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

  1. Data Collection & Preprocessing
    • Use public HAR datasets (UCF101, Kinetics, HMDB51, custom CCTV dataset).
    • Perform frame extraction, resizing, background subtraction, and normalization.
  2. Feature Extraction
    • Apply CNN-based spatial feature extraction.
    • Use temporal modeling with RNN/LSTM or 3D-CNN for motion features.
  3. Activity Recognition Model
    • Train and test deep learning models for activity classification.
    • Fine-tune models with transfer learning (e.g., ResNet, Inception, MobileNet).
  4. Anomaly Detection
    • Implement unsupervised models (e.g., Autoencoders, One-Class SVM) for suspicious activity recognition.
  5. 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.

 

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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

  1. 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).
  2. Static Analysis:
    • Run static analyzers to detect coding flaws, vulnerabilities, and maintainability issues.
    • Generate rule-based defect reports.
  3. 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.
  4. Hybrid Framework:
    • Integrate ML predictions with static analysis outputs.
    • Implement ensemble techniques to reduce false positives.
  5. 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.

 

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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

  1. Hardware Design:

    • Microcontroller (Arduino/Raspberry Pi)

    • Motor driver circuits & DC/BLDC motors

    • IR / Ultrasonic sensors for navigation & obstacle detection

    • Power supply (Battery-operated)

  2. Software Design:

    • Embedded C / Python programming for control logic

    • Path-following algorithm (Line following / Node-based navigation)

    • Obstacle avoidance algorithm

  3. 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.

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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.

 

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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

  1. To collect and preprocess e-learning datasets (Moodle, Open University Learning Analytics dataset, or Kaggle datasets).

  2. To apply Boosting algorithms for predicting student performance.

    • AdaBoost

    • Gradient Boosting (GBM)

    • XGBoost

    • LightGBM

  3. To compare these algorithms with baseline ML methods (Decision Tree, Logistic Regression).

  4. To evaluate models using metrics such as Accuracy, Precision, Recall, F1-score, and ROC-AUC.

  5. To identify important features influencing student success and failure.

  6. To design a prototype system for early student performance prediction in e-learning platforms.

4. Methodology

  1. 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.

  2. Model Development

    • Baseline Models: Logistic Regression, Decision Tree.

    • Boosting Models: AdaBoost, Gradient Boosting, XGBoost, LightGBM.

    • Hyperparameter tuning using Grid Search / Random Search.

  3. Model Evaluation

    • Performance Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC.

    • Feature Importance Analysis for interpretability.

    • Comparative analysis across boosting algorithms.

  4. 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.

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Quantum Machine Learning: Bridging Quantum Computing and ML

 

Project Synopsis

Title:

Air Quality Prediction: A Comparative Study of Machine Learning, Deep Learning, and Extreme Learning Machines

1. Introduction

Air pollution is one of the most pressing environmental challenges worldwide, directly affecting human health, ecosystems, and climate. Accurate air quality prediction is essential for policy-making, early health advisories, and sustainable urban planning. Traditional statistical models often fail to capture the non-linear and dynamic nature of air pollution data.

With the advent of Machine Learning (ML), Deep Learning (DL), and Extreme Learning Machines (ELM), predictive modeling of air quality has become more reliable. This project focuses on developing and comparing different computational approaches to predict air quality indices (AQI) using real-world datasets.

2. Problem Statement

  • Existing models for air quality forecasting often lack accuracy and robustness, especially under rapidly changing environmental conditions.

  • Different approaches (ML, DL, ELM) offer varying strengths in terms of speed, interpretability, and accuracy — but there is no consensus on which performs best.

  • A systematic comparative study is required to evaluate the effectiveness of these approaches in predicting AQI.

3. Objectives

  1. To collect and preprocess air quality datasets from sources like UCI Machine Learning Repository, Kaggle, or government air monitoring systems.

  2. To develop predictive models using:

    • Machine Learning (ML): Linear Regression, Random Forest, XGBoost.

    • Deep Learning (DL): Multilayer Perceptrons (MLP), LSTMs for time-series forecasting.

    • Extreme Learning Machines (ELM): Fast learning framework for AQI prediction.

  3. To evaluate models using metrics such as RMSE, MAE, R², and prediction accuracy.

  4. To compare ML, DL, and ELM models in terms of accuracy, computational efficiency, and scalability.

  5. To recommend the most suitable approach for real-time air quality forecasting.

4. Methodology

  1. Data Collection & Preprocessing

    • Data from air quality monitoring stations (features: PM2.5, PM10, NO₂, SO₂, CO, O₃, temperature, humidity, wind speed).

    • Handling missing values, outlier removal, normalization.

    • Feature selection using correlation analysis and importance ranking.

  2. Model Development

    • ML Models: Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression.

    • DL Models: LSTM/GRU (for time-series), CNN (for spatial-temporal data).

    • ELM Models: Single hidden layer feed-forward networks with randomized weights for fast learning.

  3. Evaluation & Comparison

    • Metrics: RMSE, MAE, MAPE, R².

    • Training time vs. prediction time comparison.

    • Robustness testing under missing/incomplete data.

  4. Visualization

    • AQI predictions over time.

    • Comparative graphs of ML, DL, and ELM performances.

5. Expected Outcomes

  • A comparative analysis of ML, DL, and ELM models for AQI prediction.

  • Identification of the most efficient and accurate approach for real-time deployment.

  • Insights into the trade-offs between accuracy and computational cost of different techniques.

  • A prototype system/dashboard to display predicted AQI levels for selected locations.

6. Applications

  • Government & Environmental Agencies: Real-time air pollution monitoring and forecasting.

  • Healthcare: Early warnings for vulnerable populations.

  • Smart Cities: Integration into IoT-enabled environmental monitoring systems.

  • Research: Benchmark dataset and model comparison for further study.

7. Tools & Technologies

  • Programming Languages: Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, ELM libraries).

  • Visualization Tools: Matplotlib, Seaborn, Plotly.

  • Datasets: UCI Air Quality dataset, Kaggle AQI datasets, or CPCB (India) / EPA (US) datasets.

  • Deployment (Optional): Streamlit / Flask-based dashboard for AQI prediction.

8. Conclusion

This project presents a comparative framework for Air Quality Prediction using Machine Learning, Deep Learning, and Extreme Learning Machines. By analyzing accuracy, computational efficiency, and real-time feasibility, the study will highlight the most effective approach for scalable and reliable AQI forecasting, contributing to public health and environmental management.

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Air Quality Prediction: Comparison of ML, DL, and Extreme Learning Machines

 

Project Synopsis

Title:

Air Quality Prediction: A Comparative Study of Machine Learning, Deep Learning, and Extreme Learning Machines

1. Introduction

Air pollution is one of the most pressing environmental challenges worldwide, directly affecting human health, ecosystems, and climate. Accurate air quality prediction is essential for policy-making, early health advisories, and sustainable urban planning. Traditional statistical models often fail to capture the non-linear and dynamic nature of air pollution data.

With the advent of Machine Learning (ML), Deep Learning (DL), and Extreme Learning Machines (ELM), predictive modeling of air quality has become more reliable. This project focuses on developing and comparing different computational approaches to predict air quality indices (AQI) using real-world datasets.

2. Problem Statement

  • Existing models for air quality forecasting often lack accuracy and robustness, especially under rapidly changing environmental conditions.

  • Different approaches (ML, DL, ELM) offer varying strengths in terms of speed, interpretability, and accuracy — but there is no consensus on which performs best.

  • A systematic comparative study is required to evaluate the effectiveness of these approaches in predicting AQI.

3. Objectives

  1. To collect and preprocess air quality datasets from sources like UCI Machine Learning Repository, Kaggle, or government air monitoring systems.

  2. To develop predictive models using:

    • Machine Learning (ML): Linear Regression, Random Forest, XGBoost.

    • Deep Learning (DL): Multilayer Perceptrons (MLP), LSTMs for time-series forecasting.

    • Extreme Learning Machines (ELM): Fast learning framework for AQI prediction.

  3. To evaluate models using metrics such as RMSE, MAE, R², and prediction accuracy.

  4. To compare ML, DL, and ELM models in terms of accuracy, computational efficiency, and scalability.

  5. To recommend the most suitable approach for real-time air quality forecasting.

4. Methodology

  1. Data Collection & Preprocessing

    • Data from air quality monitoring stations (features: PM2.5, PM10, NO₂, SO₂, CO, O₃, temperature, humidity, wind speed).

    • Handling missing values, outlier removal, normalization.

    • Feature selection using correlation analysis and importance ranking.

  2. Model Development

    • ML Models: Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression.

    • DL Models: LSTM/GRU (for time-series), CNN (for spatial-temporal data).

    • ELM Models: Single hidden layer feed-forward networks with randomized weights for fast learning.

  3. Evaluation & Comparison

    • Metrics: RMSE, MAE, MAPE, R².

    • Training time vs. prediction time comparison.

    • Robustness testing under missing/incomplete data.

  4. Visualization

    • AQI predictions over time.

    • Comparative graphs of ML, DL, and ELM performances.

5. Expected Outcomes

  • A comparative analysis of ML, DL, and ELM models for AQI prediction.

  • Identification of the most efficient and accurate approach for real-time deployment.

  • Insights into the trade-offs between accuracy and computational cost of different techniques.

  • A prototype system/dashboard to display predicted AQI levels for selected locations.

6. Applications

  • Government & Environmental Agencies: Real-time air pollution monitoring and forecasting.

  • Healthcare: Early warnings for vulnerable populations.

  • Smart Cities: Integration into IoT-enabled environmental monitoring systems.

  • Research: Benchmark dataset and model comparison for further study.

7. Tools & Technologies

  • Programming Languages: Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, ELM libraries).

  • Visualization Tools: Matplotlib, Seaborn, Plotly.

  • Datasets: UCI Air Quality dataset, Kaggle AQI datasets, or CPCB (India) / EPA (US) datasets.

  • Deployment (Optional): Streamlit / Flask-based dashboard for AQI prediction.

8. Conclusion

This project presents a comparative framework for Air Quality Prediction using Machine Learning, Deep Learning, and Extreme Learning Machines. By analyzing accuracy, computational efficiency, and real-time feasibility, the study will highlight the most effective approach for scalable and reliable AQI forecasting, contributing to public health and environmental management.

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