Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. …”
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Development Trend of Information Technology and AdvancedNuclear Power Generation by Tao ZHOU, Haolei ZHANG, Yao YAO, Chunmei LIU

“…Quantum technology can enhance the core fuel function. Artificial intelligence machine data capture and neural networks learn to process and apply information more precisely. …”
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Market Regime Identification and Variable Annuity Pricing: Analysis of COVID-19-Induced Regime Shifts in the Indian Stock Market by Mohammad Sarfraz, Guglielmo D’Amico, Dharmaraja Selvamuthu

“…Advanced methodologies, including regime-switching hidden Markov models, artificial neural networks, and Monte Carlo simulations, were applied to analyze pre- and post-COVID-19 market behavior. …”
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Intelligent Obstacle Avoidance Algorithm for Mobile Robots in Uncertain Environment by Liwei Guan, Yu Lu, Zhijie He, Xi Chen

“…In a nondeterministic environment, a mobile robot intelligent obstacle avoidance algorithm based on an improved fuzzy neural network with self-learning is firstly proposed. …”
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The underlying molecular mechanisms and biomarkers of Hip fracture combined with deep vein thrombosis based on self sequencing bioinformatics analysis by Guanghua Shi, Xiaocui Shi, Meng Zhang, Rui Cheng, Mengqing Hu, Yu Zhao, Shimei Li, Xiuxiu Li, Haiyun Ma, Pengcui Li

“…Feature genes were further refined by intersecting results from three machine learning algorithms and constructing an artificial neural network (ANN). Diagnostic performance was assessed using receiver operating characteristic (ROC) curves. …”
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Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study by Vladimir Koković, Kosta Pavlović, Andjela Mijanović, Slavko Kovačević, Ivan Mačužić, Vladimir Božović

“…We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. …”
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Leveraging dendritic complexity for neuromorphic computing by Suma G Cardwell, Mark Plagge, Luke Parker, Claire E Plunkett, David Munkvold, Paloma T Gonzalez-Bellido, Scott Koziol, Conrad James, Frances S Chance

“…Here, we present our work that aims to incorporate dendrites for ‘compute-on-wire’ in neuromorphic architectures to increase the computational complexity (e.g. number of programmable parameters, nonlinear dynamics) as well as computational efficiency (energy/compute) of artificial neural networks (ANNs). We do this by showcasing neuromorphic dendrite elements that can be leveraged for various applications. …”
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GPU Acceleration for FHEW/TFHE Bootstrapping by Yu Xiao, Feng-Hao Liu, Yu-Te Ku, Ming-Chien Ho, Chih-Fan Hsu, Ming-Ching Chang, Shih-Hao Hung, Wei-Chao Chen

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Hierarchical Information-Extreme Machine Learning of Hand Prosthesis Control System Based on Decursive Data Structure by Anatolii Dovbysh, Vladyslav Piatachenko, Mykyta Myronenko, Mykyta Suprunenko, Julius Simonovskiy

“…The method is based on adapting the input information description to maximize the probability of correct classification decisions, similar to artificial neural networks. However, unlike neural-like structures, the proposed method was developed within a functional approach to modeling cognitive processes of natural intelligence formation and decision-making. …”
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Ancillary Voltage Control Design for Adaptive Tracking Performance of Microgrid Coupled With Industrial Loads by Subrata K. Sarker, Shahriar Rahman Fahim, Niloy Sarker, Kazi Zakaria Tayef, Abu Bakar Siddique, Dristi Datta, M. A. Parvez Mahmud, Md. Fatin Ishraque, Sajal K. Das, Md Rabiul Islam Sarker, Sk. A. Shezan, Ziaur Rahman

“…Firstly, we design an intelligent adaptive control (IAC) framework made by merging with proportional-integral (PI) regulator and artificial neural network (ANN) to sustain the regulated common bus voltage over the mentioned changes. …”
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Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrix by Suprunenko M. K., Zborshchyk O. P., Sokolov O.

“…The idea of information-extreme machine learning of the control system for recognition of electromyographic biosignals, as in artificial neural networks, consists in adapting the input information description to the maximum total probability of making correct classification decisions. …”
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Ultra robust negative differential resistance memristor for hardware neuron circuit implementation by Yifei Pei, Biao Yang, Xumeng Zhang, Hui He, Yong Sun, Jianhui Zhao, Pei Chen, Zhanfeng Wang, Niefeng Sun, Shixiong Liang, Guodong Gu, Qi Liu, Shushen Li, Xiaobing Yan

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Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism by Ling Sun, Lingyan He, Hai-Hua Pan, Chang-Lin Zhai

“…Further analyses included artificial neural networks (ANN), enrichment analysis, immune infiltration, drug prediction, and molecular docking. …”
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Swiftly accessible retinomorphic hardware for in-sensor image preprocessing and recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic sensitization islan... by Kyungmoon Kwak, Kyungho Park, Jae Seong Han, Byung Ha Kang, Dong Hyun Choi, Kunho Moon, Seok Min Hong, Gwan In Kim, Ju Hyun Lee, Hyun Jae Kim

“…Here, we introduce a visible-light-driven neuromorphic vision system that integrates front-end retinomorphic photosensors with a back-end artificial neural network (ANN), employing a single neuro-inspired indium-gallium-zinc-oxide phototransistor (NIP) featuring an aluminum sensitization layer (ASL). …”
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A variable metric proximal stochastic gradient method: An application to classification problems by Pasquale Cascarano, Giorgia Franchini, Erich Kobler, Federica Porta, Andrea Sebastiani

“…Extensive numerical experiments verify that the suggested approach performs on par with state-of-the-art methods for training both statistical models for binary classification and artificial neural networks for multi-class image classification. …”
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Identifying and Diagnosing Lytic Cell Death Genes in Atherosclerosis Using Machine Learning and Bioinformatics by Zhang G, Ma R, Jin H, Zhang Q, Li W, Ding Y

“…Machine learning was used to screen characteristic LCDEGs, and an artificial neural network (ANN) model was developed. …”
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γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders by Zhong-Peng Dai, Qiang Wen, Ping Wu, Yan-Ni Zhang, Cai-Lian Fang, Meng-Yuan Dai, Hong-Liang Zhou, Huan Wang, Hao Tang, Si-Qi Zhang, Xiao-Kun Li, Jian-Song Ji, Liu-Xi Chu, Zhou-Guang Wang

“…By targeting γ oscillatory patterns and restoring healthy cross-frequency coupling, interventions may alleviate cognitive and behavioral symptoms linked to disrupted communication. This review examines clinical applications of γ neuromodulations, including enhancing cognitive function through 40 Hz multisensory stimulation in Alzheimer’s disease, improving motor function in Parkinson’s disease, controlling seizures in epilepsy, and modulating emotional dysfunctions in depression. …”
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Deconvolving X-Ray Galaxy Cluster Spectra Using a Recurrent Inference Machine by Carter Lee Rhea, Julie Hlavacek-Larrondo, Alexandre Adam, Ralph Kraft, Ákos Bogdán, Laurence Perreault-Levasseur, Marine Prunier

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Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction by Irfan Pathan, Arif Raza, Adarsh Sahu, Mohit Joshi, Yamini Sahu, Yash Patil, Mohammad Adnan Raza, Ajazuddin

“…Core AI algorithms support vector machines, random forests, graph neural networks, and transformers are examined for their applications in molecular representation, virtual screening, and ADMET property prediction. …”
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Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction by Chenxi Wang, Weiwei Zhang, Ming Ni, Qiong Wang, Chang Liu, Linbin Dai, Mengguo Zhang, Yong Shen, Feng Gao

“…We designed a multi-modal deep-learning framework that employs 3D convolutional neural networks to analyze MRI and additional neural networks to evaluate demographic data. …”
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