Machine learning
A recent study by researchers from Delhi Technological University has found that online machine learning algorithms can be effectively used for real-time air quality monitoring in Delhi, India. The study, which utilized nine popular unsupervised online anomaly detection models, demonstrated that these algorithms can detect anomalies in air quality data
Machine learning
Researchers from the Federal University of Mato Grosso do Sul (UFMS) have conducted a study to investigate the use of machine learning algorithms, specifically Support Vector Machines (SVMs), to predict soybean seed germination. The study involved the collection and analysis of a database of 1,000 soybean seed analysis samples,
Genetic research
Researchers from the University of Tabriz have developed a novel design for non-mechanical piezoelectric micropumps, which offer simplicity by eliminating moving parts. These micropumps play a crucial role in various drug delivery applications and have the potential to revolutionize the field. The proposed design includes a piezoelectric plate, a diaphragm
Genetic research
Researchers from the Department of Mechanical Engineering at TU Darmstadt, Germany, have made significant breakthroughs in optimizing staggered tube banks in cross-flow using machine learning and genetic algorithms. This innovative approach has led to the determination of optimal dimensionless transverse and longitudinal pitches that balance heat transfer enhancement and pressure
Genetic research
A new study on engineering has reported a significant breakthrough in intrusion detection systems (IDS) through a hybrid approach that integrates slime mold algorithm (SMA) and genetic algorithm (GA) within a feature selection (FS) framework. The innovative approach, known as OSMOGA, is designed to enhance search efficiency and improve detection
Genetic research
Researchers from Hubei University of Arts and Science in Xiangyang, People's Republic of China, have proposed a new method to improve product manufacturing quality using gene recombination and editing mechanism. This method involves creating an optimization model that incorporates three optimization objectives: production quality, costs, and time. The
Machine learning
Researchers from the University of California San Francisco (UCSF) have made new findings in the field of artificial intelligence. According to their research, a novel machine learning (ML) algorithm was developed to predict early and late hospital discharge in patients who have undergone transcatheter aortic valve replacement (TAVR). The study
Machine learning
Researchers from Manipal University in Rajasthan, India, have developed a novel genetic algorithm, called Inverted Swap crossover, for automated machine learning (AutoML) in Convolutional Neural Networks (CNNs). This innovative technique is designed to optimally select the best convolutional and pooling layers, activation functions, and other architectural decisions for image classification
Machine learning
Researchers at the Indian Institute of Science (IISc) and the Qatar Science and Technology Research Center (QSRTC) have developed a cutting-edge automated method to assess corrosion in industrial equipment using advanced machine learning and image analysis. This novel technique, published in npj Materials Degradation, can analyze microscope images of corroded
Machine learning
Malnutrition in children represents a severe global health concern with high prevalence and long-term consequences for growth and development. Researchers from China-Japan Friendship Hospital aimed to understand the contributory factors of child malnutrition, leading to the design of targeted prevention strategies and evidence-based interventions. The study, published in the Journal
Genetic research
A recent study on the utilization of hydrogen fuel cells in shipboard vessels highlights the need for energy conservation and emission reduction in maritime applications. The study, conducted by researchers at Dalian Maritime University, proposes an innovative real-time energy management strategy (EMS) to mitigate the challenges associated with fuel cells
Genetic research
Researchers from Shijiazhuang Tiedao University in Hebei, People's Republic of China, have developed an integrated framework for optimizing combined cooling, heating, and power (CCHP) systems coupled with thermal energy storage (TES). This framework aims to enhance operational efficiency, cost-effectiveness, and environmental sustainability. The study incorporates real-time optimization strategies,
