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1
Kartheek, Pamarthi. "Analysis on potential of artificial intelligence (AI) in fortifying cybersecurity within the telecommunications industry." Journal of Scientific and Engineering Research 11, no. 9 (2024): 54–64. https://doi.org/10.5281/zenodo.15044655.
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The expansion of cyber risks is a major concern for the security and operational integrity of the telecoms sector, which is becoming more dependent on networked digital infrastructure. In order to better comprehend and utilize the capabilities of artificial intelligence (AI) to strengthen cybersecurity in the telecoms sector, this paper provides a conceptual framework. In order to improve threat detection, mitigation, and response tactics, the framework combines the revolutionary power of AI with the specific needs of cybersecurity in telecoms. This method takes a multi-faceted view, considering the interdependence of technological aspects, human factors, and regulatory frameworks, as well as organizational and technical aspects. The AI-powered enhancement of proactive threat information gathering and analysis is the first area explored in the framework. Telecom operators may now anticipate possible weaknesses, detect unusual trends, and adjust defensive measures in advance thanks to AI's sophisticated algorithms and machine learning techniques. Second, it delves into AI-powered answers to the problems of adaptive cybersecurity procedures and dynamic risk assessment. Telecom networks may defend themselves against intrusions and breaches continuously by using real-time data analytics and automated decision-making to respond quickly to new threats. Moreover, the model highlights AI's function in enhancing human capacities via cognitive support and intelligent automation. Cybersecurity experts are free to concentrate on big-picture efforts and complicated threat scenarios thanks to AI, which automates mundane jobs and provides insights based on specific contexts. Last but not least, the framework discusses the ethics, responsibility, and openness that are crucial when using AI for telecom cybersecurity. It promotes industry-wide cooperation and ethical AI governance frameworks that put an emphasis on privacy, equity, and the reduction of prejudice. In conclusion, this theoretical framework lays forth a plan to strengthen cybersecurity resilience in the telecom industry by utilizing AI's revolutionary capabilities. This will protect vital infrastructure and guarantee the reliability of communication networks worldwide.
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Li, Lezhi. "The Current Situation and Prevention of AI Telecom Fraud: Starting from Personal Information Protection." Lecture Notes in Education Psychology and Public Media 55, no. 1 (2024): 144–52. http://dx.doi.org/10.54254/2753-7048/55/20240083.
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AI telecom fraud is an act of using artificial intelligence deep cooperation and analysis technology to create false information to lure and deceive individuals and illegal possession of money. With the rapid development of the Internet and AI, the number of AI telecom fraud cases has exploded, so how to prevent AI telecom fraud has become an important issue in society. AI telecom fraud has led to a series of problems, such as the illegal collection and malicious disclosure of personal information, criminals using AI to analyze personal information for fraud, and the industry does not adequately supervise the collection and use of personal information by companies. This article uses case analysis, normative analysis, and comparative research methods to analyze the above three problems and proposes the solution, including establishing a complete legal regulatory system for personal information protection and the prevention and control of AI telecom fraud, and strengthening industry supervision. It is hoped that by applying the above-mentioned solutions, the protection of personal information and the prevention and control of AI telecom fraud can be strengthened, and the development of AI telecom fraud prevention and control in China can be promoted.
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Farooq, Muhammad Waqas, Faiza Nawaz, and Raja Irfan Sabir. "To Gain Sustainable Competitive Advantages (SCA) Using Artificial Intelligence (AI) Over Competitors." Bulletin of Business and Economics (BBE) 13, no. 2 (2024): 1026–33. http://dx.doi.org/10.61506/01.00437.
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The motivation behind this examination is to explore the relationship between AI, DC, and SCA in the telecom business in Pakistan. The paper embraced a quantitative exploration plan and utilized a survey method to collect data from 235 telecom sector employees and managers of three distinct hierarchical levels. The paper applied SEM to examine the hypotheses and analyse the data. The paper found that artificial intelligence affected digital capacity (DC), DC meaningfully affected SCA, and DC intervened in the impact of AI on SCA. The study adds to the works on the link between AI, DC, and SCA in the telecom business. It gives experimental proof to help the hypotheses that artificial intelligence influences DC, DC influences SCA, and DC explains the impact of AI on SCA. The paper gives helpful experiences to telecom sector employees and policymakers. It suggests that telecom companies should make investments in AI technologies and applications to improve their DC, which can help them gain a competitive advantage. Also, it recommends that policymakers work with and support the telecom business to execute DC and AI because these advancements can help financial development, effectiveness, and innovation. The research aims to identify the association between AI, DC, and SCA in the telecom industry in Pakistan. It offers an original viewpoint on how artificial intelligence can improve DC and how DC can prompt SCA in the telecom business.
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Adedeji Ojo Oladejo, Omoniyi David Olufemi, Eunice Kamau, David O Mike-Ewewie, Adebayo Lateef Olajide, and Daniel Williams. "AI-driven cloud-edge synergy in telecom: An approach for real-time data processing and latency optimization." World Journal of Advanced Engineering Technology and Sciences 14, no. 3 (2025): 462–95. https://doi.org/10.30574/wjaets.2025.14.3.0166.
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In recent years, the telecommunication industry has seen significant advancements with the integration of AI, cloud computing, and edge computing. These technologies, when combined, enable telecom providers to process data more effectively, minimize latency, and enhance service delivery. This paper explores the synergy between AI, cloud, and edge computing in the telecom sector, highlighting innovative approaches to real-time data processing and latency optimization. Through a deep dive into emerging trends, this article identifies novel methodologies and applications in AI-driven cloud-edge integration, with a focus on telecom infrastructure, 5G networks, and IoT ecosystems.
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Researcher. "Cloud and AI in Telecom's Role in Smart Cities and IoT Networks." International Journal of Computer Science and Information Technology Research (IJCSITR) 4, no. 1 (2023): 47–62. https://doi.org/10.5281/zenodo.14645066.
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<em>The rise of smart cities and Internet of Things (IoT) networks has transformed urban management and services, placing telecom operators at the core of connectivity and data-driven decision-making. Leveraging Artificial Intelligence (AI) and cloud platforms, telecom operators facilitate IoT data processing, real-time analytics, and enhanced service delivery for smart cities. This paper investigates the critical role of AI and cloud technologies in enabling telecom operators to support IoT ecosystems, focusing on connectivity, data management, and real-time decision-making. Key areas of study include smart city infrastructure, AI-based traffic management, environmental monitoring, and cloud-based IoT platforms. Using a combination of qualitative and quantitative analysis, we provide insights into how telecom advances foster innovation and efficiency across smart city landscapes.</em>
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Singh, Puneet. "AI-Driven Personalization in Telecom Customer Support: Enhancing User Experience and Loyalty." Distributed Learning and Broad Applications in Scientific Research 9, no. 1 (2023): 325–63. https://doi.org/10.5281/zenodo.14989163.
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In the rapidly evolving telecom industry, the integration of Artificial Intelligence (AI) intocustomer support systems has emerged as a transformative force, significantly enhancing theuser experience and fostering customer loyalty through personalization. This paper exploresthe utilization of AI technologies in personalizing telecom customer support, emphasizing theways in which these technologies create tailored interactions that boost user satisfaction andretention. Central to this discussion is the role of advanced AI techniques, particularly NaturalLanguage Processing (NLP), which enable systems to interpret customer intents with highprecision and deliver contextually relevant responses.AI-driven personalization involves the sophisticated analysis of extensive customer data togenerate customized recommendations, optimize troubleshooting processes, and aligncommunication strategies with individual preferences. By leveraging machine learningalgorithms, telecom companies can analyze historical customer interactions, preferences, andbehaviors to predict needs and offer proactive support. This predictive capability not onlyenhances the efficiency of customer service operations but also transforms the customerexperience by providing timely and relevant solutions that are aligned with the user's uniquecontext.The application of NLP in this domain is pivotal. NLP facilitates the understanding andinterpretation of complex linguistic inputs from customers, allowing for the delivery ofresponses that are not only context-aware but also empathetic. Through techniques such assentiment analysis, entity recognition, and intent classification, AI systems can engage in moremeaningful interactions, thereby improving the overall customer support experience. Theability to process and respond to natural language inputs in a manner that reflects an understanding of customer emotions and needs is a key factor in building and maintainingcustomer trust and loyalty.To illustrate the practical impact of AI-driven personalization, this paper presents casestudies, highlighting successful implementations of AI technologies in their customer supportoperations. These case studies demonstrate how major telecom industry has leveraged AI toenhance customer engagement through personalized support channels, improve resolutiontimes, and foster greater customer satisfaction. The analysis includes detailed examinations ofAI-powered tools and strategies employed by telecom industry, such as intelligent virtualassistants and automated response systems, showcasing their effectiveness in addressingcustomer needs and preferences.Additionally, the paper discusses the contributions to developing AI-driven personalizationstrategies, emphasizing the importance of aligning technological advancements with strategicobjectives to achieve optimal outcomes. It explores how AI can be strategically integrated intocustomer support frameworks to create seamless, personalized interactions that drivecustomer loyalty and satisfaction. The discussion extends to the challenges associated withimplementing AI-driven personalization, including data privacy concerns, the need forcontinuous model training, and the integration of AI solutions with existing supportinfrastructure.The findings of this paper underscore the potential of AI to revolutionize customer supportin the telecom sector by providing highly personalized, efficient, and effective serviceexperiences. As telecom companies continue to navigate the complexities of customerengagement, the role of AI in enhancing support capabilities and driving customer loyaltybecomes increasingly critical. This research contributes to a deeper understanding of how AIcan be harnessed to deliver superior customer support, ultimately leading to increasedcustomer satisfaction and long-term loyalty in the competitive telecom industry.KeywordsAI-driven personalization, telecom customer support, Natural Language Processing (NLP),customer satisfaction, machine learning, predictive analytics, intelligent virtual assistants,automated response systems, customer engagement, data privacy
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Vaidya, Dakshaja Prakash. "AI-Augmented Green Cloud Infrastructure for Telecom Data Centers." European Journal of Computer Science and Information Technology 13, no. 33 (2025): 104–16. https://doi.org/10.37745/ejcsit.2013/vol13n33104116.
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This article presents a novel AI-augmented system for optimizing energy consumption in telecom-based cloud data centers while maintaining strict service level agreements. The article uniquely combines advanced time-series forecasting techniques with reinforcement learning to predict computational workloads and dynamically allocate resources in alignment with renewable energy availability. Unlike previous solutions that focus solely on hardware efficiency or isolated subsystems, the article provides comprehensive optimization across distributed telecom infrastructure, addressing the industry-specific challenges of continuous availability requirements and geographically dispersed resources. The article achieves significant reductions in both energy consumption and carbon emissions through intelligent workload shifting, proactive thermal management, and adaptive resource allocation. Experimental validation across multiple deployment scenarios demonstrates that substantial environmental improvements can be achieved without compromising performance, even for latency-sensitive telecom applications. Beyond the immediate operational benefits, the article provides telecom operators with enhanced capabilities for environmental reporting, regulatory compliance, and strategic sustainability planning. This article establishes a new paradigm for telecom infrastructure management that reconciles the industry's growing computational demands with increasingly urgent environmental imperatives, offering a pathway to more sustainable digital infrastructure.
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Singhb, Puneet. "Empowering Inclusion: AI-Powered Chatbots for Accessible Telecom Services." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 5, no. 1 (2024): 167–73. http://dx.doi.org/10.60087/jaigs.v5i1.184.
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The integration of artificial intelligence (AI) in the telecommunications sector has introduced groundbreaking advancements, particularly in customer service. AI-powered chatbots have emerged as vital tools for enhancing the accessibility of telecom services, especially for individuals with disabilities. This paper delves into the transformative potential of AI-powered chatbots, examining their role in making telecom services more inclusive and highlighting their social benefits. Through a detailed analysis of current technologies, case studies, and future prospects, we explore how these chat bots can bridge the accessibility gap, ensuring that all customers, regardless of their abilities, can equally benefit from telecom services.
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Suchithra, V.G., S. Prabakar, and Alemelu M. Lavanya. "AI & Tele Health: Accessibility for Rural Communities." European Journal of Arts, Humanities and Social Sciences 2, no. 2 (2025): 92–95. https://doi.org/10.59324/ejahss.2025.2(2).10.
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Rural communities face significant challenges in accessing and affordable telecom services, hindering economic growth, education and healthcare. The purpose of this study is to examine the importance and functioning Tele health in rural Sector and to explore the government initiatives to the development of quality care and well being of rural citizen. This paper demonstrates the potential of AI- enabled telecom solutions to promote digital inclusion and improve quality of life in rural communities.
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Natarajan, R. "Implementing Artificial Intelligence in CDR & Links Failure in Telecom Technology." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3309–12. http://dx.doi.org/10.22214/ijraset.2021.37127.
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This Paper is about implementing Machine Language Technology in Important day to day operations of Telecom Industry. CDR (Call Details Record) is one of the Primary Operations of Telecom service Provider for Charging Monthly Expenses to the Subscribers.Implementing AI in Telecom Links Failure is another Agenda of this Paper.
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Abdullahi Ya’u Usman and Korau Dauda Biba. "Application of Artificial Intelligence in employee recruitment decision-making process in Nigerian Telecom industry." World Journal of Advanced Research and Reviews 26, no. 3 (2025): 1049–57. https://doi.org/10.30574/wjarr.2025.26.3.1796.
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Artificial Intelligence (AI) has transformed recruitment processes across industries by enhancing efficiency, reducing biases, and improving candidate-job fit. This study evaluates the application of AI in employee recruitment decision-making within Nigeria’s telecom industry, focusing on MTN Nigeria, one of the largest telecommunications companies in Africa. In particular, the research evaluates how AI tools, such as machine learning algorithms, predictive analytics, and natural language processing, are employed to streamline and enhance the recruitment experience. By assessing MTN's use of AI-driven platforms for screening, shortlisting, and decision-making, the study examines the potential benefits and challenges of integrating such technologies in the Nigerian telecom industry. The research examines the extent to which AI-driven tools influence hiring efficiency, fairness, and quality. It employs a case study methodology, utilizing both qualitative and quantitative data to assess AI’s impact on recruitment outcomes. This research aims to provide an in-depth analysis of how AI can improve the efficiency, accuracy, and fairness of recruitment practices while reducing bias, costs, and time. Moreover, it investigates the broader implications of AI adoption in terms of its effects on recruitment transparency, employee satisfaction, and overall organizational performance. Findings will provide insights for industry stakeholders, HR professionals, and policymakers in optimizing AI applications in talent acquisition and will also contribute valuable insights for other telecom companies in Nigeria and Africa at large, assisting them in adopting AI for a more strategic, data-driven approach to talent acquisition. Through this evaluation of MTN’s recruitment practices, the study seeks to highlight both the transformative potential and limitations of AI in the fast-evolving telecom sector.
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Suchithra, V. G., S. Prabakar, and Alemelu M. Lavanya. "AI & Tele Health: Accessibility for Rural Communities." European Journal of Arts, Humanities and Social Sciences 2, no. 2 (2025): 92–95. https://doi.org/10.59324/ejahss.2025.2(2).10.
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Rural communities face significant challenges in accessing and affordable telecom services, hindering economic growth, education and healthcare. The purpose of this study is to examine the importance and functioning Tele health in rural Sector and to explore the government initiatives to the development of quality care and well being of rural citizen. This paper demonstrates the potential of AI- enabled telecom solutions to promote digital inclusion and improve quality of life in rural communities.
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Praveen Hegde and Robin Joseph Varughese. "Predictive Maintenance in Telecom: Artificial Intelligence for predicting and preventing network failures, reducing downtime and maintenance costs, and maximizing efficiency." Journal of Mechanical, Civil and Industrial Engineering 3, no. 3 (2022): 102–18. https://doi.org/10.32996/jmcie.2022.3.3.13.
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Predictive maintenance (PdM), leveraging Artificial Intelligence (AI), is transforming the telecommunications industry by enabling the prediction and prevention of network failures. This proactive strategy reduces network outages and maintenance costs while enhancing overall system performance. By employing AI technologies such as machine learning algorithms, big data analytics, and sensor data analysis, telecom operators can identify patterns and anomalies indicative of potential component failures. AI-driven models continuously monitor network health, facilitating highly accurate failure predictions and enabling timely interventions. This article examines the application of AI for PdM within the telecom sector, focusing on its impact on operational efficiency, resource optimization, and service stability. The findings highlight significant cost reductions and operational improvements achievable with PdM systems. Furthermore, the paper discusses implementation challenges and key considerations for transitioning to these systems. The future outlook for telecom PdM suggests a continued evolution towards more automated, seamless network management and an improved customer experience.
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Adedeji Ojo Oladejo, Motunrayo Adebayo, David Olufemi, Eunice Kamau, Deligent Bobie-Ansah, and Daniel Williams. "Privacy-Aware AI in cloud-telecom convergence: A federated learning framework for secure data sharing." International Journal of Science and Research Archive 15, no. 1 (2025): 005–22. https://doi.org/10.30574/ijsra.2025.15.1.0940.
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With the increasing demand for integrated cloud and telecommunications (cloud-telecom convergence), the need for privacy-preserving artificial intelligence (AI) models has never been more urgent. Federated learning (FL) has emerged as a powerful framework that facilitates secure and privacy-aware machine learning models, without the need to share raw data between entities. This paper explores the role of federated learning in ensuring secure data sharing within cloud-telecom convergence, with a focus on privacy preservation. We discuss the fundamental concepts of privacy-aware AI, cloud-telecom integration, and federated learning. Moreover, we highlight the challenges, key research directions, and practical implementations of these technologies to achieve secure and scalable data sharing in 5G/6G environments. Through a systematic review of recent advances and future trends, we demonstrate the promise of federated learning in enabling privacy-preserving AI solutions in this domain.
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Anjan, V. Hema Krishna. "“Customer Churn Prediction Using Pyspark with AI - Driven Insights”." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49946.
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Abstract - This research will forecast customer churn in the telecom industry with the help of PySpark, machine learning algorithms, and AI-powered Insights within the Azure Databricks Platform. The aim is to create a scalable and accurate model that can forecast customers as likely to churn or retain based on past behavior and demographic factors. Random Forest is chosen due to its interpretability and accuracy, and its performance is compared with Logistic Regression, SVM, and XGBoost models on precision, recall, F1-score, and ROC-AUC. Data preprocessing, feature engineering, and exploratory data analysis were carried out with great care. Model performance was measured on AUC, precision, recall, and F1-score metrics, and MLflow was utilized for experiment tracking. Generative AI was also used to interpret model outputs and create actionable business insights. Drivers of churn such as tenure, contract type, and monthly charges were determined, and strategic recommendations were provided for different customer segments. This hybrid approach demonstrates how AI-powered analytics can be utilized for customer retention support in telecom.[1] Key Words: Customer Churn Prediction, PySpark, Azure Databricks, AI-Driven Insights, Telecom Analytics, Generative AI
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Mokale, Mahesh. "Automated Debugging and Deployment for High-Performance Telecom Applications." International Scientific Journal of Engineering and Management 02, no. 11 (2023): 1–8. https://doi.org/10.55041/isjem00206.
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Abstract: High-performance telecom applications require efficient debugging and deployment strategies to ensure reliability, scalability, and seamless operations. These applications operate within highly complex and distributed environments where even minor failures or inefficiencies can result in significant service disruptions, financial losses, and customer dissatisfaction. Given the critical role telecom applications play in enabling global communication networks, minimizing downtime, optimizing system performance, and maintaining operational continuity is a top priority for telecom service providers. Automated debugging and deployment frameworks address these challenges by integrating advanced artificial intelligence (AI), machine learning (ML), and DevOps methodologies. Automated debugging solutions analyze logs and system metrics in real time, detecting anomalies, diagnosing root causes, and predicting potential failures before they impact service availability. By leveraging intelligent log analysis, anomaly detection algorithms, and self-healing mechanisms, these solutions enhance the fault tolerance and resilience of telecom applications. In addition to debugging, automated deployment frameworks streamline software releases, infrastructure updates, and configuration changes. Traditional deployment models often require manual interventions that increase the risk of errors, downtime, and inconsistent deployments across different environments. With automation-driven strategies such as Infrastructure as Code (IaC), containerization, and automated rollback mechanisms, telecom companies can achieve consistent, predictable, and secure deployments. Furthermore, modern deployment methodologies such as blue-green and canary deployments minimize disruption by allowing incremental rollouts of new software versions while ensuring service reliability. These approaches enable operators to test new releases in real-time environments with controlled user exposure, reducing the risks associated with large-scale software updates. The implementation of continuous integration and continuous deployment (CI/CD) pipelines further optimizes the development lifecycle, allowing frequent and seamless software updates without impacting ongoing operations. This white paper explores the critical challenges in debugging and deploying high-performance telecom applications and presents state-of-the-art automation strategies that were available up to 2022. By adopting AI- driven debugging techniques, robust deployment automation frameworks, and DevOps best practices, telecom providers can improve operational efficiency, enhance system resilience, reduce downtime, and accelerate time- to-market for new features and updates. Keywords: Automated Debugging, Deployment Automation, High-Performance Telecom Applications, AI-Driven Debugging, Machine Learning, DevOps, Infrastructure as Code (IaC), Continuous Integration (CI), Continuous Deployment (CD), Kubernetes, Docker, Containerization, Self-Healing Systems, Predictive Maintenance, Fault Detection, Anomaly Detection, Log Analysis, CI/CD Pipelines, Canary Deployment, Blue-Green Deployment, Automated Rollback, Disaster Recovery, Telecom Network Automation, Network Monitoring, AI-Based Root Cause Analysis, Service Orchestration, Cloud-Native Architectures, Microservices, Security Automation, Compliance Monitoring, Zero-Trust Security, Policy-Based Security Enforcement, Performance Testing, Load Balancing, Chaos Engineering, Shift-Left Testing, Regression Testing, Automated Testing, Fault Tolerance, Scalability, Operational Efficiency, Real-Time Analytics.
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Deepak Singh, Tharun Anand Reddy Sure, and Sreeram Mullankandy. "Convergence of SaaS, AI, and Telecom in Telehealth: Transforming the future of healthcare delivery through intelligent systems." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 667–78. http://dx.doi.org/10.30574/wjarr.2024.24.1.2918.
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This paper aims at identifying and analyzing how SaaS, AI, and Telecom are changing the way healthcare is delivered by focusing on telehealth. The goal is to determine the current status of these combining technologies, identify future developments and trends in intelligent telehealth systems, and analyze the opportunities and risks associated with their implementation. Following a systematic review, relevant publications published between January 2014 and January 2024 were gathered from various databases. Finally, eight studies were included for analysis, after exclusion and inclusion criteria were applied. The findings show that SaaS, AI, and Telecom are transforming telehealth by improving the remote patient monitoring, the access to care, and the moving from the reactive to the proactive approach to health. SaaS enhances adaptable and affordable approaches, while AI infuses precise identification and reasoning. Telecom provides secure and steady communication and data transfer while facing issues regarding data security, legislation acts, and disparities in infrastructure. Overall, these research studies highlight the key possibilities of developing the application of telehealth through the integration process of these technologies.
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Deepak, Singh, Anand Reddy Sure Tharun, and Mullankandy Sreeram. "Convergence of SaaS, AI, and Telecom in Telehealth: Transforming the future of healthcare delivery through intelligent systems." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 667–78. https://doi.org/10.5281/zenodo.15012146.
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This paper aims at identifying and analyzing how SaaS, AI, and Telecom are changing the way healthcare is delivered by focusing on telehealth. The goal is to determine the current status of these combining technologies, identify future developments and trends in intelligent telehealth systems, and analyze the opportunities and risks associated with their implementation. Following a systematic review, relevant publications published between January 2014 and January 2024 were gathered from various databases. Finally, eight studies were included for analysis, after exclusion and inclusion criteria were applied. The findings show that SaaS, AI, and Telecom are transforming telehealth by improving the remote patient monitoring, the access to care, and the moving from the reactive to the proactive approach to health. SaaS enhances adaptable and affordable approaches, while AI infuses precise identification and reasoning. Telecom provides secure and steady communication and data transfer while facing issues regarding data security, legislation acts, and disparities in infrastructure. Overall, these research studies highlight the key possibilities of developing the application of telehealth through the integration process of these technologies.
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Suzanne Amine, Lyn, and Golam Mostafa Khan. "Saudi telecom: an example of accelerated internationalization." Journal of Islamic Marketing 5, no. 1 (2014): 71–96. http://dx.doi.org/10.1108/jima-02-2013-0012.
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Purpose – A new case study of accelerated internationalization (AI) shows that in only two years, Saudi telecom (STC) entered markets across the Middle East, Asia, and Africa. Managerial analysis identifies reasons for success while questioning strategic choices and their implications. Theory-driven analysis reviews STC's experience in light of selected theories and frameworks. This case is also intended for teaching purposes. The paper aims to discuss these issues. Design/methodology/approach – Responding to Welch et al.'s call, the authors use “interpretive sense-making” and “contextualized explanation” and highlight environmental context in the case study development. The authors review case-based research, explain data collection problems, present managerial and theoretical analyses of the case, discuss the findings relative to the literature, and suggest directions for research. Findings – Case analysis reveals STC's focus on global portfolio development as a driver of AI. Theoretical analysis confirms the psychic distance construct and its paradox, as well as the notion of epochs of internationalization while warning that the path and stages models of internationalization are at odds with AI. The authors call for a contingency view of the resource-based view as a function of context. Research limitations/implications – Limitations arise from the use of secondary data for case development because direct access to this Saudi company was not feasible. Practical implications – AI is popular among wealthy Gulf telecoms ambitious for growth. Regional competition in the Gulf is characterized by copycat and follow-the-leader strategies which preclude elaboration of unique, inimitable or non-substitutable assets, resources or capabilities. Originality/value – This innovative approach to case development provides a rich database for probing analyses of managerial and theoretical implications of AI in a Gulf-based company.
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Kranthi Kumar Pasunuri. "Collaboration between Humans and AI in Telecom Network Automation." International Journal of Science and Research Archive 14, no. 1 (2025): 766–72. https://doi.org/10.30574/ijsra.2025.14.1.0121.
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The convergence of human expertise and artificial intelligence is revolutionizing telecom network automation, creating a synergistic approach to managing complex network infrastructures. This transformation encompasses automated operations, advanced analytics, and strategic decision-making capabilities while maintaining human oversight in critical areas. Through closed-loop automation frameworks, telecom providers are achieving enhanced operational efficiency, improved service reliability, and reduced maintenance costs. The integration of AI-driven solutions with human expertise has enabled predictive maintenance, dynamic resource allocation, and sophisticated pattern recognition while ensuring compliance and governance. As networks become increasingly complex with the integration of 5G, IoT, and edge computing technologies, this human-AI collaboration model proves essential for maintaining operational excellence and driving innovation in telecommunications infrastructure management.
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JIANG, Zhi-Hua, Dong-Ning RAO, Yun-Fei JIANG, and Hong JIANG. "AI Planning Based Parlay X Telecom Services Design." Chinese Journal of Computers 34, no. 2 (2011): 304–17. http://dx.doi.org/10.3724/sp.j.1016.2011.00304.
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Srilekha Kanakadandi. "Leveraging Generative AI in Telecom E-commerce: A Framework for Enhanced Development and Testing Optimization." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2525–33. https://doi.org/10.32628/cseit251112231.
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This article investigates the integration of generative AI technologies within telecom e-commerce platform development and testing workflows. By examining real-world implementations across multiple organizations, the research provides insights into how AI-driven approaches enhance code generation, test coverage, and API optimization in microservices architectures. The article explores the implementation of AI tools within existing CI/CD pipelines, focusing on automated test case generation, dynamic data creation, and intelligent debugging processes. Particular attention is given to security considerations and regulatory compliance, addressing the challenges of AI model explainability and training data quality. The article presents architectural frameworks and best practices for leveraging generative AI while maintaining robust security measures and performance standards. Through case studies and empirical analysis, the article demonstrates the impact of AI integration on development efficiency, test automation, and overall platform reliability. The article contributes significant insights for engineering leaders and architects seeking to implement generative AI solutions in enterprise-scale telecom e-commerce environments, while addressing potential limitations and providing strategies for risk mitigation.
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Nyongesa, Geoffrey, Kelvin Omieno, and Daniel Otanga. "Chatbot Adoption Framework for Real-Time Customer Care Support." Journal of Science, Innovation and Creativity 3, no. 2 (2024): 52–60. https://doi.org/10.58721/jsic.v3i2.865.
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Computer programs or software that communicate with humans using natural language are referred to as chatbot applications. To provide customer care support services, there are no well-formulated guidelines for the implementation of artificial intelligence chatbots in Kenyan telecom companies. This study proposes an adoption framework for deploying artificially intelligent chatbots in Kenyan telecom companies. This was accomplished by determining the current level of the installation of chatbot apps in Kenya and identifying the primary metrics that might be used as indications for the dissemination of chatbots. A review of the earlier frameworks and models on technology adoption was conducted to determine the relevant metrics. A combination of research approaches was used in this study, with questionnaires and interview schedules being used to obtain quantitative and qualitative data, respectively. To examine qualitative data, content analysis was what was used. Using tables and charts, descriptive analysis was performed on the quantitative data, and the findings were presented. AI specialists working for Safaricom PLC and the Communications Authority of Kenya were the ideal candidates for this position. From the two different telecommunications companies, a sample was selected for the research study utilizing the Delphi approach. In this approach, the researcher reaches out to experts in the area of the study to gain in-depth knowledge of the issue being investigated because they serve as a guide on aspects to consider before using AI chatbots for customer support services provision. The results showed that chatbot applications are randomly implemented, the telecom firms are ready to adopt AI chatbots for customer service support. The adoption of the framework will help the telecom industry accept a reliable chatbot application which will in turn provide faster, accurate, and reliable service support to customers thus saving time and cutting costs. The framework that was built has the potential to serve as a guiding principle in the process of implementing chatbot technology within the telecom business settings. Further research should be done on the use of AI chatbots in other sectors like healthcare and agriculture.
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Mr. Mensah Kwadwo Simon,, Francis Agyapong, Frank Baffoe, and Faustina Ampong. "Artificial Intelligence in Human Resource Management: Awareness and Perceptions Among HR Professionals in Ghana." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 5 (2025): 325–62. https://doi.org/10.51583/ijltemas.2025.140500035.
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Abstract: AI Adoption in Ghanaian HR This study investigated how HR professionals in Ghana perceive and plan to use Artificial Intelligence (AI) in their roles. Employing a mixed-methods approach, researchers surveyed 200 HR professionals across various industries (banking, telecom, education) and conducted follow-up interviews.
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Ilchenko, Mykhailo. "NEXT-GEN TELECOM AI: MASTERING PROMPT ENGINEERING FOR INNOVATION." Information and Telecommunication Sciences, no. 1 (June 24, 2025): 22–29. https://doi.org/10.20535/2411-2976.12025.22-29.
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Background. Since 2021, prompt engineering has emerged as a cornerstone of artificial intelligence (AI), revolutionising telecommunications by 2023 through optimised large language models (LLMs). Objective. This review synthesises existing research to evaluate prompt engineering’s transformative role in telecommunications, emphasising practical applications, technical challenges, and future directions. Methods. This analysis draws on 2021–2025 literature from 31 sources, including IEEE journals, ACM Transactions on Information Systems, NeurIPS proceedings, and arXiv preprints, examining prompt engineering techniques like few-shot learning, chain-of-thought prompting, multi-step prompting, Named Entity Recognition (NER), Retrieval-Augmented Generation (RAG) and more, with a telecom focus (6G and hypothesised 5G applications) contextualized within Natural Language Processing (NLP) advancements. Results. Although research on prompt engineering specifically for 5G telecommunications is currently limited, it presents substantial opportunities for optimising network performance, diagnostics, documentation handling, enhancing customer support, and driving innovation across both 5G and future 6G networks. Conclusions. Prompt engineering bridges AI capabilities with telecommunications needs, with techniques like NER and RAG contributing to the enhancement of mobile communications. The dearth of 5G-specific research highlights the urgent need for specialised LLMs in telecommunications and automated prompting to advance solutions for 5G and 6G.
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Berkovac, Doc dr Haris, Prof dr Adis Rahmanović, and Doc dr Maid Omerović. "THE TRANSFORMATIVE IMPACT OF AI ON IPTV: AI-DRIVEN IPTV MODEL." International Journal of Engineering Applied Sciences and Technology 09, no. 10 (2025): 01–17. https://doi.org/10.33564/ijeast.2025.v09i10.001.
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The integration of Artificial Intelligence (AI) into Internet Protocol Television (IPTV) is revolutionizing content delivery, user engagement, and network management. This article explores the transformative role of AI technologies—including machine learning (ML), deep learning (DL), and natural language processing (NLP)—in enhancing IPTV services. Key focus areas include personalized content recommendation, dynamic user interface optimization, AI-driven bandwidth allocation, and automated content moderation. Through a case study of the hypothetical telecom provider MODELIPTV, we demonstrate measurable improvements in user retention (27%), buffering reduction (42%), and moderation efficiency (89%). Challenges such as data privacy, algorithmic bias, and computational costs are critically analyzed. The study concludes with future research directions, emphasizing ethical AI frameworks and edge computing integration.
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Rasheed, Hasanain Salim, and Maytham Abbas Khudhair Al-Salmawi. "The Role of AI as a Mediator in Predicting Future Costs and Accounting Practices: An Empirical Study on Iraqi Telecommunication Companies." International Journal of Transformations in Business Management 15, no. 1 (2025): 41–54. https://doi.org/10.37648/ijtbm.v15i01.005.
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In this paper, we take a look at how the Iraqi telecom industry is using artificial intelligence (AI) to improve their accounting methods and foretell future expenditures. Accounting process efficiency and transparency, as well as the application of AI to increase the accuracy of cost predictions, are the primary areas of study. Three hundred and fifty managers and employees from two Iraqi telecommunications companies, Asiacell and Zain Iraq, filled out selfadministered questionnaires for this quantitative study, which made use of purposive sampling. This study used SmartPLS3 for structural equation modeling using partial least squares to examine the data. Taking into account a number of contributing aspects, the study presents concrete tactics for improved financial performance, and presents empirical data on the significance of AI as a mediator. By increasing financial reporting and significantly more accurate cost projections, artificial intelligence is helping companies adjust to the shifting financial environment. The paper also calls for greater research on the long-term financial sustainability of artificial intelligence (AI) adoption in underdeveloped nations and notes knowledge gaps in the present literature on adoption in particular in domains like telecoms.
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Singh, Puneet. "Leveraging AI for Advanced Troubleshooting in Telecommunications: Enhancing Network Reliability, Customer Satisfaction, and Social Equity." Journal of Science & Technology (JST) 2, no. 2 (2021): 99–138. https://doi.org/10.5281/zenodo.14830020.
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The integration of Artificial Intelligence (AI) in telecommunications is poised to revolutionize the industry's approach to troubleshooting, offering a transformative solution to the persistent challenges of network reliability, customer satisfaction, and social equity. This paper delves into the application of AI-driven methodologies in proactively predicting, identifying, and resolving network issues, thereby significantly enhancing the performance and dependability of telecommunication networks. The research begins with a thorough examination of the current landscape in telecommunications, highlighting the technical and operational challenges associated with traditional troubleshooting methods, which are often reactive, time-consuming, and prone to human error. These conventional approaches are increasingly inadequate in addressing the complexities of modern, large-scale networks, where the rapid proliferation of connected devices and the demand for uninterrupted services necessitate more sophisticated and efficient solutions. AI, with its ability to process vast amounts of data in real time, offers a paradigm shift in troubleshooting by enabling predictive maintenance, anomaly detection, and automated resolution processes. This paper explores the various AI techniques, including machine learning algorithms, deep learning models, and natural language processing, that are being integrated into telecom networks to facilitate advanced troubleshooting. By analyzing historical data, identifying patterns, and learning from past incidents, AI systems can preemptively address potential network failures before they impact users, thus reducing downtime and ensuring a more resilient network infrastructure. The research also addresses the technical challenges of implementing AI in telecommunications, such as the integration of AI with existing network management systems, the scalability of AI solutions in large An networks, and the need for continuous learning and adaptation of AI models to cope with evolving network dynamics. The paper provides a detailed analysis of case studies where AI-driven troubleshooting has been successfully implemented in real-world telecom scenarios. These case studies demonstrate the practical benefits of AI, including significant reductions in mean time to repair (MTTR), cost savings through optimized resource allocation, and enhanced customer satisfaction due to fewer service disruptions and faster issue resolution. Moreover, the paper emphasizes the social implications of leveraging AI in telecommunications, particularly in promoting social equity. Improved network reliability and performance, driven by AI, can enhance access to critical communication services in underserved and rural communities, bridging the digital divide and fostering greater inclusion in the digital economy. The research highlights how AI can enable telecom providers to offer more equitable services, ensuring that all segments of society benefit from reliable and high-quality telecommunications. This paper asserts that the integration of AI into telecommunications is not only a technical necessity for improving network reliability and customer satisfaction but also a crucial step toward achieving broader social equity in access to communication technologies. The findings underscore the potential of AI to transform the telecommunications industry by enabling proactive and efficient troubleshooting, ultimately leading to a more resilient, customer-centric, and socially responsible telecom infrastructure. The research contributes to the ongoing discourse on the future of telecommunications by providing insights into the practical applications of AI, the challenges that need to be addressed, and the potential social benefits that can be realized through the widespread adoption of AI-driven solutions in the industry.
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A.Chandra Sekhar, J. Vignesh, C. Nabi Harshad, et al. "Artificial Intelligence Tool For Churn Prediction Model and Customer Segmentation." international journal of engineering technology and management sciences 9, no. 2 (2025): 524–32. https://doi.org/10.46647/ijetms.2025.v09i02.066.
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The telecommunications sector has experienced remarkable expansion over the past few decades,driven by rising competition, rapid technological advancements, and ever-changing customerexpectations. To remain competitive in this evolving environment, telecom operators must prioritizecustomer retention and the delivery of personalized services. Two of the most pressing challenges inthis context are churn prediction and customer segmentation. Churn prediction involves identifyingsubscribers who are at high risk of discontinuing their service and migrating to a competitor,allowing providers to develop timely retention strategies. Meanwhile, customer segmentationfocuses on categorizing users into distinct groups based on shared traits and behavioral patterns,which enables more precise marketing campaigns, personalized service offerings, and optimizedpricing models. Traditionally, telecom providers depended on conventional statistical approachesfor churn prediction, which were often manual, limited in scalability, and insufficient for real-timedecision-making. The emergence of Artificial Intelligence (AI) and machine learning hasrevolutionized this landscape, equipping telecom companies with advanced tools to enhance churnforecasting and refine customer segmentation. These technologies have empowered companies toleverage large-scale customer data for more accurate predictions and strategic segmentation,enabling proactive engagement with at-risk customers and fostering long-term loyalty.Consequently, this research focuses on the integration of AI-driven solutions for churn predictionand customer segmentation, which has become vital for telecom firms aiming to minimize customerattrition, strengthen competitive advantage, and deliver an exceptional customer experience in anindustry shaped by constant innovation and fierce market rivalry
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Jafor, A. H. M., MD Sheam Arafat, Mir Abrar Hossain, and Mohammad Majharul Islam. "Survival Strategies for Telecom Operators in the Age of OTT Disruption: A Business Model Analysis of Revenue Diversification and Market Adaptation." American Journal of Management and Economics Innovations 07, no. 05 (2025): 09–32. https://doi.org/10.37547/tajmei/volume07issue05-02.
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The fast growth of Over-the-Top (OTT) services disrupted conventional telecom revenue streams which now demands operators to develop new business approaches. Telecom corporations need essential survival techniques to preserve profitability while operating in an OTT-dominant market structure according to this research. The research utilizes a blended methodology that combines financial data within the industry with case examples and market pattern analyses to study the success of revenue-stream redirection strategies and market transition methods. Telephone and SMS revenue streams diminished continuously because of OTT competition so telecom operators must now focus on subscription packaging, enterprise solutions along with infrastructure revenue streams. Telecom resilience depends on three main factors: affiliations with OTT providers and government rules along with investments to develop new innovations in 5G technology and AI-powered network optimization systems. This paper develops an extensive framework for digital disruption management that helps telecom operators protect their business sustainability through the technological changes. This research connects academic discoveries to industrial field experiences which generates concrete recommendations useful for telecom companies and policy makers and stakeholders involved in digital system operations. The research results demonstrate why telecom entities need to adopt innovative and agile approaches when creating their strategic plans. Future scientific research needs to study how these adaptation techniques affect telecommunication businesses across various markets through extended research periods.
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Moshed, Amer, and Sameer Al-Jabaly. "Enhancing marketing success in Jordanian telecom: Strategic IoT integration and brand relationship management for maximized consumer loyalty." Journal of Infrastructure, Policy and Development 8, no. 6 (2024): 3858. http://dx.doi.org/10.24294/jipd.v8i6.3858.
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This study explores how Jordanian telecom companies can balance Internet of Things (IoT) driven automation with maintaining genuine consumer-brand connections. It seeks strategies that blend IoT automation with personalized engagement to foster lasting consumer loyalty. Employing qualitative research via semi-structured interviews with IT and customer service managers from Jordanian telecom companies. IoT-driven automation in Jordan’s telecom sector revolutionizes consumer-brand relationships by enabling data-driven personalization. It emphasizes the importance of IoT proficiency, transformed marketing strategies, and the need to balance personalization with consumer privacy. Interviews stress the significance of maintaining authentic human connections amidst automation. Strategies for Jordanian telecom firms include integrating IoT data into CRM systems, employing omnichannel marketing, balancing automation with human interaction, adopting a consumer-centric approach, mitigating security risks, and leveraging IoT insights for adaptive services. These approaches prioritize consumer trust, personalized engagement, and agile service adaptation to meet dynamic consumer preferences. This research provides actionable strategies for telecom firms on effective IoT integration, emphasizing the need to maintain genuine consumer relationships alongside technological advancements. It highlights IoT’s transformative potential while ensuring lasting consumer loyalty and business success. Future research avenues could explore longitudinal studies and the interplay between AI and IoT in telecom services.
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Abdulwahab, Ramla, and Shankar Subramanian Iyer. "The Future of 5G and Digital Experience: Enhancing Customer Engagement and Retention in UAE Telecom." Journal of Management World 2025, no. 2 (2025): 451–60. https://doi.org/10.53935/jomw.v2024i4.972.
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The deployment of 5G technology is revolutionizing customer engagement in the telecom industry, offering faster connectivity, ultra-low latency, and seamless digital experiences. This study explores the impact of 5G-driven digital transformation on customer retention in the UAE telecom sector by examining how advanced technologies—such as AI-powered chatbots, real-time analytics, immersive AR/VR experiences, and IoT integration—enhance user engagement and satisfaction. Employing a qualitative research approach, this study conducts in-depth interviews with 15 industry experts, including telecom executives, digital transformation specialists, and customer experience strategists. Thematic analysis is applied to extract key insights on the benefits, challenges, and future potential of 5G in fostering customer loyalty. Findings reveal that while 5G enables hyper-personalization, seamless omnichannel experiences, and improved customer support mechanisms, telecom providers face significant challenges, including high infrastructure costs, data privacy concerns, and regulatory compliance. The study contributes to existing research by providing a strategic framework for telecom operators to leverage 5G in customer retention initiatives. Additionally, it offers practical implications for policymakers, industry stakeholders, and digital marketers in optimizing 5G-driven customer engagement models. By bridging the gap between technological advancements and customer loyalty strategies, this research presents actionable recommendations for sustainable growth in the UAE telecom industry.
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Sabaa, Sania, Saptaningsih Sumarmi, Basma Al-Hariry, Ahmed Soliman, and Neama Elwakeel. "The Role of Gamification and Artificial Intelligence Stimuli in Driving Customer Engagement: A Study on Saudi Telecom Users’ Ability Readiness." American Journal of Business Science Philosophy (AJBSP) 2, no. 1 (2025): 73–85. https://doi.org/10.70122/ajbsp.v2i1.28.
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This study examines the impact of gamification in marketing and artificial intelligence (AI) stimuli on customer engagement, with customer ability readiness as a moderating factor in the Saudi telecommunications sector. Using the Stimulus-Organism-Response (SOR) model, this research investigates how AI-driven personalization and gamification strategies influence customer interactions, motivation, and loyalty. A quantitative research approach was employed, utilizing Structural Equation Modeling (SEM) to analyze survey responses from 400 Saudi telecom users. The results confirm that gamification and AI stimuli positively influence customer engagement, with customer ability readiness significantly moderating these relationships. Consumers with higher technological proficiency are more likely to benefit from AI-driven and gamified experiences, while those with lower readiness may struggle to engage effectively. These findings highlight the importance of designing adaptive and inclusive marketing strategies tailored to different levels of digital proficiency. This study contributes to the theoretical literature by expanding the SOR model to include customer ability readiness as a moderating factor. It also provides practical insights for marketers in the Saudi telecom industry, emphasizing the need for personalized, user-friendly AI and gamification strategies. The findings align with Saudi Vision 2030’s goals of digital transformation and customer-centric innovation, offering valuable implications for businesses seeking to enhance customer engagement.
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Al-Kfairy, Mousa, Dheya Mustafa, Ahmed Al-Adaileh, Samah Zriqat, and Obsa Sendaba. "User acceptance of AI voice assistants in Jordan’s telecom industry." Computers in Human Behavior Reports 16 (December 2024): 100521. http://dx.doi.org/10.1016/j.chbr.2024.100521.
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Venu Madhav Nadella. "AI/ML-Driven Service Assurance: 2024 Breakthroughs Transforming Telecom Operations." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 01–07. https://doi.org/10.32996/jcsts.2025.7.4.1.
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This article explores the transformative impact of artificial intelligence and machine learning technologies on telecommunications service assurance. The industry is experiencing a paradigm shift from reactive to predictive and prescriptive approaches to network management, enabled by three key technological breakthroughs: generative AI, causal inference, and federated learning. Major telecommunications providers are implementing Large Language Models to automate incident resolution processes, reducing resolution times and improving remediation quality. Simultaneously, causal AI is advancing proactive service assurance by establishing cause-and-effect relationships between network events, enabling operators to prevent service disruptions before they occur. Federated learning implementations are solving multi-domain assurance challenges by enabling cross-operator insights while maintaining data sovereignty. Together, these technologies are not merely enhancing existing processes but fundamentally reimagining telecommunications service assurance. The convergence of these approaches promises to deliver self-diagnosing, self-optimizing networks that can anticipate and address potential issues before they impact customer experience, representing a revolutionary advancement in how service quality and reliability are managed in increasingly complex network environments.
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Chibogwu Igwe-Nmaju, Christianah Gbaja, and Chioma Onyinye Ikeh. "Redesigning customer experience through AI: A communication-centered approach in telecoms and tech-driven industries." International Journal of Science and Research Archive 10, no. 2 (2023): 1367–88. https://doi.org/10.30574/ijsra.2023.10.2.1042.
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In a rapidly evolving digital landscape, customer experience (CX) has become a decisive factor in competitive differentiation for telecom and tech-driven industries. This paper examines how artificial intelligence (AI) is reshaping CX by enabling real-time responsiveness, intelligent personalization, and operational efficiency. Beyond technological adoption, the study highlights the communication-centered approach necessary for sustaining customer trust, emotional connection, and brand coherence in human-machine interactions. Focusing on AI tools such as chatbots, voice assistants, predictive analytics, and conversational interfaces, the research analyzes how companies craft seamless, empathetic communication pathways across digital touchpoints. It explores how organizations balance automation and human oversight to avoid dehumanization, communication gaps, and frustration among users. Through case studies from leading telecom providers and tech firms, the paper investigates how AI-infused customer support systems align with service values and enhance customer loyalty. Moreover, the article delves into the role of strategic communication teams in scripting bot dialogues, calibrating tone, and ensuring consistent brand messaging across AI channels. It emphasizes the critical importance of feedback loops, adaptive learning from customer interactions, and ethical considerations in data usage. By integrating communication theory with CX design, the study offers a framework for AI implementation that preserves customer intimacy, responsiveness, and authenticity. It concludes with strategic recommendations for deploying AI technologies that not only optimize performance but also reinforce customer trust, especially in industries where technical issues, service outages, and privacy concerns can erode brand equity if not managed communicatively.
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Ang Li, Tianyi Yang, Xiaoan Zhan, Yadong Shi, and Huixiang Li. "Utilizing Data Science and AI for Customer Churn Prediction in Marketing." Journal of Theory and Practice of Engineering Science 4, no. 05 (2024): 72–79. http://dx.doi.org/10.53469/jtpes.2024.04(05).10.
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This study explores the application of data science and AI techniques in predicting customer churn within the telecommunications industry, a sector characterized by intense competition and high customer turnover rates. By analyzing historical customer data, including usage patterns and service preferences, the study aims to identify factors contributing to churn and propose targeted retention strategies to mitigate losses. Traditional classification algorithms and ensemble techniques are evaluated using the Telecom-Customer-Churn dataset, with emphasis on the underutilized Stacking ensemble method. The results demonstrate that ensemble learning algorithms, particularly the Stacking model, outperform single algorithms, with CatBoost exhibiting the highest accuracy at 0.8119, followed closely by RandomForest at 0.7902 and XGBoost at 0.7820. These findings underscore CatBoost's superior generalization capabilities, likely attributed to its adept handling of categorical features and missing values, and its ability to model complex data relationships. The study contributes to advancing understanding of ensemble models and offers valuable insights for predicting telecom customer churn, thereby aiding in the development of effective retention strategies and enhancing customer satisfaction and loyalty.
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Philip Olaseni Shoetan, Olukunle Oladipupo Amoo, Enyinaya Stefano Okafor, and Oluwabukunmi Latifat Olorunfemi. "SYNTHESIZING AI'S IMPACT ON CYBERSECURITY IN TELECOMMUNICATIONS: A CONCEPTUAL FRAMEWORK." Computer Science & IT Research Journal 5, no. 3 (2024): 594–605. http://dx.doi.org/10.51594/csitrj.v5i3.908.
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As the telecommunications sector increasingly relies on interconnected digital infrastructure, the proliferation of cyber threats poses significant challenges to security and operational integrity. This review presents a conceptual framework for understanding and harnessing the potential of artificial intelligence (AI) in fortifying cybersecurity within the telecommunications industry. The framework integrates the transformative capabilities of AI with the unique demands of cybersecurity in telecommunications, aiming to enhance threat detection, mitigation, and response strategies. It encompasses a multidimensional approach that encompasses both technical and organizational facets, recognizing the interconnectedness of technology, human factors, and regulatory environments. Firstly, the framework delves into the application of AI in bolstering proactive threat intelligence gathering and analysis. Through advanced algorithms and machine learning techniques, AI empowers telecom operators to identify anomalous patterns, predict potential vulnerabilities, and pre-emptively adapt defensive measures. Secondly, it explores AI-driven solutions for dynamic risk assessment and adaptive cybersecurity protocols. By leveraging real-time data analytics and automated decision-making, telecom networks can swiftly adapt to evolving threats and ensure continuous protection against intrusions or breaches. Furthermore, the framework emphasizes the role of AI in augmenting human capabilities through intelligent automation and cognitive assistance. By offloading routine tasks and providing context-aware insights, AI enables cybersecurity professionals to focus on strategic initiatives and complex threat scenarios. Lastly, the framework addresses the imperative of ethical considerations, accountability, and transparency in deploying AI for cybersecurity in telecommunications. It advocates for responsible AI governance frameworks that prioritize privacy, fairness, and bias mitigation while fostering collaboration across industry stakeholders. In summary, this conceptual framework provides a roadmap for harnessing AI's transformative potential to fortify cybersecurity resilience in telecommunications, thereby safeguarding critical infrastructure and ensuring the integrity of global communication networks.
 Keywords: AI, Cybersecurity, Telecommunication, Framework, Conceptual, Impact, Review.
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Mokale, Mahesh. "Scalable Data Management Strategies for Telecommunications Enterprises." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 05, no. 12 (2021): 1–8. https://doi.org/10.55041/ijsrem11159.
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In the current technological landscape marked by the widespread adoption of 5G networks, the proliferation of Internet of Things (IoT) devices, and the ever-growing need for real-time data processing, telecommunications enterprises are encountering unprecedented challenges in managing and scaling their vast volumes of data. The digital age has brought about a transformative surge in the volume, variety, and velocity of data, compelling telecom companies to reevaluate and enhance their data management strategies to remain competitive. This surge is not limited to just an increase in the number of connected devices but also encompasses the complexity of data types—ranging from structured and unstructured customer data to high-frequency network performance metrics, sensor-generated data, and real-time analytics. The sheer scale and speed at which data is generated in the telecom industry make traditional data management frameworks insufficient. Without robust, scalable strategies in place, enterprises risk inefficiencies in operations, delays in decision-making, and missed opportunities for revenue growth and innovation. Therefore, adopting a forward-looking approach to data management is no longer optional but an absolute necessity. This paper delves into a range of innovative approaches to scalable data management, highlighting how cutting-edge technologies such as cloud computing, edge computing, process automation, and artificial intelligence (AI)-driven analytics can provide telecom companies with the tools they need to thrive in this data-driven era. Cloud adoption offers unmatched scalability and flexibility, while edge computing enables low-latency processing by bringing computational power closer to the data source. Additionally, automation simplifies complex workflows, and AI-powered analytics unlock actionable insights that can drive operational efficiency, enhance customer satisfaction, and create new revenue streams. By exploring these strategies, this paper aims to provide actionable insights and a roadmap for telecom companies to not only optimize their existing data infrastructure but also align their operations with emerging industry trends, ensuring long-term growth, resilience, and competitiveness in an increasingly interconnected world. Keywords: Scalable Data Management, Telecommunications Enterprises, 5G Networks, Internet of Things (IoT), Cloud Computing, Edge Computing, AI-Driven Analytics, DataOps Methodologies, Data Federation, Real-Time Data Processing, Blockchain for Data Security, Automation in Data Management, Hyper-Personalization, Regulatory Compliance
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Ray, Debmalya. "Network Type Recognition Using Machine Learning Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem29160.
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The telecom industry is going through a massive digital transformation with the adoption of ML, AI, feedback-based automation and advanced analytics to handle the next generation of applications and services. AI concepts are not new; the algorithms used by Machine Learning and Deep Learning are being currently implemented in various industries and technology verticals. With growing data and an immense volume of information over 5G, the ability to predict data proactively, swiftly and with accuracy, is critically important. Data-driven decision-making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end-to-end network resource allocation using the concept of Network Slicing (NS). Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc). Index Terms: Supervised Learning, Feature Engineering, Python, Network Slicing, Telecom, Classification Problems, EDA – Exploratory Data Analysis.
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Al-Subaie, Norah Abdullah. "Challenges of Successful Implementation of Artificial Intelligence in Logistics Project Management at Saudi Telecom Company." مجلة العلوم الإقتصادية و الإدارية و القانونية 9, no. 3 (ملحق) (2025): 114–25. https://doi.org/10.26389/ajsrp.k100325.
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The emergence of artificial intelligence (AI) technology has the capability to drastically change logistics and supply chain management across various business and industrial sectors. Logistic project management, a crucial sector in the supply chain management of companies, is regarded as a domain where AI will play a key role in enhancing efficiency, precision, and the speed of service delivery. Saudi Arabia's Vision 2030 aims to improve connectivity with other nations both regionally and globally by refining logistics services, streamlining trade exchanges, and incorporating digital technology into these processes. The aim of this study is to identify the challenges faced when implementing AI technologies in logistics project management at Saudi Telecom Company (STC). This research employs a qualitative approach that features a literature review on the topic. The findings revealed several challenges impeding the global implementation of AI in logistics project management, which can be classified into organizational, technological, economic, data, environmental, social, and ethical issues. In the Saudi context, within the STC, four key challenges of implementing AI in logistics project management were recognized: data privacy and security, hardware infrastructure needs, insufficient employee skills and adaptability, and social acceptance. Based on the findings of this study, it can be concluded that implementing AI technology in logistics project management, when executed correctly, could reveal significant opportunities at the STC. Further experimental studies are suggested to gain a better understanding of the challenges that restrict the application of AI technology in logistics project management.
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Pothen, Vivek Aby. "Strategic Azure Cloud Migration for Telecom: Best Practices and Emerging Trends." European Journal of Computer Science and Information Technology 13, no. 19 (2025): 79–92. https://doi.org/10.37745/ejcsit.2013/vol13n197992.
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The migration of telecommunications infrastructure to cloud platforms, particularly Microsoft Azure, represents a transformative shift in how telecommunications providers manage and optimize their networks. This comprehensive article explores the imperatives driving cloud adoption in telecommunications, examining the substantial improvements in operational efficiency, cost reduction, and service reliability achieved through strategic migration initiatives. The article investigates hybrid cloud adoption strategies, the implementation of advanced Azure technologies including AI-powered analytics, Kubernetes orchestration, and serverless computing solutions. Through detailed case studies of European and Asia-Pacific telecommunications providers, the article demonstrates the practical benefits and challenges of cloud migration. The article further examines critical considerations in latency management, regulatory compliance, and multi-cloud interoperability, while exploring emerging trends in AI-driven automation, edge computing integration, and zero-trust security architectures.
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Anaam, Elham Abdulwahab, Muhamad Naser Yousef Magableh, Mohammed Hamdi, Aldeen Yousef Rashid Hmoud, and Hamood Alshalabi. "Data Mining Techniques with Electronic Customer Relationship Management for Telecommunication Company." Revista Amazonia Investiga 10, no. 48 (2021): 288–304. http://dx.doi.org/10.34069/ai/2021.48.12.30.
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Organizations must improve decisional quality, and the continuous usage of data mining techniques is a crucial issue for management. This issue mostly involves an individual's motivation to engage in the behavior. This could perhaps be characterized in terms of the working regimen. technology utilization and employee activity are the two main difficulties that this dilemma revolves around. This study aims to address the aspect associated with data mining and E-CRM in the telecom industry. The methods that are used in the current study, analysis studies of the data mining techniques are applied to E-CRM that has been identified. Moreover, PHP with the update of the DeLone and McLean methods has been used in the current study. The results show the significance in affecting the continuance used intention of data mining techniques. User satisfaction, technology, and data mining are critical predictors of employment intentions.
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Atay, Mehmet Tarik, and Munevver Turanli. "ANALYSIS OF CUSTOMER CHURN PREDICTION USING LOGISTIC REGRESSION, -NEAREST NEIGHBORS, DECISION TREE AND RANDOM FOREST ALGORITHMS." Advances and Applications in Statistics 92, no. 2 (2024): 147–69. https://doi.org/10.17654/0972361725008.
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Customer churn predictions (CCPs) and their comprehensive analysis have become prevalent in the global telecom industry over the last five years, driven by advancements in machine learning (ML) technologies. In addition, AI (artificial intelligence) and ML-based predictive methods are currently employed for CCP applications to enhance customer retention. This predictive CCP methodology streamlines customer management processes and ensures sustainable profit growth. The machine learning models focus on identifying features derived from data that is rich in various types of information. This study analyzes CCP for a specific telecom company’s customer dataset using ML methods such as logistic regression (L.R.), -nearest neighbor (-NN), decision tree (D.T.), and random forest (R.F.). The UCI Iranian telecom churn dataset was utilized, and the influence of potential factors leading to customer churn was also considered. Results show that the tuned RF method yielded the best outcomes, with churn tendency analysis achieving a higher AUC score at 0.9042 with the accuracy of 0.9562. The most important feature of the dataset affecting the customer churn was identified as complains whereas the least important feature happened to be tariff plan.
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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 quality, model selection, implementation, and ethical considerations in using customer data for predictive analytics. The paper also compares machine learning models with traditional methods, emphasizing the advantages of scalability, accuracy, and real-time processing. Furthermore, it identifies potential innovations, such as improved data integration, interpretable models, and personalized retention strategies. Finally, the paper reflects on future trends, predicting the growing role of AI and machine learning in telecommunications, particularly in customer service automation and network optimization. The review underscores the importance of adopting machine learning to reduce churn and improve customer retention while considering the field's ethical implications and future opportunities. Keywords: Customer Churn Prediction, Machine Learning, Telecommunications, Customer Retention, Predictive Analytics, AI in Telecom
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Rana, Chanderhas, and Prof Dr R. K. Pardeshi. "A Research Review: Ai and Data Science Applications in the Telecom Industry." IBMRD's Journal of Management & Research 11, no. 2 (2022): 196. http://dx.doi.org/10.17697/ibmrd/2022/v11i2/172620.
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Bikkasani, Dileesh Chandra. "Network Resiliency and Fault Tolerance through Digital Twins and Data Science." American Journal of Data, Information and Knowledge Management 6, no. 1 (2025): 1–14. https://doi.org/10.47672/ajdikm.2682.
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Purpose: As telecom networks evolve with the integration of 5G, 6G, and IoT technologies, their increasing complexity presents significant challenges to maintaining network stability. Traditional management methods are no longer sufficient to ensure the resiliency required in these dynamic environments. Materials and Methods: To address this, we explore the application of digital twin technology as a transformative solution for network operations. Digital twins enable real-time monitoring, predictive analytics, and scenario simulation by creating a dynamic, virtual representation of the telecom network. These capabilities allow for proactive identification and resolution of potential failures, enhancing predictive maintenance and supporting real-time decision-making during network anomalies. The digital twin continuously synchronizes with the live network through integration of data from diverse components, ensuring an up-to-date reflection of operational conditions. Findings: Our analysis identifies key technical and organizational challenges in implementing this approach namely, the complexity of data integration, the demand for scalable architectures, and the necessity for advanced AI-driven analytics to interpret high-volume, high-velocity data effectively. Addressing these challenges is critical to unlocking the full potential of digital twins in telecom settings. The findings suggest that digital twin technology holds substantial promise in improving network resiliency and operational efficiency. Unique Contribution to Theory, Practice and Policy: By enabling telecom operators to shift from reactive to predictive and adaptive network management, this approach offers a robust framework for future-proofing infrastructure in the face of rising complexity. The study contributes to operations research by highlighting a scalable, data-driven pathway to more resilient and reliable telecom networks through the integration of digital twins.
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Singh, Shivam Vivekanand, and Shubhang Johari. "Universal Messaging: The Growth and Potential of Rich Communication Services." International Journal of Engineering Research and Applications 14, no. 12 (2024): 74–79. https://doi.org/10.9790/9622-14127479.
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This report analyzes the evolution of Rich Communication Services (RCS) between 2020 and 2024, focusing on its role as a next-generation messaging protocol. Designed to enhance SMS/MMS, RCS integrates rich media, group chats, and secure business messaging, offering features commonly found in OTT platforms like WhatsApp and iMessage. The RCS market has expanded from $5.2 billion in 2020 to an estimated $7.5 billion by 2024, fueled by enterprise adoption, enhanced customer engagement, and 5G integration. Key applications span business messaging, advertising, transactional communication, and customer support. Despite challenges like limited iOS adoption, competition from OTT services, and high infrastructure costs, RCS presents significant investment potential for telecom operators and enterprises. The report highlights opportunities in Application-to-Person (A2P) messaging and emerging revenue streams. Trends point to the importance of 5G integration, AI-driven solutions, and telecom partnerships in overcoming adoption barriers. By leveraging RCS, businesses and MNOs can unlock competitive advantages, reclaim market share, and improve user engagement. Positioned as a secure, interoperable, and feature-rich messaging standard, RCS is transforming the communication landscape, bridging the gap between traditional telecom services and the demands of modern, multimedia-rich communication
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Tiwari, Harikesh, and Dr Chandra Kishor Pandey. "Generative AI: Crafting Tomorrow’s Creativity." International Journal of Inventive Engineering and Sciences 12, no. 1 (2025): 1–4. https://doi.org/10.35940/ijies.a1097.12010125.
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The rapid development of artificial intelligence (AI) has impacted creativity, providing artists, writers, designers, and innovators with powerful tools. This research explores the intersection of AI and human creativity, demonstrating AI’s ability to create unique content, perform tasks, and introduce new drama. Disruption poses significant challenges, including misinformation, transfer of labor, fraud, and bias in AI output. Addressing these issues requires strict regulation, greater transparency, and public awareness of the risks involved. Future efforts should focus on addressing ethical implications, ensuring transparency, and aligning technological changes with the needs of society. This is good for creating stability and balance, but people use these thing for fraud that is very harmfull for our society. Solutions include using intelligence-based search tools, improving cybersecurity, encouraging ethical behavior, and developing a skilled workforce. This study highlights the importance of balancing the benefits and risks of generative AI to foster meaningful creativity as well as its role in integrating into society. With strong ethical protections, generative AI offers many opportunities for innovation. The Telecom company should block all kinds of messages such as,(Phishing, lottery prize scams, tech support scams, love scams, bill or payment scams, tax scams, investment scams) for the customers, and also send the alert message for the scam to all the users via Voice.
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Mohamed Abdul Kadar Mohamed Jabarullah. "PredictNet: AI-enabled predictive maintenance system for telecommunications infrastructure reliability." World Journal of Advanced Research and Reviews 15, no. 3 (2022): 631–39. https://doi.org/10.30574/wjarr.2022.15.3.0954.
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This paper introduces PredictNet, a novel AI-enabled predictive maintenance system designed specifically for telecommunications infrastructure. The research addresses the critical challenge of maintaining reliability in increasingly complex telecom networks while reducing operational costs. Using machine learning algorithms and real-time sensor data, PredictNet demonstrates superior performance in predicting equipment failures before they occur. The system was implemented and tested on a mid-sized telecommunications network over a 12-month period, achieving 92.7% prediction accuracy with a mean time-to-failure prediction of 18.3 days. Results show a 43% reduction in network downtime and 37% decrease in maintenance costs compared to traditional scheduled maintenance approaches. The study validates PredictNet's effectiveness and provides a framework for implementing AI-driven predictive maintenance in telecommunications infrastructure.