Monday, 9 February 2026

Top AI & IoT-Based Smart Agriculture Project Ideas (IEEE 2026) | Best Engineering Project Maker in Nagpur

Artificial Intelligence, IoT, drones, and deep learning are revolutionizing modern agriculture by improving crop yield, disease detection, irrigation efficiency, and precision farming. This curated list of IEEE 2025 smart agriculture project ideas is ideal for engineering students searching for the best engineering project maker in Nagpur, Wardha, Amravati, Chandrapur, and nearby regions. Each project is explained in simple terms to help students select the right final-year engineering project with real-world impact.

🌱 Smart Agriculture Projects

AI-Enabled Smart Irrigation and Crop Health Monitoring System

This project uses AI and sensors to monitor soil moisture and crop health, enabling automated irrigation and reducing water wastage.

Sustainable Agriculture Through IoT-Based Plant Disease Identification and Crop Management Using Deep Learning

An eco-friendly farming solution that combines IoT sensors and deep learning to detect plant diseases early and improve crop management.

AI-Powered Crop Yield and Health Monitoring System

This system predicts crop yield and monitors plant health using AI models trained on agricultural data.

AI-Powered Precision Agriculture: Enhancing Crop Yields with Smart Analytics

A precision farming project that leverages AI analytics to optimize fertilizer usage, irrigation, and crop productivity.

Smart Crop Health Monitoring and Disease Prediction System Using IoT and Machine Learning

An intelligent agriculture system that collects real-time sensor data and predicts crop diseases using ML algorithms.

Precision Farming Redefined: IoT-Enabled Soil Monitoring and Machine Learning Approaches for Crop Recommendations

This project analyzes soil parameters and recommends suitable crops using IoT devices and machine learning models.

🚁 Drone-Based Agriculture Projects

Mung Bean Crop Health Monitoring and Disease Detection Using Drone-Based Imaging and Deep Learning

Uses drone imagery and deep learning to identify diseases and stress in mung bean crops.

Drone-Sourced Crop Segmentation and Analysis System with U-Net Deep Learning

A drone-based image segmentation system that analyzes crop patterns using the U-Net deep learning model.

Stress Segmentation of Potato Plantation from Aerial Images Using Deep Learning

Detects crop stress in potato plantations using aerial drone images and AI-based segmentation techniques.

Drone-Based Precision Agriculture Technique to Increase Crop Yield Using Machine Learning

A smart farming solution that uses drones and ML to analyze crop conditions and improve yield efficiency.

Federated Learning-Based Cotton Crop Diseases Detection Using Internet of Drones

A privacy-preserving drone network that detects cotton crop diseases using federated learning techniques.

📡 IoT-Based Agriculture Projects

A Hybrid Approach for Smart Crop Health Monitoring Using Deep Learning and IoT

Integrates IoT sensors with deep learning models for accurate crop health monitoring.

Smart Crop Monitoring and Disease Prediction Using IoT Sensors and Deep Learning Models Deployed on Edge-Cloud Architecture

An edge-cloud system that processes agricultural data locally and remotely for faster disease detection.

Remote Sensing and IoT-Based Global Crop Disease Detection and Treatment System

Uses IoT and satellite data to monitor crop diseases and suggest treatments on a global scale.

Real-Time Rice Crop Disease Monitoring: YOLOv11-Powered System with Voice Alerts and Health Scoring

A real-time rice disease detection system that provides voice alerts to farmers using YOLO-based models.

🤖 Deep Learning & Computer Vision Projects

Commercial Plant Leaf Disease Detection Using CNN, DenseNet121, and InceptionV3

Compares multiple deep learning models to identify plant leaf diseases with high accuracy.

Intelligent Pest Detection and Control in Agriculture Using Computer Vision and Deep Learning

Detects pests in crops using computer vision and deep learning to reduce pesticide misuse.

Enhancing Cotton Crop Health Monitoring by Deep Learning Models

Uses AI models to classify cotton plant and leaf diseases automatically.

AI-Driven Deep Learning for Automated Wheat Disease Detection

An automated wheat disease detection system powered by deep learning image classifiers.

Identification of Plant Diseases Using Deep Learning and Image Processing Techniques

A general-purpose AI system that identifies plant diseases using image processing and neural networks.

Deep Learning-Based Tomato Crop Health Monitoring Using ResNet101V2

Uses the ResNet101V2 model to accurately detect tomato plant diseases.

Deep Learning-Based Transfer Learning with MobileNetV2 for Crop Disease Detection

A lightweight deep learning solution suitable for mobile and edge devices in agriculture.

Potato Leaf Disease Detection Using Deep Learning

A deep learning-based approach to identify common potato leaf diseases from images.

Enhancing Crop Health Monitoring: A ResNet50 Approach to Automated Plant Disease Severity Prediction

Predicts disease severity levels using the ResNet50 deep learning architecture.

Ultrasonic Bioacoustics and Deep Learning for Early Plant Disease Prediction

An innovative project that combines sound signals and AI for early disease detection in plants.

📈 Why These Projects Are Ideal for Nagpur Engineering Students

These projects are highly suitable for students looking for:

Best engineering project maker in Nagpur
Final-year AI & IoT projects in Nagpur
IEEE agriculture projects with implementation
Mini & major projects for CSE, AI, ML, ECE, and IoT students

Nagpur and nearby agricultural regions make these projects especially relevant for real-world deployment and research-based learning.

AI-Powered Surveillance, Healthcare, and Intelligent Systems: Emerging Research Projects in 2026

Artificial Intelligence is rapidly transforming surveillance, healthcare, robotics, and smart infrastructure. Below is a curated collection of cutting-edge AI project titles from recent IEEE research, each explained in simple terms to help students, researchers, and tech enthusiasts understand their real-world impact. This guide is especially useful for engineering students looking for the best engineering project maker in Nagpur, as well as nearby areas such as Wardha, Amravati, Chandrapur, Bhandara, Gondia, Yavatmal, and Hingna MIDC, who are searching for innovative AI, ML, and IoT final-year project ideas.

🔐 Surveillance, Security & Crime Detection

AIGuard: Anomaly Detection in Surveillance Videos with YOLOv8

This project uses the YOLOv8 deep learning model to detect unusual or suspicious activities in surveillance videos, enabling faster and more accurate security responses.

Rapid Crime Response System Using AI-Based Surveillance and Dynamic Maps

An intelligent crime response framework that combines AI video surveillance with real-time mapping to alert authorities and optimize emergency response routes.

A Real-Time AI-Powered Security System with Enhanced Security Monitoring

A real-time monitoring system that leverages AI to continuously analyze surveillance feeds and identify potential security threats.

Violence and Stampede Detection in Crowd Images and Videos Using Deep Learning

This system detects violent behavior and stampede situations in crowded environments using deep learning models for public safety management.

Deep Learning Powered Video Analysis for Anomalous Event Detection

A video analytics solution that identifies abnormal events such as accidents, intrusions, or emergencies using deep neural networks.

Intelligent Approach to Crime Detection and Alert Generation

An AI-driven crime detection system that automatically recognizes suspicious activities and sends instant alerts to law enforcement agencies.

Integrating Embedded Cyber-Physical Systems in Smart Energy for AI-Enhanced Real-Time Crowd Monitoring and Threat Detection

This research integrates AI with cyber-physical systems to monitor crowds and detect threats in smart energy and public infrastructure environments.

Real-time Traffic Monitoring and Helmet Violation Detection Using YOLOv8

An automated traffic enforcement system that detects helmet violations and traffic patterns in real time using YOLOv8.

🏥 Healthcare & Public Health

The Intelligent Public Health Surveillance System

A data-driven system designed to monitor, predict, and manage public health threats using AI and large-scale health data analytics.

AI-Powered Applications in Precision Healthcare and Security Surveillance Systems

This project explores AI applications that enhance both personalized healthcare diagnostics and intelligent surveillance systems.

AI-Powered Real-Time Patient Monitoring System with Hybrid Health Anomaly Detection

A smart healthcare monitoring system that tracks patient vitals in real time and detects medical anomalies using hybrid AI models.

AI-Powered Detection of Microbial Threats in Digital Health Systems

An AI-based cybersecurity approach to identifying microbial and biological threats within digital healthcare infrastructures.

AI-Powered Social Media Surveillance for Real-Time Disease Tracking

This system analyzes social media data using AI to detect early signs of disease outbreaks and public health trends.

AI-Powered IoT System for Constant Urine Output Monitoring

An IoT-enabled healthcare solution that continuously monitors urine output in critically ill patients for early diagnosis and intervention.

🚁 Drones, Robotics & Autonomous Systems

Self-Supervised Learning with Variational Autoencoders for Anomaly Detection in Autonomous Drone Fleets

A self-supervised AI framework that enables drones to detect operational anomalies without requiring labeled training data.

Surveillance of Landmine Detection Drone

A drone-based surveillance system designed to detect landmines in hazardous areas, improving safety in post-conflict zones.

Designing Intelligent Drones and Robots for Medical and Rescue Operations

This project focuses on AI-powered drones and robots optimized for medical assistance and disaster rescue operations in challenging terrains.

Exploring the Potential of Underwater Robotics Monitoring Enhanced by AI and Sensor Integration

An advanced underwater monitoring system that combines AI and sensors for deep-sea exploration and infrastructure inspection.

🌲 Environment, Wildlife & Personal Safety

Forestguard: Edge-AI Powered Poaching Prevention and Anomaly Detection

An edge-AI solution deployed in remote forests to detect poaching activities and protect wildlife in real time.

Smart IoT and AI-Driven Physical Harassment Detection System

A personal safety system that uses IoT devices and AI algorithms to detect physical harassment and trigger emergency alerts.

🚦 Transportation & Smart Infrastructure

Temporal-Spatial Decoupled Self-Supervised Learning for Transportation Surveillance

An intelligent transportation surveillance system that detects and localizes anomalies in traffic videos using self-supervised learning.

5G-Powered Remote Sensing for Real-Time Infrastructure Monitoring

A smart infrastructure monitoring solution that uses 5G connectivity and AI-driven remote sensing for real-time analysis.

Wednesday, 10 September 2025

Human Activity Recognition from CCTV Footage

Project Synopsis

Title: Human Activity Recognition from CCTV Footage

1. Introduction

Human Activity Recognition (HAR) plays a crucial role in intelligent surveillance systems, smart cities, and public safety. With the rapid deployment of CCTV cameras in public and private spaces, there is a growing demand for automated systems that can monitor, analyze, and recognize human activities in real time. Such systems can be used for crime detection, anomaly detection, crowd monitoring, and workplace safety.

This project focuses on building a machine learning and deep learning-based HAR framework capable of recognizing different human activities (e.g., walking, running, sitting, fighting, loitering) from CCTV video footage.

2. Problem Statement

Traditional CCTV surveillance relies on human operators to monitor video streams, which is time-consuming, error-prone, and inefficient. Manual monitoring:

Leads to missed incidents due to operator fatigue.
Cannot provide real-time alerts.
Struggles with large-scale camera networks.

Therefore, there is a need for an automated activity recognition system that can process CCTV footage and classify human activities accurately and in real time.

3. Objectives

To preprocess CCTV video data and extract relevant features.
To implement deep learning-based models (CNN, RNN, LSTM, 3D-CNN) for activity recognition.
To classify activities such as walking, running, fighting, falling, or suspicious movements.
To generate real-time alerts for abnormal or suspicious activities.
To evaluate the system using accuracy, precision, recall, F1-score, and confusion matrix.

4. Proposed Approach

Data Collection & Preprocessing

Use public HAR datasets (UCF101, Kinetics, HMDB51, custom CCTV dataset).
Perform frame extraction, resizing, background subtraction, and normalization.

Feature Extraction

Apply CNN-based spatial feature extraction.
Use temporal modeling with RNN/LSTM or 3D-CNN for motion features.

Activity Recognition Model

Train and test deep learning models for activity classification.
Fine-tune models with transfer learning (e.g., ResNet, Inception, MobileNet).

Anomaly Detection

Implement unsupervised models (e.g., Autoencoders, One-Class SVM) for suspicious activity recognition.

System Deployment

Integrate the trained model with CCTV video streams.
Real-time processing and alert generation for abnormal activities.

5. Expected Outcomes

A working HAR system capable of classifying normal activities (walking, sitting, running) and detecting abnormal activities (fighting, falling, intrusion).
Improved surveillance automation and reduced human workload.
Enhanced public safety and security monitoring.
Real-time activity recognition with high accuracy.

6. Tools & Technologies

Programming Languages: Python
Deep Learning Libraries: TensorFlow, Keras, PyTorch
Computer Vision Tools: OpenCV, MediaPipe
Datasets: UCF101, HMDB51, Kinetics dataset, custom CCTV dataset
Deployment: Flask/Django (for web integration), GPU-enabled environment for training

7. Applications

Smart city surveillance systems
Crime prevention and anomaly detection
Crowd monitoring in public places (railway stations, airports, malls)
Elderly care and fall detection in healthcare
Workplace safety monitoring (factories, construction sites)

8. Conclusion

This project aims to develop a real-time Human Activity Recognition system using CCTV footage and advanced deep learning models. The system enhances surveillance by automatically detecting and classifying human activities, thereby improving safety, security, and efficiency in monitoring environments.

Improving Software Defects Detection: Machine Learning Methods and Static Analysis Tools

Project Synopsis

Title: Improving Software Defects Detection: Machine Learning Methods and Static Analysis Tools

1. Introduction

Software defects are among the most critical challenges in modern software development, leading to increased maintenance costs, reduced reliability, and potential system failures. Traditional testing and debugging techniques often fail to capture subtle and complex defects early in the development cycle. To address these challenges, this project proposes an integrated framework that leverages machine learning (ML) models alongside static analysis tools to improve software defect detection accuracy and efficiency.

2. Problem Statement

Existing defect detection techniques primarily rely on manual testing or conventional automated tools, which:

May generate a high number of false positives/negatives.
Struggle with large-scale software systems with millions of lines of code.
Lack adaptability to evolving coding patterns and practices.

Thus, there is a need for a hybrid approach that combines static analysis tools with machine learning methods to reduce false alarms, detect hidden patterns, and enhance early defect identification.

3. Objectives

To apply machine learning models (e.g., Decision Trees, Random Forest, SVM, Deep Learning) for predicting software defects using historical code metrics and defect data.
To integrate static code analysis tools (e.g., SonarQube, FindBugs, PMD, Clang Static Analyzer) for identifying common coding errors and vulnerabilities.
To design a hybrid framework combining ML predictions and static analysis insights for improved defect detection.
To evaluate the framework based on accuracy, precision, recall, and F1-score against conventional methods.
To reduce software maintenance costs and improve code quality.

4. Proposed Approach

Data Collection:

Gather open-source project datasets (e.g., PROMISE, NASA MDP, GitHub repositories) with historical defect labels.
Extract software metrics (LOC, complexity, dependencies, churn rate).

Static Analysis:

Run static analyzers to detect coding flaws, vulnerabilities, and maintainability issues.
Generate rule-based defect reports.

Machine Learning Model:

Train ML algorithms on defect-labeled data to identify defect-prone modules.
Apply feature engineering to combine code metrics + static analysis results.

Hybrid Framework:

Integrate ML predictions with static analysis outputs.
Implement ensemble techniques to reduce false positives.

Evaluation:

Compare results with standalone static analysis tools and ML-only approaches.
Use performance metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC).

5. Expected Outcomes

A hybrid defect detection system combining ML and static analysis.
Higher accuracy and lower false positives compared to existing methods.
Better identification of critical defects and vulnerabilities early in the software lifecycle.
Contribution toward improving software reliability, maintainability, and security.

6. Tools & Technologies

Programming Languages: Python, Java, C/C++ (for dataset and tool integration)
Machine Learning Frameworks: Scikit-learn, TensorFlow, PyTorch
Static Analysis Tools: SonarQube, FindBugs, PMD, Clang Static Analyzer
Datasets: PROMISE, NASA MDP, Open-source project repositories
IDE & Environment: VS Code, Eclipse, Jupyter Notebook

7. Applications

Large-scale enterprise software systems (banking, healthcare, e-commerce).
Open-source project quality assurance.
Safety-critical domains (automotive, aerospace, medical devices).
Secure software development lifecycle (SSDLC).

8. Conclusion

This project aims to enhance software defect detection by leveraging the strengths of both machine learning models and static analysis tools. The proposed framework not only improves detection accuracy but also reduces false positives, leading to more reliable, secure, and maintainable software systems.

Tuesday, 9 September 2025

Design and Development of an Automated Guided Vehicle for Material Handling Applications

Project Synopsis:

Automated Guided Vehicle (AGV)

1. Title

Design and Development of an Automated Guided Vehicle for Material Handling Applications

2. Introduction

Automated Guided Vehicles (AGVs) are driverless transport systems widely used in industries for material handling, logistics, and warehouse automation. They navigate using pre-defined paths, sensors, and intelligent control algorithms. The use of AGVs reduces human effort, improves efficiency, and ensures safety in manufacturing and distribution environments.

3. Problem Statement

Manual material handling leads to high labor costs, delays, and safety risks.
Existing transport systems are often rigid and lack flexibility in dynamic environments.
Industries require a cost-effective and reliable automated solution for material movement.

4. Objectives

To design and develop a prototype AGV capable of autonomous navigation.
To implement line following / path tracking using sensors.
To integrate obstacle detection and avoidance for safe operation.
To demonstrate efficient material transport in a controlled environment.

5. Proposed Methodology

Hardware Design:
- Microcontroller (Arduino/Raspberry Pi)
- Motor driver circuits & DC/BLDC motors
- IR / Ultrasonic sensors for navigation & obstacle detection
- Power supply (Battery-operated)
Software Design:
- Embedded C / Python programming for control logic
- Path-following algorithm (Line following / Node-based navigation)
- Obstacle avoidance algorithm
Implementation:
- Construct chassis and drive system
- Mount sensors and control system
- Test navigation on a defined track with obstacles

6. Tools & Technology

Hardware: Arduino/Raspberry Pi, Sensors (IR, Ultrasonic), Motor Driver, DC Motors, Batteries
Software: Arduino IDE / Python, Embedded C, Simulation Tools (Proteus / MATLAB for design verification)

7. Expected Outcomes

Working prototype of an AGV that can autonomously navigate along a predefined path.
Ability to detect and avoid obstacles in real-time.
Demonstration of improved efficiency and safety in material handling.

8. Applications

Industrial automation: Warehouse & factory material transport
Hospitals: Medicine and supply delivery
Airports & stations: Baggage and goods handling
Smart logistics systems

9. Conclusion

The project aims to provide a cost-effective, flexible, and intelligent AGV system that can be adapted for industrial and commercial applications. The developed prototype will showcase the potential of autonomous vehicles in reducing human labor, increasing productivity, and ensuring safety in material handling operations.

MTech Cloud Computing Project Idea List 2025

M.Tech Cloud Computing Project Idea List 2025

1. Intelligent Optimization Strategies for Hierarchical Cloud-Edge Computing in Heterogeneous Hardware Ecosystems
Proposes smart optimization for managing computation across cloud and edge devices in diverse hardware setups.

2. A Standard Model for Engineering Delivery of Large Models Based on Cloud Computing
Introduces a framework for scalable delivery of large AI/ML models via cloud infrastructure.

3. Towards Empowering Ubiquitous Computing as a Service with Cloud Analytics for Sustainable Manufacturing, Agriculture, Cities, and Buildings
Utilizes cloud analytics to support sustainability in multiple domains like smart cities and agriculture.

4. Intelligent Green Energy Solutions: Optimizing Renewable Energy for Edge Cloud Computing
Explores renewable energy integration in edge-cloud systems for sustainability.

5. How to Build Smarter Highway: An Analysis of Application Scenarios Based on Cloud Computing
Analyzes cloud-based solutions for intelligent transportation and smart highways.

6. Optimize Real-World Computer Vision Processing Performance Through The QUIC Transport Protocol And Edge Cloud Computing Model
Improves CV performance using QUIC protocol and edge-cloud integration.

7. Security Issues in Cloud Computing Using RSA Algorithm and Deployment Using Heroku
Demonstrates RSA encryption for cloud security and deployment using Heroku.

8. Performance Optimization in Cloud Computing Using Machine Learning
Applies ML for improving cloud computing performance and efficiency.

9. Optimising Resource Sharing Algorithms for Efficient Cloud Computing
Proposes new algorithms for better resource distribution in cloud environments.

10. A Methodological Model for Evaluating the Maturity of Industry Cloud Construction and Operation
Offers a model to assess how advanced and operationally efficient industry cloud systems are.

11. Optimizing Resource Scheduling for Enhanced Efficiency in Cloud Computing
Focuses on scheduling strategies to improve cloud efficiency and utilization.

12. A Cloud Computing-Enabled ESP32-CAM System for Real-Time Object Recognition with Feedback
Real-time object recognition using ESP32-CAM integrated with cloud services.

13. Enabling Moving Services in Enterprise Computing
Explores how enterprise systems can deliver dynamic services using cloud-based infrastructure.

14. Analysis of Cloud Service Providers and Computing Services in Modern IT Infrastructure
Comparative analysis of major cloud service providers in current IT systems.

15. Technology Opportunity Discovery of Cloud Computing Based on Latent Dirichlet Allocation Topic Model and Explainable Artificial Intelligence Model
Uses LDA and XAI to discover innovation opportunities in cloud technology.

16. Prevention Techniques for DDoS Attacks in Cloud Computing
Presents cloud-specific methods to defend against DDoS attacks.

17. Research on Efficient Processing Algorithm for Engineering Cost Data Using Cloud Computing Platform
Applies cloud computing for the efficient analysis of engineering budget and cost data.

18. Adaptive Resource Allocation in Cloud Computing Using Advanced AI Techniques
Uses AI for real-time and efficient allocation of cloud computing resources.

19. Exploring Arduino Board Applications in Embedded Systems: The Role of AI, Cloud Computing, and Edge Computing
Combines Arduino-based embedded systems with cloud/edge/AI for smart IoT solutions.

20. Analysis of Performance of Blockchain in Cloud Computing
Examines how blockchain technology performs when integrated with cloud systems.

21. Self-Adjustable Hybrid Metaheuristic Framework for Task Scheduling in Cloud Computing
Introduces an adaptive hybrid framework for task scheduling using metaheuristic methods.

Sunday, 31 August 2025

Boosting Algorithms for Student Performance Prediction in E-Learning

Project Synopsis

Title:

Boosting Algorithms for Student Performance Prediction in E-Learning

1. Introduction

E-learning platforms have transformed education by providing flexible and personalized learning opportunities. However, predicting student performance in these platforms is critical for designing adaptive learning paths, providing timely interventions, and improving overall educational outcomes.

Traditional statistical methods are often insufficient in capturing the complex interactions between learning behaviors, engagement patterns, and assessments. Machine Learning, especially Boosting algorithms, has emerged as a powerful solution due to its ability to combine multiple weak learners into a strong predictive model.

This project focuses on applying and evaluating Boosting techniques (AdaBoost, Gradient Boosting, XGBoost, and LightGBM) for predicting student performance in e-learning environments.

2. Problem Statement

Student performance in e-learning is influenced by multiple factors (demographics, course engagement, quizzes, time spent, interaction logs).
Early prediction of at-risk students is often challenging due to non-linear and high-dimensional data.
Existing models may lack accuracy and interpretability, leading to ineffective interventions.
Boosting algorithms offer a robust way to improve predictive performance, but their comparative effectiveness in e-learning prediction remains underexplored.

3. Objectives

To collect and preprocess e-learning datasets (Moodle, Open University Learning Analytics dataset, or Kaggle datasets).
To apply Boosting algorithms for predicting student performance.
- AdaBoost
- Gradient Boosting (GBM)
- XGBoost
- LightGBM
To compare these algorithms with baseline ML methods (Decision Tree, Logistic Regression).
To evaluate models using metrics such as Accuracy, Precision, Recall, F1-score, and ROC-AUC.
To identify important features influencing student success and failure.
To design a prototype system for early student performance prediction in e-learning platforms.

4. Methodology

Data Collection & Preprocessing
- Source: Open University Learning Analytics Dataset (OULAD) or Kaggle student datasets.
- Features: Demographics, attendance, quiz scores, time spent, forum participation, assignments.
- Preprocessing: Handling missing values, feature encoding, normalization, train-test split.
Model Development
- Baseline Models: Logistic Regression, Decision Tree.
- Boosting Models: AdaBoost, Gradient Boosting, XGBoost, LightGBM.
- Hyperparameter tuning using Grid Search / Random Search.
Model Evaluation
- Performance Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Feature Importance Analysis for interpretability.
- Comparative analysis across boosting algorithms.
Prototype Development
- Web or dashboard interface where instructors can upload student activity data and receive risk predictions.

5. Expected Outcomes

A robust predictive model for student performance in e-learning environments.
Comparative analysis of Boosting algorithms vs traditional ML models.
Identification of key behavioral and academic features affecting learning outcomes.
A decision-support tool to help educators detect at-risk students early and provide timely interventions.

6. Applications

Educational Institutions: Early detection of struggling students.
E-Learning Platforms: Personalized learning pathways.
EdTech Companies: Enhanced student analytics for engagement.
Policy Makers: Data-driven insights for improving online education quality.

7. Tools & Technologies

Programming Language: Python (Scikit-learn, XGBoost, LightGBM, CatBoost)
Data Visualization: Matplotlib, Seaborn, Plotly
Dataset: OULAD, Moodle logs, Kaggle student performance datasets
Deployment: Flask / Streamlit-based dashboard for educators

8. Conclusion

This project explores the potential of Boosting algorithms in predicting student performance within e-learning environments. By leveraging ensemble methods, the study aims to achieve high prediction accuracy, interpretability, and practical applicability, ultimately helping educators and e-learning platforms to deliver personalized and effective education.