Adaptive conflict resolution for IoT transactions: A reinforcement learning-based hybrid validation protocol.

This paper introduces a novel Reinforcement Learning-Based Hybrid Validation Protocol (RL-CC) that revolutionizes conflict resolution for time-sensitive IoT transactions through adaptive edge-cloud coordination. Efficient transaction management in sensor-based systems is crucial for maintaining data integrity and ensuring timely execution within the constraints of temporal validity. Our key innovation lies in dynamically learning optimal scheduling policies that minimize transaction aborts while maximizing throughput under varying workload conditions.

Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm.

In the human body, the skin is the main organ. Nearly 30-70% of individuals globally have skin-related health issues, for whom efficient and effective analysis is essential. A general method dermatologists use for analyzing skin illnesses is dermoscopy, which permits surveillance of the hidden structures of skin injuries, i.

Computer-aided diagnosis of Haematologic disorders detection based on spatial feature learning networks using blood cell images.

Analyzing biomedical images is vital in permitting the highest-performing imaging and numerous medical applications. Determining the analysis of the disease is an essential stage in handling the patients. Similarly, the statistical value of blood tests, the personal data of patients, and an expert estimation are necessary to diagnose a disease.

An adaptive search mechanism with convolutional learning networks for online social media text summarization and classification model.

The fast development of social media platforms has led to an unprecedented growth of daily short text content. Removing valued patterns and insights from this vast amount of textual data requires advanced methods to provide information while preserving its essential components successfully. A text summarization system takes more than one document as input and tries to give a fluent and concise summary of the most significant information in the input.

A hybrid object detection approach for visually impaired persons using pigeon-inspired optimization and deep learning models.

Visually challenged persons include a significant part of the population, and they exist all over the globe. Recently, technology has demonstrated its occurrence in each field, and state-of-the-art devices aid humans in their everyday lives. However, visually impaired people cannot view things around their atmospheres; they can only imagine the roaming surroundings.

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.