stGRL: spatial domain identification, denoising, and imputation algorithm for spatial transcriptome data based on multi-task graph contrastive representation learning by Xin Lu, Murong Zhou, Bo Gao, Fang Wang, Shuilin Jin, Qiaoming Liu, Guohua Wang

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Structure-Activity Relationship for Fe(III)-Salen-Like Complexes as Potent Anticancer Agents by Zahra Ghanbari, Mohammad R. Housaindokht, Mohammad Izadyar, Mohammad R. Bozorgmehr, Hossein Eshtiagh-Hosseini, Ahmad R. Bahrami, Maryam M. Matin, Maliheh Javan Khoshkholgh

“…The study of structure and activity relationship was performed with multiple linear regression (MLR) and artificial neural network (ANN). In nonlinear method, the adaptive neuro-fuzzy inference system (ANFIS) was applied in order to choose the most effective descriptors. …”
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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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Gravitational-wave Parameter Estimation in Non-Gaussian Noise Using Score-based Likelihood Characterization by Ronan Legin, Maximiliano Isi, Kaze W. K. Wong, Yashar Hezaveh, Laurence Perreault-Levasseur

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Non-Destructive Detection of Fillet Fish Quality Using MQ135 Gas Sensor and Neutrosophic Logic-Enhanced System by M. Y. Shams, M. R. Darwesh, Roheet Bhatnagar, N. S. A. Al-Sattary, A. A. Salama, M. S. Ghoname

“…Using the reference neural network algorithm based on chemical and physical compounds, regression coefficients (R values) achieved were 0.99, 0.98, and 0.97, respectively. …”
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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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Using artificial intelligence techniques and econometrics model for crypto-price prediction by Abhidha Verma, Jeewesh Jha

“…To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. …”
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From intelligence to autopoiesis: rethinking artificial intelligence through systems theory by Benedikt Zönnchen, Mariya Dzhimova, Gudrun Socher

“…Building on Luhmann's system theory, it is argued that classical Turing machines are not sense-making systems, as they lack both self-reference in the sense of re-entry and the ability to make contingent selections from possibilities. In contrast, artificial neural networks (ANNs) exhibit a novel, loosely coupled interaction with social systems, as they can extract patterns from societal communication. …”
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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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Deep Reinforcement Learning-Based Multi-Agent System with Advanced Actor–Critic Framework for Complex Environment by Zihao Cui, Kailian Deng, Hongtao Zhang, Zhongyi Zha, Sayed Jobaer

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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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Simulation of Quantity and Quality of Saq Aquifer Using Artificial Intelligence and Hydraulic Models by Abdul Razzaq Ghumman, Ghufran Ahmed Pasha, Md. Shafiquzzaman, Afaq Ahmad, Afzal Ahmed, Riaz Akhtar Khan, Rashid Farooq

“…Groundwater modelling with respect to its quantity and quality has been performed in this paper using Artificial Neural Networks (ANNs), Adaptive Neurofuzzy Inference System (ANFIS), and hydraulic model MODFLOW. …”
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Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis by Serena Dattola, Augusto Ielo, Giuseppe Varone, Giuseppe Varone, Alberto Cacciola, Angelo Quartarone, Lilla Bonanno

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Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications by Dasheng Wu, Na Liu, Rui Ma, Peilong Wu

“…AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. …”
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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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3D in vitro modeling of neural microenvironment through a multi-scaffold assembly approach by Cecilia Traldi, Vanessa Chiappini, Silvia Chasseur, Federica Aiello, Marina Boido, Chiara Tonda-Turo

“…The inclusion of such scaffold in the 3D bioprinted system effectively steers neural cell organization in a 3D setting, guiding neural cell elongation in a preferred direction and promoting the establishment of a functional neural network. …”
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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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Human and artificial visual systems share a computational principle for transforming binocular disparity into depth representation by Bayu Gautama Wundari, Ichiro Fujita, Hiroshi Ban

“…A deep neural network (DNN) trained for stereo vision undergoes a similar transformation across its layers, progressing through distinct phases that exploit dissimilar features to achieve coherent depth. …”
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Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food by Sreevidya R. C., Jalaja G, Sajitha N, D. Lakshmi Padmaja, S. Nagaprasad, Kumud Pant, Yekula Prasanna Kumar

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Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence by Kazi Ahnaf Alavee, Mehedi Hasan, Abu Hasnayen Zillanee, Moin Mostakim, Jia Uddin, Eduardo Silva Alvarado, Isabel de la Torre Diez, Imran Ashraf, Md Abdus Samad

“…Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. …”
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