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
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
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
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
Artificial intelligence
Researchers at Northeastern University have proposed a novel adaptive input-centric approach for edge intelligence in Industry 4.0 environments. The approach, which focuses on reducing computational overhead by pruning redundant features prior to inference, has shown significant reductions in memory usage and computation cost while maintaining competitive performance. This method
Artificial intelligence
Researchers from Prince Mohammad Bin Fahd University have published a study on the application of machine learning, specifically physics-informed neural networks (PINNs), to address the challenge of working with sparse data in fluid mechanics. Fluid mechanics often involves complex systems characterized by a large number of physical parameters, making it
Artificial intelligence
Researchers at the University of Engineering and Technology have developed an AI-driven stock market prediction model that incorporates economic factors and demonstrates significant accuracy in predicting stock market behavior. This model, based on a long short-term memory (LSTM) model, outperforms traditional ARIMA and machine learning-based random forest models. The study&
Artificial intelligence
Research conducted by the University of Ruse in Bulgaria has explored the application of artificial intelligence in predicting the b-carotene content in apricots using hyperspectral images captured in the near-infrared region during the drying process. The study focused on comparing the performance of various machine learning models, including Partial Least
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 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
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 Shahid Bahonar University of Kerman in Iran have made significant progress in understanding the thermophysical properties of alumina nanoparticles in binary mixtures of water and ionic liquids. Using advanced machine learning algorithms, specifically Cascaded Forward Neural Networks (CFNN) and Generalized Regression Neural Networks (GRNN), the team has successfully
