predictive analytics

  Background: The marriage of 5G and Artificial Intelligence (AI) has been brought forward as a key enabler of Industry 4.0 and smart city applications. These technologies solve the problem of latency, scalability, and energy use, providing technology support for real-time decision-making and efficient organization of work. Nevertheless, studies regarding their individual and collective effects in a plethora of industrial and urban contexts are still limited. Objective: The objective of this research is to assess the performance, energy saving, and expansibility of 5G and AI synergies in manufacturing, logistics, healthcare, and smart city applications and highlight their challenges and potential for further exploration. Methods: An experimental data collection, mathematical modeling and comparative analysis approach was employed. Performance indicators including latency, possible and actual throughput, power usage, and predicting achievement were measured in real pilot tests implemented in dense networks and IoT contexts. Available data were compared with other similar studies to gain an understanding of the results. Results: The conjoin with 5G and AI suggested potential optimization of process; the latency has been decreased to more than 90%, its predictive maintenance was sharpened, and its power consumption was decrease to 75%. The feasibility of extending scalability and system reliability of the protocol was confirmed in dense IoT environments, with further potential for emission reduction. Conclusion: The study identifies the use of 5G in Industry 4.0 with AI in addressing dynamic issues but potential drawback includes scalability and security. More studies should be conducted on the novel hybrid architectures and 6G integration concerning more extensive areas.

Background: The telecommunications industry is at the crossroad of change seemingly precipitated by the use of Artificial Intelligence (AI) and Machine Learning (ML). These technologies have yielded new features like network automation, prescriptive analytics, and contextual-consumer engagement, solving traditional dilemmas in service delivery and operationalization. Objective: The current article seeks to understand how AI and ML has positively affected customer experience and service provision in the telecommunication industry. The research objectives focus on how to increase KPIs to service latencies, network reliability, and customer retention while at the same time establishing the problems associated with big data large-scale implementation. Methods: Samples were gathered using systematic reviews of the current literature, meta-analysis of case studies, and assessment of industry datasets. This concerned artificial intelligence enabled operations such as dynamic resource management, real-time customer emotions analysis and real-time fault detection. Regression analysis and time series models were used in order for measuring performance indices. Results: AI and ML integration led to multifaceted advancements: a decrease of average service latency by 55%, reduction of network downtime by 70%, and an increase of maintenance predictions accuracy by 35%. The customer retention rate which had improved to 25% was also credited to better personalization of the services as well as having proper service management. AI-equipped resource allocation also raised efficiency in bandwidth utilization by 60%. Conclusion: AI and ML are positively disrupting telecommunications as they deliver remarkable enhancements in the caliber of services and client satisfaction. With all the challenges in data governance and interoperability, it is clear that their adoption promises a great chance in enhancing the current standards within the telecommunications field and creating the basis for the development of a more sophisticated environment.

Background: Digital twin (DT) technologies have become significant enablers of urban management, utilising real-time information, data analytics, and IoT connectivity to manage challenging urban issues. Nonetheless, existing studies reveal the capacity of the DTs, while their generalization, flexibility, and cross-disciplinary application for various urban environments are not thoroughly studied yet. Objective: This article aims to evaluate the effectiveness of DT technologies in improving traffic management, energy efficiency, infrastructure maintenance, and public safety across six case study cities: There are Singapore, Helsinki, Barcelona, Dubai, New York, and Tokyo. The study examines how DTs can be extended and implemented to target urban issues and how their use operational performance might be optimized. Methods: The study used quantitative data processing, on-line data analysis with factorization and machine learning, and assessment of the case studies. Quantitative measures which included traffic flow, energy loss, down time, and response to emergency situations were investigated pre and post DT application. The improvements mentioned were statistically confirmed, and the metrics of scalability and adaptability were evaluated in the course of the cities. Results: DT technologies increased traffic flow by up to 42.9%, reduced energy losses by 35%, minimum down time was 42%, emergency response was 44.9%. This was the case because the network had high IoT coverage and because DTs were applied to the context when it specifically needed them. Conclusion: The study proves that DTs can be implemented in different environments due to their flexibility to accommodate different urban conditions. AI and cross domain integration can add to the effectiveness of DT in general and both are inarguably now crucial for the management of contemporary urban environment.

Background: The telecommunication industry is currently witnessing an unparalleled growth in traffic data with a concomitant growth in the complexity of networks. As operators seek to achieve high availability of the networks, it is almost compulsory to employ the BDA for improved quality of service and increased operational performance. Objective: The study aims to provide a systematic review of the deployment of BDA in enhancing the primary characteristic indicators of telecommunications networks, to include availability of upgraded latency and throughput levels and network dependability. Methods: The research method used was summed up by quantitative analyses of the key performance parameters of the networks, along with the qualitative results of case studies conducted with major telecommunications operators. Information was collected from multiple networks as well as analyzed with the use of machine learning to be able to predict possible performance issues. Results: The study demonstrates that there is the possibility for reducing latency utilizing BDA with enhancements of up to 40%. In addition, the throughput has been raised by an average of 30% and the predictable analytics lead to 25% reducing in network downtime to improve the reliability and satisfaction of the user experience. Conclusion: The information provided in this study highlights the importance of Big Data Analytics for the telecommunication industry, proving that the proper integration can bring tangible improvements to the existing networks. One future development that constitutes the need for innovative analytical technologies is the rise in data traffic and sophisticated network requirements.