Lalit B. Damahe’s research while affiliated with Yeshwantrao Chavan College of Engineering and other places

In today's rapidly evolving digital landscape, the demand for multimedia applications is surging, driven by significant advancements in computer and storage technologies that enable efficient compression and storage of visual data in large-scale databases. However, challenges such as inaccuracy, inefficiency, and suboptimal precision and recall in image retrieval systems necessitate the development of faster and more reliable techniques for searching and retrieving images. Traditional retrieval systems often rely on RGB colour spaces, which may inadequately represent critical image information. In response, we propose a content-based image retrieval (CBIR) system that integrates advanced techniques such as quadtree segmentation alongside modern lightweight deep learning models, specifically MobileNet and EfficientNet, to enhance precision and recall. Our comparative experiments reveal that these deep learning models significantly outperform traditional methods, including SVM classifiers combined with feature extraction techniques such as Histogram of Oriented Gradients (HOG), Scale-Invariant Feature Transform (SIFT), and Speeded-Up Robust Features (SURF). Notably, MobileNet and EfficientNet achieved F1-scores of 0.87 and 0.89, respectively, with enhanced processing efficiencies that resulted in feature extraction times reduced to 20 ms and classification times down to 8 ms. This translates to rapid image retrieval times as low as 35 ms, highlighting the superior performance of modern deep learning models in enhancing both retrieval accuracy and efficiency for large-scale image databases, making them ideal for real-time applications. ARTICLE HISTORY

Chronic heart disease (CHD) is a widespread and persistent health challenge that demands immediate attention. Early detection and accurate diagnosis are essential for effective treatment and management of this condition. To overcome this difficulty, we created a state-of-the-art IoT-Based Ambulatory Blood Pressure Monitoring System that provides real-time blood pressure readings, systolic, diastolic, and pulse rates at predefined intervals. This unique technology comes with a module that forecasts CHD’s early warning score. Various machine learning algorithms employed comprise Naïve Bayes, K-Nearest Neighbors (K-NN), random forest, decision tree, and Support Vector Machine (SVM). Using Naïve Bayes, the proposed model has achieved an impressive 99.44% accuracy in predicting blood pressure, a vital aspect of real-time intensive care for CHD. This IoT-based ambulatory blood pressure monitoring (IABPM) system will provide some advancement in the field of healthcare. The system overcomes the limitations of earlier BP monitoring devices, significantly reduces healthcare costs, and efficiently detects irregularities in chronic heart diseases. By implementing this system, we can take a significant step forward in improving patient outcomes and reducing the global burden of CHD. The system’s advanced features provide an accurate and reliable diagnosis that is essential for treating and managing CHD. Overall, this IoT-based ambulatory blood pressure monitoring system is an important tool for the early identification and treatment of CHD in the field of healthcare.

The retrieval of similar type of data is performed by requesting the query, generally through personal computer. As the smart mobile devices are available at the general user, retrieval of visually similar data is now performed on these devices such as smart phones, PDA etc. A wireless medium is the key component of mobile devices and visually similar image retrieval with minimum latency is the typical issue.Hence the main objective of our proposed work is to develop the efficient mechanism for image retrieval on mobile devices. The image representation scheme V-HOG captures the useful information from the image with the help of gradient vector and the gradients are calculated by using various block size. The size of block, cell and bins are the main components which are varied and developed the V-HOG with feature values 1296, 1176, 672. The experimentation are conceded on the standard Holidays Dataset. The proposed V-HOG approach having a feature value 1296, 1176 and 672 perform better with existing HOG. In comparison with HOG, the precision and recall rate for V-HOG is improved by 27% to 90% and 7% to 32% respectively. The retrieval time is also reduced to 3% to 9%. The proposed V-HOG with 1176 feature perform well and having a good precision and recall rate.