Machine learning
Researchers at the School of Computing Science and Engineering have introduced a novel Quantum-based Coati-MobileViT (QBCMVT) model to analyze and detect malicious activities in Industrial Internet of Things (IIoT) networks. The new model addresses the limitations of existing Intrusion Detection Systems (IDS) by using a Quantum-based Coati optimization algorithm to
Machine learning
Researchers from Incheon National University have developed an integrated neural network-based method to predict traffic congestion in overhead hoist transports (OHTs) within semiconductor manufacturing facilities. This innovation leverages machine learning and deep learning approaches to optimize logistics management, ultimately leading to increased productivity. By analyzing complex traffic conditions and dynamic
Machine learning
Research has motivated the adoption of advanced remote sensing technologies and artificial intelligence-based approaches to move beyond conventional field survey methods for sustainable environmental management. A hybrid deep learning framework combining Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) has been proposed to address limitations in traditional machine learning methods,
Machine learning
Researchers at Islamic University in Kushtia, Bangladesh, have used artificial neural networks (ANN) to assess the quality of groundwater in the southwest region of the country, specifically in schools. Their study, published in the Environmental Geochemistry and Health journal, focused on the presence of iron (Fe), arsenic (As), pH, electrical
Machine learning
Researchers at the University of Valencia have made significant breakthroughs in the detection of brain tumors using machine learning algorithms, particularly One-Class Support Vector Machines (OCSVM) and Convolutional Neural Networks (CNN). The study sought to address the class imbalance issue in medical imaging datasets, which poses significant challenges for traditional
Machine learning
Researchers from the National Marine Environmental Monitoring Center have conducted a comprehensive study on the relationship between dissolved organic matter (DOM) optical characteristics and water quality in the Bohai Sea. The study, funded by several Chinese organizations, analyzed DOM distribution and nutrient concentrations in the sea and its four basins,
Machine learning
Researchers from the University of Agder have developed an autonomous robotic facility for textile sorting and recycling, leveraging advanced computer vision and machine learning technologies. The system enables real-time textile classification, localization, and sorting on a dynamically moving conveyor belt. The facility's custom-designed pneumatic gripper optimizes autonomous picking
Machine learning
Research from the University of Hong Kong has unveiled a significant breakthrough in robotics, with the development of a lightweight LiDAR-inertial-visual odometry system optimized for resource-constrained platforms. The innovative system integrates a degeneration-aware adaptive visual frame selector into error-state iterated Kalman filter (ESIKF) with sequential updates, enhancing computation efficiency while
Machine learning
Researchers at the City University of Hong Kong have developed a modular magnetic navigation system for actuating surface microwalkers, which has shown potential for designing magnetic actuation systems in clinical applications. The system utilizes a dual-sided layout, with the rotating magnet system and imaging system positioned on opposite sides of
Machine learning
Investigators from the College of Electrical Engineering at the Navy University of Engineering have made significant advancements in the design of joint waveforms for cognitive multiple input multiple output (MIMO) radar systems. Focusing on the critical challenge of multi-target detection, the researchers implemented a semi-definite programming (SDP) algorithm to optimize
Machine learning
A recent study has proposed a novel method for high-resolution seafloor mapping using laser scanners, which offers a more affordable alternative to traditional sensors. The research, conducted by the Monterey Bay Aquarium Research Institute, involves the use of a monocular camera and a laser stripe to create 3D reconstructions of
Machine learning
Researchers at Torrens University Australia have made significant contributions to the field of robotics, introducing a novel optimization framework for multi-robot exploration. The Advanced Multi-Objective Salp Swarm Algorithm Exploration Technique (AMET) has been compared to various multi-objective and single-objective exploration strategies, demonstrating superior area coverage, reduced computational overhead, and enhanced
