Fingerprint recognition is a widely used biometric authentication method in security systems. However, distorted or poor-quality fingerprints—caused by skin conditions, pressure variation, or sensor issues—can significantly degrade recognition accuracy. This project aims to automatically detect and rectify distorted fingerprints using image processing and machine learning techniques. The system includes modules for fingerprint enhancement, distortion detection, and rectification using convolutional neural networks (CNNs). By improving fingerprint quality before feature extraction and matching, the system enhances recognition performance and reduces false rejections, particularly in high-security or forensic applications.
In medical emergencies, rapid and accurate drug recommendation can be life-saving. The proposed system utilizes machine learning to suggest appropriate drugs based on patient symptoms, medical history, allergy profiles, and vital signs. By training models on a dataset of historical medical cases and drug efficacy records, the system predicts the most suitable medications for emergency conditions such as seizures, asthma attacks, heart failure, and allergic reactions. The system aims to assist paramedics, emergency room doctors, and caregivers by providing intelligent drug suggestions, reducing human error and saving crucial time.
The Text-to-Speech (TTS) Converter System is a software application that converts input text into human-like speech. This system is designed to help visually impaired individuals, people with reading disabilities, and users looking to convert written content into audio form. It takes typed or uploaded text as input and produces an audible speech output using a speech synthesis engine. The system aims to be simple, efficient, and accessible across different platforms. With advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP), modern TTS systems now produce highly realistic and context-aware voices, mimicking human intonation, pauses, and pronunciation. This project leverages these technologies to create an efficient and user-friendly TTS converter system
The AI Chatbot for College Enquiry System aims to automate the process of handling student and parent queries regarding college information such as admission procedures, course details, fee structure, faculty information, placement data, campus facilities, and more. Built using Rasa (an open-source conversational AI framework) and Python, the chatbot provides instant, accurate, and 24/7 responses to commonly asked questions, thereby reducing the workload on administrative staff and improving user experience. The system can be deployed on a website or college portal, ensuring seamless communication with stakeholders.
Automated bird species identification plays a crucial role in ecological monitoring, biodiversity assessment, and conservation efforts. This study presents a system that leverages audio signal processing and neural networks to accurately classify bird species based on their vocalizations. The proposed approach involves preprocessing raw bird song recordings using techniques such as noise reduction, Mel-frequency cepstral coefficients (MFCC) extraction, and spectrogram analysis to capture essential acoustic features. These features are then used to train a convolutional neural network (CNN) designed to learn and distinguish between the unique audio patterns of various bird species. The model demonstrates high classification accuracy across a diverse dataset of bird calls, highlighting its potential for real-time, scalable deployment in natural environments. This work contributes to the advancement of bioacoustic monitoring by offering an efficient, non-invasive tool for automated species recognition.
Natural disasters such as earthquakes, floods, wildfires, and hurricanes often cause catastrophic damage to life and infrastructure. Rapid and effective response is crucial to minimize casualties and ensure resource allocation. Traditional disaster management systems are often reactive, slow, and dependent on manual decision-making, which can delay rescue operations. This project proposes an AI-powered Disaster Response System that leverages machine learning, computer vision, natural language processing (NLP), and real-time data analytics to enhance disaster detection, prediction, and response. The system integrates multiple data sources, including satellite imagery, IoT sensors, social media feeds, and weather data, to predict disasters, assess damage. By automating early warnings, situational awareness, and resource allocation, the AI system aims to reduce human error, speed up rescue operations, and improve decision-making in emergency scenarios.
Visually impaired individuals face significant barriers in accessing technology, reading text, navigating spaces, and interacting with devices. This project proposes an AI Voice Assistant specifically designed to empower the visually impaired by allowing voice-based interaction with a computer or smartphone using speech recognition, text-to-speech, computer vision, and AI-based task automation. The system listens to user commands, processes them intelligently using Natural Language Processing (NLP), and responds with voice output. It can assist with daily tasks such as reading text aloud (OCR), recognizing objects or faces, accessing news, answering questions, sending emails, or opening applications, thus offering increased independence and accessibility.
Railway level crossings are common points of conflict between rail and road traffic, and manual gate operations often lead to accidents due to human error or delay. This project presents an Automatic Railway Gate System that automates gate control using sensors and microcontrollers. The system detects train arrival and departure in real-time and manages the opening and closing of the gate automatically, ensuring higher safety, faster operation, and minimal human intervention.
This paper consists of fraud detection and measures to automate it fully. For every bank, it has become essential for Fraud detection. Fraud is rising significantly, which ends in many damages for the banks. Transactions create unique challenges for fraud exposure due to the lack of short-term processing. The foremost task is a feasibility study of chosen fraud detection methods. With the help of models, these transactions are to be tested individually and further proceeded. We first define a detection task: attributes of the dataset, the metric choice, and any techniques to control such unbalanced datasets. This leads to the fact that the underlying pattern generating the dataset results: For example, cardholders may improve their purchasing habits over periods, and fraudsters may change their tactics. Later, we highlighted several methods used to obtain the sequential features of credit card transactions.
The issue of transcribed digit acknowledgment has for some time been an open issue in the field of example order. A few examinations have demonstrated that Neural Networks offer excellent performance in data classification. This project presents a reliable and efficient method for recognizing handwritten digits using Convolutional Neural Networks (CNN). CNNs outperform traditional neural networks due to their spatial feature extraction capabilities and improved computational efficiency. Using the MNIST dataset, which includes 70,000 grayscale images of digits (0–9), the CNN model is trained and tested to classify images with high accuracy. This is essentially a 10-class classification problem using deep learning frameworks like TensorFlow and Keras, with support from libraries such as NumPy and Pandas.
In today’s digital age, the authenticity and security of certificates play a pivotal role in various domains, including education, employment, and legal documentation. Also, in a fiercely competitive job market, the proliferation of fraudulent academic certificates poses a significant challenge. Traditional paper-based certificates are prone to forgery and tampering, leading to a growing demand for robust and efficient certificate authentication systems. Also, Traditional certificate authentication methods are time-consuming, leading to delays in verifying credentials. This project presents a novel solution – the “Certificate Authentication System using QR Code” – developed utilizing PYTHON and MySQL technologies to enhance the verification and security of certificates. The primary objective of this project is to design and implement a user-friendly, efficient, and secure system that enables the issuance and authentication of digital certificates through QR codes. This innovative approach harnesses the power of PYTHON for application development and MySQL for database management to create a seamless and reliable certificate verification process. Our system offers a versatile solution, providing options for generating Bonafide Certificates, Transfer Certificates, and Course Completion Certificates. By adopting QR code technology, the system streamlines the certificate verification process, empowering organizations to swiftly and accurately verify the authenticity of certificates. This innovative approach not only addresses the challenge of certificate fraud but also enhances the efficiency and reliability of the verification process, ensuring that employers have a dependable means to authenticate certificates and make informed hiring decisions. It represents a significant step towards the digitization and security enhancement of certificate management systems, contributing to a more trustworthy and reliable certification ecosystem.
The Network Mapper and Topology Visualizer is a tool designed to scan, map, and visually represent the structure of a computer network. It automatically discovers devices, identifies their connections, and generates a real-time graphical topology map to help administrators monitor, analyze, and manage network resources efficiently. By providing a clear view of nodes, connections, and data flow, the system simplifies network troubleshooting and optimization, ensuring better visibility and management of the overall network infrastructure.
Fake news has become a major threat to public discourse, elections, health campaigns, and even national security. With the rapid spread of false information on social media and websites, it is crucial to build systems that can automatically detect and classify news as real or fake. This project proposes a Fake News Detection System using Natural Language Processing (NLP) and Machine Learning techniques to analyze and predict the credibility of a news article based on its content. By training on a labeled dataset, the system learns linguistic patterns commonly found in fake news and accurately classifies new articles.
In this era of recent times, crime has become an evident way of making people and Society under trouble. An increasing crime factor leads to an imbalance in the Constituency of a country. In order to analyze and have a response ahead this type of Criminal activities, it is necessary to understand the crime patterns. This study imposes one such crime pattern analysis by using crime data obtained from Kaggle open source which in turn used for the prediction of most recently occurring crimes. The major aspect of this project is to estimate which type of crime contributes the most along with time period and location where it has happened. Some machine learning algorithms such as Naïve Bayes is implied in this work in order to classify among various crime patterns and the accuracy achieved was comparatively high when compared to pre composed works.
The Organ Donation Tracking System is a web application that helps manage organ donors, recipients, and organ donation requests. The system ensures transparency, facilitates tracking, and provides notifications for timely organ matching. It allows admin control, donor and recipient registration, and request monitoring. Key Features: • Donor registration with health details. • Recipient registration with organ requirements. • Admin dashboard to monitor all donors, recipients, and donation requests. • Status tracking for organ donations. • Search for matching donors for recipients. • Notifications for successful matches.
Traditional password-based authentication systems are increasingly vulnerable to phishing, password leaks, and social engineering attacks. To enhance security, this project proposes a Cloud-Based Biometric Authentication System using Python, allowing users to authenticate via biometric traits like face or fingerprint recognition, securely processed and validated over the cloud. The system captures biometric data, processes and encodes it, and stores encrypted biometric templates in the cloud. During login, the user’s biometric input is matched against the cloud-stored template using AI models to grant or deny access.
Monitoring and managing traffic is a critical need in urban areas where road congestion and violations are increasing. Traditional methods for vehicle counting, like manual surveys or sensor-based systems, are inefficient, costly, and prone to error. This project proposes a Real-Time Vehicle Detection and Counting System using OpenCV and computer vision techniques to automatically detect and count vehicles in traffic video feeds. The system leverages frame-by-frame analysis and object detection models to identify vehicles and increment counters as they cross a virtual detection line. It can be deployed for traffic management, road analytics, or surveillance in smart city environments.
Build a lightweight web-application security scanner that automatically discovers input points (URLs, query parameters, forms), performs non-destructive tests to detect potential SQL Injection (SQLi) and Cross-Site Scripting (XSS) vulnerabilities, analyzes server responses for tell-tale signs (reflection, error messages, missing input sanitization), stores results, and shows a concise report. The tool is intended for educational and defensive use (pen-testing with permission), and focuses on detection and reporting rather than exploitation.
With the rapid growth of digital communication and image sharing, securing visual data from unauthorized access has become a significant concern. Traditional encryption methods are not always suitable for image-based data due to size, format, and processing constraints. This project proposes a Visual Cryptography-Based Image Security System that uses the concept of visual cryptography to split an image into multiple secure shares such that the original image can be revealed only when the correct shares are combined. Each individual share reveals no information on its own, ensuring maximum security. The system is ideal for securing confidential images like identity proofs, medical scans, biometric data, etc.
In today’s fast-paced lifestyle, managing finances is often neglected, leading to overspending and financial instability. This project aims to develop a Python-based Personal Expense Tracker and Budgeting System that enables users to record, categorize, analyze, and visualize their income and expenses. The system allows users to set budgets, track spending habits, receive alerts when nearing limits, and view reports in real time. With an intuitive interface, this tool promotes financial awareness and encourages better spending decisions.
Academic performance prediction is essential for early intervention and student support. Traditional evaluation methods only assess outcomes after exams, which limits proactive academic guidance. This project proposes a Student Performance Prediction System using Random Forest, a supervised machine learning algorithm, to predict a student’s performance based on multiple factors such as demographics, study time, health, attendance, previous grades, parental education, and internet access. This model helps educators identify at-risk students and improve academic outcomes through data-driven insights.
Speech is one of the most natural forms of human communication, carrying not only linguistic information but also emotional states. Recognizing emotions from speech can enhance human-computer interaction, virtual assistants, healthcare, education, and security systems. Traditional machine learning methods often struggle to capture the complex temporal and spectral features of speech. In this project, a deep learning-based approach is used to detect emotions (such as happiness, sadness, anger, fear, neutral, etc.) from audio signals. The system preprocesses speech data into spectrograms or Mel-frequency cepstral coefficients (MFCCs), then applies Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN/LSTM), or hybrid architectures to classify emotions with higher accuracy.
The increasing global demand for renewable energy has made accurate prediction of solar energy generation crucial for efficient energy management and grid stability. This project focuses on developing a deep learning-based predictive model to forecast solar energy output based on historical and environmental data. By leveraging advancements in neural networks, the system aims to address the challenges posed by the intermittent and weather-dependent nature of solar energy. The proposed approach employs Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, to capture temporal dependencies in time-series data, such as solar irradiance, temperature, and weather conditions.
In India, six two-wheeler riders die every hour in road accidents. Also, we have seen that during this pandemic people wear masks, and to avoid congestion they do not wear helmets which attracted our concern and we decided to work on a project where these helmetless people can be penalized for violating traffic rules. To achieve an efficient helmet detection model, we have used the YOLOv5 object detection model using transfer learning. Further to check whether the biker is wearing a helmet or not we are using two methods, one being checking for overlapping between bounding boxes and the second method is, checking if a helmet exists in the specified range of coordinates above the motorcycle. Our model gives a mAP of 0.995 and to the best of our knowledge, we used overlapping methods for interlinking objects for finding the person not wearing a helmet. For number plate recognition we are using EasyOCR.
Social media platforms like Twitter, Facebook, and Instagram have become essential spaces for users to share opinions, feedback, and emotions. Monitoring these sentiments can provide valuable insights for businesses, governments, and researchers. This project aims to develop a Python-based Sentiment Analysis System that automatically analyzes textual posts from social media and classifies them as positive, negative, or neutral. The tool uses Natural Language Processing (NLP) and Machine Learning models to interpret the emotional tone of social media content in real time.
Recruiters often receive hundreds of resumes for a single job posting, making manual screening time-consuming and error-prone. This project automates the resume screening process using NLP techniques by matching the content of resumes with job descriptions. The system calculates a similarity score between each resume and the job post to identify the most suitable candidates, helping recruiters focus only on the most relevant applications.
Considering a growing number of criminal acts, there is an urgent need to introduce computerized command systems in security forces. This study presents a novel deep learning model specifically developed for identifying seven different categories of weapons. The suggested model utilizes the VGGNet architecture and is implemented utilizing the Keras architecture, which is built on top of the TensorFlow framework. The model is trained to recognize several types of weapons, including assault rifles, bazookas, grenades, hunting rifles, knives, handguns, and revolvers. The training procedure involves creating layers, executing processes, saving training data, determining success rates, and testing the model. A customized dataset, consisting of seven different weapon categories, has been meticulously chosen and organized to support the training of the proposed model network. We do a comparative study using the newly created dataset, specifically comparing it with established models such as VGG-16, ResNet-50, and ResNet-101. The suggested model exhibits exceptional classification accuracy, obtaining a remarkable 98.40%, outperforming the VGG-16 model (89.75% accuracy), ResNet-50 model (93.70% accuracy), and ResNet-101 model (83.33% accuracy). This research provides a vital viewpoint on the effectiveness of the suggested deep learning model in dealing with the complex problem of weapon classification, presenting encouraging outcomes that could greatly improve the capabilities of security forces in countering criminal activities.
As per the previous year’s report concerning to road crashes indicates that the principal cause of such a fatal road accidents is because of negligence behavior as well as drowsiness of driver. This problem reveals the requirement of such a system that can recognize drowsiness state of driver and gives alert signal to the driver before the occurrence of any accidents. Therefore, this proposed work has established drowsy detection as well as accident avoidance system based on the eye blink duration. Here, first the open and close state of eye are detected based on the eye aspect ratio (EAR). Further, the blink duration or count during the changes of eye state from open to close are analyzed. Then, it identifies the state of drowsiness, when blink duration becomes more than a certain limits and sends the alert message to the driver through the alarm. Our developed system has shown the accuracy of 92.5 % approx on yawning dataset (YawDD).
ReadNotify-Pro (minimal implementation) — A web application that allows users to send emails instrumented with a unique tracking pixel. When the recipient opens the email, the pixel loads from the app and the event (timestamp, IP, user agent, referer) is logged. Senders can view delivery and open history through a dashboard, retract messages, and configure self-destruct/expiry for an email. Primary goals: simple integration with existing SMTP providers (Gmail/SendGrid), accurate open logging, and admin control for sent messages.
In digital banking systems, traditional password-based authentication is increasingly vulnerable to phishing, brute-force, and credential theft. To enhance transaction security, this project proposes a Multi-Factor Authentication (MFA) system that incorporates three verification layers: One-Time Password (OTP) sent to the user’s registered email. Security Question Verification (chosen at account setup). Random Puzzle Authentication (3×3 grid puzzle), where the correct solution is generated and the answer is sent to the user’s registered mail for confirmation. The system is developed using Python Flask as the backend, with secure email handling (SMTP), database integration for user credentials, and cryptographic hashing for sensitive information. This layered approach ensures that even if one factor is compromised, unauthorized access is still prevented.
The rapid adoption of Internet of Things (IoT) devices has created new opportunities but also introduced severe security threats. Due to limited computational resources and weak authentication mechanisms, IoT devices are vulnerable to botnet attacks, where compromised devices are hijacked to launch Distributed Denial of Service (DDoS) or other malicious activities. Traditional signature-based detection systems are inadequate in identifying novel and evolving botnet traffic. This project proposes an IoT Botnet Detection System using Deep Learning, which leverages neural networks to analyze network traffic patterns and identify malicious activities. The system is capable of detecting botnet attacks with high accuracy by learning hidden traffic patterns from IoT datasets such as UNSW-NB15, Bot-IoT, or N-BaIoT. The project provides a robust framework for real-time monitoring, anomaly detection, and prevention of large-scale IoT botnet attacks.
Stress is a mental or emotional state brought on by demanding or unavoidable circumstances, also referred to as stressors. In order to prevent any unfavorable occurrences in life, it is crucial to understand human stress levels. Sleep disturbances are related to a number of physical, mental, and social problems. This study’s main objective is to investigate how human stress might be detected using machine learning algorithms based on sleep-related behaviors. The obtained dataset includes various sleep habits and stress levels. Six machine learning techniques, including Multilayer Perception (MLP), Random Forest, Support Vector Machine (SVM), Decision Trees, Naïve Bayes and Logistic Regression were utilized in the classification level after the data had been preprocessed in order to compare and obtain the most accurate results. Based on the experiment results, it can be concluded that the Naïve Bayes algorithm, when used to classify the data, can do so with 91.27% accuracy, high precision, recall, and f measure values, as well as the lowest mean absolute error (MAE) and root mean squared error rates (RMSE). We can estimate human stress levels using the study’s findings, and we can address pertinent problems as soon as possible.
The rapid urbanization of cities has led to major challenges in effective waste management. Traditional garbage collection systems are inefficient, often resulting in overflowing bins, increased pollution, and poor hygiene. This project proposes a Smart Garbage Management System using IoT and cloud technologies to automate the monitoring, scheduling, and optimization of waste collection in urban areas. The system involves smart bins equipped with sensors to monitor garbage levels in real-time, transmit data to a centralized dashboard, and dynamically notify collection units based on priority. This improves route planning, saves time, and reduces environmental hazards.
With the rapid growth of e-commerce platforms, recommending relevant products to users has become a vital part of improving user satisfaction and boosting sales. This project develops an intelligent recommendation system that suggests products to users based on their browsing behavior, purchase history, and user preferences using collaborative and content-based filtering techniques. Built using Python, the system employs machine learning models and integrates seamlessly into e-commerce platforms. Optional modules include sentiment analysis from reviews and a hybrid recommendation engine for enhanced accuracy.
With increasing cases of certificate and document forgery, there is a growing need for an automated and intelligent system to detect manipulated documents. This project proposes a system that leverages image processing, OCR (Optical Character Recognition), and AI-based tamper detection techniques to verify the authenticity of scanned certificates or digital documents. The system detects forgery by identifying anomalies such as inconsistent fonts, altered signatures, tampered seals/logos, or pixel-level discrepancies. It flags forged areas and generates a verification report. This system ensures the integrity of documents submitted in academic, corporate, and legal domains.
There are number of users who purchase products online and make payment through e- banking. There are e- banking websites who ask user to provide sensitive data such as username, password or credit card details etc. often for malicious reasons. This type of e-banking websites is known as phishing website. In order to detect and predict e-banking phishing website, we proposed an intelligent, flexible and effective system that is based on using classification Data mining algorithm. We implemented classification algorithm and techniques to extract the phishing data sets criteria to classify their legitimacy. The e-banking phishing website can be detected based on some important characteristics like URL and Domain Identity, and security and encryption criteria in the final phishing detection rate. Once user makes transaction through online when he makes payment through e-banking website our system will use data mining algorithm to detect whether the e-banking website is phishing website or not.
Hate speech on online platforms poses serious risks by promoting discrimination, harassment, and violence. Traditional keyword-based or rule-based systems are limited, as users often obfuscate offensive words (e.g., “1di0t”), mix languages, or use indirect expressions. To address this, the project proposes a deep learning-based fusion model that combines multiple textual representations—transformer-based embeddings, character-level encodings, and handcrafted linguistic features—to improve robustness and accuracy in hate speech detection. The system applies Natural Language Processing (NLP) and deep learning fusion techniques to classify text into categories such as hate speech, offensive, or normal. The model is deployed as a web application, providing a user-friendly interface where users can input text and receive real-time detection results. This ensures more effective content moderation, safer digital spaces, and support for multi-lingual or code-mixed data.
In today’s data-driven world, organizations generate vast amounts of structured and unstructured data. Data Sphere is a unified data and AI platform that allows scalable and efficient management, analysis, and visualization of big data. This project leverages Datasphere to perform data ingestion, transformation, and advanced analytics, enabling stakeholders to extract actionable insights using built-in data science tools, AI, and machine learning capabilities.
In the era of cloud computing and large-scale data sharing, organizations face the risk of data leakage due to insider threats, unauthorized access, or accidental sharing. Traditional access-control methods cannot fully prevent leaks, especially when authorized users intentionally misuse data. To address this, the project proposes a dual-layer protection system that integrates digital watermarking and cryptography. Sensitive data is first encrypted for confidentiality and then embedded with an invisible watermark that uniquely identifies the authorized recipient. If the data is leaked, forensic analysis of the watermark reveals the source of the leak. This combination ensures data confidentiality during transmission and accountability in case of breaches. The system will be deployed as a web-based platform, enabling secure data upload, encryption, distribution, and leakage detection with traceability.
As cryptocurrency adoption grows rapidly, secure and user-friendly tools to store, send, and receive digital assets are essential. This project focuses on building a Python-based Cryptocurrency Wallet that allows users to generate public/private key pairs, manage wallet balances, and securely send/receive crypto transactions via blockchain networks. This wallet will interface with existing blockchains (like Bitcoin or Ethereum) using APIs and libraries, providing a simple UI to manage digital funds securely. The system ensures wallet creation, key security, transaction integrity, and real-time balance checks.
This project proposes an automatic face naming system using discriminative affinity metrics learned from weakly labelled images. The goal is to accurately assign names to faces in a collection of images where the labels are incomplete or imprecise. By leveraging weakly labelled data, the system reduces the need for extensive manual annotation, making it scalable for large datasets. The approach involves constructing a discriminative affinity metric that models the relationship between face pairs based on visual features and available weak labels. A metric learning framework is employed to refine these affinities, enhancing the separability of different identities in the feature space. The methodology includes data preprocessing, feature extraction using deep learning models, and affinity metric learning through supervised and semi-supervised strategies. Experimental results demonstrate improved face naming accuracy compared to traditional methods, showcasing the effectiveness of the proposed technique in handling weakly labelled datasets. This system has potential applications in media analysis, security, and large-scale image retrieval systems.
Air and noise pollution are two of the most harmful environmental threats in urban areas, impacting both human health and ecosystem balance. This project presents an real-time monitoring system that measures air quality (e.g., PM2.5, CO2) and ambient noise levels. The system collects data through smart sensors and transmits it to a centralized server for visualization and analysis. The goal is to provide accurate, location-based pollution data that can assist governments, environmental agencies, and the public in understanding pollution trends and taking preventive actions.
