A neural network is a computational model inspired by the structure and function of the human brain. It consists of interconnected artificial neurons that process and transmit information, enabling machine learning tasks such as pattern recognition, classification, and regression by learning from labeled data.
Researchers unveil RetNet, a novel machine-learning framework utilizing voxelized potential energy surfaces processed through a 3D convolutional neural network (CNN) for superior gas adsorption predictions in metal-organic frameworks (MOFs). Demonstrating exceptional performance with minimal training data, RetNet's versatility extends beyond reticular chemistry, showcasing its potential impact on predicting properties in diverse materials.
Chinese researchers introduce a novel approach, inspired by random forest, for constructing deep neural networks using fragmented images and ensemble learning. Demonstrating enhanced accuracy and stability on image classification datasets, the method offers a practical and efficient solution, reducing technical complexity and hardware requirements in deep learning applications.
Researchers unveil EfficientBioAI, a user-friendly toolkit using advanced model compression techniques to enhance AI-based microscopy image analysis. Demonstrating significant gains in latency reduction, energy conservation, and adaptability across bioimaging tasks, it emerges as a pivotal 'plug-and-play' solution for the bioimaging AI community, promising a more efficient and accessible future.
Researchers from India, Australia, and Hungary introduce a robust model employing a cascade classifier and a vision transformer to detect potholes and traffic signs in challenging conditions on Indian roads. The algorithm, showcasing impressive accuracy and outperforming existing methods, holds promise for improving road safety, infrastructure maintenance, and integration with intelligent transport systems and autonomous vehicles
Researchers present ReAInet, a novel vision model aligning with human brain activity based on non-invasive EEG recordings. The model, derived from the CORnet-S architecture, demonstrates higher similarity to human brain representations, improving adversarial robustness and capturing individual variability, thereby paving the way for more brain-like artificial intelligence systems in computer vision.
Researchers unveil a paradigm-shifting development in artificial intelligence through memristor-based neural networks, showcasing exceptional energy efficiency and the ability to operate autonomously with energy harvesters. The resilient binarized neural network, optimized for extreme-edge applications and solar-powered adaptability, eliminates the need for calibration, promising groundbreaking advancements in self-powered AI for health, safety, and environment monitoring.
The MMSS_MKR framework revolutionizes music recommendation systems by integrating knowledge graphs and multi-task learning approaches. Offering robust solutions to data sparsity and cold start issues, this innovative model, combining prediction techniques and enhanced loss functions, outperforms existing methodologies. The study not only presents significant improvements in music recommendation accuracy but also outlines promising avenues for future exploration.
Researchers explored the integration of Deep Neural Operator Network (DeepONet) as a robust surrogate modeling method for digital twin (DT) technology in nuclear energy systems. DeepONet's unique architecture, trained with various operational conditions, showcased unparalleled accuracy and speed, positioning it as a promising algorithm for real-time predictions in complex particle transport problems.
This research explores the factors influencing the adoption of ChatGPT, a large language model, among Arabic-speaking university students. The study introduces the TAME-ChatGPT instrument, validating its effectiveness in assessing student attitudes, and identifies socio-demographic and cognitive factors that impact the integration of ChatGPT in higher education, emphasizing the need for tailored approaches and ethical considerations in its implementation.
Researchers present a novel myoelectric control (MEC) framework employing Bayesian optimization to enhance convolutional neural network (CNN)-based gesture recognition systems using surface electromyogram (sEMG) signals. The study demonstrates improved accuracy and generalization, crucial for advancing prosthetic devices and human-computer interfaces, and highlights the potential for broader applications in diverse sEMG signal types and neural network architectures.
Researchers from multiple countries introduced a groundbreaking method using machine learning (ML) models to predict the effluent soluble chemical oxygen demand (SCOD) in a two-stage anaerobic onsite sanitation system. Outperforming conventional models, the ML approach, led by the artificial neural network (ANN), not only enhances prediction accuracy but also offers simplicity, speed, and reliability in optimizing and controlling wastewater treatment processes, marking a significant leap in sustainable sanitation technology.
In a groundbreaking study, researchers revolutionized mine fire simulations by integrating neural networks with the Fire Dynamics Simulator (FDS) software. The hybrid approach provided rapid and accurate estimates of environmental parameters during mine fires, offering crucial insights for timely emergency decision-making in confined spaces.
Researchers from Iran and Turkey showcase the power of machine learning, employing artificial neural networks (ANN) and support vector regression (SVR) to analyze the optical properties of zinc titanate nanocomposite. The study compares these machine learning techniques with the conventional nonlinear regression method, revealing superior accuracy and efficiency in assessing spectroscopic ellipsometry data, offering insights into the nanocomposite's potential applications in diverse fields.
This article introduces LC-Net, a novel convolutional neural network (CNN) model designed for precise leaf counting in rosette plants, addressing challenges in plant phenotyping. Leveraging SegNet for superior leaf segmentation, LC-Net incorporates both original and segmented leaf images, showcasing robustness and outperforming existing models in accurate leaf counting, offering a promising advancement for agricultural research and high-throughput plant breeding efforts.
This study introduces an advanced AI-driven model for optimizing the conjunctive operation of groundwater and surface water resources. Focused on mitigating water scarcity challenges in semiarid regions like Iran, the hybrid simulation-optimization model, integrating symbiotic organism search and moth swarm algorithms with an artificial neural network (ANN) simulator, outperforms traditional methods, offering a powerful solution for sustainable water resource management in arid environments.
This research pioneers the use of acoustic emission and artificial neural networks (ANN) to detect partial discharge (PD) in ceramic insulators, crucial for electrical system reliability. With a focus on defects caused by environmental factors, the study achieved a 96.03% recognition rate using ANNs, further validated by support vector machine (SVM) and K-nearest neighbor (KNN) algorithms, showcasing a significant advancement in real-time monitoring for electrical power network safety.
Researchers unveil LGN, a groundbreaking graph neural network (GNN)-based fusion model, addressing the limitations of existing protein-ligand binding affinity prediction methods. The study demonstrates the model's superiority, emphasizing the importance of incorporating ligand information and evaluating stability and performance for advancing drug discovery in computational biology.
Scientists present a groundbreaking study published in Scientific Reports, introducing an intelligent transfer learning technique utilizing deep learning, particularly a convolutional neural network (CNN), to predict diseases in black pepper leaves. The research showcases the potential of advanced technologies in plant health monitoring, offering a comprehensive approach from dataset acquisition to the development of deep neural network models for early-stage leaf disease identification in agriculture.
Canadian researchers at Western University and the Vector Institute unveil a groundbreaking method employing deep neural networks to predict the memorability of face photographs. Outperforming previous models, this innovation demonstrates near-human consistency and versatility in handling different face shapes, with potential applications spanning social media, advertising, education, security, and entertainment.
Researchers unveil a groundbreaking approach to tackle escalating construction solid waste challenges through a machine vision (MV) algorithm. By automating the generation and annotation of synthetic datasets, the study significantly enhances efficiency and accuracy, demonstrating superior performance in construction waste sorting over manually labeled datasets, paving the way for sustainable urban waste management.
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