Unveiling Arabic named entity recognition using natural language processing with artificial intelligence approach on Moroccan dialect.

Named entity recognition (NER) is a significant natural language processing task (NLP) in several applications, including question-answering and data retrieval. NER's primary goal is to classify, discover, and extract named entities into predetermined classes: location, person, and organization. Arabic NER is a tedious process due to its unique characteristics and complexity.

Enhancing medical response efficiency in real-time large crowd environments via smart coverage and deep learning for stable ecological health monitoring.

Festivals and city-wide mass events are prevalent in human societies worldwide, drawing large crowds. Such events range from concerts with a dozen attendees to large-scale actions with thousands of viewers. It is the highest priority for each organizer of such an occasion to be capable of upholding a higher standard of safety and minimizing the danger of events, especially medical emergencies.

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.

An intelligent ransomware based cyberthreat detection model using multi head attention-based recurrent neural networks with optimization algorithm in IoT environment.

The rapid growth of the Internet of Things (IoT) and its extensive use in many regions, such as smart homes, healthcare, and vehicles, have made IoT security increasingly critical. Ransomware is an advanced and adjustable threat influencing users globally, limiting admittance to their data or systems over models like file encryption or screen locking. Traditional ransomware detection methods frequently drop, deprived of the ability to combat these threats successfully.

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.