Lalit Damahe’s research while affiliated with Yashwantrao Chavan College Of Science Karad 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

Machine learning offers a powerful and versatile approach to flood susceptibility mapping, enabling us to leverage complex data and improve prediction accuracy. Given the plethora of available techniques and the challenges in selecting the optimal approach, this study investigates prominent ML algorithms for flood susceptibility mapping (FSM) in the Wardha River sub-basin, India. Seven machine learning algorithms, viz. support vector machine (SVM), extreme gradient boosting (XGB), artificial neural network (ANN), generalized linear model (GLM), gradient boosting machine (GBM), random forest (RF), and linear discriminant analysis (LDA), were evaluated at varying spatial resolutions (30 m, 50 m, 100 m, and 200 m). Seven flood-inducing factors (elevation, flow accumulation, topographic wetness index, slope, rainfall, land use, and drain density) were considered. Model performance was assessed using sensitivity, specificity, area under the curve (AUC), overall correlation, overall standard deviation ratio, and overall root mean square difference (RMSD). The impact of spatial resolution on models’ accuracy was analysed. SVM, GBM, and RF were significantly affected, while ANN, GLM, and XGB were less sensitive. LDA excelled in execution time and spatial resolution resilience. The overall ranking of models was executed based on their accuracy, AUC, and execution time. XGB outperformed GBM and RF, securing first place, while SVM ranked last. GLM, ANN, and LDA ranked third to fifth. The results highlighted the importance of algorithm selection in accurately mapping flood susceptibility, particularly when working with varying spatial resolution data. The study findings can inform the decision-making process for implementing FSM using these machine learning algorithms.

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.