Brain stroke is the leading cause of death and disability globally; hence, early identification and prediction are critical for better patient outcomes. Traditional diagnostic procedures, such as manually interpreting clinical images, are time consuming and error prone. This research investigates the use of hybrid deep learning models, such as recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs), to improve stroke prediction accuracy. The current study compared the performance of these individual models with the developed hybrid model on the brain stroke dataset. By merging these models, we reached an overall accuracy of 96% in identifying stroke risk as low, medium, or high. This categorization may offer healthcare practitioners actionable insights by assisting them and allowing them to make better decisions. This technique represents a substantial improvement in stroke prediction and preventive healthcare practices. The model’s performance can further be tested with more complicated clinical and demographic data that will help to generalize the model for real-world clinical applications. Furthermore, combining this hybrid model with electronic health records (EHR) systems can also assist in early identification, tailored therapies, and improved stroke management, enhancing patient outcomes and lowering healthcare costs.

It is important to identify and locate glacial lakes for assessing any potential hazard. This study presents a combination of semi-automatic method Double-Window Flexible Pace Search (DFPS) and edge detection technique to identify glacial lakes using Sentinel 2A satellite data. Initially, Normalized Difference Water Index (NDWI) has been used to identify water and non-water areas, while DFPS and Edge detection technique has been used to identify an optimum threshold value to distinguish between water and shadow areas. The optimal threshold from DFPS process is 0.21, while threshold value of gradient magnitude using edge detection process is 0.318. The number of glacial lakes identified using the above algorithm is in close agreement with previously published results on glacial lakes in Gangotri glacier using different techniques. Thus, a combination of DFPS and edge detection process has successfully segregated glacial lakes from other features present in Gangotri glacier.

In the Himalayas, rising number of glacial lakes with increase in area of existing lakes is a matter of serious concern and correct mapping is an important step towards regular monitoring of these glacial lakes. This study used Double Window Flexible Pace Search method to identify an optimal threshold of Normalized Difference Water Index value on multi-spectral Sentinel-2 image of 31^st October 2020. A threshold value of 0.30 is obtained that resulted in identification of 410 glacial lakes with an estimated total area of about 29.86 ± 6.85 km². The results are found to be in close agreement with previously published research article on Sikkim Himalayas and boundaries of classified lakes are validated by overlaying on high resolution PlanetScope dataset for the same date. Hence, this study presents an effective and simple way for mapping of glacial lakes that may further be extended for regular monitoring of glacial lakes in the region.

Lung cancer is a major global health threat, affecting millions annually and resulting in severe complications and high mortality rates, particularly when diagnosed late. It remains one of the leading causes of cancer-related deaths worldwide, often detected at advanced stages due to the lack of early symptoms. This study introduces a novel hybrid machine learning model aimed at enhancing early detection accuracy and improving patient outcomes. By integrating traditional machine learning classifiers with deep learning techniques, the proposed framework optimizes feature selection, hyperparameter tuning, and data-balancing strategies, such as Adaptive Synthetic Sampling (ADASYN). A comparative evaluation with existing models demonstrated substantial improvements in predictive accuracy, ranging from 0.44% to 9.69%, with Gradient Boosting and Random Forest models achieving the highest classification performance. The study highlights the importance of hybrid methodologies in refining lung cancer diagnostics, ensuring robust, scalable, and clinically viable predictive models.