Mobile network operators constantly optimize their networks to ensure superior service quality and coverage. This optimization is crucial for maintaining an optimal user experience and requires extensive data collection and analysis. One of the primary methods for gathering this data is through drive tests, where technical teams use specialized equipment to collect signal information across various regions. However, drive tests are both costly and time-consuming, and they face challenges such as traffic conditions, environmental factors, and limited access to certain areas. These constraints make it difficult to replicate drive tests under similar conditions. In this study, we propose a method that enables operators to predict received signal strength at specific locations using data from other drive test points. By reducing the need for widespread drive tests, this approach allows operators to save time and resources while still obtaining the necessary data to optimize their networks and mitigate the challenges associated with traditional drive tests.

In today's rapidly evolving technological landscape, organizations face the challenge of integrating external insights into their decision-making processes to stay competitive. To address this issue, this study proposes a method that combines topic modeling, expert knowledge inputs, and reinforcement learning (RL) to enhance the detection of technological changes. The method has four main steps: (1) Build a relevant topic model, starting with textual data like documents and reports to find key themes. (2) Create aspect-based topic models. Experts use curated keywords to build models that showcase key domain-specific aspects. (3) Iterative analysis and RL driven refinement: We examine metrics such as topic magnitude, similarity, entropy shifts, and how models change over time. We optimize topic selection with RL. Our reward function balances the diversity and similarity of the topics. (4) Synthesis and operational integration: Each iteration provides insights. In the final phase, the experts check these insights and reach new conclusions. These conclusions are designed for use in the firm's operational processes. The application is tested by forecasting trends in quantum communication. Results demonstrate the method's effectiveness in identifying, ranking, and tracking trends that align with expert input, providing a robust tool for exploring evolving technological landscapes. This research offers a scalable and adaptive solution for organizations to make informed strategic decisions in dynamic environments.

Topic modeling often requires examining topics from multiple perspectives to uncover hidden patterns, especially in less explored areas. This paper presents an approach to address this need, utilizing weighted keywords from various aspects derived from a domain knowledge. The research method starts with standard topic modeling. Then, it adds a process consisting of four key steps. First, it defines keywords for each aspect. Second, it gives weights to these keywords based on their relevance. Third, it calculates relevance scores for aspect-weighted keywords and topic keywords to create aspect-topic models. Fourth, it uses these scores to tune relevant new documents. Finally, the generated topic models are interpreted and validated. The findings show that top-scoring documents are more likely to be about the same aspect of a topic. This highlights the model's effectiveness in finding the related documents to the aspects.