A Very Brief Introduction to Machine Learning With Applications to Communication Systems

Maximum Likelihood from Incomplete Data Via the EM Algorithm

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Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268

Book Review: Pattern Recognition and Machine Learning

Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066

Learning Internal Representations by Error Propagation

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Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612

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Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

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Reinforcement Learning: An Introduction

Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,825

Loihi: A Neuromorphic Manycore Processor with On-Chip Learning

Davies, M., Srinivasa, N., Lin, T.-H., Chinya, G. N., Cao, Y., Choday, S. H., Dimou, G. D., Joshi, P., Imam, N., Jain, S., et al. (2018) · IEEE Micro · doi:10.1109/mm.2018.112130359 · Cited by 4,186

A Sentiment Analysis Approach for Abusive Content Detection using Improved Dataset

Abdou, A. L. N. N., & Tagne, E. F. (2021) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci54926.2021.00283 · Cited by 1

КЛЮЧОВІ НАПРЯМКИ ДОСЛІДЖЕНЬ СТІЛЬНИКОВИХ МЕРЕЖ НА ШЛЯХУ ДО 6G (ОГЛЯД)

Одарченко, Р. С., Dyka, T., Жарова, О., Odarchenko, M., Жога, В., & Слободян, О. (2022) · Science-based technologies · doi:10.18372/2310-5461.55.16904 · Cited by 1

User Selection and Pairing for Future Power Domain Non-Orthogonal Multiple Access (PD-NOMA) using Deep Learning Techniques

Kavitha, G., & Deny, J. (2022) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v10i1s.5884 · Cited by 1

Learning of Doppler Tolerant Radar Detectors for Noise Waveforms

Wensell, K. P., Zhou, J., Haimovich, A. M., Young, E. A., & Vo, L. T. (2023) · Conference on Information Sciences and Systems · doi:10.1109/ciss56502.2023.10089751 · Cited by 1

Vector Control of PMSM Drive in Electric Vehicles Using SVM Regression Approach

Tom, A. M., & Daya, J. L. F. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-2100-3_28 · Cited by 1

Companies’ E-waste Estimation Based on General Equilibrium Theory Context and Random Forest Regression Algorithm in Cameroon: Case Study of SMEs Implementing ISO 14001:2015

Djoukoue, G. T., Teledjieu, I. D., & Bai, S. (2023) · Journal of Management Science & Engineering research · doi:10.30564/jmser.v6i2.5862 · Cited by 1

Adaptive Approach for Dynamic Spectrum Utilization in Wireless Communication System

Niranjan, S. K., Sanjay, L. N., & Anil, B. N. (2023) · International Journal of Performability Engineering · doi:10.23940/ijpe.23.08.p5.526535 · Cited by 1

Customer churn prediction in telecommunication company

Biomantoro, D., & Kusuma, G. P. (2023) · AIP conference proceedings · doi:10.1063/5.0128741 · Cited by 1

Error-Tolerant Techniques for Classifiers Beyond Neural Networks for Dependable Machine Learning

Liu, S., Reviriego, P., Tang, X., & Lombardi, F. (2024) · Springer eBooks · doi:10.1007/978-3-031-42478-6_7 · Cited by 1

Analysis of a Scheduling System as a Proposal for Automatic Generation Using Machine Learning

Mitre, R. E., Quezada, A., Aguiñaga, A. R., Jiménez, S., & Téllez, A. C. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-54235-0_6 · Cited by 1

Design of a Deep Learning based Intelligent Receiver for a Wireless Communication System

Drakshayini, M. N., Kounte, M. R., & Ravindra, C. (2024) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.120132 · Cited by 1

From Theory to Practice: Implementing Meta-Learning in 6G Wireless Infrastructure

Zeshan, A., Ouameur, M. A., Alam, M. Z., & Le, T.-A. D. (2024) · Telecom · doi:10.3390/telecom5040063 · Cited by 1

Evaluating The Integration Of Ai In Digital Marketing Automation

Ahuja, P. R. (2024) · African Journal of Biomedical Research · doi:10.53555/ajbr.v27i4s.5665 · Cited by 1

Channel estimation in ISAC-IRS systems using machine learning approaches

Liu, Y., Al-Nahhal, I., Dobre, O. A., & Wang, F. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-322143-9.00012-6 · Cited by 1

Long-Term Water Temperature Forecasting in Fish Spawning Grounds Downstream of Hydropower Stations Using Machine Learning

Zhang, D., Ma, Y., Jiang, A., Ren, Y., Lin, J., Peng, Q., & Jin, T. (2025) · Sustainability · doi:10.3390/su17104514 · Cited by 1

Comparative Analysis of CNN and LSTM for Channel Distribution Learning and Classification in Beyond 5G

Kaya, O., & Shah, A. F. M. S. (2025) · International Symposium on Innovative Approaches in Smart Technologies (ISAS) · doi:10.1109/isas66241.2025.11101891 · Cited by 1

Scalable Min-Max Optimization via Primal-Dual Exact Pareto Optimization

Park, S., Vlaski, S., & Hanzo, L. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226560 · Cited by 1

Cancer Prediction and Insurance Eligibility using Machine Learning Techniques

More, S. S., Lobo, V. B., Laban, R. M., Panchal, S., Patil, M., & Pathak, G. (2020) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces48766.2020.9137936 · Cited by 1

DeEQ: Deep Equalization for Large MIMO Systems.

Hummert, M., Wübben, D., & Dekorsy, A. (2020) · WSA · Cited by 1

PlaPhy-SMD: A Database System of Plants and their Phytochemicals Integrated with Small-Molecule Drugs and their Condition and Gene Associations with Clustering and Product Ingredient Matching

Recario, J. M., Solano, G. A., & Lao, A. R. (2020) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa50023.2020.9284382 · Cited by 1

Investigating batch normalization in spoken language understanding

Jagdale, S., & Shah, M. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1812/1/012022 · Cited by 1

Hierarchical RL for Load Balancing and QoS Management in Multi-Access Networks

Ornatelli, A., Tortorelli, A., Giuseppi, A., & Priscoli, F. D. (2021) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med51440.2021.9480246 · Cited by 1

Leveraging Advanced Analytics to Understand the Impact of the COVID-19 Pandemic on Trends in Substance Use Disorders

Kleczyk, E. J., Bana, J., & Arora, R. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.99639 · Cited by 1

Applying Machine Learning Algorithms to Predict Endometriosis Onset

Kleczyk, E. J., Yadav, T., & Amirtharaj, S. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.101391 · Cited by 1

Multi-class Weather Classification: Comparative Analysis of Machine Learning Algorithms

Mishra, A., Roy, G. K., & Singla, K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-5689-7_27 · Cited by 1

Video object segmentation for automatic image annotation of ethernet connectors with environment mapping and 3D projection

Dantas, M. S. M., Dreyer, P., Bezerra, D., Reis, G. A. D. J., de Souza, R. G., Lins, S., Kelner, J., & Sadok, D. F. H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13128-z · Cited by 1

Joint Fine Time Synchronization and Channel Estimation Using Deep Learning for Wireless Communication Systems

Wang, C.-L., & Hsieh, C.-C. (2022) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2022-spring54318.2022.9860932 · Cited by 1

Joint Design of Radar Waveform and Detector via End-to-end Learning with Waveform ConstraintsPreprint

Jiang, W., Haimovich, A. M., & Simeone, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.09694 · Cited by 1

End-to-End Deep Learning in Optical Fibre Communication Systems

Karanov, B. (2020) · UCL Discovery (University College London) · Cited by 1

Challenges in Wireless Communication Channel Characteristic Identification using Machine Learning: A ReviewPreprint

Kulkarni, N., Nalbalwar, S. L., & Nandgaonkar, A. B. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3916357 · Cited by 1

A Detailed Analysis of the Multi-Class Classification Problem in Network Intrusion Detection using Resampling Techniques

Ahsan, R. (2021) · Carleton University's Institutional Repository (MacOdrum Library, Carleton University) · doi:10.22215/etd/2021-14657 · Cited by 1

Downlink Video Communications for Enhanced Hybrid NOMA using Deep Learning ModelPreprint

Kaveri, V., & Deepa, T. (2022) · Research Square · doi:10.21203/rs.3.rs-2055180/v1 · Cited by 1

Application of Multivariate Statistical Analysis and Machine Learning to Sensory Data Analysis

Pinheiro, A. C. M., Filho, E. G. A., Ribeiro, M. N., Rodrigues, J. F., Pimentel, T. C., Silva, L. K. D. F., Rodrigues, S., Esmerino, E. A., & da Cruz, A. G. (2023) · doi:10.1002/9781119619383.ch4 · Cited by 1

Improved accuracy for estrous cycle staging using supervised object detectionPreprint

Babaev, B., Goyal, S., & Ross, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.08.593231 · Cited by 1

Towards enforcing network slicing in 5G networks

Bakri, S. (2021) · doi:10.70675/6801939bz3184z4784z9441z72d292b51ee0 · Cited by 1

A Machine Learning Approach to Genome Assessment

Thrash, C. (2019) · Mississippi State University

Machine Learning in 5G Multimedia Communications

Milovanovic, D. A., Bojkovic, Z. S., & Kukolj, D. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-5225-9643-1.ch017

Semi-automatic Annotation of OCT Images for CNN Training

Schleier, S., Stolz, N., Langner, H., Hasan, R., Roschke, C., & Ritter, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49059-1_49

Update-Based Machine Learning Classification of Hierarchical Symbols in a Slowly Varying Two-Way Relay Channel

Kolář, J., Sýkora, J., & Hron, P. (2020) · Mathematics · doi:10.3390/math8112007

Resource allocation optimization for future wireless communication systems

Su, B. (2020) · Lancaster EPrints (Lancaster University) · doi:10.17635/lancaster/thesis/1115

Contextual multi-armed bandits with structured payoffs

Qureshi, M. A. (2020) · Bilkent University Institutional Repository (Bilkent University)

ConCrete MAP: Learning a Probabilistic Relaxation of Discrete Variables for Soft Estimation with Low Complexity.Preprint

Beck, E., Bockelmann, C., & Dekorsy, A. (2021) · arXiv (Cornell University)

Developing an automated fall armyworm identification, early warning and monitoring system using a convolution neural network

Chulu, F. (2020)

Applications of Machine Learning in Steganography for Data Protection and Privacy

Bartere, M. M., Bohra, S., Adakane, P. K., & Kumar, B. S. (2021) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-7998-7160-6.ch014

Frame Detection with Deep Learning

Yıldırım, M., & SOKULLU, R. İ. (2021) · Celal Bayar Üniversitesi Fen Bilimleri Dergisi · doi:10.18466/cbayarfbe.693942

Machine Learning for Channel Estimation and Hybrid Beamforming in Millimeter-Wave Wireless Networks

Peken, T. (2021) · UA Campus Repository (The University of Arizona)

Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexity: CMDNetPreprint

Beck, E., Bockelmann, C., & Dekorsy, A. (2021) · arXiv (Cornell University)

Bio medical image retrieval using support vector machine

Surya, S. (2021) · AIP conference proceedings · doi:10.1063/5.0058251

Data Handling Approach for Machine Learning in Wireless Communication: A Survey

Kulkarni, N. S., Nalbalwar, S. L., & Nandgaonkar, A. B. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-2008-9_10

A Novel Approach for Women Security with Information Fusion for Multi-Sensory Data

Suryawanshi, P. K., Kadam, A., N.Patil, V., & Rao, G. V. R. S. (2021) · Turkish Online Journal of Qualitative Inquiry

Deep Clustering and Deep Network Compression

Alqahtani, A. (2021) · Cronfa (Swansea University) · doi:10.23889/suthesis.57269

Developing novel radio resource management techniques for 5G and beyond

Yazar, A. (2020)

An Outlook on the Interplay of Machine Learning and Reconfigurable Intelligent Surfaces: An Overview of Opportunities and LimitationsPreprint

Mohjazi, L., Zoha, A., Bariah, L., Muhaidat, S., Sofotasios, P. C., Imran, M. A., & Dobre, O. A. (2021) · doi:10.36227/techrxiv.16606718.v1

Design Simulation and Performance Assessment of Improved Channel Estimation for Millimeter Wave Massive MIMO Systems

Sharma, A., Sharma, A. K., & Balai, L. N. (2021) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v9i5.5486

Endometriosis - Recent Advances, New Perspectives and Treatments

Gonçalves, G. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.95742 · Cited by 1

The Genomics of Industrial Process through the Qualia of Markovian BehaviourPreprint

Danishvar, M., Danishvar, S., & Mousavi, A. (2022) · doi:10.36227/techrxiv.19102523.v1

The Genomics of Industrial Process through the Qualia of Markovian BehaviourPreprint

Danishvar, M., Danishvar, S., & Mousavi, A. (2022) · doi:10.36227/techrxiv.19102523

Resource allocation for federated learning enabled edge intelligence

Lim, B. W. Y. (2022) · doi:10.32657/10356/156418

HUMAN ACTIVITY RECOGNITION USING ACCELEROMETER DATA WITH MULTI CLASS SVM

Nair, A. M. (2019) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2019.v04i04.007

An Outlook on the Interplay of Machine Learning and Reconfigurable Intelligent Surfaces: An Overview of Opportunities and LimitationsPreprint

Mohjazi, L., Zoha, A., Bariah, L., Muhaidat, S., Sofotasios, P. C., Imran, M. A., & Dobre, O. A. (2021) · doi:10.36227/techrxiv.16606718

2018 IndexIEEE Transactions on Cognitive Communications and NetworkingVol. 4

(2018) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2018.2889420

MalDC: Malicious Software Detection and Classification using Machine Learning

Moon, J., Kim, S., Jangyong, P., Lee, J., Kim, K., & Song, J. (2022) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2022.05.004 · Cited by 1

Button Press Detection from EEG Signals Using Deep Learning

Kuzucu, E. E. (2022) · International Siberian workshops and tutorials on electron devices and materials · doi:10.1109/edm55285.2022.9855191

Implementing 3GPP Code Division for Non-Orthogonal Multiple Access in 5G

Al-Dulaimi, A. M. K., Al-Dulaimi, O. M. K., & Al-Dulaimi, M. K. H. (2022) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit56059.2022.9932803 · Cited by 1

Blockchain-Enabled Hierarchical Federated Learning in Resource-Constrained Power Systems

Yang, H., Chen, L., Cheng, K., Zhang, L., Shen, P., Chen, J., Guo, S., & Li, Q. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-6901-0_21

A practical evaluation of AutoML tools for binary, multiclass, and multilabel classificationPreprint

Aragão, M. V. C., Afonso, A. G., Ferraz, R. C., Ferreira, R. G., & Leite, S. G. (2023) · doi:10.36227/techrxiv.21792959

Fast Adaptation of Radar Detection via Online Meta-learning

Khan, Z., Jiang, W., Haimovich, A. M., Govoni, M. A., Garner, T. J., & Simeone, O. (2022) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf56349.2022.10051849

Webpage Classification Using Ensemble Machine Learning

Altaee, H. (2023) · Iraqi Journal of Intelligent Computing and Informatics (IJICI) · doi:10.52940/ijici.v2i1.27

Novel Low-complexity Neural Network Aided Detection for Faster-than-Nyquist (FTN) Signalling in ISI Channel

Abdelsamie, A. (2023) · doi:10.22215/etd/2023-15378

A novel pricing algorithm using supervised machine learning for Malaysia’s E-hailing

Qureshi, M. N., Lazam, N. A. M., & Jalil, A. (2023) · AIP conference proceedings · doi:10.1063/5.0133090

Review on impact of deep learning in resolving gait related challenges

Misal, A. K., Chaubey, A., Chaubey, S., Agrawal, D., & Chaurasiya, U. (2023) · AIP conference proceedings · doi:10.1063/5.0133602

Taxonomy and Implications of Machine Learning for Internet of Things: Qualities, Uses and Algorithms

Goswami, S. A., Patel, K. D., Raval, H. Y., & Parikh, S. M. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1410-4_14

On the Role of Taylor’s Formula in Machine Learning

Kärkkäinen, T. (2023) · Computational methods in applied sciences · doi:10.1007/978-3-031-29082-4_16

DSP optimization technique based on deep learning to account for the effects of fiber nonlinearity

Yang, J. Y. (2023) · Highlights in Science Engineering and Technology · doi:10.54097/hset.v53i.9683

Prediction of Channel Stability to Optimize Signalling Overhead in 5G Networks using ML Algorithm

Fathima, A. (2023) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem25166

Computer Vision Based AutoML Platform

ŞAHİN, B., & Boyacı, A. (2023) · Turkish Journal of Science and Technology · doi:10.55525/tjst.1266144

Modèle logistique de désertion à travers des techniques de régression et un arbre de décision pour l'efficience dans la destination des ressources : Le cas d'une université privée chilienne

Gutiérrez, B., Cortés, R., & Dehnhardt, M. (2023) · Revue Internationale des Économistes de Langue Française · doi:10.18559/rielf.2023.1.6

Smart Service Function Chain System for Dynamic Traffic Steering using Reinforcement Learning (CHRL)

Nadhum, A., & Al-Saadi, A. (2023) · Karbala International Journal of Modern Science · doi:10.33640/2405-609x.3326

An End-to-End Auto-Encoder Algorithm for Hardware-Impaired Transceivers Based on Meta and Joint Learning

ElFar, S. H., & Ikki, S. (2023) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom58138.2023.10299733

Identificação de padrões de hipertensão arterial via aprendizado de máquina

Fernandez, M. D. (2022) · doi:10.47749/t/unicamp.2022.1245156

Braiding drive data processing through rote learning

Chen, B., Xu, H., Yang, Q., Zhao, Z., & You, S. (2024) · Intelligent Decision Technologies · doi:10.3233/idt-230374

Planning déficit irrigation strategies for woody crops under different irrigation wáter availability scenarios

Rajjaf, A. T. (2023) · doi:10.31428/10317/13740

Recognition of Signal Modulation Pattern Based on Multi-task Self-supervised Learning

Cheng, D., Wu, X., Xie, Z., Cui, Z., Li, Q., Tong, E., Niu, W., Wei, Z., & Zhao, X. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-57919-6_3

Research on the Application of Artificial Intelligence in Mobile Communication Systems

Liu, L. (2023) · International Signal Processing, Communications and Engineering Management Conference (ISPCEM) · doi:10.1109/ispcem60569.2023.00130

Enhancing Electricity Demand Forecasting Accuracy Through Hybrid Models and Deep Learning Techniques: A Systematic Literature Review

Mba, A., Agyeman, A. Y., & Tetteh, S. G. (2024) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2024.130908

Modelling an Autonomous PV System Using Machine Learning: Study of the Contribution of Different Environmental Parameters on Power

Cisse, Y., Ndiaye, A., & Sarr, A. (2024) · International Conference on Electrical and Computer Engineering Researches (ICECER) · doi:10.1109/icecer62944.2024.10920443

ML-Based Low-Density Spreading Code Design

Yang, H., Kulhandjian, M., Kulhandjian, H., Jakubisin, D. J., Cheng, X., Bondada, K. S., & Yang, Y. (2024) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf60004.2024.10942725

Prediction Model of Indigofera Forage Production Using Regression Machine Learning Method

Akhirina, T. Y., Nurdiati, S., Kusuma, W. A., & Abdullah, L. (2024) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic64337.2024.10956202

Assessment of Machine Learning Techniques Through Accuracy Estimation

Divya, G. (2025) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem50038

Machine Learning Doppler‐Tolerant One‐Bit Radar Detectors

Wensell, K. P., Zhou, C., Haimovich, A. M., Khreishah, A. A., Lozneanu, B., Cannizzo, B., Young, E. A., & Vo, L. T. (2025) · IET Radar Sonar & Navigation · doi:10.1049/rsn2.70011

Deep learning detector for downlink IM-NOMA

Gian, T., Pham, N.-H., Pham, V.-C., Nguyễn, T. H., Nguyen, T. T., & Luong, T. V. (2025) · Wireless Networks · doi:10.1007/s11276-025-03985-5

Applications of AI in Modern/Smart Power Grids: A Short Review

Avdaković, S., Dedović, M. M., Hasković, E., Jašarević, Z., & Žugor, A. (2025) · International Conference on Smart and Sustainable Technologies (SpliTech) · doi:10.23919/splitech65624.2025.11091387

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

Hybrid time synchronization and ANN-based M-QAM demodulation for enhanced performance in OFDM and MIMO-OFDM systems

Kotb, M., Abdel‐Haleem, M. R., Hassan, A. Y., Pławiak, P., & Mohra, A. S. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11624-5

Adaptive security framework for multi-environment networks using ensemble data drift detection and incremental deep learning

Rustam, F., & Jurcut, A. D. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104219

A hybrid intelligent framework for energy consumption classification of mechanical equipment in the steel industry based on machine learning and metaheuristic optimization

Liu, D. (2025) · Chemical Product and Process Modeling · doi:10.1515/cppm-2025-0021

Joint Power Control and User Assignment in RIS-based NOMA: A Multi-kernel Neural Network Approach

Habachi, O. (2025) · ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM) · doi:10.1109/mswim67937.2025.11309047

Integrating Edge Computing into 5G Networks: Toward Real-Time, Resilient Connectivity

Alzoubi, S., & Saleh, A. M. (2026) · Studies in systems, decision and control · doi:10.1007/978-3-032-07220-7_52

Integrating AI and RAN for 6G Networks: Architecture, Implementation, and Future Vision

Joshi, N., Raut, S. Y., & Bharati, M. (2025) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon67554.2025.11379271

Introduction of machine learning with applications to communication system

Kumar, J. R. R., Chobe, S., Nikam, S., Zanwar, S. R., Borawake, M. P., & Hirolikar, D. S. (2023) · Journal of Autonomous Intelligence · doi:10.32629/jai.v6i3.1244 · Cited by 1

DataDriven Approaches for Communication Systems: Algorithms, Benchmark Collections, and Architectural Models

Dr.A.Swetha, D. A. S., AKSHAYA, T., PRASANNA, T., & ABHIRAM, P. (2026) · International Journal of Novel Research and Development · doi:10.56975/ijnrd.v11i4.313310

A review of applications of supervised learning to future networks

Bilson, S., & Thompson, A. (2022) · doi:10.47120/npl.ms40 · Cited by 3

The Convergence of Machine Learning and CommunicationsPreprint

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