Data mining
Researchers at the Nanjing University of Science and Technology have proposed a novel end-to-end framework for automatically generating comprehensive research workflows from full-text academic papers. The framework, which utilizes Natural Language Processing (NLP) techniques and machine learning algorithms, has been tested in the Natural Language Processing (NLP) domain and has
Algorithms
A recent study published in Earth Science Informatics has investigated the use of machine learning (ML) algorithms for identifying runways and associated airfield features using open-source satellite imagery. The research, conducted by the Birla Institute of Technology, employed three data pathways, including SAR, optical, and fused SAR-optical data, to train
Algorithms
A recent study published in Earth Science Informatics has investigated the use of machine learning (ML) algorithms for identifying runways and associated airfield features using open-source satellite imagery. The research, conducted by the Birla Institute of Technology, employed three data pathways, including SAR, optical, and fused SAR-optical data, to train
Data mining
Researchers at Isfahan University of Technology in Iran have conducted a comprehensive study on rule extraction methods in data mining, highlighting the importance of these methods in various domains such as expert systems, decision support, and automated control. The team analyzed 19 studies selected from 678 articles collected from reputable
Asian medicine
A team of researchers from Shanghai Normal University has applied data mining techniques to analyze electronic medical records from a Traditional Chinese Medicine clinic, yielding valuable insights into the diagnostic features and prescription patterns of influenza-like illnesses. The study, published in the Journal of Multidisciplinary Healthcare, aimed to describe diagnostic
Machine learning
Researchers have made significant strides in developing algorithms that can accurately predict sarcopenia, a condition characterized by muscle wasting and weakness. A recent study published in Frontiers in Public Health found that unsupervised machine learning algorithms, specifically Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold partitioning, outperformed MRI-based
Machine learning
A team of researchers from Tongji University in Shanghai, People's Republic of China, has made significant discoveries in the field of pancreatic cancer, identifying a new biomarker and shedding light on the regulatory mechanisms underlying the disease. The study, published in The FASEB Journal, aimed to examine the
Machine learning
A team of researchers from the Renmin Hospital of Wuhan University, in collaboration with international partners, has made a groundbreaking discovery in the field of personalized medicine. By leveraging machine learning algorithms, the team has developed a highly accurate prognostic model for gastric cancer, a highly heterogeneous disease that requires
Algorithms
Researchers at Northeastern University have made a significant breakthrough in the field of multiobjective reinforcement learning (MORL) by developing a nonlinear algorithm that can efficiently handle multiobjective Markov decision processes (MOMDPs) with nonconvex Pareto fronts. The new algorithm, called MORL/D-VR, uses a Tchebycheff approach to transform the given MOMDP
Machine learning
Rapid urbanization and climate change create significant challenges for land cover dynamics and demographic patterns in growing cities. A recent study employs advanced geospatial methods and machine learning algorithms to examine the complex relationships between seasonal land surface temperature (LST) variations and land use/land cover (LULC) changes in urban
Machine learning
Researchers at the NED University of Engineering and Technology in Pakistan have made a significant breakthrough in machine learning, utilizing fuzzy logic to improve data handling and interpretation of epidemiological datasets. By employing fuzzy logic, the researchers have developed algorithms that consistently deliver reliable results and offer a novel approach
Machine learning
A study published in the Journal of Surgical Research has demonstrated the efficacy of a machine learning algorithm-based tumor-stroma ratio in stratifying the prognosis of intrahepatic cholangiocarcinoma (iCCA) patients after radical surgery. The research, conducted by a team of scientists from Sun Yat-Sen University, aimed to quantify tumor components and
