The perceptron: A probabilistic model for information storage and organization in the brain.

A logical calculus of the ideas immanent in nervous activity

McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566

The Organization of Behavior

Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825

Representation of Events in Nerve Nets and Finite Automata

Kleene, S. (1951) · Princeton University Press eBooks · doi:10.1515/9781400882618-002 · Cited by 1,927

Probabilistic Logic and the Synthesis of Reliable Organisms from Unreliable Components

von Neumann, J. (1956) · Cited by 1,652

Untitled work

(1952) · Medical Entomology and Zoology · Cited by 1,436

The Sensory Order.

Kneale, M., & von Hayek, F. A. (1954) · The Philosophical Quarterly · doi:10.2307/2216396 · Cited by 494

Human Nature in the Light of Psychopathology

Goldstein, K. (1940) · Harvard University Press eBooks · doi:10.4159/harvard.9780674492103 · Cited by 286

The cell assembly: Mark II.

Milner, P. M. (1957) · Psychological Review · doi:10.1037/h0042287 · Cited by 235

Human Nature in the Light of Psychopathology.

L., H. A., & Goldstein, K. (1941) · The Journal of Philosophy · doi:10.2307/2017294 · Cited by 126

Why the Mind is in the Head?

McCulloch, W. S. (1950) · dialectica · doi:10.1111/j.1746-8361.1950.tb01019.x · Cited by 73

Anomaly detection for IIoT: analyzing Edge-IIoTset dataset with varied class distributions

Ferhi, W., Moussaoui, D., Hadjila, M., & Bouidaine, A. B. (2025) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2025-25-5-876-887 · Cited by 1

Artificial neural network and the prospect of AGI: an argument from architecture

Liu, C., & Ye, B. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00561-w · Cited by 1

A comparative analysis of regression algorithms and a real world application of multivariable energy signatures

Eiraudo, S., Schiera, D. S., Barbierato, L., Trifirò, A., Bottaccioli, L., & Lanzini, A. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100641 · Cited by 1

Designing a double deep reinforcement learning selection tool for resilient demand prediction

Benziane, B. A., Lardeux, B., Mcharek, A., & Jridi, M. (2025) · Journal of Industrial and Production Engineering · doi:10.1080/21681015.2025.2580997 · Cited by 1

Building causation links in stochastic nonlinear systems from data

Chibbaro, S., Furtlehner, C., Marchetta, T., Pantea, A.-T., & Rossetti, D. (2026) · Physical review. E · doi:10.1103/kdyd-tc6n · Cited by 1

Filozofska analiza mehanizmov kognitivnega modeliranja in umetne inteligence v izobraževanju

Aberšek, B., & Aberšek, M. K. (2025) · doi:10.18690/um.fnm.4.2025 · Cited by 1

From evidence to insight: designing Spain’s new digital framework for public policy evaluation

Martín, M. P. S., & de León, F. G. (2025) · Journal of Policy Evaluation · doi:10.4995/jpeval.2025.23954 · Cited by 1

Analysis of cumulative damage in water distribution system using machine learning based corrosion depth prediction models

Kwon, H. J. (2025) · Journal of The Korean Society of Water and Wastewater · doi:10.11001/jksww.2025.39.5.313 · Cited by 1

Unsupervised Operator Learning Approach for Dissipative Equations via Onsager Principle

Chang, Z., Wen, Z., & Zhao, X. (2026) · SIAM Journal on Scientific Computing · doi:10.1137/25m1786763 · Cited by 1

Multiplicative Stochastic Gradient Descent for fast and robust deep learning training

Kirtas, M., Passalis, N., & Tefas, A. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227594 · Cited by 1

The potential of machine learning in classifying latin American water isotope values and its implication for forensic provenancing

Delgado, T. A., Tillquist, R. C., & Bartelink, E. J. (2025) · Forensic Science International · doi:10.1016/j.forsciint.2025.112725 · Cited by 1

HeteroX: An interpretable Dual-tier Heterogeneous Graph for sustainable investment decisions

Taghikhah, F. R., Hosseinpour, S., & Gao, J. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111640 · Cited by 1

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis

Weiden, M., Kalloor, J., Kubiatowicz, J. D., & Iancu, C. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00062 · Cited by 1

SmartSafetyNet: A Hybrid Deep Learning Model for Predicting Traffic Accident Severity and Enhancing Road Safety Measures

Mustafa, G., Sartori, C., Hyder, A., Anwer, A., Adnan, M., Magsi, A., & Younis, M. (2025) · International Conference on Electrical, Communication, and Computer Engineering (ICECCE) · doi:10.1109/icecce67514.2025.11258030 · Cited by 1

Rolling bearing fault diagnosis based on dual attention mechanism

Wu, S., Chen, F., & Chen, Q. (2025) · International Conference on Machine Manufacturing and Intelligent Control (ICMMIC) · doi:10.1109/icmmic66805.2025.11264879 · Cited by 1

Enhancing PRRT Outcome Prediction in Neuroendocrine Tumors: Aggregated Multi-Lesion PET Radiomics Incorporating Inter-Tumor Heterogeneity

Sabouri, M., Hajianfar, G., Gharibi, O., Sardouei, A. R., Menda, Y., Dundar, A., Zamboni, C. G., Jain, S., Kruzer, M., Zaidi, H., et al. (2025) · Cancers · doi:10.3390/cancers17233887 · Cited by 1

A unified framework for lifted training and inversion approaches

Wang, X., Valavanis, A., Mahmood, A., Mang, A., Benning, M., & Repetti, A. (2026) · Handbook of numerical analysis · doi:10.1016/bs.hna.2026.03.001 · Cited by 1

Cluster-based adaptive sampling methodology for systems modeling

Ahmad, M., Yin, J., Moreno-Benito, M., Kumarasamy, S., Rao, H. N., Mustakis, J. G., & Karimi, I. A. (2025) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2025.109527 · Cited by 1

A systematic review and comprehensive evaluation of artificial intelligence approaches for prediction flood susceptibility

Farzad, R., Sharafati, A., & Kheyruri, Y. (2025) · Acta Geophysica · doi:10.1007/s11600-025-01769-1 · Cited by 1

Using Artificial Intelligence Systems in Modern Neurobehavioral Research

Apukhtin, K. V., Zolotova, A. E., Leunenko, A. O., Perfilova, V. N., & Kalueff, A. V. (2025) · Journal of Evolutionary Biochemistry and Physiology · doi:10.1134/s0022093025060080 · Cited by 1

Research on a lightweight intrusion detection model based on multimodal fusion

Li, Y., & Yao, J. (2025) · International Conference on Network Communication and Information Security (ICNCIS) · doi:10.1109/icncis67521.2025.11296132 · Cited by 1

Brain-inspired, interpretable, resonant recurrent neural networks

Kramer, M. (2026) · Physical Review Research · doi:10.1103/phlp-rhny · Cited by 1

Machine learning, memory and efficiency in cryptocurrency markets

Li, S., Yarovaya, L., & Mishra, T. (2025) · Journal of International Financial Markets Institutions and Money · doi:10.1016/j.intfin.2025.102210 · Cited by 1

State-of-Health Estimation for Lithium-Ion Batteries Using Lightweight Features from the Constant Current Charging Phase

Pietrocola, G., Porpora, F., Molinara, M., Gerevini, L., Vitelli, M., Marrocco, C., & Bria, A. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4963-4_26 · Cited by 1

Anforderungen an die KI-Kompetenz von Beschäftigten der öffentlichen Verwaltung im Kontext des EU AI Acts

Piesold, R.-R., & Krause, T. (2025) · Springer eBooks · doi:10.1007/978-3-658-48425-5_6 · Cited by 1

Optimization of multi-orifice synthetic jet configuration using flow and heat transfer characteristics coupled with machine learning

Kumar, R., Mirikar, D., Rehman, M. Z. U., Bhatnagar, A., Kumar, N., & Yadav, H. (2025) · International Communications in Heat and Mass Transfer · doi:10.1016/j.icheatmasstransfer.2025.110160 · Cited by 1

Preemptive deep tunnel operation using revised long short-term memory for improving resilience

Lee, E. H. (2025) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2025.107349 · Cited by 1

Prognostication of electrical properties of polyaniline composites using machine learning approaches

Bharadwaj, R. V., Vishnuvardhan, T. K., Deepak, A., Murugendrappa, M. V., & Sudhakar, A. (2025) · Materials Today Communications · doi:10.1016/j.mtcomm.2025.114511 · Cited by 1

Sensor-based room inhabitance monitoring using robust ML models compatible with large datasets / real-time datastreams

Pintea, A. (2025) · JUCS - Journal of Universal Computer Science · doi:10.3897/jucs.150393 · Cited by 1

Using Machine Learning in Forecasting and Data Analysis

Bіlichenko, O., & Radzevych, T. (2025) · Modern Economics · doi:10.31521/modecon.v50(2025)-01 · Cited by 1

Machine learning models for predicting isotropic surface roughness effects in squeeze-film dampers

Rice, T., Wagner, J., Lau, R., Barberan, C. J., Hicks, I. V., & Higgs, C. F. (2025) · Tribology International · doi:10.1016/j.triboint.2025.111622 · Cited by 1

Intelligent Educational Technologies for Personalization in STEM Education: A Bibliometric Analysis

Djambazova, E., Terzieva, V. T., & Paunova-Hubenova, E. (2025) · International Conference on Big Data, Knowledge and Control Systems Engineering (BdKCSE) · doi:10.1109/bdkcse67969.2025.11300532 · Cited by 1

Artificial Neural Networks

Ghosh, A. K. (2025) · Springer eBooks · doi:10.1007/978-981-96-8362-8_15 · Cited by 1

A Comparison Between Kolmogorov-Arnold and Multilayer Perceptron Networks for State of Health Estimation of Lithium-Ion Batteries

Bueno, R. I., Bianchi, R. A. C., & Giacomini, R. C. (2026) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ae335d · Cited by 1

Genetic risk predictions using deep learning models with summary data

Wang, A. Y., Xiao, E., Cheng, J., & Shen, X. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1657021 · Cited by 1

Adaptive Model Integration for Stock Price Forecasting With CNN‐ATTN‐BiLSTM

Xiao, Y., He, C., Yi, M., & Hu, Y. (2026) · Journal of Forecasting · doi:10.1002/for.70098 · Cited by 1

AI in Layman’s life

Bhargava, V., Kar, A., Gupta, S., Yadav, C., Singh, K., & Lata, P. (2026) · AI and Ethics · doi:10.1007/s43681-025-00946-8 · Cited by 1

La Inteligencia Artificial desde la perspectiva de los Estudios Sociales de la Tecnología

Copa, A. O. (2025) · Revista Punto Cero · doi:10.35319/puntocero.202551421 · Cited by 1

PsycHin: Finding the Trade-Off With Heterogeneous Information Network Toward Psychometric Preference Assessment

Wang, Q., Wen, X., Hou, J., Zhong, H., & Zhang, Y. (2026) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2026.3653641 · Cited by 1

Künstliche Intelligenz in der Risikostratifikation des akuten Koronarsyndroms

Büscher, A., Plagwitz, L., Blaschke, J., Heider, D., & Eckardt, L. (2026) · Herz · doi:10.1007/s00059-025-05360-8 · Cited by 1

Predicting Gene Functions Using Machine Learning Techniques

Dutta, S., Ghosh, S., Sarkar, A., & Sinha, D. (2026) · CABI biotechnology series. · doi:10.1079/9781800628649.0014 · Cited by 1

Dietary patterns and chronic prostatitis: a symptom severity prediction model based on nutritional clustering and machine learning

Wang, Z., Wu, W. D., Wen, B., Xu, Z., Xi, J., & Zhang, Y. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1660430 · Cited by 1

Integrating physics and machine learning for unified seismic forward modeling and reservoir property inversion

Zayier, Y., Yalikun, Y., Cheng, Y., & Yang, D. (2026) · Scientific Reports · doi:10.1038/s41598-026-36501-6 · Cited by 1

An Automated Data Efficient Morphometric Approach to Define Global Lentic and Lotic Inland Waters

Sharma, A., Narayanan, M., & Ilampooranan, I. (2026) · Water Resources Research · doi:10.1029/2025wr040137 · Cited by 1

A novel hybrid CNN framework integrating HOG and FP-growth for low-resolution image recognition

Tanan, N., & Al-halabi, M. F. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-025-05071-x · Cited by 1

Big Data Management in the Era of Artificial Intelligence

Abu‐Zidan, F. M., & Eid, H. (2026) · Hot topics in acute care surgery and trauma · doi:10.1007/978-3-032-09357-8_26 · Cited by 1

Cut-SOAP: A Machine Learning Descriptor for Rapid Screening of Molecular Adsorption Energetics

Calderan, F. V., Andriani, K. F., Felício-Sousa, P., Pinheiro, G. A., Silva, J. L. F. D., & Quiles, M. G. (2026) · ACS Omega · doi:10.1021/acsomega.5c10055 · Cited by 1

EEG-based cognitive state classification using a leakage-safe stacked ensemble learning approach

Islam, M. R., Hasan, R., Layek, M. A., & Rahaman, M. A. (2026) · Next research. · doi:10.1016/j.nexres.2026.101411 · Cited by 1

Quantum Neural Network Realization of XOR on a Desktop Quantum Computer

Tee Hui, T. E. O., Lin, Q., & Fu, Y. (2026) · Sensors · doi:10.3390/s26030854 · Cited by 1

Common, Uncommon Sense: Visiting the Human‐Versus‐AI Argument

Ahmed, D., Dechang, Goldstein, M. A., Stremekiene, D., & Bhutta, U. S. (2026) · Journal of Economic Surveys · doi:10.1111/joes.70066 · Cited by 1

Mining Chinese historical sources at scale: A machine learning approach to Qing state capacity

Keller, W., Shiue, C. H., & Yan, S. (2026) · Historical Methods A Journal of Quantitative and Interdisciplinary History · doi:10.1080/01615440.2025.2596395 · Cited by 1

Introduction to Object Detection and Localization

Elhanashi, A., & Saponara, S. (2026) · Springer eBooks · doi:10.1007/978-981-95-4846-0_1 · Cited by 1

Coupled Impacts of Road Construction and Vehicle Emissions on Spatiotemporal Dynamics of Roadside Particulate Matter Pollution

Liu, K., Xu, J., Zhang, F., Meng, Z., Hu, X., & Wang, Z. (2026) · Water Air & Soil Pollution · doi:10.1007/s11270-025-09068-7 · Cited by 1

Regime Change Hypothesis: Foundations for Decoupled Dynamics in Neural Network Training

Pérez-Corral, C., Fernández-Hernández, A., Mestre, J. I., Dolz, M. F., Duato, J. L., & Quintana-Ortí, E. S. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729598 · Cited by 1

ContinuousViT: A physics-aware spatially continuous vision transformer for high-fidelity flow and thermal field prediction

Ma, H., Wang, Z., Wang, X., Zhao, Y., Jia, Y., Chen, Z., & Zhang, W. (2026) · Aerospace Science and Technology · doi:10.1016/j.ast.2026.111916 · Cited by 1

Nanozymes: Promising frontiers in detection and nanoremediation of environmental pollutants

Mughe, H. C., Kola, H. A., Erşan, M. S., Darland, D. C., & Zhao, J. X. (2026) · Energy & Environmental Sustainability · doi:10.1016/j.eesus.2026.100070 · Cited by 1

Neural cryptography: Synergy and conflict between neural networks and cryptographic systems

Zhong, Y., Gu, J., & Yu, X. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133084 · Cited by 1

A semi-supervised performance prediction and resource adaptive allocation for cloud-native microservices

Cao, R., Hao, J., & Xia, H. (2026) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2026.104450 · Cited by 1

A measurement-based calibration approach for highly scalable timing and energy modeling of EdgeAI multi-core systems

Dariol, Q., Nours, S. L., Pillement, S., Stemmer, R., Helms, D., & Grüttner, K. (2026) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2026.103738 · Cited by 1

FRCL-SARNet : A lightweight SAR ship detection network based on feature refinement and dual cross level fusion

Wang, Y., Li, Z., Wang, T., Hu, Y., Bai, Z., Ding, G., & Geng, X. (2026) · Digital Signal Processing · doi:10.1016/j.dsp.2026.106005 · Cited by 1

Machine learning-supported prognosis of electron beam welding quality in ITER vacuum vessel manufacturing

de Zuniga, M. O., Fèbvre, M., Prinja, N. K., Camacho, A. M., & Rodríguez-Prieto, Á. (2026) · Nuclear Engineering and Technology · doi:10.1016/j.net.2026.104205 · Cited by 1

Machine-Learning-Driven Intelligent Memory System Design: From On-Chip Caches to Storage

Bera, R., Nadig, R., & Mutlu, O. (2026) · IEEE Micro · doi:10.1109/mm.2026.3667076 · Cited by 1

Hybrid Kolmogorov-Arnold and xLSTM network for enhanced RUL prediction of lithium batteries

Hu, Y., Ge, Q., Tian, Z., Zhang, Z., & Zhang, F. (2026) · Statistical Theory and Related Fields · doi:10.1080/24754269.2026.2635735 · Cited by 1

Advancing Sustainable and Equitable STEM Education: A GAN-CNN Integrated Model for Precise Learning Diagnosis and Individualized Instruction

Tsai, W.-L., Wu, L. Y., & Chen, K. (2026) · Sustainability · doi:10.3390/su18052481 · Cited by 1

Intelligent Computational Algorithms Based on Neural Networks: A Survey

Yang, M., Zhu, S., Zhang, H., & Wang, Y. (2026) · Computational Intelligence · doi:10.1111/coin.70193 · Cited by 1

Advances in aggregation methods for multi-model ensembles in climate science: A comprehensive review

Shiru, M. S., Song, Y.-H., Shahid, S., Sa’adi, Z., & Chung, ‪. (2026) · Physics and Chemistry of the Earth Parts A/B/C · doi:10.1016/j.pce.2026.104391 · Cited by 1

Development of Artificial Neural Network Model for Prediction of Interference Factor of Body-Wing Missile

Yoo, H., Jun, D., Nam, H., Lee, B., Kim, K., & Kim, H. (2026) · International Journal of Aeronautical and Space Sciences · doi:10.1007/s42405-026-01166-y · Cited by 1

Deep learning search for gravitational waves from compact binary coalescence

Mobilia, L., Canton, T. D., & Guidi, G. M. (2026) · Physical review. D/Physical review. D. · doi:10.1103/28ng-mj9x · Cited by 1

Comprehensive machine learning model comparison for Cherenkov and Scintillation light separation due to particle interactions

Tas, M., Tiras, E., Kizilkaya, D., Yagiz, M. A., & Kandemir, M. (2026) · Physica Scripta · doi:10.1088/1402-4896/ae5132 · Cited by 1

Knowledge-Embedded Hypergraph Neural Networks

Feng, Y., Zhang, Y., Du, S., Ying, S., Wu, Z., & Gao, Y. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3674800 · Cited by 1

Artificial Neural Network Model for Predicting Fatigue Endurance Limit of Hot Mix Asphalt Using Uniaxial Tension–Compression Tests

Acharjee, P. K., Isied, M. M., Souliman, M. I., Karki, S. J., Ahmed, T., & Saygili, G. (2026) · International Journal of Pavement Research and Technology · doi:10.1007/s42947-026-00742-5 · Cited by 1

Operational Solar Flare Ensemble Forecasting Model for Full-disk Active Regions

Li, M., Cui, Y., Luo, B., Wang, J., Liu, S., & Wang, X. (2026) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ae4741 · Cited by 1

Ordinal evolutionary artificial neural networks for predicting diabetic nephropathy progression

Gómez-Orellana, A. M., Bernardini, M., Ayllón-Gavilán, R., Vargas, V.-M., Gutiérrez, P. A., Hervás-Martínez, C., & Romeo, L. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115153 · Cited by 1

Deep learning-driven soil quality prediction: integrating feature selection for enhanced accuracy

Aydın, A., Pacci, S., & Dengız, O. (2026) · Advances in Space Research · doi:10.1016/j.asr.2026.03.094 · Cited by 1

Comparative Performance of LSTM, ANN, and GAM in Predicting Precipitation and Temperature Anomalies Under Accelerated Warming: Evidence from Thohoyandou, South Africa (1990–2025)

Mukhaninga, M., Sigauke, C., & Ravele, T. (2026) · Earth · doi:10.3390/earth7020057 · Cited by 1

KAN-AST: Kolmogorov-Arnold Network based Audio Spectrogram Transformer for Audio Classification

Dat, P. T., & Dat, T. H. (2025) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru65441.2025.11434708 · Cited by 1

Hybrid seismic automation: Integrating Artificial Intelligence and deterministic methods in velocity analysis

da Luz, M. A. L., & Vasconcelos, M. A. R. (2026) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2026.106259 · Cited by 1

SLMFL: Structural and semantic fusion for fault localization based on Small Language Models

Fan, X., Hu, J., Zheng, W., Liu, Y., & Gao, S. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108143 · Cited by 1

Role of Artificial Intelligence in Critical Mineral Landscape

Tewari, A., Dubey, R. S., & Bhargava, S. K. (2026) · Springer eBooks · doi:10.1007/978-981-95-4693-0_11 · Cited by 1

Enhancing event reconstruction for γ-ray particle detector arrays using transformers

Pirke, M., Son, Y., Glombitza, J., Schneider, M., Watson, I. J., & van Eldik, C. (2026) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2026/09/144 · Cited by 1

Polynomial Perceptrons for Compact, Robust, and Interpretable Machine Learning Models

Aldana-Bobadilla, E., Molina-Villegas, A., Cesar-Hernandez, J., & Garza-Fabre, M. (2026) · Entropy · doi:10.3390/e28040453 · Cited by 1

A Novel Machine Learning-Based Method for Predicting the Compressive Strength of Recycled Aggregate Concrete

Ajir, K., Toufigh, V., & Ghaemian, M. (2026) · Journal of Testing and Evaluation · doi:10.1520/jte20240256 · Cited by 1

Data-Driven Methods in Spectroscopy

Ness, M. (2026) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev-astro-052722-095654 · Cited by 1

Fluid thinking about collective intelligence

Werfel, J. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01211-1 · Cited by 1

A synthetic dataset is sufficient to measure the height of thermally fatigued surface-breaking cracks using convolutional neural networks

Gaffney, J., Beckingham, T., Colquitt, D. J., Christian, W. J. R., Daniels, W., Sarris, G., Madine, K. H., Song, Z., & Haslinger, S. G. (2026) · NDT & E International · doi:10.1016/j.ndteint.2026.103848 · Cited by 1

Current Applications of Artificial Intelligence and Machine Learning in Plant Functional Genomics

Singh, S. S. S., Mishra, S., Amaan, A. R. I. F., Garg, P., & Srivastava, P. (2026) · Wiley eBooks · doi:10.1002/9781394330485.ch6 · Cited by 1

Hyperdimensional Computing Regression: Foundations, Algorithms, and Comparative Review

Salahudeen, T., Elfadel, I. M., & Maalouf, M. (2026) · IEEE Access · doi:10.1109/access.2026.3690317 · Cited by 1

Cross-dimensional principal component analysis

Fan, N., Zhang, L. X., & Liu, R. (2026) · Digital Signal Processing · doi:10.1016/j.dsp.2026.106227 · Cited by 1

Deep Neural Cryptography

Gérault, D., Hambitzer, A., Ronen, E., & Adi Shamir, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-25333-0_18 · Cited by 1

Modulation strategies of nanozyme activity applied to food quality and safety analysis: From rational design to intelligent assistance

Li, H., Gao, M., Hu, X., Pan, M., & Wang, S. (2026) · Advanced Composites and Hybrid Materials · doi:10.1007/s42114-026-01806-7 · Cited by 1

Artificial Intelligence and Medicine-Ten Lessons Learned (and Forgotten): 1970-2026

Maojo, V., & Kulikowski, C. A. (2026) · ANALES RANM · doi:10.32440/ar.2026.143.01.rev05 · Cited by 1

State of the Art: Analysis of Deep Learning Techniques in Images Acquired in an Aquatic Environment

López-Vázquez, V., Satama-Bermeo, G., Raheem, H. I., & López-Guede, J. M. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8050131 · Cited by 1

Facial Expression Recognition in Anime and Manga Characters: A Comparative Study of Vision Transformers and Convolutional Neural Networks

Parrillo, M., Santoro, E., Laura, L., & Rughetti, V. (2026) · Information · doi:10.3390/info17050484 · Cited by 1

Ensemble Approach for Financial Time Series Modeling

Nannoolal, A., & Engelbrecht, A. P. (2026) · Algorithms · doi:10.3390/a19050404 · Cited by 1

The Evolution of Artificial Intelligence Paradigms – Implications for Performance, Scalability, and Responsible Deployment

Lima, A. (2026) · Premier Journal of Artificial Intelligence · doi:10.70389/pjai.100022 · Cited by 1

A biologically plausible recurrent spiking architecture for modeling nonlinear dynamic mechanical systems

Bhaskaran, V., Tandale, S. B., Polydoras, V., Korcsak-Gorzo, A., Morrison, A., Diesmann, M., & Stoffel, M. (2026) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2026.119081 · Cited by 1

Machine Behaviour Design and Management

Tan, H., Wu, Y., & Zhang, Y. (2026) · Springer eBooks · doi:10.1007/978-981-95-5585-7_20 · Cited by 1

Uncertainty Reduction in Streamflow Prediction Using Mixture Density Networks

Kumar, N., & Roy, T. (2026) · Journal of Hydrologic Engineering · doi:10.1061/jhyeff.heeng-6781 · Cited by 1

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