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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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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Opportunities and Challenges of Brain-on-a-Chip Interfaces by Wenwei Shao, Weiwei Meng, Jiachen Zuo, Xiaohong Li, Dong Ming

“…Biological neural networks inherently grant BoCI systems neuro-inspired computational properties—such as ultralow energy consumption and dynamic plasticity—that surpass those of conventional artificial intelligence. …”
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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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A compositional model for effort‐aware Just‐In‐Time defect prediction on android apps by Kunsong Zhao, Zhou Xu, Meng Yan, Lei Xue, Wei Li, Gemma Catolino

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Multimodal Data Fusion Framework for Early Prediction of Autism Spectrum Disorder by Mohemmed Sha, Hussein Al-Dossary, Mohamudha Parveen Rahamathulla

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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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General Cultural Competence of a Future Teacher in a Changing World by O. R. Neradovskaya, V. A. Starodubtsev

“…The paper analyzes the content оf the concept of “general cultural competence of a future teacher” and its dialectical development in a changing world under the influence of various factors: the new technological order; the complexity and diversity of various social environments; the risks of environmental, energy and epidemic crises; the emergence of artificial intelligence and learning neural networks; and the large consequences of small (at first glance) events in public life. …”
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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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Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images by Fadwa Alrowais, Mohammed Alqahtani, Jahangir Khan, Achraf Ben Miled, Da’ad Albalawneh, Abdulwhab Alkharashi, Samah Al Zanin, Radwa Marzouk

“…Moreover, the hybrid of convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) model is employed for the brain stroke classification process. …”
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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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Model of differential diagnosis of nervous system lesions in young children by T. S. Krivonogova, O. M. Gerget, V. A. Zhelev, E. V. Golikova, E. V. Mikhalev, E. V. Loshkova, A. L. Solnyshko

“…To create a mathematical model for differential diagnosis of hypoxic-ischemic and traumatic lesions of the nervous system, 12 indicators of neurosonography and 4 indicators of peripheral capillary blood lymphocyte morphology (area and perimeter of the nucleus, area and perimeter of the cell) were taken. A self-learning artificial neural network was used in the construction of the diagnostic model, the operation of which was reproduced using the created software application. …”
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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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