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| Teaching | Research | Industry |
|---|---|---|
| 72 | 24 | 48 |
| Committee Name | Start Date | End Date |
|---|---|---|
| DEPARTMENTAL MAGAZINE AND ACTIVITY COMMITTEE | 07-09-2022 | 03-06-2026 |
| DEPARTMENTAL ACADEMIC COUNCIL | 01-07-2017 | 03-06-2026 |
| Title | Patent Number | Patent Office | Date | Status | Country | Application No. | Abstract | Expiration Date |
|---|---|---|---|---|---|---|---|---|
| INTELLIGENT IOTENABLED MACHINE LEARNING SYSTEM FOR ENVIRONMENTAL POLLUTION–BASED EARLY CANCER DETECTION IN SMART CITIES | IN202621020491 A1 | Chennai | 10-04-2026 | publish | india | 202621020491 | The present invention provides a framework incorporates sensor networks designed to track pollutants in air, water, and soil alongside wearable health technology that gathers physiological data. A machine learning algorithm processes the integrated environmental and health information to detect early indicators of cancer risk associated with pollution exposure. The system offers immediate alerts, preventive suggestions, and predictive analytics, thereby facilitating proactive healthcare measures and promoting wellness management within smart cities. | 10-02-2034 |
| IOT BASED APPLE SWEETNESS MEASUREMENT AND FRUIT DISEASEPREDICTION USING IMAGE PROCESSING TECHNIQUES AND DEEPLEARNING BASED ON HUMAN-COMPUTER INTERACTION FORINDUSTRY 4.0 | 202341015772 | kolkata | 17-03-2023 | Published | india | 202341015772 | Disease identification is one of the most difficult elements of agricultural research. When attempting to diagnose a plant's ailment, agricultural specialists usually reference a range of resources. Misdiagnosing ill plants can occasionally lead to the unnecessary administration of pesticides, which can have catastrophic effects on agriculture. Automated disease detection systems are the primary means of increasing their use and obtaining more precise and early disease diagnosis. This is significant for farmers because the alternative is time-consuming and costly. In order to successfully separate the diseased leaf from the healthy leaves, it must be cut into little pieces. Digital noise, which varies from image to image and is influenced by variables such as background, shape, and brightness, making it more difficult to identify a sick photo. To improve the image quality of apple leaf scans so that diseases can be identified and classified, the brightness preserving dynamic fuzzy histogram equalisation technique was created.Determine an apple's flavour by tasting it and examining its leaves. In the following section, we will compare the effectiveness of the proposed strategy to that of other common techniques for improving things. By isolating the area of interest in pictures of sick leaves against a green background, our method surpasses existing segmentation techniques. Examining the Jaccard index, the Dice coefficient, and the precision at this time. The suggested segmentation methodology outperforms the best existing techniques. It is 99.8 percent accurate in distinguishing apple ill leaves from a living backdrop. | 14-11-2030 |