Artificial intelligence
Research from the Silesian University of Technology, in collaboration with Polish institutions, has achieved a significant milestone in the development of artificial intelligence for Unexploded Ordnance (UXO) classification. By leveraging a high-fidelity digital twin and a custom Convolutional Neural Network (CNN), the team has demonstrated an 84.65% balanced accuracy
Artificial intelligence
Researchers at Centre de Recherche de l'Institut Universitaire de Geriatrie de Montreal have made significant strides in developing artificial neural networks (ANNs) that are aligned with brain dynamics. The study involved training ANNs in the field of artificial intelligence (AI) to model brain processes, with the goal of
Machine learning
A team of researchers from Tecnologico Nacional de Mexico has made a significant discovery in the field of artificial neural networks. Their study, published in the journal Nova Scientia, found that a Multi-Layer Perceptron (MLP) outperformed traditional regression models in predicting dissolved oxygen levels in water. The researchers utilized data
Machine learning
Researchers have developed neural networks that accurately reproduce proton and C chemical shifts obtained from oB97X-D/6-31G* density functional model GIAO calculations. These networks support uncharged, closed-shell molecules comprising H, C, N, O, F, S, Cl, and Br. The development involved training the networks to 2.7 million equilibrium geometry
Machine learning
Artificial intelligence researchers at the University of California, Davis, have made significant advancements in the detection of regional wall motion abnormalities (RWMA) using electrocardiogram (ECG) data. The team utilized classical machine learning (ML) methods and one-dimensional convolutional neural network (1D CNN) models to analyze ECG data from 3,750 unique
Machine learning
A recent study published in Frontiers in Pharmacology has found that Quinoline Yellow, a synthetic dye commonly used as an additive in food products, poses significant health risks to humans. Researchers from the Medical University of Warsaw used a combination of computational and in vivo models to investigate the toxic
Machine learning
Artificial neural networks have emerged as a promising solution for predicting midterm electrical load demands. According to a recent study from Sulaimani Polytechnic University, researchers utilized a medium-term load forecasting model based on artificial neural networks (ANNs) to predict the electrical demand for Duhok city up to the year 2025.
Machine learning
Researchers from the Faculty of Engineering and Computing at Liwa University have developed a novel strategy that integrates Internet of Things (IoT) devices and Artificial Neural Networks (ANNs) to optimize energy usage and promote sustainability in residential settings. The study highlights the rapid expansion of IoT devices as a promising
Machine learning
A new study has been conducted on the topic of environmental impact and construction costs in road infrastructure projects. Researchers from the Korea Institute of Civil Engineering and Building Technology have proposed a machine learning-based predictive framework using artificial neural networks and deep neural networks to accurately estimate these costs.
Machine learning
Researchers from Jilin University have made a significant breakthrough in solving the Dirac equation, a fundamental problem in quantum mechanics. Using a deep neural network and an unsupervised machine learning technique, the team was able to accurately solve the equation not only for the ground state but also for low-lying
Machine learning
Researchers from Seoul National University have made a significant breakthrough in neuromorphic computing, developing an IGZO-based optoelectronic neuromorphic system integrated with light guides. This innovative design demonstrates high retention through negative gate bias and successfully emulates synaptic plasticity such as long-term potentiation and long-term depression. The proposed IGZO-based neuron circuit
Machine learning
Researchers have proposed a new data-driven approach to modeling material behavior under various pressure-temperature-volume conditions, which is crucial for understanding complex materials and their applications. The traditional models rely on thermodynamic assumptions and are limited by scalability and extrapolability, whereas the new method, called EOSNN, uses a neural network-based physics-informed
