Artificial neural networks

Artificial intelligence

Breakthrough in Artificial Intelligence: Nonreciprocal Neural Networks for Decoupled Bidirectional Analog Computing

Researchers from Zhejiang University have made a significant discovery in the field of artificial intelligence, introducing a nonreciprocal neural network that leverages enhanced magneto-optical effects to decouple forward and backward paths in computing. This innovation enables the creation of integrated perception-response systems, which are essential for various applications, including image

Machine learning

Artificial Intelligence Assists Precision Glass Thermoforming with Neural Networks

Researchers at Hong Kong Polytechnic University have made a breakthrough in using artificial intelligence to improve the precision glass thermoforming process. By developing a surrogate model based on a dimensionless back-propagation neural network (BPNN), they have been able to accurately predict forming errors and compensate for them in mold design.

Artificial intelligence

Artificial Neural Networks Revolutionize Wastewater Treatment through Photo-Fenton Degradation

Artificial intelligence has become a game-changer in various fields, including scientific research and water treatment. A recent study published in Water Science and Engineering has shed light on the potential of artificial neural networks (ANNs) in optimizing the photo-Fenton process for wastewater treatment. This revolutionary approach has shown remarkable promise

Artificial intelligence

Vulnerabilities in Deep Neural Networks Expose Safety-Critical Artificial Intelligence Systems to Backdoors

Cybersecurity researchers from the University of Electronic Science and Technology of China have conducted an in-depth analysis of backdoor attacks on deep neural networks (DNNs), revealing a previously unknown vulnerability that affects the efficacy of DNN-based applications. The study, published in the Journal of Electronic Science and Technology, highlights the

Machine learning

Artificial Neural Networks for Monitoring pH and Acidity in Yogurt Fermentation

Researchers from the Universidad Nacional del Altiplano have successfully developed artificial neural network (ANN) models to predict pH and titratable acidity in yogurt fermentation using CIELAB color parameters. The study, funded by the Consejo Nacional De Ciencia, TecnologiA E InnovacioN Tecnologica, aimed to improve process control in industrial yogurt production

Machine learning

Artificial Neural Networks Proven to Speed Up Power Flow Calculations by up to 45x

Researchers at Universitat Politecnica de Catalunya have successfully utilized artificial neural networks (ANNs) and XGBoost techniques to simulate power flows in a medium-voltage grid in Norway with significant speed-up factors. This groundbreaking study, supported by the European Union, demonstrates the potential of machine learning (ML) models in managing power systems

Machine learning

Enhanced Engine Misfire Diagnosis through Artificial Neural Networks

Researchers from Minia University have developed an advanced engine misfire diagnosis system using artificial neural networks (ANNs). The innovative approach combines vibration and acoustic emission (AE) signals to improve detection accuracy. By integrating multiple sensors and employing Bayesian Optimization techniques, the ANN model achieved an impressive 94.11% accuracy in

Machine learning

Machine Learning Predicts Echoic Verbal Behavior in Children with Autism Spectrum Disorder

Researchers have made a significant breakthrough in predicting the likelihood of children with Autism Spectrum Disorder (ASD) developing echoic verbal behavior using machine learning strategies. By analyzing 143 case records of children assessed with the Verbal Behavior-Milestones Assessment and Placement Profile (VB-MAPP), a multilayer perceptron (MLP) model achieved 100% accuracy

Machine learning

Novel ANN-Based Classification of Spike-Wave Activity in EEG Recordings

Researchers have developed a new tool, called the Spike-Wave discharge Artificial Neural Network (SWAN), to detect and classify spike-wave discharges in absence epilepsy. This automated tool uses spectrograms to evaluate complex spatiotemporal patterns in electroencephalographic (EEG) recordings. SWAN has been shown to achieve high precision (0.96) and sensitivity (0.

Machine learning

Machine Learning Algorithms Accurately Determine Sex from CT Images

Researchers from Karabuk University have developed a machine learning model that can accurately determine sex from computed tomography (CT) images of the skull. The study analyzed CT images of 200 individuals, aged 19-65 years, and used various machine learning algorithms to estimate sex based on morphometric measurements from the facial

Artificial intelligence

Huawei Develops Advanced Neural Network Model Training Method

Huawei Technologies Co., Ltd. has been assigned a patent (No. US 12406488 B2) developed by inventors Chuanyun Deng, Guanfu Chen, and Shaohua Tang for "Neural network model training method, image processing method, and apparatus." This breakthrough in artificial intelligence involves a novel approach to training neural network models,

Machine learning

Machine Learning Technique Improves Resistivity Logging Data in Oil and Gas Industry

Researchers at Shandong University have introduced a machine learning technique based on "training while drilling" to enhance the integrity and accuracy of resistivity logging data in the oil and gas industry. This approach integrates a Long Short-Term Memory (LSTM) neural network into embedded devices, allowing for real-time downhole