In today's digital world, processing multilingual documents is critical for business, legal tasks, and information retrieval. This study describes a Multilingual Document Processing System that uses Optical Character Recognition (OCR) and Retrieval-Augmented Generation (RAG) to extract, query and summarize text in multiple languages. The system employs advanced OCR models to correctly recognize text from scanned documents, images, and handwriting in various scripts. By incorporating RAG, it improves comprehension and response generation, allowing users to retrieve and summarize information in English even when the original language is different. This approach takes advantage of recent advances in natural language processing, large language models (LLM), and multimodal AI to address challenges in multilingual data accessibility, knowledge synthesis, and real-time communication. The system provides a scalable AI-driven solution to improve document processing, eliminate language barriers, and increase global user engagement. AWS services support scalable document processing but cold starts in AWS Lambda hinder real time tasks.
Social media platforms have become the nerve center of campaign discussion in regards to global political landscape. Political candidates are wielding social media as an influential instrument to advance their own election campaigns. In this paper, we have perused the online exchanges and conversations among supporters of different political parties in the intense environment of US Presidential Election 2024. Sentiment scores, abusive speech and stance detection of the tweets have been considered for understanding the perspective of voters on social media platforms. Each post has been considered whether it is a hate speech which contributes in polarization in the various conversation obtained from mainstream social media platform X/Twitter. A novel model has been proposed that factors in sentiment, stance and and hate speech for opinion dynamics estimation. Our method has been evaluated on various publicly available datasets and satisfactory results have been obtained. It is observed that there is higher level of opinion polarization when hate speech is involved.
This study proposes a novel fixed-time tracking control methodology tailored for second-order multi-agent systems (MASs) operating over directed communication networks and experiencing communication delays. Recent studies have explored fixed-time consensus in MASs with nonlinear dynamics and communication delays. The control design employs a Lyapunov–Krasovskii (L-K) functional framework, integrating Hölder's inequality and a Lipschitz-like condition to ensure system stability. Notably, this approach eliminates the dependence on Linear Matrix Inequalities (LMIs), which are prevalent in traditional control strategies but often lead to increased computational complexity. By circumventing LMIs, the proposed method enhances computational efficiency, making it suitable for largescale MAS applications. The control protocol guarantees that all agents achieve consensus tracking of the leader's trajectory within a predetermined fixed time, regardless of initial conditions. Theoretical analyses are substantiated through numerical simulations, demonstrating the effectiveness and robustness of the proposed scheme in achieving rapid convergence and maintaining stability in the presence of communication delays.
This study developed the Digital Banks PH Notebook, a mobile application designed to support financial literacy among Filipinos by optimizing savings through high-yield digital banking platforms. The application featured a savings portfolio tracker, interest forecasting calculator, and savings goal management to address gaps in financial planning and savings behavior. Development followed a blended Agile methodology integrating Scrum, Extreme Programming, and Feature-Driven Development, ensuring iterative improvements aligned with user needs. Software quality was assessed using the ISO/IEC 25010 model, while qualitative feedback was analyzed through word cloud visualization to capture user sentiment and key focus areas. Findings indicated that the application effectively enhanced users' understanding of savings strategies and promoted responsible saving practices. The tool successfully connected the opportunities presented by digital banking with the practical requirements of financial education, providing users with actionable insights to manage their savings more strategically. By leveraging agile development practices and rigorous evaluation frameworks, the project demonstrated that technology-driven solutions can play a significant role in advancing financial literacy and supporting sustainable financial behaviors in an evolving digital economy.
This research presents the MySecureMap system, a geolocation-based recommender system designed for real-time air quality visualization and safetyfocused navigation. The system leverages the Air Quality Index (AQI) data provided by both government and private sources. The proposed mechanism has integrated the Long Short-Term Memory (LSTM) neural network's machine learning technique which is capable of predicting the AQI levels and providing the route recommendations. The ultimate goal of the proposed system aims to protect users from the hazardous air pollutants by automatically identifying the risk zones and suggesting the safe routes which are visualized on the map. This paper discusses the proposed approach and the system architecture, the design and the development of the proposed MySecureMap system, and the performance evaluation, highlighting its usability and effectiveness in enhancing the environmental awareness and public health. The performance evaluation results shown that the proposed system met the acceptable accuracy with Mean absolute percent error (MAPE) of 2.39% when comparing the predicted forecast value with the real AQI data observed for the next 12 hours.
