Kakinada Institute of Engineering and Technology for Women
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PREDICTIVE ANALYTICS-BASED DECISION SUPPORT SYSTEM FOR LAND INVESTMENT USING AI
Land investment is considered a promising long-term financial strategy, but evaluating the true potential of land plots is often difficult due to limited analytical tools and heavy reliance on brokers. Most real estate platforms mainly focus on built properties and provide only basic information for raw land evaluation. This research proposes a predictive analytics-based decision support system that utilizes artificial intelligence to assist investors in making informed land investment decisions. The system integrates multiple datasets including historical land price trends, geographic location data, transportation accessibility, environmental indicators, and safety statistics. Machine learning algorithms such as Linear Regression, Random Forest, and K-Means Clustering are applied to analyze patterns and forecast future land value appreciation. A composite Investment Score is calculated by combining predicted growth with civic and environmental factors. Users can filter plots based on budget and visualize results through an interactive map interface. The platform also generates analytical reports summarizing investment potential and comparative insights. The modular architecture allows future expansion to multiple cities and integration with additional data sources. This approach enhances transparency and enables data-driven real estate investment decisions.
A role-based web application called the Unified College Management Platform was created to streamline and digitize academic management procedures in educational establishments. The platform has an interactive and user-friendly interface thanks to the use of HTML, CSS, and JavaScript for the frontend and Django as the backend framework. Administrators, faculty, and students are the three primary user roles that it is intended to support, each of which has unique functionalities. In order to ensure well-organized administrative control, the Administrator module offers secure login and makes it possible to manage academic years, semesters, faculty information, and user accounts. Instructors can upload and manage academic resources in the Faculty module, including study materials, prior year question papers in PDF format, syllabus information (including titles, descriptions, topics, and learning videos), and key questions to help students prepare for exams.. Users can choose their branch and academic year, register via email-based authentication, and quickly access all pertinent academic resources, including study guides, exam-related documents, and syllabus content. The Unified College Management Platform lowers manual labor, increases academic management efficiency, and offers safe and practical access to educational resources by centralizing academic data and resources. The system also provides a scalable framework that can accommodate future features like notifications, academic performance tracking, and integration with cutting-edge learning management systems. It also improves communication between students and faculty.
YOLOv8-Based Helmet and Plate Detection with Bilingual output
Road accidents and traffic violations, particularly the non-usage of helmets and unregistered vehicles, remain a serious concern in developing nations. Manual monitoring by traffic police is often inefficient and error-prone. To address this challenge, we propose an Automatic Helmet and License Plate Detection System with Bilingual Output using YOLOv8. The system integrates real-time helmet detection and license plate recognition with bilingual (English and regional language) feedback, ensuring wider accessibility and usability. Leveraging YOLOv8’s advanced object detection capabilities, the model achieves high accuracy and real-time performance.. Experimental results demonstrate that our approach achieves an mAP of over 90% for helmet detection and license plate recognition, with an average inference speed of 35 FPS on GPU. The proposed system can be deployed for intelligent traffic monitoring, law enforcement, and smart city applications.
A SMART LIBRARY INVENTORY SYSTEM BASED ON BOOK PINE TEXT RECOGNITION
Efficient management of library resources is essential for educational institutions, research centers, and public libraries. Traditional library inventory systems rely heavily on manual verification or barcode scanning techniques, which require significant human effort and time. Although technologies such as RFID have improved automation, their implementation cost remains high for many institutions. This research proposes a smart library inventory system that uses computer vision and optical character recognition (OCR) techniques to identify books directly from the text printed on their spines. The system captures images of bookshelves using a camera, detects book spines from the image, and extracts textual information such as book titles and author names. The extracted text is the matched with a digital library database to verify the presence and location of books. This automated approach minimizes manual effort, improves inventory accuracy, and speeds up the library management process. The proposed system demonstrates how artificial intelligence and image processing can be integrated into library management systems to enhance efficiency and reliability. The study also discusses system design, implementation methods, challenges, and potential improvements for future research.
AN INTELLENCE STUDENT IQ PREDICTION SYSTEM USING ASSESSMENT DATA
This research aims to develop a comprehensive student assessment model for predicting Intelligence Quotient (IQ) using advanced machine learning techniques. The primary objective is to provide a holistic evaluation of students’ academic aptitude and career readiness. To ensure robustness, data is collected from multiple sources, including academic records, faculty evaluations, and socio-economic background information. Academic performance, represented through Grade Point Average (GPA) and subject-wise marks, provides a quantitative measure of students’ scholastic achievements. In addition, qualitative insights from professors enhance the evaluation by assessing key attributes such as analytical thinking, problem-solving ability, classroom participation, and overall behavioural patterns. These multi-dimensional features enable the model to capture both intellectual capabilities and behavioural characteristics that influence student potential.
ENHANCING VEHICULAR COMMUNICATION RELIABILITY THROUGH BLOCKCHAIN-ENABLED INCENTIVES
Agriculture remains the backbone of India’s economy, yet farmers often face the dual uncertainty of unpredictable crop yields and fluctuating market prices. While several existing solutions address yield forecasting, they fail to incorporate profitability, leaving farmers vulnerable to income instability. This project proposes a Profit-Aware Crop Forecasting System that integrates machine learning–based yield prediction models (Random Forest, XGBoost, Linear Regression, SVR) with market price forecasting models (ARIMA, Prophet, LSTM). The system estimates profitability by combining predicted yield with market price data, deducting input costs, and recommending the most profitable crop. The goal is to empower farmers with reliable, data-driven decisions for sustainable farming and improved income stability.
PROFIT-AWARE CROP FORECASTING USING MACHINE LEARNING AND MARKET DATA
Recently, AI-driven journaling tools have emerged as supportive resources for mental health by integrating mood tracking with natural language analysis. Building upon earlier systems such as MindScape, which combines journaling with behavioral information like sleep patterns and location data, and Resonance, which demonstrated that AI-based memory suggestions can enhance mood and reduce depressive symptoms, our application allows users to record daily mood ratings along with written reflections. These entries are analyzed using spaCy-powered techniques including sentiment analysis, entity recognition, and LDA topic modeling to identify recurring themes and emotional patterns. A custom Crisis Detection Model further improves user safety by evaluating keywords, mood variations, and sentiment thresholds to detect potential crisis situations, assess severity, log incidents, trigger in-app alerts, and recommend suitable resources. Developed with Flask and secured through Flask-Login, the system anonymizes journal entries to maintain privacy while functioning as a supportive digital companion that encourages emotional awareness, early detection of distress, and reflective self-care. Planned future enhancements include multilingual support and analysis of long-term mood trends.
END-TO-END AI SOLUTION FOR UPI FRAUD MONITORING
With the rapid adoption of Unified Payments Interface (UPI) for digital transactions, the risk of fraudulent activities has also increased significantly. To address this challenge, we propose a novel approach to detect UPI fraud by analyzing transaction details such as the bank book name, transaction ID, and transaction amount. Our method employs three machine learning algorithms: Random Forest, K-Nearest Neighbors (KNN), and Decision Tree. The system operates by processing provided transaction details to classify the transaction outcome as either "Transaction Failed: Incorrect Details Entered" or "Transaction Successful: Details Verified and Processed." Experimental evaluation suggests that these algorithms effectively distinguish between genuine and fraudulent transactions, demonstrating high accuracy and potential for integration into real-world financial systems to provide users with an additional layer of protection against unauthorized activities.
DEVELOPMENT OF A SMART ACCIDENT DETECTION AND ALERT SYSTEM USING SENSOR DATA AND GPS-GSM COMMUNICATION
The intelligent accident detection system not only enhances safety but also reduces the time gap between an accident and medical assistance, which is often critical for saving lives. Unlike conventional vehicle safety mechanisms that are expensive and vehicle-specific, this solution is compact, cost-effective, and easily adaptable to helmets, making it highly practical for two-wheeler riders. The use of an accelerometer or vibration sensor ensures accurate crash detection, minimizing false alerts caused by minor shocks or bumps. Furthermore, the system can be extended to support cloud integration for centralized monitoring, IOT-based dashboards for live tracking, and voice call alerts in addition to SMS, making it more versatile. The rechargeable power supply with energy-efficient design allows long-term operation, while the architecture ensures easy upgrades with newer technologies like 4G/5G modules for faster communication. Its integration into daily commuting gear like helmets ensures that the safety mechanism is always with the rider, providing peace of mind for both the user and their families.
AN AI-DRIVEN CLOUD-BASED SMART SURVEILLANCE SYSTEM FOR FIRE AND SMOKE DETECTION
Fire incidents in residential, commercial, and industrial environments pose a serious threat to human life and infrastructure. Conventional fire detection systems rely mainly on smoke or temperature sensors, which often result in delayed responses and increased damage. To overcome these limitations, this research presents a Smart Fire Surveillance System that integrates Artificial Intelligence (AI) with cloud technology for intelligent fire monitoring. The proposed system utilizes surveillance cameras to continuously capture video streams and applies AI-based computer vision techniques to identify fire and smoke patterns in real time. Once a fire event is detected, the system transmits the detection results to a cloud platform where the information is securely stored and analyzed. The cloud infrastructure enables remote monitoring, centralized data management, and instant notifications to users through web or mobile applications. By combining AI-based visual detection with scalable cloud services, the system provides faster detection, minimizes false alarms, and improves response time. This solution is suitable for deployment in smart cities, industries, commercial buildings, and residential areas, offering an efficient and cost-effective approach to fire safety management.
SMART FARMING PREDICTION SYSTEM USING EXPLAINABLE AI
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that primarily affects memory, thinking, and behavior. Early detection of Alzheimer's is critical for managing symptoms and improving the quality of life for patients. In this project, we build a computer-based system to predict the early stages of Alzheimer’s disease. The system combines different kinds of information, such as brain scans (MRI and PET), lab tests (like blood or spinal fluid markers), genetic risk factors, and memory test results. After cleaning and organizing the data, we train both traditional machine learning models and modern deep learning methods to find patterns linked to early Alzheimer’s. We then test how well these models can tell apart healthy people, those with mild cognitive problems, and those already showing early Alzheimer’s. We will first train the models using the large ADNI database and then check their accuracy on another independent dataset to make sure the results are reliable. Along with accuracy, we focus on making the predictions explainable so doctors can understand which features are most important. This work aims to support earlier and more reliable detection of Alzheimer’s disease in real-world healthcare.
AUTOMATED LEUKEMIA DETECTION USING HYBRID DEEP LEARNING AND MEDICAL IMAGE ANALYSIS
Leukemia is a life-threatening cancer of the blood and bone marrow, where early detection plays a crucial role in improving patient outcomes. Traditional methods of examining blood smears under a microscope are time-consuming and prone to human error. This study explores the use of deep learning techniques, specifically convolutional neural networks (CNNs), to automatically analyze blood smear images and detect signs of leukemia. The proposed system is trained on labeled datasets to identify abnormal white blood cells with high accuracy. Experimental results demonstrate that deep learning models can significantly enhance the speed and reliability of leukemia diagnosis, offering an effective decision-support tool for hematologists and reducing diagnostic delays. . A CNN architecture, such as ResNet or VGG16 with transfer learning, will be trained and validated to classify blood smear images as healthy or leukemic. By training models on large annotated datasets, the system can accurately classify and highlight abnormal leukocytes, significantly reducing diagnostic time and increasing consistency. Experimental results demonstrate that deep learning approaches can achieve high sensitivity and specificity, indicating their potential to assist hematologists in early and reliable leukemia diagnosis.
CAREER RECOMMENDATION USING MACHINE LEARNING
In the modern digital era, video content has emerged as one of the most impactful forms of communication, widely used in education, marketing, entertainment, and social media. However, the process of creating high-quality videos traditionally demands significant time, technical expertise, and expensive resources. This project, titled AI Video Studio (VideoGen), aims to simplify and revolutionize this process by utilizing the power of Generative Artificial Intelligence introduces Smart Career Advisor, a web-based machine learning application designed to provide personalized career recommendations based on user input. The system utilizes a trained machine learning model to suggest optimal career paths by analyzing user responses to a standardized questionnaire capturing interests, skills, and preferences. It is implemented using Python and the Flask web framework, ensuring efficient data handling and user interaction. The backend model is serialized using joblib for rapid inference and seamlessly integrates with the web interface to deliver real-time predictions. With the growing importance of informed career choices in today’s competitive environment, this system aims to support students, educators, and career counselors by offering an intelligent, data-driven guidance tool, Machine Learning, Flask, Web Application, Smart Advisor, User Personalization.
TEXT AND IMAGE TO-VIDEO GENERATION USING GENERATIVE AI
In the modern digital era, video content has emerged as one of the most impactful forms of communication, widely used in education, marketing, entertainment, and social media. However, the process of creating high-quality videos traditionally demands significant time, technical expertise, and expensive resources. This project, titled AI Video Studio (VideoGen), aims to simplify and revolutionize this process by utilizing the power of Generative Artificial Intelligence. The proposed system is a web-based application that enables users to generate high-definition videos using simple text descriptions or reference images. By integrating Google’s advanced Veo model through the Vertex AI platform, the system interprets user inputs and produces short, visually appealing video clips with minimal effort. The backend of the application is developed using Flask (Python), ensuring efficient handling of user requests and secure communication with AI services, while SQLite is used for reliable data storage and management.
A MACHINE LEARNING APPROACH TO FOOD IMAGE RECOGNITION AND CALORIE ESTIMATION
The project addresses the growing need for health awareness and lifestyle management through smart dietary tracking. It proposes an AI-driven system to help users achieve personalized health and fitness goals. The system uses image processing and machine learning algorithms to automatically recognize food items and calculate accurate calorie values. Deep learning techniques are applied to analyze dietary intake and provide personalized meal recommendations based on the user's health goals, preferences, and medical conditions. An integrated tracking system enables users to: 1. Monitor daily food intake 2. Track nutritional progress over time 3. Adjust dietary goals using real-time insights
RAG-BASED CONVERSATIONAL AGENT FOR COLLEGE WEBSITE NAVIGATION
Educational institutions often face challenges in providing timely and accurate responses to numerous student queries related to admissions, courses, fee structures, and campus policies. Traditional communication methods such as emails and help desks are limited in scalability and efficiency, resulting in delays and a poor user experience. Addressing these limitations requires an automated, intelligent solution capable of understanding natural language queries and delivering reliable, context-based information. This project introduces an AI-powered college chatbot that combines Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) to provide accurate and conversational responses. The system retrieves information from verified sources, such as the college website and official documents, reducing hallucinations and ensuring data reliability. It employs a structured data pipeline involving web scraping, PDF parsing, embedding generation, and semantic search using a vector database. Key features include multi-language support, voice interaction, and secure authentication for personalized queries. The chatbot is deployed on the official college website, delivering a scalable and efficient solution that enhances accessibility, accuracy, and overall user experience in academic information services.
DETECTION OF APP RANKING FRAUD AND MALWARE IN GOOGLE PLAY STORE USING MACHINE LEARNING TECHNIQUES
The rise of mobile applications has significantly contributed to the growth of digital ecosystems, with platforms like Google Play hosting millions of apps used by billions of users worldwide. However, the openness of such marketplaces has made them a prime target for malicious activities such as search rank fraud and malware distribution. Search rank fraud involves deceptive techniques including fake reviews, inflated ratings, and manipulated install counts, which artificially boost an app’s ranking and mislead genuine users. Malware, on the other hand, poses severe risks by stealing sensitive information, tracking user activity, or compromising device functionality. This paper introduces Fair Play, a detection framework designed to identify both search rank fraud and malware in Google Play. The system integrates behavioural analysis, co-review graph modelling, temporal tracking, and linguistic evaluation to uncover fraudulent patterns. By combining static and dynamic analysis techniques with supervised machine learning, Fair Play achieves high accuracy in classifying malicious and fraudulent applications. Experimental evaluation demonstrates that the system can detect fraudulent apps with more than 97% accuracy and malware with over 95% accuracy, thereby enhancing marketplace trust and ensuring user safety.
Drowsiness and Fatigue of drivers are amongst the significant causes of road accidents. Every year, they increase the amounts of deaths and fatalities injuries globally. In this paper, a module for Advanced Driver Assistance System (ADAS) is presented to reduce the number of accidents due to drivers fatigue and hence increase the transportation safety; this system deals with automatic driver drowsiness detection based on visual information and Artificial Intelligence. We propose an algorithm to locate, track, and analyze both the drivers face and eyes to measure PERCLOS, a scientifically supported measure of drowsiness associated with slow eye closure.The aim of this project is to develop a smart system that can detect when a person, especially a driver, is feeling sleepy or drowsy. The system uses a camera to monitor the person’s face and eyes. With the help of Artificial Intelligence (AI) and computer vision, it checks for signs like eye closure, blinking rate, and head movement. If it finds signs of drowsiness, it gives an alert to wake the person up and prevent accidents.
AN INTELLIGENT CLINICAL DECISION SUPPORT SYSTEM FOR HEART DISEASE PREDICTION USING CNN
Cardiovascular diseases remain one of the most critical health problems worldwide, leading to millions of deaths every year. Early prediction and diagnosis of heart disease are essential for effective treatment and prevention. This project proposes an Intelligent Clinical Decision Support System (CDSS) that utilizes Convolutional Neural Networks (CNN) to predict the risk of heart disease using clinical and medical data. The system integrates machine learning and deep learning techniques to assist healthcare professionals in making accurate and timely medical decisions. Traditional heart disease prediction approaches mainly rely on statistical analysis or classical machine learning algorithms such as Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines. Although these techniques provide reasonable prediction accuracy, they often require manual feature extraction and may fail to capture complex nonlinear relationships in medical data. To overcome these limitations, the proposed system applies CNN-based deep learning models capable of automatically learning important features from patient datasets. The CNN model analyzes patient attributes such as age, blood pressure, cholesterol level, ECG results, and other medical parameters to determine the likelihood of heart disease. By integrating intelligent learning capabilities with a clinical decision support framework, the system improves prediction accuracy and supports doctors in diagnosing cardiovascular conditions more efficiently.
DATA-DRIVEN CRIME ANALYSIS AND PREDICTION USING MACHINE LEARNING
Crime analysis has become an essential tool for law enforcement agencies and public safety organizations. With the rapid growth of urban populations and digital records, large volumes of crime data are generated daily. This paper presents a data-driven crime analytics and prediction system designed to assist both citizens and law enforcement authorities. The proposed system integrates data preprocessing, exploratory analysis, hotspot detection, and machine learning-based crime prediction within an interactive web platform. Crime datasets uploaded in various formats are automatically processed, cleaned, and analysed to extract meaningful insights. Visualization techniques are used to display crime trends, hotspot areas, and risk indicators. Machine learning models such as Random Forest, Logistic Regression, and time-series forecasting methods are used to predict crime occurrences and future crime trends. The system provides two interfaces: a public portal for crime awareness and safety recommendations, and an administrative dashboard for law enforcement decision support. Experimental results demonstrate that the proposed framework can effectively analyze crime patterns and provide predictive insights that assist authorities in proactive policing strategies.
MACHINE LEARNING BASED SKILL GAP ANALYSIS FOR INTELLIGENT CAREER RECOMMENDATION
In the modern recruitment landscape, organizations receive a large number of job applications for a single position, making manual resume screening both time-consuming and inefficient. At the same time, many job seekers lack proper guidance to evaluate whether their resumes meet industry expectations or contain the required skills for particular job roles. This research presents an AI-Based Resume Analyzer and Career Recommendation System designed to automate the resume evaluation process and assist users in improving their career prospects. The proposed system utilizes machine learning and natural language processing techniques to analyze resumes, extract relevant information such as skills, education, and experience, and generate meaningful insights. Based on the extracted information, the system identifies skill gaps and provides recommendations for suitable career paths. This helps users understand the requirements of different job roles and improve their resumes accordingly. The system is implemented using ReactJS for the frontend interface, FastAPI for backend services, and Python-based machine learning models for resume analysis. The proposed solution aims to simplify resume evaluation, reduce manual effort in recruitment processes, and provide intelligent career guidance for students and job seekers.
FAKE REVIEW DETECTION SYSTEM USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING
Online platforms heavily rely on user reviews for decision-making, making the identification of fake reviews crucial. This paper presents a novel machine learning-based Fake Review Detection System that incorporates advanced linguistic and semantic analysis. The model's robustness is demonstrated through comprehensive evaluation metrics, showcasing its efficacy in real-world scenarios. Technological innovations in the backend system ensure seamless integration, scalability, and reliability. The outcome contributes to a deeper understanding of linguistic cues, providing a valuable tool for maintaining trust in online platforms. Numerous approaches are employed in detecting fake reviews, predominantly focusing on the linguistic cues of reviewers while overlooking their non-linguistic behaviors. This study identifies various non-linguistic behavioral traits of online reviewers and assesses their significance in detecting fake reviews compared to linguistic cues. Empirical findings from real-world online reviews demonstrate that integrating non-linguistic reviewer characteristics can substantially enhance the efficacy of fake review detection models.
AI-POWERED VIRTUAL PERSONAL FINANCE ASSISTANT FOR SMART BUDGETING AND EXPENSE MANAGEMENT
The rapid growth of digital transactions has made personal finance management increasingly complex and essential. This project, titled “AI-Powered Virtual Personal Finance Assistant for Smart Budgeting and Expense Management,” focuses on developing an intelligent system to manage income, expenses, and savings efficiently. It uses Artificial Intelligence and Machine Learning to analyze spending patterns and automate expense categorization. The system provides personalized budgeting recommendations based on user behavior. Natural Language Processing enables easy interaction through a conversational interface. This makes financial management more accessible and user-friendly. The application generates real-time financial insights for better understanding. It can also predict future expenses using historical data. Smart alerts help users avoid overspending and stay within budget. The platform ensures secure handling of sensitive financial information. It presents data through charts and summaries for better visualization. The assistant also suggests effective saving strategies. It can recommend suitable investment options based on user goals. The system promotes smarter financial planning and discipline. Overall, it enhances financial decision-making and improves users’ financial well-being through an automated and user-centric approach.
AI-BASED REAL-TIME AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM
The increasing density of vehicles in modern transportation systems has created a critical need for automated and intelligent monitoring solutions. Vehicle number plate detection and recognition systems have become essential for applications such as traffic regulation, surveillance, toll automation, and identification of unauthorized or stolen vehicles. This research presents an advanced real-time vehicle number plate recognition framework that integrates computer vision and deep learning techniques for efficient performance. The proposed system processes live video streams or image inputs to detect moving vehicles and accurately localize their number plates using a deep learning–based object detection model. The extracted plate regions are further processed through Optical Character Recognition (OCR) to convert visual text into digital format. Image preprocessing techniques, including noise reduction, contrast enhancement, and grayscale conversion, are employed to improve detection accuracy under varying environmental conditions such as poor lighting, motion blur, and different viewing angles. To enhance reliability, the system combines a YOLO-based detection approach with Tesseract OCR for robust character recognition. The recognized license plate data is systematically stored in a database and automatically verified against a predefined list of flagged or unauthorized vehicles. The developed framework demonstrates high accuracy, real-time capability, and scalability, making it suitable for intelligent transportation systems. It offers a cost-effective and automated solution for vehicle identification, contributing to improved road safety, efficient traffic management, and enhanced security monitoring.
AN INTELLIGENT DRUG RECOMMENDATION FRAMEWORK USING PATIENT FEEDBACK AND REINFORCEMENT LEARNING
In the current technology, everyone started taking suggestions from the machines. The advancement in technology has also evolved in medical industry. Most people are following drug recommendation systems. Traditional drug recommendation systems often rely only on a understands text and voice inputs from patients and recommends drugs based on their symptoms, while CNN models identify drugs from pill images. Reinforcement learning is then used to continuously improve the recommendations by learning from patient feedback and outcomes over time. Existing systems that use facial data do not ensure user privacy; however, our proposed system avoids using facial data, thereby providing better privacy protection.
AI-POWERED FACIAL EMOTION ANALYZER USING CNN
Facial emotion recognition has become an important research area in artificial intelligence due to its wide range of applications in human-computer interaction, healthcare, education, and security systems. Understanding human emotions through facial expressions helps machines interact with users in a more natural and intelligent way. This project proposes an AI-powered facial emotion analyzer using Convolutional Neural Networks (CNN) to detect emotions from facial images in real time. The system captures facial images using a camera or input image and processes them using deep learning techniques to identify emotions such as happy, sad, angry, surprised, neutral, and fear. Traditional emotion recognition systems mainly rely on manual feature extraction and classical machine learning algorithms such as Support Vector Machines, Decision Trees, and K-Nearest Neighbors. These approaches require handcrafted features and often fail to capture complex facial patterns. To overcome these limitations, the proposed system uses CNN-based deep learning models that automatically learn facial features from images. The CNN model analyzes facial landmarks, expressions, and patterns to classify emotions accurately. By integrating deep learning with image processing techniques, the system improves emotion detection accuracy and provides real-time emotion prediction. The proposed system can be used in smart classrooms, mental health monitoring, customer feedback analysis, and human-computer interaction applications.
Transformer-Driven Contextual Analysis for Fraud Detection in Conversational Voice Data
Voice-based fraud, commonly referred to as voice phishing or vishing, has become a rapidly growing cybersecurity threat in modern communication systems. Fraudsters frequently exploit real-time voice conversations to impersonate legitimate entities such as banking representatives, government officials, or service providers in order to manipulate victims into revealing sensitive information including passwords, banking credentials, and one-time passwords. Traditional fraud detection systems largely rely on rule-based filtering mechanisms and predefined keyword matching techniques. Although such methods are capable of identifying known fraud patterns, they often fail to detect emerging scam strategies that rely on contextual manipulation and dynamic conversational tactics. Recent advancements in Natural Language Processing have introduced Large Language Models capable of analyzing conversational context with high accuracy. However, these models often require substantial computational resources and are difficult to deploy efficiently in real-time environments due to high latency and infrastructure requirements. These limitations highlight the need for more efficient contextual analysis techniques capable of detecting fraudulent intent in conversational data while maintaining computational efficiency.This research proposes a transformer-driven contextual analysis framework for detecting fraud in conversational voice data. The proposed approach processes voice conversations by converting audio signals into textual transcripts using speech recognition techniques, followed by text preprocessing and contextual language analysis using a lightweight transformer model. By analyzing semantic relationships and conversational patterns, the system identifies linguistic cues associated with fraudulent behavior such as urgency-based requests, impersonation tactics, and attempts to obtain confidential information.Experimental evaluation demonstrates that transformer-based contextual analysis can achieve high detection accuracy while maintaining significantly lower computational overhead compared to large language model approaches. The proposed framework therefore provides an efficient and scalable solution for detecting voice-based fraud in real-time communication systems.
Machine Learning-Based Forecasting of Air Quality Index Using Data Analytics
Air pollution is a growing environmental issue that significantly affects human health and ecological balance. The Air Quality Index (AQI) is commonly used to indicate pollution levels, but most existing systems only provide current or past information without predicting future conditions. To address this limitation, this study presents a machine learning-based approach for forecasting AQI using historical pollutant data and environmental parameters. Data from various sources is processed and analyzed to identify patterns that influence air quality. The proposed system applies machine learning models to predict future AQI values and analyze pollution trends over time. It also includes visualization techniques to present results in a clear and understandable manner. By providing early predictions and meaningful insights, the system helps individuals and authorities take timely actions to reduce health risks and manage air pollution effectively. The system is designed to be scalable and adaptable across different regions using diverse data sources. It ensures efficient performance with minimal computational overhead, making it suitable for real-time applications. Overall, the approach enhances proactive environmental monitoring and supports sustainable decision-making.
AN ADAPTIVE DEEP LEARNING APPROACH FOR REAL-TIME INTRUSION DETECTION
In an increasingly digitized world, safeguarding network environments against diverse cyber threats has become imperative. This paper presents an Adaptive Deep Learning Framework for Real-Time Network Intrusion Detection, developed to enhance the detection and classification of intrusions while accommodating the evolving nature of network traffic. The proposed framework integrates a Stacked Convolutional Autoencoder for effective feature extraction and dimensionality reduction, coupled with a Gated Convolution mechanism to capture temporal dependencies within data streams. To address challenges such as class imbalance and the scarcity of labeled data, we employ a Conditional Generative Adversarial Network (CGAN) for synthetic data generation, enriching the training dataset and improving model robustness. CGAN is employed to generate synthetic data for minority classes, thereby augmenting the training dataset and improving the detection capabilities of the model. This approach significantly enhances the robustness of our system against various types of attacks, including zero-day vulnerabilities and polymorphic threats Our experimental results demonstrate that the proposed system achieves a high detection rate with significantly reduced false positives, thereby ensuring a reliable defense against unauthorized access and other cyber threats. This research contributes to the field of cybersecurity by providing an adaptive solution that not only enhances detection performance but also enables real-time analysis, making it suitable for deployment in dynamic network environments.
ASSISTIVE OBJECT DETECTION FOR VISUALLY IMPAIRED USING DEEP LEARNING
Vision loss affects over 200 million people worldwide, making everyday tasks challenging and reducing independence. To address this, we propose an Android-based assistive application that leverages deep learning for real-time object detection and auditory feedback. The system integrates You Only Look Once(YOLO) for fast detection and (Single Shot Detector(SSD ) for accurate recognition on mobile devices. Using TensorFlow Lite APIs, the models are optimized to run efficiently on smartphones, enabling complex machine learning tasks in resource-constrained environments. Once objects are identified, the TextToSpeech API provides immediate audio feedback, allowing visually impaired users to understand their surroundings. This combination of deep learning and auditory assistance enhances safety, mobility, and independence in daily life.
DATA-TO-SOUND MAPPING: INTEGRATING VISUAL AND AUDITORY ANALYTICS
This project focuses on transforming numerical data into sound using the concept of sonification, providing an alternative to conventional visual analysis methods. By mapping data values to auditory features such as pitch, loudness, and duration, the system enables users to “listen” to patterns and insights that might not always be clearly visible in graphical representations. This interactive approach improves data engagement and can benefit researchers, analysts, as well as individuals with visual impairments. The applications of this system extend to domains such as scientific research, education, accessibility technologies, and creative multimedia fields. The project introduces a sonification workflow that first converts numerical datasets into RGB color images and then transforms the color information into sound signals. Initially, a dataset is arranged within a structured two-dimensional matrix, which is visualized as an RGB image. Each pixel in the image contains red, green, and blue components that are interpreted as sound parameters such as frequencies and amplitudes. Through this layered transformation, the system creates a bridge between visual and auditory forms of data representation, allowing users to interpret patterns more intuitively. The approach can support multiple applications including data exploration, assistive tools for visually impaired users, and creative audio-visual experiments. By linking numerical values, color representations, and sound signals, the proposed method supports analytical applications while offering new insights into datasets and user interaction. Additionally, it opens opportunities for future developments in multimodal data representation systems that combine visual, auditory, and interactive elements to improve data understanding.
A COMPUTER VISION-BASED FRAMEWORK FOR AUTOMATED ROAD ACCIDENT DETECTION AND RAPID EMERGENCY RESPONSE
Road accidents are a major cause of fatalities and severe injuries worldwide, often due to delays in accident detection and emergency response. With the increasing deployment of surveillance cameras in urban areas, computer vision and deep learning techniques can be utilized for real-time traffic monitoring. This paper proposes a computer vision-based framework for automated road accident detection using CCTV surveillance systems. The system employs deep learning models such as YOLOv8 for object detection, Mask R-CNN for segmentation, and DeepSORT for object tracking to analyze vehicle movements and identify abnormal events like collisions and sudden stops. Upon detecting a potential accident, the system automatically generates alerts and notifies emergency responders with relevant information. Experimental results demonstrate that the proposed framework achieves high detection accuracy while maintaining real-time performance, enabling faster emergency response and improved road safety.
SALES FORECASTING USING MACHINE LEARNING AND DATA SCIENCE
In today’s competitive business environment, In competitive markets, predicting future sales accurately is essential for aligning inventory planning with business strategies. This study focuses on using machine learning to improve such forecasts. This project aims to build a machine learning based model to predict future sales using historical data. The model examines variables like product type, regional sales, previous demand patterns, discounts, holidays, and seasonal behaviours to generate insights that support more strategic business planning.The performance of these models is evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The dataset used is pre-processed and split into training and testing sets to ensure the model’s generalization capabilities. The results indicate that applying machine learning enhances forecasting precision, offering noticeable improvements over conventional statistical approaches. This solution can help businesses optimize inventory levels, allocate resources efficiently, and increase customer satisfaction by ensuring product availability.
BRAIN TUMOR CLASSIFICATION AND DETECTION USING DEEP LEARNING
Brain tumors represent a critical global health challenge, where timely and accurate detection can significantly improve treatment outcomes and survival rates. Manual analysis of MRI scans is prone to delays and misinterpretations due to human error and workload. This project presents a deep learning-based system for automatic classification of brain tumors using Convolutional Neural Networks (CNNs). The model is trained on a diverse MRI dataset sourced from Kaggle and classifies five tumor types: Glioblastoma, Meningioma, Astrocytoma, Pituitary Adenoma, and Medulloblastoma.Our approach employs transfer learning with VGG16 and MobileNet, comparing their performance against traditional machine learning classifiers like Random Forest, Naive Bayes, and Decision Tree. Preprocessing and data augmentation enhance model robustness. The trained models are deployed as a Django-based web application, providing users with real-time predictions, precautionary suggestions, and treatment guidance.
CONTEXT AWARE DEEP LEARNING FRAMEWORK FOR WEATHER RESILIENT TRAFFIC SIGN DETECTION
Road accidents are a leading cause of fatalities worldwide, and one major factor is drivers failing to notice or obey traffic signs due to distractions, poor visibility, or weather conditions. Advanced Driver Assistance Systems (ADAS) exist in luxury vehicles, but they are often expensive and not optimized for developing regions such as India. This work presents a Real-Time Traffic Sign Detection System using deep learning with the YOLOv5 architecture, integrated with image preprocessing and post-processing modules to enhance accuracy in diverse road conditions. The proposed system is trained on publicly available traffic sign datasets and optimized for real-time performance on resource constrained devices. Experimental results demonstrate that the system achieves reliable detection performance, making it a promising step toward cost-effective road safety solutions. Furthermore, the modular design of the system enables easy integration with edge devices such as Raspberry Pi or NVIDIA Jetson, making it suitable for deployment in smart vehicles and intelligent transportation systems. The results indicate the potential of this approach to reduce human error, improve driver awareness, and contribute to the development of safer road infrastructures.
EMOTION AWARE MUSIC RECOMMENDATION SYSTEM USING REAL TIME FACIAL EXPRESSION ANALYSIS
Music plays an important role in influencing human emotions and mental well-being. Traditional music recommendation systems rely mainly on user preferences, listening history, or manual selection, which may not always reflect the user’s current emotional state. This project presents an Emotion Aware Music Recommendation System using Real Time Facial Expression Analysis that automatically detects the user’s mood and recommends suitable music accordingly. The proposed system utilizes a web-based application built with Python Flask as the backend and a browser-based interface for real-time user interaction. Facial images captured through the user’s webcam are processed and analyzed using the DeepFace emotion recognition framework, which identifies emotions such as happiness, sadness, anger, surprise, fear, neutral, and disgust from facial expressions. The detected emotion is then mapped to an appropriate set of songs stored in the system or retrieved from online platforms such as Spotify or YouTube, enabling personalized music recommendations. The system processes captured images by decoding base64 image data, performing facial emotion analysis, and returning recommended songs based on the dominant emotion detected. . Experimental evaluation demonstrates that the proposed system provides quick and context-aware music recommendations, improving user experience by aligning music suggestions with the user's real-time emotional state. This approach enhances traditional recommendation methods by incorporating affective computing and real-time emotion detection for intelligent and adaptive music selection.
INTELLIGENT VIDEO CONTENT FILTERING SYSTEM USING AL
Intelligent video content filtering systems are important for ensuring safe and appropriate content across digital platforms such as social media, online streaming services, educational websites, and mobile applications. Traditional content moderation systems rely on human reviewers to continuously monitor large volumes of video data, which can lead to fatigue, inconsistency, and missed detection of inappropriate or harmful content. As a result, sensitive material such as violence, explicit scenes, or unsafe activities may not be identified in time. To address this problem, this project proposes an Intelligent Video Content Filtering System using Artificial Intelligence and deep learning models such as CNN and YOLO (You Only Look Once). The system processes video streams and analyze frames in real time to detect inappropriate content such as violence, explicit visuals, or harmful activities. Detected elements are highlighted with bounding boxes and classified based on predefined categories, making it easier to identify and filter unsafe content.
AN INTELLIGENT MACHINE LEARNING FRAMEWORK FOR WATER QUALITY PREDICTION
Water is a vital natural resource that supports human health, agriculture, and industrial development, making the monitoring and management of water quality extremely important for sustainable environmental protection. However, increasing urbanization, industrial activities, and agricultural runoff have significantly contributed to the deterioration of water bodies, creating a need for intelligent and efficient monitoring systems. This study presents an intelligent machine learning framework for water quality prediction and monitoring that analyzes historical and real-time data to accurately assess the condition of water resources. The proposed framework incorporates data preprocessing, feature selection, and machine learning algorithms to evaluate important water quality parameters such as pH, dissolved oxygen, turbidity, and temperature. By identifying hidden patterns and relationships within the data, the model can effectively predict future water quality levels and detect potential contamination risks. The performance of the model is validated using appropriate evaluation techniques to ensure accuracy and reliability. Overall, the proposed framework offers an efficient and scalable solution for continuous water quality monitoring, helping environmental agencies and policymakers take proactive measures for sustainable water resource management and public health protection.
COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR CROP YIELD PREDICTION
Crop yield prediction plays a critical role in agricultural planning, food security, and economic stability. Accurate forecasting enables farmers, policymakers, and agricultural stakeholders to make informed decisions regarding crop selection, resource allocation, and risk management. With the increasing availability of agricultural data and advancements in computational techniques, machine learning has emerged as an effective approach for improving prediction accuracy. This study presents a comparative analysis of multiple machine learning algorithms for predicting crop yield based on historical agricultural data. The dataset used includes key parameters such as rainfall, temperature, soil characteristics, humidity, and previous yield records. These features are selected due to their significant influence on crop productivity. Three widely used machine learning models—Linear Regression, Decision Tree, and Random Forest—are implemented and evaluated for their predictive performance. The models are trained and tested using standard preprocessing techniques, including data cleaning, normalization, and feature selection, to ensure data quality and consistency. Performance evaluation is conducted using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R² score). The experimental results demonstrate that ensemble-based approaches, particularly Random Forest, outperform traditional models in terms of accuracy, robustness, and generalization capability. The findings highlight the importance of selecting appropriate algorithms and relevant features to enhance prediction performance. The proposed approach provides a data-driven framework that can assist farmers and agricultural planners in optimizing crop productivity and improving decision-making processes in modern agriculture.
REAL-TIME STOCK MARKET PRICE PREDICTION USING MACHINE LEARNING
After the COVID-19 ended, the global economy gradually recovered. Due to the nonlinearity, complexity, and high noise of financial time series, stock price prediction has become one of the most challenging tasks in the stock market. we propose a real-time stock market price prediction based on Long Short-Term Memory (LSTM) to simultaneously improve the fitting and accuracy of stock price prediction. Key algorithms like LSTM (Long Short-Term Memory), ARIMA (Auto Regressive Integrated Moving Average), and Random Forest will be explored for their effectiveness in forecasting stock trends. Data preprocessing and feature engineering techniques are applied to improve model reliability and prediction accuracy. The developed system integrates an interactive dashboard that visualizes both historical and predicted stock trends, enabling users to monitor market movements and gain insights into potential price behavior. By combining machine learning models with real-time data acquisition and visualization, the proposed approach provides a practical tool for supporting data-driven financial analysis and decision-making.
ML-BASED FRAUD DETECTION FOR SECURING CREDIT CARD
Credit cards are used a lot for online payments because they are fast and easy to use. But as more people use credit cards, fraud has also increased. This fraud causes big money losses for both users and banks. In this study, the main goal is to detect credit card fraud using machine learning methods. We used public data for this study, which had challenges like very few fraud cases, changing fraud patterns, and many false alerts.Many earlier studies have used machine learning methods like Decision Trees, Random Forest,LSTM, CNN and stacking classifier, these methods give high accuracy. So, we tested machine learning methods to improve the results., we used machine learning models, which gave better results than before. Then, we applied five models which are machine learning algorithms. We tried different numbers of layers, training rounds (epochs), and updated models to get the best performance. Our final model gave very high results.
ENHANCING IOT SECURITY THROUGH ML-BASED ADVERSARIAL ATTACK DETECTION
Adversarial attacks are an increasing concern for AI systems, especially in IoT, where devices rely on accurate and consistent data for decision-making. This project proposes a lightweight machine learning approach to detect such attacks, using reliable algorithms like Random Forest and Logistic Regression instead of complex deep learning models. The system begins with data preprocessing to clean and prepare incoming data, after which the models identify unusual patterns that may indicate adversarial activity. By avoiding computationally heavy techniques, the approach remains efficient and practical. A user-friendly web interface built with Flask allows users to upload data and receive instant feedback. Overall, the project delivers an accessible, effective, and straightforward tool to enhance IoT security and defend against adversarial threats.
MACHINE LEARNING APPROACHES FOR WEED IDENTIFICATION IN PRECISION AGRICULTURE
Agriculture plays a crucial role in ensuring global food security, and improving crop productivity has become an important objective in modern farming practices. One of the major challenges faced by farmers is the presence of weeds that compete with crops for nutrients, water, sunlight, and space, leading to significant reductions in crop yield and quality. Traditional weed management methods often rely on manual inspection and uniform herbicide spraying, which are labor-intensive, time-consuming, and may result in excessive chemical usage that can harm the environment. To address these challenges, this study presents a machine learning-based approach for weed identification in precision agriculture that utilizes image processing and intelligent data analysis techniques to automatically distinguish weeds from crop plants. The proposed system involves several stages including image data collection, preprocessing, feature extraction, and machine learning model training to accurately classify plant species present in agricultural fields. Machine learning algorithms such as Support Vector Machine (SVM), Random Forest, Decision Tree, and Convolutional Neural Networks (CNN) are used to analyze plant characteristics such as shape, color, and texture for accurate weed detection. By learning patterns from agricultural datasets, the system can effectively identify weeds and crops with improved accuracy.
INTELLIGENT SYSTEM FOR FUEL CONSUMPTION PREDICTION USING MACHINE LEARNING
Modern vehicles are increasingly dependent on Electronic Control Units (ECUs) that regulate and monitor critical subsystems, such as the engine, transmission, and braking systems. With the integration of the Internet of Things (IoT) and advanced sensor technologies, ECUs generate vast amounts of real-time data, including vehicle speed, engine output, throttle position, gear selection, acceleration, engine load, and fuel consumption data. Leveraging these data, the present study investigated the application of machine learning (ML) techniques for two key objectives: (i) real-time prediction of fuel consumption and (ii) classification of driving behavior profiles categorized as aggressive, moderate, and economical.The research methodology comprised data preprocessing, feature selection, and cross-validation to ensure the accuracy, robustness, and generalizability of the results. Among the evaluated models, Ridge Regression demonstrated strong predictive capability owing to its effectiveness in addressing multicollinearity within high-dimensional data. The experimental results revealed enhanced accuracy in both fuel consumption prediction and driver profile classification, validating the feasibility of machine learning approaches in this domain.The findings highlight the potential of ML-driven vehicle data analytics for advancing fuel efficiency, lowering operational costs, and mitigating environmental impacts, such as carbon emissions. This study contributes to the development of intelligent transportation systems and smart mobility solutions, where real-time predictive modeling can support eco-driving strategies, adaptive vehicle control, and sustainable fleet management.
A COMPUTER VISION BASED APPROACH FOR ACCURATE ROAD LANE DETECTION
This project focuses on detecting road lane lines using Python programming and the OpenCV library. It is mainly useful for driver assistance and basic self-driving systems. By analyzing the road images or videos taken from a front-facing camera, the system can identify lane markings and draw lines over them. This helps the vehicle stay within its lane and improves road safety for both drivers and passengers.We start by converting the road image into a simpler format using grayscale and removing noise using filters like Gaussian blur. Then, we apply edge detection to highlight the lane boundaries. A region of interest is selected to focus only on the road part, and then we use the Hough Line Transform to detect the actual lane lines. The detected lines are then drawn clearly on the image, showing the path of the lane. This method is low-cost and works well in good lighting and road conditions. It can be improved further by adding curve detection and handling shadows or faded lane marks. This project shows how image processing in Python with OpenCV can be used to solve real-world traffic problems in a simple and effective way.
AUTOMATED SURVELLIANCE AND MONITORING SYSTEM USING MACHINE LEARNING
Automated surveillance and monitoring systems are important for maintaining security in crowded public places such as railway stations, airports, shopping malls, and event venues. Traditional CCTV systems rely on human operators to continuously watch multiple video feeds, which can lead to fatigue, distraction, and missed incidents. Because of this, suspicious activities or security threats may not be detected in time.To address this problem, this project proposes an Automated Surveillance and Monitoring System using the YOLO (You Only Look Once) object detection model. The system processes live CCTV video streams and uses YOLO to detect objects such as people, vehicles, and suspicious items in real time. Detected objects are highlighted with bounding boxes, making it easier to monitor activities and identify unusual situations such as unattended objects or unauthorized access.When suspicious activity is detected, the system generates instant alerts for security personnel, allowing quick response to potential threats. By automating object detection and monitoring using YOLO, the system reduces human workload and improves the efficiency and accuracy of surveillance in public areas.
TRAFFIC SIGN DETECTION FOR VISUALLY IMPAIRED
Traffic signs are essential elements of road safety, providing warnings, directions, and regulations to drivers and pedestrians. However, these signs are primarily visual, making them inaccessible to blind and visually impaired individuals. The lack of awareness about traffic signs exposes visually impaired people to significant risks while navigating roads. This project proposes a deep learning–based assistive system designed to detect and interpret traffic signs in real time. The system leverages object detection models such as Convolutional Neural Networks (CNNs) and YOLO (You Only Look Once) to recognize traffic signs from live video feeds. Once a sign is detected, the result is converted into speech using a Text-to-Speech (TTS) engine, thereby providing immediate auditory feedback to the user. The framework is designed for deployment on lightweight, portable platforms such as smartphones and Raspberry Pi, making it affordable and practical. By combining deep learning with accessible technologies, this system improves navigation safety and independence for visually impaired people.
VENUE BOOKING PLATFORM WITH REAL-TIME AVAILABILITY
In the evolving landscape of event planning, the search for suitable venues remains a complex and often time-consuming process. This project introduces an innovative venue booking platform designed to streamline and enhance the experience of discovering, evaluating, and booking event venues online. By aggregating a wide range of venue types including wedding halls, banquet halls, conference centres, outdoor spaces, luxury venues, and small event locations — into a centralized and user-friendly digital platform, it simplifies decision- making for event organizers and individuals alike. The platform offers a comprehensive directory of venues, each with detailed profiles featuring high-quality images, location information, pricing, available packages, and optional services such as catering, decoration, parking, and seating arrangements. This level of transparency enables users to make faster, data-driven decisions without the need for multiple offline inquiries or visits. For venue owners, the platform introduces a subscription-based model that not only supports visibility and lead generation but also provides access to a suite of professional tools. These include a dynamic booking calendar, real-time reservation management, an analytics dashboard for tracking engagement and bookings, and workflow tools to streamline daily operations. This empowers venue providers to operate more efficiently, reduce manual overhead, and improve customer service.
Real-Time Opinion on Twitter Sentiment Analysis Using Machine Learning Algorithms
In recent years, social media platforms have become a major source of public opinion and user-generated content. Twitter, in particular, allows users to express their thoughts and reactions on various topics in real time. Analyzing these opinions can provide valuable insights into public perception, trends, and behaviors. This project focuses on real-time opinion mining on Twitter using machine learning algorithms.The system collects tweets related to specific keywords or hashtags through the Twitter API. The collected data is then preprocessed to remove noise such as URLs, special characters, and stop words in order to improve data quality. After preprocessing, feature extraction techniques such as Bag of Words or TF-IDF are used to convert textual information into numerical representations suitable for machine learning models.Various machine learning algorithms are applied to classify tweets into positive, negative, and neutral sentiments. The performance of the trained models is evaluated using metrics such as accuracy, precision, recall, and F1-score. Finally, the results are presented through visualizations such as graphs and dashboards, enabling users to easily understand sentiment trends.The proposed system helps in analyzing large volumes of social media data efficiently and provides useful insights for applications such as market analysis, brand monitoring, political analysis, and decision making.
ALZHEIMER’S DISEASE DETECTION USING CLINICAL DATA
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that primarily affects memory, thinking, and behavior. Early detection of Alzheimer's is critical for managing symptoms and improving the quality of life for patients. In this project, we build a computer-based system to predict the early stages of Alzheimer’s disease. The system combines different kinds of information, such as brain scans (MRI and PET), lab tests (like blood or spinal fluid markers), genetic risk factors, and memory test results. After cleaning and organizing the data, we train both traditional machine learning models and modern deep learning methods to find patterns linked to early.Alzheimer’s. We then test how well these models can tell apart healthy people, those with mild cognitive problems, and those already showing early Alzheimer’s. We will first train the models using the large ADNI database and then check their accuracy on another independent dataset to make sure the results are reliable. Along with accuracy, we focus on making the predictions explainable so doctors can understand which features are most important. This work aims to support earlier and more reliable detection of Alzheimer’s disease in real-world healthcare.