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21-04-2009 |
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MCA |
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193459 |
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HINDUISM |
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1-447854748 |
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01-01-2008 |
| Teaching | Research | Industry |
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| 180 | 24 | 12 |
| Date | Title | Conference | DOI | Link |
|---|---|---|---|---|
| 02-10-2025 | Enhancing Stock Price Prediction: A Utility-Based Approach with Selective Parameters | International Conference on Data Mining and Information Security, 2024 | DOI | View |
| 23-12-2024 | Predictive Modeling of Maternal Child Health Challenges Through Machine Learning Analysis | Proceedings of Third International Conference on Advanced Computing and ApplicationsICACA | DOI | View |
| Committee Name | Start Date | End Date |
|---|---|---|
| MCA FRESHER'S WELCOME 2025-26 | 12-05-2026 | 12-05-2026 |
| DEPARTMENTAL ACADEMIC COMMITTEE (MCA) | 07-01-2022 | 03-06-2026 |
| Title | Patent Number | Patent Office | Date | Status | Country | Application No. | Abstract | Expiration Date |
|---|---|---|---|---|---|---|---|---|
| MACHINE LEARNING-POWERED VIBRATION ANALYZER FOR MACHINES | 466378-001 | Kolkata | 07-11-2025 | Published, Journal No is 45/2025 07/11/2025 | India | 212747 | The continuous monitoring of machine vibrations is essential for early fault detection and maintenance optimization in industrial systems. This paper presents a Machine Learning-Powered Vibration Analyzer designed to enhance the accuracy and efficiency of machine health diagnostics. Traditional vibration analysis methods rely heavily on manual interpretation of time-domain and frequency-domain signals, which can be time-consuming and prone to human error. The proposed system employs machine learning algorithms to automatically extract relevant features from vibration signals and classify the machine’s operational state. Using supervised learning techniques such as Support Vector Machines and Random Forests, the model identifies patterns associated with imbalance, misalignment, and bearing faults. The system is trained and validated on vibration datasets collected from multiple machines under varying load conditions. Experimental results demonstrate that the ML-based analyzer achieves higher diagnostic accurac | 07-11-2030 |
| AI-Powered Traffic Management Device | 6471256 | 41 High St, Chesham HP5 1BW, United Kingdom, CHESHAM, HP5 1BW | 21-09-2025 | Granted/Published | UK | 6471256 | An intelligent system that uses artificial intelligence to monitor, analyze, and control traffic flow in real time. The device leverages sensors and predictive algorithms to optimize traffic signals, reduce congestion, and improve road safety. | 11-09-2030 |