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

Neural network solution to the cogeneration system by using coal

Özel, Y., Güney, İ., & Arca, E. (2008) · Dspace Repository (Marmara Üniversitesi) · Cited by 11

Graphics Processing Unit Computation of Neural Networks

Davis, C. E. (2005) · Cited by 11

Novel time series analysis approach for prediction of dialysis in critically ill patients using echo-state networks

Verplancke, T., Looy, S. V., Steurbaut, K., Benoît, D., Turck, F. D., Moor, G. D., & Decruyenaere, J. M. A. (2009) · Critical Care · doi:10.1186/cc7420 · Cited by 11

Silicon retina: image compression by associative neural network based on code and graph theories

Tanaka, M. (2002) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.1990.112037 · Cited by 11

Application of Neural Networks to a Predictive Extended Corresponding States Model for Pure Halocarbon Thermodynamics

Scalabrin, G., Piazza, L., & Cristofoli, G. (2002) · International Journal of Thermophysics · doi:10.1023/a:1013992608159 · Cited by 11

Development of Neural Units with Higher-Order Synaptic Operations and their Applications to Logic Circuits and Control Problems

Redlapalli, S. (2004) · University Library - University of Saskatchewan (University of Saskatchewan) · Cited by 11

Statistical Query Algorithms for Stochastic Convex Optimization

Feldman, V., Guzmán, C., & Vempala, S. (2015) · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · doi:10.1137/1.9781611974782.82 · Cited by 11

SOLVING SHORTEST PATH PROBLEM USING GRAVITATIONAL SEARCH ALGORITHM AND NEURAL NETWORKS

Sinaie, S. (2010) · Cited by 11

Hand Gesture Recognition for Sign Language Transcription

Vazquez, I. (2017) · doi:10.18122/b2b136 · Cited by 11

Believing the Bot - Model Risk in the Era of Deep Learning

Richman, R., von Rummell, N., & Wuthrich, M. V. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3444833 · Cited by 10

Wide Neural Networks with Bottlenecks are Deep Gaussian ProcessesPreprint

Agrawal, D., Papamarkou, T., & Hinkle, J. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.00921 · Cited by 11

Cryptanalysis of Round-Reduced SIMON32 Based on Deep Learning.Preprint

Hou, Z., Ren, J., & Chen, S. (2021) · IACR Cryptology ePrint Archive · Cited by 11

Spike-inspired Rank Coding for Fast and Accurate Recurrent Neural NetworksPreprint

Jeffares, A., Guo, Q., Stenetorp, P., & Moraitis, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02865 · Cited by 11

Backpropagation

Munro, P. (2010) · Springer eBooks · doi:10.1007/978-0-387-30164-8_51 · Cited by 11

Artificial Neural Network and Deep Learning: Fundamentals and TheoryPreprint

Hammad, M. (2024) · arXiv (Cornell University) · doi:10.13140/rg.2.2.18262.66889 · Cited by 11

Data fusion for NDE signal characterization

Lim, J. (2001) · doi:10.31274/rtd-180813-9931 · Cited by 11

Deep multi-modal fusion network with gated unit for breast cancer survival prediction

Yuan, H., & Xu, H. (2024) · Computer Methods in Biomechanics and Biomedical Engineering · doi:10.1080/10255842.2023.2211188 · Cited by 10

Granular neural networks with a reference frame

Chen, Y., Zhang, X., Zhuang, Y., Yao, B., & Lin, B. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110147 · Cited by 10

Can docking scoring functions guarantee success in virtual screening?

Velásquez-López, Y., Tejera, E., & Pérez‐Castillo, Y. (2022) · Annual reports in medicinal chemistry · doi:10.1016/bs.armc.2022.08.008 · Cited by 10

Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices

Shchanikov, S., Bordanov, I., Kucherik, A. O., Gryaznov, E. G., & Mikhaylov, A. N. (2023) · Applied Sciences · doi:10.3390/app132413309 · Cited by 10

Enhancing Monthly Streamflow Prediction Using Meteorological Factors and Machine Learning Models in the Upper Colorado River Basin

Thota, S., Nassar, A., Boubrahimi, S. F., Hamdi, S. M., & Hosseinzadeh, P. (2024) · Hydrology · doi:10.3390/hydrology11050066 · Cited by 10

Double-quantitative multi-granularity kernel fuzzy rough sets model and its application in rheumatoid arthritis risk assessment

Dai, X., Sun, B., Bai, J., Ye, J., & Chu, X. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02144-0 · Cited by 10

Real Time Bimodal Emotion Recognition using Hybridized Deep Learning Techniques

Bhat, A. A., Kavitha, S., Satapathy, S. M., & Kavipriya, J. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.168 · Cited by 10

Comparative analysis of RMSE and MAP metrics for evaluating CNN and LSTM models

Kaur, G., & Saini, S. (2024) · AIP conference proceedings · doi:10.1063/5.0221565 · Cited by 10

Engineering Sequestration-Based Biomolecular Classifiers with Shared Resources

Moghimianavval, H., Gispert, I., Restrepo-Castillo, S., Corning, O. B. W. H., Liu, A. P., & Samaniego, C. C. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00270 · Cited by 10

Forecasting ETF Performance: A Comparative Study of Deep Learning Models and the Fama-French Three-Factor Model

Shih, K., Wang, Y., Kao, I.-C., & Lai, F.-M. (2024) · Mathematics · doi:10.3390/math12193158 · Cited by 10

Novel approach to solving Schwarzschild black hole perturbation equations via physics informed neural networks

Patel, N., Aykutalp, A., & Laguna, P. (2024) · General Relativity and Gravitation · doi:10.1007/s10714-024-03322-9 · Cited by 10

Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency

Śliwko, L. (2024) · IEEE Access · doi:10.1109/access.2024.3520422 · Cited by 10

Dmixnet: a dendritic multi-layered perceptron architecture for image recognition

Xu, W., Song, Y., Gupta, S., Jia, D., Tang, J., Lei, Z., & Gao, S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11123-y · Cited by 10

Physics-informed neural networks for a highly nonlinear dynamic system

Bărbulescu, R., Ciuprina, G., Duca, A., Machado, P., & Silveira, L. M. (2025) · Journal of Mathematics in Industry · doi:10.1186/s13362-025-00172-1 · Cited by 10

A comprehensive review on computer vision analysis of aerial data

Tetarwal, V., Kaur, M., & Kumar, S. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111206 · Cited by 10

Advancements and future directions of artificial intelligence in tumor imaging: A comprehensive review of techniques and applications

Xue, J., Yu, J., Mao, Q., & Gu, X. (2025) · EngMedicine · doi:10.1016/j.engmed.2025.100086 · Cited by 10

Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope?

Baily, M. N., Byrne, D. M., Kane, A. T., & Soto, P. E. (2025) · Finance and Economics Discussion Series · doi:10.17016/feds.2025.053 · Cited by 11

Deep learning, transformers and graph neural networks: a linear algebra perspective

Baggag, A., & Saad, Y. (2025) · Numerical Algorithms · doi:10.1007/s11075-025-02218-2 · Cited by 10

Navigating the knowledge problem in the era of open AI and polycentric governance

Makridis, C. A., & Lazanski, D. (2025) · The Review of Austrian Economics · doi:10.1007/s11138-025-00708-z · Cited by 10

A review of the applications and challenges of applying partial least squares (PLS) to exposomics research

Galindo‐Prieto, B., & Mudway, I. S. (2025) · Analytica Chimica Acta · doi:10.1016/j.aca.2025.344826 · Cited by 10

A blending approach to transfinite interpolation on compact disks using neural network operators

Bhat, A. A., Khan, A., Iliyas, M., Khan, K., & Mursaleen, M. (2025) · Iranian Journal of Science · doi:10.1007/s40995-025-01905-y · Cited by 10

Borsuk-Ulam and Replicable Learning of Large-Margin Halfspaces

Blondal, A., Hatami, H., Hatami, P., Lalov, C., & Tretiak, S. (2026) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3798129.3800771 · Cited by 10

MetaboLM: a metabolomic language model for multi-disease early prediction and risk stratification

Qiu, S., Guo, J., Zhang, Z., Liang, H., You, H., Hu, Y., Liu, G., & Wang, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-66163-3 · Cited by 10

DeepForestVision : Automated wildlife identification for camera traps of African tropical forests

Magaldi, H., Cornette, R., Tibesigwa, J. J., Katumba, R., Rugonge, H., Amarasekaran, B., Anderson, N., Cappelle, N., Cardoso, A. W., Cornélis, D., et al. (2025) · Ecological Solutions and Evidence · doi:10.1002/2688-8319.70167 · Cited by 10

Survey of Quantization-Aware Training (QAT) Applications in Deep Learning Quantization

Zhang, J. (2025) · International Symposium on Artificial Intelligence and Computational Social Sciences (AICSS) · doi:10.1145/3776759.3776826 · Cited by 10

Artificial intelligence-driven food safety and quality control

Zhou, X., Li, Z., Huang, X., Hu, X., Li, Z., Shi, J., & Zou, X. (2026) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-026-04088-1 · Cited by 10

PMv Neuronal Firing May Be Driven by a Movement Command Trajectory within Multidimensional Gaussian Fields

Agarwal, R., Thakor, N. V., Sarma, S. V., & Massaquoi, S. G. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2643-14.2015 · Cited by 10

Preliminary results for neuroevolutionary optimization phase order generation for static compilation

Sher, G. I., Martin, K., & Dechev, D. (2014) · doi:10.1145/2568326.2568328 · Cited by 10

Latent Structured Perceptrons for Large-Scale Learning with Hidden Information

Sun, X., Matsuzaki, T., & Li, W. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.129 · Cited by 10

Tighter Perceptron with improved dual use of cached data for model representation and validation

Wang, Z., & Vučetić, S. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178978 · Cited by 10

Global Artificial Bee Colony Algorithm for Boolean Function Classification

Shah, H., Ghazali, R., & Nawi, N. M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36546-1_2 · Cited by 10

Selection Stability as a Means of Biomarker Discovery in Classification

Schirra, L.-R., Lausser, L. M., & Kestler, H. A. (2016) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-319-25226-1_7 · Cited by 10

Artificial Intelligence for Modeling and Control of Nonlinear Phenomena in Internal Combustion Engines

Scafati, F. T., Lavorgna, M., Mancaruso, E., & Vaglieco, B. M. (2017) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-319-67140-6_1 · Cited by 10

Digitale Transformation in der Urologie – Chance, Risiko oder Notwendigkeit?

Loch, T., Witzsch, U. H. F., & Reis, G. (2021) · Der Urologe · doi:10.1007/s00120-021-01610-9 · Cited by 10

Effective Kernelized Online Learning in Language Processing Tasks

Filice, S., Castellucci, G., Croce, D., & Basili, R. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06028-6_29 · Cited by 10

Sparse CapsNet with explicit regularizer

Shi, R., Niu, L., & Zhou, R. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108486 · Cited by 10

A DFN-based method for fast prediction of ships' added resistance in heading waves

Duan, W., Ke, Y., Huang, L., Yu, J., & Ma, S. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2021.110484 · Cited by 10

Intelligent Systems for Sustainable Person-Centered Healthcare

Kriksciuniene, D., & Sakalauskas, V. (2022) · Intelligent systems reference library · doi:10.1007/978-3-030-79353-1 · Cited by 10

Biomarker identification by reversing the learning mechanism of an autoencoder and recursive feature elimination

Abir, F. A., Shovan, S. M., Hasan, M. A. M., Sayeed, A., & Shin, J. (2022) · Molecular Omics · doi:10.1039/d1mo00467k · Cited by 10

Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements

Khalil, A., Handawi, K. B. A., Chamseddine, I., Mohsen, Z., Nour, A. A., Feghali, R., & Kokkolaras, M. (2022) · Viruses · doi:10.3390/v14071414 · Cited by 10

Yakubovich's method of recursive objective inequalities in machine learning

Lipkovich, M. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.07.301 · Cited by 10

ulisse: A tool for one-shot sky exploration and its application for detection of active galactic nuclei

Doorenbos, L., Torbaniuk, O., Cavuoti, S., Paolillo, M., Longo, G., Brescia, M., Sznitman, R., & Márquez-Neila, P. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202243900 · Cited by 10

Extending the Universal Approximation Theorem for a Broad Class of Hypercomplex-Valued Neural Networks

Vital, W. L., Vieira, G. H. A., & Valle, M. E. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21689-3_45 · Cited by 10

ML Approach to Improve the Costs and Reliability of a Wireless Sensor Network

Ayanoglu, M., & Uysal, I. (2023) · Sensors · doi:10.3390/s23094303 · Cited by 10

Predictions for Bending Strain at the Tower Bottom of Offshore Wind Turbine Based on the LSTM Model

Lee, S., Kang, S.-J., & Lee, G.-S. (2023) · Energies · doi:10.3390/en16134922 · Cited by 10

Machine Learning Approach for Frozen Tuna Freshness Inspection Using Low-Frequency A-Mode Ultrasound

Sakai, A., Yagi, M., Yasutomi, S., Mizuno, K., Suzuki, K., & Goto, K. (2023) · IEEE Access · doi:10.1109/access.2023.3319400 · Cited by 10

Optimizing Hyperspectral Imaging Classification Performance with CNN and Batch Normalization

Zhang, G., & Abdulla, W. H. (2023) · Applied Spectroscopy Practica · doi:10.1177/27551857231204622 · Cited by 10

Towards Improved Heliosphere Sky Map Estimation with Theseus

Osthus, D., Weaver, B., Beesley, L. J., Moran, K. R., Stricklin, M. A., Zirnstein, E. J., Janzen, P. H., & Reisenfeld, D. B. (2023) · Technometrics · doi:10.1080/00401706.2023.2271017 · Cited by 10

Tutorial: Deep learning prediction of thermophysical properties for liquid multicomponent alloys

Xiao, R., Liu, K., Ruan, Y., Hu, L., & Wei, B. (2023) · Journal of Applied Physics · doi:10.1063/5.0173250 · Cited by 10

Computationally Aware Surrogate Models for the Hydrodynamic Response Characterization of Floating Spar-Type Offshore Wind Turbine

Ilardi, D., Kalikatzarakis, M., Oneto, L., Collu, M., & Coraddu, A. (2023) · IEEE Access · doi:10.1109/access.2023.3343874 · Cited by 10

Subspace Learning Machine (Slm): Methodology and Performance Evaluation

Fu, H., Yang, Y., Mishra, V. K., & Kuo, C. ‐. J. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104058 · Cited by 10

The use of artificial neural networks and decision trees: Implications for health-care research

Smith, S., & McConnell, S. (2024) · Open Computer Science · doi:10.1515/comp-2022-0279 · Cited by 10

Artificial Neural Network Learning, Attention, and Memory

Manca, V. (2024) · Information · doi:10.3390/info15070387 · Cited by 10

Artificial Intelligence in Healthcare With an Emphasis on Public Health

Mudey, A. B., Dhonde, A. S., & Chandrachood, M. (2024) · Cureus · doi:10.7759/cureus.67503 · Cited by 10

PathMLP: Smooth path towards high-order homophily

Zhou, J., Xie, C., Gong, S., Qian, J., Yu, S., Xuan, Q., & Yang, X. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106650 · Cited by 10

Comprehensive drought risk assessment and mapping in Taiwan: An ANP-ANN ensemble approach

Liou, Y., Vo, T.-H., Tran, D.-P., & Bui, H.-A. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.175835 · Cited by 10

Ionic surfactants critical micelle concentration prediction in water/organic solvent mixtures by artificial neural network

Soria-López, A., García-Martí, M., Barreiro, E., & Mejuto, J. C. (2024) · Tenside Surfactants Detergents · doi:10.1515/tsd-2024-2623 · Cited by 10

Large Language Models for Link Stealing Attacks Against Graph Neural Networks

Guan, F., Zhu, T., Sun, H., Zhou, W., & Yu, P. S. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3489427 · Cited by 10

A spiking photonic neural network of 40 000 neurons, trained with latency and rank-order coding for leveraging sparsity

Talukder, R., Skalli, A., Porté, X., Thorpe, S. J., & Brunner, D. (2025) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/addee7 · Cited by 10

Shear Wave Velocity Prediction with Hyperparameter Optimization

Bekdaş, G., Aydın, Y., Işıkdağ, Ü., Ni̇gdeli, S. M., Hajebi, D., Kim, T.-H., & Geem, Z. W. (2025) · Information · doi:10.3390/info16010060 · Cited by 10

Enhancing Short-Term Load Forecasting Through K-Shape Clustering and Deep Learning Integration

Zhang, W., Cheng, M., Xiang, Q., & Li, Q. (2025) · IEEE Access · doi:10.1109/access.2025.3541574 · Cited by 10

Swamped with Too Many Articles? GraphRAG Makes Getting Started Easy

Ngangmeni, J., & Rawat, D. B. (2025) · AI · doi:10.3390/ai6030047 · Cited by 10

Overcoming classic challenges for artificial neural networks by providing incentives and practice

Irie, K., & Lake, B. M. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01121-8 · Cited by 10

Fuzzy Neural Networks Applied to Fault Diagnosis

Calado, J. M. F., & da Costa, J. S. (2006) · Springer eBooks · doi:10.1007/978-1-84628-631-5_10 · Cited by 10

An Improved Gbest Guided Artificial Bee Colony (IGGABC) Algorithm for Classification and Prediction Tasks

Shah, H., Herawan, T., Ghazali, R., Naseem, R., Aziz, M. A., & Abawajy, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12637-1_70 · Cited by 10

An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain

Mencía, E. L., & Fürnkranz, J. (2007) · LWA · Cited by 10

A multi objective approach to evolving artificial neural networks for coronary heart disease classification

Shenfield, A., & Rostami, S. (2015) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2015.7300294 · Cited by 10

Joint layer based deep learning framework for bilingual machine transliteration

Sanjanaashree, P., & Kumar, M. A. (2014) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2014.6968553 · Cited by 10

Artificial neural network based prediction model of daily global solar radiation in Morocco

Mejdoul, R., Taqi, M., & Belouaggadia, N. (2013) · Journal of Renewable and Sustainable Energy · doi:10.1063/1.4854817 · Cited by 10

Neural network meta‐model of fibrous materials based on discrete‐event simulation. Part 2: neural network meta‐model

Cherkassky, A. (2010) · Journal of the Textile Institute · doi:10.1080/00405000.2010.488079 · Cited by 10

Liquid computing on and off the edge of chaos with a striatal microcircuit

Toledo-Suárez, C. D., Duarte, R., & Morrison, A. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00130 · Cited by 10

Data Analytics for Pervasive Health

Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2015) · Cited by 10

Data Mining for Bioinformatics: Design with Oversampling and Performance Evaluation

Tsai, M.-F., & Yu, S.-S. (2015) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-015-0094-8 · Cited by 10

Prediction of Refractive Index of Polymers Using Artificial Neural Networks

Astray, G., Cid, A., Moldes, O., Ferreiro-Lage, J. A., Gálvez, J. F., & Mejuto, J. C. (2011) · Journal of Chemical & Engineering Data · doi:10.1021/je200071p · Cited by 10

Performing Diagnostics & Prognostics On Simulated Engine Failures Using Neural Networks

Macmann, O. B., Seitz, T. M., Behbahani, A., & Cohen, K. D. O. (2016) · AIAA/SAE/ASEE Joint Propulsion Conference (JPC) · doi:10.2514/6.2016-4807 · Cited by 10

Smart-MLlib: A High-Performance Machine-Learning Library

Siegal, D., Guo, J., & Agrawal, G. (2016) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster.2016.49 · Cited by 10

Efficient determination of the number of weak learners in AdaBoost

Htike, K. K. (2016) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2016.1266038 · Cited by 10

Feasibility of a screening tool for obesity diagnosis in Mexican children from a vulnerable community of Me'Phaa ethnicity in the State of Guerrero, Mexico

Rios-Julian, N., Alarcón-Paredes, A., Alonso-Silverio, G. A., Hernández-Rosales, D. E., & Guzmán‐Guzmán, I. P. (2017) · Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE) · doi:10.1109/gmepe-pahce.2017.7972105 · Cited by 10

Review and possible development direction of the methods for modeling of soil pollutants spatial distribution

Tarasov, D. A., Medvedev, A., Sergeev, A., & Buevich, A. (2017) · AIP conference proceedings · doi:10.1063/1.4992211 · Cited by 10

Traditional machine learning for limited angle tomography

Huang, Y., Lu, Y., Taubmann, O., Lauritsch, G., & Maier, A. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1851-2 · Cited by 10

Use of a Deep Belief Network for Small High-Level Abstraction Data Sets Using Artificial Intelligence with Rule Extraction

Hayashi, Y. (2018) · Neural Computation · doi:10.1162/neco_a_01139 · Cited by 10

A lossless online Bayesian classifier

Nguyen, T. T. T., Nguyen, T. T., Sharma, R., & Liew, A. W. (2019) · Information Sciences · doi:10.1016/j.ins.2019.03.031 · Cited by 10

Machine learning for flux regression in discrete fracture networks

Berrone, S., Santa, F. D., Pieraccini, S., & Vaccarino, F. (2021) · GEM - International Journal on Geomathematics · doi:10.1007/s13137-021-00176-0 · Cited by 10

Function and Pattern Extrapolation with Product-Unit Networks

Dellen, B. K., Jaekel, U., & Wolnitza, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22741-8_13 · Cited by 10

THE PERCEPTRON: A PROBABILISTIC MODEL FOR INFORMATION STORAGE AND ORGANIZATION

Brain, I. T., & Rosenblatt, F. F. (2013)

The perception: a probabilistic model for information storage and organization in the brain

Rosenblatt, F. F. (1988) · The MIT Press eBooks · Cited by 78

(1958) F. Rosenblatt, "The perceptron: a probabilistic model for information storage and organization in the brain," Psychological Review 65: 386-408

Rosenblatt, F. F. (1988) · The MIT Press eBooks · doi:10.7551/mitpress/4943.003.0010 · Cited by 13

Perception, memory and providing information

Upton, D. (2013) · doi:10.4324/9781315846972-11

Brain mechanisms of perception and memory from neuron to behavior

Ono, T., & Toyama, I. S. O. B. M. O. P. A. M. F. N. T. B. 1. (1993)