Artificial intelligence Defect Detection Robustness inReal-time Non-Destructive Testing of Metal Surfaces by Chaoyu Dong, Jovian Sanjaya Putra, Andrew A. Malcolm

“…Through machine learning methods and deep neural networks, it is possible for AI systems to learn from diverse datasets and accurately identify defects across various applications. …”
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Seismic Vibration Control and Multi-Objective Optimization of Transmission Tower with Tuned Mass Damper Under Near-Fault Pulse-like Ground Motions by Ying Lin, Tao Liu

“…Based on the obtained analysis results, artificial neural network (ANN) is trained to predict the vibration reduction ratios of peak responses and the corresponding vibration reduction cost. …”
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Artificial intelligence approach to magnetohydrodynamic flow of non-Newtonian fluids over a wedge: Thermophoresis and Brownian motion effects by Talal Taha, Sohaib Abdal, Liaqat Ali, Rana Muhammad Zulqarnain, Se-Jin Yook

“…Further optimization and simplification of the analysis are achieved by using an artificial neural networking base Levenberg Marquardt algorithm (ANN-LMA) model. …”
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Tree networks of real-world data: Analysis of efficiency and spatiotemporal scales by Davide Cipollini, Lambert Schomaker

“…Hierarchical tree structures are common in many real-world systems, from tree roots and branches to neuronal dendrites and biologically inspired artificial neural networks, as well as in technological networks for organizing and searching complex datasets of high-dimensional patterns. …”
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A facile photonics reconfigurable memristor with dynamically allocated neurons and synapses functions by Zhenyu Zhou, Lulu Wang, Gongjie Liu, Yuchen Li, Zhiyuan Guan, Zixuan Zhang, Pengfei Li, Yifei Pei, Jianhui Zhao, Jiameng Sun, Yahong Wang, Yiduo Shao, Xiaobing Yan

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Crack-ConvT Net: A Convolutional Transformer Network for Crack Segmentation in Underwater Dams by Pengfei Shi, Hongzhu Chen, Zaiming Geng, Xinnan Fan, Yuanxue Xin

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Reconfigurable neuromorphic functions in antiferroelectric transistors through coupled polarization switching and charge trapping dynamics by Jing Gao, Yu-Chieh Chien, Jiali Huo, Lingqi Li, Haofei Zheng, Heng Xiang, Kah-Wee Ang

“…Additionally, we further demonstrate synaptic and neuronal functions for implementing unsupervised learning rules and spiking behavior in spiking neural networks. …”
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Expanding structural insights into DNA packaging apparatus and endolysin LysSA05 function of Epsilon15 bacteriophage by Muhammad Saleem Iqbal Khan, Ju Wu, Shenlin Ji, Demeng Tan, Bingrui Sui, Shanshan Peng, Jinbiao Zhan, Jiajun Yin

“…Concurrently, we characterized LysSA05, a dual-acting endolysin harboring a glycoside hydrolase 19 (GH19) catalytic domain accommodating peptidoglycan (PG) residues N-acetylmuramic acid (NAM) and N-acetylglucosamine (NAG) through structural docking, indicating plausible binding interactions that promote hydrolysis support vector machine (SVM), random forest (RF), discriminant analysis (DA), artificial neural network (ANN) and physicochemical scanning identified an amphipathic helix (residues 59-112) with predicted antimicrobial peptide (AMP)-like properties. …”
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Optimizing Graphene Oxide Content in Cellulose Matrices: A Comprehensive Review on Enhancing the Structural and Functional Performance of Composites by Ghazaleh Ramezani, Ion Stiharu, Theo G. M. van de Ven, Vahe Nerguizian

“…Various optimization techniques, including response surface methodology, particle swarm optimization, and artificial neural networks, have been employed to identify optimal graphene concentrations and processing conditions. …”
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AI-Driven Integration of Deep Learning With Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction by Kecheng Huang, Chujun Wu, Rongpeng Pi, Jieyu Fang

“…AI frameworks, particularly convolutional neural networks and hybrid models like TD-CNNLSTM-LungNet, demonstrate exceptional performance in detecting pulmonary inflammation and stratifying hypoxemia risk, achieving up to 96.57% accuracy in pneumonia subtype differentiation and an area under the curve of 0.96 for postoperative hypoxemia prediction. …”
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Deep learning algorithm on H&E whole slide images to characterize TP53 alterations frequency and spatial distribution in breast cancer by Chiara Frascarelli, Konstantinos Venetis, Antonio Marra, Eltjona Mane, Mariia Ivanova, Giulia Cursano, Francesca Maria Porta, Alberto Concardi, Arnaud Gerard Michel Ceol, Annarosa Farina, Carmen Criscitiello, Giuseppe Curigliano, Elena Guerini-Rocco, Nicola Fusco

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Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry by Valeria Di Stefano, Martina D’Angelo, Francesco Monaco, Annarita Vignapiano, Vassilis Martiadis, Eugenia Barone, Michele Fornaro, Luca Steardo, Marco Solmi, Mirko Manchia, Luca Steardo

“…This manuscript explores how the integration of these technologies has unveiled key insights into schizophrenia’s structural and functional neural anomalies. fMRI research highlights disruptions in crucial brain regions like the prefrontal cortex and hippocampus, alongside impaired connectivity within networks such as the default mode network (DMN). …”
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Classifying kidney disease using a dense layers deep learning model by Amal Al-Rasheed, Sheikh Muhammad Saqib, Muhammad Zubair Asghar, Tehseen Mazhar, Asim Seedahmed Ali Osman, Mohammad Shahid, Muhammad Iqbal, Muhammad Amir Khan

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Ultrasensitive bionic photonic-electronic skin with wide red-shift mechanochromic response by Tianqi Wang, Lei Chen, Haogeng Liu, Haoran Zhu, Zijie Zeng, Yixin Lu, Ping Zhang, Yaofei Chen, Yue Huang, Gui-Shi Liu, Yunhan Luo, Zhe Chen

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Intelligent Dispatch Decision-Making for UHVDC Blocking Fault Based on Deep Learning by Xiaonan YANG, Bo SUN, Yansheng LANG

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Sunflower-based butterfly optimization algorithm with enhanced RNN for the harmonics elimination in multilevel inverter by V. Mohan, G. Krithiga, M. Thamil Alagan, V. Sathya

“…Enhanced Recurrent Neural Network (ERNN) shows a kind of recurrent neural network in which the hidden neurons are tweaked by SF-BOA with the goal of minimizing THD. …”
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Optimizing Renewable Energy Systems Placement Through Advanced Deep Learning and Evolutionary Algorithms by Konstantinos Stergiou, Theodoros Karakasidis

“…This study introduces GREENIA, a novel artificial intelligence (AI)-powered framework for optimizing RES placement that holistically integrates machine learning (gated recurrent unit neural networks with swish activation functions and attention layers), evolutionary optimization algorithms (Jaya), and Shapley additive explanations (SHAPs). …”
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Machine learning for active sites prediction of quinoline derivatives by Jie Sun, Zi-Hao Li, Yi-Fei Yang, Shu-Yu Zhang

“…In this study, a generalizable approach to predict site selectivity is accomplished by using artificial neural network (ANN), which is suitable for the site prediction of derivatives of quinoline. …”
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A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

“…To evaluate the proposed method, eight DL models — Feedforward Neural Network, Recurrent Neural Network, Deep Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU) and Bidirectional GRU were trained on selected features using different FS methods, as well as complete dataset. …”
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Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting by Mohini Yadav, Majid Jamil, Mohammad Rizwan, Richa Kapoor

“…RBFNNs and CNNs are trained in two phases using the functional link artificial neural network (FLANN) optimization method with a deep learning structure. …”
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