Machine learning
Investigations into the application of machine learning in gas sensing have led to breakthroughs in the detection of hydrogen gas at room temperature. Researchers from South Korea have developed a method that utilizes UV light to reduce the sensing temperature down to room temperature, eliminating the need for micro-heaters and
Artificial neural networks
Researchers at the PSG Institute of Technology and Applied Research have made a groundbreaking discovery in the field of artificial neural networks, successfully developing a model to predict the functional fatigue behavior of shape memory alloys (SMAs). The novel approach utilizes a feed-forward backpropagation artificial neural network (ANN) to capture
Machine learning
In the era of web applications, security vulnerabilities expose sensitive data and organizational integrity to sophisticated attacks. Researchers from the Faculty of Engineering and Natural Sciences at Sivas University of Science and Technology have developed a novel approach, UniEmbed, to detect cross-site scripting (XSS) and SQL injection attacks using machine
Machine learning
Fresh data on robotics and artificial intelligence are presented in a new report from Delft University of Technology. According to the research, locomotion over granular terrain poses significant challenges for autonomous robotic systems, particularly in coastal regions characterized by loose, shifting sands. To optimize the locomotion on these challenging terrains,
Exercise
Researchers from Chongqing Normal University have developed a model for classifying physical activity types using healthcare wearable devices and optimized machine learning (ML) methods. The model, which utilizes XGBoost as the primary ML algorithm, has potential applications in continuous activity monitoring for rehabilitation and remote patient care in clinical settings.
Machine learning
Investigators at King's College London have made a groundbreaking discovery in the field of artificial intelligence. According to a recent report, the researchers have introduced a novel deep learning architecture called Limit Order Book Transformer (LiT), which outperforms traditional machine learning methods and state-of-the-art deep learning baselines in
Machine learning
Researchers at Chitkara University in Himachal Pradesh, India, have developed an AI-powered system to predict rainfall using the MapReduce framework and convolutional long short-term memory (ConvLSTM) method. The innovative system, which combines adaptive salp-stochastic-gradient-descent-based ConvLSTM (adaptive S-SGD-based ConvLSTM), has demonstrated high prediction accuracy, achieving minimal values for Mean Square Error
Machine learning
Investigations at Tarim University led to a groundbreaking innovation in robotics motion planning. A team of researchers, spearheaded by Xu Li, devised a novel framework incorporating an enhanced A* algorithm and a modified Timed Elastic Band (TEB) strategy. This innovative approach aims to address both global and local planning simultaneously,
Machine learning
Researchers at the University of Science and Technology Beijing have introduced a novel system identification method for developing a dynamic model of flapping-wing robots, presenting a significant advancement in robotics. This method constructs an independent frame for each flapping-wing, enhancing the authenticity of the model's structure. The team
Atoms
Researchers at Tsinghua University have made a significant breakthrough in the field of artificial intelligence, particularly in quantum information processing. The study, published in the npj Quantum Information journal, explores the application of SU(d)-symmetric random unitaries in diverse contexts, including information scrambling, covariant quantum error correcting random codes,
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
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
