Machine learning
Researchers at the Islamic University have made significant strides in developing a system for early detection and management of wheat rust, a devastating disease that can lead to significant losses in crop yields. The system utilizes IoT-enabled sensor networks and machine learning algorithms to identify and mitigate the spread of
Machine learning
A team of researchers at the First Affiliated Hospital of Xi'an Jiaotong University in Xi'an, People's Republic of China, has made a groundbreaking discovery in the field of personalized medicine for liver cancer patients. The research team, led by Jiayi Zhang, integrated multi-omics data
Machine learning
Researchers at Jiangsu Health Vocational College in China have developed a novel framework for accurate exercise posture recognition and health indicator prediction, showcasing significant potential for applications in personal fitness coaching, rehabilitation monitoring, and preventive healthcare. The innovative system combines improved convolutional neural networks with spatiotemporal attention mechanisms to enhance
Machine learning
Researchers at Islamic University have developed an Artificial Intelligence-based system, AWRM-AI, to optimize agricultural water resource management, reducing waste and ensuring crops receive the precise amount of water needed. This innovative approach leverages IoT sensors to monitor environmental variables and uses AI algorithms to analyze the data and make real-time
Machine learning
Researchers from the Chaitanya Bharathi Institute of Technology have proposed a new framework for detecting Distributed Denial of Service (DDoS) threats in Internet of Things (IoT) environments using advanced techniques. The Metaheuristic-Optimized Blockchain Framework for Attack Detection using a Deep Learning Model (MOBCF-ADDLM) method employs blockchain technology to mitigate DDoS
Machine learning
Researchers at Al-Balqa' Applied University have proposed a novel approach to enhancing the cybersecurity of Internet of Things (IoT) devices through machine learning-based penetration testing. The study, published in the Applied Computer Science journal, builds on the concept of the BDI (Belief-Desire-Intention) model, which is used to simulate human-like
Machine learning
Researchers from Universiti Kebangsaan Malaysia have developed a novel tri-band Multiple Input Multiple Output (MIMO) antenna module that employs metamaterial (MTM) technology to enhance performance. The compact antenna module, measuring 36 x 36 x 1.6 mm, operates at 3.5 GHz, 5.2 GHz, and 28 GHz and shows
Machine learning
New research conducted by the University of Jeddah has yielded promising results in the field of Machine Learning, specifically in the development of a smart deep learning model for enhanced IoT intrusion detection. The study aimed to address the limitations of existing approaches in processing large volumes of data and
Machine learning
Researchers at Fuyang Normal University have developed a new scheme for maximizing energy transmission efficiency and minimizing cost in distributed power sources. The scheme, based on the integration of the improved particle swarm optimization algorithm and the improved ant colony optimization algorithm (IPSOACO), was tested on the IEEE 33-node system
Machine learning
In a groundbreaking study published in Applied Intelligence, researchers from National Taiwan University of Science and Technology have proposed a novel multivariate monitoring method that integrates a stacked gated recurrent unit (S-GRU) with a self-adaptive particle swarm optimization (PSO), dubbed S-GRU-PSO. This innovative approach has been shown to achieve superior
Machine learning
Research from the School of Software at Nanchang Hangkong University in the People's Republic of China has made new findings on Machine Learning. The study, which focused on the problem of restricting team members' individuality and comprehensive development in the discipline competition teaching mode, proposed a Competition
Machine learning
A team of researchers has proposed an anonymous and provably secure lightweight authentication protocol for unmanned aerial vehicle-assisted internet of autonomous vehicles (IoAVs). The protocol, called SLAP-IoAV, uses exclusive-OR operations, elliptic-curve cryptography, collision-resistant one-way hashing, and concatenation to ensure robust security. The researchers found that SLAP-IoAV is secure against several
