Artificial neural networks

Advances in Homomorphic Encryption for Secure Machine Learning Inference

Researchers at Isik University have published a groundbreaking study on homomorphic encryption (HE) for secure machine learning inference in sensitive environments such as healthcare and finance. Their research focuses on efficiently handling non-linear activation functions in artificial neural networks (ANNs) under homomorphic encryption. The study introduces a lightweight, ANN-based estimator

Generalized Standard Material Networks: A Machine Learning Framework for Understanding Material Behavior

Researchers at Friedrich-Alexander-University Erlangen-Nurnberg (FAU) have developed a novel machine learning framework called Generalized Standard Material Networks, which utilizes convex neural networks to learn the mechanical behavior of complex materials. This framework, supported by the European Research Council (ERC), SNF, Switzerland, and the German Research Foundation (DFG), aims to provide

Artificial neural networks

Multiscale Thermodynamics-Informed Neural Network for Nonlinear Structural Computations of Recycled Thermoplastic Composites

Researchers at the University of Lorraine have developed a novel approach to predict the nonlinear, anisotropic response of recycled glass fiber-reinforced polyamide 6 composites. The Multiscale Thermodynamics-Informed Neural Network (MuTINN) framework integrates thermodynamic principles with artificial neural networks to capture the evolution of internal state variables and Helmholtz free energy.

Artificial neural networks

Advanced Neural Network Approaches for Optimal Check Dam Site Selection in Sub-tropical Climates

Researchers at American University have developed a new method for identifying optimal check dam sites in the Kangsabati River Basin using advanced neural network models. The study, which was supported by Fundacao para a Ciencia e a Tecnologia (FCT), used Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Recurrent

Artificial neural networks

AI Accelerates Materials Design and Development in Nanotechnology

Nanotechnology researchers at Johns Hopkins University have discovered that artificial intelligence (AI) models can significantly accelerate materials design and development. By employing convolutional neural networks (CNN) models, the researchers were able to characterize DNA origami nanostructures, which have numerous applications in biomedicine. The study, published in the Journal of Chemical

Artificial neural networks

Fine-Grained Hierarchical Singular Value Decomposition for Convolutional Neural Networks Compression and Acceleration

Research into convolutional neural networks (CNNs) has led to significant advancements in the field of computer vision, especially in industrial-embedded scenarios. Despite the increasing availability of modern artificial intelligence chips, making CNNs more lightweight remains a crucial challenge. A new study proposes a novel matrix decomposition method, termed hierarchical singular

Artificial neural networks

Breakthrough in Machine Learning: Researchers Develop New Model for Predicting Material Properties

Researchers at the University of São Paulo (USP) have made a significant breakthrough in the field of machine learning, developing a new model for predicting the mechanical properties of wire rods subjected to cold plastic deformations. The study, published in the Journal of Engineering, demonstrates the effectiveness of using pre-trained

Artificial neural networks

Advances in Brain Cancer Diagnosis through Deep Learning and Simulated Annealing

Researchers at Ibn Tofail University in Morocco have made significant progress in developing a more accurate and efficient method for classifying brain tumors using Magnetic Resonance Imaging (MRI) scans. By employing a Convolutional Neural Network (CNN) architecture optimized with Simulated Annealing (SA), the team achieved a validation accuracy of 98.

Artificial neural networks

Breakthrough in Brain Cancer Diagnosis: Ensemble-Based Convolutional Neural Networks

Researchers at the University of Sevilla have developed an innovative approach to classify brain tumors using ensemble-based deep learning models. This method has shown significant promise in achieving high accuracy while maintaining interpretability for clinical use. By combining the strengths of multiple Convolutional Neural Network (CNN) architectures and incorporating explainability

Artificial neural networks

Artificial Neural Networks Optimize Waste Heat Recovery in Energy Consumption and Greenhouse Gas Emissions

Investigations into waste heat recovery have reached a crucial milestone with the publication of a new report on artificial neural networks. The researchers from the Autonomous University of Morelos in Mexico, supported by the SecretariA De Ciencia Humanidades, TecnologiA E InnovacioN, have made significant strides in optimizing energy consumption and

Artificial neural networks

Content Adaptive Neural Network Based In-Loop Filter in VVC Using Dual Query Transformer

Researchers from Xidian University in Xi'an, People's Republic of China, have made a significant breakthrough in the field of neural networks for video compression. By proposing a novel content adaptive neural network-based in-loop filter (NNLF) using a dual query transformer (DQT), they have achieved impressive results

Artificial neural networks

Understanding Social Difficulties in Autism Spectrum Disorder

Researchers are working to better understand the neurobiological aspects of autism spectrum disorder (ASD), which remains a complex condition due to its heterogeneity and the limitations of traditional neuroimaging methods. A recent study proposed a novel approach using interpretable graph neural networks (GNNs) to analyze inter-brain synchrony (IBS) during dyadic

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