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Thematische Bibliographien / Machine Learning in Telecommunications / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Machine Learning in Telecommunications“

Autor: Grafiati

Veröffentlicht am 7. Juni 2025

Zuletzt aktualisiert am 2. August 2025

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1

Krishnan R, CV Krishnaveni, and AV Krishna Prasad. "Telecom Churn Prediction using Machine Learning." World Journal of Advanced Engineering Technology and Sciences 7, no. 2 (2022): 087–96. http://dx.doi.org/10.30574/wjaets.2022.7.2.0130.

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In every industry, customers are crucial. Customer churn can have a variety of effects and have a negative influence on sales. Analysis and forecasting of customer turnover must be a key component of any business. We will analyze and forecast customer turnover in the telecom industry in our study. The study of consumer behavior is crucial for the telecommunications sector in order to identify those customers who are most likely to cancel their subscriptions. Because there is so much data available and the market is becoming more competitive, businesses are spending more time trying to keep the

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Shaikh Shabbir, Mohammed Juned, Pradip Sitaram Ingle, Sagar Shrikrishna Dharamkar, and Ravindra Bhika Phase. "Online Fraud Call Detection: A Machine Learning Approach for Real-Time Identification and Prevention." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem04773.

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Detection of Online Fraud Calls, A Machine Learning Method for Real-Time Identification and Prevention The rise of telecommunication fraud has become a major issue in the digital era, resulting in annual losses up to billions of dollars due to false calls. This research introduces a robust machine learning system for the real-time detection of online fraudulent calls. Our suggested system amalgamates various detection methodologies, including voice pattern analysis, behavioral profiling, and network traffic surveillance, to discern anomalous calling patterns. The system utilizes a hybrid metho

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Bagam, Naveen, Sai Krishna Shiramshetty, Mouna Mothey, Sri Nikhil Annam, and Santhosh Bussa. "Machine Learning Applications in Telecom and Banking." Integrated Journal for Research in Arts and Humanities 4, no. 6 (2024): 57–69. http://dx.doi.org/10.55544/ijrah.4.6.8.

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The uses of machine learning (ML) in the banking and telecommunication sectors are investigated over the course of this research paper. The results of the article indicate that by means of enhanced customer experience, identification of fraudulent behaviour, risk management, and operational efficiency, machine learning algorithms are changing these sectors. This article covers several machine learning methods including supervised and unsupervised learning, deep learning, reinforcement learning, and others together with their particular uses in the banking and telecommunications sectors especia

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Praveen, Halingali, Kumar Santosh, Desai Sanket, and S. Alagoudar Punith. "Survey on Applications of Machine Learning." Journal of Research and Review: Machine Learning 1, no. 2 (2025): 29–35. https://doi.org/10.5281/zenodo.14922864.

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<em>Machine learning (ML) has transformed research methodologies across technical disciplines by enabling data-driven decision-making and predictive analytics. This paper explores ML’s core principles, issues and future developments, emphasizing its impact on optimizing research processes in fields such as engineering, materials science, and telecommunications. Key ML paradigms, including supervised, unsupervised, and reinforcement learning, are analyzed in the context of technical applications. Additionally, this study addresses challenges such as data quality and model interpretability

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Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen. "Implementing machine learning techniques for customer retention and churn prediction in telecommunications." Computer Science & IT Research Journal 5, no. 8 (2024): 2011–25. http://dx.doi.org/10.51594/csitrj.v5i8.1489.

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This review paper explores the application of machine learning techniques in predicting customer churn and enhancing customer retention within the telecommunications industry. The paper begins by discussing the significance of customer churn, its causes, and the limitations of traditional churn prediction methods. It then delves into machine learning algorithms, including decision trees, support vector machines, and ensemble methods. It highlights their effectiveness in handling large and complex datasets typical of the telecom sector. The discussion extends to the challenges faced in data qua

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Maddipudi, Sreenu. "The Role of Artificial Intelligence in Data Migration for Telecommunications." International Scientific Journal of Engineering and Management 04, no. 02 (2025): 1–7. https://doi.org/10.55041/isjem02254.

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The telecommunications industry has witnessed rapid digital transformation in recent years, with data migration playing a critical role in adapting to new technologies and improving service delivery. As organizations move from legacy systems to modern cloud-based architectures, the complexity and scale of data migration processes increase. Traditional migration methods often face challenges such as downtime, data integrity issues, and the need for manual intervention. Artificial Intelligence (AI) has emerged as a transformative solution, offering automated, intelligent, and scalable approaches

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Huh, Jae-Hyuk, and Woongsup Lee. "Machine Learning based Churn Prediction in Telecommunications." Journal of the Korea Institute of Information and Communication Engineering 27, no. 8 (2023): 1016–19. http://dx.doi.org/10.6109/jkiice.2023.27.8.1016.

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KOKO, Joe BALANGA, Guylit KIALA LUTUMBA, Francis KANGA SALU, et al. "Machine Learning-based Customer Churn Analysis in Telecommunications Using Support Vector Machines." Asian Journal of Research in Computer Science 18, no. 5 (2025): 187–203. https://doi.org/10.9734/ajrcos/2025/v18i5648.

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Faced with globalization and increasing competition, the information available via the Internet and the many connected objects continues to increase. This explosion of data, often heterogeneous and from diverse sources, poses major challenges in terms of storage, analysis and exploitation. This paper is the result of the present research on the analysis and classification of churning customers in a telecommunications company. These data, often heterogeneous and coming from various sources, require in-depth analysis as well as new storage and exploration paradigms to extract value from them. Th

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Nagappan Nagappan Palaniappan. "Intelligent Network Management: Integration of AI/ML Technologies in Modern Telecommunications Infrastructure." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2938–46. https://doi.org/10.32628/cseit251112310.

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This article examines the transformative impact of artificial intelligence and machine learning technologies on modern telecommunications infrastructure, with a particular focus on network traffic optimization. It presents a comprehensive analysis of how AI-driven solutions are revolutionizing predictive maintenance protocols, real-time traffic management, and anomaly detection in telecommunication networks. Through an exploration of reinforcement learning applications in dynamic routing and quality of service optimization, this article demonstrates significant improvements in network reliabil

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Samuel Olaoluwa Folorunsho, Olubunmi Adeolu Adenekan, Chinedu Ezeigweneme, Ike Chidiebere Somadina, and Patrick Azuka Okeleke. "Developing smart cities with telecommunications: Building connected and sustainable urban environments." Engineering Science & Technology Journal 5, no. 8 (2024): 2492–519. http://dx.doi.org/10.51594/estj.v5i8.1441.

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The rapid urbanization experienced globally has necessitated the development of smart cities, integrating advanced telecommunications to create connected and sustainable urban environments. This review paper explores the pivotal role of telecommunications in the evolution of smart cities, highlighting its impact on enhancing connectivity, improving public services, and fostering sustainable development. By leveraging Internet of Things (IoT) technologies, telecommunications infrastructure facilitates real-time data exchange and efficient resource management, essential for smart city operations

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Pidpalyi, Oleksandr. "Future prospects: AI and machine learning in cloud-based SIP trunking." Вісник Черкаського державного технологічного університету 29, no. 1 (2024): 24–35. http://dx.doi.org/10.62660/bcstu/1.2024.24.

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The relevance of the study lies in the consideration of artificial intelligence and machine learning as one of the most important technologies that determine the future of the telecommunications industry. Integration of artificial intelligence and machine learning into cloud-based Session Initiative Protocol trunking solutions can potentially significantly improve the efficiency, performance, and security of these solutions. The purpose of the study was to analyse the possibilities of integrating artificial intelligence and machine learning in cloud-based Session Initiation Protocol trunking s

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BUSAYO, Temitayo, Olusola IGBEKOYI, Oluyinka OLUWAGBADE, Yinka ADEWARA, Muyiwa DAGUNDURO, and Yinka BOLUWAJI. "Artificial Intelligence and Service Quality of Telecommunication Firms in Nigeria." Journal of Economics, Finance and Accounting Studies 5, no. 3 (2023): 203–14. http://dx.doi.org/10.32996/jefas.2023.5.3.16.

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Globally, artificial intelligence (AI) technology spans various industries, but relatively little attention is given to the use of AI technologies by telecommunication industries. This study evaluated the effect of AI on the service quality of telecommunications companies in Nigeria, specifically the effect of data mining, machine learning, and chatbots on the service quality of these firms. The research employed a survey research design, and its population was heterogeneous. A sample size of 400 participants was chosen using Taro Yamane's formula, and the Cronbach alpha test yielded an averag

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Dai, Xuhao. "Machine learning based churn prediction in telecom." Highlights in Science, Engineering and Technology 68 (October 9, 2023): 212–22. http://dx.doi.org/10.54097/hset.v68i.12068.

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In the new era of continuous social development, the level of information technology service construction is also increasing, among which the telecommunications service industry has also developed into a relatively large business cluster under this trend. Due to fierce commercial competition, telecommunication companies also face the problem of losing customers. Asking telecom companies to determine the trend of customer churn from the huge amount of customer information data has become a challenge for all telecom companies to solve. This paper takes a telecom company as a reference, using a l

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Zhouwei Gang, Qianyin Rao, Lin Guo, Lin Xi, Zezhong Feng, and Qian Deng. "Applying machine learning in network topology optimization." ITU Journal on Future and Evolving Technologies 2, no. 4 (2021): 91–99. http://dx.doi.org/10.52953/fkid2877.

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Nowadays, telecommunications have become an indispensable part of our life, 5G technology brings better network speeds, helps the AR and VR industry, and connects everything. It will deeply change our society. Transmission is the vessel of telecommunications. While the vessel is not so healthy, some of them are overloaded, meanwhile, others still have lots of capacity. It not only affects the customer experience, but also affects the development of communication services because of a resources problem. A transmission network is composed of transmission nodes and links. So that the possible top

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Hossain, Mohammad Raquibul. "Predicting Customer Churn in Telecommunications with Machine Learning Models." Asian Journal of Research in Computer Science 18, no. 1 (2025): 53–66. https://doi.org/10.9734/ajrcos/2025/v18i1548.

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Customer churn is an important issue in businesses and service sectors including telecommunication industries. Prediction of potential customer churn can be very useful in these fields and it can help to improve customer retention significantly by providing personalized offering to reduce potential churn. This paper mainly focused on customer churn prediction using machine learning (ML) models and Iranian Telco customer churn dataset. Different reasons or variables are involved in customer dissatisfaction or indication of customer’s churn. If ML models are trained with such essential and cruci

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Khlaponin, Y. I., N. H. Qasim, and D. M. Tarasiuk. "FORECASTING THE STATE OF TELECOMMUNICATION NETWORKS USING QUANTILE AND LOGISTIC REGRESSION METHODS." Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University, no. 80 (2023): 91–97. http://dx.doi.org/10.17721/2519-481x/2023/80-10.

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In today's modern world, the ubiquity of information technology has intertwined telecommunications systems with every facet of human life. It's challenging to fathom a world where you're disconnected from the "World Wide Web" or unable to exchange data instantly via the intricate web of modern mobile devices. The vitality of staying connected online cannot be overstated, and ensuring the smooth functioning of telecommunications systems is paramount. This paper delves into the pivotal task of predicting and managing the performance of these networks, employing quantile and logical regression te

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Xiao, Chuyan. "Application of Machine Learning to Customer Churn Risk Prediction." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 152–57. http://dx.doi.org/10.54097/bk8rsb44.

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Accompanied by technological globalization and upswing of telecommunication industry in the 21st century, the number of operators is springing up like mushrooms after rain in the market and that intensifies the industry competition environment due to the unprecedented growth trend and the challenges. As a research hotspot in the field of business analysis, prediction of customer attrition risk possesses some extensive range pertaining to applications within global marketing, telecommunications and other fields. Due to the complex relationship between customer information and the products used,

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Koudouridis, Georgios P., Serveh Shalmashi, and Reza Moosavi. "An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications." Telecom 5, no. 1 (2024): 98–121. http://dx.doi.org/10.3390/telecom5010006.

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The purpose of this survey study is to shed light on the importance of knowledge usage and knowledge-driven applications in telecommunication systems and businesses. To this end, we first define a classification of the different knowledge-based approaches in terms of knowledge representations and reasoning formalisms. Further, we define a set of qualitative criteria and evaluate the different categories for their suitability and usefulness in telecommunications. From the evaluation results, we could conclude that different use cases are better served by different knowledge-based approaches. Fu

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Bhattarai, Aayush, Elisha Shrestha, and Ram Prasad Sapkota. "Customer Churn Prediction for Imbalanced Class Distribution of Data in Business Sector." Journal of Advanced College of Engineering and Management 5 (December 13, 2019): 101–10. http://dx.doi.org/10.3126/jacem.v5i0.26693.

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Churners are those people who are about to transfer their business to a competitor or simply who cancel a subscription to a service. This paper is based on a specific business sector, which is telecommunication sector. With a churn rate of 30%, the telecommunication sector takes the first place on the list. In this paper, we present some advanced data mining methodologies which predicts customer churn in the pre-paid mobile telecommunications industry using a call detail records dataset. To implement the predictive models, we initially propose and then apply four machine learning algorithms: R

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Abdellah, A., and A. Koucheryavy. "SURVEY ON ARTIFICIAL INTELLIGENCE TECHNIQUES IN 5G NETWORKS." Telecom IT 8, no. 1 (2020): 1–10. http://dx.doi.org/10.31854/2307-1303-2020-8-1-1-10.

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Research subject. Fifth-generation telecommunication networks are currently the determining direction of telecommunications development as a whole. At the same time, the complexity of the processes of functioning of fifth-generation telecommunication networks increases by an order of magnitude compared to existing networks. All this requires the use of new technologies, including artificial intelligence, to ensure the stable functioning of telecommunication networks. Method. System analysis. Core results. The scientific tasks for the fifth generation communication networks, in which the use of

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Uddin, Mohammad Main, Md Mamunar Rashid, Mahmudul Hasan, Md Alamgir Hossain, and Yuantao Fang. "Investigating Corporate Environmental Risk Disclosure Using Machine Learning Algorithm." Sustainability 14, no. 16 (2022): 10316. http://dx.doi.org/10.3390/su141610316.

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The volume of the environmental risk disclosure in the annual reports of firms in the pharmaceutical and chemical, tannery, telecommunications, and paper and printing industries listed on the Dhaka Stock Exchange (DSE) in Bangladesh was analyzed in this paper. The research used a content analysis of the annual reports of 43 companies that represented four DSE sectors. To quantify the level of environmental risk disclosure reporting practiced by corporations in their annual reports, the authors established the ERDIPCI for the pharmaceutical and chemical industry, the ERDITI for the tannery indu

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DEUSSOM DJOMADJI, Eric Michel, Bequerelle MATEMTSAP MBOU, Aurelle TCHAGNA KOUANOU, Michael EKONDE SONE, and Parfait BAYONBOG. "Machine learning-based approach for designing and implementing a collaborative fraud detection model through CDR and traffic analysis." Transactions on Machine Learning and Artificial Intelligence 10, no. 4 (2022): 46–58. http://dx.doi.org/10.14738/tmlai.104.12854.

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Fraud in telecommunications networks is a constantly growing phenomenon that causes enormous financial losses for both the individual user and the telecommunications operators. We can denote many researchers who have proposed various approaches to provide a solution to this problem, but still need to be improve to ensure the efficiency. Detecting fraud is difficult and, it's no surprise that many frauds schemes have serious limitations. Different types of fraud may require different systems, each with different procedures, parameter adjustments, database interfaces, and case management tools a

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Hangarge, Pallavi, Gunjan Jadhav, Vaishnavi Janagave, Sujal Kadam, and Prof P. S. Pise. "Customer Churn Prediction Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2374–76. http://dx.doi.org/10.22214/ijraset.2023.50646.

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Abstract: Customer churn is a serious problem in the telecommunications industry and occurs more often. Customer churn is the percentage of customers that stopped using your company's product or service during a certain time frame. One of the most important problems in predicting customer churn is the imbalanced data, which has been tried to be solved and compared with different methods. The machine learning algorithms will be use in this paper are Decision Tree, Support Vector Machine, Random Forest . Also, the performance of support vector were better than other algorithms.

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Tebu, Grace, and Aaron Izang. "Customer Churn Prediction in the Telecommunication Industry Over the Last Decade: A Systematic Review." Asian Journal of Research in Computer Science 18, no. 4 (2025): 256–71. https://doi.org/10.9734/ajrcos/2025/v18i4618.

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Aims: This study explores the application of machine learning algorithms in predicting customer churn within the telecommunications sector. By analyzing various predictive models, the study identifies key factors influencing churn and assesses how data integration enhances predictive accuracy. Study Design: A systematic literature review was conducted to evaluate existing research on churn prediction models and their effectiveness in the telecommunications industry. Place and Duration of Study: The study reviews published research from various academic and industry sources over the past decade

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Olajide Soji Osundare, Chidiebere Somadina Ike, Ololade Gilbert Fakeyede, and Adebimpe Bolatito Ige. "Application of Machine Learning in Detecting Fraud in Telecommunication-Based Financial Transactions." Computer Science & IT Research Journal 4, no. 3 (2023): 458–77. http://dx.doi.org/10.51594/csitrj.v4i3.1499.

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The increasing integration of telecommunications with financial services has brought about significant advancements in the accessibility and efficiency of financial transactions. However, this convergence has also led to a rise in fraudulent activities, posing substantial risks to both service providers and users. The application of machine learning (ML) in detecting fraud within telecommunication-based financial transactions offers a promising solution to these challenges. This abstract explores the potential of ML techniques to enhance the detection and prevention of fraud in this domain. Ma

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Binele Abana, Alphonse, Patrick Dany Bavoua Kenfack, Paul Salomon Ngohe Ekam, Emmanuel Tonye, and Lionel Nyobe Makani. "Modeling the Customer Experience Using Machine Learning for Optimizing the Performance of Telecommunications Networks: Case of Mobile Networks." International Journal of Advanced Engineering and Management Research 09, no. 05 (2024): 151–71. http://dx.doi.org/10.51505/ijaemr.2024.9511.

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Customer experience is a major issue for telecommunications companies seeking to offer quality and personalized services in an environment where customer expectations are constantly evolving. Measuring this experience is a complex challenge, particularly with regard to the impact of network performance on customer usage and, consequently, on their satisfaction. In this context, an in-depth analysis on the relationships between network performance indicators and customer experience was carried out in the network of the telecommunications operator Orange Cameroon (OCM). A method based on machine

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Loukili, Manal. "Supervised Learning Algorithms for Predicting Customer Churn with Hyperparameter Optimization." International Journal of Advances in Soft Computing and its Applications 14, no. 3 (2022): 50–63. http://dx.doi.org/10.15849/ijasca.221128.04.

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Abstract Churn risk is one of the most worrying issues in the telecommunications industry. The methods for predicting churn have been improved to a great extent by the remarkable developments in the word of artificial intelligence and machine learning. In this context, a comparative study of four machine learning models was conducted. The first phase consists of data preprocessing, followed by feature analysis. In the third phase, feature selection. Then, the data is split into the training set and the test set. During the prediction phase, some of the commonly used predictive models were adop

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Desai, Aishwarya Sandeep. "Machine Learning Approaches in 5G Networks." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1691–97. http://dx.doi.org/10.22214/ijraset.2024.63400.

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Abstract: As the deployment of 5G networks accelerates globally, these networks are set to revolutionize the telecommunications landscape by providing unprecedented data speeds, ultra-low latency, and massive connectivity for devices across diverse applications. However, the increasing complexity and dynamic nature of 5G networks necessitate advanced approaches to optimize performance and manage resources efficiently. Machine learning (ML) emerges as a pivotal technology capable of addressing these challenges through data-driven insights and autonomous decision-making processes. This paper del

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Akbar, Teuku Alif Rafi, and Catur Apriono. "Machine Learning Predictive Models Analysis on Telecommunications Service Churn Rate." Green Intelligent Systems and Applications 3, no. 1 (2023): 22–34. http://dx.doi.org/10.53623/gisa.v3i1.249.

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Customer churn frequently occurs in the telecommunications industry, which provides services and can be detrimental to companies. A predictive model can be useful in determining and analyzing the causes of churn actions taken by customers. This paper aims to analyze and implement machine learning models to predict churn actions using Kaggle data on customer churn. The models considered for this research include the XG Boost Classifier algorithm, Bernoulli Naïve Bayes, and Decision Tree algorithms. The research covers the steps of data preparation, cleaning, and transformation, exploratory data

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Kirti, Vasdev. "Churn Prediction in Telecommunications Using Geospatial and Machine Learning Techniques." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 13, no. 1 (2025): 1–7. https://doi.org/10.5281/zenodo.14607920.

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Churn prediction is a critical focus area in the telecommunications industry due to its direct impact on customer retention and revenue. Leveraging geospatial and machine learning (ML) techniques, businesses can better understand customer behavior, identify at-risk customers, and implement targeted retention strategies. This paper explores theoretical underpinnings, case studies, and practical applications, emphasizing the integration of geospatial data with advanced ML models. The research also discusses challenges, datasets, and potential future developments in this domain

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P., Sai Krishna, Sai Harsha Vardhan Reddy K., Srikar K., Gobinda Prasad Acharya Dr., and Rama Swamy DR. "Handoff using Machine Learning Techniques." International Journal of Innovative Science and Research Technology 7, no. 3 (2022): 613–16. https://doi.org/10.5281/zenodo.6406715.

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This paper demonstrates about Implementation of Handoff Techniques through Machine Learning Algorithms in Tele communications. A handoff is the process of transferring an active call or data session from one cell in a cellular network to another, or from one channel within a cell to another. Cellular networks are made up of cells, each of which can provide telecommunications services to customers roaming through the network. Each cell has a limited region and number of subscribers it can serve. A handoff occurs when any of these two thresholds is reached. When a certain mobile tower's capa

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Ouyang, Jiangshan. "Research for Machine Learning Enhance the Customer Retention Rate." Advances in Economics, Management and Political Sciences 153, no. 1 (2025): 34–39. https://doi.org/10.54254/2754-1169/2024.19471.

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Customer retention is important for businesses that want to maintain long-term profitability. This is especially true in industries such as telecommunications and media. Machine learning is a powerful tool. Businesses might use it to find clients who are likely to leave. And to keep them, offer focused treatments. This article explores how machine learning models can use customer data to predict customer churn. The model includes logistic regression (LR), decision tree (DT), random forest (RF), and gradient boosting trees (GBT). Analyze factors such as customer retention period, monthly fees,

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Demir, Buse, and Övgü Öztürk Ergün. "Customer Churn Prediction with Machine Learning Methods In Telecommunication Industry." Advances in Artificial Intelligence Research 5, no. 1 (2025): 32–41. https://doi.org/10.54569/aair.1709274.

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With the emergence of new competitors and increasing investments in telecommunication services, change often occurs and hence importance of marketing strategies and customer behavior prediction have become an important demand for companies. New regulations and technologies increase competition among mobile operators. Since acquiring a new customer is more expensive than acquiring active customers, companies seek solutions to reduce the churn rate. Therefore, telecommunications companies want to analyze the concept of the customer's desire to change service provider and take necessary measures

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Deepika, J., T. Senthil, C. Rajan, and A. Surendar. "Machine learning algorithms: a background artifact." International Journal of Engineering & Technology 7, no. 1.1 (2017): 143. http://dx.doi.org/10.14419/ijet.v7i1.1.9214.

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With the greater development of technology and automation human history is predominantly updated. The technology movement shifted from large mainframes to PCs to cloud when computing the available data for a larger period. This has happened only due to the advent of many tools and practices, that elevated the next generation in computing. A large number of techniques has been developed so far to automate such computing. Research dragged towards training the computers to behave similar to human intelligence. Here the diversity of machine learning came into play for knowledge discovery. Machine

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Chandrasekhar Katasani. "Predicting Device Faults in Telecom Using Real-Time Streaming, Cloud Technologies, and Machine Learning." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 575–82. https://doi.org/10.32628/cseit25111263.

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This article presents a comprehensive framework for predicting device faults in telecommunication networks using real-time streaming, cloud technologies, and machine learning approaches. The article explores the integration of advanced analytics with traditional network maintenance strategies to create a proactive fault detection system. By leveraging multiple data sources, including device telemetry, historical failure records, and environmental factors, the system enables early detection and prevention of potential network issues. The framework encompasses various components, from robust dat

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Basanta Lingthep and Dinesh Chandra Misra. "ANALYZING KEY COMPONENTS OF MACHINE LEARNINGBASED CYBERSECURITY AWARENESS PROGRAMS IN TELECOMMUNICATION NETWORKS." Scientific Digest : Journal of Applied Engineering 13, no. 7(1) (2025): 102–10. https://doi.org/10.70864/joae.2025.v13.i7(1).pp102-110.

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This study explores the key constituents of machine learning-based program of cybersecurity awareness programs in telecommunication networks. Increasing complexity of cyber threats and attacks in the telecommunications sector has rendered traditional means of cybersecurity training insufficient to monitor dynamic security issues. This study looks at the possibility of machine learning models like Graph Attention Networks (GATs) to bolster cybersecurity awareness programs. The research is of a quantitative nature as survey responses have been used to gauge the efficiency of training modules dri

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Yousuf, Adnan. "5G Resource Allocation for Efficient Usage of Bandwidth using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 2408–21. http://dx.doi.org/10.22214/ijraset.2024.63462.

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Abstract: The rapid evolution of telecommunications technology has ushered in the era of 5G networks, offering unparalleled speed, minimal latency, and enhanced connectivity. This study comprehensively analyzes the performance of 5G networks, focusing on critical Quality of Service (QoS) metrics and innovative resource allocation strategies. Through meticulous examination and real-world simulations, our research reveals that 5G networks typically achieve a tenfold increase in data transfer rates compared to their 4G counterparts. Moreover, our findings demonstrate a substantial 30% reduction i

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phetvilai, sithong. "Comparative Analysis of Logistic Regression and Random Forest Models for Customer Churn Prediction in Laos." Souphanouvong University Journal Multidisciplinary Research and Development 11, no. 3 (2025): 01–10. https://doi.org/10.69692/sujmrd110301.

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Customer churn poses a significant challenge in the telecommunications industry, as it directly impacts both revenue and long-term customer retention. This study leverages real-world customer data from TPLUS Digital to evaluate the effectiveness of machine learning techniques in predicting customer churn. The research aims to assess the learning speed and testing capability of machine learning models and compare their performance in churn prediction. Two widely used models Logistic Regression (LR) and Random Forest (RF) were employed and evaluated using various performance metrics, including t

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Jabbar, Ma’shum Abdul, and Suharjito Suharjito. "Fraud Detection Call Detail Record Using Machine Learning in Telecommunications Company." Advances in Science, Technology and Engineering Systems Journal 5, no. 4 (2020): 63–69. http://dx.doi.org/10.25046/aj050409.

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Cox, Tony, and George Bell. "A machine learning approach to process improvement in a telecommunications company." Annals of Operations Research 65, no. 1 (1996): 21–34. http://dx.doi.org/10.1007/bf02187325.

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Nneka Adaobi Ochuba, Enyinaya Stefano Okafor, Olatunji Akinrinola, Olukunle Oladipupo Amoo, and Favour Oluwadamilare Usman. "ENHANCING CUSTOMER SERVICE IN SATELLITE TELECOMMUNICATIONS: A REVIEW OF DATA-DRIVEN INSIGHTS AND METHODOLOGIES FOR PERSONALIZED SERVICE OFFERINGS." International Journal of Management & Entrepreneurship Research 6, no. 3 (2024): 582–93. http://dx.doi.org/10.51594/ijmer.v6i3.846.

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Enhancing customer service in satellite telecommunications is a critical focus area for companies seeking to differentiate themselves in a competitive market. This Review reviews data-driven insights and methodologies aimed at delivering personalized service offerings to satellite telecommunications customers. Personalized service offerings leverage data analytics to tailor services to individual customer needs and preferences. By analyzing customer data, such as usage patterns, service history, and feedback, satellite telecommunications companies can gain valuable insights into customer behav

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Shi, Congming, Wen Wang, Shoulin Wei, and Feiya Lv. "System for Recommending Telecommunication Packages Based on the Deep and Cross Network." Wireless Communications and Mobile Computing 2022 (April 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/2100841.

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With the evolution of the 5th generation mobile network (5G), the telecommunications industry has considerably affected livelihoods and resulted in the development of national economies worldwide. To increase revenue per customer and secure long-term contracts with users, telecommunications firms and enterprises have launched diverse types of telecommunication packages to satisfy varied user requirements. Several systems for recommending telecommunication packages have been recently proposed. However, extracting effective feature information from large and complex consumption data remains chal

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Zhuk, Andrei. "UNSUPERVISED MACHINE LEARNING AND VECTOR MODELS IN DESIGNING AND OPTIMIZATION OF TELECOM RETAIL CHANNELS." American Journal of Engineering and Technology 6, no. 10 (2024): 23–32. http://dx.doi.org/10.37547/tajet/volume06issue10-04.

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This paper examines the use of unsupervised machine learning and vector models in the design and optimization of retail channels for telecommunications services. Unsupervised machine learning allows you to analyze and identify hidden patterns in large volumes of untagged data, which is especially important in a dynamically changing consumer market. Vector models, in turn, provide high accuracy of demand forecasting and inventory management, contributing to an increase in the efficiency of trading channels. The synergy of these technologies allows companies to improve customer experience, optim

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Aksoy, Ceren, Ayhan Küçükmanisa, and Zeynep Hilal Kilimci. "Forecasting Customer Churn using Machine Learning and Deep Learning Approaches." Kocaeli Journal of Science and Engineering 8, no. 1 (2025): 60–70. https://doi.org/10.34088/kojose.1526621.

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Customer churn forecasting is a challenging task recommended for churn prevention for companies operating in various industries such as banking, telecommunications, and insurance. Forecasting customer churn is very important for many companies because gaining potential customers usually costs more than retaining present ones. That is why companies, analysts, and researchers are center on analyzing the dynamics behind customer churn behaviors. In this study, we present a comparative study for the purpose of forecasting customer churn employing publicly available datasets, namely, IBM Watson and

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Tang, Thomas. "Comparison of machine learning methods for estimating customer churn in the telecommunication industry." Applied and Computational Engineering 17, no. 1 (2023): 157–62. http://dx.doi.org/10.54254/2755-2721/17/20230928.

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With the rising competition in business, particularly in the telecommunications industry, there has been a growing emphasis on churn prediction. This is attributed to the higher cost involved in attracting new customers than retaining the remaining ones. In telecom churn analysis, the primary goal is to accurately estimate churn behavior by identifying customers who are at risk of leaving. Another objective is to determine the primary reasons for customer churn. Manually predicting the churn in telecommunications is expensive, tedious, and time-consuming. To relieve the burden, machine learnin

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Kamalesh, Jain, and Mahant Kshitij. "Intelligent Network Optimization: A Machine Learning Approach to Dynamic Network Management in Telecommunications." Sarcouncil Journal of Multidisciplinary 4, no. 12 (2024): 1–7. https://doi.org/10.5281/zenodo.14423256.

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The rapid evolution of telecommunications networks, driven by the growth of 5G, IoT, and edge computing, has introduced unprecedented complexity and dynamic challenges. Traditional network management approaches, reliant on static rule-based systems, are insufficient to address the real-time demands of modern networks. This study explores the integration of machine learning (ML) into dynamic network management, focusing on traffic prediction, resource allocation, and fault detection. Advanced ML models, including LSTMs, reinforcement learning, and autoencoders, were implemented and evaluated fo

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Nneka Adaobi Ochuba, David Olanrewaju Olutimehin, Olusegun Gbenga Odunaiya, and Oluwatobi Timothy Soyombo. "THE EVOLUTION OF QUALITY ASSURANCE AND SERVICE IMPROVEMENT IN SATELLITE TELECOMMUNICATIONS THROUGH ANALYTICS: A REVIEW OF INITIATIVES AND THEIR IMPACTS." Engineering Science & Technology Journal 5, no. 3 (2024): 1060–71. http://dx.doi.org/10.51594/estj.v5i3.958.

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Satellite telecommunications have undergone significant evolution, driven by the need for enhanced quality assurance and service improvement. This paper provides a comprehensive review of the initiatives undertaken in this field, focusing on the integration of analytics to achieve these objectives. Over the years, advancements in satellite technology have enabled the collection of vast amounts of data, presenting both challenges and opportunities. Leveraging analytics has emerged as a crucial strategy to extract actionable insights from this data, thereby enhancing quality assurance and servic

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Zatonatska, Tetiana, Yana Fareniuk, and Viktor Shpyrko. "Churn Rate Modeling for Telecommunication Operators Using Data Science Methods." Marketing and Management of Innovations 14, no. 2 (2023): 163–73. http://dx.doi.org/10.21272/mmi.2023.2-15.

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The telecommunication company functioned in the market with extremely high competitiveness. Attracting new customers needs 5-10 times more expenses than maintaining an existing one. As a result, effective customer churn management and analysis of the reasons for customer churn are vital tasks for telecommunication operators. As a result, predicting subscriber churn by switching on the competitors becomes very important. Data Science and machine learning create enormous opportunities for solving this task to evaluate customer satisfaction with company services, determine factors that cause disa

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Kristian Vieri, Jhon, Tb Ai Munandar, and Dwi Budi Srisulistiowati. "Exclusive Clustering Technique for Customer Segmentation in National Telecommunications Companies." International Journal of Information Technology and Computer Science Applications 1, no. 1 (2023): 51–57. http://dx.doi.org/10.58776/ijitcsa.v1i1.19.

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This study aims to empirically examine consumer behavior based on customer transaction history. Analyzing consumer behavior can provide very useful information for businesses in making decisions, particularly business decisions toward customers, in order to survive in such intense competition.Companies are becoming faster and more precise in reading environmental conditions and predicting what conditions may occur as a result of machine learning technology.This technology can also assist companies in making decisions that are more targeted according to actual secondary data provided for resear

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Zeng, Wanrou. "Telecommunications Industry: Analysis on Customer Attrition Prediction and Segmentation." BCP Business & Management 38 (March 2, 2023): 2811–19. http://dx.doi.org/10.54691/bcpbm.v38i.4195.

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Churn prediction is essential for the survival of every business as it allows proactive action planning, whereas customer segmentation is an effective grouping approach commonly used for product marketing and customer relationship management. This paper analyzes the current development of customer attrition prediction and segmentation for the telecommunications (telco) industry, and explores the integration of both approaches that results in an actionable matrix framework for customer retention. This paper consists of four parts: review and analyses on the machine learning techniques used for

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