Machine learning
Researchers at the University of Porto have developed a machine learning algorithm to identify and assess the impact of partial shading on photovoltaic panels. According to the study, partial shading can reduce production and accelerate aging of these panels. The algorithm, which utilizes K-means clustering and a Long Short-Term Memory
Machine learning
Researchers from Huzhou University have developed an optimized algorithm for long-distance target localization (LTLO) based on single-robot moving path planning to address the problem of low positioning accuracy for long-distance static targets. By introducing constraints on stopping position selection and non-redundant locations, the algorithm improves the positioning accuracy for long-distance
Machine learning
A recent study has demonstrated the effectiveness of machine learning algorithms in solving routing, modulation, and spectrum allocation (RMSA) problems in optical networks. The research, conducted at the Federal University Pernambuco, utilized a classification model to propose an algorithm that predicts routes according to call request information and network link
Artificial intelligence
Researchers from Tehran University of Medical Sciences conducted a systematic review and meta-analysis to evaluate the diagnostic performance of artificial intelligence (AI) algorithms in detecting pancreatic ductal adenocarcinoma (PDAC) from other types of pancreatic lesions using computed tomography (CT) scans. The study, published in the Journal of Imaging Informatics In
Machine learning
Research conducted in south China has revealed the significant impact of acid rain on soil chemistry and heavy metal pollution. The study, published in the journal Minerals, investigated the environmental effects of acid rain-induced heavy metal migration in the Siding Lead-Zinc mining area. The findings suggest that acid rain enhances
Machine learning
Research from the University of Gondar in Ethiopia has analyzed data from 147,466 men to identify predictors of tobacco use in Sub-Saharan Africa. The study utilized machine learning algorithms to determine the key factors influencing tobacco use among men in the region between 2018 and 2023. The findings highlight
Machine learning
Researchers at the Vellore Institute of Technology in Tamil Nadu, India, have conducted a comprehensive review of the Deep Q-Network (DQN) algorithm, a type of machine learning that enables sequential decision-making. The team focused on the pivotal aspects of DQN, including the deep neural network and experience replay, and explored
Machine learning
Researchers have developed new dynamic programming algorithms within the NUPACK software suite, enabling the analysis of equilibrium base-pairing properties for complex systems containing mixed-material nucleic acid strands. Currently, calculations are limited to single-material systems, but the new algorithms can analyze mixed-material systems, which are critical for modern applications in vitro,
Machine learning
A new study by researchers from Veer Kunwar Singh University has explored the potential of artificial intelligence in enhancing e-learning experiences for students in India's educational institutions. The study, which analyzed data from 605 students from various schools across India, focused on the impact of AI-driven social learning
Machine learning
Research from Islamic University of Najaf, Iraq, has introduced a new machine learning algorithm to predict pest and disease management in sustainable food and agriculture. The algorithm, called Pest and Disease Management Machine Learning Algorithm (PDM MLA), utilizes data-driven predictive modeling to analyze weather, soil parameters, and crop health data
Machine learning
A novel approach to quantification of active pharmaceutical ingredients in ophthalmic preparations has been developed, addressing a significant challenge in sustainability research. The study, led by researchers from King Abdulaziz University, employs machine learning-enhanced UV-spectrophotometric chemometric models to concurrently quantify latanoprost, netarsudil, benzalkonium chloride, and two related compounds. This breakthrough
Machine learning
Data from a recent study have shed new light on the capabilities of artificial intelligence (AI) in predicting preconception outcomes. Researchers from Army Medical University have used five machine-learning algorithms to analyze data from a cohort of 4,097 couples, recruited from the Chongqing Health Center for Women and Children,
