February 2024

Top 10 ethical hacking projects Ethical hacking is a proactive defense strategy where authorized professionals test systems for vulnerabilities before malicious actors misuse them. In this article, we’ll explore ten intriguing ethical hacking projects designed to enhance your skills and contribute positively to the cybersecurity landscape. These projects cover a wide range of ethical hacking activities, from creating viruses for educational purposes to developing phishing website checkers. Let’s dip in and explore each ethical hacking project idea. 1. User Authentication System User authentication is like a lock that protects sensitive information. It guarantees that only authorized individuals have access to digital spaces. However, the growing complexity of cyber threats makes these systems vulnerable. The User Authentication System ethical hacking project aims to strengthen the security of sensitive information. Developers and security professionals can use ethical hacking principles to build robust authentication systems to withstand cyber threats. This project combines security and ethical practices to provide a safer digital experience for users everywhere. Pyton import sqlite3 import hashlib import os def hash_password(password): salt = os.urandom(32) key = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) return salt + key def verify_password(username, password): connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('SELECT salt, key FROM users WHERE username = ?', (username,)) user = cursor.fetchone() connection.close() if user: salt = user[0] key = user[1] hashed_password = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) return hashed_password == key else: return False def register(username, password): try: connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(password) cursor.execute('INSERT INTO users (username, salt, key) VALUES (?, ?, ?)', (username, hashed_password[:32], hashed_password[32:])) connection.commit() print(f"User {username} registered successfully!") except sqlite3.IntegrityError: print(f"User {username} already exists!") finally: connection.close() def login(username, password): connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('SELECT * FROM users WHERE username = ?', (username,)) user = cursor.fetchone() connection.close() if user: if user[2] >= 3: print("Account locked. Too many failed login attempts.") else: salt = user[1] key = user[2] hashed_password = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) if hashed_password == key: print(f"Welcome back, {username}!") else: print("Invalid username or password.") connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('UPDATE users SET attempts = attempts + 1 WHERE username = ?', (username,)) connection.commit() connection.close() else: print("Invalid username or password.") def change_password(username, old_password, new_password): if verify_password(username, old_password): connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(new_password) cursor.execute('UPDATE users SET salt = ?, key = ? WHERE username = ?', (hashed_password[:32], hashed_password[32:], username)) connection.commit() connection.close() print(f"Password changed successfully for {username}!") else: print("Invalid username or password.") def reset_password(username, new_password): connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(new_password) cursor.execute('UPDATE users SET salt = ?, key = ? WHERE username = ?', (hashed_password[:32], hashed_password[32:], username)) connection.commit() connection.close() print(f"Password reset successfully for {username}!") def main(): connection = sqlite3.connect('users.db') cursor = connection.cursor() # Create a table to store user credentials cursor.execute(''' CREATE TABLE IF NOT EXISTS users ( id INTEGER PRIMARY KEY AUTOINCREMENT, username TEXT UNIQUE NOT NULL, salt TEXT NOT NULL, key TEXT NOT NULL, attempts INTEGER DEFAULT 0 ); ''') connection.commit() connection.close() # Register a user register('alice', 'password123') # Login with the registered user login('alice', 'password123') # Try to register the same user again register('alice', 'password123') # Try to login with incorrect credentials login('alice', 'wrongpassword') # Change password change_password('alice', 'password123', 'newpassword456') # Login with the new password login('alice', 'newpassword456') # Reset password reset_password('alice', 'resetpassword789') # Login with the reset password login('alice', 'resetpassword789') if __name__ == "__main__": main() import sqlite3 import hashlib import os def hash_password(password): salt = os.urandom(32) key = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) return salt + key def verify_password(username, password): connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('SELECT salt, key FROM users WHERE username = ?', (username,)) user = cursor.fetchone() connection.close() if user: salt = user[0] key = user[1] hashed_password = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) return hashed_password == key else: return False def register(username, password): try: connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(password) cursor.execute('INSERT INTO users (username, salt, key) VALUES (?, ?, ?)', (username, hashed_password[:32], hashed_password[32:])) connection.commit() print(f"User {username} registered successfully!") except sqlite3.IntegrityError: print(f"User {username} already exists!") finally: connection.close() def login(username, password): connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('SELECT * FROM users WHERE username = ?', (username,)) user = cursor.fetchone() connection.close() if user: if user[2] >= 3: print("Account locked. Too many failed login attempts.") else: salt = user[1] key = user[2] hashed_password = hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 100000) if hashed_password == key: print(f"Welcome back, {username}!") else: print("Invalid username or password.") connection = sqlite3.connect('users.db') cursor = connection.cursor() cursor.execute('UPDATE users SET attempts = attempts + 1 WHERE username = ?', (username,)) connection.commit() connection.close() else: print("Invalid username or password.") def change_password(username, old_password, new_password): if verify_password(username, old_password): connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(new_password) cursor.execute('UPDATE users SET salt = ?, key = ? WHERE username = ?', (hashed_password[:32], hashed_password[32:], username)) connection.commit() connection.close() print(f"Password changed successfully for {username}!") else: print("Invalid username or password.") def reset_password(username, new_password): connection = sqlite3.connect('users.db') cursor = connection.cursor() hashed_password = hash_password(new_password) cursor.execute('UPDATE users SET salt = ?, key = ? WHERE username = ?', (hashed_password[:32], hashed_password[32:], username)) connection.commit() connection.close() print(f"Password reset successfully for {username}!") def main(): connection = sqlite3.connect('users.db') cursor = connection.cursor() # Create a table to store user credentials cursor.execute(''' CREATE TABLE IF NOT EXISTS users ( id INTEGER PRIMARY KEY AUTOINCREMENT, username TEXT UNIQUE NOT NULL, salt TEXT NOT NULL, key TEXT NOT NULL, attempts INTEGER DEFAULT 0 ); ''') connection.commit() connection.close() # Register a user register('alice', 'password123') # Login with the registered user login('alice', 'password123') # Try to register the same user again register('alice', 'password123') # Try to login with incorrect credentials login('alice', 'wrongpassword') # Change password change_password('alice', 'password123', 'newpassword456') # Login with the new password login('alice', 'newpassword456') # Reset password reset_password('alice', 'resetpassword789') # Login with the reset password login('alice', 'resetpassword789') if __name__ == "__main__": main() 2. Phishing Simulation Phishing simulation is a way to test and train people to recognize and stop phishing attacks. These attacks trick people into sharing sensitive information by pretending to be trustworthy sources. The project aims to help individuals and organizations understand these tactics and protect against them. The phishing simulation system creates fake phishing emails or messages to imitate real-world scenarios. It includes fake links, deceptive content,

Best 30 machine learning final year project This article provides 30 unique machine learning final-year projects, offering exceptional academic and professional growth opportunities. These projects provide valuable concepts essential for computer science students in their final year. The most significant benefit of machine learning is that it opens up new options and makes it possible to create incredible projects. 1. Fabric Defect Detection Introduction: Detecting fabric defects is crucial in the textile industry for high-quality production. It involves identifying imperfections like stains, holes, or misleading, which impact product quality and customer satisfaction. Machine learning enhances detection efficiency, automating the process for more accurate inspection. This fabric defect detection system uses a Convolutional Neural Network (CNN) algorithm. CNNs are deep learning algorithms specifically designed for image recognition tasks. The algorithm learns to identify standard and defective fabrics by studying labeled images with distinct visual characteristics of defects. Problem Statement: The textile industry needs help maintaining consistent product quality due to the limitations of the manual inspection process. Human Inspectors might miss minor defects, which could affect the quality of the product. Automating defect detection using machine learning provides a better and faster solution to identify defects in real time during manufacturing. Source Code 2. Plant Disease Detection Introduction: In agriculture, plant health is crucial for a good harvest. Diseases can harm plants and reduce food production. The system can rapidly and accurately identify plant diseases, allowing farmers to intervene promptly. Convolutional Neural Networks (CNNs) are algorithms that recognize image patterns, making them ideal for plant disease detection. Problem Statement: Traditional disease detection methods are often time-consuming and rely heavily on human expertise. The Plant Disease Detection system enables farmers to swiftly and precisely identify plant diseases. This system allows them to take targeted actions to prevent losses. Source Code 3. Credit Card Fraud Detection Introduction: Credit card fraud detection uses machine learning to protect financial transactions by identifying unusual activity. It works by analyzing patterns to spot any signs of fraudulent behavior. One key algorithm used in Credit Card Fraud Detection is the Random Forest algorithm. This intelligent system combines the predictions of multiple decision trees to enhance accuracy.  Problem Statement: Credit card fraud detection is crucial due to increasing online fraud. Traditional systems struggle to keep up, so advanced machine-learning algorithms are necessary.   Source Code 4. Phishing Website Detection Introduction: The “Phishing Website Detection” system is a vital tool that detects and stops deceptive online activities. Phishing websites deceive users into giving away sensitive information, a significant cybersecurity threat. This system uses machine learning to identify and reduce these websites’ risks effectively. Problem Statement: Phishing attacks are a significant problem in the digital age. Hackers create fake websites to trick people. This project aims to create a system that can quickly find and stop these fake websites, making the internet safer. Source Code 5. Heart Disease Detection Introduction: The “Heart Disease Detection” system uses machine learning to predict and identify the likelihood of heart diseases in individuals. Detecting cardiovascular diseases early is crucial for preventing deaths caused by them since they are a significant cause of death globally. The system assesses the risk of heart disease based on various health parameters. The algorithm used is a Support Vector Machine (SVM). SVM is good at classification tasks, like predicting if someone is at risk of heart disease. Problem Statement: To reduce heart disease, we must identify at-risk people. Traditional methods that rely on manual analysis may need to be more effective in efficiently managing the complexity of diverse health data.   Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning Source Code 6. Breast Cancer Detection Introduction: The “Breast Cancer Detection” system uses machine learning to help diagnose breast cancer early. Breast cancer is a widespread and potentially fatal illness, and detecting it early greatly enhances treatment results. This system uses advanced machine learning to analyze medical data and identify meaningful patterns related to breast cancer. Problem Statement: The challenge in breast cancer diagnosis lies in accurately distinguishing between benign and malignant tumors, especially in the early stages when symptoms may not be apparent. Traditional diagnostic methods may have limitations, so this system solved these limitations.   Source Code 7. House Price Prediction Introduction: House Price Prediction is an intelligent application that uses machine learning to estimate the price of houses based on various factors. Whether you’re a home buyer, seller, or just curious about real estate, this system provides valuable insights into property values. We use data and algorithms to improve the accuracy and accessibility of predicting house prices. Problem Statement: The real estate market is complex, and determining the fair market value of a house involves considering numerous variables. Buyers and sellers often need help to accurately determine the value of a property, which can result in mispricing and financial dissatisfaction. House Price Estimation aims to tackle this problem using machine learning algorithms to offer accurate and data-driven predictions. Source Code 8. Big Mart Sales Prediction Machine Learning Project Introduction: This Project aims to revolutionize the retail industry by using predictive analytics to forecast sales. It’s like having a crystal ball that helps store owners prepare for how much of their products will be sold. This system allows for better inventory management and ensures customers find what they need when needed. Our project used a machine learning algorithm known as regression. In essence, regression helps us understand the relationship between various factors (like product visibility, store size, and promotional activities) and the sales of products. We train the algorithm using historical sales data, enabling it to predict future sales based on these learned patterns. Problem Statement: One of the significant challenges retailers face is the uncertainty surrounding product demand. This system often leads to overstocking or understocking, affecting profits and customer satisfaction. Source Code 9. Stock Prices Predictor using Time Series Introduction: Predicting stock prices is complex but essential in finance. Our project aims to forecast future stock prices by analyzing historical data using time series analysis and machine learning.

Fabric Defect Detection Fabric inspection is an essential step in every textile industry. Fabric detection is an important part of maintaining the fabric’s quality. The automatic fabric fault detection system is required to reduce the cost and waste of time. Quality inspection is a major aspect of the modern industrial manufacturing process. Due to a lack of consistency in quality inspection, defective fabric may be introduced to the market. This causes the industry’s name at stake, leading to heavy losses. With this concept moving forward, a new detection method, which has high detection accuracy and detection speed, is needed to replace the manual work currently used this problem can be resolved by using initial image processing techniques, and then a designed AI system can start working on finding the defect in the fabric. Our designed system is responsible for the quality of the fabric by capturing images from the rolling fabric with a camera. What is Fabric Defect Detection? Fabric defect detection is the technique of identifying and categorizing defects in textile materials like yarns, textiles, and garments. These defects can include missing threads, holes, stains, and gaps in the fabric or knitting. Detecting these defects is critical to keeping product quality and guaranteeing customer happiness. Traditional Methods vs. Deep Learning Traditionally, humans performed manual inspections to discover fabric defects. This method is difficult, not adaptable, and prone to errors. Deep learning systems, on the other hand, are capable of accurately identifying defects in fabric images through autonomous analysis. Objectives The major objectives of this project are:  Fabric inspection is done completely automatic, with no human involvement.  By using the latest trend Al (Machine Learning), the system self-learns by detecting the new defects on the fabric. The faulty fabric is automatically stamped by the robotic arm. Methodology Explanation Of Block Diagram As we can see from the block diagram, here we use the camera to capture the fabric, after capturing the image of the fabric the DIP(Digital Machine Learning) module does image processing through its algorithms & by this, the trained system performs Machine Learning(ML). After this the system decides whether the fabric is defective or non-defective if it’s defective then the robotic arm stamp at that particular area of the fabric, or if it is nondefective then the fabric passed through the roller and the process repeats Features The system is 24/7 capable for operation. The system works efficiently. The system is fully automatic, no human involvement.  AI based system with continuous self-learning. Challenges in Fabric Defect Detection Despite its benefits, fabric defect detection using deep learning poses some challenges:  Data Quality: Deep learning algorithms require substantial, high-quality labeled data to train efficiently. This can be difficult to get, especially in unusual or complex abnormalities.  Deep learning algorithms are complex and require specialized knowledge to create and deploy.  Interpretability: Deep learning algorithms might need help grasping how they make judgments.  Generalization: Deep learning algorithms may need help generalizing to new, previously unknown faults, particularly if they are underrepresented in training data. Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning Fabric defect detection using deep learning To make a fabric defect detection system using deep learning, download a dataset from Kaggle. # Importing Libraries import os import cv2 import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split # Importing Libraries import os import cv2 import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split This block imports necessary libraries for file operations, image processing, numerical operations, deep learning, and dataset splitting. # Function to load and preprocess images def load_images(folder_path): images = [] for filename in os.listdir(folder_path): img = cv2.imread(os.path.join(folder_path, filename)) img = cv2.resize(img, (224, 224)) # Resize images if needed img = img / 255.0 # Normalize pixel values images.append(img) return np.array(images) # Function to load and preprocess images def load_images(folder_path): images = [] for filename in os.listdir(folder_path): img = cv2.imread(os.path.join(folder_path, filename)) img = cv2.resize(img, (224, 224)) # Resize images if needed img = img / 255.0 # Normalize pixel values images.append(img) return np.array(images) This block defines a function load_images that takes a folder path as input and returns a numpy array of preprocessed images. It reads each image using OpenCV (cv2), resizes it to 224×224 pixels, and normalizes pixel values to the range [0, 1].   # Load images from folders defect_images = load_images("Defect_images") non_defect_images = load_images("nondefect") # Load images from folders defect_images = load_images("Defect_images") non_defect_images = load_images("nondefect") This block loads images from two folders: “Defect_images” and “nondefect”. It uses the load_images function defined earlier to preprocess the images. labels = np.zeros((len(defect_images) + len(non_defect_images), 2)) labels[:len(defect_images), 0] = 1 labels[len(defect_images):, 1] = 1 labels = np.zeros((len(defect_images) + len(non_defect_images), 2)) labels[:len(defect_images), 0] = 1 labels[len(defect_images):, 1] = 1 This block creates labels for the images. It creates a numpy array of zeros with dimensions (len(defect_images) + len(non_defect_images), 2). It sets the first column to 1 for defect images and the second column to 1 for non-defect images. all_images = np.concatenate((defect_images, non_defect_images), axis=0) all_images = np.concatenate((defect_images, non_defect_images), axis=0) This block concatenates the defect and non-defect images into a single numpy array called all_images. # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(all_images, labels, test_size=0.2, random_state=42) # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(all_images, labels, test_size=0.2, random_state=42) This block splits the data into training and testing sets using the train_test_split function from sklearn.model_selection. It uses 80% of the data for training and 20% for testing.   # Define and train a simple CNN model model = tf.keras.Sequential([ tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(64, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(2, activation='softmax') ]) # Define and train a simple CNN model model = tf.keras.Sequential([ tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(64, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(2, activation='softmax') ]) This

Best 60 artificial intelligence projects for final year Selecting an AI project for the final year is an excellent opportunity to showcase your skills and boost your grades. Artificial intelligence has a profound impact on various industries, to such an extent that an artificial intelligence project has the potential to bring about significant changes. This article will give you 60 unique Artificial intelligence final year project ideas. To help you source code is also there with every project. 1. Bitcoin Price Prediction Using Machine Learning Introduction: Bitcoin is a decentralized digital money uncontrolled by any financial organization or government and uses data encryption for protection. Nowadays, demand for bitcoin also increases day by day because the price of bitcoin is currently high. So, building a Bitcoin price prediction is an AI-based system that predicts the future value of Bitcoin. It uses machine learning algorithms to predict the future value of Bitcoin. In this project, machine learning algorithms learn from historical data, market trends, and other external factors. However, maintaining accuracy in this project takes a lot of work.   Problem Statement: The cryptocurrency market, particularly Bitcoin, is renowned for its sudden and unpredictable price fluctuations. Investors frequently want assistance in comprehending and projecting future market movements. The “Bitcoin Price Prediction Using Machine Learning” gadget solves this issue by employing cutting-edge AI methods to sift through vast amounts of historical data and spot trends that could influence future Bitcoin fees. Source Code 2. Fake News Detection In Social Media Introduction: Sometimes, we have too much information and don’t know what is true or false. Fake news is when people lie or make mistakes on the internet. Fake news can make people confused or angry. Fake news detection system aims to detect false information and report flagged content. It will help in validating the news on the internet. Problem Statement: Fake news is a big problem with social media as a primary news source. Misleading content can have serious consequences, influencing public opinions and potentially causing harm. The “Fake News Detection in Social Media” system uses AI algorithms to analyze content shared on social media platforms. The goal is to help users differentiate between reliable and unreliable information, creating a more informed online community. Source Code 3. Credit Fraud Detection Using Machine Learning Introduction: With the rise in the digital world, credit card fraud has become challenging. Frauds involving credit cards are simple and easy to target. Online payment options have expanded due to e-commerce and numerous other websites, raising the possibility of online fraud. Due to increased fraud, academics are currently analyzing and detecting online transaction fraud using various machine learning techniques. To stop this, you can develop a credit card fraud detection system as your final year project. This system uses machine learning techniques to examine credit card transactions for unusual patterns and behaviours that may indicate possible fraud. Problem Statement: The threat of credit card fraud is a significant concern for financial institutions and individuals. As technology progresses, so do the techniques fraudsters employ to exploit vulnerabilities in the payment system. Traditional rule-based fraud detection systems often struggle to adapt to these evolving tactics. The system solved this challenge by utilizing AI algorithms that can learn and adjust to novel patterns of fraud behaviour. Source Code 4. Liver Disease Detection Using Neural Networks and SVM Classification Introduction: People with liver disorders need to receive medical attention when it’s needed. It is crucial to find the illness before it progresses past a stage where it can be cured. Interestingly, studies of patients with liver problems have led to a great deal of knowledge on the development of organs. Based on the information that may be obtained by testing the patient, machine learning helps identify the condition early on. The system focused on early identification and diagnosis of liver diseases. By integrating neural networks and Support Vector Machine (SVM) classification, this system analyzes medical data to provide accurate predictions regarding the presence of liver diseases. Problem Statement: the traditional liver diagnostic methods may have limitations in accuracy and speed. For this problem, some people know when he reaches the last stage and the causes of patient death. So, the system aims to address these limitations by harnessing the power of artificial intelligence. Source Code 5. Detection of Phishing Attacks Using Machine Learning Algorithm Introduction: Today, fake messages and internet scams are growing daily. Phishing is also an internet scam; attackers send fake messages in this scam. These fake messages come from trusted sources. These projects help detect phishing attacks and protect clients from phishing fraud by assisting them in recognizing and blocking fake websites. This system prevents the theft of important information. Organizations also benefit by maintaining their brand and reputation. So, as a final year project selection, this project is best, and if you are choosing these kinds of projects, you will get great grades in your final year. This system uses advanced machine learning algorithms to analyze emails, URLs, and web content. It can tell the difference between legitimate messages and phishing attempts. Problem Statement: Traditional rule-based and signature-based approaches to phishing detection may need help to keep pace with the dynamic nature of these attacks. The “Detection of Phishing Attacks Using Machine Learning Algorithm” system uses AI algorithms to learn and adapt to new phishing patterns. This project aims to protect users and organizations from phishing attacks using intelligent and proactive defense measures. Source Code 6. Comparison Of Machine Learning Approaches for Twitter Sentiment Analysis Introduction: The important discipline of sentiment analysis in natural language processing includes hate speech and offensive statement identification. Sentiment analysis detects and analyzes emotional tones in text to understand and evaluate beliefs and attitudes. Because of its capacity for understanding and interpreting human emotions as they are expressed in textual data, Sentiment Analysis has been increasingly well-known in recent years. Sentiment analysis has become critical for researchers, corporations, and organizations trying to understand public opinion, consumer feedback, and societal trends as communication becomes increasingly deeply embedded on digital platforms. Sentiment Analysis

Best 13 IOT project ideas for final year students The Internet of Things, or IoT, is an exciting field that has become very popular recently. It involves providing commonplace gadgets and internet access to upgrade their intelligence and productivity. Working on IoT projects can be an excellent opportunity for final-year students to apply the knowledge and skills they have learnt in their studies and obtain practical experience. This article explores the best 13 IoT project ideas for final-year students to consider. 1. Smart Grocery Monitoring System Our daily lives revolve around the kitchen. With daily schedules becoming more hectic, automation of the kitchen and groceries is essential. Keeping an eye on the groceries by hand takes a lot of time and effort. The smart grocery management system makes use of the Internet of Things to monitor the grocery levels in the kitchen through smart sensors. This technology assists consumers by automatically tracking their food purchases and placing new orders as needed. A smart system for managing groceries helps the user keep accurate track of the household groceries and place independent reorders when required. This Smart sensors are used in the system’s development to monitor the quantity of the grocery items at regular intervals and transmit a signal and quantity data to the server. The information is compared to a threshold number, and if the amount of data received is less, the user receives a notification and can click to rearrange the item. 2. Smart Home Door Lock System Security in the workplace or business setting is a serious risk that everyone must deal with, whether at home or on the road. In this busy, competitive environment, when people can’t find a way to secure their secret belongings physically, security systems are one of the biggest concerns. Rather, they look for a different approach that offers simpler and more dependable security. In the modern era, everything is interconnected via networks, enabling anybody to access information from anywhere in the globe. So, Build a Smart Home Door Lock System is a project designed to improve home security and convenience. The Smart Home Door Lock System uses IoT devices, such as smart locks and connected sensors, to enable remote monitoring and control of door access. The system allows homeowners to manage door security efficiently, even when away from home. 3. Smart Office Security System Security is very important in any office environment. A secure workplace is essential to the efficient running of everyday activities, whether it is for safeguarding sensitive information, guaranteeing the security of workers and guests, or stopping illegal entry. You know that the highly technologically grown world of today may make traditional security measures insufficient on their own. So, the Smart Office Security System final year project is a new approach to secure office assets. The Smart Office Security System employs IOT technology by connecting various security devices to a central hub, enabling effective communication and data sharing. 4. Smart Home Temperature Control System The Smart Home Temperature Control System is an innovative project that uses Internet of Things (IoT) technology to enhance the comfort and energy efficiency of residential space. The main purpose of this system is to create temperature-controlled home automation. Your AC home appliances, such as the fan, heater, cooler, and lightbulbs, will be under the direction of the system architecture. Let’s say you are hot and bothered while seated in a room. You now want your fan or cooler to turn “ON” automatically and turn “OFF” when the temperature returns to normal in the room. You’ve come to the correct spot if you’ve been looking for a project like that. This project will help you in setting up automatic temperature control for your home appliances. 5. IoT-Based Weather Monitoring System We provide an intelligent online weather reporting system. We have implemented a system that enables online reporting of weather parameters. It eliminates the need for weather prediction agencies by allowing anyone to monitor the weather conditions immediately online. With the help of IOT devices, the system monitors the weather and provides real-time information to the user. This system used humidity sensors. Humidity sensors help continuously check for rain and temperature. In this system, if a sensor detects a sudden change in temperature or a significant shift in wind direction, the system can provide immediate updates and alerts. 6. Health Monitoring System Using IoT In today’s technologically advanced century, humans have become machines that work around the clock every day to get two meals in a day. He lost focus on the most crucial aspect of this race: his health. Since there were initially so many systems created, we learned about several flaws and characteristics of earlier work by carefully reviewing these systems. These systems utilize the newest, most popular technology, known as IoT. As a result, one of the most significant uses of IoT is the health monitoring system. Today, people normally face many health issues daily, such as high blood pressure, heart rate, and ECG. So, with the help of IOT technology, devices use blood pressure, heart rate, and ECG sensors with various microcontrollers in these systems to obtain readings from the sensing elements and receive them by the notification sensor. This system also recommends the doctor and, in an emergency, also tells what the patient is doing right now. 7. IoT-Based Temperature And Humidity Monitoring System In the environment, temperature and humidity are important parameters. The coronavirus spreads quickly in cold temperatures and high humidity during the COVID-19 pandemic. A lot of people are aware of the environment in which they live. This final year IOT-based project helps to monitor the temperature and humidity in the atmosphere. The proposed system is an innovative approach for tracking and reporting in real time the temperature and humidity at different locations. The cloud server stores the real-time data from the sensors, and emails are sent to the specified users, enabling them to access the data remotely via the internet. The IoT-Based Temperature and Humidity Monitoring System

Top 7 Cybersecurity Final Year Projects I have compiled a list of 7 cybersecurity final year projects. These 7 projects will help you in improving your skills and grades. 1. Cloud Security Monitoring and Incident Response First on my list of cybersecurity final year projects is Cloud Security Monitoring and Incident Response. This system creates a solution to safeguard cloud environments by continuously monitoring potential security threats and responding promptly to incidents. This system includes data from various cloud services, network logs, and security configurations. The Cloud Security Monitoring and Incident Response system utilizes advanced cybersecurity measures to detect anomalies, assess security events, and orchestrate timely responses, ensuring the resilience of cloud infrastructure. 2. Cyber Threat Intelligence Platform for Proactive Defense The next cybersecurity final year project is Cyber Threat Intelligence Platform for Proactive Defense. This system is designed as a comprehensive solution that collects, analyzes, and distributes threat intelligence to support proactive defence strategies. This system includes data from various sources, such as network logs, incident reports, and external threat feeds. The Cyber Threat Intelligence Platform used advanced cybersecurity methodologies to aggregate and analyze this information, providing organizations with actionable insights to prevent risks associated with online use early. Recommended Reading Stock Price Prediction system using Machine Learning Real-Time Object Detection Using Machine Learning Ecommerce Sales Prediction using Machine Learning 3. Machine Learning-Based Intrusion Detection System With cyber threats becoming more advanced, traditional intrusion detection systems often must catch up. This project explores the field of machine learning to develop intelligent intrusion detection systems. By analyzing network traffic patterns and identifying anomalies in real time, these systems can effectively threaten cyber attacks before they cause significant damage. 4. Online Fund Transfers with DES Encryption Build online fund transfer with Des Encryption to simplify online funds transfer securely. The facilities offered by Internet banking make it simple to transfer funds. However, a powerful cryptographic technique called DES is employed to keep it safe. A unique OTP feature is available on this platform to approve transactions, adding security. 5. Web Vulnerability Scanner The web vulnerability scanner is like a tool that built to examine web pages and web apps in a structured way to find any possible security issues. It will test for common online risks like SQL injection, cross-site scripting (XSS), and unsecured direct object references using automated techniques like scanning and fuzzing. The vulnerability scanner will produce reports that list all vulnerabilities found and suggest ways to fix them. The project aims to improve the general security of web-based systems by offering users a simple yet powerful method for locating and fixing vulnerabilities in web applications. 6. IoT Security Enhancement through Blockchain Integration An IoT Security Enhancement system through Blockchain Integration for a cybersecurity-related project developed a solution that used blockchain technology to enhance the security of Internet of Things (IoT) devices and networks. The IoT Security Enhancement system used cybersecurity measures by integrating blockchain to establish a decentralized and tamper-resistant ledger for securely recording and verifying IoT device transactions. Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning 7. Biometric Authentication System This project uses biometric authentication systems to improve security measures. From fingerprint scanners to facial recognition technology, biometrics offer a more muscular stranger and user-friendly access control approach, minimizing the risk of unauthorized access. Recommended Reading AI Music Composer using Machine Learning Real-Time Object Detection Using Machine Learning 30 Creative Final Year Projects with Source Code What are some essential skills for undertaking cybersecurity final-year projects? Proficiency in programming languages, understanding of networking concepts, familiarity with cybersecurity tools and frameworks, and critical thinking skills are essential for success in cybersecurity projects. What are some potential career opportunities in cybersecurity after completing a final-year project? Completing a cybersecurity final year project can open doors to various career paths, including cybersecurity analyst, penetration tester, security consultant, and incident responder. Additionally, it demonstrates your practical skills and commitment to the field, enhancing your employability. How can I choose the right cybersecurity final year project? Consider your interests and strengths, and choose a project that aligns with current industry trends and challenges. Collaborating with industry partners or mentors can also provide valuable insights. 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Best 21 HTML CSS JavaScript projects with source code I’ve heard you’re starting with web development projects. It’s a great idea, but when you’re working alone, finding a significant project might be challenging. Therefore, this article presents 21 projects using HTML, CSS, and JavaScript with source code. Here, projects are listed from beginners to experts who are great at practising projects using HTML, CSS, and JavaScript with source code. 1. To-Do List App Are you ready to go into the dynamic world of web development? This to-do list app is perfect for you as a beginner. Crafted with HTML for structure, CSS for style, and JavaScript for interactivity, this project will teach you the foundations of creating a responsive and user-friendly application. To do list app source code HTML <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>To-Do List</title> <style> body { font-family: Arial, sans-serif; } .container { max-width: 600px; margin: 0 auto; padding: 20px; background-color: #f4f4f4; border-radius: 5px; box-shadow: 0px 0px 10px rgba(0,0,0,0.1); } h1 { text-align: center; color: #4CAF50; } input[type="text"] { width: 100%; padding: 10px; margin-bottom: 10px; border: 1px solid #ccc; border-radius: 5px; } button { padding: 10px 20px; background-color: #4CAF50; color: white; border: none; border-radius: 5px; cursor: pointer; transition: background-color 0.3s ease; } button:hover { background-color: #45a049; } ul { list-style-type: none; padding: 0; } li { padding: 10px; border-bottom: 1px solid #ccc; } li:last-child { border-bottom: none; } </style> <script> function addTask() { var taskInput = document.getElementById("taskInput"); var taskList = document.getElementById("taskList"); if (taskInput.value === "") { alert("Please enter a task."); return; } var li = document.createElement("li"); li.textContent = taskInput.value; taskList.appendChild(li); taskInput.value = ""; } </script> </head> <body> <div class="container"> <h1>To-Do List</h1> <input type="text" id="taskInput" placeholder="Add new task"> <button onclick="addTask()">Add Task</button> <ul id="taskList"></ul> </div> </body> </html> <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>To-Do List</title> <style> body { font-family: Arial, sans-serif; } .container { max-width: 600px; margin: 0 auto; padding: 20px; background-color: #f4f4f4; border-radius: 5px; box-shadow: 0px 0px 10px rgba(0,0,0,0.1); } h1 { text-align: center; color: #4CAF50; } input[type="text"] { width: 100%; padding: 10px; margin-bottom: 10px; border: 1px solid #ccc; border-radius: 5px; } button { padding: 10px 20px; background-color: #4CAF50; color: white; border: none; border-radius: 5px; cursor: pointer; transition: background-color 0.3s ease; } button:hover { background-color: #45a049; } ul { list-style-type: none; padding: 0; } li { padding: 10px; border-bottom: 1px solid #ccc; } li:last-child { border-bottom: none; } </style> <script> function addTask() { var taskInput = document.getElementById("taskInput"); var taskList = document.getElementById("taskList"); if (taskInput.value === "") { alert("Please enter a task."); return; } var li = document.createElement("li"); li.textContent = taskInput.value; taskList.appendChild(li); taskInput.value = ""; } </script> </head> <body> <div class="container"> <h1>To-Do List</h1> <input type="text" id="taskInput" placeholder="Add new task"> <button onclick="addTask()">Add Task</button> <ul id="taskList"></ul> </div> </body> </html> Output 2. Calculator Build a calculator using HTML for the UI, CSS for styling, and JavaScript to handle user inputs and perform calculations. This beginner-level project is both practical and visually rewarding. Calculator source code HTML <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Calculator</title> <style> body { font-family: Arial, sans-serif; background-color: #f4f4f4; display: flex; justify-content: center; align-items: center; height: 100vh; } .calculator { width: 300px; background-color: #fff; border-radius: 10px; padding: 20px; box-shadow: 0 0 10px rgba(0,0,0,0.1); } input[type="button"] { width: 50px; height: 50px; font-size: 20px; margin: 5px; border: none; border-radius: 5px; background-color: #4CAF50; color: white; cursor: pointer; transition: background-color 0.3s ease; } input[type="button"]:hover { background-color: #45a049; } input[type="text"] { width: calc(100% – 10px); padding: 10px; margin-bottom: 10px; border: 1px solid #ccc; border-radius: 5px; font-size: 20px; text-align: right; } </style> <script> function calculate() { var input = document.getElementById("input").value; var result = eval(input); document.getElementById("input").value = result; } function clearInput() { document.getElementById("input").value = ""; } </script> </head> <body> <div class="calculator"> <input type="text" id="input" readonly> <input type="button" value="C" onclick="clearInput()"> <input type="button" value="7" onclick="document.getElementById('input').value += '7'"> <input type="button" value="8" onclick="document.getElementById('input').value += '8'"> <input type="button" value="9" onclick="document.getElementById('input').value += '9'"> <input type="button" value="/" onclick="document.getElementById('input').value += '/'"> <input type="button" value="4" onclick="document.getElementById('input').value += '4'"> <input type="button" value="5" onclick="document.getElementById('input').value += '5'"> <input type="button" value="6" onclick="document.getElementById('input').value += '6'"> <input type="button" value="*" onclick="document.getElementById('input').value += '*'"> <input type="button" value="1" onclick="document.getElementById('input').value += '1'"> <input type="button" value="2" onclick="document.getElementById('input').value += '2'"> <input type="button" value="3" onclick="document.getElementById('input').value += '3'"> <input type="button" value="-" onclick="document.getElementById('input').value += '-'"> <input type="button" value="0" onclick="document.getElementById('input').value += '0'"> <input type="button" value="." onclick="document.getElementById('input').value += '.'"> <input type="button" value="=" onclick="calculate()"> <input type="button" value="+" onclick="document.getElementById('input').value += '+'"> </div> </body> </html> <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Calculator</title> <style> body { font-family: Arial, sans-serif; background-color: #f4f4f4; display: flex; justify-content: center; align-items: center; height: 100vh; } .calculator { width: 300px; background-color: #fff; border-radius: 10px; padding: 20px; box-shadow: 0 0 10px rgba(0,0,0,0.1); } input[type="button"] { width: 50px; height: 50px; font-size: 20px; margin: 5px; border: none; border-radius: 5px; background-color: #4CAF50; color: white; cursor: pointer; transition: background-color 0.3s ease; } input[type="button"]:hover { background-color: #45a049; } input[type="text"] { width: calc(100% – 10px); padding: 10px; margin-bottom: 10px; border: 1px solid #ccc; border-radius: 5px; font-size: 20px; text-align: right; } </style> <script> function calculate() { var input = document.getElementById("input").value; var result = eval(input); document.getElementById("input").value = result; } function clearInput() { document.getElementById("input").value = ""; } </script> </head> <body> <div class="calculator"> <input type="text" id="input" readonly> <input type="button" value="C" onclick="clearInput()"> <input type="button" value="7" onclick="document.getElementById('input').value += '7'"> <input type="button" value="8" onclick="document.getElementById('input').value += '8'"> <input type="button" value="9" onclick="document.getElementById('input').value += '9'"> <input type="button" value="/" onclick="document.getElementById('input').value += '/'"> <input type="button" value="4" onclick="document.getElementById('input').value += '4'"> <input type="button" value="5" onclick="document.getElementById('input').value += '5'"> <input type="button" value="6" onclick="document.getElementById('input').value += '6'"> <input type="button" value="*" onclick="document.getElementById('input').value += '*'"> <input type="button" value="1" onclick="document.getElementById('input').value += '1'"> <input type="button" value="2" onclick="document.getElementById('input').value += '2'"> <input type="button" value="3" onclick="document.getElementById('input').value += '3'"> <input type="button" value="-" onclick="document.getElementById('input').value += '-'"> <input type="button" value="0" onclick="document.getElementById('input').value += '0'"> <input type="button" value="." onclick="document.getElementById('input').value += '.'"> <input type="button" value="=" onclick="calculate()"> <input type="button" value="+" onclick="document.getElementById('input').value += '+'"> </div> </body> </html> Output 3. Blogging Website Build a blogging platform where users can create, edit, and publish blog posts with features like comments and user authentication. This project uses HTML for post structure, CSS for styling, and JavaScript for user authentication and dynamic content updates. A perfect project

Best 17 blockchain projects for beginners If you are a beginner in programming and are interested in blockchain projects, sometimes blockchain projects seem a bit puzzling but don’t worry. This article gives 17 blockchain projects for beginners. Throughout this article, we’ll walk you through the fundamentals, ensuring you start your blockchain journey with crystal-clear understanding and straightforward language. Let’s explore the ideas of blockchain projects for beginners. Blockchain projects for beginners 1. Blockchain-based To-Do List Creating a Blockchain-based To-Do List system is an excellent project for beginners, and we’ll break down the system design in a simple and easy-to-understand way. Each task is a digital block. Adding a new task creates a chain of blocks, which is stored in many computers worldwide – making it super safe. To use it, you’ll have a simple app where you can add, edit, and complete tasks.An Angular/Ionic app that enables users to create, manage, and mark off completed tasks would serve as the front end. The Ionic framework, an open-source mobile hybrid framework for developing cross-platform mobile apps, and Angular will be used in its build. The user’s data will be safely saved on the blockchain backend, which will remain unchangeable and only accessible by authorized individuals granted access by the account owner. 2. Cryptocurrency Price Tracker A Cryptocurrency Price Tracker system for beginners is like building your digital assistant to monitor cryptocurrency prices.In this system, you can type in the names of the cryptocurrencies you’re interested in, like Bitcoin or Ethereum. Once you hit enter, the system will fetch the latest prices online and display them on your screen.In our system, every cryptocurrency price is a block on the blockchain. This block contains information about the currency, like its current value. When a change happens, say Bitcoin’s price increases, a new block is added to the chain. This block is then linked to the previous ones, forming a chain of information. 3. Token Wallet Tracker Designing a Token Wallet Tracker system for beginners is like making a digital piggy bank for your digital tokens – it’s simple. It helps you keep an eye on your virtual money. The Token Wallet Tracker project is designed to monitor and manage digital token wallets. It acts as a digital assistant for users to keep track of their cryptocurrency holdings, providing insights into transactions, balances, and overall portfolio performance. Here’s how it works: Your Digital Wallet: First, you’ll have a digital wallet. It’s where you store your tokens, like a pocket in your virtual world. This wallet has a unique address – think of it as your wallet’s home address on the internet. Tracker Interface: Now, picture a simple app or webpage where you can type in your wallet’s address. When you hit enter, the system quickly checks your wallet on the blockchain (the big digital book that records all transactions). Token Count: After its mission, the Tracker tells you how many tokens you have in your wallet. It’s like your wallet reporting to you, saying, “Hey, you’ve got this many tokens right now!” Easy Updates: The beauty is this Tracker updates in real time. So, sending or receiving tokens is like the map instantly showing the new treasure in your wallet.   4. Decentralized Voting System A Blockchain-based voting system project is best for beginners, and we’ll break down the system design. A higher degree of security and transparency can be achieved using the blockchain’s decentralized and unchangeable properties, guaranteeing the voting procedure’s validity and reliability.  Here’s how it works: Every Vote Counts: In a decentralized voting system, each vote is like a digital token recorded on a blockchain – a secure and unchangeable ledger. Transparency and Security: Because the blockchain is transparent and tamper-proof, everyone can see the votes without knowing who cast them. Accessibility and Convenience: With a decentralized voting system, voters can participate from anywhere with an internet connection, making the process more accessible and convenient for everyone.   5. Blockchain-based Supply Chain Tracker Designing a Blockchain-based Supply Chain Tracker is like building a digital detective that traces the journey of products from creation to your hands. It’s a system that ensures accessibility and reliability in the supply chain. Each product gets a digital passport on the blockchain, containing essential details like where it was made, its journey, and necessary stops. Every time the product moves, a new entry is added to its digital passport, showing its current location and status.Anyone in the supply chain – from manufacturers to retailers – can view and trust the information on the blockchain. It’s like a see-through container for your product’s journey. The Supply Chain Tracker gives real-time updates. Want to know where your product is right now? Just check the blockchain, and you’ll see the latest stop on its digital journey. 6. Blockchain-based Blogging Platform As a beginner’s level in blockchain, this system is very easy and straightforward. Think of it as a digital notebook on the blockchain where your articles are safe, unchangeable, and open for the world to see. Each article you write becomes a digital block on the blockchain, recording your thoughts and words. Once there, it’s like a permanent page in your digital book.The blockchain ensures that once your article is published, it can’t be altered by anyone, making it transparent and tamper-proof. It’s like having a personal printing press where your words are set in stone.Unlike traditional blogging platforms that rely on a central authority, a Blockchain-based Blogging Platform is decentralized.Readers can trust that what they’re reading is authentic and unchanged. It’s like having a digital timestamp on your articles, proving they existed at a specific time and in a particular form. 7. Smart Contract System The Smart Contract System is a revolutionary platform that automates and executes self-executing contracts using blockchain technology. Users input the terms and conditions of an agreement into the system, defining the rules and actions to be taken upon fulfilment. The system automatically executes the terms when predefined conditions are met, eliminating the need for intermediaries

13 Python Projects for Final Year Students with Source Code When starting a project, the idea is the most critical component. The idea is an integral part of the final-year project. This article gives you 13 advanced Python projects for final-year because You must have a unique idea to gain the supervisor’s approval for your final-year project. Python Projects For Final Year Students 1. Fake Logo Detection System This project helps people identify fake logos online. In the digital world, it’s hard to tell if a logo is real or fake. This system aims to solve this problem using Python to create a tool to identify bogus logos, making it easier for users to trust the logos they see. Many fake logos on the internet can cause business trouble and confuse customers. This system addresses the need for a reliable tool to quickly check if a logo is real or fake. Doing this helps businesses protect their brand and customers from false information. To use the Fake Logo Detection System, users upload images or files with logos. The system then checks if the logo is real or fake. It gives a clear message about the logo’s authenticity and may also add visual marks on the logo to show potential issues. This project aims to build trust and keep online logos genuine and trustworthy. Source Code 2. Skin Disease Detection System Using CNN The Skin Disease Detection System Using CNN is like a helpful friend for checking skin problems using pictures. It uses an intelligent technology called Convolutional Neural Networks (CNNs) with Python to look at skin images and tell you if there might be a skin issue. This makes it easy for people to monitor their skin health and know when to ask a doctor for help. Sometimes, it’s hard to notice or understand skin problems, which can delay getting the right help. This system is here to solve that problem. Using Python and intelligent technology allows you upload pictures of your skin, giving you quick information about possible skin conditions. You can detect any issues and talk to a doctor sooner. You need to upload pictures of your skin into the system. It will then give you a report about possible skin issues it finds. The report might include details about the type of skin condition, its seriousness, and suggestions to see a doctor. The system wants to make it easy for you to understand the results. Source Code 3. Heart Disease Detection System Heart disease is a common and severe health issue that affects millions of people worldwide. Sometimes, doctors can’t detect heart disease in the early stage because heart problems can start quietly and become severe. It is not found in the early stage, and that is the cause of patient death. So, Heart disease detection uses Python to detect possible heart issues early. It uses machine learning algorithms to check key health details and tell you if there might be a risk of heart disease. The purpose of this project is to give everyone an easy way to monitor their heart health and get advice from a doctor when needed. You must share health information, like age, blood pressure, and cholesterol levels. The system then gives you a report showing if there might be a risk of heart disease. The report helps you understand your heart health and decide whether to make lifestyle changes. Source Code 4. Advanced Healthcare Chat Bot using Python Today, in the modern world, health care has grown to be an essential component of our living. On the other hand, seeing a doctor for help on every health concern has become quite tricky. So, this project aims to develop an artificial intelligence medical chatbot to assist in problem diagnosis. Building this project used natural language processing so that users communicate with the chatbot effectively. The project aims to identify the health issue, summarise the problem, and recommend the doctor. Source Code 5. Online Election System Using Python In a world, every country has conducted elections, and those people who live in the country have the right to vote for a particular party that they want to give power to. But you also know a billion people who vote, and it is a time-consuming process. Also, the government faced issues like people fighting outside the polling station, long queues and manual counting. This project aims to simplify the voting procedure by providing a simple online voting interface. Building this system used authentication and encryption techniques; for this technique, the system guarantees the accuracy and correctness of the election results.  The Online Election System addresses these issues by providing a digital platform that allows voters to participate in elections from the comfort of their homes. Additionally, it minimizes the time and budget of traditional paper-based voting methods, improving election accessibility and efficiency for every eligible voter. Source Code 6. Ecommerce Website Live Visitor Tracking System E-commerce is frequently used to purchase or sell products online. Almost anything you can think of can be purchased without leaving your home. Online shops accept orders around the clock and can be reached anywhere with an Internet connection. The e-commerce industry has grown significantly in the age of the Internet. However, the e-commerce sector faces numerous challenges. Some difficulties encountered are online identity verification, competitive analysis, keeping customers loyal, competing with manufacturers and retailers, and many other issues. A visitor monitoring system allows us to keep track of every person who visits the website, as well as the products they are most interested in and other information. This system solves that by tracking visitors live. It wants to show store owners which products are popular, how long people stay on the site, and what pages they like. By knowing this, store owners can improve the website and sell more. The system watches what visitors are doing on the website in real-time. It collects information like which pages visitors look at, what products they check out, and how long they stay. Source Code 7. Credit Card Fraud Detection With the rise in the digital world, credit card fraud has become challenging. Many fraudsters use phishing websites to steal your