A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection.

Protecting data from management is a significant task at present. Digital images are the most general data representation. Images might be employed in many areas like social media, the military, evidence in courts, intelligence fields, security purposes, and newspapers.

Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment.

In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications.

Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection.

Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. These problems include deducing the unknown properties of biological structures or tissues from the observed imaging data, presenting a unique challenge in decoding intricate biological phenomena. Regarding disease detection, this technique has played a critical role in optimizing diagnostic efficiency by extracting meaningful insights from different imaging modalities like molecular imaging, MRI, and CT scans.

Feature Subset Selection for Malware Detection in Smart IoT Platforms.

Malicious software ("malware") has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling approach for malware detection.