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

Maximum Likelihood from Incomplete Data Via the EM Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444

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

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501

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

Untitled work

(1999) · Cited by 12,854

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

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260

Untitled work

(2012) · Cited by 9,062

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

Channel-Adaptive Quantization for Wireless Federated Learning

Lin, X., Liu, Y., & Chen, F. (2021) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc52777.2021.9580257 · Cited by 5

WLAN Channel Status Duration Prediction for Audio and Video Services Using Probabilistic Neural Networks

Hou, Y., & Denno, S. (2024) · IEEE Access · doi:10.1109/access.2024.3365188 · Cited by 4

AI in Telecommunication: Revolutionizing Signal Processing and Communication Networks in Smart Grid

Dewangan, S., Manoharan, G., Minz, S. S., Kumar, S., Talukder, M. B., & Arora, K. (2025) · International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI) · doi:10.1109/ic3ecsbhi63591.2025.10991163 · Cited by 4

Analysis of Software Engineering Practices in General Software and Machine Learning Startups

Lakha, B., Bhetwal, K., & Eisty, N. U. (2023) · IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA) · doi:10.1109/sera57763.2023.10197836 · Cited by 4

Deep Learning Based Intrusion Detection for Cybersecurity in Unmanned Aerial Vehicles Network

Niyonsaba, S., Konaté, K., & Soidridine, M. M. (2024) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet61485.2024.10698453 · Cited by 4

Optimizing AI-Driven Efficient Communication and Pioneering 6G Network Architecture

Goutham, N., Singh, K., & Manjul, M. (2024) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs62700.2024.10840672 · Cited by 4

Uncertainty-Aware and Reliable Neural MIMO Receivers via Modular Bayesian Deep Learning

Raviv, T., Park, S., Simeone, O., & Shlezinger, N. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3579989 · Cited by 4

Prediction of Optimal Torques from Gait Analysis Applying the Machine Learning Concepts

al., B. S. K. E. A. B. S. K. E., & TJPRC (2019) · International journal of mechanical and production engineering research and development · doi:10.24247/ijmperdaug201969 · Cited by 4

On Equivalence of Neural Network Receivers

Careem, M. A. A., Dutta, A., & Thawdar, N. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500703 · Cited by 4

Performance Evaluation For Identifying Best Feature Selector And Machine Learning Algorithm For Optimizing Crop Recommendation System

Janrao, S., & Shah, D. (2020) · IEEE Congreso Bienal de Argentina (ARGENCON) · doi:10.1109/argencon49523.2020.9505474 · Cited by 4

AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks Through Accuracy Gradient

Nikfam, F., Marchisio, A., Martina, M., & Shafique, M. (2022) · IEEE Access · doi:10.1109/access.2022.3213734 · Cited by 4

5G and 6G Wireless Communication

Sikkanan, S., Seerangurayar, T., Sikkannan, K. P., & Kasthuri, M. (2023) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-6684-7000-8.ch015 · Cited by 4

Learning Channel Codes from Data: Performance Guarantees in the Finite Blocklength Regime

Bernardo, N. I., Zhu, J., & Evans, J. (2023) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit54713.2023.10206556 · Cited by 4

On the Robustness of Deep Learning-Aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge

Chen, C.-H., Karanov, B., van Houtum, W. J., Wu, Y. X., Young, A., & Alvarado, A. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10570860 · Cited by 4

Task-Based Information Compression for Multi-Agent Communication Problems with Channel Rate Constraints.Preprint

Mostaani, A., Vu, T. X., Chatzinotas, S., & Ottersten, B. (2020) · arXiv (Cornell University) · Cited by 4

A Brief Review on Spiking Neural Network - A Biological InspirationPreprint

Rafi, T. H. (2021) · Preprints.org · doi:10.20944/preprints202104.0202.v1 · Cited by 3

Unsupervised Learning in Accordance With New Aspects of Artificial Intelligence

Sharma, R., Saxena, K., & Rana, A. (2021) · Wiley eBooks · doi:10.1002/9781119785873.ch17 · Cited by 4

Machine Learning Approach to Delay Spread Estimation in Industrial Environments

Hosseinzadeh, M., Prete, S. D., Fuschini, F., Barbiroli, M., Vitucci, E. M., & Degli‐Esposti, V. (2024) · European Conference on Antennas and Propagation · doi:10.23919/eucap60739.2024.10500975 · Cited by 3

Power Control for NN-Based Wireless Distributed Inference With Improved Model Calibration

Hou, Q., Lee, M., Yu, G., & Cai, Y. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3417305 · Cited by 3

A novel method for estrous cycle staging using supervised object detection

Babaev, B., Goyal, S., Arora, T., Autry, A. E., & Ross, R. (2025) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-024-00020-x · Cited by 4

Self-Evolving Wireless Communications: A Novel Intelligence Trend for 6G and Beyond

Qian, L., Yang, P., Wu, G., Tang, W., & Chen, Z. (2024) · International Conference on Future Communications and Networks (FCN) · doi:10.1109/fcn64323.2024.10984554 · Cited by 3

Modeling and predicting the transmission efficiency of communication devices under joint noise and vibration disturbances

Hamrouni, C., & Alutaybi, A. (2025) · Sound&Vibration · doi:10.59400/sv2112 · Cited by 3

An Introduction to Neural Networks in SCMA

Madhura, K., Rukmini, M. S. S., & Raut, R. D. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-08222-8 · Cited by 3

An application of 8-APSK modulation for the uplink using SVD-SCMA

Vidal-Beltrán, S., López-Bonilla, J. L., & Martínez‐Piñón, F. (2021) · IEEE Latin America Transactions · doi:10.1109/tla.2021.9477276 · Cited by 3

Smart Cities Ecosystem in the Modern Digital Age: An Introduction

França, R. P., Monteiro, A. C. B., Arthur, R., & Iano, Y. (2021) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-030-72139-8_3 · Cited by 4

Machine and Deep Learning Algorithms and Applications

Shanthamallu, U. S., & Spanias, A. (2021) · Synthesis lectures on signal processing · doi:10.2200/s01135ed1v01y202109spr022 · Cited by 3

Machine Learning Scaled Belief Propagation for Short Codes

Hummert, M., Wübben, D., & Dekorsy, A. (2021) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2021-fall52928.2021.9625308 · Cited by 3

Swarm Exploration and Communications: A First Step towards Mutually-Aware Integration by Probabilistic Learning

Beck, E., Shin, B.-S., Wang, S., Wiedemann, T., Shutin, D., & Dekorsy, A. (2023) · Electronics · doi:10.3390/electronics12081908 · Cited by 3

Adaptive Modulation Classification with Deep Learning for Various Number of Users and Performance Validation

G., V. K. R. P., & Meena, M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9379-4_21 · Cited by 3

Deep Learning-Based Demodulation in Impulse Noise Channels

Andersson, A., Hägglund, K., & Axell, E. (2023) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom58377.2023.10356304 · Cited by 3

Customer Churn Prediction Using Machine Learning

Patil, K. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem29907 · Cited by 3

Transferable Multi-Fidelity Bayesian Optimization for Radio Resource Management

Zhang, Y., Park, S., & Simeone, O. (2024) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc60668.2024.10694025 · Cited by 3

Invited paper: Towards robust end-to-end neural network-based transceivers for short reach fiber links

Karanov, B. (2024) · Optical Fiber Technology · doi:10.1016/j.yofte.2024.104069 · Cited by 3

Towards Grounding of Formulae

Asakura, T., Greiner-Petter, A., Aizawa, A., & Miyao, Y. (2020) · Workshop on Scholarly Document Processing (SDP) · doi:10.18653/v1/2020.sdp-1.16 · Cited by 3

Learning Assisted Demand Charge Mitigation for Workplace Electric Vehicle Charging

Chaudhari, K., Connor, P., Comden, J., & King, J. (2022) · IEEE Access · doi:10.1109/access.2022.3172334 · Cited by 4

Machine Learning-based Flexible Payload Power Resource Allocation for Non-orthogonal SATCOM

Zhu, Y., Hofmann, C. A., & Knopp, A. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000745 · Cited by 3

Segmentation and Clustering of Time Series Data

Tiwari, S. (2023) · 2023 International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat57137.2023.10080820 · Cited by 3

Massive Connectivity and Low-Latency for Next-Generation Internet of Things: A Filtered OFDM-based Deep Learning Approach

Hussain, S., Hussain, A., Aftab, M. W., Kundi, H., Ullah, I., Ali, A., Rasool, S., Khan, U. A., & Al‐Jawahry, H. M. (2023) · Journal of Smart Internet of Things · doi:10.2478/jsiot-2023-0014 · Cited by 6

Black-Box and Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks.Preprint

Nikoloska, I., & Simeone, O. (2021) · arXiv (Cornell University) · Cited by 3

Regression‐Based Machine Learning Approaches for Daily Streamflow Modeling

Samadi, V. S., Tabas, S. S., Wilson, C., & Hitchcock, D. R. (2023) · Special publications · doi:10.1002/9781119639268.ch5 · Cited by 3

Monitoring subsurface conditions of pavement structures using Ultra-wideband radar technology

Todkar, S. S. (2019) · theses.fr (ABES) · doi:10.70675/1d45298ezbff6z478bz81c9z0d494b56a65a · Cited by 3

MODELOS MACHINE LEARNING EN GEOMECÁNICA MINERA PARA EL CONTROL EFICAZ DE PERFORACIÓN

Cruz, H. J. P. (2023) · REVISTA DE INVESTIGACIONES · doi:10.26788/ri.v12i1.4311 · Cited by 2

Application of computer simulation system and convolutional neural network in video teaching of ethnic music

Xu, Z. (2023) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-023-02177-8 · Cited by 2

Fast optimization of cache-enabled Cloud-RAN using determinantal point process

Bsebsu, A., Zheng, G., & Lambotharan, S. (2021) · Physical Communication · doi:10.1016/j.phycom.2021.101292 · Cited by 2

Systematic Literature Review: Human Gait Cycle Model Using Image-Temporal Feature

Jamal, I. H., Izhar, C. A. A., Fauzilan, A. S., Maruzuki, M. I. F., Hussain, Z., & Rahman, M. F. A. (2021) · IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) · doi:10.1109/icraie52900.2021.9703989 · Cited by 2

Massive MIMO System—Overview, Challenges, and Course of Future Research

Shailender, S., Khera, S., Singh, S. P., & Jyoti (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-8892-8_16 · Cited by 2

Supporting Democracy through Content-Neutral Social Media Policies

Quarles, C. L. (2023) · Journal of Science Policy & Governance · doi:10.38126/jspg220108 · Cited by 2

Realizing Intelligence in 6G Communications: A Review

Arshad, R., & Muzzammel, R. (2023) · International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE) · doi:10.1109/etecte59617.2023.10396677 · Cited by 2

RIS-aided Systems under Faulty Configuration

de Souza, P. H. C., & Mendes, L. L. (2023) · IEEE Conference on Standards for Communications and Networking · doi:10.1109/cscn60443.2023.10453174 · Cited by 2

A Deep Learning Method for Open-Set Transmission Mode Recognition Based on Multiresolution Time-Frequency Spectrograms

Lin, H., Dai, X., & Wang, C. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3427113 · Cited by 2

A Review of Artificial Intelligence Applications in Contemporary Computer Network Technologies

Lutepo, A., & Zhang, K. (2024) · Communications and Network · doi:10.4236/cn.2024.163005 · Cited by 4

Rethinking Outage in Federated Learning: An Adaptive Retransmission Design

Lin, X., Liu, Y., & Chen, F. (2025) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2025.3621847 · Cited by 2

Federated Learning at Mobile Edge Networks: A Tutorial

Lim, W. Y. B., Ng, J. S., Xiong, Z., Niyato, D. T., & Miao, C. (2022) · Wireless networks · doi:10.1007/978-3-031-07838-5_1 · Cited by 4

A Fiber-Optic Channel Modeled Through BiLSTM Technique

Song, Y., Wang, D., Zhang, M., & Cui, Q. (2019) · International Conference on Optical Communications and Networks (ICOCN) · doi:10.1109/icocn.2019.8933906 · Cited by 2

On Training Neural Network Decoders of Rate Compatible Polar Codes via Transfer Learning

Lee, H., Seo, E. Y., Ju, H., & Kim, S. (2020) · Entropy · doi:10.3390/e22050496 · Cited by 2

Reinforcement Learning-Based Resource Allocation for Adaptive Transmission and Retransmission Scheme for URLLC in 5G

Pradhan, A., & Das, S. (2020) · Algorithms for intelligent systems · doi:10.1007/978-981-15-5243-4_13 · Cited by 2

Machine Learning Techniques for Underwater Wireless Sensor Networks

Rani, D., Sangwan, A., Sangwan, A., & Singh, T. (2020) · Advances in environmental engineering and green technologies book series · doi:10.4018/978-1-7998-3640-7.ch013 · Cited by 2

Busy/Idle Duration Prediction for Video and Audio WLAN Traffics Using Autoregressive Predictor with Data Categorization

Hou, Y., Kawasaki, S., & Denno, S. (2021) · International Conference on Advanced Communication Technology · doi:10.23919/icact51234.2021.9370597 · Cited by 2

Performance Evaluation of the Supervised Machine Learning Algorithms Using R

Maurya, P., & Srivastava, N. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-0171-2_38 · Cited by 2

Machine learning for short reach optical fiber systems

Karanov, B., Bayvel, P., & Schmalen, L. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-385227-2.00010-3 · Cited by 2

Real-Time Human–Machine Interaction Through Voice Augmentation Using Artificial Intelligence

Sumaiya, M. N., Sreekanth, B. V., Akash, U. S., Kala, A. S., & Gowda, G. M. D. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2940-3_10 · Cited by 2

Multi-source Transfer Learning for Signal Detection over a Fading Channel with Co-channel Interference

Zheng, Z., Tong, X., Yu, X., Xu, X., & Huang, S. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838790 · Cited by 2

PAC Learnability for Reliable Communication Over Discrete Memoryless Channels

Liu, J., Zhang, W., & Poor, H. V. (2024) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit57864.2024.10619315 · Cited by 2

A Neural Network-Aided Viterbi Receiver for Joint Equalization and Decoding

Ou, H.-M., Teng, C.-F., Tsai, W.-C., & Wu, A.-Y. A. (2020) · Machine learning for signal processing ... · doi:10.1109/mlsp49062.2020.9231847 · Cited by 2

A Family of Hybrid Federated and Centralized Learning Architectures in Machine Learning.

Elbir, A. M., & Çöleri, S. (2021) · Open Repository and Bibliography (University of Luxembourg) · Cited by 2

Start Making Sense: Semantic Plane Filtering and Control for Post-5G ConnectivityPreprint

Popovski, P., & Simeone, O. (2019) · arXiv.org · doi:10.48550/arxiv.1901.06337 · Cited by 2

An Outlook on the Interplay of AI and Software-Defined Metasurfaces.Preprint

Mohjazi, L., Zoha, A., Bariah, L., Muhaidat, S., Sofotasios, P. C., Imran, M. A., & Dobre, O. A. (2020) · arXiv (Cornell University) · Cited by 2

A Variational Auto-Encoder Approach for Image Transmission in Wireless ChannelPreprint

Estiri, A. H., Sabramooz, M. R., Banaei, A., Dehghan, A. H., Jamialahmadi, B., & Siavoshani, M. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.03967 · Cited by 2

Index Modulation Multiple Access via Deep Learning based Detection

Dhanke, S. S., Sharma, S., Kumar, A., & Mandloi, M. (2020) · IEEE 5G World Forum (5GWF) · doi:10.1109/5gwf49715.2020.9221097 · Cited by 2

A Framework for Prediction in a Fog-Based Tactile Internet Architecture for Remote Phobia Treatment

Rasouli, F. (2020) · Spectrum Research Repository (Concordia University) · Cited by 2

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., de Figueiredo, F. A. P., & Mafra, S. B. (2024) · Research Square · doi:10.21203/rs.3.rs-4172933/v1 · Cited by 2

Proposal of a Mathematical and Computational Method for Determining the Optimal Number of Clusters in the K-Means AlgorithmPreprint

Golondrino, G. E. C., Barrera, D., & Barreto, S. (2024) · Preprints.org · doi:10.20944/preprints202407.2325.v1 · Cited by 2

Review of Deep Learning and Interpretability

Joshi, H., Rajeswari, K., & Joshi, S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-5301-8_37 · Cited by 1

Neural joint source-channel coding via Bernoulli latent straight-through estimator

Seo, J., Kim, S., & Kang, J. (2022) · Journal of Communications and Networks · doi:10.23919/jcn.2022.000043 · Cited by 1

Ferramentas de machine learning na gestão de doenças crónicas: uma scoping review

Pinto, I., Sá, M. S. F., Alves, A. M., Sousa, M. T. B. P., Carvalho, A. V. F., & Moreira, C. T. P. (2023) · Revista de Investigação & Inovação em Saúde · doi:10.37914/riis.v7.359 · Cited by 1

Análisis de datos para la optimización eficiente de horarios y Aprendizaje Automático

Mitre, R. E., Quezada, Á., Lobato, B. Y. M., & Garza, A. A. (2024) · Revista Perspectivas · doi:10.47187/perspectivas.6.2.223 · Cited by 1

Image Classification of Forest Fires Using Machine and Deep Learning

Davis, M., & Shekaramiz, M. (2024) · Intermountain Engineering, Technology and Computing (IETC) · doi:10.1109/ietc61393.2024.10564291 · Cited by 1

Integrating Semantic Communication and Human Decision-Making into an End-to-End Sensing-Decision Framework

Beck, E., Lin, H.-Y., Rückert, P., Bao, Y., von Helversen, B., Fehrler, S., Tracht, K., & Dekorsy, A. (2026) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2026.3652845 · Cited by 1

Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks

Kim, H., Kim, Y.-J., & Kim, W.-T. (2025) · Applied Sciences · doi:10.3390/app15010428 · Cited by 1

Classification Based on the Support Vector Machine for Determining Operational Targets for Controlling Electricity Usage With Conventional Meters: A Case Study of Industrial and Business Tariff Customers From PT PLN (Persero) Indonesia

Arisona, G., Taruna, A. P., Irwanto, D., Bestari, A. B., & Juniawan, W. (2025) · IEEE Access · doi:10.1109/access.2025.3529295 · Cited by 3

Exploring Sentiment Patterns in ChatGPT Interactions: A Machine and Deep Learning Approach to Sentiment Classification

Jonathan, A., Halim, C. K., Wulandhari, L. A., & Nabiilah, G. Z. (2025) · International Conference on Smart Computing, IoT and Machine Learning (SIML) · doi:10.1109/siml65326.2025.11081155 · Cited by 2

Matching Game Preferences Through Dialogical Large Language Models: A Perspective

Fabre, R., Egret, D., & Bellot, P. (2025) · Applied Sciences · doi:10.3390/app15158307 · Cited by 1

Online Optimization for Learning to Communicate Over Time-Correlated Channels

Wu, Z., Li, J., Xu, Z., Sun, S., & Liu, J. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3595551 · Cited by 1

Study and analysis for improving the efficiency in Massive MIMO systems- a deep learning based approach

Penilop, P. S., Meenakshi, M., & Anand, D. (2024) · International Conference on Communication, Computing, Smart Materials and Devices (ICCCSMD) · doi:10.1109/icccsmd63546.2024.11015239 · Cited by 1

Analyzing the Influence of Twitter Public Sentiment on Starbucks Stock Direction: A Naive Bayes Classification and Statistical Approach

Mandiri, S. B. P., Pramesti, D., & Hamami, F. (2025) · International Conference on Data Science and Its Applications (ICoDSA) · doi:10.1109/icodsa67155.2025.11157385 · Cited by 1

AI-Based Predictive Maintenance for 5G Infrastructure Using Digital Twins

Rajakakarlapudi, R. V. (2025) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic64892.2025.11158441 · Cited by 1

Decision Feedback In-Context Symbol Detection Over Block-Fading Channels

Fan, L., Yang, J., Shen, C., & Brown, C. L. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161684 · Cited by 1

Deep Reinforcement Learning-Based Intelligent Water Level Control: From Simulation to Embedded Implementation

Cusihuallpa-Huamanttupa, K., Sacoto-Cabrera, E. J., Coaquira-Castillo, R. J., Mego, L. W. U., Herrera-Levano, J. C., Concha-Ramos, Y., & Moreno-Cardenas, E. (2025) · Sensors · doi:10.3390/s26010245 · Cited by 3

Machine Learning-Driven Social IoT: Advancing Inter-Connectivity, Intelligence, and Autonomous Systems

Das, C., Das, B., Bose, A., Mandal, A., Mishra, A. K., & Raghuvanshi, C. S. (2026) · Information systems engineering and management · doi:10.1007/978-3-032-10122-8_10 · Cited by 2

Designing with Algorithms: A Historical Journey of Artificial Intelligence

Agkathidis, A., Hudert, M., & Medel-Vera, C. (2026) · SpringerBriefs in architectural design and technology · doi:10.1007/978-981-95-0760-3_1 · Cited by 1

Symbiotic radios for future ambient IoT

Wang, J., Zhou, H., Liang, Y., & Sun, S. (2026) · Science China Information Sciences · doi:10.1007/s11432-025-4922-8 · Cited by 1

HUMAN ACTIVITY RECOGNITION USING ACCELEROMETER DATA WITH MULTI CLASS SVM

., A. M. N. (2019) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2019.v04i03.061 · Cited by 1

Application of Autoencoders for Latent Pattern Analysis in Image Time Series

He, J. (2020) · Clark Digital Commons (Clark University) · Cited by 1

Machine Learning for Database Management Systems

Sai Tanishq, N. (2020) · International Journal Of Engineering And Computer Science · doi:10.18535/ijecs/v9i08.4520 · Cited by 1

A tree structure-based classification of diabetic retinopathy stages using convolutional neural network

Peiris, M. S. H., & Sotheeswaran, S. (2021) · International Research Conference on Smart Computing and Systems Engineering (SCSE) · doi:10.1109/scse53661.2021.9568361 · Cited by 1

Busy/Idle Duration Prediction for Video and Audio WLAN Traffics Using Autoregressive Predictor with Data Categorization

Hou, Y., Kawasaki, S., & Denno, S. (2022) · International Conference on Advanced Communication Technology · doi:10.23919/icact53585.2022.9728885 · Cited by 1

Machine Learning in 5G Multimedia Communications

Milovanovic, D. A., Bojkovic, Z. S., & Kukolj, D. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-7708-0.ch010 · Cited by 1

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

Samek, W., Stańczak, S., & Wiegand, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.08299 · Cited by 25

Machine Learning for Wireless Communication

Thanki, R. M., Borisagar, K. R., & Diwan, A. (2025) · Synthesis lectures on communications · doi:10.1007/978-3-031-94117-7