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Deep Learning and Intelligent Robots in Government

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Deep Learning in Intelligent Power and Energy Systems

Mota, B., Pinto, T. D. O., Vale, Z., & Ramos, C. A. (2022) · Wiley eBooks · doi:10.1002/9781119834052.ch3 · Cited by 1

Artificial Intelligence in the Tribology: Review

Mahadeshwara, M. R., Kumar, S., & Dastidar, A. G. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-5482-5_31 · Cited by 1

Cognitive Therapy and the Three Waves: Advantages, Disadvantages and Rapprochement

Leahy, R. L. (2022) · Springer eBooks · doi:10.1007/978-3-031-11677-3_11 · Cited by 1

Introducing AI in Medicine

Cohen, T., Patel, V. L., & Shortliffe, E. H. (2022) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-09108-7_1 · Cited by 1

Evaluation of Quality of Neural Network Models and Discriminant Analysis in ROPO Forecasting

Michalska-Dudek, I., & Dudek, A. (2022) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-031-10190-8_14 · Cited by 1

MeFirst ranking and multiple dichotomies : Via Linear Programming and Neural Networks

George Nagy, A., & Krishnamoorthy, M. S. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956323 · Cited by 1

Tools for Machine Learning

Alvo, M. (2022) · Springer series in the data sciences · doi:10.1007/978-3-031-06784-6_11 · Cited by 1

Compactness of Neural Networks

Miyajima, K., & Yamazaki, H. (2022) · Formalized Mathematics · doi:10.2478/forma-2022-0002 · Cited by 1

Spiking image processing unit based on neural analog of Boolean logic operations

Nazari, S., Keyanfar, A., & Hulle, M. M. V. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09917-9 · Cited by 1

Machine Learning for Face Recognition in Shallow Data

Phan, N., Zhou, J., Tao, Y., & Almakki, M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-21438-7_74 · Cited by 1

Data-driven Surrogate Modeling using Deep Learning for Uncertainty Quantification of Random Fields

Palar, P. S., Stevenson, R., Amalinadhi, C., & Zuhal, L. R. (2023) · AIAA SciTech Forum · doi:10.2514/6.2023-2044 · Cited by 1

Discussion on the Application of Ai in Nuclear Medicine

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Machine Learning, Deep Learning und Artificial Intelligence

Gronwald, K.-D. (2023) · Springer eBooks · doi:10.1007/978-3-658-39099-0_6 · Cited by 1

Application of Reinforcement Learning for the Design and Optimization of Pass Schedules in Hot Rolling

Idzik, C., Gerlach, J., Lohmar, J., Bailly, D. B., & Hirt, G. (2023) · Lecture notes in production engineering · doi:10.1007/978-3-031-18318-8_8 · Cited by 1

Introduction

Chen, Y., Li, L., Li, W., Guo, Q., Du, Z., & Xu, Z. (2023) · Elsevier eBooks · doi:10.1016/b978-0-32-395399-3.00007-x · Cited by 1

Evolving Interpretable Classification Models via Readability-Enhanced Genetic Programming

Abreu, J. V. T. D. S., Martins, D. M. L., & Neto, F. B. D. L. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022164 · Cited by 1

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Lang, V. (2022) · Springer eBooks · doi:10.1007/978-3-662-66285-4_4 · Cited by 1

Identifying steady state in the network dynamics of spiking neural networks

George, V. K., Gupta, A., & Silva, G. A. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13913 · Cited by 1

History, Core Concepts, and Role of AI in Clinical Medicine

Palm, C. (2023) · AI in Clinical Medicine · doi:10.1002/9781119790686.ch5 · Cited by 1

Smart Pruning of Deep Neural Networks Using Curve Fitting and Evolution of Weights

Islam, A., & Belhouari, S. B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25891-6_6 · Cited by 1

Numerical Representations of Chemical Data for Structure‐Based Machine Learning

Na, G. S. (2023) · Wiley eBooks · doi:10.1002/9781119819783.ch7 · Cited by 1

Zone-wise surrogate modelling (ZSM) of univariate systems

Srinivas, S. V., & Karimi, I. A. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108249 · Cited by 1

A review on image captioning system from artificial intelligence, machine learning and deep learning techniques

Revathi, B. S., & Meena, K. A. (2022) · i-manager’s Journal on Image Processing · doi:10.26634/jip.9.3.19054 · Cited by 1

Learning a simple multilayer perceptron with PSO

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Complex-Valued Neural Networks with Application to Wireless Communication: A Review

Iverson, S., & Liang, Q. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1256-8_49 · Cited by 1

Machine Learning with Neural Networks

Crowley, J. L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-24349-3_3 · Cited by 1

Computational Models of Categorization

Kurtz, K. J. (2023) · Cambridge University Press eBooks · doi:10.1017/9781108755610.015 · Cited by 1

Computational Modeling of Basic Cognitive Functionalities

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Clinical decision support methods

Greenes, R. A., & Fiol, G. D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91200-6.00019-x · Cited by 1

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Şen, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-29555-3_8 · Cited by 1

Enhanced Dense Layers Using a Quadratic Transformation Function

Gundawar, A., & Lodha, S. (2023) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-28324-6_1 · Cited by 1

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Fundamental Considerations on Representation Learning for Multimodal Processing

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Machine Learning Techniques for Acoustic Data Processing in Additive Manufacturing In Situ Process Monitoring: A Review

Taheri, H., & Zafar, S. (2023) · Materials Evaluation · doi:10.32548/2023.me-04356 · Cited by 1

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Towards ARSPI-Net: development of an efficient hybrid deep learning framework

Lane, A., Tang, W., & Nelson, B. D. (2023) · IEEE Long Island Systems, Applications and Technology Conference (LISAT) · doi:10.1109/lisat58403.2023.10179592 · Cited by 1

Application of Deep Learning Methods in Sports and Health Field

宋, 凯. (2023) · Advances in Applied Mathematics · doi:10.12677/aam.2023.127331 · Cited by 1

Walter Burns at SemEval-2023 Task 5: NLP-CIMAT - Leveraging Model Ensembles for Clickbait Spoiling

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Large-scale process models using deep learning

Gopaluni, R. B., Cao, L., & Cao, Y. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-823869-1.00009-0 · Cited by 1

A Neural-Network-Based Competition between Short-Lived Particle Candidates in the CBM Experiment at FAIR

Belousov, A., Kisel, I., & Lakos, R. (2023) · Algorithms · doi:10.3390/a16080383 · Cited by 1

Analyzing and Filtering Multimedia Digital Evidence with Artificial Intelligence

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Molecular Fingerprint Based and Machine Learning Driven QSAR for Bioconcentration Pathways Determination

Nascimben, M., Spriano, S., Rimondini, L., & Venturin, M. (2023) · SEMA SIMAI Springer series · doi:10.1007/978-3-031-35715-2_7 · Cited by 1

Multimedia Data Learning

Zhang, Z. M., & Zhang, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_19 · Cited by 1

What Does A Typical CNN “See” In An Emotional Facial Image?

Sannasi, M. V., Kyritsis, M., & Gray, K. L. H. (2023) · Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science · doi:10.11159/mvml23.114 · Cited by 1

Simulation-Based Material Property Analysis of 3D Woven Materials Using Artificial Neural Network

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A REVIEW ON ROLE OF AI IN DAMAGE ASSESSMENT INLAMINATED COMPOSITE STRUCTURES

Singh, A. P., Asgar, M. E., Ranjan, R., Kaushik, Y., Reji, J. K., Tyagi, T., & HMRITM (2023) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2023.v08i02.034 · Cited by 1

Multi-Activation Dendritic Neural Network (MA-DNN) Working Example of Dendritic-Based Artificial Neural Network

Tomov, K., & Момчева, Г. (2023) · Cybernetics and Information Technologies · doi:10.2478/cait-2023-0030 · Cited by 1

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Crow Way: An Optimization Technique for generating the Weight and Bias in Deep CNN

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Cómo los tecnólogos "responden" a lo que suele ser llamado "imagen". Una respuesta arqueológica medial al "cuestionamiento sobre la ontología cambiante de la imagen"

Vilchis, M. Á. L., Colín, M. S., & Ernst, W. (2023) · ElOrnitorrinco tachado · doi:10.36677/eot.v0i17.21567 · Cited by 1

Brain-inspired evolving and spiking connectionist systems

Kasabov, N. K. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-96104-2.00007-5 · Cited by 1

Implementation Deep Learning Method On Decision Support System For Smart Indonesia Card Scholarship At University Of KH. Bahaudin Mudhary Madura

Bachtiar, M. I., & Hidayat, R. (2023) · Technium Romanian Journal of Applied Sciences and Technology · doi:10.47577/technium.v17i.10095 · Cited by 1

Review and Analysis of the Development of Artificial Neural Networks

Bilokon, O. (2023) · Cybernetics and Computer Technologies · doi:10.34229/2707-451x.23.3.6 · Cited by 1

Artificial Neural Networks

Cerulli, G. (2023) · Statisctics and computing/Statistics and computing · doi:10.1007/978-3-031-41337-7_6 · Cited by 1

A COMPREHENSIVE OVERVIEW AND EVALUATION OF LINK PREDICTION TECHNIQUES

Brown, L., & Nel, S. (2023) · The South African Journal of Industrial Engineering · doi:10.7166/34-3-2960 · Cited by 1

From algorithmic thinking to thinking machines

Pasquinelli, M. (2023) · AI critique · doi:10.14361/9783839467664-005 · Cited by 1

Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis

Alfakeeh, A. S., Sharif, M. S., Zorto, A. D., & Pillonetto, T. (2023) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2023/8819052 · Cited by 1

Neural Networks

Acito, F. (2023) · Springer eBooks · doi:10.1007/978-3-031-45630-5_11 · Cited by 1

Artificial Intelligence in Phycochemicals Recognition

Dullius, A., Buffon, G., Faria, M., & Giuliatti, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-42026-9_5 · Cited by 1

Why psychologists should embrace rather than abandon DNNs

Yovel, G., & Abudarham, N. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x2300167x · Cited by 1

From Digital Twins to Digital Selves and Beyond – Perspectives for Social Modelling in a Trans-Humanist World

Barachini, F., & Stary, C. (2023) · Journal of Information & Knowledge Management · doi:10.1142/s0219649223500703 · Cited by 1

Redes neurais artificiais na modelagem de laminadores de encruamento

Rodrigues, I. P., Rodrigues, G. A., & dos Santos, B. L. (2023) · Cadernos UniFOA · doi:10.47385/cadunifoa.v18.n53.4672 · Cited by 1

Learning-Based Cryptography

Shandilya, S. K., Datta, A., & Nagar, A. K. (2023) · Studies in computational intelligence · doi:10.1007/978-981-99-7081-0_3 · Cited by 1

Нейрон на основі адаптивного нечіткого перетворення для сучасних моделей штучних нейронних мереж

Бодянський, Є. В., & Kostiuk, S. (2023) · International Scientific Technical Journal Problems of Control and Informatics · doi:10.34229/1028-0979-2023-6-7 · Cited by 1

A Projection Neuronal Smart WEB

Corredor, B. Y. A., Crespo, R. A. G., Montenegro, C., & Martelo, C. A. S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-44131-8_33 · Cited by 1

Data, machine learning, first-principles, and hybrid models in the petrochemical industry

Du, D., & Schmal, J. P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99135-3.00011-7 · Cited by 1

Mixtron: Bandit Online Multiclass Prediction with Implicit Feedback

Feng, W., Shi, H., Zhao, P., & Gao, X. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00115 · Cited by 1

Three Gradient-Based Optimization Methods and Their Comparison

李, 晶. (2024) · Statistics and Applications · doi:10.12677/sa.2024.131003 · Cited by 1

Erkennungsverfahren für KI-generierte Texte: Überblick und Architekturentwurf

Pröhl, T., Mohrhardt, R., Förster, N., Putzier, E., & Zarnekow, R. (2024) · HMD Praxis der Wirtschaftsinformatik · doi:10.1365/s40702-024-01051-w · Cited by 1

Gender Detection of Day-Old Chicks from Vocalization Data: An Experimental Study

Basu, J., Roy, R. K., Pal, M., Kannan, A., Ghosh, T. K., Ray, H., Ghosh, A., Rao, S. V. R., & CHATTERJEE, R. N. (2023) · IEEE India Conference · doi:10.1109/indicon59947.2023.10440685 · Cited by 1

Convolutional Neural Network in Deep Learning for Object Tracking: A Review

Dubey, U., & Barskar, R. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-8129-8_28 · Cited by 1

Prediction of Tunnelling-Induced Settlement Trough by Artificial Neural Networks

Soranzo, E., Pock, C., Guardiani, C., Wang, Y., & Wu, W. (2024) · Springer series in geomechanics and geoengineering · doi:10.1007/978-3-031-52159-1_9 · Cited by 1

A Hybrid Machine Learning Enabled Tourism Recommender System Providing a Context-Aware Experience to Tourists in Ireland

Lemma, T., Byrne, W., & Tadisetti, S. (2023) · International Conference on Advanced Engineering and Technologies (ICONNIC) · doi:10.1109/iconnic59854.2023.10467263 · Cited by 1

Machine Learning, Deep Learning und Artificial Intelligence

Gronwald, K.-D. (2024) · Springer eBooks · doi:10.1007/978-3-662-68668-3_6 · Cited by 1

Renal cell classification in highly multiplexed microscopy imaging using a biology-based decision tree classifier

Casella, G., Durkee, M. S., Ai, J., Cao, T., Ghosh, D., Andrade, M. S., Chang, A., Giger, M. L., & Clark, M. R. (2024) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3008519 · Cited by 1

User tendency-based rating scaling in online trading networks

Jeong, S., Kim, J., Lee, B. S., & Lim, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0297903 · Cited by 1

Computergenerierter Zufall als kreatives Moment in Malerei und Literatur

de Vries, A. (2024) · Digital humanities · doi:10.14361/9783839470039-004 · Cited by 1

Análisis comparativo de redes neuronales profundas para la predicción del precio de mercado de bitcoin

López-García, A. (2023) · Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA · doi:10.24310/recta.24.1.2023.19858 · Cited by 1

A Brief Overview of Machine Learning

Škrlj, B. (2024) · SpringerBriefs in computer science · doi:10.1007/978-3-031-57016-2_1 · Cited by 1

Quality evaluation of rice grains for sake brewing with a deep convolutional neural network

Taisuke, K. O. D. O., Nishimura, T., FUJIWARA, H., & WAKAI, Y. (2021) · JOURNAL OF THE BREWING SOCIETY OF JAPAN · doi:10.6013/jbrewsocjapan.116.415 · Cited by 1

An optimal control framework for adaptive neural ODEs

Aghili, J., & Mula, O. (2024) · Advances in Computational Mathematics · doi:10.1007/s10444-024-10149-0 · Cited by 1

Sophisticated State-Aware Roll Force Prediction

Sparrer, S., & Frenzel, J. (2024) · Iron & Steel Technology Conference and Exposition (AISTech) · doi:10.33313/388/117 · Cited by 1

Neural networks and types of error metrics used for flood predictions: review from 2015 to 2021

Ruezzene, C. B., Rocha, M., Parizotto, D., de Lemos, R. M. T., Barbosa, M. A. G. A., de Miranda, R. B., & Mauad, F. F. (2024) · Caderno Pedagógico · doi:10.54033/cadpedv21n6-069 · Cited by 1

Felügyelt gépi tanulási módszerek alkalmazása a pszichológiai kutatásokban

Hajdú, N., Szászi, B., Aczél, B., & Nagy, T. (2024) · Magyar Pszichológiai Szemle · doi:10.1556/0016.2024.00052 · Cited by 1

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TOYOSHI, T., & Wada, Y. (2024) · Transactions of the JSME (in Japanese) · doi:10.1299/transjsme.24-00072 · Cited by 1

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Development of Linear, Nonlinear and Hybrid Models for Forecasting Sugarcane Yield

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