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

HCNN-PSI: A hybrid CNN with partial semantic information for space target recognition

Yang, X., Wu, T., Wang, N., Huang, Y., Song, B., & Gao, X. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107531 · Cited by 30

Application of Deep Learning in Dentistry and Implantology

Kang, D., Duong, H. P., & Park, J. (2020) · The Korean Academy of Oral and Maxillofacial Implantology · doi:10.32542/implantology.202015 · Cited by 30

Hate speech detection in Twitter using hybrid embeddings and improved cuckoo search-based neural networks

Ayo, F. E., Folorunso, O. O., Ibharalu, F. T., & Osinuga, I. A. (2020) · International Journal of Intelligent Computing and Cybernetics · doi:10.1108/ijicc-06-2020-0061 · Cited by 30

Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules

Maldonado, F., Varghese, C., Rajagopalan, S., Duan, F., Balar, A., Lakhani, D. A., Antic, S., Massion, P. P., Johnson, T. F., Karwoski, R. A., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.02485-2020 · Cited by 30

In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins

Anteghini, M., dos Santos, V. A. P. M., & Saccenti, E. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22126409 · Cited by 30

Machine learning methods to model multicellular complexity and tissue specificity

Sealfon, R. S. G., Wong, A. K., & Troyanskaya, O. G. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00339-3 · Cited by 30

Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study

Hur, S., Min, J. Y., Yoo, J., Kim, K., Chung, C. R., Dykes, P. C., & Cha, W. C. (2021) · Journal of Medical Internet Research · doi:10.2196/23508 · Cited by 30

Risk Prediction for Winter Road Accidents on Expressways

Kim, D., Jung, S., & Yoon, S. (2021) · Applied Sciences · doi:10.3390/app11209534 · Cited by 30

Heart disease prediction using convolutional neural network

Sharma, A. K., Pal, T., & Jaiswal, V. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822706-0.00012-3 · Cited by 30

The Future Will Not Be Calculated: Neural Nets, Neoliberalism, and Reactionary Politics

Halpern, O. (2021) · Critical Inquiry · doi:10.1086/717313 · Cited by 30

Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

Li, H., Huang, K., Qin, Z., & Sun, C. (2022) · Energies · doi:10.3390/en15030726 · Cited by 30

Photometric Stereo-Based Defect Detection System for Steel Components Manufacturing Using a Deep Segmentation Network

Saiz, F. A., Barandiarán, Í., Arbelaiz, A., & Graña, M. (2022) · Sensors · doi:10.3390/s22030882 · Cited by 30

Defining Artificial Intelligence

Fogel, D. B. (2022) · Wiley eBooks · doi:10.1002/9781119815075.ch7 · Cited by 30

Machine learning-facilitated multiscale imaging for energy materials

Zhang, G., Song, Y., Zhao, W., An, H., & Wang, J. (2022) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2022.101008 · Cited by 30

A Biologically Interfaced Evolvable Organic Pattern Classifier

Gerasimov, J. Y., Tu, D., Hitaishi, V. P., Harikesh, P. C., Yang, C., Abrahamsson, T., Rad, M., Donahue, M. J., Ejneby, M. S., Magnus Berggren, A., et al. (2023) · Advanced Science · doi:10.1002/advs.202207023 · Cited by 30

Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio

Baghbani, A., Nguyen, M. D., Alnedawi, A., Milne, N., Baumgartl, T., & Abuel-Naga, H. (2023) · Applied Sciences · doi:10.3390/app13084934 · Cited by 30

Joshua 4.0: Packing, PRO, and Paraphrases

Ganitkevitch, J., Cao, Y., Weese, J., Post, M., & Callison-Burch, C. (2013) · Cited by 30

A family of online boosting algorithms

Babenko, B., Yang, M., & Belongie, S. (2009) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2009.5457453 · Cited by 30

A No-Go Theorem for One-Layer Feedforward Networks

Giusti, C., & Itskov, V. (2014) · Neural Computation · doi:10.1162/neco_a_00657 · Cited by 30

Selective sampling on graphs for classification

Gu, Q., Aggarwal, C. C., Liu, J., & Han, J. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487641 · Cited by 30

Theory: Foundations of Artificial Neural Networks

Buscema, M. (1998) · Substance Use & Misuse · doi:10.3109/10826089809115858 · Cited by 35

Decision Making for Reduction of Nonproductive Time through an Integrated Lost Circulation Prediction

Moazzeni, A., Nabaei, M., & Jegarluei, S. G. (2012) · Petroleum Science and Technology · doi:10.1080/10916466.2010.495961 · Cited by 30

3-D inversion of borehole-to-surface electrical data using a back-propagation neural network

Ho, T. L. (2008) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2008.06.002 · Cited by 30

Online Learning with (Multiple) Kernels: A Review

Diethe, T., & Girolami, M. (2012) · Neural Computation · doi:10.1162/neco_a_00406 · Cited by 30

Statistical Script Learning with Recurrent Neural Networks

Pichotta, K., & Mooney, R. J. (2016) · doi:10.18653/v1/w16-6003 · Cited by 30

The effect of weight errors on neural networks

Arechiga, A. P., & Michaels, A. J. (2018) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2018.8301749 · Cited by 30

Application of natural computation inspired method in community detection

Zhang, W., Zhang, R., Shang, R., Li, J., & Jiao, L. (2018) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2018.09.186 · Cited by 30

Deep Learning for Quantitative Cardiac MRI

Tao, Q., Lelieveldt, B. P. F., & van der Geest, R. J. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.19.21927 · Cited by 30

Creating Business Intelligence through Machine Learning: An Effective Business Decision Making Tool

Reshi, Y. S., & Khan, R. A. (2014) · Journals & Books Hosting (International Knowledge Sharing Platform) · Cited by 30

Linear Classifiers

Theodoridis, S., & Koutroumbas, K. (2009) · Elsevier eBooks · doi:10.1016/b978-1-59749-272-0.50005-0 · Cited by 30

DESIGN OF NEURAL NETWORK FILTERS

Larsen, J. O. (1996) · Cited by 30

Prediction of gas solubilities in ionic liquids

Oliferenko, A. A., Oliferenko, P. V., Seddon, K. R., & Torrecilla, J. S. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp20336c · Cited by 30

Group method of data handling (GMDH): Review and experience

Mehra, R. К. (1977) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.1977.271540 · Cited by 30

Classification of particles in stratiform clouds using the 33 and 95 GHz polarimetric cloud profiling radar system (CPRS)

Lohmeier, S. P., Sekelsky, S. M., Firda, J. M., Sadowy, G. A., & McIntosh, R. E. (1997) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.563264 · Cited by 30

Parallel algorithms/architectures for neural networks

Hwang, J., & Kung, S. (1989) · The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology · doi:10.1007/bf02427796 · Cited by 30

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition ApplicationsPreprint

Roffo, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05933 · Cited by 30

Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and AlgorithmsPreprint

Blondel, M., Martins, A. F. T., & Niculae, V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09717 · Cited by 30

Technical report: supervised training of convolutional spiking neural networks with PyTorchPreprint

Zimmer, R., Pellegrini, T., Singh, S. F., & Masquelier, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.10124 · Cited by 30

The contribution of GenAI to business analytics

Salazar, A. J., & Kunc, M. H. (2025) · Journal of Business Analytics · doi:10.1080/2573234x.2024.2435835 · Cited by 30

Towards Lower Bounds on the Depth of ReLU Neural Networks

Hertrich, C., Basu, A., Summa, M. D., & Skutella, M. (2021) · Padua Research Archive (University of Padova) · Cited by 30

A feasibility study of machine learning-assisted alloy design using wrought aluminum alloys as an example

Soofi, Y. J., Rahman, M. A., Gu, Y., & Liu, J. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111783 · Cited by 29

Deep learning enabled surrogate model of complex food processes for rapid prediction

Ghosh, D., & Datta, A. K. (2023) · Chemical Engineering Science · doi:10.1016/j.ces.2023.118515 · Cited by 29

Artificial intelligence literacy for the language industry – with particular emphasis on recent large language models such as GPT-4

Krüger, R. (2023) · Lebende Sprachen · doi:10.1515/les-2023-0024 · Cited by 31

A Review of Artificial Neural Networks Applications in Maritime Industry

Assani, N., Matić, P., Kaštelan, N., & Čavka, I. R. (2023) · IEEE Access · doi:10.1109/access.2023.3341690 · Cited by 29

Improving the accuracy of short-term multiphase production forecasts in unconventional tight oil reservoirs using contextual Bi-directional long short-term memory

Kocoglu, Y., Gorell, S. B., Emadi, H., Eyinla, D. S., Bolouri, F., Kocoglu, Y. C., & Arora, A. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2024.212688 · Cited by 29

From GPUs to AI and quantum: three waves of acceleration in bioinformatics

Schmidt, B., & Hildebrandt, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103990 · Cited by 29

Opportunities and Challenges in Applying AI to Evolutionary Morphology

He, Y., Mulqueeney, J. M., Watt, E. C., Salili‐James, A., Barber, N. S., Camaiti, M., Hunt, E. S. E., Kippax-Chui, O., Knapp, A., Lanzetti, A., et al. (2024) · Integrative Organismal Biology · doi:10.1093/iob/obae036 · Cited by 32

Predictive modeling of membrane reactor efficiency using advanced artificial neural networks for green hydrogen production

Mahmoudi, M., Ghaemi, A., Kelishami, A. R., & Movahedirad, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-75068-y · Cited by 29

Deep learning paradigm shift in composite materials performance prediction: A review of state-of-the-art applications

Yan, Y., Zhang, Y., Wu, X., Guo, Q., Zhang, D., Li, C., Liu, Y., Zhang, J., An, L., Wang, Z., et al. (2025) · Composites Communications · doi:10.1016/j.coco.2025.102542 · Cited by 29

Decentralized Federated Learning with Non-IID Data: Challenges, Trends, and Future Opportunities

Chung, W., Lo, C.-A., Lin, Y.-J., Chen, Z.-H., & Hung, C. (2025) · ACM Computing Surveys · doi:10.1145/3785657 · Cited by 29

Discovering interpretable structural dynamic response using physics-informed Kolmogorov-Arnold network

Du, K., Li, Z., Wang, X., Luo, H., Wang, Z., & Wen, R. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114468 · Cited by 29

Neural network modeling of ternary solubilities of 2-naphthol in supercritical CO2 : A comparative study

Bakhbakhi, Y. (2011) · Mathematical and Computer Modelling · doi:10.1016/j.mcm.2011.11.051 · Cited by 29

Extracting the internal representation of faces from human brain activity: An analogue to reverse correlation

Hansen, B. C., Thompson, B. S., Hess, R. F., & Ellemberg, D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.02.021 · Cited by 29

A Feeling for the Algorithm: Working Knowledge and Big Data in Biology

Stevens, H. (2017) · Osiris · doi:10.1086/693516 · Cited by 29

Variational inference based bayes online classifiers with concept drift adaptation

Nguyen, T. T. T., Nguyen, T. T., Liew, A. W., & Wang, S. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.04.007 · Cited by 29

Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model

Jung, W.-M., Park, I.-S., Lee, Y., Kim, C.-E., Lee, H., Hahm, D., Park, H., Jang, B.-H., & Chae, Y. (2018) · Frontiers of Medicine · doi:10.1007/s11684-017-0582-z · Cited by 29

A supervised learning framework for learning management systems

Olivé, D. M., Huynh, D. Q., Reynolds, M. A., Dougiamas, M., & Wiese, D. (2018) · International Conference on Data Science, E-Learning and Information Systems (DATA) · doi:10.1145/3279996.3280014 · Cited by 29

What does the mind learn? A comparison of human and machine learning representations

Spicer, J., & Sanborn, A. N. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.02.004 · Cited by 29

A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease

Putatunda, S. (2020) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct50063.2020.9198447 · Cited by 29

Optimization of Hyper-parameter for CNN Model using Genetic Algorithm

Yoo, J., Yoon, H.-I., Kim, H.-G., Yoon, H.-S., & Han, S.-S. (2019) · International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) · doi:10.1109/icecie47765.2019.8974762 · Cited by 29

The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)

Asomani, S. N., Yuan, J., Wang, L., Appiah, D., & Adu-Poku, K. A. (2020) · Energies · doi:10.3390/en13092271 · Cited by 29

Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts

Gonthier, N., Ladjal, S., & Gousseau, Y. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103299 · Cited by 29

Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica

Sarmiento-Ramos, J. L. (2020) · Revista UIS Ingenierías · doi:10.18273/revuin.v19n4-2020001 · Cited by 29

The Role of Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Disorders

Tonozuka, R., Mukai, S., & Itoi, T. (2020) · Diagnostics · doi:10.3390/diagnostics11010018 · Cited by 29

Artificial intelligence in molecular imaging

Herskovits, E. H. (2021) · Annals of Translational Medicine · doi:10.21037/atm-20-6191 · Cited by 29

Deterministic Multi-kernel based extreme learning machine for pattern classification

Ahuja, B., & Vishwakarma, V. P. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115308 · Cited by 29

Forecasting inflation in Latin American countries using a SARIMA–LSTM combination

Peirano, R., Kristjanpoller, W., & Minutolo, M. C. (2021) · Soft Computing · doi:10.1007/s00500-021-06016-5 · Cited by 29

Hold-out strategy for selecting learning models: Application to categorization subjected to presentation orders

Mezzadri, G., Laloë, T., Mathy, F., & Reynaud-Bouret, P. (2022) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2022.102691 · Cited by 29

A data-driven approach to predicting band gap, excitation, and emission energies for Eu 2+ -activated phosphors

Park, C., Lee, J.-W., Kim, M., Lee, B. D., Singh, S. P., Park, W. B., & Sohn, K. (2021) · Inorganic Chemistry Frontiers · doi:10.1039/d1qi00766a · Cited by 30

A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: An artificial neural network approach

Aboufazeli, S., Jahani, A., & Farahpour, M. (2021) · MethodsX · doi:10.1016/j.mex.2021.101489 · Cited by 29

Predicting Inflow Rate of the Soyang River Dam Using Deep Learning Techniques

Lee, S., & Kim, J. (2021) · Water · doi:10.3390/w13172447 · Cited by 29

Potential role of artificial intelligence in craniofacial surgery

Ryu, J. Y., Chung, H. Y., & Choi, K. Y. (2021) · Archives of Craniofacial Surgery · doi:10.7181/acfs.2021.00507 · Cited by 29

Evolution of Artificial Intelligence-Powered Technologies in Biomedical Research and Healthcare

Díaz-Flores, E., Meyer, T., & Giorkallos, A. (2022) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2021_189 · Cited by 29

Deep learning methods for predicting fluid forces in dense particle suspensions

Raj, N. A., Cao, Z., Muralidhar, N., Tafti, D. K., & Karpatne, A. (2022) · Powder Technology · doi:10.1016/j.powtec.2022.117303 · Cited by 29

The Receptron: a device for the implementation of information processing systems based on complex nanostructured systems

Martini, G., Mirigliano, M., Paroli, B., & Milani, P. (2022) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ac665c · Cited by 29

Shale gas geological “sweet spot” parameter prediction method and its application based on convolutional neural network

Qin, Z., & Xu, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-19711-6 · Cited by 29

Using machine learning to analyze longitudinal data: A tutorial guide and best‐practice recommendations for social science researchers

Sheetal, A., Jiang, Z., & Milia, L. V. D. (2022) · Applied Psychology · doi:10.1111/apps.12435 · Cited by 29

Artificial Intelligence for Neurosurgery : Current State and Future Directions

Noh, S. H., Cho, P. G., Kim, K. N., Kim, S. H., & Shin, D. A. (2022) · Journal of Korean Neurosurgical Society · doi:10.3340/jkns.2022.0130 · Cited by 29

Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data

Al-Ramini, A., Hassan, M., Fallahtafti, F., Takallou, M. A., Rahman, H., Qolomany, B., Pipinos, I. I., Alsaleem, F., & Myers, S. A. (2022) · Sensors · doi:10.3390/s22197432 · Cited by 29

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

Fahmy, D. M., Alksas, A., Elnakib, A. A., Mahmoud, A. H., Kandil, H., Khalil, A., Ghazal, M. A., Bogaert, E. V., Contractor, S. G., & El-Baz, A. S. (2022) · Cancers · doi:10.3390/cancers14246123 · Cited by 29

Multi-source and multimodal data fusion for improved management of a wastewater treatment plant

Strelet, E., Peng, Y., Castillo, I., Rendall, R., Wang, Z., Joswiak, M. N., Braun, B., Chiang, L. H., & Reis, M. S. (2023) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2023.111530 · Cited by 29

Predicting PM2.5 levels and exceedance days using machine learning methods

Gao, Z., Do, K., Li, Z., Jiang, X., Maji, K. J., Ivey, C. E., & Russell, A. G. (2024) · Atmospheric Environment · doi:10.1016/j.atmosenv.2024.120396 · Cited by 29

Unlocking the Potential of Generative Artificial Intelligence in Drug Discovery

Romanelli, V., Cerchia, C., & Lavecchia, A. (2024) · Springer eBooks · doi:10.1007/978-3-031-46238-2_3 · Cited by 29

Modeling the evolution of complex genetic systems: The gene network family tree

Fierst, J. L., & Phillips, P. C. (2014) · Journal of Experimental Zoology Part B Molecular and Developmental Evolution · doi:10.1002/jez.b.22597 · Cited by 29

Use of Artificial Neural Networks for Predicting Crude Oil Effect on CO2 Corrosion of Carbon Steels

Hernández, S., Nešić, S., Weckman, G. R., & Ghai, V. V. (2005) · NACE CORROSION Conference · doi:10.5006/c2005-05554 · Cited by 29

Learning from interpretations

Wachman, G., & Khardon, R. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273615 · Cited by 29

Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity

D’Souza, P., Liu, S., & Hahnloser, R. H. R. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0909394107 · Cited by 29

Hybrid Segmentation – Artificial Neural Network Classification of High Resolution Hyperspectral Imagery for Site-Specific Herbicide Management in Agriculture

Eddy, P., Smith, A. M., Hill, B. D., Peddle, D. R., Coburn, C. A., & Blackshaw, R. E. (2008) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.74.10.1249 · Cited by 29

Forecasting Crude Oil Price Using Artificial Neural Networks: A Literature Survey

Hamdi, M., & Aloui, C. (2015) · RePEc: Research Papers in Economics · Cited by 29

Acoustic Detection of Human Activities in Natural Environments

Ntalampiras, S., Potamitis, I., & Fakotakis, N. (2012) · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · Cited by 29

Supervised Image Classification Using Deep Convolutional Wavelets Network

Hassairi, S., Ejbali, R., & Zaied, M. (2015) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2015.49 · Cited by 29

Trial-dependent psychometric functions accounting for perceptual learning in 2-AFC discrimination tasks

Kattner, F., Cochrane, A., & Green, C. S. (2017) · Journal of Vision · doi:10.1167/17.11.3 · Cited by 29

Artificial neural networks approach to predicting rut depth of asphalt concrete by using of visco-elastic parameters

Kamboozia, N., Ziari, H., & Behbahani, H. (2017) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2017.10.088 · Cited by 29

Arabic Text Classification: A Review

Mohammad, A. H. (2019) · Modern Applied Science · doi:10.5539/mas.v13n5p88 · Cited by 29

Inception neural network for complete intersection Calabi–Yau 3-folds

Erbin, H., & Finotello, R. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abda61 · Cited by 29

History of Artificial Intelligence

Guizzi, A., Denard, P. J., Collin, P., & Lädermann, A. (2024) · Springer eBooks · doi:10.1007/978-3-031-70310-2_1 · Cited by 29

Self-optimising Production Systems

Schmitt, R. H., Brecher, C., Corves, B. J., Gries, T., Jeschke, S., Klocke, F., Loosen, P., Michaeli, W., Müller, R. H., Poprawe, R., et al. (2011) · Springer eBooks · doi:10.1007/978-3-642-21067-9_6 · Cited by 29

Artificial Neural Networks and Their use in Chemistry

Peterson, K. L. (2000) · Reviews in computational chemistry · doi:10.1002/9780470125939.ch2 · Cited by 29

FEEDFORWARD NEURAL NETWORKS FOR THE IDENTIFICATION OF DYNAMIC PROCESSES

Thibault, J. (1991) · Chemical Engineering Communications · doi:10.1080/00986449108911521 · Cited by 29

Support Vector Machines for Classification: A Statistical Portrait

Lee, Y. (2009) · Methods in molecular biology · doi:10.1007/978-1-60761-580-4_11 · Cited by 29

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)