Application of artificial intelligence for commodity identification for customs purposes by Galina Yu. Fedotova, Anna Yu. Komelova

“…As a model, the neural network of artificial intelligence developed by the World Customs Organization is used. …”
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Neural correlates of human fear conditioning and sources of variability in 2199 individuals by Joaquim Radua, Hannah S. Savage, Enric Vilajosana, Alec Jamieson, Birgit Abler, Fredrik Åhs, Tom Beckers, Narcís Cardoner, Josh M. Cisler, Juliana B. Diniz, Dominik R. Bach, Sigrid Elsenbruch, Steven G. Greening, Daphne J. Holt, Antonia N. Kaczkurkin, Andreas Keil, Merel Kindt, Kathrin Koch, Kevin S. LaBar, Charlene L. Lam, Christine L. Larson, Tina B. Lonsdorf, Christian J. Merz, Katie A. McLaughlin, Yuval Neria, Daniel S. Pine, Carien M. van Reekum, Alexander J. Shackman, Carles Soriano-Mas, Victor I. Spoormaker, Daniel M. Stout, Benjamin Straube, Thomas Straube, Lauri Tuominen, Renée M. Visser, Laura Ahumada, Volker Arolt, Marcelo C. Batistuzzo, Paulo R. Bazán, Emma E. Biggs, Marta Cano, Pamela Chavarría-Elizondo, Samuel E. Cooper, Udo Dannlowski, Víctor de la Peña-Arteaga, Stephanie N. DeCross, Katharina Domschke, Mana R. Ehlers, John L. Graner, Alfons O. Hamm, Martin J. Herrmann, Ashley A. Huggins, Adriane Icenhour, Asier Juaneda-Seguí, Markus Junghoefer, Tilo Kircher, Katja Koelkebeck, Manuel Kuhn, Franziska Labrenz, Shmuel M. Lissek, Martin Lotze, Ulrike Lueken, Jürgen Margraf, Ignacio Martínez-Zalacaín, Robert Moeck, Jayne Morriss, María Ortuño, Andre Pittig, Daniel Porta-Casteras, Jan Richter, Isabelle C. Ridderbusch, Winfried Rief, Kati Roesmann, Jörgen Rosén, Alena N. Rußmann, Rachel Sjouwerman, Jennifer Spohrs, Andreas Ströhle, Benjamin Suarez-Jimenez, Martin Ulrich, Hans-Ulrich Wittchen, Xi Zhu, Lea Waller, Henrik Walter, Paul M. Thompson, Janna Marie Bas-Hoogendam, Nynke A. Groenewold, Dan J. Stein, Nic J. Van der Wee, Joseph E. Dunsmoor, Andre F. Marquand, Ben J. Harrison, Miquel A. Fullana

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Evaluation of Data Mining and Artificial Intelligence Methods to Predict Daily Precipitation by Yaseen Ahmed Hamaamin

“…In this study, data mining techniques were applied to metrological data to predict daily precipitation using Multilinear regression (MLR) along with two artificial intelligence (AI) techniques, specifically Artificial Neural Networks (ANN) and Neuro-Fuzzy Inference System (ANFIS). …”
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THE PROBLEM OF ENERGY EFFICIENCY OF SYSTEMS BASED ON ARTIFICIAL INTELLIGENCE OF THE INTERNET OF THINGS (AIOT) by С. В. Войтко

“…It was determined that integrating artificial intelligence functionality into the IoT infrastructure provides the opportunity to store big data for processing using artificial intelligence elements capable of conducting predictive analysis to make informed management decisions. …”
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Length Estimation of Pneumatic Artificial Muscle with Optical Fiber Sensor Using Machine Learning by Yilei Ni, Shuichi Wakimoto, Weihang Tian, Yuichiro Toda, Takefumi Kanda, Daisuke Yamaguchi

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Estimation of Positive‐, Negative‐, and Zero‐Sequence Current and Voltage Phasors of UIPC VSCs for Short‐Circuit Faults in Transmission Lines by Babak Bahadori, Ali Nahavandi, Mahyar Abasi

“…This research presents an estimation scheme utilizing artificial neural networks to determine the magnitude and phase angle of voltage and current in voltage source converters of the UIPC during short‐circuit faults in transmission lines. …”
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High-fidelity EEG feature-engineered taxonomy for bruxism and PLMS prognostication through pioneering and avant-garde ML frameworks by Shivam Tiwari, Deepak Arora, Barkha Bhardwaj

“…Using K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) with Radial Basis Function (RBF), Bruxism categorization was accomplished. …”
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Criminal Law Challenged by Crossing into Virtual Reality by Mahdi Karimi

“…It should be noted that* the current criminal policy approach requires re-evaluation and reassessment in line with the current advancement of neural technologies and artificial intelligence to effectively monitor and address the challenges and consequences arising from the development of these technologies. …”
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Advancements in Cerebellar Modeling and Its Practical Applications: A Comprehensive Review by Shaojia Huang, Tao Xu, Jiaqing Chen, Jiajia Huang, Zhikun Wang, Ya Ke, Wing Ho Yung

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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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Starch–Glycerol-Based Hydrogel Memristors for Bio-Inspired Auditory Neuron Applications by Jiachu Xie, Yuehang Ju, Zhenwei Zhang, Dianzhong Wen, Lu Wang

“…Against this backdrop, memristor devices, capable of mimicking the computational functions of brain neural networks, have emerged as key components in neuromorphic systems. …”
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Mathematical modelling and optimization of cutting conditions in turning operation on MDN 350 steel with carbide inserts by Syed Adil, A. Krishnaiah, D. Srinivas Rao

“…The machining performance indicators of the first set are optimized using graphical method of contour plots. Artificial neural networks technique, which is well known for its versatility to model linear as well as non-linear data, is used to express the surface roughness as a function of tool geometrical variables. …”
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A Fog Computing-Based Cost-Effective Smart Health Monitoring Device for Infectious Disease Applications by Saranya Govindakumar, Vijayalakshmi Sankaran, Paramasivam Alagumariappan, Bhaskar Kosuru Bojji Raju, Daniel Ford

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GAN-enhanced deep learning for improved Alzheimer's disease classification and longitudinal brain change analysis by Purushottam Pandey, Surbhi Bhatia Khan, Surbhi Bhatia Khan, Surbhi Bhatia Khan, Jyoti Pruthi, Eid Albalawi, Ali Algarni, Ahlam Almusharraf

“…Alzheimer's disease (AD) is commonly defined by a progressive decline in cognitive functions and memory. Early detection is crucial to mitigate the devastating impacts of AD, which can significantly impair a person's quality of life. …”
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ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact by Xi Liu, Hui Hwang Goh, Haonan Xie, Tingting He, Weng Kean Yew, Dongdong Zhang, Wei Dai, Tonni Agustiono Kurniawan

“…Experimental findings indicate that the suggested model achieves 99.94% accuracy on pristine data and 98.21% accuracy on noisy data, markedly surpassing established techniques such as artificial neural networks (ANN), ResNet, random forests (RF), multi-scale SE-ResNet, and other ResNet-based approaches. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…For each rock class, we applied regression techniques—such as random forest (RF), k-nearest neighbors (k-NN), extreme gradient boosting (XGB), and artificial neural networks (ANN)—to estimate the logarithm of capillary pressure. …”
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Enhancing Postural Monitoring in Wheelchair Users Through Context Classification by Nerea Perez, Aitziber Mancisidor, Itziar Cabanes, Patrick Vermander

“…These models have been developed using machine learning techniques, such as K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). …”
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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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Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFIS by Paresh Kulkarni, Satish Chinchanikar

“…This study develops artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models to predict cutting force, surface roughness, and tool life during Inconel 718 turning with a hybrid nanofluid under minimum quantity lubrication. …”
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Performance Investigation of Coated Carbide Tools in Milling Procedures by Paschalis Charalampous

“…Based on this dataset, artificial intelligence (AI) models, including artificial neural network (ANN), k-nearest neighbors (KNN), and support vector regression (SVR), were developed in order to predict the tool’s life as a function of the milling conditions. …”
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