Deep Learning: Foundations and Concepts

Investigating Brain Activity of Children with Autism Spectrum Disorder During STEM-Related Cognitive Tasks

Penmetsa, H., Abbasi, R., Yellamilli, N., Winkelman, K., Chan, J., Hwang, J., & Cho, K. T. (2025) · Information · doi:10.3390/info16100880 · Cited by 1

Lumped Parameter Thermal Network for Automotive Components: Modeling, Simulation, System Identification, and Parameter Estimation

Kehe, M., Enke, W., & Rottengruber, H. S. (2025) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2025-01-5078 · Cited by 1

RSNet: A Neural Network Model for Picking of Seismic Wave Arrival Times Based on Mesofluctuations

Nigmatullin, R. K., & Imashev, S. A. (2025) · Journal of Volcanology and Seismology · doi:10.1134/s0742046325700289 · Cited by 1

AXE: A Task Decomposition Approach to Learned LSM Tuning

Huynh, A., Saha, A., Chaudhari, H. A., & Athanassoulis, M. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3773731.3773735 · Cited by 1

Estimating the Rectified Linear Unit Activation Function in Deep Learning Using the Grouping-Adjusted Median Estimator: An Evaluation of Max Pooling

Nawata, K. (2026) · Open Journal of Statistics · doi:10.4236/ojs.2026.161001 · Cited by 1

Physics-Conditioned Transformer for Surrogate Modelling of Aerofoil Pressure Distribution

Anhichem, M., Tagarielli, V. L., Montomoli, F., & Timme, S. (2026) · AIAA SciTech Forum · doi:10.2514/6.2026-0687 · Cited by 1

Beyond the Algorithm: A Critical Synthesis for Human-Centered AI in K-12 STEM Education

Lodhi, S. S., & Roehrig, G. (2026) · AI and Ethics · doi:10.1007/s43681-026-00994-8 · Cited by 7

Generalized quantum Hadamard test for machine learning

Mehta, V., Choudhury, A., & Roy, U. (2026) · Quantum Information Processing · doi:10.1007/s11128-026-05164-7 · Cited by 1

Deconstructing deep imbalanced regression: a comprehensive review and experimental evaluation

Pütz, N. C., Brandt, J. U., Hilbert, M., Raponi, E., Bäck, T., & Bartz–Beielstein, T. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11570-1 · Cited by 1

Absorbing state dynamics of stochastic gradient descent

Zhang, G., & Martiniani, S. (2026) · Physical review. E · doi:10.1103/sbv7-syp7 · Cited by 6

High-resolution automated mapping of potential Aedes larval container habitats using drone imagery and supervised machine learning in Dar es Salaam, Tanzania

Hahm, M., Guthula, V. B., Chilaule, R. V. E., Gominski, D., Limwagu, A. J., Chaki, E., Okumu, F. O., Tingitana, L., Hermund, A., Tusting, L. S., et al. (2026) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0014361 · Cited by 1

Deep learning for image-based structural element damage assessments in post-earthquake buildings: a systematic review

ABEYSURIYA, K. G., Ciupala, M. A., Ghorashi, S. A., & İlki̇, A. (2026) · AI in Civil Engineering · doi:10.1007/s43503-026-00099-5 · Cited by 1

Eleven quick tips to reduce overfitting in machine learning

Chicco, D., & Oneto, L. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01246-y · Cited by 1

Neural Network-Integrated Multistatic Sensing for Joint Angle Estimation in Cell-Free JCAS Systems

Ayten, F., Ilter, M. C., Jain, A., Lohan, E. S., & Valkama, M. (2025) · IEEE International Symposium on Joint Communications & Sensing (JC&S) · doi:10.1109/jcs64661.2025.10880641 · Cited by 1

Introduction

Krist, C., Kubsch, M., & Wulff, P. (2025) · Springer texts in education · doi:10.1007/978-3-031-74227-9_1 · Cited by 1

Toward Measuring and Understanding the Overvalidation Phenomena

Mori, F., Ciná, A. E., Roli, F., Anguita, D., & Oneto, L. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00288 · Cited by 1

PetNet: Detection of Diseases in Dog Skin

Gomez-Lozano, A., & Montalvo, P. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-84078-4_16 · Cited by 1

Machine Learning and Deep Learning

Nichelatti, M. (2025) · TELe-Health · doi:10.1007/978-3-031-81709-0_7 · Cited by 1

Leveraging Land Cover Priors for Isoprene Emission Super-Resolution

Ummerle, C., Giganti, A., Mandelli, S., Bestagini, P., & Tubaro, S. (2025) · Remote Sensing · doi:10.3390/rs17101715 · Cited by 1

Deep Learning-based Grading Model for Middle School Physical Fitness Test of Table Tennis

You, Y. (2024) · International Conference on Machine Intelligence and Digital Applications (MIDA) · doi:10.1145/3662739.3669986

Evaluation of Portuguese Population’s Perspectives on Chemical Innovations for Sustainable Development

Figueiredo, M., & Vicente, H. (2024) · Journal of Sustainability Research · doi:10.20900/jsr20240057

Deep Learning Primer

Jain, A. K., Ross, A. A., Nandakumar, K., & Swearingen, T. (2024) · Texts in computer science · doi:10.1007/978-3-031-61675-4_2

Advanced Low-Cost Technology for Assessing Metal Accumulation in the Body of a Metropolitan Resident Based on a Neural Network Model

Tunakova, Y. A., Новикова, С. В., Валиев, В. С., Danilaev, M. P., & Faizullin, R. (2024) · Sensors · doi:10.3390/s24227157

Parameter Estimation Using Artificial Intelligence

Lecca, P. (2024) · SpringerBriefs in statistics · doi:10.1007/978-3-031-74748-9_5

On Divergence-Free Neural ODE for Classification

Jarraya, Z., Drumetz, L., Benaïchouche, S., Salem, D. B., & Rousseau, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78186-5_17

人工知能システムを監督するための様々な技術の紹介

Ishida, T. (2024) · Denshi Jouhou Tsuushin Gakkai Tsuushin Sosaieti magajin/B-plus/Denshi Jouhou Tsuushin Gakkai Tsuushin Sosaeti magajin · doi:10.1587/bplus.18.232

Large Language Models: Ethics and Norms in the European Union

Ciullo, M. (2024) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine62247.2024.10795986

Application of Deep Learning to Generate Auto Player Mode in Car Based Game

Patadiya, K., Jain, R., Moteriya, J., Palaniappan, D., Kumar, P., & Premavathi, T. (2024) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn63059.2024.10847417

Moritz Hardt, Benjamin Recht: Patterns, Predictions, and Actions

Voigtlaender, F. (2025) · Mathematische Semesterberichte · doi:10.1007/s00591-025-00386-9

Convolutional Neural Networks: Key Components and ArchitecturesPreprint

Fischer, M. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5125488

Modelo de visión artificial basada en redes neuronales convolucionales para identificación de mazorca negra en plantaciones de cacao

Villalobos-Culqui, C., Valles-Coral, M. A., Plata, C. E. G. R., & Tuesta-Hidalgo, O. A. (2025) · Revista Cientifica de Sistemas e Informatica · doi:10.51252/rcsi.v5i1.678

Power System Transient Stability Prediction with a CNN Trained with Steady-State Input Variables

Ramirez-Gonzalez, M., Sevilla, F. R. S., & Korba, P. (2024) · International Conference on Power and Energy Engineering (ICPEE) · doi:10.1109/icpee64457.2024.10949969

DDU-Net: A Domain Decomposition-Based CNN for High-Resolution Image Segmentation on Multiple GPUs

Verburg, C., Heinlein, A., & Cyr, E. C. (2025) · IEEE Access · doi:10.1109/access.2025.3561033 · Cited by 4

AI-driven inorganic materials by design

Zhu, R. (2024) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/183779

Learning with Almost Invariant Sets in Neural Oscillatory ODEs

Inui, Y., Konishi, T., & Kawahara, Y. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6954-7_24

The First- and Second-Order Features Adjoint Sensitivity Analysis Methodologies for Neural Integro-Differential Equations of Volterra Type: Mathematical Framework and Illustrative Application to a Nonlinear Heat Conduction Model

Cacuci, D. G. (2025) · Journal of Nuclear Engineering · doi:10.3390/jne6030024

MSPKD: multi spatial projectors for knowledge distillation in semantic segmentation

Park, Y., Hwang, J., Jeong, S.-M., & Lee, E. C. (2025) · Machine Vision and Applications · doi:10.1007/s00138-025-01721-9

The First- and Second-Order Features Adjoint Sensitivity Analysis Methodologies for Fredholm-Type Neural Integro-Differential Equations: I. Mathematical Framework

Cacuci, D. G. (2025) · Processes · doi:10.3390/pr13072258

The First- and Second-Order Features Adjoint Sensitivity Analysis Methodologies for Fredholm-Type Neural Integro-Differential Equations: An Illustrative Application to a Heat Transfer Model—Part II

Cacuci, D. G. (2025) · Processes · doi:10.3390/pr13072265

Segmentação de Objetos em Tempo Real na Fruticultura: Contribuições para Agricultura 4.0

dos Santos, V. R., & Guedes, E. B. (2025) · Anais do Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA) · doi:10.5753/wcama.2025.7970

Physics Informed Neural Networks: Fundamentals and Application to Phase Field Models

Haridasan, N., Krishnaveni, V. S., Sandra, S., & Abhijith, M. S. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-89983-6_5

Além do Desempenho: Um Estudo da Confiabilidade de Detectores de Deepfakes

Lopes, L., Laroca, R., & Gregio, A. R. A. (2025) · Simpósio Brasileiro de Cibersegurança (SBSeg) · doi:10.5753/sbseg.2025.11431

The Impact of Data Representation on Predicting Aircraft Component Removals

Boleto, G., Sommariva, G., Oneto, L., Stefano Rovetta, A., Calvo, A. B., Martorano, L., Porretti, C., & Anguita, D. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-00137-5_8

Deep Learning for Regular Raster Spatio-Temporal Prediction: An Overview

Capone, V., Casolaro, A., & Camastra, F. (2025) · Information · doi:10.3390/info16100917

Classification Filtering

Bayram, İ. (2025) · Machine learning for signal processing ... · doi:10.1109/mlsp62443.2025.11204245

Towards a Taxonomy for Application of Machine Learning and Artificial Intelligence in Building and District Energy Management Systems

Lichtenegger, K., Ahammer, F., Schopper, F., Muschick, D., & Gölles, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-03098-6_7

IE10.com. Reconstructing Latin inscriptions with Aeneas

Krause, T. B. (2025) · Folia Linguistica · doi:10.1515/flin-2025-2027

A Hierarchical Nonparametric Learning Approach to Estimate the Origin-Destination Matrix Using Volunteered Geographic Information and Population Statistics

Aghayan, P., Ghaffari, H., Basit, A., Moser, G., & Sacco, N. (2025) · International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) · doi:10.1109/mt-its68460.2025.11223543

Model Concepts for Language Processing

Müller, H. M. (2025) · Springer eBooks · doi:10.1007/978-3-662-71906-0_2

Data-driven predictions for dark photon and millicharged particle production

Allison, E., & Blinov, N. (2026) · Physical review. D/Physical review. D. · doi:10.1103/vzmz-jbck

Artificial Neural Networks for Decision-Making Industry-Wide Applications and Innovations

Sirajudeen, N. A., Thayanandeswari, C. S. S., & Maheen, A. M. (2025) · Artificial intelligence · doi:10.5772/intechopen.1012770

Let’s Talk AI with Computer Science Expert Jakob Rehof

Rehof, J., & Steffen, B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-09008-9_28

Optimizing the Object using Real-Time Computer Vision and Neural Network

Aguero, R., Noah Olson, D., & Selwyn, J. (2025) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.f4684.15011025

Aydi: An Al-Powered App to Teach Saudi Sign Language

Alwadei, S. A., AL-Sama, A., Alkhunjuf, F., Alharthi, A., Madkhli, S. A., & Kulayb, M. A. (2025) · IEEE International Conference on e-Business Engineering (ICEBE) · doi:10.1109/icebe68123.2025.00022

Resilient Anomaly Detection in Ocean Drifters with Unsupervised Learning, Deep Learning Models, and Energy-Efficient Recovery

Guo, C. A., Zhao, J., & Pinsky, E. (2026) · Oceans · doi:10.3390/oceans7010005

Fake Date Tests: Can We Trust In-sample Accuracy of LLMs in Macroeconomic Forecasting?Preprint

Eliseev, A., & Seleznev, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6035916

Development of an Automated Knee Rehabilitation Device

Handalian, L., Ferreira, E. D., Hernández-Martínez, E. G., & Ramírez‐Neria, M. (2025) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit66093.2025.11321529

Learning DynamicsPreprint

Hermosis, E. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.5998659

Urban local climate zone classification through deep learning using spatio-temporal thermal imagery

van Capel, M., Rafiee, A., & Lindenbergh, R. (2026) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2026.101889

Center of Gravity Estimation for Rotary-Wing Aircraft Using Neural Networks

Davis, A. (2026) · AIAA SciTech Forum · doi:10.2514/6.2026-0195

Simulation of multivariate extremes: A Wasserstein–Aitchison GAN approach

Lhaut, S., Rootzén, H., & Segers, J. (2026) · Extremes · doi:10.1007/s10687-026-00530-1

Shape-aware deep learning for models of production

Wei, Z., Sang, H., Prokhorov, A. B., & Ma, Y. (2026) · Journal of Productivity Analysis · doi:10.1007/s11123-026-00797-3 · Cited by 1

Efficient parameter selection in laser welding via Bayesian optimization

Haas, M., Steinhoff, R., Powell, J., Zaiß, F., Wahl, J., Hagenlocher, C., & Michalowski, A. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-026-02799-2

Enhancing deep hashing with graph filters and autoencoder-based embeddings

Kim, S., Kim, K., Min, S., Kim, W., Hwang, D., & Kim, D. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3644

Euclid Quick Data Release (Q1)

Bazzanini, L., Angora, G., Bergamini, P., Meneghetti, M., Rosati, P., Acebrón, A., Grillo, C., Lombardi, M., Ratta, R., Fogliardi, M., et al. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202557590 · Cited by 4

Efficient Object Detection and Range Prediction in a Poorly Lit Environment Using a Modified GAN

Ramsai, N., Satish, K., Hrishikesh, V. R., & Sridharan, K. (2025) · International Conference on Robotics, Automation and Artificial Intelligence (RAAI) · doi:10.1109/raai67517.2025.11423364

ML-Based Prediction Framework for Varying Traffic Signal Control Strategies and Its GLOSA-Application

Malena, K., Link, C., Gausemeier, S., & Trächtler, A. (2025) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc60802.2025.11423716

Bruk av utbredelsesmodeller for arter og naturtyper i arealplanlegging og naturforvaltning

Simensen, T., Bryn, A., Chipperfield, J. D., Halvorsen, R., Naas, A. E., Töpper, J. P., Vandvik, V., & Velle, L. G. (2026) · Kart og plan · doi:10.18261/kp.119.1.3

Detecting Phase Transitions from Data Using Generative Learning

Zhou, X. K., Mi, Y., & Zhang, P. (2026) · Entropy · doi:10.3390/e28040406

Methodology and tooling for energy-efficient neural networks computation and optimization

Waltsburger, H. (2024) · theses.fr (ABES) · doi:10.70675/6aadb508z47bfz4dc6z95b6zbd5a77514a0a

Feasibility of Interactions and Network Inference of Online Social Networks

Papanastasiou, E. (2024) · theses.fr (ABES) · doi:10.70675/dca5b034z74b1z47b5z80e7z67e704a701d5

Estimating global input relevance and enforcing sparse representations with a scalable spectral neural network approach

Chicchi, L., Buffoni, L., Febbe, D., Giambagli, L., Marino, R., & Fanelli, D. (2026) · Communications Physics · doi:10.1038/s42005-026-02614-7 · Cited by 2

Méthodes d'apprentissage pour l'amélioration des récepteurs MIMO/NOMA basés sur des algorithmes bayésiens itératifs

Michon, A. (2025) · theses.fr (ABES) · doi:10.70675/b22bfe42zb077z4c7cz9978z406381a55a7d

Neural network quantization methods for FPGA on-board processing of satellite images

Gernigon, C. (2024) · theses.fr (ABES) · doi:10.70675/889e2f05z3097z4bccz9415z7a6540dd1211

Designing Agentic AI-Based Screening for Portfolio InvestmentPreprint

Caner, M., Capponi, A., Sun, N., & Tan, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6461607

Empowering Credit Unions with Explainable AI: A Hierarchical Loan Approval SVM Model

Novillo, E., & Hurtado, R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-19852-5_14

Deep Learning-Based Robust Head Pose Estimation

Spoleto, A. J., Staiano, A., Hauber, G., Lettiero, M., Barra, P., & Camastra, F. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-981-95-4072-3_34

Emotion Recognition Tool for Brain-Computer Interface

Angelis, D. D., Manco, M., Garzia, E., Nardo, E. D., & Ciaramella, A. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-981-95-4072-3_11

Research on Adaptive Fault Feature Tracking Method and Explainability of Vacuum Interrupters Based on ResNet-SHAP

Gong, J., & Tan, Y. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-7330-1_56

Rethinking reference data quality: the role of mixed pixels in remote sensing classification

Corrêa, S. P. L. P., Reis, M. S., da Silva, M. P., dos Santos, J. A., Oliveira, H. N., Oliveira, J. M. G., Körting, T. S., dos Santos, A. P., & Dutra, L. V. (2026) · International Journal of Remote Sensing · doi:10.1080/01431161.2026.2664863

Multiclass Audio Detection of Violent Events Using Convolutional Neural Networks

Merit, K., & Beladgham, M. (2026) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661825701275

An Exploratory Framework for Applying Machine Learning Proxy Models in Quantitative Finance Education: A Project-Based Learning Approach

Lu, J. (2026) · International Conference on Big Data and Informatization Education (ICBDIE) · doi:10.1145/3806980.3806982

Machine Learning for Diagnosis and Differentiation of Central Disorders of Hypersomnolence: A Systematic Review

Helmy, A., Fregolente, L. G., von Gernler, M., Morand, R., van der Meer, J., Schmidt, M. H., Mougiakakou, S. G., Tzovara, A., & Bassetti, C. L. (2026) · European Journal of Neurology · doi:10.1111/ene.70661

Hyperjerk Oscillator with One Nonlinear Element

Pham, V., & Munoz-Pacheco, J. M. (2026) · Springer eBooks · doi:10.1007/978-3-032-25152-7_3

Transformer-Based Sleep Staging Using Continuous Photoplethysmography Signals

Quino, J. A. P., Cárdenas, D. A. C., de Toledo, M. A. F., Dias, F. M., Ribeiro, E., Krieger, J. E., & Gutierrez, M. A. (2026) · doi:10.5753/sbcas.2026.21548

Evaluating a multiplex diagnostic test using partially ordered Bayes classifier

Cheung, Y. K., & Kuhn, L. (2026) · The Annals of Applied Statistics · doi:10.1214/26-aoas2156

Infusing Computational Science Into Computer Science: A Nifty SIMD Assignment

Schoder, J., Bosse, T., & Bücker, H. M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29918-5_34

The Janus-Faced Nature of AI in STEM Education

Wulff, P. (2026) · Maarif global education series · doi:10.1007/978-981-95-9023-0_4

Latent diffusion model for super-resolution of rock CT images

Tomami, K., Okamoto, A., & Omori, T. (2026) · Nonlinear Theory and Its Applications IEICE · doi:10.1587/nolta.17.751

Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings

Westny, T., Axelsson, D., Olofsson, B., & Frisk, E. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37461-5_3

Artificial neural networks and multivariate approaches to identifying donor parents for yield and downy mildew tolerance in cucumber ( Cucumis sativus L.)

Das, S., Mohanta, R., Roy, S., Sarkar, D., Mukherjee, A., Sarkar, A., Chattopadhyay, A., & Pandit, M. K. (2026) · International Journal of Vegetable Science · doi:10.1080/19315260.2026.2695690

Defining Artificial Intelligence Content Knowledge (AI-CK) for Teachers with a Data-Driven and Expert-Validated Approach

Robinig, W., & Steinbauer-Wagner, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30784-2_18

Machine Learning: A Concise Introduction, 2nd Edition, Steven W.Knox Wiley, 2026, xiv + 419 pages, $109.95, hardcover ISBN: 978‐1‐394‐32525‐2

Young, A. (2026) · International Statistical Review · doi:10.1111/insr.70055

Adaptive Wavelet-Based Function Neural Approach with L2 Regularization for Heart Disease Classification

Padhan, B., & Senapati, M. R. (2026) · AI Computer Science and Robotics Technology · doi:10.5772/acrt.20250221

Deep learning embedded latent class joint modelling of time-to-event and longitudinal data

Harris, T., Rad, N. N., & Skhosana, S. B. (2026) · Statistical Methods in Medical Research · doi:10.1177/09622802261465334

Exploiting the Latent Space of Deep AutoEncoders for the Identification of Signal Pulses in Noisy Time-Series

Anastasi, G. A., Albergo, S., NAPOLI, M. D., Pino, N., Puglia, S. M. R., & Tricomi, A. R. (2026) · Particles · doi:10.3390/particles9030085

Fault Detection and Diagnosis Within Battery Systems Using Autoencoders and Explainable AI

Theiler, M., Karaali, C., & Endisch, C. (2026) · International Power Electronics Conference (IPEC) · doi:10.23919/ipec-nagasaki2026-ec64663.2026.11596599

Digital Twin-Enabled Smart Driver Whole-Body Vibration Monitoring for Intelligent Transportation Systems

Goutham, B., & S., N. A. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-8954-7.ch011

Predicting Tourist Revisit Intention Using Machine Learning: A Comparative Analysis of Models and Feature Importance

Pangaribuan, K. D. C., Soegiarto, D., Ervina, E., Mansur, D. M., Moorthy, K., & Pratondo, A. (2026) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict71158.2026.11620950

Performance of Sequential and Quickest Detection Based on Machine Learning

Harvey, R. J., Braca, P., Millefiori, L. M., & Willett, P. (2026) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2026.3718771

Deep Learning Foundations

Jo, T. (2023) · Springer eBooks · doi:10.1007/978-3-031-32879-4 · Cited by 4

Neural Networks and Deep Learning

Aggarwal, C. C. (2023) · Springer eBooks · doi:10.1007/978-3-031-29642-0 · Cited by 190

Deep Learning with Python

Ketkar, N. S. (2017) · Apress eBooks · doi:10.1007/978-1-4842-2766-4 · Cited by 477

Deep Learning: Concepts and Architectures

(2019) · Studies in computational intelligence · doi:10.1007/978-3-030-31756-0 · Cited by 65

Advances in Deep Learning

Wani, M. A., Bhat, F. A., Afzal, S., & Khan, A. I. (2019) · Studies in big data · doi:10.1007/978-981-13-6794-6 · Cited by 243