Machine learning
Researchers at Umea University have developed a novel approach to improve the effectiveness of Generative Adversarial Networks (GANs) in denoising medical images. The study, published in the journal AI Open, introduces a new loss function that captures complex relationships within images, enabling the GANs to generate high-quality images from noisy
Machine learning
Researchers at Bohai University have developed an adaptive finite-time neural optimal control technique for stochastic nonlinear systems (SNSs) using reinforcement learning (RL). According to the study, the proposed method simplifies the control scheme and addresses the singularity problem that occurs in traditional backstepping optimal control techniques. The technique uses radial
Machine learning
Research has identified rain-driven urban surface water flooding as a prevalent natural disaster, causing disruptions, economic losses, and casualties. The Centre for Water Systems at the University of Exeter has developed a novel deep learning method utilizing convolutional neural networks (CNNs) to assess flood hazards. The model incorporates three techniques
Machine learning
A recent study conducted at Kyushu University in Japan has made significant advancements in the field of artificial intelligence by proposing a hybrid machine learning model for estimating peak particle velocity (PPV) in surface mining operations. The research team, led by Kesalopa Gaopale, developed a Sparrow Search Algorithm-optimized Artificial Neural
Machine learning
Recent research conducted by University of Maryland Eastern Shore (UMES) has shed new light on the effectiveness of machine learning techniques in predicting corn prices. The study, led by Raksha Khadka and her team, evaluated the performance of traditional econometric models, as well as advanced machine learning and hybrid approaches,
Machine learning
Millions of people worldwide live with the challenges of amputation, which profoundly impacts their physical, emotional, and psychological well-being. Researchers at the University of Wollongong have made significant strides in developing artificial intelligence for prosthetic hand control, with a focus on improving myoelectric hand gesture recognition. Despite advancements, high rejection
Machine learning
Researchers from Taylor's University have developed a hybrid machine learning classification algorithm to detect the polarity and key aspects mentioned in Amazon product reviews. The algorithm, named Soft Voting Hybrid Algorithm (SVHA), was implemented by training and testing a voting classifier using soft voting, which produced the final
Machine learning
Research from the University of Queensland has identified artificial intelligence (AI) as a potential solution to mitigate the negative impact of administrative tasks on healthcare workers' mental health. The study, published in the International Journal of Medical Informatics, found that AI can streamline workflow processes, reduce administrative burden, and
Machine learning
Researchers at the University of California have developed a deep learning method for optimizing monoclonal antibody production processes, resulting in a 28.1% increase in antibody titer and a 27.9% improvement in volumetric productivity. The method uses a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture and was validated
Machine learning
A team of researchers from Madhav Institute of Technology and Science has made significant strides in developing a general artificial intelligence solution that can solve different problems across various domains without the need for re-training. The breakthrough is the result of a unique approach that combines machine learning and genetic
Machine learning
Researchers from Shiraz University of Technology have developed an explainable AI approach to intrusion detection in the Internet of Things (IoT). This method uses a lightweight one-dimensional convolutional neural network (1D-CNN) that allows for the interpretation of results and identifies the most important features in the model. The proposed method
Machine learning
Researchers at Vanderbilt University have made a significant breakthrough in artificial intelligence, enhancing the performance of Graph Neural Networks (GNNs) in social network classification tasks. By utilizing average controllability and a novel rank encoding method, the team has developed a new strategy for constructing expressive node features, addressing the limitation
