Machine learning
A team of researchers from the University of Geneva (UNIGE) has developed a machine learning-based system that can detect colorectal cancer from simple stool samples with high accuracy, potentially revolutionizing the diagnosis of this disease. Using a comprehensive catalogue of human gut microbiota subspecies, the researchers created a model that
Machine learning
Effectual, an AWS Premier Tier Services Partner, has joined forces with SOLV Energy, a leading renewable energy services provider, to develop an innovative Generative AI (Gen AI) solution aimed at revolutionizing the work of field technicians. The Gen AI solution, powered by AWS's capabilities in artificial intelligence and
Machine learning
Research from the Department of Science and Technology has highlighted the growing need for accurate and real-time air quality monitoring in urban environments. The study, which utilized a novel embedded machine learning approach with edge computing, demonstrated the effectiveness of low-cost sensor enhancement in improving air quality monitoring and control.
Machine learning
Researchers at the Military University of Technology in Warsaw, Poland, have developed a machine learning model to assess the severity of Parkinson's disease symptoms using data from wearable and smartphone sensors. The study explores the use of machine learning to predict the severities of individual symptoms, such as
Machine learning
Recent research from Poona College of Pharmacy in Maharashtra, India, has shed new light on the transformative advancements in drug discovery, clinical development, and post-market surveillance fueled by the integration of artificial intelligence (AI) and machine learning (ML) into pharmaceutical sciences. According to the study, AI has revolutionized modern pharmacotherapy,
Machine learning
Researchers from Yili Normal University in Xinjiang, China, have employed a novel methodological framework integrating morphological spatial pattern analysis, circuit theory, and machine learning models to investigate the spatiotemporal evolution and optimization of ecological networks in arid areas. The study, published in Environmental and Sustainability Indicators, reveals significant improvements in
Machine learning
Punjab, India's agricultural sector is a crucial aspect of the country's economy, with a significant portion of the population dependent on it. To ensure sustainable development and effective management of agricultural lands, researchers have turned to remote sensing and machine learning techniques to classify and monitor
Machine learning
Researchers from the Chinese Academy of Sciences have utilized artificial intelligence to improve the interpretation of ecological drivers in grassland ecosystems in arid regions. A new study combines machine learning with temporal autocorrelation metrics to assess resilience in these ecosystems. Key Takeaways: * The study employed Random Forest and SHAP analysis
Machine learning
Researchers from Hainan Medical University have made a significant contribution to the field of personalized medicine, leveraging machine learning algorithms to identify potential drug candidates for colon cancer. According to a new study, the team employed computational oncology to advance multi-targeted therapies for the disease, addressing challenges in understanding molecular
Machine learning
Circle Pharma, a clinical-stage biopharmaceutical company, has announced an agreement with Eli Lilly and Company to leverage Lilly's AI/ML platform, TuneLab, to enhance its MXMOtm platform in developing macrocycle therapeutics for difficult-to-treat cancers. This collaboration is part of Lilly's Catalyze360 model, which aims to empower
Machine learning
As the adoption of Artificial Intelligence (AI) in Nigeria gains momentum, business leaders are facing a pressing need to grasp the fundamentals of this technology to stay competitive. AI is no longer a futuristic concept but an essential tool for survival and growth. This article breaks down key AI terms
Machine learning
Researchers at the National University of Defense Technology have developed a novel domain-adaptive entity resolution model that tackles the challenge of obtaining annotated data for entity resolution tasks. The proposed model utilizes semi-supervised learning to reduce the distributional difference between source and target domains, achieving comparable performance to supervised baseline
