Pavan Vadapalli

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What Is Dijkstra Algorithm Shortest Path Algorithm: Explained with Examples The Dutch computer scientist Edsger Dijkstra in 1959, spoke about the shortest path algorithm that could be applied to a weighted graph. This graph can be of two types – directed or undirected. A precondition of the graph is that there should be a non-negative value on its very edge. Edsger named this algorithm ‘Dijkstra’s Algorithm’. This blog will explore Dijkstra’s shortest path algorithm and ways to implement it in various programming languages.  Understanding Graphs Graphs are non-linear data structures depicting the connections between elements known as vertices or nodes. The arcs or lines forming the connection between two nodes in a graph are termed edges. Simply put, a graph comprises a set of Edges (E) and vertices (V). This graph can be denoted G (V, E). Two graph nodes connect only when an edge exists between them. Graph components Edges – Edges, also called arcs, are lines connecting two vertices or graph nodes. These are. Vertices – Basic graph elements, also known as nodes, vertices depict real-life people and objects. Graph Types Graphs can be broadly classified into directed and undirected graphs. 1. Directed Graph These graphs consist of edges with direction. The edges denote a one-way relationship in such graphs where a single direction traverses each edge. The figure above shows a simple directed graph consisting of five edges and four nodes. Arrows are used in place of simple lines to denote directed edges.  2. Undirected Graphs Graphs with an edge but no fixed direction are known as undirected graphs. In these graphs, the edge denotes a two-way relationship where we can communicate in both directions. The figure above shows a simple undirected graph comprising six edges and six nodes. Learn more about this topic in our detailed blog post Weighted Graph Weighted graphs are those where each edge is assigned a ‘weight’ or ‘cost.’ This weight can represent time, distance or anything representing the connection between the nodes or vertices it links. Dijkstra’s Algorithm considers these weights as essential elements. The image above shows the weighted graph with a number beside each edge, signifying the weight of the corresponding edge. Introduction to Dijkstra’s Algorithm Alternately called single source shortest path algorithm, Dijkstra’s Algorithm is used to figure out the shortest path in weighted graphs or the shortest distance between the starting node and target node in weighted graphs. It uses the weights of the edges to find the route that minimises the total weight or distance between the starting node and the other nodes.  This algorithmic process provides the shortest distance from a precise source node to all other nodes inside a graph. This differs from the minimum spanning tree since the shortest path between two nodes might not include all the graph nodes. Why Do We Use Dijkstra’s Algorithm? Dijkstra’s Algorithm is used in GPS devices to find the shortest path between your current location and your destination. Additionally, Dijkstra’s Algorithm in computer networks is used for routing protocols. A Step-by-Step Guide to Implementing Dijkstra’s Algorithm Look at some of the important features of the algorithm before moving on to Dijkstra’s Algorithm steps for implementing the algorithm.  Dijkstra’s Algorithm starts from the source node. The algorithm examines the whole graph to find the shortest path between that node and all other nodes. It keeps track of the presently recognised shortest path from each node to the starting node. It updates the values if it can find a different shortest path. After the algorithm has found the shortest distance from the source node to another node, it marks the node as ‘visited’ and adds it to the path. This process continues until the path contains all nodes in the graph. With the help of this, a path is created connecting the source node to all other nodes. This path is created following the probable shortest distance to reach each node.  Let’s move on to the step-by-step process of implementing Dijkstra’s Algorithm. Mark all vertices as unvisited. Mark the source node with a present distance of 0 while marking the other nodes as infinity. Fix the source node as the current node. Analyse all the unvisited neighbours of the current node and calculate their distances. Add the present distance of the current node to the edge’s (connecting current node and neighbour node) weight to calculate the distance. Compare the most recent distance to the distance already assigned to the neighbouring node, then set that distance as the new current distance for that node. Consider all the current node’s unvisited neighbours after that and mark the current node as visited. An algorithm has ended if the destination node has been marked as visited. If not, select the unvisited node marked with the smallest distance and fix it as the latest current node. Repeat the process once more from step 4. Dijkstra’s Shortest Path Algorithm Example For a better understanding, consider the illustration below to explain Dijkstra’s Algorithm with examples. Begin with a weighted graph. Select a source node and mark it as 0. Assign infinity to the other nodes. Move to each node and update the path distance. If the path distance of the adjacent node is smaller than the new path distance, it is unnecessary to update it. You must avoid updating the path distances of nodes you have visited. We choose the unvisited node with the smallest path distance after each iteration. That is why choose 5 before 7. You may notice how the rightmost node gets its path distance updated twice. Repeat the steps until all the nodes have been visited. Understanding Pseudocode for Dijkstra’s Algorithm Now that we have a fair grasp of Dijkstra Algorithm example, let’s dive into the pseudocode for Dijkstra Algorithm. Keep a record of the path length of every vertex. Keep each vertex’s path length inside an array with size n, where n is the total number of vertices. Find the shortest path and the distance of that path. To overcome this issue, map each vertex to the vertex that last updated its path distance. After completing the algorithm, try backtracking the destination vertex to the source vertex to find the path. Use a minimum Priority Queue to find the vertex with the smallest path length efficiently.  Now look at this pseudocode of the above example. Pseudocode: function Dijkstra Algorithm(Graph, source node) // iterating through the nodes in Graph and set their distances to INFINITY for each node N in Graph: distance[N] = INFINITY previous N = NULL If N != source_node, add N to Priority Queue G // setting the distance of the source node of the Graph to 0 distance source_node]=0 // iterating until the Priority Queue G is not empty while G is NOT empty: // selecting a node Q having the least distance and marking it as visited Q = node in G with the least distance mark Q visited // iterating through the unvisited neighbouring nodes of the node Q and performing relaxation accordingly for each unvisited neighbour node N of Q temporary distance = distance[Q] + distance between(Q, N) if the temporary distance is less than the given distance of the path to the Node. updating the resultant distance with the minimum value if temporary distance < distance[N] distance[N]:= temporary distance previousNO //returning the final list of distance return distance[], previous[] In the pseudocode above, a function is built with two parameters — the source node and the Graph made up of the nodes. In this function, each node in the Graph has been iterated through their initial distance set to INFINITY, and the previous node value set to NULL. Additionally, before adding each node to the priority queue, it was checked to confirm it was not a source node.  The source node’s length is set to 0. After going through each node in the priority queue once, the closest one is chosen and marked as visited. It is repeated through the selected node’s unexplored neighbours and relaxed wherever necessary.  Finally, the original and temporary distances between the source and destination nodes are compared and updated with the resulting distance with the minimum value and the prior node information. For the last step, we returned the final list of distances with the prior node information. Check out our free technology courses to get an edge over the competition. Using Dijkstra’s Algorithm in Various Programming Languages This section will describe the implementation of the algorithm in various programming languages. Dijkstra’s Algorithm C Code Use the following code to implement Dijkstra Algorithm in C. File: DijkstraAlgorithm.c // Implementation of Dijkstra's Algorithm in C // importing the standard I/O header file #include <stdio.h> // defining some constants #define INF 9999 #define MAX 10 // prototyping of the function void DijkstraAlgorithm(int Graph[MAX][MAX], int size, int start); // defining the function for Dijkstra's Algorithm void DijkstraAlgorithm(int Graph[MAX][MAX], int size, int start) { int cost[MAX][MAX], distance[MAX], previous[MAX]; int visited_nodes[MAX], counter, minimum_distance, next_node, i, JE // creating cost matrix for (i = 0; i < size; i++) for (j = 0; j < size; j++) if (Graphi [i][j] == 0) cost[i][j] = INF; else cost[i][j]= Graphjn:[i][j]; for (i = 0; i < size; i++) { distance[i] = cost[start][i]; previous[i] = start; visited_nodes[i] = 0; } distance[start] = 0; visited_nodes[start] = 1; counter = 1; while (counter < size - 1) { minimum distance = INF; for (i = 0; i < size; i++) if (distance[i] < minimum_distance && !visited_nodes[j]) { minimum distance = distance[i]; next_node = i; } visited_nodes[next_node] =1; for (i = 0; i < size; i++) if (!visited_nodes[i]) if (minimum_distance + cost[next_node][i] < distance[i]) { distance[i] = minimum_distance + cost[next_node][i]; previous[i] = next_node; } counter++; } // printing the distance for (i=0; i< size; i++) if (i != start) { printf("\nDistance from the Source Node to %d: %d", i, distance[i]); } } // main function int main(){ // defining variables int Graph[MAX][MAX], i, j, size, source; // declaring the size of the matrix size = 7; // declaring the nodes of graph Graph[0][0] = 0; Graph[0][1] = 4; Graph[0][2] = 0; Graph[0][3] = 0; Graph[0][4] = 0; Graph[0][5] = 8; Graph[0][6] = 0; Graph[1][0] = 4; Graph[1][1] <= 0; Graph[1][2] = 8: Graph[1][3] = 0: Graph[1][4] = 0; Graph[1][5] = 11; Graph[1][6] = 0; Graph[2][0] = 0; Graph[2][1] = 8: Graph[2][2] <= 0; Graph[2][3] = 7: Graph[2][4] = 0; Graph [2][5] = 4; Graph[2][6] = 0; Graph[3][0] = 0; Graph [3][1] = 0; Graph[3][2] <= 7 Graph[3][3] <=0 Graph[3][4] = 9, Graph[3]][5] = 14; Graph[3][6]= 0; Graph [4][0] = 0; Graph [4][1] = 0; Graph[4][2] = 0; Graph[4][3]= 9; Graph[4][4] = 0; Graph[4][5] = 10; Graph[4][6] = 2: Graph[5][0] = 0; Graph[5][1] = 0; Graph[5][2] = 4; Graph [5][3] = 14 Graph [5][4] = 10; Graph [5][5]= 0; Graph[5][6]= 2; Graph[6][0] = 0; Graph[6][1]=0; Graph[6][2] = 0; Graph[6][3] = 0; Graph[6][4] = 2; Graph[8][5] = 0; Graph[8][6] = 1; source= 0; //calling the DijkstraAlgorithm() function by passing the Graph, the number of nodes and the source node Dijkstra Algorithm(Graph, size, source); return 0; } Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Dijkstra Algorithm C++ Code Use the following code to implement Dijkstra’s Algorithm in C++. File: DijkstraAlgorithm.cpp // Implementation of Dijkstra's Algorithm in C++ // importing the required header files #include <iostream> #include <vector> // defining constant #define MAX_INT 10000000 // using the standard namespace using namespace std; // prototyping of the DijkstraAlgorithm() function void DijkstraAlgorithm(); // main function int main(){ DijkstraAlgorithm(); return 0; } // declaring the classes class Vertex; class Edge; // prototyping the functions void Dijkstra(); vector<Vertex*>* Adjacent Remaining Nodes(Vertex" vertex); Vertex Extract_Smallest(vector<Vertex*>& vertices); int Distance(Vertex vertexOne, Vertex* vertexTwo); bool Contains(vector<Vertex">& vertices, Vertex vertex); vold Print Shortest Route To(Vertex" des); // instantiating the classes vector<Vertex"> vertices; vector<Edge"> edges; // defining the class for the vertices of the graph class Vertex{ public: Vertex(char id) : id(id), prev(NULL), distance_from_start(MAX_INT) { vertices.push_back(this); } public: char id; Vertex* prev; int distance_from_start; }; // defining the class for the edges of the graph class Edge { public: Edge(Vertex* vertexOne, Vertex vertexTwo, int distance) : vertexOne(vertexOne), vertexTwo(vertexTwo), distance(distance) { edges.push_back(this); } bool Connects(Vertex* vertexOne, Vertex vertexTwo) { return( (vertexOne == this->vertexOne && vertex Two == this->vertexTwo) || (vertexOne == this->vertexTwo && vertexTwo == this->vertexOne)); } public: Vertex vertexOne: Vertex vertexTwo: int distance; }; // defining the function to collect the details of the graph void DijkstraAlgorithm() { // declaring some vertices Vertex vertex_a= new Vertex('A'); Vertex vertex_b = new Vertex('B'); Vertex vertex_c = new Vertex('C'); Vertex vertex_d = new Vertex('D'); Vertex vertex_e = new Vertex('E'); Vertex vertex_f = new Vertex('F'); Vertex vertex_g = new Vertex('G'); // declaring some edges Edge* edge_1 = new Edge(vertex a, vertex_c, 1); Edge* edge_2 = new Edge(vertex a, vertex_d, 2); Edge* edge_3 = new Edge(vertex b, vertex_c, 2); Edge* edge_4 = new Edge(vertex c, vertex_d, 1): Edge* edge_5 = new Edge(vertex b, vertex_f, 3); Edge* edge_6 = new Edge(vertex c, vertex_e, 3); Edge* edge_7 = new Edge(vertex e, vertex_f, 2); Edge* edge_8 = new Edge(vertex d, vertex_g, 1); Edge* edge_9= new Edge(vertex g, vertex_f, 1); vertex a distance from start = 0; // setting a start vertex // calling the Dijkstra() function to find the shortest route possible Dijkstra(); //calling the prient_shortest_route_to() function to print the shortest route from the Source vertex to the destination vertex Print_shortest_Route_To(vertex_f); // defining the function for Dijkstra's Algorithmn void Dijkstra(){ while (vertices.size() > 0) { Vertex smallest = Extract Smallest(vertices); vector<Vertex adjacent nodes = Adjacent_Remaining_Nodes(smallest); const int size = adjacent_nodes -> size(); for (int i = 0; i < size; ++i) { Vertex adjacent = adjacent nodes → at); int distance = Distance(smallest, adjacent) + smallest -> distance_from_start; if (distance < adjacent -> distance_from_start) { adjacent->distance from start = distance: adjacent -> prev = smallest; } } delete adjacent_nodes; } } // defining the function to find the vertex with the shortest distance, removing it, and returning it Vertex* Extract Smallest(vector<Vertex">& vertices) int size = vertices.size(); if (size == 0) return NULL; int smallest_position = 0; Vertex* smallest = vertices.at(0); for (int i = 1; i < size; ++i) { Vertex* current = vertices.at(i); if (current ->distance_from_start < smallest -> distance_from_start) smallest=current; smallest_position=i; } } vertices.erase(vertices.begin() + smallest_position); return smallest; } // defining the function to return all vertices adjacent to 'vertex' which are still in the vertices collection. vector<Vertex*>* Adjacent Remaining Nodes(Vertex" vertex) { vector<Vertex"> adjacent nodes = new vector<Vertex">(); const int size = edges.size(); for (int i = 0; i < size; ++i) { Edge* edge = edges.at(i); Vertex adjacent = NULL; if (edge -> vertexOne == vertex) { adjacent = edge >> vertexTwo; }else if (edge -> vertexTwo == vertex) { adjacent = edge-> vertexOne; } if (adjacent && Contains(vertices, adjacent)) { adjacent nodes -> push_back(adjacent); } } return adjacent nodes; } // defining the function to return distance between two connected vertices int Distance(Vertex* vertexOne, Vertex* vertexTwo) { const int size = edges.size(); for (int i = 0; i < size; ++i) { Edge* edge = edges.at(i); if (edge -> Connects(vertexOne, vertexTwo)) { return edge -> distance; } } return -1; // should never happen } // defining the function to check if the 'vertices' vector contains 'vertex' bool Contains(vector<Vertex*>& vertices, Vertex* vertex) { const int size = vertices.size(); for (int i = 0; i < size; ++i) { if (vertex == vertices.at(i)) {} return true; } } return false; } // defining the function to print the shortest route to the destination vold Print_Shortest_Route _To(Vertex* des) { Vertex" prev = des; cout << "Distance from start: " << des -> distance_from_start << endl; while (prev) { cout << prev -> id <<""; prev = prev-> prev; } cout << endl; } Dijkstra Algorithm Java Code Use the following code to implement Dijkstra’s Algorithm in Java programming language. File: DijkstraAlgorithm.java // Implementation of Dijkstra's Algorithm in Java // defining the public class for Dijkstra's Algorithm public class DijkstraAlgorithm { // defining the method to implement Dijkstra's Algorithm public void dijkstraAlgorithm(int[][] graph, int source) { // number of nodes int nodes = graph.length; boolean[] visited_vertex = new boolean[nodes]; int[] dist = new int[nodes]; for (int i=0; i<nodes; i++){ visited_vertex] = false; dist[i] = Integer.MAX_VALUE; } // Distance of self loop is zero dist[source] = 0; for (int i=0; i<nodes, i++) { //Updating the distance between neighbouring vertex and source vertex int u= find_min_distance(dist, visited vertex); visited_vertex[u] = true; // Updating the distances of all the neighbouring vertices for (int v=0; y < nodes: v++) { if (visited vertex(v) && graph[u][v]! = 0 && (dist[u] + graph[u][v] < dist{v})) { dist[v] = dist[u] + graph[u][v]; } } } for (int i=0; i < dist.length; i++) { System.out.println(String format("Distance from Vertex %s to Vertex %s is %s", source, i, dist[i])); } } //definding the medhod to find the minimum distance privae static int find_min_distance(int[]dist, boolean[] visited_vertex) { int minimum_distance = integer.Max_value; int mininum_distance_vertex =-1; for (int i=0; i<dist. length; i++){ if (visited vertex) && dist[i] < minimum_distance) { minimum_distance = dist[i]} minimum distance vertex=i; } } retum minimum_distance_vertex; } // main function public static void main(String[] args) { // declaring the nodes of the graphs int graph[][] = new int[][]{ {0,1,1,2,0,0,0}, {0,0,2,0,0,3,0}, {1,2,0,1,3,0,0}, {2,0,1,0,2,0,1}, {0,0,3,0,0,2,0}, {0,3,0,0,2,0,1}, {0,2,0,1,0,1,0} }; //instantiating the DijkstraAlgorithm() class DijkstraAlgorithm Test = new DijkstraAlgorithm()) // calling the dijkstraAlgorithm() method to find the shortest distance from the source node to the destination node Test.dijkstraAlgorithm(graph, 0) } } Dijkstra Algorithm Python Code Use the following code to implement Dijkstra’s Algorithm in Python. File: DikstraAlgorithm.py #Implementation of Dijkstra's Algorithm in Python #importing the sys module import sys #declaring the list of nodes for the graph nodes=[ [ 0, 0, 1, 0, 1, 0, 0] [0, 0, 1, 0, 0, 1, 0], [1, 1, 0, 1, 1, 0, 0 [1, 0, 1, 0, 0, 0, 1], [0, 0, 1, 0, 0, 1, 0] [0, 1, 0, 0, 1, 0, 1, ] [ 0, 0, 0, 1, 0, 1, 0] ] #declaring the list of edges for the graph edges = [ [0,0,1,0,2,0,0], [0, 0, 2, 0, 0, 3, 0], [1,2,0,1,3,0,0], [2, 0, 1, 0, 0, 0, 1], [0,0,3,0,0,2, 0], [0, 3, 0, 0, 2, 0, 1], [0, 0, 0, 1, 0, 1,0] ] # declaring the list of edges for the graph edges=[ [ 0, 0, 1, 0, 2, 0, 0], [ 0, 0, 2, 0, 0, 3, 0], [ 1, 2, 0, 1, 3, 0, 0], ( 2, 0, 1, 0, 0, 0, 1, 1], [ 0, 0, 3, 0, 0, 2, 0], [0, 3, 0, 0, 2, 0, 1], [ 0, 0, 0, 1, 0, 1, 0], ] #defining the function to find which node is to be visited next def toBevisited(): global visitedAndDistance V=-10 for index in range(numberOfNodes); If visitedAndDistance[index][0] == 0 and (v <0 or visitedAndDistance index][1]<= visitedAndDistance[v][1]): v=index return v #finding the number of nodes in the graph numberOfNodes = len(nodes[0]) visitedAndDistance = [[0, 0] for i in range(numberOfNodes - 1): visitedAndDistance.append([0, sys.maxsize]) for node in range(numberOfNodes): #finding the next node to be visited toVisit = toBeVisited() for neighborIndex in range(numberOfNodes) #updating the new distances if nodes to Visit][neighborIndex]== 1 and visitedAndDistance(neighborinbox[[0] ==0: newDistance = visitedAndDistance toVisit][1] + edges[toVisit][neighborindex] if visitedAndDistance neighborfndex][1] > newDistance: visitedAndDistance[neighborIndex][1] = newDistance visitedAndDistance(toVisit [0] =1 i=0 #printing the distance for distance in visitedAndDistance: print("Distance of", chr(ord("A") + i), "from source node", distance[1]) i=i+1 In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Real-life Applications of Dijkstra’s Algorithm Mentioned below are some real-world applications of Dijkstra’s Algorithm. Mobile Network Every transmission line in a mobile network consists of a bandwidth, ‘B’. The transmission line’s highest supported frequency is known as the bandwidth. In general, a line reduces a signal if the signal frequency is higher in that line. The amount of data transmitted over a line is measured as bandwidth.  Let’s imagine a city as a graph, where the graph nodes represent the switching station and the edges represent the transmission lines. The weight of the edges represents the bandwidth, or ‘B’. As a result, the mobile network can also be considered a type of shortest-distance problem that can be resolved using Dijkstra’s Algorithm. Google Maps We often try to find the distance between two cities interlinked with many routes or paths. We resort to Google Maps to show us the minimal distance. This is only possible because Dijkstra’s Algorithm aids the application in determining which portion of the path is shortest between two specified places.  Consider India as a network, with the cities and locations acting as the nodes and the routes connecting them as the edges. It is possible to figure out the shortest paths between any two cities or locations using Dijkstra’s Algorithm. Flight Programme Let’s consider that a person needs software to create a customer flight schedule. A database containing all flights and airports is available to the agent. The flights also consist of departure and arrival timings in addition to the origin airport, flight number and destination. Here, the agents can apply Dijkstra’s Algorithm to compute the earliest arrival time for the chosen destination from the original airport and the specified start time. Pros and Cons of Dijkstra’s Algorithm Dijkstra’s Algorithm comes with its own set of advantages and disadvantages.  Advantages Dijkstra’s Algorithm has a nearly linear space and time complexity. It can only be used with directed weighted graphs. This graph’s edges must be non-negative. Calculating the shortest distance from a single node to all other nodes is possible by using Dijkstra’s Algorithm. It is also possible to measure the shortest path from a source node to a destination node by ending the algorithm after we reach the shortest path for the destination node. Disadvantages Dijkstra’s Algorithm cannot handle negative edges. This algorithm performs an obscured exploration. This takes up too much time during processing. Maintenance is required to keep track of the visited nodes. This algorithm cannot measure the exact shortest distance since it enters the acyclic graph. Check Out upGrad’s Software Development Courses to upskill yourself. Conclusion Dijkstra’s Algorithm is useful for finding the shortest path between a source node and all other nodes in a graph. An in-depth knowledge of this algorithm is crucial for data scientists, along with the know-how to implement it in various programming languages. Enrol in an online data science course to understand it in detail and learn more about Dijkstra’s shortest path algorithm example and its real-world applications.  FAQs

What are Natural Language Processing Projects? NLP project ideas advanced encompass various applications and research areas that leverage computational techniques to understand, manipulate, and generate human language. These projects harness the power of artificial intelligence and machine learning to process and analyze textual data in ways that mimic human understanding and communication. Here are some key aspects and examples of NLP projects: 1. Text Classification NLP can be used to classify text documents into predefined categories automatically. This is useful in sentiment analysis, spam detection, and topic categorization. For instance, classifying customer reviews as positive or negative to gauge product sentiment. 2. Named Entity Recognition (NER) NLP models can identify and categorize entities such as names of people, organizations, locations, and dates within text. This is crucial for information extraction tasks like news article analysis or document summarization. 3. Machine Translation Projects in this domain focus on developing algorithms that translate text from one language to another. Prominent examples include Google Translate and neural machine translation models. 4. Text Generation NLP models like GPT-3 can generate human-like text, making them useful for content generation, chatbots, and creative writing applications. 5. Question-Answering Systems These nlp project ideas involve building systems that can understand questions posed in natural language and provide relevant answers. IBM’s Watson is a well-known example. 6. Speech Recognition While technically part of the broader field of speech processing, NLP techniques are used in transcribing spoken language into written text, as seen in applications like voice assistants (e.g., Siri and Alexa). 7. Text Summarization NLP can automatically generate concise summaries of lengthy texts, making it easier to digest information from news articles, research papers, or legal documents. 8. Sentiment Analysis Analyzing social media data and customer reviews to determine public sentiment toward products, services, or political issues is a common NLP application. 9. Language Modeling Creating and fine-tuning language models, such as BERT and GPT, for various downstream tasks forms the core of many NLP projects. These models learn to represent and understand language in a generalized manner. What are the Different Best Platforms to Work on Natural Language Processing Projects? Here are some of the best platforms for nlp projects for final year: 1. Python and Libraries Python is the most popular programming language for NLP due to its extensive libraries and frameworks. Libraries like NLTK, spaCy, gensim, and the Transformers library by Hugging Face provide essential NLP functionalities and pre-trained models. 2. TensorFlow and PyTorch These deep learning frameworks provide powerful tools for building and training neural network models, including NLP models. Researchers and developers can choose between them based on their preferences. 3. Google Colab For cloud-based NLP development, Google Colab offers free access to GPU and TPU resources, making it an excellent choice for training large NLP models without needing high-end hardware. 4. SpaCy SpaCy is a fast and efficient NLP library that excels at various NLP tasks, including tokenization, named entity recognition, and part-of-speech tagging. It also offers pre-trained models for multiple languages. 5. Docker Docker containers can create reproducible and portable NLP environments, ensuring consistency across development and deployment stages. 6. AWS, Azure, and Google Cloud These cloud platforms offer scalable compute resources and specialized NLP services like Amazon Comprehend, Azure Text Analytics, and Google Cloud NLP, simplifying the deployment of NLP solutions at scale. 7. Kaggle Kaggle provides datasets, competitions, and a collaborative platform for NLP practitioners to share code and insights. It’s a great resource for learning and benchmarking NLP models. 8. GitHub GitHub is a repository for NLP project code, facilitating collaboration and version control. Many NLP libraries and models are open-source and hosted on GitHub. 9. Apache Spark Apache Spark can be used for handling large-scale NLP tasks for distributed data processing and machine learning. NLP Projects & Topics Natural Language Processing or NLP is an AI component concerned with the interaction between human language and computers. When you are a beginner in the field of software development, it can be tricky to find NLP based projects that match your learning needs. So, we have collated some examples to get you started. So, if you are a ML beginner, the best thing you can do is work on some NLP projects. We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting NLP projects which beginners can work on to put their knowledge to test. In this article, you will find top NLP project ideas for beginners to get hands-on experience on NLP. But first, let’s address the more pertinent question that must be lurking in your mind: why to build NLP projects? When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience. NLP is all about analyzing and representing human language computationally. It equips computers to respond using context clues just like a human would. Some everyday applications of NLP around us include spell check, autocomplete, spam filters, voice text messaging, and virtual assistants like Alexa, Siri, etc. As you start working on NLP projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career. In the last few years, NLP has garnered considerable attention across industries. And the rise of technologies like text and speech recognition, sentiment analysis, and machine-to-human communications, has inspired several innovations. Research suggests that the global NLP market will hit US$ 28.6 billion in market value in 2026.  When it comes to building real-life applications, knowledge of machine learning basics is crucial. However, it is not essential to have an intensive background in mathematics or theoretical computer science. With a project-based approach, you can develop and train your models even without technical credentials. Learn more about NLP Applications. To help you in this journey, we have compiled a list of NLP project ideas, which are inspired by actual software products sold by companies. You can use these resources to brush up your ML fundamentals, understand their applications, and pick up new skills during the implementation stage. The more you experiment with different NLP projects, the more knowledge you gain. Before we dive into our lineup of NLP projects, let us first note the explanatory structure.  The project implementation plan All the nlp projects for final year included in this article will have a similar architecture, which is given below: Implementing a pre-trained model Deploying the model as an API Connecting the API to your main application This pattern is known as real-time inference and brings in multiple benefits to your NLP design. Firstly, it offloads your main application to a server that is built explicitly for ML models. So, it makes the computation process less cumbersome. Next, it lets you incorporate predictions via an API. And finally, it enables you to deploy the APIs and automate the entire infrastructure by using open-source tools, such as Cortex.  Here is a summary of how you can deploy machine learning models with Cortex: Write a Python script to serve up predictions. Write a configuration file to define your deployment. Run ‘cortex deploys’ from your command line. Now that we have given you the outline let us move on to our list!  Must Read: Free deep learning course! So, here are a few NLP Projects which beginners can work on: NLP Project Ideas This list of NLP projects for students is suited for beginners, intermediates & experts. These NLP projects will get you going with all the practicalities you need to succeed in your career. Further, if you’re looking for NLP based projects for final year, this list should get you going. So, without further ado, let’s jump straight into some NLP projects that will strengthen your base and allow you to climb up the ladder. This list is also great for Natural Language Processing projects in Python.  Here are some NLP project idea that should help you take a step forward in the right direction. 1. A customer support bot One of the best ideas to start experimenting you hands-on projects on nlp for students is working on customer support bot. A conventional chatbot answers basic customer queries and routine requests with canned responses. But these bots cannot recognize more nuanced questions. So, support bots are now equipped with artificial intelligence and machine learning technologies to overcome these limitations. In addition to understanding and comparing user inputs, they can generate answers to questions on their own without pre-written responses.  For example, Reply.ai has built a custom ML-powered bot to provide customer support. According to the company, an average organization can take care of almost 40 % of its inbound support requests with their tool. Now, let us describe the model required to implement a project inspired by this product.  You can use Microsoft’s DialoGPT, which is a pre-trained dialogue response generation model. It extends the systems of PyTorch Transformers (from Hugging Face) and GPT-2 (from OpenAI) to return answers to the text queries entered. You can run an entire DialoGPT deployment with Cortex. There are several repositories available online for you to clone. Once you have deployed the API, connect it to your front-end UI, and enhance your customer service efficiency! Read: How to make chatbot in Python? 2. A language identifier Have you noticed that Google Chrome can detect which language in which a web page is written? It can do so by using a language identifier based on a neural network model.  This is an excellent nlp project in python for beginners. The process of determining the language of a particular body of text involves rummaging through different dialects, slangs, common words between different languages, and the use of multiple languages in one page. But with machine learning, this task becomes a lot simpler. You can construct your own language identifier with the fastText model by Facebook. The model is an extension of the word2vec tool and uses word embeddings to understand a language. Here, word vectors allow you to map a word based on its semantics — for instance, upon subtracting the vector for “male” from the vector for “king” and adding the vector for “female,” you will end up with the vector for “queen.” A distinctive characteristic of fastText is that it can understand obscure words by breaking them down into n-grams. When it is given an unfamiliar word, it analyzes the smaller n-grams, or the familiar roots present within it to find the meaning. Deploying fastTExt as an API is quite straightforward, especially when you can take help from online repositories. 3. An ML-powered autocomplete feature Autocomplete typically functions via the key value lookup, wherein the incomplete terms entered by the user are compared to a dictionary to suggest possible options of words. This feature can be taken up a notch with machine learning by predicting the next words or phrases in your message. Here, the model will be trained on user inputs instead of referencing a static dictionary. A prime example of an ML-based autocomplete is Gmail’s ‘Smart Reply’ option, which generates relevant replies to your emails. Now, let us see how you can build such a feature.  For this advanced nlp projects, you can use the RoBERTa language model. It was introduced at Facebook by improving Google’s BERT technique. Its training methodology and computing power outperform other models in many NLP metrics. To receive your prediction using this model, you would first need to load a pre-trained RoBERTa through PyTorch Hub. Then, use the built-in method of fill_mask(), which would let you pass in a string and guide your direction to where RoBERTa would predict the next word or phrase. After this, you can deploy RoBERTa as an API and write a front-end function to query your model with user input. Mentioning NLP projects can help your resume look much more interesting than others. 4. A predictive text generator This is one of the interesting NLP projects. Have you ever heard of the game AI Dungeon 2? It is a classic example of a text adventure game built using the GPT-2 prediction model. The game is trained on an archive of interactive fiction and demonstrates the wonders of auto-generated text by coming up with open-ended storylines. Although machine learning in the area of game development is still at a nascent stage, it is set to transform experiences in the near future. Learn how python performs in game development. DeepTabNine serves as another example of auto-generated text. It is an ML-powered coding autocomplete for a variety of programming languages. You can install it as an add-on to use within your IDE and benefit from fast and accurate code suggestions. Let us see how you can create your own version of this NLP tool.  You should go for Open AI’s GPT-2 model for this project. It is particularly easy to implement a full pre-trained model and to interact with it thereafter. You can refer to online tutorials to deploy it using the Cortex platform. And this is the perfect idea for your next NLP project! Read: Machine Learning Project Ideas 5. A media monitor One of the best ideas to start experimenting you hands-on NLP projects for students is working on media monitor. In the modern business environment, user opinion is a crucial denominator of your brand’s success. Customers can openly share how they feel about your products on social media and other digital platforms. Therefore, today’s businesses want to track online mentions of their brand. The most significant fillip to these monitoring efforts has come from the use of machine learning.  For example, the analytics platform Keyhole can filter all the posts in your social media stream and provide you with a sentiment timeline that displays the positive, neutral, or negative opinion. Similarly, an ML-backed sift through news sites. Take the case of the financial sector where organizations can apply NLP to gauge the sentiment about their company from digital news sources.  Such media analytics can also improve customer service. For example, providers of financial services can monitor and gain insights from relevant news events (such as oil spills) to assist clients who have holdings in that industry.  You can follow these steps to execute a project on this topic:  Use the SequenceTagger framework from the Flair library. (Flair is an open-source repository built on PyTorch that excels in dealing with Named Entity Recognition problems.) Use Cortex’s Predictor API to implement Flair. We are currently experiencing an exponential increase in data from the internet, personal devices, and social media. And with the rising business need for harnessing value from this largely unstructured data, the use of NLP instruments will dominate the industry in the coming years. Such developments will also jumpstart the momentum for innovations and breakthroughs, which will impact not only the big players but also influence small businesses to introduce workarounds.  Also read: AI Project Ideas and Topics for Beginners Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses Natural Language Processing Techniques to Use in Python Making computers read unorganized texts and extract useful information from them is the aim of natural language processing (NLP). Many NLP approaches can be implemented using a few lines of Python code, courtesy of accessible libraries like NLTK, and spaCy. These approaches can also work great as NLP topics for presentation.  Here are some techniques of Natural Language Processing projects in Python –  Named Entity Recognition or NER – A technique called named entity recognition is used to find and categorise named entities in text into groups like people, organisations, places, expressions of times, amounts, percentages, etc. It is used to improve content classification, customer service, recommendation systems, and search engine algorithms, among other things. Analysis of Sentiment – One of the most well-known NLP approaches, sentiment analysis examines text (such as comments, reviews, or documents) to identify whether the information is good, poor, or indifferent. Numerous industries, including banking, healthcare, and customer service, can use it. BoW or Bag of Words – A format that transforms text into stationary variables is called the Bag of Words (BoW) model. This makes it easier for us to convert text to numbers to be used in machine learning. The model is simply interested in the number of terms in the text and isn’t focused on word order. It may be used for document categorisation, information retrieval, and NLP. Cleaning raw text, tokenisation, constructing a vocabulary, and creating vectors are all steps in the normal BoW approach. TF-IDF (Term Frequency – Inverse Document Frequency) – The TF-IDF calculates “weights” that describe how significant a word is in the document.  The quantity of documents that include a term reduces the TF-IDF value, which rises according to the frequency of its use in the document. Simply said, the phrase is rare, more distinctive, or more important the higher the TF-IDF score, and vice versa. It has uses in information retrieval, similar to how browsers try to yield results that are most pertinent to your request.  TF and IDF are calculated in different ways.  TF = (Number of duplicate words in a document) / (Number of words in a document) IDF = Log {(Number of documents) / (Number of documents with the word)} Wordcloud – A common method for locating keywords in a document is word clouds. In a Wordcloud, words that are used more frequently have larger, stronger fonts, while those that are used less frequently have smaller, thinner fonts. With the ‘Wordcloud’ library and the ‘stylecloud’ module, you can create simplistic Wordclouds in Python. This makes NLP projects in Python very successful.  In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses NLP Research Topics –  To ace NLP projects in Python, it is necessary to conduct thorough research. Here are some NLP research topics that will help you in your thesis and also work great as NLP topics for presentation –  Biomedical Text Mining Computer Vision and also NLP Deep Linguistic Processing Controlled Natural Language Language Resources and also Architectures for NLP Sentiment Analysis and also Opinion Mining NLP includes Artificial Intelligence Issues includes Natural language understanding and also Creation Extraction of Actionable Intelligence also from Social Media Efficient Information also Extraction Techniques Use of Rule also based Approach or Statistical Approach Topic Modelling in Web data Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Conclusion In this article, we covered some NLP projects that will help you implement ML models with rudimentary knowledge software development. We also discussed the real-world applicability and functionality of these products. So, use these topics as reference points to hone your practical skills and propel your career and business forward!  Only by working with tools and practise can you understand how infrastructures work in reality. Now go ahead and put to test all the knowledge that you’ve gathered through our NLP projects guide to build your very own NLP projects! If you wish to improve your NLP skills, you need to get your hands on these NLP projects. If you’re interested to learn more about machine learning online course, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

With the rapid evolution of technology, the world is seeing a subsequent shift to online for everything. The Internet is the one-stop solution for everything from storing relevant documents to conducting financial transactions. However, this also means increased threats to cyberspace through hacking, identity theft, etc. Web hacking generally targets the areas that can destroy your important applications. SQL injection is a common approach to harming data-driven applications. SQL injection attacks are generally performed through any application input or web page. Attackers search for vulnerabilities and loopholes in a web page or application to execute malicious commands. This blog will comprehensively answer ‘What is SQL injection and how to prevent it’.  What Is SQL Injection? SQL injection is a web security attack and vulnerability performed by executing malicious codes. The attacker gains access to the application of a database and damages sensitive data by either making changes to it or stealing it. Injection attackers incorporate SQL queries to change, modify, update, or delete sensitive information from the database. Recent years have seen an alarming increase in SQL injection attacks and security breaches.  These attacks may also affect the server or back-end infrastructure, sometimes escalating to DDoS attacks.  The Intention Behind an SQL Injection The most prevalent question when discussing SQL injection is, ‘What is the purpose of an SQL injection?’ The main motive of SQL injection attackers is to access sensitive information in a database.  The purpose of an SQL injection is to exploit vulnerabilities in a software application’s security by manipulating the input fields or parameters that interact with a database using Structured Query Language (SQL). This process aims to damage sensitive data such as updating, modifying, deleting, or stealing it with malicious intentions. This exploitation can have various malicious intentions, and it seriously threatens the confidentiality, integrity, and availability of data within a database.  SQL Injection Types SQL injection is a widespread cybersecurity threat that comes in various forms, each with its own methods and goals. Depending on their potential to damage sensitive data, it can be classified into three broad categories as described below: 1. In-band SQL Injection In this type of SQL injection, the attacker launches malicious commands on the same communication channel used for deriving information. It is one of the most effective and straightforward SQL injection attacks, thus making it one of the most used.  In-band SQL injection can be divided into the following sub-categories: Error-based SQL injection: This is the type of SQL injection where an attacker attacks in a way that produces error messages in the database. People with the affected database will see the error messages, and the attacker will gain access to sensitive information about the features and structure of the database. Union-based SQL injection: Attackers use the UNION SQL operator to combine their malicious query with a legitimate one in the application’s database. This can allow them to extract data from other tables or manipulate the query’s result.  Check out our free technology courses to get an edge over the competition. 2. Inferential (Blind) SQL Injection In Inferential SQL injection, the attacker does not mess with the immediate web page but proceeds in a way that sends data payloads to the main server. This process is also known as blind SQL injection. Attackers use this technique when they can’t view the application’s responses directly. They infer the data’s existence or values by observing how the application responds to their queries over time. Blind SQL injections are difficult and slower to execute but can be dangerous as they identify the behavioural patterns of the server. Inferential SQL injection can also be divided into two sub-categories, as illustrated below: Boolean-based SQL injection: Here, the attacker writes an SQL command as a query and sends it to the database, asking the application to return a response. The response depends upon the query being true or false. The HTTP results of the query may portray some changes or can remain the same. The attacker then analyses whether the message is true or false.  Time-based SQL injection: The attacker initiates a SQL query to the database, prompting the system to wait briefly before responding, usually for a few seconds. The time period of the response from the database allows the attacker to evaluate the legitimacy of the query in terms of true or false. Based on the query results, an HTTP result will be generated immediately or after some time. The attacker can then evaluate whether the status of the message is true or false even without accessing the information of the database. 3. Out-of-bound SQL injection Out-of-bound SQL injection cannot be performed when certain database features are missing. This is an infamous type of SQL injection that depends upon the functionalities of a database server. The attacker cannot launch this attack if certain functionalities are not enabled. While configuring, it may look like a database administrator issue. This injection attack is used when the attacker cannot use the same communication channel to launch an attack as in the case of in-band SQL injection. The attacker can carry out this attack even if the database server is unstable and slow. This method is based on the ability of the server to forward HTTP or DNS requests to pass on sensitive data to the attacker. Executing a SQL Injection Attack To know ‘what is SQL injection attack‘ is, one must also understand how an SQL injection attack is conducted. To launch an SQL injection attack, the attacker locates the vulnerable user inputs in a web application or page. The attacker creates harmful input content through malicious payloads and sends it as user input, followed by executing malicious SQL commands in the database containing important data. SQL is a programming language that writes queries and commands to manage the data stored in relational databases. It is generally used to update, modify, access, or delete data. Organisations largely store their sensitive data in SQL databases. SQL commands are sometimes applied to execute the operating system’s commands. Therefore, a successful SQL injection attack may result in very serious outcomes. Check Out upGrad’s Software Development Courses to upskill yourself. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript What Are Some Examples of SQL Injection? Here are some of the most common examples of SQL injection attacks that will help you better understand the concept along with the commands: Example 1: The first example depicts how an attacker uses SQL commands to gain access to a database and act as an administrator. The attacker writes commands on a web server to authenticate with a username and password.   In the following example, the table name is ‘users’, and the requested column names are ‘username’ and ‘password’. # Define POST variables uname = request.POST[‘username’] passwd = request.POST[‘password’] # SQL query vulnerable to SQLi sql = “SELECT id FROM users WHERE username = ”’ + uname + “’ AND password=”’ + passwd + “”’ # Execute the SQL statement database.execute(sql) These SQL commands are vulnerable inputs, and the attacker can easily alter or modify any user input. For instance, the attacker can alter the password field and set it to: password' OR 1=1 Therefore, in this case, the database will execute the following SQL command: SELECT id FROM users WHERE username='username' AND password='password' OR 1=1'   Because of the command mentioned above, the ‘where’ clause will return the result of the first ID, and the value of the username and password is immaterial. In this way, an attacker gains unauthorised access to the database and also gets the privileges of an administrator. The attacker can further manipulate the database by executing the following query: MySQL, MSSQL, Oracle, PostgreSQL, SQLite ‘ OR ‘1’=’1’ -- ‘ OR ‘1’=’1’ /* – MySQL ‘ OR ‘1’=’1’ # – Access (using null characters) ‘ OR ‘1’=’1’ %00 ‘ OR ‘1’=’1’ %16 Example 2: Union-based SQL injection example The union operator is the main feature of launching an SQL injection attack here. In this type of attack, the attackers can combine the outcomes of two select statements to return a single result.  Like a legitimate user, the attacker sends an HTTP request to a vulnerable web page. The payload sent by the attacker can alter and modify the query using the union operator that is generally attached to the malicious SQL command. The result of the chosen statement will show the outcome of the original query combined with that of the malicious query.  The following SQL commands show the example of union-based SQL injection: GET http://testphp.vulnweb.com/artists.php?artist=1 HTTP/1.1 Host: testphp.vulnweb.com   GET http://testphp.vulnweb.com/artists.php?artist=-1 UNION SELECT 1,2,3 HTTP/1.1 Host: testphp.vulnweb.com   GET http://testphp.vulnweb.com/artists.php?artist=-1 UNION SELECT 1,pass,cc FROM users WHERE uname=’test’ HTTP/1.1 Host: testphp.vulnweb.com   SQL Injection Attack: Preventive Measures Now that we have covered the what and how of SQL injection attacks, the next question is, ‘What are the solutions for injection attacks?’ Preventing injection attacks is not easy. Implement the following preventive techniques to protect your data from SQL injection attacks: Implement parameterised queries and prepared statements: You may use parameterised queries, which help analyse and treat the SQL statements securely. Only those SQL commands parameterised with safety features will be executed in this case. It allows the database to record only prepared statements and eliminate fake commands. Object-oriented mapping: This is a great way of securing your data from SQL injection attacks. Companies nowadays use object-oriented relational mapping frameworks over traditional mapping tools. Object-oriented mapping offers seamless conversion of SQL results into codes. It helps developers keep the data safe against SQL mapping. To answer ‘what is SQLmap used for’, it tests the vulnerabilities in web applications and web pages so the attacker can easily access the database.  Escaping inputs: This is a new way of protecting your data from SQL injection attacks, where many programming languages have some standard functions for data protection. One should be alert while applying escape characters in the SQL statements and commands. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Conclusion Web hacking using SQL injection can take advantage of a company’s database and damage it. These attacks can manipulate the database server in charge of the company’s web applications. Any company that uses an SQL database is vulnerable to SQL injection attacks. These attacks can cause irreversible damage to databases and servers, resulting in far-reaching losses in terms of finance and reputation.  Understanding these attacks is crucial for developers and security professionals to protect applications and databases from such vulnerabilities. Proper input validation, parameterised queries, and regular security assessments are essential in preventing SQL injection attacks. Enrol in an online cybersecurity course to gain in-depth knowledge on ‘what is SQL injection in cybersecurity’ and the various kinds of SQL injection. 

Learning about MATLAB can be tedious. It’s capable of performing many tasks and solving highly complex problems of different domains. If you’ve been learning about MATLAB, you’d surely want to test your skills. The best way to do so is through working on MATLAB project ideas. That’s why in this article, we’ve brought you a detailed list of the same.  We have projects of matlab for beginners a gentle approach of multiple skill levels. Whether you’re a beginner or an expert, you’d find a brain-teasing project here. What is MATLAB? MATLAB is a programming platform for scientists and engineers. It uses the MATLAB language, combining matrix and array mathematics with design processes and iterative analysis. By using MATLAB, you can create algorithms, analyze data, build models, and apply them. MATLAB’s apps, built-in functions, and language allow you to use different methods to solve a particular problem. MATLAB finds applications in many areas, including control systems, communications, machine learning, computational biology, and deep learning.  What are the Skills That You Will Acquire Through MATLAB Projects? Engaging in matlab for beginners projects offers a diverse range of skills that are valuable across various industries and fields of study. MATLAB, a powerful programming and numerical computing platform, enables individuals to tackle complex problems, conduct data analysis, and develop innovative solutions. Here are some skills you can acquire through MATLAB projects: 1. Programming Proficiency Matlab simulation projects involve writing code, which helps you develop strong programming skills. You’ll learn about variables, data structures, loops, and conditional statements, which are fundamental concepts in programming. 2. Data Analysis and Visualization It helps in excels in data analysis and visualization. Through projects, you’ll gain expertise in importing, processing, and visualizing data, which is crucial in fields like data science, finance, and engineering. 3. Algorithm Development It allows individual to develop and implement algorithms efficiently. On top of that, you’ll also learn about designing and optimizing algorithms for tasks like, image processing, signal processing, and machine learning. 4. Mathematical Modeling ML is widely used for mathematical modeling and simulations. You’ll acquire skills in creating mathematical models of real-world phenomena and simulating their behavior. 5. Image and Signal Processing MATLAB is renowned for its capabilities in image and signal processing. You’ll learn how to enhance images, analyze signals, and extract meaningful information from them. 6. Machine Learning It offers extensive tools and libraries for machine learning. Through projects, you can develop skills in building and training machine learning models for tasks like classification, regression, and clustering. 7. Numerical Optimization MATLAB is ideal for solving optimization problems. You’ll gain experience in formulating and solving optimization problems, which are valuable in engineering and operations research. 8. Simulink Simulink, a MATLAB toolbox, is used for modeling and simulating dynamic systems. You can acquire skills in system modeling and control design, which are essential in fields like robotics and control engineering. 9. Parallel and Distributed Computing MATLAB allows you to leverage parallel and distributed computing resources. Learning to distribute your computations efficiently is valuable for handling large datasets and complex simulations. 10. Problem-Solving Skills The projects often involve tackling real-world problems. You’ll develop problem-solving skills by breaking down complex challenges into manageable tasks and finding creative solutions. 11. Collaboration and Documentation Working on projects in MATLAB encourages collaboration and the documentation of your code and findings, which are essential skills for teamwork and knowledge sharing. 12. Project Management Managing and completing MATLAB projects requires organizational skills, time management, and goal setting, which are transferable to various professional settings. Why Opt for MATLAB Projects? Engaging in MATLAB projects offers several compelling reasons: 1. Practical Application MATLAB is a versatile platform used in academia and industry for solving real-world issues. Through projects, you can apply theoretical knowledge to practical scenarios, enhancing your understanding and skills. 2. Skill Development MATLAB projects cultivate a wide range of skills, including programming, data analysis, and mathematical modeling, which are highly transferable and sought after in many professions. 3. Interdisciplinary Applications MATLAB is not limited to a specific field; it’s used in diverse domains such as engineering, finance, biology, and physics. This versatility allows you to explore various areas of interest and adapt your skills to different contexts. 4. Research Opportunities MATLAB is a common tool in research. Engaging in MATLAB projects can open doors to research collaborations, enabling you to contribute to cutting-edge advancements in your field of study. 5. Career Advancement Proficiency in MATLAB can be a valuable asset on your resume, making you more attractive to employers in technical and scientific fields. 6. Problem-Solving MATLAB projects often involve complex problem-solving, honing your ability to analyze challenges, devise solutions, and make informed decisions. 7. Portfolio Building Completing MATLAB projects creates a portfolio showcasing your practical skills and problem-solving abilities, which can impress potential employers or academic institutions. 8. Personal Growth Working on projects in MATLAB fosters perseverance, creativity, and self-confidence as you overcome obstacles and see tangible results. Join the ML Courses online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. MATLAB Project Ideas The following are some of the most exciting matlab projects with source code so that you can test your skills. Let’s get started: 1. Build a Car Parking Indicator Parking a car can be tricky. It requires precision and a lot of practice. You can use MATLAB to make things easier for the driver, however, by building a car parking indicator. You can take inspiration from various parking indicator systems.  An automated car parking indicator would alert the driver when the car is too close to an object. This way, the driver can avoid those objects and turn the vehicle accordingly. You can build a car parking indicator for private parking spaces or open spaces. Such a system can have many benefits: The driver would save time and park his/her car more efficiently. Parking spaces would also be used more efficiently. The chances of a vehicle getting damaged would decrease drastically. Your system can guide the driver to a nearby suitable parking space. You can take it a step further and add the functionality of suggesting a parking space only if it’s available. Maybe your system can determine if a car park has open slots or not, and it can indicate a parking space to the driver of the vehicle accordingly. The sensors can co-ordinate and help in guiding the driver to an open and nearby parking slot. Here’s more info on this car parking indicator project.  2. Use Artificial Neural Network for Image Encryption Privacy issues have become highly prevalent in recent years. This is one of the best matlab project ideas for mechanical engineering for you on this list if you take an interest in cybersecurity and cryptography. You can perform image encryption by taking the help of Artificial Neural Networks (ANNs in short).  Image encryption can prevent unauthorized parties from viewing and accessing images. This way, your data can remain safe. In simple terms, image encryption hides its information. In image encryption, you convert the original plaintext into ciphertext (which can seem like a bunch of nonsense). You can save and transmit this ciphertext over your network, and at the receiver’s end, the ciphertext would convert into the original plaintext.  Neural Networks are machines that behave similarly to how a human brain functions. You can encrypt images on the sender’s end through one ANN and use another ANN to decrypt the image on the receiver’s end. You can use MATLAB to build a complete image encryption system that uses Artificial Neural Networks. After completing this project, you’d be familiar with cryptography as well.  3. Design and Apply an Electronic Differential System An Electronic Differential System allows vehicles to balance them better while turning or running on curved paths. Automotive manufacturers use this system in place of the mechanical differential. This system provides every driving wheel with the required torque and enables multiple wheel speeds.  In a curved path, the vehicle’s inner and outer wheels would have different rotation speeds as the inner wheels would require a smaller radius. An Electronic Differential System uses the motor speed signals and steering wheel command signal to determine the required power for every wheel, so they get the necessary torque. Must Read: Free nlp online course! It’s an advanced technology that offers many advantages, which its mechanical counterpart fails in providing. For example, the electronic differential is lighter than mechanical differential in terms of weight. The wheel with the least traction wouldn’t limit the torque as it would with a mechanic differential. These systems respond faster and offer many functionalities unavailable in the other one, such as traction control. You can use ml projects for final year to design and implement an electronic differential system. You’ll need to create an embedded system design as well for better application. Also try: 13 Exciting IoT Project Ideas & Topics For Beginners 4. Build a MATLAB Based Inspection System with Image Processing In this project, you’ll build a MATLAB-based inspection system. Machine vision is becoming an accessible technology in the manufacturing industry because of its versatility. And one of the most significant areas where machine vision can find use is in the inspection stage of product development. Quality inspection is necessary to make sure the product doesn’t have any defects.  You can use MATLAB to create an automated inspection system, and you’ll have to employ image processing. With machine vision image processing, you can perform multiple tasks at once: Counting the number of dark and light pixels Discovering blobs of joined pixels in an image Segmenting a part of an image or change the representation Recognizing patterns in an image by matching templates Reading barcode and 2D code. You can perform many other tasks with machine vision. Your automated inspection system would have to determine whether to accept the final product or reject it. It will make the manufacturing process far more efficient and effective.  Read : 5 Ways Intelligent Automation Helps Your Business Grow 5. Perform Image Encryption and Verification with Chaotic Maps The project is a little different from the one we’ve discussed previously. In this project, you’ll use chaotic maps to encrypt images on the block and steam levels. There is n number of chaotic maps present that generate keys for encryption, so there would be n number of equations involved. Every equation can have n number of constants.  All of these constants would have specific values (random numbers). You can use a neural network to produce a particular series of numbers for image encryption. For image authentication, you’d have to create a simple algorithm to ensure that the sender and receivers are the right people.  Chaos maps would make the encryption secure through substituting the image with the cover image and encrypting the former n times. Such secure encryption would ensure that your end product remains free from brute force attacks and differential attacks.  Also try: Python Project Ideas and Topics 6. Measure an Object’s Diameter in an Image by using MATLAB Computer vision is a prominent field of study. It finds applications in many areas due to its unique utility. You can use MATLAB to measure an object’s diameter in an image.  This application can find uses in many areas where you can’t find the diameter of an object physically. For example, suppose you need to measure the size of a building. In this case, the physical measurement would be nearly impossible, so you’ll need to use computer vision. Your MATLAB script should first import the image, separate the required object from the background, and in the end, use MATLAB functions to find the object’s diameter. While this project might seem quite simple, it will help you showcase your image processing skills while also highlighting your knowledge of multiple MATLAB functions. Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses 7. Use MATLAB to Automate Certificate Generation This project is also among the beginner-level MATLAB project ideas. In this project, you’ll create an automated certificate generator by using MATLAB. Many institutions certify companies according to their performance and achievements. Educational institutions also generate report cards and certificates for their students. You can create an automated certificate generator, which will make this process efficient and straightforward.  This project idea might seem too simple, but you can make it complicated by adding the functionality of generating detailed reports for large datasets.  8. Create Light Animations with MATLAB and Arduino This is one of the beginner level MATLAB projects on our list. In this project, you’ll use MATLAB and Arduino to create a graphical user interface to control the lighting patterns of multiple lights. By controlling their lighting pattern, you can create various light animations. Using a GUI will allow you to perform many other tasks while running the animation.  We recommend using Arduino Uno for this project. It’d be the hardware of this project, and the software would be the Arduino IDE. You can connect the Arduino Uno board with the required lights. After you’ve connected Arduino Uno with MATLAB, you’ll be able to create simple light animations with the same.  It’s an easy project, but it’ll surely help you explore real-life MATLAB applications and help you realize its versatility. After you’ve made simple light animations, you can take this project a step further and add more lights to create more complex animations.  9. Log Sensor Data in MS Excel This project requires you to use Arduino Uno with MATLAB to log sensor data in MS Excel. You can add LM35 (a temperature sensor) to your Arduino interface, which would connect to MATLAB through ArduinoIO.  Once you’ve connected Arduino with MATLAB, you’ll need to create a program that transmits the sensor’s data into an Excel sheet. You’ll need to have MS Excel installed on your PC to complete this project. Once you’ve finished this project, you’d have a graphic user interface that allows you to see the logs of the sensor data. To take it a step further, you can add more sensors and log their data into the same excel file (or in multiple different files). This project will give you plenty of experience in using GUI with MATLAB.  10. Simulate an Artificial Neural Network Artificial Neural Networks are machines that imitate the functioning of a human brain. Their purpose is to mimic the behavior of a mind and act accordingly. In this project, you can simulate an ANN by creating models and training them.  Before you work on this project, you should be familiar with the basic concepts of artificial intelligence and machine learning. You’ll first need to create a data model that takes particular input and generates a particular output. First, you’ll need to train the model by giving it a list of inputs and outputs. Once you’ve prepared the model, you’d give the model a data list with no outputs.  After completing this project, you’d be familiar with artificial intelligence, machine learning, and relevant technologies.  11. Analyze and Design an Antenna While everything is becoming wireless, their connectivity relies largely on antennas. An antenna’s design can have a significant impact on its connection, power consumption, and data retention capabilities. The design should make the antenna compact while allowing it to have a substantial beam width to perform information transmission without any loss.  It’s an excellent project for anyone interested in electronics and communications. You should be familiar with the workings of antennas before you work on this project, however. For example, you should know about the ideal antenna pattern and how a real antenna works. You should also be familiar with the Yagi-Uda antenna, which is the most common TV antenna you see on rooftops. You can estimate (approximately) the operating frequency of such an antenna by viewing its length. You can build a MATLAB program that can perform such estimation with high accuracy and give you the required results.  12. Build a Circuit Design Calculator To build a circuit, you must calculate the component values by using the circuit theory and its formulae. Circuit theory is among the oldest and essential branches of electrical engineering. And its calculations take a lot of time and effort. You can create a MATLAB program that can perform those calculations and help an engineer design a better circuit. Not only will such a system save the user a lot of time, but it will also enhance the accuracy of circuit analysis by minimizing human error.  Your program can analyze and figure out circuit design with inductors, transistors, diodes, capacitors, and other critical components. The program can design highly complex circuits and solve problems accordingly.  In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses 13. Compress Images without Loss Modern cameras have become capable of taking highly detailed images. But an increase in an image’s level of detail also leads to a rise in its size. That’s why image compression technologies have become prevalent. You can use MATLAB to perform image compression as well.  In this project, you would aim to compress an image without compromising its quality. In other words, you’ll have to perform lossless image compression. To do so, you can use the discrete cosine transform algorithm. To find out how much loss took place while compressing the image, you can derive the mean-square error (also known as MSE) of your process. To implement these algorithms in MATLAB, you’ll have to use the required functions.  Also Read: Machine Learning Project Ideas 14. Perform Real-Time Face Detection with MATLAB Face detection can find applications in many areas. You can use face detection capabilities for image enhancement, security, as well as surveillance. While it’s quite natural for us humans to detect faces, we can’t say the same about computers. A simple change in lighting can cause various intra-class variations, that’s why it’s a complicated issue for machines.  You can build a MATLAB-based face detection system, and you can use the Viola-Jones algorithm. There are many other facial recognition algorithms, but we have chosen the viola-jones algorithm for this project.  It first creates a detector object, then takes the primary image, finds the necessary features, and annotates them. This project will give you experience working with facial recognition technology, which has gained popularity in many fields.  Know more: TensorFlow Object Detection Tutorial For Beginners 15. Build Laser Guidance for a Vehicle In this project, you’d develop a program that can use lasers to inform the vehicle of upcoming road conditions. This technology can be really helpful for harsh terrains (such as snowy roads, dirt roads, etc.). You’d need to develop an algorithm in MATLAB that converts the scan sequences into readable data so the user can see what kind of terrain is up ahead. This way, the driver can prepare him or herself accordingly and drive safely. An autonomous vehicle can use this technology, as well.  This project will help you get familiar with the application of MATLAB in automotive engineering. It’ll also help you understand how autonomous vehicles work. You can learn more about this project here.  Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau   Learn More About MATLAB We hope you liked our list of MATLAB project ideas. We’ve kept it as accessible as possible. You can bookmark it for future reference. This list would’ve also shown how versatile and powerful this technology is. From electronics to AI, you can use it in various industries for multiple applications. If you’re interested to learn more about MATLAB, machine learning, and its relevant topics, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms. You’ll find plenty of valuable resources to answer your questions. Refer to your Network! If you know someone, who would benefit from our specially curated programs? Kindly fill in this form to register their interest. We would assist them to upskill with the right program, and get them a highest possible pre-applied fee-waiver up to ₹70,000/- You earn referral incentives worth up to ₹80,000 for each friend that signs up for a paid programme! Read more about our referral incentives here.

Cybersecurity has never been more critical than now. With the ever-present threat of cyberattacks, there’s a growing demand for skilled professionals who can protect systems and data from malicious hackers. The term ‘hacker’ originally referred to a skilled programmer proficient in computer operating systems and machine code. However, the term has now evolved to refer to someone active in hacking operations. To counter the attacks of black hat hackers came into existence ethical hackers. Ethical hackers are positioned at the front line of defence owing to their knowledge to improve the system’s vulnerability of business organisations.  This blog presents the nitty-gritty of ethical hacker requirements, answering the pertinent question, ‘How to become a certified ethical hacker?’.  Understanding Ethical Hacking Before learning how to learn ethical hacking step by step, it is important to understand the basic concept of ethical hacking. Computer systems employ a range of security measures to protect stored data. Ethical hacking is a process of testing the strength of these security implementations. Ethical hackers simulate real-world hacking scenarios to detect underlying security flaws and fix those vulnerabilities in time. If not fixed, these vulnerabilities remain exposed to malicious hackers exposing confidential data, thus putting the company’s resources and reputation at stake.  A few alternative ethical hacking names are security and white hat hacking. Ethical hacking is integral to keeping security intact in computer systems or networks. Business organisations persistently check for vulnerabilities to protect their systems and networks by seeking ethical hackers’ services. Definition of an Ethical Hacker Ethical hackers are cybersecurity professionals who use hacking skills to protect computer systems and networks against cyberattacks. Ethical hackers differ from criminal hackers as they are authorised to test the vulnerabilities of computer systems by simulating an actual attack. Ethical hackers follow similar methods and use the same tools as criminal hackers to carry out hacking. Ethical hackers have greater insight into computer networking, cryptography, and programming. The skills required for an ethical hacker are typically approved by CEH (Certified Ethical Hacker) certification. Role of Ethical Hacker Before diving into “How to become an ethical hacker from scratch?”, learning what the role entails is essential. Ethical hacking is an authorised process of discovering potential cybersecurity threats and data breaches. Business organisations hire ethical hackers for their expertise in strengthening security systems. Their primary role is to look for ways criminal hackers can exploit security flaws to exploit confidential data maliciously.  Ethical hackers must be well acquainted with the working model of the system to which they provide protection. To fix security vulnerabilities, they need to look for different attacking methods that will possibly expose the data to malicious hackers. Additionally, ethical hackers must have a knack for continuous learning to keep themselves updated with the rapidly evolving hacking techniques. How To Become an Ethical Hacker Step-by-Step in 2023? Below are the steps prospective candidates must follow to become a hacker ethically: Step 1: Gain hands-on experience in LINUX/UNIX LINUX/UNIX is an open-source operating system that ascertains superior security for computer systems. It is one of the classic, widely used operating systems. Hence, being familiar with LINUX is important to become an ethical hacker.  Step 2: Master the mother programming language along with others C is referred to as the mother of all programming languages due to its primitive origin. C is used to write LINUX/UNIX completely. Hence, as an ethical hacker, it is important to be well-versed in a programming language that gives them the power to use the open-source LINUX operating system according to their wish.  Learning multiple programming languages eases operating a piece of code. Python, JavaScript, PHP, C++, and SQL are a few programming languages that are best for hackers to learn.   Step 3: Become adept at being anonymous The most significant step to follow in ethical hacking is to hide one’s identity. Ethical hackers must learn to master the skill of being anonymous to eliminate their traces online. They can use Anonsurf, MacChanger, and Proxychains to camouflage their identities.  Step 4: Learn about networking concepts in detail A good grasp of knowledge in networks and protocols aids in vulnerability exploitation. Gaining insights into various networking tools like Wireshark, Nmap, etc., helps execute smooth operations.  Step 5: Travel across the dark web The dark web refers to the hidden part of the web that remains invisible to search engines. Special software access and authorisation are required to penetrate it. Knowing the technology of the dark web is imperative for ethical hackers.  Step 6: Learn cryptography The encryption and decryption involved in cryptography are used in various aspects of information security, like data integrity, authentication, confidentiality, etc. An ethical hacker must learn to identify and break the encryption when required.  Step 7: Dig deeper into hacking Covering hacking topics like penetration testing, SQL injections, vulnerability assessments, and others is rewarding. An ethical hacker should know the latest security changes and system security tools.  Step 8: Always look for vulnerabilities Vulnerabilities in a system are loopholes leading to security breaches. An ethical hacker must identify and use these loopholes to exploit the system. A few vulnerability identification tools are OpenVAS Vulnerability Scanner, Nmap Vulnerability Scanner, Nessus Vulnerability Scanner, and Wapiti Vulnerability Scanner.  Check out our free technology courses to get an edge over the competition. How to Become an Ethical Hacker? – Skills Required Students often wonder how to become an ethical hacker after 12th. Anyone with the below-listed skills can become an ethical hacker:  A good command of programming languages  In-depth knowledge of networking  Proficiency in scripting  Ability to operate multiple operating systems Expertise in the backend database Exposure to servers and search engines Proficient in using the available tools in the market Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Career Stages in Ethical Hacking Patience is the key to establishing a career in ethical hacking. The field provides high-ranking job profiles to the aspirants in due course of time. Nevertheless, a great career start is also achievable within a short time.  1. Kick-starting  Aspirants who wonder how to become a certified ethical hacker can kick-start their careers by acquiring a degree in computer science. Alternatively, candidates can take up the CompTIA certification, comprising a two-part exam.  2. Network Supporting  The various activities involved in this stage are installing security programs, monitoring and updating, testing vulnerabilities, etc. This stage allows hackers to gain practical experience that will help to upscale their skills for future reference.  3. Network Engineering  Once the hackers gain experience in network support, they can escalate into network engineering. It offers a well-paid job profile with the authority of designing networks. This stage marks the beginning of becoming a security expert. Hence, obtaining certain security certifications, such as CISSP, Security+, etc., is mandatory.  4. Working as an expert in Information Security In this crucial stage as an ethical hacker, you will be introduced to information security for the first time. As an information analyst, the hackers will examine the system and strengthen the network’s security through firewalls and other relevant software. Ethical hackers must be well-versed in penetration testing and know tools like Tufin, AlgoSec, RedSeal, etc.  In this stage, ethical hackers can opt for CEH (Certified Ethical Hacker) certification as the course will help them to evolve into efficient ethical hackers.  Check Out upGrad’s Software Development Courses to upskill yourself. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Why Becoming an Ethical Hacker Is Beneficial? A few benefits of becoming an ethical hacker are:  Introduces the hidden aspects  Ethical hackers implement new techniques and discover new security breaches that remain unknown to the organisation. Ethical hacking introduces concepts like web application security that help establish an excellent career.  Makes the hackers’ mindset easy to understand Sound knowledge of ethical hacking enables white hat hackers to comprehend how black hat hackers think. This is of utmost importance when searching for security vulnerabilities. Offers high-paying jobs  A certified ethical hacker is better suited to land a high-paying job at a reputable corporation. With the demand for certified cybersecurity experts rising daily, the supply is still lagging. Hence, this is a lucrative career opportunity with a great future outlook. Gives liberty in picking out your field of work Once you become an ethical hacker, you can choose the field you want to work in based on your goals and passion. You can work in a multinational company or own a small start-up.  In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Salary Details for Certified Ethical Hackers: Top 5 Countries With the rapid growth of cyber threats, the need for proficient ethical hackers has risen multifold, and demand will likely rise in the coming years. This, combined with the development of AI and ML, positions ethical hackers attractively in the market. The tables below show the salary ranges for a few job roles, including India, in ethical hacking based in different countries:  Canada  Average annual salary- C$88,000 Job Title Average annual salary range Security Analyst C$50,000 – C$1.01 lakh Information Security Analyst C$57,000 – C$1.05 lakh Cyber Security Manager C$1.04 lakh – C$1.57 lakh Senior Security Consultant C$87,000 – C$1.16 lakh Security Engineer C$86,000 – C$1.33 lakh   Australia  Average annual salary- AU$1 lakh Job Title Average annual salary range Cyber Security Analyst AU$60,000 – AU$1.29 lakh Security Engineer AU$77,000 – AU$1.11 lakh Penetration Tester AU$58,000 – AU$1.38 lakh  Security Architect, IT AU$1.35 lakh – AU$1.76 lakh Chief Information Security Officer AU$1.42 lakh – AU$2.41 lakh   Singapore Average annual salary- S$69,000  Job Title Average annual salary range Cyber Security Engineer S$6000 – S$1.47 lakh Security Engineer S$33,000 – S$1.05 lakh Penetration Tester S$5000 – S$1.13 lakh Information Security Analyst S$54,000 – S$1.03 lakh Information Security Manager S$78,000 – S$1.5 lakh   UAE Average annual salary- AED 1.58 lakh Job Title Average annual salary range Cyber Security Engineer AED 14,000 – AED 5.49 lakh Security Engineer AED 49,000 – AED 2.36 lakh Penetration Tester AED 15,000 – AED 1.71 lakh Security Analyst AED 1.02 lakh – AED 2.92 lakh Chief Information Security Officer AED 2.95 lakh – AED5.60 lakh   India Average annual salary- INR 7.26 lakh    Job Title Average annual salary range Security Engineer  INR 2.96 lakh – INR 2m Penetration Tester INR 1.98 lakh – INR 2m Security Analyst INR 2.65 lakh – INR 9.95 lakh Information Security Analyst INR 2.43 lakh – INR 1m Certified Ethical Hacker INR 1.99 lakh – INR 5m Conclusion  Given the job opportunities, ethical hacking is an excellent career choice in the current market. The field holds immense opportunities for cybersecurity aspirants. The certified courses shape the students to fit their job roles with industry-relevant skills. Explore this lucrative career by enrolling in a cybersecurity course online and enhancing marketability.

Managing domain names and directing traffic efficiently is pivotal in the intricate web of cloud computing and web hosting. Enter AWS Route 53, Amazon Web Services’ highly versatile and scalable Domain Name System (DNS) web service. If you’ve ever wondered how websites find their way to your browser or how to create resilient and performant infrastructure, you’re in the right place. This blog will unravel the mysteries of Amazon Route 53 and provide a comprehensive understanding of how it works and its vital role in modern internet architecture. What Is Route 53 in AWS? Amazon Route 53 is a robust cloud service offering essential functionalities like DNS management, domain name registration, and health-checking services. Developers and businesses use it as a reliable, cost-effective way of leading end users directly to Internet applications.  Route 53 in AWS is an intermediary between user requests and infrastructure in AWS (e.g. instances, load balancers, and storage services) or external infrastructure outside it – such as external websites accessed via API calls).  Characteristics of AWS Route 53  Amazon Route 53 offers many features that elevate its DNS and traffic routing capabilities. Here are the significant functionalities that make Route 53 a powerful tool in the AWS ecosystem: 1. Route 53 resolver It lets you get recursive DNS for your Amazon VPCs, AWS Outposts, or on-premises networks. You can create conditional forwarding rules and endpoints to resolve custom names, whether hosted in private hosted zones or on your on-premises DNS servers. 2. Resolver on Outpost endpoints Establish a Connection between Route 53 Resolvers on Outpost racks and DNS servers in your on-premises data centre. This facilitates DNS query resolution between on-premises resources and Outposts racks. 3. Route 53 resolver DNS firewall Protects recursive DNS queries within Route 53 Resolver by enabling the creation of domain lists and firewall rules for outbound DNS traffic. 4. Application Recovery Controller (ARC)   Readiness check Maintain ongoing audits of your resources distributed across Availability Zones or Regions to ensure their readiness for recovery. Routing control Using integrated on/off switches with DNS records allows you to effectively manage traffic failover during incidents. Safety rules Ensures compliance with specific rules during failover, safeguarding automated recovery actions and maintaining system availability. 5. Traffic flow Offers beneficial traffic management on a global scale, routing end users based on geo-proximity, health, latency, and more, optimising application performance. Understanding the Working of Amazon Route 53  Amazon Route 53 is crucial in translating human-readable domain names into IP addresses and making websites and applications accessible online. Here’s how it works: 1. DNS resolution  When users enter a domain name (like www.example.com) in their browser, their device sends a DNS query to a DNS resolver. 2. DNS resolver  The DNS resolver, which could be provided by an internet service provider or a third-party service, forwards the query to a DNS authoritative server. 3. DNS authoritative server  This server is responsible for storing DNS records for the domain. In the case of Route 53, it manages these records. 4. Record retrieval  The authoritative server retrieves the DNS records associated with the requested domain, which include IP addresses or other relevant information. 5. Response  The DNS resolver receives the records and returns the IP address to the user’s device. 6. Connection  The user’s device uses the obtained IP address to connect with the appropriate server hosting the web application. Check out our free technology courses to get an edge over the competition. Benefits of Amazon Route 53 Amazon Route 53 offers a range of benefits that make it a valuable tool for managing domain names and DNS services. Some are: High availability  Route 53 has a global network of DNS servers strategically distributed worldwide, reducing latency and resolving DNS queries promptly. This makes it ideal for critical applications requiring uninterrupted service. Scalability  Route 53 can effortlessly handle many DNS queries, making it suitable for both small websites and large-scale applications. It automatically scales to manage increased traffic, ensuring consistent performance. Load balancing  Route 53 supports weighted routing, allowing you to distribute traffic across multiple resources or regions based on specified weights. This load-balancing capability ensures optimal resource utilisation and high availability. DNS traffic flow policy  You can create traffic policies to control how Route 53 responds to DNS queries, enabling advanced traffic management and routing scenarios. Health checks  Route 53 can perform health checks on web servers or load balancers. If a resource fails a health check, Route 53 will stop routing traffic to it until it passes the check again. Integration with AWS services  Route 53 integrates with other AWS services, simplifying DNS management for AWS resources like EC2 instances, S3 buckets, and load balancers. This integration ensures that DNS records are automatically updated as resources change. AWS Domain registration  Route 53 also provides domain registration and management services. Users need not register domains through a domain registrar before configuring Route 53 for DNS management. They can simply use Route 53 to manage and register domains. Cost-effective  Route 53 offers a pay-as-you-go pricing model, making it cost-effective for businesses of all sizes. You only pay for the DNS queries and hosted zones you use. Some Amazon Route 53 Limitations to Keep in Mind While Amazon Route 53 is a powerful and reliable DNS service, it does have some limitations, as mentioned below: Complexity  For users unfamiliar with DNS and AWS routing policies, Route 53’s advanced features can be complex to configure correctly. Misconfigurations can lead to service disruptions or suboptimal routing. AWS domain pricing  While Route 53 offers competitive pricing, costs can increase as DNS queries and hosted zones increase. Users should carefully monitor their usage and select the most cost-effective AWS routing policy. Propagation delay  DNS changes can take time to propagate globally, affecting how quickly updates to DNS records take effect. Route 53 has a low propagation delay, but it’s not instant. Limited logging  Route 53 provides basic logging and metrics, but users seeking more comprehensive DNS traffic analysis may need third-party tools or services. No reverse DNS (rDNS) for EC2  While Route 53 supports rDNS for certain AWS domain resources, it doesn’t support it for Amazon Elastic Compute Cloud (EC2) instances. Users requiring rDNS for EC2 instances must use other methods. Check Out upGrad’s Software Development Courses to upskill yourself. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Amazon Route 53 Alternatives to Choose From Here are some notable alternatives to Amazon Route 53: 1. Google Cloud DNS  Google Cloud DNS is Google’s managed DNS service. It offers low-latency and reliable DNS resolution, integration with Google Cloud services, and global anycast IP addresses. It’s suitable for those already using Google Cloud Platform. 2. Cloudflare  Cloudflare provides DNS services with a focus on security and performance. It offers protection against DDoS attacks, global content delivery, and analytics. Cloudflare’s free plan includes basic DNS management. 3. Microsoft Azure DNS  Microsoft’s Azure DNS is tightly integrated with Azure services and provides scalable and reliable DNS hosting. It’s a good choice for those using Azure as their cloud platform. AWS Route 53 Security Features Here are some key security features and practices associated with Amazon Route 53: DDoS mitigation Route 53 is designed to scale and absorb DDoS attacks automatically. It can help protect your domain names and ensure their availability during attacks. Domain name registration protection AWS offers a Domain Name System Security Extensions (DNSSEC) service for added security in domain name registration, helping to prevent DNS-related attacks. Private DNS Route 53 supports private DNS namespaces that can only be resolved within your VPCs. This is useful for internal applications that should not be accessible over the public internet. Encryption Route 53 supports DNS over HTTPS (DoH) and DNS over TLS (DoT), adding an extra layer of encryption to DNS queries and responses. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Routing Policies Amazon Route 53 Offers Here are some key Amazon Route 53 routing policies that help achieve various availability, performance, and failover goals: Simple routing policy In this straightforward policy, your DNS records can be linked with one or more resources, such as an Elastic Load Balancer or S3 bucket, to distribute traffic evenly among them. This method ensures traffic flows seamlessly. Weighted routing policy With this policy, you can assign different weights to different resources. For example, you might direct 70% of the traffic to one resource and 30% to another. It’s useful for load-balancing traffic across resources with varying capacities. Failover routing policy  Failover allows you to configure a primary and a secondary resource (usually in a different location or region). Route 53 automatically directs traffic to the secondary resource if the primary resource becomes unavailable. This is useful for creating high-availability configurations. Geolocation routing policy  Geolocation routing enables you to direct user traffic based on their geographical location, for instance, routing European users towards resources located in a European data centre while sending North American users towards resources in a North American one. Weighted alias record  This policy is similar to the weighted AWS Route 53 routing policy but can be used with alias records that route traffic to AWS resources like ELB, CloudFront distributions, or S3 buckets. Understanding the Route 53 Pricing Structure Here’s a breakdown of the AWS Route 53 pricing details: DNS zones The first 25 hosted DNS zones cost $0.50 per zone per month. Beyond the initial 25 zones, additional zones are charged at $0.10 per zone per month. 2. Policy records For each DNS name (e.g., www.abc.com), the Route 53 cost is $50 per Standard query for the first query. For queries beyond the first, the AWS domain pricing is tiered: $0.40 per million queries for the first billion queries per month. $0.20 per million queries per month for queries beyond the initial billion. 3. Latency-based routing queries Route 53 charges $0.60 per million for the first billion monthly queries. For queries exceeding the initial billion, the price drops to $0.30 per million queries per month. 4. Geo-based queries For the first billion queries per month, Route 53 charges $0.70 per million queries. Subsequent queries are billed at $0.35 per million queries per month. 5. Health checks The first 50 AWS endpoints are covered at no charge. Beyond the initial 50 endpoints, each additional endpoint incurs a fee of $0.50 per endpoint per month. 6. Domain registration Domain registration in AWS costs vary based on the top-level domain (TLD) and are determined according to the pricing sheet. Use the AWS Pricing Calculator or Route 53 pricing page on the AWS website to estimate costs based on your usage patterns and requirements. Route 53 charges may depend upon factors like the number of hosted zones, DNS queries per second scaled across geographic locations, and how widely distributed resources are. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Conclusion In the dynamic world of cloud services and networking, Amazon Route 53 remains a cornerstone for many enterprises, facilitating the seamless flow of data and services across the internet’s vast landscape. Moreover, alternative DNS and traffic management solutions may emerge as the cloud computing landscape evolves, offering different features and pricing models. Businesses must stay informed about these alternatives to make strategic choices for their infrastructure.

Keeping up with the fast-paced nature of project development fueled by technological progress is challenging, to say the least. To function efficiently, organisations have to quickly respond to changes and adapt to the needs of the development projects. For this, organisations have adopted some methodologies and frameworks of project management like Agile, Scrum, Kanban, etc., which ensure the smooth execution of the projects.  However, each of these project management methodologies is different from one another. How to be certain which one to opt for while completing a project? In this blog, we will discuss in detail what is Agile and Scrum methodology each and what are the differences between the two.   What Is Agile Methodology? If you are new to software development and unclear about What is Agile methodology in project management?” the answer is simple. Agile is an approach to project management that incorporates the use of short iterative steps towards the attainment of project completion. But what makes Agile the most often preferred choice? The reasons are many. With the help of Agile, one can work flexibly on projects, adapt to changes quickly, and maintain constant communication. In Agile methodology, the small incremental steps help monitor the progress smoothly and make it easier to incorporate the changes as and when required. This methodology focuses on quick delivery and easy adaptability rather than following a strict plan.  Agile methodology is a process of continuous feedback. It was initially incorporated for the purpose of software development but is now being used for a variety of projects.  What Is Scrum Methodology? The question that follows next is What is Scrum project management? Scrum is a framework within Agile methodology that organisations use to execute tasks in an organised manner and achieve a common goal. If you are wondering what is Agile Scrum methodology, here is the explanation. While Agile is a project management philosophy, Scrum is an Agile framework. Scrum helps break down the project into smaller plans, reducing its scope. Teams working on the project can allot distinct timelines for each activity.  The use of Scrum is found in both software development as well as in other tasks like research and development. Scrum uses several tools, meetings, and roles for the smooth execution of a project delivery. In Scrum, teams manage themselves independently, learn from experiences, and quickly adapt to changes.  Agile vs Scrum: Head-to-Head Comparison Now that we have covered what is Scrum in Agile, look at the table below to understand the difference between Agile and Scrum based on certain parameters. Parameter  Agile  Scrum Methodology Agile is an approach to project management.  It is a framework of Agile methodology. Prioritisation In Agile, prioritisation means deciding the order in which the Agile team will work on achieving the goals of a project.  In Scrum, prioritisation takes place based on value.  Alternative Waterfall is a good alternative to Agile. Kanban is a good alternative to Scrum. Delivery  Agile ensures continuous delivery so that any change can be incorporated immediately.  In Scrum, delivery is made after the completion of each sprint.  Collaboration mode  Members from various cross-functional teams collaborate with one another for the execution of a task.  The task is operated through regular sprint meetings, sprint planning, and retrospectives.  Design and execution  Simple.  Innovative.  Flexibility  Low.  High.  Leadership The project execution is taken care of by a Project Head.  Cross-functional teams work in collaboration with one another to ensure the smooth execution of the projects.  Agile Methodology vs. Scrum Mentioned below is how Agile and Scrum are different from one another based on each of the parameters.  What they are To answer ‘what is Agile process’, Agile is a project management methodology based on an incremental and iterative approach. Whereas Scrum is an Agile implementation where customers are delivered incremental builds every two or three weeks.  Scope of use Agile can be used in projects managed by an expert management team. It can be used in small environments. On the other hand, Scrum is suitable for use in projects subject to frequent change.  Leadership Leadership is an important aspect of Agile methodology. There is a project head who takes the mantle of the project. Scrum incorporates the use of a cross-functional team that has good self-organising abilities. The entire team is responsible for the progress of a project.  Flexibility While Agile is slightly rigid, and there is little room for changes to be incorporated frequently, Scrum’s ability to react quickly is noteworthy.  Roles In Agile, several cross-functional teams interact face-to-face and collaborate to execute tasks effectively. In Scrum, fixed tasks are assigned to individuals designated as Scrum masters, product owners, and team members.  Delivery In Agile, the progress must be delivered to the customers frequently to seek their feedback regularly. In Scrum, the builds are delivered to the clients after each sprint so they can leave their feedback on the same and suggest any change they might be looking for.  Design and execution  In Agile, the design and execution must be kept simple, whereas in Scrum, the design and execution can be made experimental and innovative.  Check out our free technology courses to get an edge over the competition. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Agile and Scrum: A Study of Their Similarities By now, we have already understood that Agile and Scrum are complementary, so we cannot draw many comparisons between the two.  Now that we have seen the Scrum and Agile differences, let us glance through the similarities.  Adaptability: Both Agile and Scrum are designed in a way that can adapt to changes. They do not follow a fixed plan. This helps to deal with the ever-changing demands of the clients.  Provide similar outcomes: To be able to measure the progress, it is important to focus on the outcomes. Agile and Scrum help speed up the development process by allowing faster results and better transparency.  Foster collaboration: Both Agile and Scrum facilitate collaboration among the team members. As for Scrum, there are daily 15-minute events where all the members, like the Scrum Master and Product Owner, participate.  Organisations can reach their sprint goal faster through these meetings and adjust to changes accordingly. This helps to improve communication and collaboration between the cross-functional teams and build trust between them. Check Out upGrad’s Software Development Courses to upskill yourself. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Agile vs. Other Methodologies Each of the project management methodologies has its own advantages and disadvantages, making them suitable for a certain kind of project. In this blog, we have discussed what is Agile project management, what is Scrum methodology, and what is the difference between Agile and Scrum till now.  Let us now look at the key differences between Agile, Waterfall, and Kanban so you can thoroughly understand them and make an informed decision while choosing the right project management framework.  1. Agile vs. Waterfall Are you wondering what differentiates Agile from the Waterfall methodology? Agile and Waterfall are distinct project management methodologies that aid in successfully completing a project. The Waterfall methodology follows a linear approach to management, whereas the Agile methodology is flexible and can support changes at the different stages of the project. In the Waterfall model, a sequential order is followed. The project development team moves to the next development phase only after completing the previous step.  In Agile methodology, the project development follows a constant iterative development and testing process. Unlike the Waterfall model, the development and testing activities occur simultaneously. This also facilitates seamless communication between customers, managers, developers, and testers.  2. Agile vs. Kanban Agile methodology is a new approach to software development that emphasises continuous iterative development occurring throughout the software development life cycle. This allows the provision to incorporate changes according to the immediate requirements and feedback of the clients.  On the other hand, Kanban is a project management methodology which acts like a visual signal that ensures that work is noticeable to the people. There is a board known as the Kanban Board, which displays the task workflows. This way, the flow of tasks can be optimised between the different teams. This also helps to maintain coordination between the teams- each team can remain updated about the progress of another team throughout the development phase.  Agile vs. Scrum: Which One Is the Best? Picking the best-suited project management approach can be difficult despite understanding what is Agile and Scrum. While Agile is a project management approach, Scrum is a part of this wide Agile umbrella.  Agile is best suited for ongoing projects that lack clarity from the beginning. It is ideally suited for industries prone to constant and unpredictable changes. Agile is a broader approach that stresses flexibility, collaboration, and adaptability.  On the other hand, Scrum is a framework encompassed within the Agile methodology. It provides a structured approach and helps manage complex projects through iterative development.  However, based on the project’s requirements, one can choose between either. Agile may be better suited if a project needs to follow an adaptive and flexible approach. For projects that require specific roles, artefacts like Product Backlog, Sprint Backlog, etc., and events like Daily Standup, Sprint Review, Scrum is more suitable. You can also look for the difference between Scrum and Kanban, both Agile frameworks, and choose whichever fits best to your project requirements. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Conclusion There is no one-size-fits-all when choosing the most appropriate project management method. Based on the requirements of the organisation, its goals and objectives, and other criteria, one has to look for the methodology that will suit them the best.  Agile is a broad philosophy guiding software development with flexibility at its core, while Scrum is a specific framework within the Agile universe, offering a structured approach to project management. Teams can choose to adopt Agile principles and customise their processes or follow the more defined structure of Scrum. If you look forward to upskilling or knowing more about these project management methodologies, you can enrol in one of the certification courses offered by platforms like upGrad. These courses will help you better understand concepts like Agile methodology vs. Scrum, what is Waterfall methodology, Kanban vs. Scrum, and so on. Knowledge of these frameworks will help you make informed decisions and better execute a project.

In the ever-evolving landscape of cloud computing, Azure Active Directory or Azure AD has emerged as a cornerstone in identity and access management. Whether you’re a seasoned IT professional or just beginning to explore the intricacies of cloud services, understanding Azure AD is essential.  Managing several user logins simultaneously can be challenging at times. Access to Azure services can simplify and alleviate the burden of their work. Services such as SQL database, machine learning, and Azure active directory domain services can be made available to employees by assigning a unique user ID and password for each service. Azure AD makes it easier for the administrator to operate multiple user logins.  This comprehensive guide aims to demystify Azure AD, providing you with a thorough understanding of what it is, how it works, and why it’s a pivotal component in the Microsoft Azure ecosystem. Read through the blog to learn more about AD domain service. Understanding the Concept of Azure Active Directory Azure Active Directory can be defined as a multi-tenant and cloud-based directory of Microsoft. Besides this, Azure Active Directory also performs Microsoft’s identity management service. With the help of Azure AD, the employees of an organisation can sign up and access multiple services. These services remain accessible everywhere over the cloud and require just one set of login credentials. Azure AD is designed to facilitate secure authentication and authorisation while simplifying the management of user identities and access. The two types of resources that Azure AD gives access to the employees are:  External resources- These resources include Microsoft Office 365, SaaS applications, the Azure portal, etc.  Internal resources- These resources include the apps that are on your corporate network alongside the apps designed by your own organisation.  A traditional on-premise setup with Active Directory can be amalgamated with Azure AD by simply using AD Connect. This will help manage the accessibility of the cloud application.  Windows Azure Active Directory: How Does It Work? Microsoft’s newly designed Azure AD supports cloud infrastructure using REST APIs for data transmission. The data from one system passes to other cloud systems and applications that support REST.  Azure AD has a flat structure in a single tenant. For example, imagine the tenant as a circle, and that circle surrounds your data. You can have control over the data that is inside the tenant. However, you can apply control over your data only until it leaves the circle. 1. Users and Groups These are the building blocks for Azure AD. Users can be further categorised into groups that behave identically. Users in Azure AD can be both from outside and inside. This implies that you can let people join your organisation’s tenant from outside and grant them certain permissions that make them a part of your organisation. When approached correctly, this acts as an additional security to the organisation’s data.  2. Adding User and Groups to Azure AD The different ways users and groups can be added to the Microsoft Azure Active Directory are:  Using Connect Azure AD to sync users from Windows AD. The enterprises that have Windows AD already mostly opt for this method.  Manually creating users in the Azure AD Management Portal.  Using PowerShell to add new users. If not installed, connect to Azure AD Powershell by installing the Microsoft Online Powershell Module.  Programming the process with the help of the Azure AD Graph API.  3. Customer Domains Adding a customer domain to Azure AD enhances the user’s experience while migrating to the new system. This is how a default Azure AD domain looks:  @notarealdomain.onmicrosoft.com  Once you configure Azure AD domain services, your users can work more conveniently, thus improving user experience.  Check out our free technology courses to get an edge over the competition. Windows AD vs Azure AD: Studying the Comparisons The table below shows the difference between Windows Active Directory and Azure Active Directory:  Field  Windows Active Directory Azure Active Directory Authentication Windows Active Directory uses Kerberos and NTLM for authentication. Azure Active Directory uses cloud-based protocols. Communication Uses a Lightweight Directory Access Protocol (LDAP) for communication. Uses Representational State Transfer (REST) APIs for communication. Entitlement Management Administrators assign users to groups. Administrators organise users into groups.  Network Organisation The network organisation in Windows Active Directory comprises organisational units, domains, and forests. The network organisation in Azure Active Directory is a flat structure of users and groups.  Desktops Desktops are governed by Group Policy (GPOs). Desktops can use Microsoft Intune to join. Devices There is no mobile device management. Mobile device management exists. Servers  Manages servers by GPOs or other on-premise servers. Manages servers by using domain services. Reasons for Using Azure AD: The Benefits  In today’s world, where remote work and cloud services are the norm, securing user identities and managing access to resources is paramount. Azure AD offers a robust solution to these challenges, providing a foundation for secure, seamless, and efficient identity management and access control.  Below are a few reasons why using Azure AD can benefit users: Boosts security Azure AD implements certain authentication policies like multi-factor authentication and conditional access that are more powerful. This ensures that the accessibility to the company’s resources is limited only to authorised users.  Centralises management Azure AD enables centralised management for user identities. This feature lets you create, modify, and delete users from any connected application and service. This does not require managing each application separately. Hence, this acts as a time-saver and reduces the chance of errors.  Highly scalable This means adding and removing users and applications can be easily done. Business organisations can benefit from it as they scale up or down.  Carries out a smooth integration  Azure AD provides a seamless integration that makes managing user identities easier. You can work with many applications and services simultaneously, including Microsoft 365.  Cost-effectiveness Azure AD is a cloud-based solution that eliminates the need to purchase on-premises hardware and software. Hence, it helps save money while getting the job done. Azure Active Directory pricing is flexible, with multiple options available.  Check Out upGrad’s Software Development Courses to upskill yourself. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Features and Licensing of Azure AD The two licenses that give access to Azure AD are- Azure AD Premium licenses Microsoft Online Services You can access all the non-paid Azure features with a Microsoft Azure license or Microsoft 365.  The Power BI Premium licenses below give access to Azure Premium features: Premium P1 Premium P2 licenses Below are the features of Azure AD: Authentication Azure Active Directory offers strong authentication services. It has a feature that enables users to manage and reset self-service passwords. Application Management It uses services like the My Apps portal, Application Proxy, SaaS apps, etc., to manage cloud and on-premises apps.  Business-to-Business Under Azure AD, managing guests and external partners has become easy. You can also maintain your own corporate data simultaneously.  Business-to-Customer (B2C) Azure Directory permits users to customise others’ interaction with their apps. For example, users can customise how others can log in, sign up, or handle their profiles. Reports and monitoring Users can acquire reports of the security and usage patterns in their work environment.  Protection of identity It helps in threat detection and risk-based authentication. It also resolves suspicious actions, if any.  Identity governance  You can manage the identity of your organisation through business partners, vendors, app access controls, etc.  User enterprise Azure AD provides the management of license assignments and app access. You can set up representatives through groups and administrative roles.  Privileged Identity Management (PIM) With this feature, users gain access to the resources of Azure AD Directory Services. This also includes Microsoft Online Services such as Microsoft 365 and Intune.  Azure AD for developers  The apps that can be built with the help of Azure AD can sign in to all the Microsoft identities. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Azure Active Directory Connect Active AD Connect combines the on-premise directories with Azure Active Directory. The amalgamation provides accessibility to both cloud and on-premise resources with a common identity.  The features of Azure AD Connect are:  Synchronises a hashed user with Azure AD through an on-premise AD password. Provides a pass-through authentication through which users can have a similar password on-premise and on the cloud. Validates the identification of users and groups by matching them with the cloud. Acts as a central monitoring system. Azure AD: Common Attacks Against It With the easy accessibility to the internet, Azure AD is prone to brute force attacks. The attackers mostly use deceptive usernames and passwords to intrude into Azure AD accounts. This method of attack is known as credential stuffing.  Another widespread attack is the phishing method. In this method, credential theft occurs, giving the attackers direct access to your tenant.  Azure skeleton key attack is an attack on Azure AD Connect. This method of attacking occurs when the server, Azure Agent, is installed. The attackers take advantage of the Pass-Through Authentication in this method.  Other types of attacks include Man-in-the-Middle attacks, DDoS attacks, token theft and replay attacks among others. Azure AD: Securing and Managing Devices Azure Active Directory login supports a strong password policy with multi-factor authentication that can resist force attacks. By staying vigilant and implementing security measures, organisations can significantly reduce the risk of security breaches and protect their Azure AD environment from common attacks.  Some best practices that can mitigate these threats and enhance Azure AD security include: Implement multi-factor authentication for an added layer of security. Encourage users to create strong passwords and change them regularly. Use Azure AD ID Protection to detect and mitigate risks. Establish policies based on location, risk and device to control access. Constantly monitor user and administrative activities for suspicious behaviour. Educate users about security best practices, including recognising and avoiding phishing attempts.  In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Conclusion  Azure AD acts as the identity control plane in a cloud-based or hybrid environment, ensuring users have secure and seamless access to resources. It centralises identity management, offers robust security features, and integrates with various applications and services, making it a fundamental component in modern cloud-based IT ecosystems. 

As a Bachelor of Computer Applications (BCA) student, one of the essential aspects of the curriculum is completing a project that showcases their expertise and knowledge in the field of computer science. The BCA final year project topics enable students to apply their theoretical knowledge to practical use and demonstrate their creativity and problem-solving skills. Enroll for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. This article will explore the top 13 project ideas for BCA students. However, if you are a BCA fresher, these can also be an exceptional starting point to enhance your skills.  How to Choose a Project Topic in Computer Science? Choosing a major project for BCA final year is a critical decision that can influence your academic and professional journey. Whether you’re a student working on a class project, a researcher exploring new avenues, or a developer building a portfolio, selecting the right topic is essential for success. Here’s a comprehensive guide to help you make an informed decision. Identify Your Interests and Strengths Start by assessing your interests and strengths. What areas of computer science fascinate you the most? Are you more inclined towards software development, artificial intelligence, data science, cybersecurity, networking, or something else? Consider your existing knowledge and skills – tackling a topic you’re comfortable with can boost your confidence and productivity. Narrow Down the Scope Computer science is vast, so narrowing your focus is important. Instead of choosing a broad topic like “machine learning,” consider a specific subtopic, such as “image classification using convolutional neural networks.” A narrower scope allows you to delve deeper into the subject and produce a more impactful project. Stay Updated with Trends Keep yourself informed about the latest trends and advancements in computer science. Follow conferences, journals, and online communities to discover emerging technologies and research areas. Projects related to current trends showcase your relevance and provide opportunities for collaboration and networking. Research Existing Work Before finalizing BCA project topics, research existing projects and literature. This helps you understand what has already been done, identify gaps in knowledge, and refine your project idea. Building upon existing work with innovative twists can lead to unique and valuable contributions. Consider Practicality While ambitious projects are exciting, ensure they are feasible given your available resources – time, expertise, and tools. A practical project that you can complete successfully is more valuable than an overly ambitious one left incomplete. Address Real-World Problems Solving real-world problems adds a sense of purpose to your project. Consider how your project can address challenges in sectors like healthcare, education, environment, or business. Projects with practical applications tend to be more impactful and attractive to potential employers or collaborators. Consult with Professors or Mentors If you’re a student, consult your professors or mentors for guidance. They can provide insights into relevant research areas, suggest potential project topics, and help you refine your ideas. Their experience can help you avoid common pitfalls and ensure your project is on the right track. Balance Challenge and Familiarity While you should choose a topic that challenges you, don’t go too far beyond your comfort zone. Striking a balance between learning something new and leveraging your existing skills ensures you can progress steadily without getting overwhelmed. Brainstorm and Evaluate Brainstorm a list of potential project topics. For each idea, evaluate its significance, feasibility, potential impact, and personal interest. Create a matrix or scoring system to compare and rank these ideas objectively. Plan for Long-Term Engagement Consider how your project can lead to future opportunities. Will it open doors for further research, career growth, or skill development? A project with the potential for long-term engagement can be more rewarding in the grand scheme. Stay Adaptable As you dive into your chosen project, be open to adjustments. Your initial plan may evolve as you gain deeper insights into the topic. Flexibility is key to accommodating unexpected challenges and opportunities. Selecting a project topic in computer science needs careful consideration of the learner’s interests, skills, practicality, relevance, and long-term goals. It’s a decision that can shape your academic and professional trajectory, so invest time in researching, brainstorming, and consulting with mentors. Remember that choosing a topic is as important as the topic itself, as it sets the stage for a successful and fulfilling project experience. Top BCA Projects for Final Year and Beginners Web Development Web development is essential in today’s digital age, as the internet has become the primary mode of communication, information sharing, and commerce. With the rise of e-commerce and online marketplaces, web development has become a lucrative field with ample job opportunities. As a result of this, doing a project in the field of web development opens students up to varied career opportunities. Here are some of the ideas you can explore in the field of web development:  1. E-Commerce Website By developing an e-commerce website, students can learn how to create an online store and implement various features such as shopping carts, payment gateways, and order tracking systems. E-commerce is a rapidly growing field with increasing demand, and developing an e-commerce website can provide students with practical experience and a valuable skill set. An example of an e-commerce website is Amazon, one of the world’s largest online marketplaces. 2. Content Management System A content management system (CMS) is a software application that enables users to create, manage, and publish digital content, such as websites, blogs, and social media posts. By developing a CMS, students can learn how to build a customisable and scalable platform that allows content creators to collaborate and publish content efficiently.  3. Web Application Developing a web application requires knowledge of programming languages, frameworks, and web development tools. By building a web application, students can learn how to develop robust and scalable software solutions that can be accessed from anywhere.  4. Responsive Website A responsive website is a website that can adapt its layout and content based on the device’s screen size, such as desktops, laptops, tablets, and smartphones. Developing a responsive website requires knowledge of HTML, CSS, JavaScript, and various front-end frameworks. With the increasing use of mobile devices to access the internet, responsive website development is a critical skill for web developers. Candidates can seek inspiration for their responsive website from one such leading example, which is Airbnb. 5. Social Network Website A social network website is a platform that allows users to connect and communicate with each other through various features such as messaging, profile pages, and news feeds. By developing a social network website, students can learn how to create a scalable and interactive platform that allows users to share information and engage with each other. With a wide range of people using social media platforms as a medium to connect with their loved ones, creative projects on social media websites can be an excellent project for BCA final year candidates.  Mobile App Development Mobile app development is a rapidly growing field as smartphones and tablets have become ubiquitous, and people increasingly use mobile apps for various tasks such as communication, entertainment, and productivity.  Here are some of the project ideas and topics that you can explore in the domain of mobile app development:  6. Android App Development Android is the most popular mobile operating system, with a market share of over 70%. Developing Android apps requires knowledge of Java, Android SDK, and various development tools. By developing Android apps, students can learn how to create robust and scalable apps that can be distributed through the Google Play Store.  7. iOS App Development iOS is the second most popular mobile operating system, with a market share of around 28%. Developing iOS apps requires knowledge of Swift, iOS SDK, and various development tools. By developing iOS apps, students can learn how to create highly valued apps for the iPhone and iPad. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification Data Science and Analytics Data science and analytics involve extracting, processing, and analysing data to gain insights and make informed decisions. With the rise of big data and the increasing importance of data-driven decision-making, data science and analytics have become essential fields in various industries, including finance, healthcare, and marketing. Some of the most sought-after BCA final year project topics that you can explore in the field of data science and analytics include:  8. Data Visualisation Data visualisation is data representation in graphical or visual formats such as charts, graphs, and maps. Developing data visualisation skills enables students to present complex data in an easy-to-understand and visually appealing manner. By working on data visualisation projects, students can explore the implementation of various data modelling and visualisation tools for effective representation. A COVID-19 dashboard is an example of an efficient data visualisation project which provides real-time data on the spread of the virus. 9. Machine Learning By approaching ML projects, students can navigate how to develop predictive models for diverse applications such as image recognition, natural language processing, and recommendation systems. Machine learning is a highly sought-after skill in the market, with various job opportunities available. Hence, working on an ML project, such as creating a recommendation system used by Netflix to suggest movies and TV shows to users, can offer an edge to your candidature.  10. Data Mining Data mining is widely implemented in various industries, such as finance, healthcare, and marketing, to gain insights and make informed decisions. Working on data mining projects such as those implemented in Blockchain, your candidature would be able to exhibit your knowledge of in-demand data mining expertise. 11. Natural Language Processing Natural language processing (NLP) involves the development of algorithms and models that can understand and analyse human language. Students can learn how to develop chatbots, language translators, and sentiment analysis tools by developing NLP projects. The best NLP project for BCA final year candidates can work on may include creating a chatbot.  Software Development The field of software development can be seen as evergreen – in the sense that there will always be good job opportunities for skilled software developers. With that in mind, here are some of the project ideas that you can explore if you’re willing to work in the field of software development:  12. Desktop Application Aspirants can work on creating user-friendly and functional software solutions for various applications such as productivity tools, entertainment, and education. Examples may include spreadsheets, project management tools, etc.  13. Game Development Game development is one of the most engaging BCA final year project topics, which enables learners to explore the tech as well as the creative part of working on a project. Building interactive computer games eligible to run on gaming consoles, computers, mobile phones, or other smart devices can be an excellent method to flaunt your development skills. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Importance of BCA project ideas and topics Working on developing BCA projects under any of the mentioned domains enables candidates to gain practical experience and valuable skills that are highly sought after in the job market. It is worth noting that the market potential for these project ideas is vast, with various job opportunities available in various industries. Following the unprecedented growth of the computer science field worldwide, candidates with a BCA degree and a strong candidature are highly likely to get recruited by leading tech firms such as Accenture, Capgemini, Google, Deloitte and more! Along with working on project topics for BCA final year and completing your bachelor’s in the domain, obtaining an MSc or an MBA in a specialised field can significantly strengthen your candidature, further enabling learners to acquire astonishing work opportunities. You can also check out our free courses offered by upGrad in Management, Data Science, Machine Learning, Digital Marketing, and Technology. All of these courses have top-notch learning resources, weekly live lectures, industry assignments, and a certificate of course completion – all free of cost! Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Conclusion In conclusion, BCA 6th sem project topics provide students with a wealth of opportunities to gain in-demand skills and work experience for a deep understanding. These projects heavily contribute to elevating your chance of bagging lucrative industry opportunities as well as boosting your experience across domains.  As a BCA final year student of a fresh graduate, if you’re aiming to obtain in-demand skills, upGrad’s Master’s in AI and ML Certification offered under Liverpool John Moores University can be an excellent addition to your career!  The course enables learners to participate in learning a dynamic curriculum with topics such as NLP, AI Strategy, Exploratory Data Analytics, Data Lifecycle, and more, helping them become industry professionals in no time!