Algorithms
Researchers at Beijing Wuzi University have made new findings in the field of information technology, shedding light on the optimal consensus problem for discrete-time multi-agent systems with input constraints. The study aims to investigate the optimal protocol that enables all followers to reach consensus with the leader while minimizing the
Machine learning
Researchers from the University of Hassan 2 Casablanca have conducted a detailed analysis of land cover changes in the Fez province of Morocco, utilizing machine learning and deep learning techniques to generate high-resolution land cover maps. This study, spanning from 2014 to 2022, aimed to identify the most suitable algorithm
Machine learning
Researchers at the School of Advanced Engineering in India have conducted a study to explore the impact of dust accumulation on photovoltaic (PV) system power output using artificial neural networks (ANN) and machine learning (ML) based modeling approaches. The study utilized a 60 W PV module under controlled laboratory conditions
Machine learning
Research conducted by Universiti Teknologi PETRONAS has demonstrated the potential of machine learning algorithms in predicting gas sorption capacity in heterogeneous porous materials, with a focus on shale gas. The study utilized extreme gradient boosting (XGBoost) and artificial neural networks (ANN) optimized with artificial bee colony (ABC) and particle swarm
Artificial intelligence
Artificial intelligence and machine learning have transformed the industrial sector, enabling it to digitize all production processes and generate vast amounts of data from sensors and actuators. This data provides valuable insights into machine behavior, enabling optimization and predicting potential malfunctions. Researchers at Escuela Politecnica Nacional have proposed a hybrid
Machine learning
Researchers have conducted a systematic review to evaluate the effectiveness of machine learning algorithms in predicting anemia among various populations in Ethiopia. The study found that machine learning algorithms, particularly random forest, offer promising tools for accurately predicting anemia in Ethiopia by integrating a wide range of socio-demographic and health-related
Machine learning
New research from the University of Parma has found that a combination of design of experiments and machine learning algorithms can effectively optimize 3D printing parameters for Fused Deposition Modeling (FDM) technology. The study, published in the journal Scientific Reports, explores the influence of various printing parameters on the mechanical
Machine learning
A recent study published in the journal Applied Research in Quality of Life investigates online discourse on dehumanization and violence among polarized and non-apathetic neutral individuals. Researchers analyzed over 39 million user-generated comments from two online forums in Hong Kong during the 2019 social unrest. The study found that non-apathetic
Artificial intelligence
GoldRhein Exchange, a cutting-edge trading platform, has unveiled an artificial intelligence-driven risk management engine designed to detect and neutralize threats in real-time across trading operations. This innovative system utilizes machine learning algorithms, dynamic behavioral models, and predictive analytics to identify suspicious trading patterns and flag abnormal fund movements. The AI
Artificial intelligence
Researchers at the University of Hassan II of Casablanca have developed a machine learning-based system for assessing physical education activities and predicting student performance. According to the study, the system uses a dataset of 600 students and evaluates their performance in physical tests and athletic activities. The machine learning model
Machine learning
Artificial intelligence (AI) techniques can significantly improve the accuracy of precipitation forecasting by leveraging metrological data. Researchers from the University of Sulaimani have conducted a study using Multilinear regression (MLR), Artificial Neural Networks (ANN), and Neuro-Fuzzy Inference System (ANFIS) to predict daily precipitation. The study found that both AI systems
Machine learning
Researchers at the China University of Petroleum have developed an integrated data-driven framework for forecasting tight gas production using machine learning algorithms, feature selection, and fracturing optimization. The study aimed to identify the most significant factors affecting tight gas productivity and to provide evidence-based recommendations to refine development strategies and
