Machine learning
A recent investigation by the Computer Science Department has introduced a novel hybrid multiobjective wrapper method for feature selection, which combines the technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with a multiobjective Crow Search Algorithm (CSA) to improve classification accuracy. The study aims to address the challenges
Machine learning
A new research study from Leipzig University Hospital has identified the potential of using Graph Neural Networks (GNNs) in clinical data analysis, particularly in diagnosing sepsis from blood count data. The study, published in PLOS ONE, finds that GNNs can achieve superior results compared to traditional machine learning algorithms when
Machine learning
Researchers at the University of California Los Angeles (UCLA) have successfully validated a machine learning algorithm that generates cellularity prediction maps (CPM) from multiparametric MRI data to non-invasively evaluate tumor cellularity in brain cancer patients. The study, published in the Journal of Neuro-Oncology, found that the CPM values accurately predicted
Machine learning
Researchers have developed OocystMeter, a software that uses machine learning to automate the segmentation of malaria oocysts from images of mosquito midguts. The tool helps overcome challenges in determining oocyst size and numbers by leveraging a high-quality dataset of 11,178 Plasmodium falciparum oocysts in Anopheles gambiae midguts annotated by
Machine learning
Trinity Life Sciences, a leader in advisory, insights, and analytics for the life sciences industry, in collaboration with Sanofi, has made a significant breakthrough in the early detection of Type 1 Diabetes (T1D). A study, titled "Identification of Earlier Stage Autoimmune Type 1 Diabetes Using Machine Learning Algorithms,"
Machine learning
A team of researchers from Fujian, People's Republic of China, has developed a novel prognostic signature for breast cancer patients by integrating multi-omics data and machine learning algorithms. The study, published in Scientific Reports, used transcriptomic and gene expression data from TCGA and GEO databases to identify 72
Machine learning
Recent research from the Punjab Remote Sensing Centre, supported by the University of Pecs, has demonstrated the effectiveness of machine learning algorithms in assessing wheat lodging. The study utilized multi-temporal Sentinel-2 data to analyze the temporal characteristics of crop phenology and detect lodging during the end of March and April
Machine learning
Researchers from the Birla Institute of Technology and Science in Hyderabad, India, have employed machine learning techniques to predict power loss in vanadium redox flow batteries (VRFBs). This breakthrough could greatly enhance the efficiency and scalability of VRFBs, a crucial technology for the transition to renewable energy. The study demonstrates
Algorithms
Research conducted at the University of Guelph in Ontario, Canada, has demonstrated the potential of machine learning algorithms in estimating nitrous oxide (N2O) emissions in agricultural landscapes. A team of researchers, led by Uttam Ghimire, employed multiple algorithms, including random forest regression (RFR), support vector regression (SVR), and artificial neural
Machine learning
A recent study published in JAMA Network Open demonstrates the superior reproducibility and prognostic associations of an artificial intelligence (AI) algorithm in quantifying tumor-infiltrating lymphocytes (TIL) in melanoma patients compared to traditional pathologist-read methods. The study, led by Thazin N. Aung, Ph.D., from Yale University School of Medicine, analyzed
Machine learning
Research has shown that both biotic and abiotic stresses pose significant threats to crop plant growth and productivity, including maize worldwide. Identifying genes and associated networks underlying stress resistance responses in maize is crucial for developing stress-resistant varieties. A recent study from Louisiana State University employed a meta-transcriptome approach to
Machine learning
Scientists at the National Research Council Italy (CNR) have made a groundbreaking discovery in the field of artificial intelligence. By developing a machine learning-based algorithm called CIAO, they have significantly improved the accuracy of satellite-derived Chlorophyll-a estimates in the Arctic Ocean. The research, published in the journal Science of Remote
