CALL US
(0343)-2501353,2504106
Mob: +91-6297128554 | Email: info@bcrec.ac.in
CALL US
|
22-08-2006 |
|
|
MCA |
|
|
193727 |
|
|
O+ |
|
|
HINDUISM |
|
|
1-446145117 |
|
|
24-09-2001 |
| Teaching | Research | Industry |
|---|---|---|
| 240 | 36 | 0 |
| Date | Title | Journal | DOI | Link |
|---|---|---|---|---|
| 04-03-2023 | A Novel Image Encryption Technique with Four Stage Bit-Interspersing and A 4D-Hyperchaotic System | ECTI Transactionson Computer and Information Technology | DOI | View |
| Date | Title | Conference | DOI | Link |
|---|---|---|---|---|
| 01-05-2026 | Performance Analysis of Supervised Learning Algorithms for Real-Time Weed Detection: A Comparative Study | International Conference on Smart Systems and Wireless Communication | DOI | View |
| 09-10-2025 | A predictive analytics approach of Brain Stoke using machine learning | International Conference on Computing, Intelligence, and Application (CIACON) | DOI | View |
| 25-07-2023 | Color Image Encryption Using Hybrid Three-Scroll Unified Chaotic Attractor and 6D Hyperchaotic System | 4th International Conference on Communication and Intelligent Systems ICCIS 2022 | DOI | View |
| Committee Name | Start Date | End Date |
|---|---|---|
| INSTITUTE MOOCS COMMITTEE | 25-06-2026 | 08-08-2026 |
| TIME TABLE (CLASS) MANAGEMENT COMMITTEE | 22-06-2026 | 08-08-2026 |
| MCA BUDGET COMMITTEE | 23-07-2025 | 08-08-2026 |
| DEPARTMENTAL ACADEMIC AUDIT COMMITTEE (MCA) | 06-01-2023 | 08-08-2026 |
| DEPARTMENTAL ROUTINE COMMITEE (MCA) | 06-01-2023 | 08-08-2026 |
| DEPARTMENTAL ACADEMIC COMMITTEE (MCA) | 07-01-2022 | 08-08-2026 |
| Title | Patent Number | Patent Office | Date | Status | Country | Application No. | Abstract | Expiration Date |
|---|---|---|---|---|---|---|---|---|
| MACHINE LEARNING-POWERED VIBRATION ANALYZER FOR MACHINE | 466378-001 | Office of the Controller General of Patents, Designs and Trade Marks, Government of India (Kolkata) | 07-11-2025 | Published | India | 466378-001 | A machine learning-powered vibration analyzer for machines is disclosed for real-time condition monitoring, fault detection, and predictive maintenance of industrial equipment. The system comprises one or more vibration sensors configured to acquire vibration signals from a machine during operation, a signal processing module to filter and extract relevant time-domain and frequency-domain features, and a machine learning engine trained to identify normal and abnormal operating conditions. The analyzer classifies machine health by detecting faults such as bearing wear, shaft misalignment, imbalance, looseness, and gear defects based on learned vibration patterns. The system generates maintenance alerts, fault severity estimates, and diagnostic reports to enable early intervention and reduce unplanned downtime. The disclosed invention may be implemented as a standalone embedded device or integrated with Industrial IoT platforms for continuous remote monitoring, data logging, and performance analytics, thereby improving equipment reliability, operational efficiency, and maintenance planning. | 19-07-2035 |
| AI-EMBEDDED CYBER SECURE NETWORK ROUTER WITH REAL-TIME THREAT ANALYSIS | 6478249 | Intellectual Property Office (IPO), UK | 17-10-2025 | Granted | UK | 6478249 | The patent “AI-Embedded Cyber Secure Network Router with Real-Time Threat Analysis” describes an intelligent router that uses AI technology for improved cybersecurity. The router combines machine learning technology to detect, analyze, and prevent potential threats. The technology is always online and continuously monitors the network for any potential threats and reacts quickly to the rising cyber threats. The router uses AI technology, which ensures optimal security, scalability, and resilience during data transmission and prevents any unauthorized or malignant activity. | 08-10-2030 |
| AI-POWERED ADAPTIVE LEARNING HEADSET | 460835-001 | Office of the Controller General of Patents, Designs and Trade Marks, Government of India (Kolkata) | 19-09-2025 | Published | India | 460835-001 | An AI-powered adaptive learning headset is disclosed for delivering personalized, intelligent, and immersive educational experiences by continuously monitoring learner engagement and cognitive performance. The headset comprises audio-visual output devices, biometric and motion sensors, a processing unit, wireless communication modules, and an artificial intelligence engine configured to collect and analyze real-time physiological, behavioral, and learning interaction data. Machine learning algorithms dynamically assess parameters such as attention level, learning pace, comprehension, fatigue, and user preferences to personalize instructional content, adjust lesson difficulty, recommend learning activities, and provide real-time feedback. The system supports voice interaction, gesture recognition, adaptive assessments, and cloud-based synchronization for continuous learning analytics. The disclosed headset may be employed in educational institutions, corporate training, healthcare education, and remote learning environments to improve learner engagement, knowledge retention, accessibility, and overall learning outcomes through intelligent, data-driven content adaptation. | 31-05-2035 |