Genetic research
Research has made significant advancements in addressing global climate change and energy crises through the development of low-carbon integrated energy systems (IESs). A recent study proposed an improved genetic algorithm (IGA) to optimize the multi-objective low-carbon operations of IESs, aiming to minimize both operating costs and carbon emissions. The IGA
Machine learning
A new study from the School of Geosciences and Info-Physics has utilized machine learning techniques to predict the fracability index of shale gas formations, offering a faster and more cost-effective alternative to traditional experimental methods. The research, focusing on the Upper Ordovician Wufeng to Lower Silurian Longmaxi Formation in the
Machine learning
Researchers at the First People's Hospital in Guizhou, China, have developed a novel pathomics model that reliably predicts breast cancer prognosis using machine learning algorithms. The study, published in the journal PLOS ONE, analyzed the expression of checkpoint kinase 1 (CHEK1) in breast cancer tissue and found that
Machine learning
Investigations into machine learning have recently led to the publication of a new report, focusing on the role of automated software systems in controlling essential operations. The research team from Singidunum University, funded by the Science Fund of the Republic of Serbia and the Intelligent Multi-Agent Control and Optimization applied
Machine learning
A new study from the Department of Technology at Shanghai Palan DataRx Co. Ltd. has developed a machine learning model tool for identifying postoperative patients with major bleeding based on electronic medical records. The researchers used data from a database of 2,000 patients who underwent in-hospital tumor resection surgery
Machine learning
Researchers have been working with gridded livestock distribution datasets for years, using them in various fields like epidemiology, livestock impact assessment, and territory management. These datasets are based on national or sub-national censuses downscaled using machine learning algorithms and spatial-explicit environmental predictors. One notable dataset is the Gridded Livestock of
Machine learning
Researchers from Peking University recently conducted a network meta-analysis to compare the performance of various machine learning algorithms in predicting surgical site infections (SSI). The study, published in the journal Computers in Biology and Medicine, aimed to identify the best-performing algorithm for building SSI predictive models. The researchers found that
Machine learning
A new study has successfully developed an interpretable machine-learning approach that accurately predicts lithium-ion battery discharge capacity under variable operating conditions using minimal input descriptors. Funded by the Interdisciplinary Research Center for Construction & Building Materials (IRC-CBM), King Fahd University of Petroleum & Minerals, the research used three nature-inspired hybrid
Machine learning
Researchers at the University of Alberta have successfully developed an artificial intelligence system to detect camera rotation in minimally invasive surgery (MIS) procedures, mitigating the difficulty and cognitive load on surgeons. This innovative approach leverages a convolutional neural network (CNN) to automatically correct camera rotation issues during MIS procedures. The
Genetic research
Research conducted by a team of experts at Technical University has made significant strides in the field of robotics, particularly in the context of laparoscopic pancreatic surgery. The study focused on optimizing the design of a 3-DOF parallel robot to enhance its kinematic performance, workspace accessibility, and precision. By integrating
Machine learning
Researchers at the University of New South Wales have found that integrating Explainable Artificial Intelligence (XAI) with machine learning (ML) algorithms significantly improves clinician trust in ML-driven decisions for glaucoma detection. By providing a deeper understanding of the logic and reasoning behind ML algorithms, XAI techniques enhance the transparency and
Machine learning
Air quality has become a significant environmental concern globally due to the rapid development of society and industry. Predicting and rating air quality for many cities remains a challenge. Recent research from the University of Hull has explored the potential of machine learning algorithms in addressing this issue. By developing
