As telecommunication service providers shifting their focus to analyzing user behavior for package design and marketing interventions, a critical challenge lies in developing a unified, end-to-end framework capable of modeling long-term and periodic user behavior sequences with diverse time granularities, multi-modal data inputs, and heterogeneous labels. This paper introduces GTS-LUM, a novel user behavior model that redefines modeling paradigms in telecommunication settings. GTS-LUM adopts a (multi-modal) encoder-adapter-LLM decoder architecture, enhanced with several telecom-specific innovations. Specifically, the model incorporates an advanced timestamp processing method to handle varying time granularities. It also supports multi-modal data inputs -- including structured tables and behavior co-occurrence graphs -- and aligns these with semantic information extracted by a tokenizer using a Q-former structure. Additionally, GTS-LUM integrates a front-placed target-aware mechanism to highlight historical behaviors most relevant to the target. Extensive experiments on industrial dataset validate the effectiveness of this end-to-end framework and also demonstrate that GTS-LUM outperforms LLM4Rec approaches which are popular in recommendation systems, offering an effective and generalizing solution for user behavior modeling in telecommunications.

Data is the main fuel of a successful machine learning model. A dataset may contain sensitive individual records e.g. personal health records, financial data, industrial information, etc. Training a model using this sensitive data has become a new privacy concern when someone uses third-party cloud computing. Trained models also suffer privacy attacks which leads to the leaking of sensitive information of the training data. This study is conducted to preserve the privacy of training data in the context of customer churn prediction modeling for the telecommunications industry (TCI). In this work, we propose a framework for privacy-preserving customer churn prediction (PPCCP) model in the cloud environment. We have proposed a novel approach which is a combination of Generative Adversarial Networks (GANs) and adaptive Weight-of-Evidence (aWOE). Synthetic data is generated from GANs, and aWOE is applied on the synthetic training dataset before feeding the data to the classification algorithms. Our experiments were carried out using eight different machine learning (ML) classifiers on three openly accessible datasets from the telecommunication sector. We then evaluated the performance using six commonly employed evaluation metrics. In addition to presenting a data privacy analysis, we also performed a statistical significance test. The training and prediction processes achieve data privacy and the prediction classifiers achieve high prediction performance (87.1\% in terms of F-Measure for GANs-aWOE based Na\"{\i}ve Bayes model). In contrast to earlier studies, our suggested approach demonstrates a prediction enhancement of up to 28.9\% and 27.9\% in terms of accuracy and F-measure, respectively.

Customer churn, the discontinuation of services by existing customers, poses a significant challenge to the telecommunications industry. This paper proposes a novel adaptive ensemble learning framework for highly accurate customer churn prediction. The framework integrates multiple base models, including XGBoost, LightGBM, LSTM, a Multi-Layer Perceptron (MLP) neural network, and Support Vector Machine (SVM). These models are strategically combined using a stacking ensemble method, further enhanced by meta-feature generation from base model predictions. A rigorous data preprocessing pipeline, coupled with a multi-faceted feature engineering approach, optimizes model performance. The framework is evaluated on three publicly available telecom churn datasets, demonstrating substantial accuracy improvements over state-of-the-art techniques. The research achieves a remarkable 99.28% accuracy, signifying a major advancement in churn prediction.The implications of this research for developing proactive customer retention strategies withinthe telecommunications industry are discussed.

Large Language Models (LLMs) are continuously being applied in a more diverse set of contexts. At their current state, however, even state-of-the-art LLMs such as Generative Pre-Trained Transformer 4 (GTP-4) have challenges when extracting information from real-world technical documentation without a heavy preprocessing. One such area with real-world technical documentation is telecommunications engineering, which could greatly benefit from domain-specific LLMs. The unique format and overall structure of telecommunications internal specifications differs greatly from standard English and thus it is evident that the application of out-of-the-box Natural Language Processing (NLP) tools is not a viable option. In this article, we outline the limitations of out-of-the-box NLP tools for processing technical information generated by telecommunications experts, and expand the concept of Technical Language Processing (TLP) to the telecommunication domain. Additionally, we explore the effect of domain-specific LLMs in the work of Specification Engineers, emphasizing the potential benefits of adopting domain-specific LLMs to speed up the training of experts in different telecommunications fields.

Are you considering how to use AI in telecommunications for your business? In this article, we explore the potential of AI and its implications for digital transformation. Discover how using AI can help your business reach new heights and stay ahead of the competition in an ever-evolving world of technology. What is AI and what does it do? AI is a process of programming computers to make decisions for themselves.

In the course of time, data science has proved its high value and efficiency. Data scientists find more and more new ways to implement big data solutions in daily life. Nowadays data is a fuel needed for a successful company. Telecommunication companies are not an exception. Due to these circumstances, they cannot afford not to use data science.

Sweden's attitude towards innovation is perhaps best exemplified by the Swedish innovation agency, Vinnova, a government agency founded in 2001 based on a series of predecessors going back to at least 1968. The innovation agency functions much like its counterparts in other countries, similarly to the Finnish Funding Agency for Technology and Innovation (Tekes) in neighbouring Finland, and to the part of the US National Science Foundation (NSF) that does seed funding on the other side of the Atlantic. The Swedish government gives Vinnova more than €300m each year to invest through grants to different kinds of actors, which might be small companies, research institutes, large competence centres, or consortia of companies working together on projects. Vinnova invests this money along 10 different themes, including sustainable industry and digital transformation. To report on the social and economic effects of its funding, the agency produces two impact studies annually.

In every facet of life, challenges keep coming, and overcoming them is all we have learned so far, and that's how AI is surprising every industry with its capabilities to enrich businesses. Now, automation and AI technology is the new technological advancement adopted by the telecom industry to solve challenges like network failures, improper resource utilization, managing bandwidth requirements, and issues related to customer support. According to a study, the global AI market in the telecom industry is expected to grow by $8.63 billion between 2022 and 2026, at a CAGR of 47.33 %. The telecommunications industry is experimenting and delivering new innovative concepts to businesses using artificial intelligence. AI capabilities are extracted for business use from collecting necessary data such as customer profiles, log behaviors, mobile devices, networks, service utilization, sales data, geo-location intelligence, and billing to assist customers better.

According to Markets & amp; Markets, the telecommunications industry's world market for synthetic brains will attain a whopping 2.5 billion greenbacks through 2022. It is no extra arguable whether or not the speedy emergence of AI will impact, or possibly disrupt most businesses. The telecommunications enterprise is no different. As per Markets & amp; Markets, the telecommunications industry's world market for synthetic talent will attain a whopping 2.5 billion bucks with the aid of 2022. The emergence of AI, Data Science, and Machine Learning will allow Telecom corporations to beautify their performance, make greater investments, and profit.

The application of Artificial Intelligence in the telecommunication industry has gained quite a much traction in the recent past and for the right reasons. The role of the telecommunications industry in today's world has expanded beyond the provision of simple phone and internet interaction services for individuals and corporates. In the current era of the Internet of Things (IoT), telecommunication companies have leveraged mobile and broadband services to take center stage in technological growth and innovation. That is not all; educated prospects point to a future commercial world where Artificial intelligence is vital. For example, Technavio, a leading market research, and advisory firm globally, expects growth in technology to continue for the foreseeable future and record a Compounded Annual Growth Rate (CAGR) of above 42% next year.