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

Interdisciplinary Consensus RE.GA.IN.: What's New? Part 2: Rare Forms of Gastritis, Gastritis and Gastric Microbiota, Epidemiology of Gastritis and Associated Pretumor and Tumor Changes

Bordin, D. S., Игоревич, М. С., Livzan, M. A., Gaus, O. V., & Maev, I. V. (2024) · Effective Pharmacotherapy · doi:10.33978/2307-3586-2024-20-18-30-41 · Cited by 1

Reducing Resource Usage for Continuous Model Updating and Predictive Query Answering in Graph Streams

Liu, Q., King, A., & Ge, T. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00208 · Cited by 1

Imaging Histopathology of Breast Cancer for Detection

Thabet, M. R. H., & Maghfiroh, H. (2024) · IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA) · doi:10.1109/mi-sta61267.2024.10599634 · Cited by 1

Use of Neural Network Technologies in the Educational Activities of Primary School Teachers

Ovchinnikova, A. Z., Solovyeva, T., & Soo, V. (2024) · International Conference on Technology Enhanced Learning in Higher Education (TELE) · doi:10.1109/tele62556.2024.10605644 · Cited by 1

Differentially Private Extreme Learning Machine

Ono, H., Phuong, T. T., & Phong, L. T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-68208-7_14 · Cited by 1

Fusion of Activation Functions: An Alternative to Improving Prediction Accuracy in Artificial Neural Networks

Akodia, J. A., Dzidonu, C. K., Boison, D. K., & Kisembe, P. (2024) · World Journal of Engineering and Technology · doi:10.4236/wjet.2024.124051 · Cited by 1

An Improved Count-Based Classifier for Categorical Data

Singh, S. S. K., & Chauhan, A. (2024) · IEEE Access · doi:10.1109/access.2024.3454770 · Cited by 1

Feasibility of a Personal Neuromorphic Emulation

Tucker, D. M., & Luu, P. (2024) · Entropy · doi:10.3390/e26090759 · Cited by 1

Deeply Learned Cervical Vertebrae Maturation Staging in CT Images

Zhang, F., Zheng, L., Lin, C., Huang, L., Bai, Y., Chen, Y., & Luó, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650502 · Cited by 1

Machine Learning of Nonlinear Dynamical Systems with Control Parameters Using Feedforward Neural Networks

Sakaguchi, H. (2024) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.93.105001 · Cited by 1

Split-Boost Neural Networks

Cestari, R. G., Maroni, G., Cannelli, L., Piga, D., & Formentin, S. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.08.535 · Cited by 1

Gearbox Fault Detection Using Continuous Wavelet Transform and Vision Transformer (ViT)

Asadian, A., Riyazi, Y., & Ayati, M. (2024) · doi:10.1109/icee63041.2024.10668386 · Cited by 1

基于机器学习方法的短期太阳爆发活动预报模型综述

黄, 鑫., 赵, 忠., 钟, 昱., 徐, 龙., Marianna, B. K., & Erdélyi, R. (2024) · SCIENTIA SINICA Terrae · doi:10.1360/n072023-0208 · Cited by 1

A hybrid decomposition-based Machine Learning approach for predicting subsurface temperature in the Arabian Sea

Malavika, A. R., Pai, M. L., & Johny, K. (2024) · Modeling Earth Systems and Environment · doi:10.1007/s40808-024-02167-0 · Cited by 1

SCML-GNN: A Graph Neural Network Model Leveraging Sensor Causality and Meta-Learning for Mechanical Fault Classification

Xiao, Z., Wang, Y., Tan, G., He, D., Li, J., & Zhou, W. G. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001424560111 · Cited by 1

Neuropunk revolution: energy consumption aspects of modern AI

Talanov, M. O. (2024) · Scientific School Dynamics of Complex Networks and their Applications (DCNA) · doi:10.1109/dcna63495.2024.10718402 · Cited by 1

HybridBooth: Hybrid Prompt Inversion for Efficient Subject-Driven Generation

Guan, S., Ge, Y., Tai, Y. Y., Yang, J. J., Li, W., & You, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72673-6_22 · Cited by 1

On-chip Memory in Accelerator-based Systems: A System Technology Co-Optimization (STCO) Perspective for Emerging Device Technologies

Sahoo, S. S., Abdi, D. B., Ryckaert, J., Myers, J., & Biswas, D. (2024) · IEEE International System-on-Chip Conference (SOCC) · doi:10.1109/socc62300.2024.10737780 · Cited by 1

RPS: A Generic Reservoir Patterns Sampler

Diop, L., Plantevit, M., & Soulet, A. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825601 · Cited by 1

AI-Enhanced Ecological Learning Spaces

Ilic, P., & Sato‐Ilic, M. (2024) · Intelligent systems reference library · doi:10.1007/978-3-031-71232-6_2 · Cited by 1

Investigating Spatially Correlated Patterns in Adversarial Images

Chattopadhyay, N., Zhi, L. Y. E., Xing, B. T. B., Chattopadhyay, A., & Shafique, M. S. (2024) · IEEE International Conference on Image Processing Workshops (ICIPW) · doi:10.1109/icipcw64161.2024.10769132 · Cited by 1

ZoDIAC: Zoneup Dropout Injection Attention Calculation

Zohourianshahzadi, Z., Boult, T. E., & Kalita, J. K. (2024) · Conference on AI, Science, Engineering, and Technology (AIxSET) · doi:10.1109/aixset62544.2024.00008 · Cited by 1

Prediction of the Static and Dynamic Characteristics of Subframe Rubber Bushings Using Deep Learning

Kim, J., & Park, S. (2024) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas63016.2024.10773314 · Cited by 1

On the Design of an Inference Circuit for Tiny Machine Learning

Santos, T. H. D., Meinhardt, C., & Cabrera, F. L. (2024) · Symposium on Internet of Things (SIoT) · doi:10.1109/siot63830.2024.10780553 · Cited by 1

KPNet: Towards a Parameterized Implicit 2D Cloth Rendering

Scharnagl, B., & Groth, C. (2024) · International Conference on Frontiers of Signal Processing (ICFSP) · doi:10.1109/icfsp62546.2024.10785404 · Cited by 1

Combination of GNN and MNL: a new model for dealing with multi-classification tasks

Li, Q., Yuan, X., & Xie, J. (2024) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2024-3548 · Cited by 1

Data Mining Methods Application to Solve the Oil and Gas Flow Regimes of Oil Well Production Classification Problem

Mikhaylov, I. S., Varshavskii, P. R., Fomina, M., & Sidorov, K. O. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-77688-5_38 · Cited by 1

Dissecting a small artificial neural network

Yang, X., Arora, K., & Bachmann, M. (2024) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8121/ad9dc6 · Cited by 1

FROM ORIGINS TO INNOVATIONS: AI'S ROLE AND THE COST IMPACT ON COMPUTER VISION

Tirana, K., & Bejleri, E. (2024) · AGORA INTERNATIONAL JOURNAL OF ECONOMICAL SCIENCES · doi:10.15837/aijes.v18i2.6960 · Cited by 1

Classification of Melanoma Cancer Using Deep Convolutional Neural Networks

Güneş, A., & Dönmez, E. (2024) · Journal of Advanced Research in Natural and Applied Sciences · doi:10.28979/jarnas.1505804 · Cited by 1

Association Rule Learning

Liu, Z. “. (2025) · Springer eBooks · doi:10.1007/978-3-031-75953-6_13 · Cited by 1

Dimensionality cutback and deep learning algorithms efficacy as to the breast cancer diagnostic dataset

Chuiko, G. P., & Гончаров, Д. (2024) · RADIOELECTRONIC AND COMPUTER SYSTEMS · doi:10.32620/reks.2024.4.08 · Cited by 1

LEGO Minifigure Recognition Based on Deep Learing

Wang, P. (2024) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai62207.2024.10826947 · Cited by 1

Deciphering Distress: Stress Detection in White Leghorn Birds Through Vocalization Analysis

Basu, J., Roy, R. K., Pal, M., Ghosh, T. K., Ray, H., Ghosh, A., Kannan, A., Rao, S. V. R., BHANJA, S. K., & CHATTERJEE, R. N. (2024) · Springer eBooks · doi:10.1007/978-981-97-5157-0_50 · Cited by 1

MAGIC: Mobile App for Gender Identification of Chick from Vocalization Data Analysis

Basu, J., Roy, R. K., Pal, M., Ghosh, T. K., Ray, H., Ghosh, A., Kannan, A., Rao, S. V. R., & CHATTERJEE, R. N. (2024) · Springer eBooks · doi:10.1007/978-981-97-5157-0_28 · Cited by 1

New Paradigms of Disaster Mitigation Decision Support Systems Through Applications Leveraging Earth Observations and Machine Learning Approaches

Mayer, T. J., Bhandari, B., Uddin, K., Shrestha, M., Thapa, R. B., & Meyer, F. J. (2025) · GIScience and geo-environmental modelling · doi:10.1007/978-3-031-76189-8_2 · Cited by 1

Del Ars Magna a la inteligencia artificial generativa

Llavori, R. B. (2025) · Revista de Senología y Patología Mamaria · doi:10.1016/j.senol.2024.100663 · Cited by 1

BEN-RS-ANN: An Innovative Approach for Revealing Emotion from Bengali Text with Exposure to Polysemy Resolution

Seal, T., Bhattacharya, A., Das, S., Patra, S., Dawn, D. D., Khan, A., Setua, S. K., & Pal, R. K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-9762-2_13 · Cited by 1

Deep Learning

Lederer, J. (2025) · Statisctics and computing/Statistics and computing · doi:10.1007/978-3-031-30276-3_8 · Cited by 1

Investigating the “stickiness” of waste-inducing behaviors before and during the COVID-19 pandemic in Massachusetts, USA: A geospatial and machine learning analysis

Schmitz, G. M. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.145259 · Cited by 1

Visual Perception Inspired Deep Learning Models: A Study of Visual Attention and Information Interaction

Wei, B., Zhao, Y., Hao, K., & Gao, L. (2024) · International Conference on Machine Vision, Image Processing and Imaging Technology (MVIPIT) · doi:10.1109/mvipit65697.2024.00019 · Cited by 1

Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)

Svoboda, J., Ruesch, M., Liechti, D., Jones, C. L., Volpi, M., Zehnder, M., & Schweizer, J. (2025) · Geoscientific model development · doi:10.5194/gmd-18-1829-2025 · Cited by 1

Revolutionizing Pedagogical Methods in Architectural Design Studios: Incorporating AI and Repertory Grid Technique for Enhanced Brand Image Design

Albdour, A., & Agiel, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-85649-5_46 · Cited by 1

On-Road Water Detection and Water Film Height Estimation using a Time-of-Flight Camera

Puščul, D., Schönlieb, A., Ramsauer, A., & Lex, C. (2024) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc58415.2024.10920134 · Cited by 1

Forecasting the Weekly Spot Oil Price Using a Hybrid Model ARMA-GARCH-MLP and Prophet Forecasting Method

Salaheddine, S.-H., Nacer, B., & Mohammed, M. (2025) · ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH · doi:10.24818/18423264/59.1.25.10 · Cited by 1

Nobel prize in physics 2024: ideas that transcend physics

Vicente, C. J. P. (2025) · Europhysics news · doi:10.1051/epn/2025105 · Cited by 1

Integrated Habitat Assessment of a Protected Fish Species in the Upper Yangtze River, China: Connectivity and Suitability

Bai, X., Niu, Z., Yu, Y., Xue, M., Qin, L., & Zhang, P. (2025) · Hydroecology and engineering. · doi:10.70322/hee.2025.10005 · Cited by 1

Predictive Modelling for Sustainable Pilgrim Flow Management on the Camino de Santiago

(España), U. D. S. D. C., Lema, Y. A., Gómez, I. N., (España), U. D. S. D. C., Araújo, M. L. D. R., & (España), U. D. S. D. C. (2025) · PASOS Revista de Turismo y Patrimonio Cultural · doi:10.25145/j.pasos.2025.23.029 · Cited by 1

Pruning and Optimizing Large Language Models in an Era of GPU Scarcity

Islam, A., Belhouari, S. B., & Bermak, A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-86623-4_12 · Cited by 1

Deep Learning

Kumari, P., Swarnalatha, K. S., & Pandey, P. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0735-0.ch002 · Cited by 1

Benchmarking CNN Architectures for Tool Classification: Evaluating CNN Performance on a Unique Dataset Generated by Novel Image Acquisition System

Bilal, M., Podishetti, R., Großmann, D., & Bregulla, M. (2025) · IEEE Access · doi:10.1109/access.2025.3574785 · Cited by 1

A Review of Research on the Thermo‐Force Characteristics of Scramjet Engine Wall Based on Neural Networks

Qi, C., Wang, C., Binhao, L., Lin, Y. P., Zibo, Z., & Wenzhong, J. (2025) · International Journal of Energy Research · doi:10.1155/er/3241787 · Cited by 1

A history of psychology as it happened to me

Kintsch, W. (2025) · Discourse Processes · doi:10.1080/0163853x.2025.2511585 · Cited by 1

Start Time End Time Integration (STETI): Method for Including Recent Data to Analyze Trends in Kidney Cancer Survival

Chaduka, T., Berleant, D., Bauer, M., Tsai, P.-H., & Tu, S. (2025) · Healthcare · doi:10.3390/healthcare13121451 · Cited by 1

Conspiracy to Commit: Information Pollution, Artificial Intelligence, and Real-World Hate Crime

Aziani, A., Giudice, M. V. L., & Yazdi, A. S. (2025) · European Journal on Criminal Policy and Research · doi:10.1007/s10610-025-09629-w · Cited by 1

An Energy-Efficient IF Spiking Neuron with Enhanced Spike Encoding Capability

Kirtonia, P., Williams, S., Khalil, K., & Bayoumi, M. (2025) · IEEE Dallas Circuits and Systems Conference (DCAS) · doi:10.1109/dcas65331.2025.11045476 · Cited by 1

El cerebro social como base de la inteligencia artificial

Guzmán-Sandoval, V. M., Delgado‐Enciso, I., Santana-Mancilla, P. C., Cervantes-Kardasch, V. H., Fajardo-Flores, S. B., & Rodriguez-Sanchez, I. P. (2025) · Biología y Sociedad · doi:10.29105/bys8.16-198 · Cited by 1

Study on bicycle stability and self-stabilization mechanism by dynamic-and-data-driven surrogate modeling

Miao, X., & Liu, C. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-11524-2 · Cited by 1

Large Language Models for Dummies

Calamo, M., & Marinacci, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-92285-5_3 · Cited by 1

A spatial explainable deep learning framework for prediction classification of hydrological drought in ungauged basin

Wang, Q., Su, X., Wu, H., Xiao, Y., & Yang, Y. (2025) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2025.102653 · Cited by 1

Feasibility Study of Neural Network-based Classification of Conveyor Belt Damage Using Partial DiagBelt Data

Rzeszowska, A., Jurdziak, L., & Błażej, R. (2025) · Civil And Environmental Engineering Reports · doi:10.59440/ceer/205939 · Cited by 1

Evaluating soil salinity patterns in Tehran Province: An artificial neural network model incorporating satellite images and geomorphometric parameters

Marvi, M. S. P., & moharami, S. (2025) · Advances in Space Research · doi:10.1016/j.asr.2025.07.074 · Cited by 1

Stochastic simulation of microenvironment comfort and greenhouse gas emission of residential building using Monte-Carlo and hybrid intelligence paradigms

Li, Z., Zhao, Y., Suman, S. K., & Bardhan, A. (2025) · Environment Development and Sustainability · doi:10.1007/s10668-025-06635-0 · Cited by 1

1956 Artificial Intelligence Is Born

ÇİLHOROZ, İ. A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-3099-0.ch001 · Cited by 1

Support vector machines for optimal channel decoding

Rovella, G. D. B., Benammar, M., Benaddi, T., & Méric, H. (2025) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-025-02493-6 · Cited by 1

Isometric representations in neural networks improve robustness

Beshkov, K., Verhellen, J., & Lepperød, M. E. (2025) · Scientific Reports · doi:10.1038/s41598-025-20619-0 · Cited by 1

Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives

Vergadou, N., & Constantoudis, V. (2025) · ChemPlusChem · doi:10.1002/cplu.202500267 · Cited by 1

Deep continual multitask out-of-hospital incident severity assessment from changing clinical features

Ferri, P., Sáez, C., Castro, A. F.-D., Sánchez-Cuesta, P., & Garcia-Gomez, J. M. (2025) · Health Information Science and Systems · doi:10.1007/s13755-025-00395-x · Cited by 1

Machine learning in health informatics: making better use of domain experts

Brodley, C. E., & Wallace, B. (2012) · Cited by 1

Cerebellar Memory, Long-Term Depression, and Long-Term Potentiation

Barlow, J. S. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511529771.008 · Cited by 1

Forecasting Order Quantity for Treadmill Part by Comparison of Time Series Forecast Technologies and Artificial Intelligence Methods

Chang, F. M. (2013) · Lecture notes in mechanical engineering · doi:10.1007/978-3-319-00557-7_57 · Cited by 1

A new hybrid system for information security

El-Bakry, H. M., & Mastorakis, N. E. (2012) · Cited by 1

Evolutionary granular kernel machines

Zhang, Y., & Jin, B. (2007) · Cited by 1

Benchmarking an artificial neural network tuned by a genetic algorithm

Blomström, K. (2012) · Cited by 1

Sistemas Complejos. Sistemas en el Límite del Caos

Tovar, L. A. R. (2025) · Investigación Administrativa · doi:10.35426/iasc · Cited by 1

MTGAN-KAN: A New Physics-Driven Wasserstein Generative Adversarial Kolmogorov–Arnold Network for 2-D Magnetotelluric Inversion

Yang, F., Liu, Y., Yin, C., Su, Y., Ke, Z., Ma, X., & Rong, Z. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3631788 · Cited by 1

MTMD: A Multi-task Multi-domain Framework for Unified Ad Lightweight Ranking at Pinterest

Yang, X., Yin, P., Engle, A., Zhuang, J., & Leng, L. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-16358-5_3 · Cited by 1

Generative diffusion for perceptron problems: statistical physics analysis and efficient algorithms

Straziota, D., Demyanenko, E., Baldassi, C., & Lucibello, C. (2025) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ae8ceb · Cited by 1

Vision Pipelines and Optimizations

Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_8 · Cited by 1

Topics in Information Theory and Machine Learning

Nugues, P. (2014) · Cognitive technologies · doi:10.1007/978-3-642-41464-0_4 · Cited by 1

A world according to artificial neural networks

Schuster, A. (2003) · Journal of Telecommunications and Information Technology · doi:10.26636/jtit.2003.3.184 · Cited by 1

Combining Connectionist and Dynamic Systems Principles in Models of Development: The Case of Analogical Completion

Mareschal, D., Leech, R., & Cooper, R. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195300598.003.0010 · Cited by 1

Reguła PID uczenia sztucznych neuronów

Bartman, J. (2009) · Metody Informatyki Stosowanej · Cited by 1

Wpływ opisu danych na efektywność uczenia oraz pracy sztucznej sieci neuronowej na przykładzie identyfikacji białek

Bartman, J. (2013) · Edukacja – Technika – Informatyka · Cited by 1

Teaching Decision Tree Classification Using Microsoft Excel

Ataman, K., Kulick, G., & Sim, T. (2011) · INFORMS Transactions on Education · doi:10.1287/ited.1110.0060 · Cited by 1

Empleo de redes neuronales de base radial a un modelo econométrico de exportación de tomate

Mesa, J. C. P., Valenciano, J. D. P., & Manguin, J.-P. L. (2007) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 1

Yapay Sinir Ağları Analizi İle İmalat Süreçlerinin İyileştirilmesi

Aka, S., & Akyüz, G. (2018) · Ege Akademik Bakis (Ege Academic Review) · doi:10.21121/eab.2018237354 · Cited by 1

All-Implicants Neural Networks for Efficient Boolean Function Representation

Buffoni, F., Gianini, G., Damiani, E., & Granitzer, M. (2018) · IEEE International Conference on Cognitive Computing (ICCC) · doi:10.1109/iccc.2018.00019 · Cited by 1

Mining smartphone generated data for user action recognition – Preliminary assessment

Fijalkowski, J., Ganzha, M., Paprzycki, M., Fidanova, S. S., Lirkov, I., Bădică, C., & Ivanović, M. D. (2018) · AIP conference proceedings · doi:10.1063/1.5064928 · Cited by 1

Constraint breeding during on-line incremental learning

Moreton, E. (2019) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/6f9x-6411 · Cited by 1

HIPSTER

Bevan, A. J., Charman, T. P., & Hays, J. (2019) · EPJ Web of Conferences · doi:10.1051/epjconf/201921406027 · Cited by 1

Building an Arabic Semantic Lexicon for Hajj

Batarfi, O., Yehia, M., & Ezz, A. (2019) · International Journal of Computer Applications · doi:10.5120/ijca2019918325 · Cited by 1

Standard and Inception-based Encoder-Decoder Neural Networks for Predicting the Solution Convergence of Design Optimization Algorithms

O'Neill, N. J. (2019) · CU Scholar (University of Colorado Boulder) · Cited by 1

Analytic Philosophy of Language

Soames, S. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.004 · Cited by 1

Phenomenology Meets Philosophy of Mind and Language

Smith, D. W. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.049 · Cited by 1

Data Analysis and Machine Learning: on Long Memory Commodity Time Series

Ni, T. (2019) · University of Liverpool · doi:10.17638/03061246 · Cited by 1

Edge detection for Roof Images using Transfer Learning

Ahmed, A., Byun, Y.-C., & Hazra, D. (2019) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis46139.2019.8940304 · Cited by 1

Artificial neural network employment for element determination in Mugil cephalus by ICP OES in Pontal Bay, Brazil

Batista, M. A. S., Santos, L. N., Chagas, B. C., Lôbo, I. P., Novaes, C. G., Guedes, W. N., de Jesus, R. M., Amorim, F. A. C., Pacheco, C. S. V., Moreira, L. S., et al. (2020) · Analytical Methods · doi:10.1039/d0ay00799d · Cited by 1

МОДЕЛИ ОЦЕНКИ ИННОВАЦИОННЫХ ПРОЕКТОВ С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ

Зайцев, Р. Г., Комиссарова, М. А., & Ткачев, Д. И. (2020) · Bulletin of the South-Russian state technical University (NPI) Series Socio-economic Sciences · doi:10.17213/2075-2067-2020-2-63-67 · Cited by 1

A COMPARATIVE STUDY ON THE LOGISTIC REGRESSION AND NAÏVE BAYES MODELS UPON MEDICAL DATA THROUGH A MACHINE LEARNING APPROACH

Parthasarathy, S., & Venkataraman, M. (2020) · Advances in Mathematics Scientific Journal · doi:10.37418/amsj.9.5.42 · Cited by 1

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)