Machine learning
Researchers at the Indian Institute of Technology (IIT) Kanpur have successfully utilized a combination of air pollutant data, meteorological parameters, urbanization indicators, spectral indices, and elevation data to predict land surface temperature (LST) in Bengaluru over a five-year period. The study, published in the journal Natural Hazards, employed advanced machine
Machine learning
Researchers at the University of Groningen have developed a novel recursive Bayesian estimation framework using B-splines for continuous-time 6-DoF dynamic motion estimation. This framework, known as RESPLE, improves upon existing systems by achieving comparable or superior estimation accuracy and robustness while attaining real-time efficiency. The study presents extensive real-world evaluations
Machine learning
A recent study published in the Journal of Hydrology and Hydromechanics has revealed that land-use change is a significant driver of environmental degradation and increasing drought risk. The research, conducted by investigators from Slovak University of Technology, aimed to assess drought dynamics in the South Malang Plateau, East Java, by
Machine learning
Coffee demand continues to rise globally, and producing countries face challenges due to climate change, resulting in yield losses. Researchers from the State University of Campinas (UNICAMP) have developed a novel approach to identify coffee cultivation using machine learning. This innovative method proposes to identify coffee cultivation considering four phenological
Machine learning
Research conducted by the Chinese Academy of Sciences in Changchun, People's Republic of China, has highlighted the critical need for improved water quality monitoring and prediction as the world grapples with the intensifying challenges of climate change and human activities. The study emphasizes the importance of integrating remote
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
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
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 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
