February 2024

Hate Speech Detection Using Machine Learning Today, the world of the internet and social media provides a platform where people freely express their opinions and thoughts in text form. But some people used their freedom in the wrong way to direct hate towards individuals for race, religion and many more things. For this, cyber security organizations increase the number of cases day by day for cyberbullying. Also, many organizations have found a solution to detect hateful speech. So, in the digital era machine learning is now important for detecting hate speech. In this article, we will examine the concept of hate speech, the issues associated with detecting it, and how machine learning can help. What is Hate Speech? Hate speech is defined as any speech, act, behavior, writing, or display that may cause violence or adverse action against or by a specific individual or group or that criticism or threatens a specific individual or group because of characteristics such as race, ethnicity, religion, sexual orientation, disability, or gender. The Challenges in Detecting Hate Speech Because hate speech is constantly changing and language is personal, it can be challenging to identify it. Because hate speech can appear in various ways and settings, it can be challenging to define and recognize. Furthermore, hate speech can also be hidden and sensitive, which makes it harder to recognize using normal methods. Machine Learning and Hate Speech Detection It is possible to train machine learning algorithms to detect patterns in text data and, using these patterns, identify hate speech. More accurate detection is made possible by machine learning algorithms that can distinguish between hate speech and non-hate speech through the analysis of vast datasets of text data. So, for this, you should train the machine learning model for an accurate dataset. The Future of Hate Speech Detection The identification of hate speech appears to have a bright future as long as technology keeps developing. Machine learning algorithms are evolving and getting better at accurately identifying hate speech. Furthermore, complicated text data analysis and comprehension are becoming more straightforward due to advances in natural language processing. We hope these developments will create a more secure and welcoming online community. Recommended Reading Stock Price Prediction system using Machine Learning Real-Time Object Detection Using Machine Learning Ecommerce Sales Prediction using Machine Learning How to build Hate Speech Detection using Machine Learning? To build  hate speech detection system in python you need to follow these steps. Step 1: Download dataset First of all, you need to download the dataset. This dataset contains multiple tweets. And our system will identify if a tweet is offensive or not. You can download the dataset from here. Download here: twitter_dataset Step 2: Import Libraries These lines import the necessary libraries for data manipulation (pandas and numpy), text processing (nltk), and machine learning (sklearn). Pyton import pandas as pd import numpy as np from sklearn.feature_extraction.text import CountVectorizer from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier import re import nltk from nltk.util import pr import pandas as pd import numpy as np from sklearn.feature_extraction.text import CountVectorizer from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier import re import nltk from nltk.util import pr Step 3: Initializing Stemmer and Stopwords Here, we initialize a stemmer and stopwords. The stemmer is used to reduce words to their base form, and stopwords are words that are commonly used in a language and are not considered significant for analysis. Pyton stemmer=nltk.SnowballStemmer("english") from nltk.corpus import stopwords import string stopword=set(stopwords.words("english")) stemmer=nltk.SnowballStemmer("english") from nltk.corpus import stopwords import string stopword=set(stopwords.words("english")) Step 4: Loading the Data This line loads the dataset from the CSV file “twitter_data.csv” into a pandas DataFrame called data. Pyton data=pd.read_csv("twitter_data.csv") data=pd.read_csv("twitter_data.csv") Step 5: Preprocessing the Data These lines create a new column called labels based on the class column. The class column is mapped to the corresponding label (0 to “Hate speech”, 1 to “Not offensive”, and 2 to “Neutral”). Then, we select only the tweet and labels columns for further processing. Pyton data['labels']=data['class'].map({0:"Hate speech",1:"Not offensive",2:"Neutral"}) data = data[["tweet","labels"]] data['labels']=data['class'].map({0:"Hate speech",1:"Not offensive",2:"Neutral"}) data = data[["tweet","labels"]] Step 6: Tokenization and Stemming This function tokenizes the text (splits it into words), removes stopwords and non-alphabetic characters, and then stems the words using the stemmer. Pyton def tokenize_stem(text): tokens = nltk.word_tokenize(text) tokens = [word for word in tokens if word not in stopword] tokens = [word for word in tokens if word.isalpha()] tokens = [stemmer.stem(word) for word in tokens] return tokens def tokenize_stem(text): tokens = nltk.word_tokenize(text) tokens = [word for word in tokens if word not in stopword] tokens = [word for word in tokens if word.isalpha()] tokens = [stemmer.stem(word) for word in tokens] return tokens Step 7: Vectorization This code creates a CountVectorizer object with the tokenize_stem function as the tokenizer. It then fits and transforms the tweet column of the data DataFrame into a sparse matrix X. Pyton vectorizer = CountVectorizer(tokenizer=tokenize_stem) X = vectorizer.fit_transform(data['tweet']) vectorizer = CountVectorizer(tokenizer=tokenize_stem) X = vectorizer.fit_transform(data['tweet']) Step 8: Splitting the Data This line splits the data into training and testing sets. X_train and y_train contain the features and labels for the training set, while X_test and y_test contain the features and labels for the testing set. Pyton X_train, X_test, y_train, y_test = train_test_split(X, data['labels'], test_size=0.2, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, data['labels'], test_size=0.2, random_state=42) Step 9: Training the Classifier This code creates a DecisionTreeClassifier object and trains it on the training data. Pyton classifier = DecisionTreeClassifier() classifier.fit(X_train, y_train) classifier = DecisionTreeClassifier() classifier.fit(X_train, y_train) Step 10: Taking User Input This line prompts the user to enter a tweet. Pyton tweet = input("Enter your tweet: ") tweet = input("Enter your tweet: ") Step 11: Vectorizing User Input: This code vectorizes the user input tweet using the same CountVectorizer object. Pyton tweet_vector = vectorizer.transform([tweet]) tweet_vector = vectorizer.transform([tweet]) Step 12: Predicting the Label This line uses the trained classifier to predict the label for the user input tweet. Pyton prediction = classifier.predict(tweet_vector) prediction = classifier.predict(tweet_vector) Step 12: Predicting the Label This line prints the predicted label for the user input tweet.

How to Download image in HTML? You want to add the functionality of downloading images in HTML. And you want to use something other than JavaScript. You want to do this in HTML and CSS. Well, in this article, I am going to help you, and you will learn how to download an image in HTML. But you should know HTML and CSS. If you are a beginner, don’t worry. I will give you the source code for this feature. But I will also tell you how to download images in HTML, CSS and JavaScript. Javascript is essential if you want to make your website interactive. So, In this article, I will tell you two methods to download images in HTML. One method is downloading images using HTML and CSS; the other uses HTML, CSS and JavaScript. Method 1: Download image in HTML Step 1: Create a Button in HTML Step 2: Design this button in CSS Step 3: Add functionality in HTML Note: Image should be in same folder where you are creating HTML file. Creating a Button in HTML First of all, we will create an HTML Button. Here is the source code to create a button in HTML. HTML <!DOCTYPE html> <html> <head> <title>Download image in HTML</title> </head> <body> <br><br> <button>Download Now</button> </body> </html> <!DOCTYPE html> <html> <head> <title>Download image in HTML</title> </head> <body> <br><br> <button>Download Now</button> </body> </html> Creating a Button in HTML As we have created a button, we will style and make it beautiful. CSS <style type="text/css"> *{ margin: 0; padding: 0; } body{ text-align: center; } button{ width: 150px; height: 30px; background-color: orangered; color:white; border:2px solid orangered; font-size: 18px; font-family: sans-serif; border-radius: 5px; } button:hover{ background-color: transparent; color:orangered; } </style> <style type="text/css"> *{ margin: 0; padding: 0; } body{ text-align: center; } button{ width: 150px; height: 30px; background-color: orangered; color:white; border:2px solid orangered; font-size: 18px; font-family: sans-serif; border-radius: 5px; } button:hover{ background-color: transparent; color:orangered; } </style> Adding download image functionality in HTML Now, we are ready to add download image functionality in HTML. Here is the complete code for you. HTML <!DOCTYPE html> <html> <head> <title>Download image in HTML/title> </head> <style type="text/css"> *{ margin: 0; padding: 0; } body{ text-align: center; } button{ width: 150px; height: 30px; background-color: orangered; color:white; border:2px solid orangered; font-size: 18px; font-family: sans-serif; border-radius: 5px; } button:hover{ background-color: transparent; color:orangered; } </style> <body> <br><br> <a href="webpage.zip"><button>Download Now</button></a> </body> </html> <!DOCTYPE html> <html> <head> <title>Download image in HTML/title> </head> <style type="text/css"> *{ margin: 0; padding: 0; } body{ text-align: center; } button{ width: 150px; height: 30px; background-color: orangered; color:white; border:2px solid orangered; font-size: 18px; font-family: sans-serif; border-radius: 5px; } button:hover{ background-color: transparent; color:orangered; } </style> <body> <br><br> <a href="webpage.zip"><button>Download Now</button></a> </body> </html> Recommended Reading 10 web development projects for beginners How to change color of text in JavaScript How to make portfolio website in Javascript? Method 2: Download image in HTML, CSS and JavaScript Step 1: Create a Button in HTML Step 2: Design this button in CSS Step 3: Add JavaScript to add functionality Creating a Button in HTML and CSS Here we will create a HTML button and than we style it in CSS. HTML <!DOCTYPE html> <html> <head> <title>Download Image on Button Click</title> <style> body { display: flex; justify-content: center; align-items: center; height: 100vh; margin: 0; background-color: #f5f5f5; } button { padding: 10px 20px; font-size: 16px; cursor: pointer; background-color: #FF8C00; color: #fff; border: none; border-radius: 5px; } button:hover { background-color: #FF4500; } </style> </head> <body> <button id="downloadButton">Download Image</button> </body> </html> <!DOCTYPE html> <html> <head> <title>Download Image on Button Click</title> <style> body { display: flex; justify-content: center; align-items: center; height: 100vh; margin: 0; background-color: #f5f5f5; } button { padding: 10px 20px; font-size: 16px; cursor: pointer; background-color: #FF8C00; color: #fff; border: none; border-radius: 5px; } button:hover { background-color: #FF4500; } </style> </head> <body> <button id="downloadButton">Download Image</button> </body> </html> Adding JavaScript and download functionality HTML <!DOCTYPE html> <html> <head> <title>Download Image on Button Click</title> <link rel="stylesheet" type="text/css" href="styles.css"> </head> <style> button { padding: 10px 20px; font-size: 16px; cursor: pointer; } </style> <body> <button id="downloadButton">Download Image</button> <script> document.getElementById('downloadButton').addEventListener('click', function() { var link = document.createElement('a'); link.href = 'image.jpg'; link.download = 'image.jpg'; document.body.appendChild(link); link.click(); document.body.removeChild(link); }); </script> </body> </html> <!DOCTYPE html> <html> <head> <title>Download Image on Button Click</title> <link rel="stylesheet" type="text/css" href="styles.css"> </head> <style> button { padding: 10px 20px; font-size: 16px; cursor: pointer; } </style> <body> <button id="downloadButton">Download Image</button> <script> document.getElementById('downloadButton').addEventListener('click', function() { var link = document.createElement('a'); link.href = 'image.jpg'; link.download = 'image.jpg'; document.body.appendChild(link); link.click(); document.body.removeChild(link); }); </script> </body> </html> Recommended Reading 10 web development projects for beginners HTML Course How to change color of text in JavaScript JavaScript course CSS Tutorial Can I use any image on the internet in my HTML code? No, you should only use images that you have permission to use. This includes images in the public field, images licensed for usage, and images designed by you. What is the best image format for the web? JPEG is best for images, PNG for graphics, and GIF for animations. How can I optimize images for the web? You can optimize images for the web by reducing the file size of the image, using the appropriate image format, and using image compression tools. Final Year Projects Data Science Projects Blockchain Projects Python Projects Cyber Security Projects Web dev Projects IOT Projects C++ Projects Top 20 Machine Learning Project Ideas for Final Years with Code 10 Deep Learning Projects for Final Year in 2024 10 Advance Final Year Project Ideas with Source Code Realtime Object Detection AI Music Composer project with source code Stock market Price Prediction using machine learning E Commerce sales forecasting using machine learning c++ Projects for beginners Credit Card Fraud detection using machine learning 30 Final Year Project Ideas for IT Students Fake news detection using machine learning source code 10 Web Development Projects for beginners Hand Gesture Recognition in python portfolio website using javascript 10 advanced JavaScript project ideas for experts in 2024 Python Projects For Beginners with Source Code Data Science Projects with Source

Best 20 cyber security final year projects In a time where cyberattacks and data breaches are common, protecting your project from possible weaknesses is critical. By guiding you through the complexities of cybersecurity, this article will ensure that your final-year project satisfies academic requirements and remains strong in the face of technological problems. This article lists the 20 best cybersecurity final-year projects. These cybersecurity final year projects will help to get good grades in academics. What is Cyber Security? Cybersecurity is a combination of technologies, practices, and policies to protect against harm, malware, viruses, hacking, data theft, and unauthorized access to networks, devices, programs, and data. Security of the privacy of all business data against internal and external attacks and natural disaster-related errors is the main objective of cyber security. 1. Secure Mobile App Development Framework In my list, the first Cyber security final year project is a secure mobile app. It aims to ensure the safety and integrity of mobile applications. This system includes the mobile app source code, user authentication details, and encryption keys. The Secure Mobile App Development Framework utilizes cybersecurity protocols to process and safeguard this data, aiming to create applications resistant to security threats. The Secure Mobile App Development Framework employs cybersecurity measures such as secure coding practices, encryption algorithms, and regular security updates. The system also provides developers with tools for code analysis, vulnerability scanning, and adherence to cybersecurity best practices. 2. Biometric Data Protection Measuring and examining an individual’s particular behavioral or physical characteristics is called biometrics. Fingerprints, iris patterns, face features, voice patterns, hand geometry, and behavioral patterns like typing and rhythm are some of these characteristics. Since biometrics can identify people with great accuracy by utilizing their unique features, it is mainly used for identification and authentication. Biometrics plays an integral part in cybersecurity by strengthening the security of numerous systems and procedures. Defense-in-depth (layers of protection) can be supported using biometrics for identity identification and multi-factor authentication. The Biometric Data Protection system utilizes cybersecurity protocols to process, store, and authenticate this data securely to prevent unauthorized access and protect individuals’ privacy. 3. Vulnerability Scanning For IoT Designing an IoT vulnerability scanning system in a cybersecurity-related project involves finding and fixing possible security weaknesses in Internet of Things (IoT) devices. The Vulnerability Scanning System utilizes cybersecurity protocols to assess, analyze, and report on IoT devices’ security posture, aiming to address potential risks proactively.   4. Cybersecurity Awareness Training This project aims to inform and train people about security measures, possible risks, and best practices, which is part of developing a cybersecurity awareness training system for a cybersecurity-related project. The Cybersecurity Awareness Training system utilizes cybersecurity protocols to deliver practical and engaging training sessions to enhance users’ awareness and understanding of cybersecurity principles. The Cybersecurity Awareness Training system employs cybersecurity measures such as secure content delivery, encrypted user data, and authentication protocols to ensure the confidentiality and integrity of training materials. 5. Supply Chain Security Assessment Creating a Supply Chain Security Assessment system for a cybersecurity-related project involves developing a solution to evaluate and improve the security of the entire supply chain. The Supply Chain Security Assessment system uses cybersecurity protocols to conduct thorough assessments, identify vulnerabilities, and recommend security measures, aiming to strengthen the overall resilience of the supply chain.   6. Ransomware Mitigation Tools Ransomware Mitigation Tools are suitable for cybersecurity final-year projects. It aims to create a solution to detect, prevent, and respond to ransomware attacks. This system includes real-time data from network activities, endpoint behavior, and threat intelligence feeds. The Ransomware Mitigation Tools utilize cybersecurity protocols to analyze, identify, and neutralize potential ransomware threats to protect systems and data from unauthorized encryption.   7. Threat Hunting Platform Threat Hunting Platform for a cybersecurity-related project aims to create a solution to proactively search for and identify potential threats within an organization’s network. Threat hunting, often known as cyber threat hunting, is the proactive practice of examining a company’s network to find and remove dangers. Companies better understand how secure they are and how to strengthen it by doing this.  Cyber threat-hunting technologies create various approaches based on data collected by security analysts and threat intelligence. In addition to using user and entity behavior analytics to monitor and defend the network and operating systems, these cyber threat-hunting tools also use this technology. The Threat Hunting Platform employs cybersecurity measures such as machine learning algorithms, behavior analysis, and secure data storage. 8. Securing Smart Cities You are securing Smart Cities in a cybersecurity final year to get good marks for the examiner in your final year. This system measures to protect the interconnected systems and data within urban environments. This system includes data from innovative city components like IoT devices, sensors, and infrastructure networks. The Securing Smart Cities system utilizes advanced cybersecurity protocols to assess vulnerabilities, monitor activities, and respond to potential threats, aiming to ensure the integrity and security of innovative city operations. 9. Privacy Preserving Data Analytics A group of methods known as “privacy-preserving data” enables data analysis without disclosing the identities of the subjects whose data is being examined. They are creating a Privacy-Preserving Data Analytics system for a cybersecurity-related project to develop a solution that allows organizations to analyze data while protecting individual privacy. The input for this system includes sensitive data sets, user preferences, and analysis requirements. The Privacy-Preserving Data Analytics system utilizes advanced cybersecurity protocols to anonymize, encrypt, and process data, ensuring that personally identifiable information is protected during the analysis process. 10. AI enhanced Cyber Insurance This project aims to reduce cyber risks for insurance purposes; a solution using artificial intelligence must be developed for a cybersecurity-related project to create an AI-enhanced Cyber Insurance system. The AI-enhanced Cyber Insurance system uses advanced cybersecurity algorithms to analyze risk factors, identify possible dangers, and suggest insurance plans customized to the particular requirements of protected organizations. 11. Federated Learning For Privacy What is Federated Learning? This method of machine learning is distributed and involves training a

Top 10 Java final year projects with source code One of the most asked questions while selecting a topic for a final year or semester project is “Java Final Year Projects.” You start to wonder at that point, “What topic should you choose for your Final year project?” “Java, you have moved our world,” Think over this statement momentarily. One of the most widely used programming languages is Java. Additionally, it serves as the server-side language for most back-end development jobs, including Big Data and Android programming. Java is used in gaming, numerical computation, desktop, and other mobile computing. Also, Java is the most used programming language in the IT sector, with applications across practically all software development domains.  As a new software developer, we advise you to begin working on real-time and fully functional real-time Java applications. This article gives ten Java final-year projects. 1. Electricity Bill Management System It is one of the Good Java final year projects. The developer can experience every aspect of web development with this project, which also calls for a robust database on the back end. This system aims to simplify electricity bill management, provide accurate information, promote energy efficiency, and enhance the overall user experience. This system includes user data such as customer information, meter readings, and utility rate details. The system uses Java to process this information and perform calculations based on the electricity consumption. Java is employed to handle the logic for bill generation, ensuring accurate calculations and adherence to specific utility rate structures. Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning 2. Library management software Maintaining responsibility for a library has always been challenging. Having this system in place allows librarians to monitor every book. They will be able to learn everything there is to know. This system includes book details, user information, and transaction records. Java processes and manages this information, ensuring smooth library operations. Java is applied to implement the logic behind these features, enabling seamless data retrieval and interaction between the software components. The system also provides administrators with tools for cataloging, monitoring inventory, and generating reports. 3. Courses Management System This project is used to provide a user-friendly platform for managing educational courses. In this System, Enrollment records, student information, and course specifics are included. Java is utilized to process this data, facilitating the organization and manipulation of information related to courses and students. The system also offers administrators tools for course scheduling, grade management, and generating comprehensive reports. 4. Supply Chain Management system A user-friendly and effective platform for managing the supply chain may be created in this final year project using the Java programming language. This system includes data related to inventory, orders, and logistics. Java is utilized to process and manage this data, facilitating smooth coordination and optimization of the supply chain. The system also provides tools for supply chain managers to monitor and analyze key performance indicators, improving overall supply chain efficiency.   5. Smart City Project This Project is among the unique ideas for a Java project. If you can finish it successfully, you’ll receive high grades from your teachers. A Smart City Project involves the application of the Java programming language to create an integrated and intelligent urban management system. This Project includes data from various sources, such as sensors, public services, and infrastructure databases. Java is employed to process and manage this diverse data, facilitating the implementation of intelligent functionalities within the city. Because of Java’s flexibility, attractive user interfaces, effective data processing, and creative algorithms for effective urban planning are all made available. 6. Hospital Management System Creating a Hospital Management System of management system improves patient care and healthcare processes and gives hospital administrators valuable tools for resource management and overall healthcare delivery. This system includes patient records, medical data, and administrative details. Java processes and organizes this information, ensuring smooth coordination and optimization of hospital processes. The system also provides: Healthcare professionals with tools for monitoring patient care. Accessing medical histories. Facilitating timely and accurate decision-making.   Recommended Reading Stock Price Prediction system using Machine Learning Real-Time Object Detection Using Machine Learning Ecommerce Sales Prediction using Machine Learning 7. Online Banking System It aims to provide customers convenient and secure access to their financial information, enabling efficient online banking operations while prioritizing data protection and user experience. This system includes user account details, transaction data, and security information. Java is utilized to process and manage this data, ensuring the secure and efficient execution of online banking operations. The system also provides users with tools for managing their accounts, transferring funds, and accessing various banking services online. 8. Data Visualization Software The Data Visualization Software System aims to empower users with a deeper understanding of their data, facilitating data-driven decision-making through visually compelling and accessible representations of complex information. The input for this system includes diverse data sources, analytical models, and user preferences. Java is employed to process and organize this information, enabling the creation of visually engaging and informative data visualizations. The system also provides users with tools for customizing visualizations, exploring data details, and sharing insights with others. 9. Email Client Software They are creating an Email Client Software System utilizing Java programming to develop a user-friendly platform for managing and sending emails. This system includes user credentials, email content, and configuration settings. Java processes and collects this data, ensuring secure and efficient email communication. The system also provides users tools for composing emails, attaching files, and organizing their email inboxes. Recommended Reading AI Music Composer using Machine Learning Real-Time Object Detection Using Machine Learning 30 Creative Final Year Projects with Source Code 10. Online CV builder System It is one of the greatest and most ambitious concepts for a Java project. With the minimal information provided by the user, this system will produce a fully functional resume for him. Such a strategy is always dependable for last-minute CV preparation. This

Best 20 IOT projects for Final Year Are you a final-year student wishing to develop your skills in IOT technology? So, this article gives you the 20 most amazing IOT projects for the final year. These IOT projects are important for various industries and will help you get good grades in final year. What is Internet Of Things (IOT)? The network of physical items, or “things,” implemented with sensors, software, and other technologies to communicate and exchange data with other devices and systems over the Internet is known as the Internet of Things (IoT). These gadgets might be anything from simple everyday objects to highly advanced industrial instruments. Machines and computers are not the only types of IoT devices. Anything with a sensor and a unique identifier (UID) can be a part of the Internet of Things. Why should you develop IOT project for the final year? Developing projects based on the Internet of Things (IoT) in the Final year offers various advantages and aligns with the modern needs of the technology environment. Firstly, IoT projects allow students to discover and use current technology, improving their abilities and keeping up with market trends. In future, it will also help to get higher position jobs in the market, especially in the data science field. 20 IOT projects for Final Year with Source Code 1. Smart Agriculture System using IOT The first IOT project for the final year on my list is the smart agriculture system. Agriculture is important worldwide, and IoT is responsible for upgrading agriculture by utilizing advanced methods and tools to manage crops, land, and animals. Build this project we used IOT devices such as soil moisture sensors, weather stations, and crop health monitors. With the help of these IOT devices, we collect real time information about soil conditions, weather patterns, and the overall health of crops. These projects help the farmers to plan their activities based on upcoming conditions. In this project, the main feature is that when farmers use this system if soil moisture levels are detected to be low, the system can trigger an automated irrigation process. 2. Industrial IOT For Predictive Maintenance The next IOT project for the final year is the Industrial IOT system for Predictive Maintenance. This project helps industries improve their machinery. With the help of IOT systems, industrial companies monitor and check the maintenance of machinery. The features of this project are that it predicts the maintenance of machinery, predicts potential issues, and schedules maintenance alerts. When industrial companies use this system, they easily know that machinery now requires maintenance, whether it is further used or not, and which part of the machinery is default. 3. Health Monitoring Wearable using IOT According to the annual health report, 15% of the world’s population faces different health problems. Sometimes, patients face many challenges when going to the clinic. So, this wearable health monitoring system helps continue health monitoring and augment clinical treatment. This system gathers the user’s health through sensors used in the system. These sensors help monitor the user, such as the user’s heart rate, body temperature, and physical activity. This system sends notifications that the device is paired to a smartphone app. This IOT project can be a good project for the final year. Also, it is very helpful for every user. 4. Smart Traffic Management using IOT Nowadays, in urban mobility, ongoing population growth increases, and the result of the traffic congestion problem also increases day by day. As cities grow, there is an increased requirement to satisfy sustainability goals while examining traffic management techniques. So, the traditional methods of managing urban mobility in this day and age are showing their limitations. Additionally, the demand for an effective traffic control system is rising. All sizes of cities need digital solutions driven by technology to monitor and control traffic. They can assist in maintaining traffic volume, traffic jams at intersections, and clogged networks. So, this final year of the IOT project utilizes IOT technology to improve traffic movement and reduce congestion. With the help of sensors used in the system, real-time traffic conditions, vehicle movements and road occupancy information are collected. 5. Environmental Monitoring using IOT Nowadays, air pollution is becoming a bigger problem. To ensure everyone has a better future and a healthy lifestyle, the environment must be monitored and maintained under control. Here, this final year project offer an IOT-based environmental monitoring system that enables us to keep an eye on and assess the current state of the environment in specific locations. The device employs air sensors to detect the presence of dangerous chemicals or compounds in the atmosphere. It then continuously sends this data to a microcontroller, which then uses IOT to report it to an online server. When air quality sensor detects a rise in pollutant levels, the system can trigger alerts and send notifications. The objective of developing a smart city is to raise the standard of living by using technology to satisfy the demands of citizens and increase service efficiency. 6. Smart Waste Management using IOT Waste management is a global issue these days. According to World Bank data, at least 33% of the 2.01 billion tonnes of municipal solid waste produced worldwide each year is not managed in an environmentally responsible way. The Internet of Things is one technology that governments can employ to enhance waste management. IoT technologies provide evidence of this, as they are already integrated into common supply chains. The features of this system include Optimized waste collection routes, realtime monitoring of bin fill levels, and data on the types of waste generated. 7. Water Quality Monitoring using IOT It aims to assess and manage water quality in various environments continuously. This system is gathered through IoT devices such as water quality sensors deployed in water bodies. These sensors measure parameters like pH levels, chemical concentrations, and temperature. The Water Quality Monitoring System includes: Real-time data on water quality. Alerts for potential issues. Comprehensive reports are accessible to relevant authorities.

10 Unique C++ Projects with Source Code The C++ programming language is widely recognized for offering numerous advantages compared to the C language. The most interesting feature of this language is its support for object-oriented programming, which opens up a completely new realm of possibilities. We are pleased to provide you with over 10 C++ projects with source code for learners. One of the most popular programming languages that is widely used in many different industries, including games, operating systems, web browsers, and database management systems, is C++. Because it incorporates object-oriented programming concepts, such as the use of specified classes, into the C programming language, some refer to C++ as “C with classes.” Over time, C++ has continued to be a very useful language for computer programming as well as to teach new programmers how object-oriented programming operates. C++ Projects with Source Code 1. Bank Management System Project This project is a simple C++ Project with source code. Bank Management System Project in C++ uses C++ to manage banking operations like account creation, deposits, and withdrawals. Users interact with the system, providing input for tasks such as updating account details. C++ is chosen for its efficiency and versatility, allowing the implementation of features like data processing and user interfaces. The output includes updated account balances and transaction receipts displayed to users. C++ #include <iostream> #include <map> #include <string> using namespace std; class Bank { private: map<string, double> accounts; public: void createAccount(const string& name, double initialDeposit) { if (accounts.find(name) != accounts.end()) { cout << "Account already exists!" << endl; } else { accounts[name] = initialDeposit; cout << "Account created successfully!" << endl; } } void deposit(const string& name, double amount) { if (accounts.find(name) != accounts.end()) { accounts[name] += amount; cout << "Amount deposited successfully!" << endl; } else { cout << "Account does not exist!" << endl; } } void withdraw(const string& name, double amount) { if (accounts.find(name) != accounts.end()) { if (accounts[name] >= amount) { accounts[name] -= amount; cout << "Amount withdrawn successfully!" << endl; } else { cout << "Insufficient balance!" << endl; } } else { cout << "Account does not exist!" << endl; } } void checkBalance(const string& name) { if (accounts.find(name) != accounts.end()) { cout << "Balance for account " << name << " is: " << accounts[name] << endl; } else { cout << "Account does not exist!" << endl; } } }; int main() { Bank bank; // Sample usage bank.createAccount("Alice", 1000); bank.deposit("Alice", 500); bank.checkBalance("Alice"); bank.withdraw("Alice", 200); bank.checkBalance("Alice"); return 0; } #include <iostream> #include <map> #include <string> using namespace std; class Bank { private: map<string, double> accounts; public: void createAccount(const string& name, double initialDeposit) { if (accounts.find(name) != accounts.end()) { cout << "Account already exists!" << endl; } else { accounts[name] = initialDeposit; cout << "Account created successfully!" << endl; } } void deposit(const string& name, double amount) { if (accounts.find(name) != accounts.end()) { accounts[name] += amount; cout << "Amount deposited successfully!" << endl; } else { cout << "Account does not exist!" << endl; } } void withdraw(const string& name, double amount) { if (accounts.find(name) != accounts.end()) { if (accounts[name] >= amount) { accounts[name] -= amount; cout << "Amount withdrawn successfully!" << endl; } else { cout << "Insufficient balance!" << endl; } } else { cout << "Account does not exist!" << endl; } } void checkBalance(const string& name) { if (accounts.find(name) != accounts.end()) { cout << "Balance for account " << name << " is: " << accounts[name] << endl; } else { cout << "Account does not exist!" << endl; } } }; int main() { Bank bank; // Sample usage bank.createAccount("Alice", 1000); bank.deposit("Alice", 500); bank.checkBalance("Alice"); bank.withdraw("Alice", 200); bank.checkBalance("Alice"); return 0; } 2. Train Reservation System Project The concept of the project is a train reservation system to reserve train tickets for multiple destinations. In this project, C++ is used to implement the logic for seat availability, reservation confirmation, and the generation of booking records. C++ is utilized to handle data processing, implement user interfaces, and manage the overall flow of the reservation application. The language allows for implementing key functionalities required for a reservation system, providing users with a straightforward and reliable platform to manage their train travel plans. C++ #include <iostream> #include <map> #include <vector> #include <string> using namespace std; class TrainReservationSystem { private: map<int, int> availableSeats; // Map to store available seats for each train public: TrainReservationSystem() { // Initialize available seats for each train availableSeats[1] = 50; availableSeats[2] = 50; availableSeats[3] = 50; } void displayAvailableSeats() { cout << "Available Seats:" << endl; for (const auto& pair : availableSeats) { cout << "Train " << pair.first << ": " << pair.second << " seats" << endl; } } void bookTicket(int trainNumber, int numTickets) { if (availableSeats.find(trainNumber) != availableSeats.end()) { if (availableSeats[trainNumber] >= numTickets) { availableSeats[trainNumber] -= numTickets; cout << numTickets << " ticket(s) booked successfully for Train " << trainNumber << endl; } else { cout << "Insufficient seats available for Train " << trainNumber << endl; } } else { cout << "Invalid Train Number" << endl; } } void cancelTicket(int trainNumber, int numTickets) { if (availableSeats.find(trainNumber) != availableSeats.end()) { availableSeats[trainNumber] += numTickets; cout << numTickets << " ticket(s) canceled successfully for Train " << trainNumber << endl; } else { cout << "Invalid Train Number" << endl; } } }; int main() { TrainReservationSystem trainSystem; int choice, trainNumber, numTickets; do { cout << "nTrain Reservation System" << endl; cout << "1. Display Available Seats" << endl; cout << "2. Book Ticket" << endl; cout << "3. Cancel Ticket" << endl; cout << "4. Exit" << endl; cout << "Enter your choice: "; cin >> choice; switch(choice) { case 1: trainSystem.displayAvailableSeats(); break; case 2: cout << "Enter Train Number: "; cin >> trainNumber; cout << "Enter Number of Tickets: "; cin >> numTickets; trainSystem.bookTicket(trainNumber, numTickets); break; case 3: cout << "Enter Train Number: "; cin >> trainNumber; cout << "Enter Number of Tickets: "; cin >> numTickets; trainSystem.cancelTicket(trainNumber, numTickets); break; case 4: cout

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Can I upgrade to a paid plan on 000webhost? Yes, 000webhost offers paid plans with additional features and resources for those who require more advanced hosting solutions. You can upgrade your plan at any time to unlock premium features. Final Year Projects Data Science Projects Blockchain Projects Python Projects Cyber Security Projects Web dev Projects IOT Projects C++ Projects Top 20 Machine Learning Project Ideas for Final Years with Code 10 Deep Learning Projects for Final Year in 2024 Realtime Object Detection 10 Advance Final Year Project

Best 20 artificial intelligence projects for final year As a computer science student, you may question how to leave a long-term mark on your final year project. Selecting a project in the final year is important for your grades and future life. Artificial intelligence projects for last year can help you get more chances and improve your resume. Artificial Intelligence is a dynamic and evolving field that offers various possibilities. In this article, we will explore a variety of innovative and interesting Artificial Intelligence projects for the final year. What is artificial Intelligence? When many people hear the term artificial intelligence, they usually think of robots. But artificial intelligence has almost every element of our lives, from voice assistants to independent vehicles. Artificial Intelligence is also defined   An Intelligent Being Made by Mankind Able to carry out tasks wisely, even without clear instructions. Able to reason and behave humanely and reasonably.In your final year, diving into the world of AI allows you to add to this rapidly developing area. AI projects display technological expertise and show your ability to deal with real-world problems. Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning Artificial Intelligence projects for final year with source code 1. Augmented Reality- Based Learning Platform For Distance Education The first artificial intelligence project for the final year is an augmented reality-based learning platform. Building a digital environment where students utilize gadgets to access interactive instructional information is the first step in creating a raised reality-based learning platform for distance learning. Through increased reality capabilities, the platform improves student learning by enabling them to visualize concepts and participate in virtual activities. Source Code 2. Development of a low cost Air pollution Monitoring System  A Low-Cost Air Pollution Monitoring System is designing a solution that allows easy and affordable air quality tracking. In this system, input comes from sensors that measure various pollutants in the air, such as particulate matter and gases. These sensors are placed strategically in different locations. The data collected is then processed and sent to a central system. The output is accessible information about air quality levels, which can be displayed on a user-friendly interface or made available through a mobile app. This project aims to provide an affordable and efficient way for communities to monitor their air quality, enabling individuals to make informed decisions about their health and environment.   Research Paper 3. Real Time Video Analytics and Object Detection This project is a very simple artificial intelligence project for the final year. Real-time video Analytics and Object Detection systems involve developing a technology that instantly analyzes videos and identifies objects within them. In this system, the input consists of video feeds from cameras or other sources, and advanced algorithms process this information in real time. The system can detect and recognize objects, people, or events within the video frames. The output is valuable insights and data, such as identifying specific objects or tracking movement patterns. Source Code 4. Design and implementation of a Smart Home Using Artificial Intelligence It aims to make homes more convenient and energy efficient while providing users with an easy-to-use and adaptable home. A Smart Home using Artificial Intelligence involves creating a system where everyday devices are connected and automated through intelligent algorithms. In this setup, the input comes from various smart devices like thermostats, lights, and sensors, which can be controlled and monitored through a central hub. The system uses artificial intelligence to learn user preferences and adjust their routines. The output is a seamlessly automated home environment where AI algorithms manage tasks such as changing temperature, controlling lighting, and predicting user needs. Source Code 5. Development of Real Time Traffic Monitoring System It helps cities manage traffic flow, reduce congestion, and enhance overall transportation efficiency. This system involves creating a solution that continuously observes and analyzes traffic conditions. This system’s input consists of data from various sources, such as cameras, sensors, and GPS devices, capturing real-time information about vehicle movements and congestion. The system processes this data instantly to generate useful output, such as live traffic updates, congestion alerts, and suggested alternate routes. This design aims to provide commuters with timely and accurate information, allowing them to make informed decisions about their travel routes. Source Code 6. Design and Implementation Of an automated Parking System This artificial intelligence for the final year project can simplify and optimize parking, reducing the time and effort required to find a parking spot. Designing and implementing an Automated Parking System involves creating a solution where parking vehicles are streamlined and automated. This system’s input comes from sensors and cameras that monitor available parking spaces. As a car approaches, the system processes this data to guide the driver to an open parking spot. The output is an automated parking experience, where the system controls entry barriers, shows the driver available space, and manages the parking process. Source Code 7. Design and Implementation of Autonomous Drone System An Autonomous Drone System involves creating a technology where drones can operate independently with minimal human intervention. In this system, the input comes from pre-programmed instructions, GPS coordinates, or real-time data from sensors on the drone. The system processes this information to navigate, avoid obstacles, and perform tasks. The output is the autonomous operation of the drone, capable of tasks such as surveillance, package delivery, or capturing aerial imagery. Source Code 8. Artificial Intelligence In Health Care It aims to utilize artificial intelligence to improve healthcare procedures, enhance diagnostics, and ultimately improve patient results. This System involves incorporating smart technology to enhance medical processes. This System gathers input from patient records, medical imaging, and real-time monitoring devices. Artificial Intelligence processes this information, providing valuable insights such as personalized treatment recommendations, early disease detection, and efficient management of medical records. The output is a more efficient and adaptive healthcare system that assists medical professionals in making informed decisions and delivering improved patient care.

Plant Disease Detection using Machine Learning with Source Code Ensuring that crops are healthy and full of nutrients is very important for long-term food production. However, diseases that affect plant leaves can slow growth and yield, a problem for farms worldwide. Conventional methods for finding diseases often involve inspecting things by hand, which can take a long time and lead to mistakes. Here comes machine learning, which has changed the game in agriculture by creating new ways to quickly and correctly find leaf diseases. Understanding Leaf Diseases Before getting into the details of machine learning-based detection, it’s important to understand the basics of leaf diseases. These diseases include a variety of fungal, bacterial, and viral infections, as well as physiological defects, all of which cause unique symptoms on the leaves of plants. From white mold to bacterial disease, each disease has a distinct appearance, making early detection difficult for farmers. The Limitations of Traditional Methods In the past, farmers had to look at leaves by hand to find leaf diseases. This method worked to some extent, but it takes a lot of work and is easy to overlook. Also, how different people see things can make diagnoses inconsistent. More effective ways to find diseases are needed because farming areas are growing, and diseases are becoming more complicated. Enter Machine Learning Traditional methods of finding diseases have drawbacks that machine learning systems could help fix. These algorithms can accurately tell the difference between different signs by using huge datasets containing images of healthy and sick leaves. Machine learning models can quickly find signs of disease through pattern recognition and classification, which lets people take action before they get infected. Training the Algorithm The functioning of machine learning-based detection systems depends on the algorithm’s training. This involves adding labelled images of healthy and diseased leaves into the model, allowing it to gain knowledge of each state’s unique characteristics. With increased data analysis iteration, the algorithm shows enhanced precision in classifying leaves images, which creates a foundation for strong disease detection. Image Preprocessing Techniques Preprocessing techniques extract important information and improve image quality before feeding the images into the machine-learning model. Resizing, normalization, and augmentation are a few strategies used to standardize the input data and enhance the algorithm’s performance. The dataset is refined through preprocessing so that the model can concentrate on identifying significant patterns in the images. Real-Time Detection and Monitoring The best thing about plant disease detection using machine learning is that it can monitor crop health in real-time. With sensors and recording devices built in, these systems can constantly look over fields for signs of disease and inform farmers immediately about any possible threats. This proactive method allows people to step in quickly, reducing crop losses and increasing yields. Scalability and Adaptability Another significant advantage of machine learning in agriculture is its scalability and adaptation across different crops and conditions. Like previous approaches, which may be limited to certain diseases or plant species, machine learning algorithms can be trained to detect a wide range of leaf diseases. Furthermore, these models can adjust to changing disease patterns, ensuring their value in dynamic agricultural settings. Challenges and Considerations Even though machine learning has a lot of potential for detecting leaf diseases, there are certain difficulties. Some aspects that need to be carefully considered are data availability, model interpretability, and implementation costs. Another major challenge is guaranteeing the algorithms’ durability and generalizability under various growing situations. Plant Disease Detection Source Code To create a plant disease detection in Python. First, download the dataset from here and then follow these steps: 1. Importing Libraries The required libraries, including matplotlib, torch, torchvision, pandas, and numpy, are imported into this block. Additionally, it imports particular modules from torchvision, including the SubsetRandomSampler from torch.utils.data.sampler and datasets, transformations, models, nn, and functional. import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch from torchvision import datasets, transforms, models from torch.utils.data.sampler import SubsetRandomSampler import torch.nn as nn import torch.nn.functional as F from datetime import datetime import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch from torchvision import datasets, transforms, models from torch.utils.data.sampler import SubsetRandomSampler import torch.nn as nn import torch.nn.functional as F from datetime import datetime 2. Defining Image Transformations This block defines a set of image preprocessing transformations that use torchvision transforms. Compose. These changes include scaling, center cropping, and turning photos into tensors. transform = transforms.Compose( [transforms.Resize(255), transforms.CenterCrop(224), transforms.ToTensor()] ) transform = transforms.Compose( [transforms.Resize(255), transforms.CenterCrop(224), transforms.ToTensor()] ) 3. Loading Dataset This block loads images from a folder (“dataset_images”) using torchvision’s datasets.ImageFolder and applies the defined transformations. dataset = datasets.ImageFolder("dataset_images", transform=transform) dataset = datasets.ImageFolder("dataset_images", transform=transform) 4.Splitting Dataset Indices This block randomly shuffles the indices of the dataset and splits them into train, validation, and test indices based on predefined proportions. indices = list(range(len(dataset))) split = int(np.floor(0.85 * len(dataset))) validation = int(np.floor(0.70 * split)) train_indices, validation_indices, test_indices = ( indices[:validation], indices[validation:split], indices[split:], ) indices = list(range(len(dataset))) split = int(np.floor(0.85 * len(dataset))) validation = int(np.floor(0.70 * split)) train_indices, validation_indices, test_indices = ( indices[:validation], indices[validation:split], indices[split:], ) 5. Defining Model Architecture This block defines a CNN class inheriting from nn.Module, specifying the architecture with convolutional and dense layers. class CNN(nn.Module): def __init__(self, K): super(CNN, self).__init__() self.conv_layers = nn.Sequential( # conv1 nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(32), nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(32), nn.MaxPool2d(2), # conv2 nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(64), nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(64), nn.MaxPool2d(2), # conv3 nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(128), nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(128), nn.MaxPool2d(2), # conv4 nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(256), nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(256), nn.MaxPool2d(2), ) self.dense_layers = nn.Sequential( nn.Dropout(0.4), nn.Linear(50176, 1024), nn.ReLU(), nn.Dropout(0.4), nn.Linear(1024, K), ) def forward(self, X): out = self.conv_layers(X) # Flatten out = out.view(-1, 50176) # Fully connected out = self.dense_layers(out) return out class CNN(nn.Module): def __init__(self, K): super(CNN, self).__init__() self.conv_layers = nn.Sequential( # conv1 nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(32), nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding=1), nn.ReLU(), nn.BatchNorm2d(32), nn.MaxPool2d(2), # conv2 nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3,

15 Exciting blockchain project Ideas with Source Code Blockchain Project ideas world where innovation and decentralization converge to reshape industries. Blockchain project technology, originally designed to support cryptocurrencies like Bitcoin, has evolved into a versatile tool with the potential to revolutionize various sectors. I am going to present 15 creative blockchain project ideas for the final year project and also to gain the supervisor’s approval for your final-year project. What is Block chain? Blockchain technology is a decentralized and distributed ledger that records transactions across multiple computers. Each transaction is secured using cryptography, creating a tamper-resistant and transparent system. The basic concept involves a chain of blocks, where each block contains a list of transactions linked together through cryptographic hashes. 15 Block Chain Project Ideas with Source Code 1. Block chain project in Healthcare Data Management The healthcare industry struggles with enormous volumes of private patient information, but it’s often spread out in different places. In this project, blockchain can enhance data security by creating a decentralized and secure ledger for storing patient records. This system ensures privacy, interoperability, and transparency in managing sensitive medical information. In this system, the chain ensures patients have control over who accesses their data and for what purpose. This transparency in data usage fosters trust between patients and healthcare providers, addressing concerns about privacy breaches. Source Code 2. Block chain voting system The next one is the blockchain voting system. This blockchain project is one of the most impactful and can be a good project for the final year. Creating this system using advanced technology to fix problems in traditional voting, like fraud. Using a tamper-proof digital ledger called a blockchain makes voting secure and clear. We pick a good blockchain platform and build smart contracts that define who can vote and how. The voting interface is made simple for everyone to use. A solid system verifies and records votes on the blockchain, ensuring they can’t be changed. The results are shown openly for trust. Security measures, like encryption, protect against attacks. Thorough testing, deploying on a reliable blockchain, and teaching users ensure a trustworthy system. Continuous monitoring, improvements, and following laws keep the Blockchain Voting System working well over time. Source Code Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning 3. Decentralized File Storage The next blockchain project idea is decentralized file storage. A decentralized file storage system involves creating a system where files are distributed across multiple nodes, ensuring reliability, security, and scalability. In such a system, users input their files into smaller pieces or encrypted chunks. These fragments are distributed across various nodes in the network, eliminating the need for a central server. The decentralized nature ensures no single point of failure exists, providing robustness and reducing the risk of data loss. When users want to retrieve their files, the system locates and assembles the necessary fragments from different nodes, reconstructing the original data. This approach enhances data privacy and security and promotes scalability, as the system can efficiently handle a growing volume of information. Source Code 4. Block chain powered prediction market A blockchain-powered prediction market system involves building a platform where people can predict outcomes and make bets using blockchain technology. In this system, users input predictions or bets, and the blockchain records these transactions securely. The design ensures the information is decentralized and cannot be tampered with. When an event occurs, the blockchain automatically executes and settles the bets based on the outcome. Users can then easily retrieve their winnings directly through the blockchain. This decentralized approach removes the need for a middleman, making the prediction market more transparent and resistant to manipulation. It also allows people from anywhere to participate, promoting a global and inclusive prediction market. Source Code 5. Decentralized Autonomous Vehicles Decentralized Autonomous Vehicles (DAV) system involves creating a network where vehicles can operate independently without a central authority. In this system, input comes from the cars, which use sensors and data to make decisions about their movements and actions. Each vehicle is like a smart robot on the road, constantly communicating with others to navigate safely. The output is a coordinated traffic system where vehicles work together, sharing information through a decentralized network powered by blockchain. This design allows vehicles to make decisions collectively, helping avoid accidents and optimize traffic flow. Moreover, it ensures no single control centre is needed, making the system more resilient to failures or attacks. The DAV system aims to create a safer, more efficient, and decentralized way for autonomous vehicles to operate on the roads. Source Code 6. Block Chain based Supply Chain Financing This blockchain project idea is very unique. This system involves creating a digital platform that helps businesses in the supply chain get financing more easily. In this system, enterprises input information about their transactions and supply chain activities onto the blockchain. This input could include details about the products, shipments, and payments. The blockchain securely records and verifies these transactions, creating a transparent and unchangeable history. The system’s output is that this recorded information can be used to secure financing. Banks or lenders can trust the data on the blockchain, making it easier for businesses to get loans or other financial support based on their reliable and transparent supply chain activities. This approach reduces the risk of fraud and provides a more efficient and accessible way for businesses in the supply chain to access the financing they need to grow and operate smoothly. Source Code 7. Block Chain Based gaming Blockchain-based gaming system involves creating a platform where players can enjoy games with added security, transparency, and unique ownership features. In this system, input comes from players who use the blockchain to buy, sell, and trade in-game items or characters. The blockchain records these transactions securely, making them verifiable and tamper-resistant. The output is a gaming environment where players truly own their in-game assets. Because of the blockchain’s decentralized nature,