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Omni-ia game: prototipo local-first para la generación asistida de insumos de videojuegos mediante inteligencia artificial, con validación preliminar en Unity

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Precision-Driven Self-organization: From Brain Evolution to Engineering

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Perancangan Smart Decision NPC Game Turn-Based Menggunakan MLP Dengan MCTS Sebagai Generator Keputusan

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Assessing Uncertainty in Performance Modeling: Conformal Prediction vs. Bayesian Regression

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Deep Learning From a Control Vantage Point: Feedforward, Residual, Transformers, Large Language Models, And Selective State-Space Models

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Artificial Neural Network: Architecture, Learning Algorithms, Applications and Challenges

Bele, M. D. A. S. B., & Telkhade, A. V. T. I. P. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-38737

DE-optimized machine learning for compressive strength prediction of glass powder and fly ash geopolymers

Elyazidi, A., Berroug, F., Boukhattem, L., & Bouferra, R. (2026) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2026.e06617

Artificial Intelligence in Beer Brewing: A Comprehensive Review

Rewak, K., Zygadło, K., & Jackowski, M. (2026) · Beverages · doi:10.3390/beverages12100118

Applications of deep learning in fluorescence microscopic imaging

Xiong, Z., Wang, W., Xu, H., Yan, R., An, S., Wen, K., Gao, P., Zheng, J., Han, Y., & Gao, P. (2026) · PhotoniX Life · doi:10.3724/pxlife.2026-0004

A Fourier-graph neural operator for transient isothermal two-phase mold filling relevant to metal casting

Minete, E. M., Immertreu, M., Teichmann, F., & Müller, S. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.113310

Initialization and Optimization of Neural Networks with Ranking Layers

Kalogiannis, I. (2026) · DSpace - NTUA (National Technical University of Athens) · doi:10.26240/heal.ntua.33757

The Evolution of Deep Neural Architectures A Review in Machine LearningPreprint

Oberoi, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7576139

A Modular VHDL Framework for Deterministic FPGA Acceleration of MR Fingerprinting Reconstruction

Ricchi, M., Alfonsi, F., Barbieri, M., Retico, A., Gabrielli, A., Brizi, L., & Testa, C. M. (2026) · Applied Sciences · doi:10.3390/app16199935

Recent Advances in Generative Modeling

Selvi, S. T., & Visalakshi, P. (2026) · Wiley eBooks · doi:10.1002/9781394422395.ch1

Leveraging machine learning algorithms for designing analog and radio frequency integrated circuits

Khanam, R., Ojha, S., Afroj, M., & Singh, N. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41652-1.00002-4

Unraveling pollutant–catalyst–oxidant matching in persulfate-based advanced oxidation processes through interpretable deep learning

Du, S., Zhuang, W., Liu, S., Chen, X., Plakas, K. V., Ma, J., Wang, A., & Wang, W. (2026) · Water Research · doi:10.1016/j.watres.2026.127126

Machine learning-based prediction of hysteretic behavior for flexure-dominated FRP-confined circular RC columns

Hou, Y., Zhou, W., Wang, Z., & Zuo, Z. (2026) · Journal of Building Engineering · doi:10.1016/j.jobe.2026.117492

Operative process analysis in laparoscopic cholecystectomy for clinically informed computer vision

Tranter‐Entwistle, I. (2025) · Otago University Research Archive (University of Otago)

Machine learning models for complex trait prediction

Collienne, K. (2024) · Otago University Research Archive (University of Otago)

Comprehensive Analysis of Partition Methods and Neural Network Architectures for Prediction in Cryptocurrency Time Series

Beleña, L., & Hidalgo, P. (2026) · IEEE Access · doi:10.1109/access.2026.3742317

Dự báo rủi ro thanh khoản tại các ngân hàng thương mại: Tiếp cận học máy

Anh, T. N., & Minh, N. N. (2026) · Tạp chí Kinh tế và Phát triển · doi:10.33301/jed.vi.3118

SNP-based Modeling Reveal Breeding and Sowing Co-Adaption Potential in Stabilizing Wheat Phenology under Global Warming

Yan, W., Li, F., Hao, C., Li, Y., Zhou, M., Yang, X., Fang, Q., Lv, S., Xu, Z., Liu, L., et al. (2026) · Plant Phenomics · doi:10.1016/j.plaphe.2026.100293

Blast Performance Enhancement of RC Columns Using Machine Learning

Ibrahim, K., Elbelbisi, A. H., Salama, A., Eraky, A., & Emad, S. (2026) · Buildings · doi:10.3390/buildings16204045

Divine, Familial, and Human-Value Inspirations for Artificial Intelligence: A Conceptual Framework Linking Genetic Algorithms, Particle Swarm Optimisation, Artificial Neural Networks, and the Krishna and Rama ArchetypesPreprint

Gajawada, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7611385

Network of Quantum Neural Networks

Tokgoz, E., & Desiboyina, S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28454-9_30

A Two-Stage Neural Network Framework for Swimming Direction Control of an Underwater Walking Robot: Kinematic Validation via Simulation and In-Air Experiments

Choi, H., Kim, M., & Yoo, S.-Y. (2026) · Applied Sciences · doi:10.3390/app162010177

Neural Networks and Deep Learning

Samruddhi, P. P. A. W. L., & Bhadake, D. H. B. J. P. L. S. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-38859

Leveraging Automation and Machine Learning To Bridge the Sustainability Data Gap

Sletten, E. T., Fiedler, L., Dwivedi, P., Dorokhov, I., Seeberger, P. H., & Tyrikos‐Ergas, T. (2026) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.6c02432

Incorporating Visual Features for Image Retrieval via Scene Graphs

Georgoulopoulos, D. (2026) · DSpace - NTUA (National Technical University of Athens) · doi:10.26240/heal.ntua.33796

Chemometrics, machine learning and artificial intelligence in Laser-Induced Breakdown Spectroscopy: a critical review of the state of the art (2001–2026)

Duponchel, L. (2026) · Spectrochimica Acta Part B Atomic Spectroscopy · doi:10.1016/j.sab.2026.107661

Reasoning Models of Artificial Intelligence Think Like Humans DoPreprint

Xiang, J., Blakely, E. Q., & Schlinger, H. D. (2026) · Research Square · doi:10.21203/rs.3.rs-11132673/v1

Evaluation of tree based statistical learning supporting the bespoke manufacturing of low volume, super luxury cars

Pooley, J. (2026) · Open Access at Essex (University of Essex) · doi:10.5526/err-00043998

Machine-Learning-Assisted Tuning of Exchange–Correlation Functional Parameters for the eXact-Integral Simplified Tamm-Dancoff Approximation Method

Wang, S.-R., Xiao, D., Fang, Q., Liu, X., & Cui, G. (2026) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.6c04024

Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics

Basse, K., Szeitz, B., Suits, F., & Horvatovich, P. L. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5519-1_13

Análise tomográfica de redes neurais aplicada a problemas quânticos de muitos corpos

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Comparative Evaluation of CPA and Machine Learning-based power analysis attacks on AES, PRESENT, and ASCON

Javeed, K., Almheiri, M., Alnaqbi, D., & Alhammadi, F. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.111600

Constructing and Verifying a Generalized Model for PM10 Prediction Based on Atmospheric Pollutants and Meteorological Factors

Gu, J., Fu, D., Zhang, Z., Zhang, Y., Li, X., & Gao, Y. (2026) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi15100475

Machine Learning in Health Care

Schell, U., & Scherl, C. (2026) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003668404

MODE: Subtractive Causal Representation via Manifold Orthogonality on Graphs

Han, X., Zhou, M., & Wei, W. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729590

A hybrid data-centric machine learning for lost circulation prediction using imbalanced drilling and geological datasets

Al-Salih, O. G., Guangjian, D., & Al‐Mudhafar, W. J. (2026) · Scientific Reports · doi:10.1038/s41598-026-75043-9

3D inverse heat conduction analysis of a heterogeneous rock mass: application to Lascaux Cave

Lharti, H., Salmon, F., Sirieix, C., Riss, J., & Lacanette, D. (2026) · International Journal of Thermal Sciences · doi:10.1016/j.ijthermalsci.2026.111445

The Architecture of Artificial Intelligence Models: From Foundations to Advanced

Polat, Ö. (2026) · doi:10.59537/saupress.2583

Regimes of error and the epistemology of learning: From cybernetics to large language models

Zimmermann, O., & Tau, R. (2026) · Cognitive Systems Research · doi:10.1016/j.cogsys.2026.101527

Artificial Intelligence Tools in Qualitative Research II

Demirkol, M., Candan Çiftçi, F., Başaran, M., Kalender, B., Deniz, İ. D., Arıcı, M., Polat, Ö., Döş, B., Yokuş, E., Kalınkara, Y., et al. (2026) · doi:10.59537/saupress.2577

Tool life prediction integrating cutting power: a comparative analysis of machine learning models

de Souza, L. M. S., Cesario, M. D. C., SILVA, C. E. S. D., Gaudêncio, J. H. D., & Pereira, J. L. J. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-19385-8

Maschinelles Lernen

Borth, D., Hüllermaier, E., & Kauermann, G. (2026) · Springer eBooks · doi:10.1007/978-3-662-73474-2_4

Is There Meaning Without Anyone Meaning Anything? A Structuralist Proposal for Artificial Semantics

Cianciaruso, F. M. (2026) · Global Philosophy · doi:10.1007/s10516-026-09824-2

Probabilistic Unlearning-Based Defense for Robust and Efficient Edge Learning

Emami, A., Zhao, Q., Yang, J., Ponzina, F., Asgarinejad, F., Rosing, T., & Akşanlı, B. (2026) · Electronics · doi:10.3390/electronics15204879

Limitations of Computational Morality: Can We Morally Enhance Ourselves via AI?

Richter, F. (2026) · Science and Engineering Ethics · doi:10.1007/s11948-026-00634-8

Bias characteristics and estimation of the FORMOSAT-7/COSMIC-2 radio occultation bending angle in the lower troposphere with a machine learning algorithm

Pham, G. H., Yang, S.-C., Chen, S.-Y., Huang, C.-Y., & Wang, K.-N. (2026) · GPS Solutions · doi:10.1007/s10291-026-02160-3

Memristive Neural Networks-Assisted Training Algorithms: Current State and Future Challenges

Samardžić, N., Vranjković, V. S., Teodorovic, P., Maričić, J., Shchanikov, S., Gordleeva, S., Mikhaylov, A. N., Talanov, M. O., Kazantsev, V., STRUHARIK, R. J. R., et al. (2026) · Electronics · doi:10.3390/electronics15214892

A Data-Driven Approach to Geological Modeling Using Geophysical Proxies

Hernández, H., Alberdi, E., & Goti, A. (2026) · Mining Metallurgy & Exploration · doi:10.1007/s42461-026-01747-1

The Collaborative Nature of Intelligence

Crowley, J. L. (2026) · Springer eBooks · doi:10.1007/978-3-031-61050-9_32-1

Using artificial intelligence in psychology: features and role of chatbots in modern psychological support

Шевченко, О. Ю. (2026) · INSTITUTIONAL REPOSITORY OF ODESSA NATIONAL UNIVERSITY NAMED I.I. Mechnykova (Odesa I.I.Mechnikov National University)

Generative AI and tissue modeling for the analysis of bioimages

Román, J. Á. A. S. (2026) · Deposito de Investigacion Universidad de Sevilla (University of Seville)

Machine learning models for classifying optimal solutions in housing vulnerability situations

Maeso, J. A. A. (2026) · Deposito de Investigacion Universidad de Sevilla (University of Seville)

Predictive Scheduling of Incoming Goods Using Machine Learning and Deep Learning Methods

Rechenauer, J. S. M. (2026) · reposiTUm (TU Wien) · doi:10.34726/hss.2026.144705

Evaluation of Multi-class Intrusion Detection Models Using Machine Learning Techniques

Palma-Gonzalez, M. A., López-Vivanco, A. D. J., Gonzalez-Gutierrez, L. Y., Pérez-Fajardo, A. K., Ventura-Molina, E., & Aldape-Pérez, M. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-42785-4_22

Understanding computation through biological self-organization

Youk, H. (2026) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2026.100612

LLM Enables Coherent Fusion for High-Dynamic UAV Detection in Bistatic MIMO Radar

Wang, M., Rui, Y., Wang, Y., Chen, D., Li, X., Greco, M., & Gini, F. (2026) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion67272.2026.11726773

Exploring relationships of microplastic characteristics, soil properties and enzyme activities with greenhouse gas emissions using machine learning

Deng, A., Li, D., Cai, Y., Hu, X., Zhu, Z., Wang, Y., Chen, J., Hu, C., & Qiu, X. (2026) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2026.125671

A Baseline Machine Learning Benchmark for Permission-Based Android Ransomware Detection: Comparative Evaluation of Random Forest, XGBoost, Support Vector Machines, Neural Networks, Decision Trees, and Linear Regression

Khan, M. A., Nadeem, M., Soomro, M., Ayub, M., Khan, M. A., & Rani, M. (2026) · Kashmir Journal of Academic Research and Development · doi:10.71317/kjard.2.10.2026.919

Effect of molecular-filament orientation on conductance in tyrosine-based amino acid synaptic devices

Berco, D. (2026) · Journal of Applied Physics · doi:10.1063/5.0353574

On the VC Dimension of Autoregressive Linear Threshold FunctionsPreprint

Doron-Arad, I., Mehalel, I., & Mossel, E. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2610.36970

A novel integrated workflow for deep geothermal reservoir characterization using core data, optimized coupled petrophysical–seismic modeling, and machine learning: A case study from the SW Paris Basin, France

Pwavodi, J., Maurel, C., Bordenave, A., Stopin, A., Ortiz, A., Faure, A., Lasseur, É., Beccaletto, L., Briais, J., Issautier, B., et al. (2026) · Geothermics · doi:10.1016/j.geothermics.2026.103880

Deep Learning Ensemble Approach for Classification of Indigenous Pig Breeds in India

Devi, S. J. (2026) · The Indian Journal of Animal Sciences · doi:10.56093/ijans.v96i8.172968

Optical masonry waste analysis using machine learning

Walz, J., & Linß, E. (2026) · ce/papers · doi:10.1002/cepa.70971

AI integration for transforming care of patients with liver diseases

Rudra, O. S., Karthik, E. N., & Bhat, M. (2026) · Hepatology International · doi:10.1007/s12072-026-11156-7

Graph neural network enhancement via Kolmogorov–Arnold layers for reliable autism spectrum disorder classification

Madhav, A. G. K., Mishra, S., Satapathy, S. K., & Mohanty, S. N. (2026) · Journal of Neurorestoratology · doi:10.1016/j.jnrt.2026.100349

Dilution prediction in data-scarce underground mines using a robust statistical approach, population-based adaptive metaheuristics, and neural networks

Mariz, J. L. V., Ferraz, T. S. G., Marinésio Pinheiro de, L. I. M. A., & Silva, R. M. D. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45276-5.00012-7

Zastosowanie narzędzi AI w praktyce akademickiej

Popović, M., & Błaszczyk, M. (2026) · Wydawnictwo Uniwersytetu Łódzkiego eBooks · doi:10.18778/8445-081-9

Decomposition of Perceptual Scores in Multi-Component IQA: Pathways to Enhanced Model Interpretability and Efficiency

Трыкин, А. М., & Турлапов, В. (n.d.) · CyberLeninK (CyberLeninka)

Chemistry-transforming applications of artificial intelligence

Medvedev, M. G., Serov, N., Chaliy, V. A., Ilin, E. A., Prolomov, I. V., Eremeyeva, M. A., Schneider, M. Y., Mezentsev, I. A., Artamonov, D. V., Boiko, D. A., et al. (2026) · Chemical Society Reviews · doi:10.1039/d4cs01100g

Artificial Industrial Intelligence: Bioinspired Technology for Big Data Analysis and Comprehensive Assessment of its Effectiveness

Samigulina, G. A., Самигулина, З., & Bekeshev, D. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.10.316

Artificial neural networks

(2026) · De Gruyter eBooks · doi:10.1515/9783112189054-016

Metaphysical Assumptions as the Foundation for Artificial Intelligence

Alo, F. C. (2026) · Izu Journal of Critical Perspectives · doi:10.68251/izu.4.1.6

ANN validation and RSM optimization of Cattaneo-Christov heat flux in MHD stenotic artery trihybrid nanofluid flow

Kumari, S., & Bhatt, R. (2026) · Discover Nano · doi:10.1186/s11671-026-05085-5

Transient CFD Trained RNN for Digital Twin Application

Diederich, M., & Beni̇m, A. C. (2026) · Springer eBooks · doi:10.1007/978-981-95-5253-5_26-1

High Energy Additive Manufacturing of Advanced Materials

(2026) · Frontiers research topics · doi:10.3389/978-2-8325-8959-5

Geometric algebra neural networks for the simulation of the shallow water equations

Bonilla, J. P. P. (2026) · Repositorio Universidad Distrital

Advancement and trends in electrical machines using deep learning algorithms

Gupta, S., Das, R., Anusha, G., Kumar, M., Kuppusamy, Y., & Amruthalakshmi, M. R. (2026) · International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT) · doi:10.1109/ce2ct68422.2026.11756134

Data analysis approaches in untargeted metabolomics

Troisi, J., & Lombardi, M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-23677-8.00003-1

Reconstruction Method and Evolutionary Characteristics of Photolysis Rates in the Beijing-Tianjin-Hebei Region Based on a Deep Learning Algorithm

Zheng, W., Zhao, S., Xiujuan, Z., Zhao, X., Wang, F., Wang, L., Jingda, 刘. L., Liu, Y., Zhaohui, H., Liang, S., et al. (2026) · Atmospheric Pollution Research · doi:10.1016/j.apr.2026.103266

Modelling choices: The conflict between realism and prediction performance

Tsoleridis, P., Hancock, T. O., Hess, S., & Sfeir, G. (2026) · Journal of Choice Modelling · doi:10.1016/j.jocm.2026.100636

Evaluation of Machine Learning Models for TT-POCUS Based Prognostic Prediction of Did-Not-Finish and First-Treatment Failure Cattle Diagnosed with Bovine Respiratory Disease

Kunnary, H. E., White, B. J., Feitoza, L. F. B. B., Larson, R. L., Zimmermann, J. A. R., & Spore, T. J. (2026) · Veterinary Sciences · doi:10.3390/vetsci13111199

A Survey on Deep Learning Theory in a State Space Perspective

Zhang, T., Wen, H., Tan, K., Li, L., Zhang, L., Song, D., Li, L., Cui, Y., & Wang, Z. (2026) · IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO) · doi:10.1109/3m-nano69510.2026.11759782

AI-powered image analysis of fungal infections: A review of advances in research and diagnostics

Svensson, C., & Figge, M. T. (2026) · Therapeutic Advances in Infectious Disease · doi:10.1177/20499361261500532

Wastewater's total influent estimation and performance modeling

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Computers and the Human Brain

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Bayesian opponent modeling in adversarial game environments.

Baker, R. J. S. (2010) · OpenGrey (Institut de l'Information Scientifique et Technique)

Segmentation supervisée d'actions à partir de primitives haut niveau dans des flux vidéos

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Scalable Event-Driven Modelling Architectures for Neuromimetic Hardware

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Monte Carlo comparison of back-propagation, conjugate-gradient, and finite-difference training algorithms for multilayer perceptrons

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Représentation dynamique dans le cortex préfrontal : comparaison entre reservoir computing et neurophysiologie du primate

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Taxonomy of Feature Description Attributes

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Nonlinear dynamics of pattern recognition and optimization

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HUMAN INSPIRED PATTERN RECOGNITION VIA LOCAL INVARIANT FEATURES

Maestas, D. R. (2013) · UNM’s Digital Repository (University of New Mexico)

Projeto de controladores suplementares de amortecimento utilizando redes neurais artificiais

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THE PERCEPTRON: A PROBABILISTIC MODEL FOR INFORMATION STORAGE AND ORGANIZATION

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