Machine learning
Research by Wael Khreich and Jad Doughman at the American University of Beirut has led to the development of a novel data-centric system for detecting gender biases in language. This system, called Genderly, aims to identify and address the personal and societal consequences of biased language, including exclusion, labor force
Machine learning
Researchers at the Getulio Vargas Foundation conducted a study on the effectiveness of binding precedents in the Brazilian Supreme Court, highlighting concerns about their ability to reduce repetitive demands. The team used a unique framework to analyze five binding precedents and found that they tend to fail in this direction,
Machine learning
Researchers at the University of Canberra conducted a study to examine people's first impressions of a humanoid chatbot, specifically 'Pepper', a robot outfitted with ChatGPT, in a public venue. The study, published in the journal Scientific Reports, aimed to understand the affective, social, and material forces
Machine learning
A new study has made a significant advancement in predicting breast cancer recurrence, a persistent challenge in treating the disease. Researchers from the First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, in collaboration with international partners, have developed an immune-related gene signature (IRGS) that demonstrates outstanding predictive performance
Machine learning
New research by Gachon University has shed light on the potential of integrating machine learning and 3D motion capture to effectively classify and diagnose neurological disorders such as incomplete tetraplegia, incomplete paraplegia, and cauda equina syndrome. These conditions significantly reduce patients' quality of life, primarily due to impaired motor
Machine learning
Researchers at a leading institution have made a breakthrough in understanding the genetic basis of growth-related traits in snapper (Chrysophrys auratus), a species commonly farmed for food. The study, published on biorxiv.org, involves the analysis of phenotypic and genomic data from a selectively bred population of snapper. The researchers
Machine learning
A new method for recognizing small targets in computer vision has been proposed by researchers from the School of Electrical and Information Engineering, Zhuhai Coll Sci & Technol. The method, based on an improved version of You Only Look Once Version 8 (YOLOv8), addresses the challenge of reduced detection accuracy
Machine learning
Research conducted by Chih-Yuan Tseng and colleagues from Sinoveda Canada Inc. has developed a novel machine learning approach to predict drug dissolution profiles in the gastrointestinal tract. The study, published in the Journal of Chemical Information and Modeling, presents a two-stage machine learning approach that integrates physics-informed neural networks (PINNs)
Machine learning
Research at the International University of Business Agriculture and Technology in Bangladesh has led to the development of a novel hybrid framework combining advanced machine learning and deep learning techniques to improve the accuracy of drug-target interaction (DTI) predictions. The framework overcomes challenges such as data imbalance and biochemical representation
Machine learning
Researchers at the Nanjing University of Information Science and Technology (NUIST) have successfully integrated quantum computing with classical machine learning models, resulting in high accuracy in object detection. The team, led by researchers Chengjun Zhang and Wenbin Yu, proposed a novel approach that combines transfer learning and multi-channel quantum convolutional
Machine learning
Research suggests that financial and logistical barriers hinder access to high-quality human sports coaches, leading to disparities in the availability of effective training. Artificial intelligence (AI) coaches, powered by Large Language Models, may offer a solution to augment human coaches by supporting or autonomously performing specific coaching tasks within targeted
Machine learning
Research has found that maize-soybean intercropping is a sustainable farming practice that optimizes resource use efficiency and improves yield potential. A new study proposes a Fuzzy-Optimized Hybrid Ensemble Model (FOHEM) to improve yield prediction in these systems. The model integrates stacked ensemble machine learning algorithms with a fuzzy inference system
