Machine learning
Researchers at the University of Tabriz in Iran have developed a machine learning model using multilayer neural networks and decision trees to predict precipitation in the country's regions. The model, which was tested on a dataset of 24-hour cumulative precipitation data, showed a high level of accuracy in
Artificial intelligence
Researchers have made a significant leap in the field of neuromorphic computing by introducing a hybrid photonic-electronic reservoir computing (RC) system that overcomes the challenges of traditional software-based neural networks. This innovative approach, developed by a team from AMO GmbH, combines passive photonic reservoirs with electronic readout layers, resulting in
Machine learning
Accurate duration estimation is crucial for construction projects to maintain efficiency and quality control during early phases. However, traditional methodologies suffer due to limited data available in initial project planning stages. Researchers at the University of Jordan have proposed an innovative approach using artificial neural networks (ANNs) to address these
Artificial intelligence
In a recent study, researchers from the Department of Management Sciences at Kuei-Hu Chang, Roc Mil Acad, Kaohsiung 830, Taiwan, have developed a novel decision-making system that leverages group experience to improve multi-criteria decision-making (MCDM) problems. The system, based on a feedforward neural network, effectively captures group experience through a
Artificial intelligence
Researchers at China Agricultural University have proposed a cognitive modeling framework (CMF) that integrates cognitive science and artificial intelligence, achieving state-of-the-art performance in computer vision tasks. This framework, which combines functional abstraction, operator structuring, and program agent, addresses the divide between cognitive science and artificial intelligence. The CMF, along with
Machine learning
Despite the growing adoption of green buildings, robust and data-driven approaches for assessing and predicting project risks remain limited. Research from the East China University of Science and Technology proposed an innovative hybrid framework combining the fuzzy analytic hierarchy process (FAHP), multilayer perceptron neural networks (MLPNNs), and particle swarm optimization
Artificial intelligence
Researchers at Ho Chi Minh City University of Education have conducted a comparative analysis of human and neural network translations of A.S. Pushkin's poem "I Loved You" into Vietnamese. The study reveals the limitations of neural networks in capturing the aesthetic elements of the original
Artificial intelligence
A groundbreaking study from the University of Miami Miller School of Medicine, in collaboration with Siemens Medical Solutions, has developed a deep-learning algorithm that can automatically identify lung segments from CT scans with near-radiologist-level accuracy. The research, published in the Journal of Applied Clinical Medical Physics, uses a multi-stage, 3D
Machine learning
Researchers at Koc University have made significant strides in machine learning by designing a photonic neural network that leverages spatiotemporal chaos to enhance data classification accuracy. This innovative approach has shown promising results in multiple domains, including biomedical imaging, fashion, and satellite geospatial analysis. The study's findings underscore
Machine learning
A new study has introduced a novel machine learning-based strategy for generating supercapacitor reference current to optimize energy distribution in Battery Electric Vehicles (BEV) and Hybrid Battery Electric Vehicles (HBEV). Researchers from the School of Electrical Engineering have developed a Long Short-Term Memory (LSTM) neural network trained using real-world drive
Machine learning
Modern network infrastructures have significantly improved global connectivity but have also escalated network security challenges as sophisticated cyberattacks increasingly target vital systems. Intrusion Detection Systems (IDSs) play a crucial role in identifying and mitigating these threats. Recent advances in machine-learning-based IDSs have shown promise in detecting evolving attack patterns. Researchers
Machine learning
Researchers at Fudan University in Shanghai, China have proposed a machine learning-enhanced model-based optical proximity correction (MBOPC) framework that employs a convolutional neural network (CNN) to predict mask edge imaging thresholds. This innovation addresses accuracy bottlenecks in traditional MBOPC techniques due to physical modeling errors. The CNN-based variable threshold strategy
