Abstract

Abstract
In today’s era, most of the people are suffering with chronic diseases because of their lifestyle, food habits and reduction in physical activities. Diabetes is one of the most common chronic diseases which has affected to the people of all ages. Diabetes complication arises in human body due to increase of blood glucose (sugar) level than the normal level. Type-2 diabetes is considered as one of the most prevalent endocrine disorders. In this circumstance, we have tried to apply Machine learning algorithm to create the statistical prediction based model that people having diabetes can be aware of their prevalence. The aim of this paper is to detect the prevalence of diabetes relevant complications among patients with Type-2 diabetes mellitus. The processing and statistical analysis we used are Scikit-Learn, and Pandas for Python. We also have used unsupervised Machine Learning approaches known as Artificial Neural Network (ANN) and K-means Clustering for developing classification system based prediction model to judge Type-2 diabetes mellitus chronic diseases.

Abstract
The most broadly perceived relative directions are right, left, up, down, backward and forward. This research paper presents a new computational technique to learn human's relative directions, where one intelligent computer can learn any human's right, left, up, down, backward and forward or different relative directions. The present paper portrays models describing the essential structures of relative direction learning process between human and intelligent machine. We developed two proficient algorithms for solving this approach. In our experiment we propose Human Relative Direction Learning (HRDL) algorithm for learning human's relative directions and Human Direction Identification (HDI) algorithm for tracking any human position and identity human's relative directions from different direction points.

Abstract
Nowadays, due to the growing demand for interdisciplinary research and innovation, different scientific communities pay substantial attention to cross-domain collaboration. However, having only information retrieval technologies in hands might be not enough to find prospective collaborators due to the large volume of stored bibliographic records in scholarly databases and unawareness about emerging cross-disciplinary trends. To address this issue, the endorsement of the cross-disciplinary scientific alliances have been introduced as a new tool for scientific research and technological modernization. In this paper, we use a state-of-art knowledge representation technique named Knowledge Graphs (KGs) and demonstrate how clustering of learned KGs embeddings helps to build a cross-disciplinary co-author recommendation system. © 2021, Springer Nature Switzerland AG.

Abstract
The relationship of a patient with a hospital from admission to discharge is often kept in a series of textual documents that describe the patient’s journey. These documents are important to analyze the different steps of the clinical process and to make aggregated studies of the paths of patients in the hospital. In this paper, we explore the potential of Large Language Models (LLMs) to generate realistic and comprehensive patient journeys in European Portuguese, addressing the scarcity of medical data in this specific context. We employed Google’s Gemini 1.5 Flash model and utilized a dataset of 285 European Portuguese published case reports from the SPMI website, published by the Portuguese Society of Internal Medicine, as references for generating synthetic medical reports. Our methodology involves a sequential approach to generating a synthetic patient journey. Initially, we generate an admission report, followed by a discharge report. Subsequently, we generate a comprehensive patient journey that integrates the admission, multiple daily progress reports, and the discharge into a cohesive narrative. This end-to-end process ensures a realistic and detailed representation of the patient’s clinical pathway as a patient’s journey. The generated reports were rigorously evaluated by medical and linguistic professionals, as well as automatic metrics to measure the inclusion of key medical entities, similarity to the case report, and correct Portuguese variant. Both qualitative and quantitative evaluations confirmed that the generated synthetic reports are predominantly written in European Portuguese without the loss of important medical information from the case reports. This work contributes to developing high-quality synthetic medical data for training LLMs and advancing AI-driven healthcare applications in under-resourced language settings. © 2025 Copyright for this paper by its authors.