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
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
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
Research conducted at Northeastern University in Shenyang, People's Republic of China, has led to a groundbreaking study on machine learning and support vector machines. The study proposes an asymmetrical optical fiber Mach-Zehnder interferometer (MZI) that utilizes support vector machine (SVM) and artificial neural network (ANN) to sense vector
Machine learning
Researchers from Jeonbuk National University have made significant advancements in the field of robotics by developing a Wheeled Inverted Pendulum with a Fan (WIPF). This innovative system incorporates a fan-generated bidirectional thrust force as an additional control input, making it fully actuated and enhancing stability. However, the limited bandwidth of
Machine learning
Researchers at the School of Computer Science and Engineering have made a breakthrough in artificial intelligence by developing a novel approach to detecting hairfall trends over time. The study presents a time-aware anomaly detection framework, HairSentinel, which utilizes deep learning models to analyze user-provided data and identify sudden increases or
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
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,
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
Machine learning
A new patent has been granted to Semes Co. Ltd. for a carriage robot and tower lift design specifically tailored for semiconductor manufacturing. This innovative design addresses the challenges of transporting multiple containers of varying sizes in a vertical direction, without compromising transport efficiency. The patented invention provides a solution
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
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.
