Title: Advancing Text Summarization with Enhanced RoBERTa and Knowledge Graph Integration
Author:
R. Suganya
Department of Computer Science and Engineering, RAAK College of Engineering and Technology, Puducherry, India
email: suganyasmvec@gmail.com
Pages: 41-49
DOI: https://doi.org/10.55306/CJIESN.2025.010104
Abstract:

Especially in the era of big data, extraction and summarization of short but meaningful phrases are confronted by more and more natural language processing tools. In this work we propose the context-aware summarization methods with the enhancements both based on pre-trained models Enhanced RoBERTa with HEAD architectures by structured domain information. It is grounded on auditory attention and augments a general purpose transformer stack with knowledge-driven features to obtain more coherent, informative and semantically faithful summaries. Training and Evaluation 4.1 Corpus and Preprocessing We have corpus and preprocessing pipeline as follows. We evaluate the proposed framework using the CNN/DailyMail dataset and compare it with a variety of baselines on standard ROUGE metrics. The experimental results are quite promising for context-depth capturing and improving the quality of the automatic summaries as compared to the baselines. Overall, this work contributes to the development of text summarization by migrating from transformer-based contextual learning to integration of structured knowledge, and provides scalable and adaptable methods for intelligent information retrieval.
Key Words: Transformer Models, Enhanced RoBERTa, Abstractive Summarization, Information Retrieval, ROUGE Evaluation, Natural Language Processing.
Citation: R. Suganya., “Advancing Text Summarization with Enhanced RoBERTa and Knowledge Graph Integration,” Ci-STEM Journal of Intelligent Engineering Systems and NetworksDigital Technologies and Expert Systems, Vol. 1(1), pp. 41-49, 2025, doi: 10.55306/CJIESN.2025.010104

Title: Diabetes Prediction Using Different Classification Algorithms on Data Mining – A Survey
Authors:
Raja Rao Chatla
Board of Practical Training Eastern Region, Kolkata, India.
email: crrao@bopter.gov.in
Soumitra Kumar Mandal
Department of Electrical Engineering, National Institute of Technical Teachers Training and Research, Kolkata, West Bengal, India.
email: skmandal@nitttrkol.ac.in
Pages: 50-60
DOI: https://doi.org/10.55306/CJIESN.2025.010105
Abstract:

Healthcare generates volume of data daily in different formats such as notes, reports, images and a numbers pool and so forth. But there is no scientific instrument in medicine to study this information. The data from this point on may be mined for information that can be utilized by media experts to predict future steps in the process. Cardiopathy is the most common cause of death for the general population. Early identification and risk perception is essential for the patients’ drugs and analysis of specialists. Data mining is a process that extracts information from the collected data and structures it for further use. In the present study, we pay attention to such medical decision learning design based on diabetes data and establish a smart therapeutic choice emotional supporting network for doctors. The primary objective of this study is to develop a intelligent diabetic disease prediction for analyzing diabetes malady by using database of diabetes patients. Health data are by its nature unpredictable and always changing, which makes it extremely hard to deal with. In order to tackle the challenges mentioned above, some studies have presented a range of ML approaches for detecting and prognosis of illness. This article compares a number of diabetes prediction models to find an approach for diagnosing the disease. The research aims to shed light on different methods of diagnosis for the disease, enabling patients to get treated more quickly. The prediction of the product, glucose level in blood is also predicted with advance technology, Variety of Machine Learning techniques e.g., Neural Network (NN), SVM classifier, Data mining Techniques etc which are used here for predicting result. The study hopes to find a faster and more efficient way of diagnosing the condition, so that patients can receive treatment earlier.
Key Words: Cardiopathy, Data Mining Techniques, Diagnosing, Healthcare, Intelligent Diabetic Disease Prediction, Machine Learning, Neural Network (NN), SVM classifier.
Citation: C. R. Rao., et al., “Diabetes Prediction Using Different Classification Algorithms on Data Mining – A Survey,” Ci-STEM Journal of Intelligent Engineering Systems and NetworksDigital Technologies and Expert Systems, Vol. 1(1), pp. 50-60, 2025, doi: 10.55306/CJIESN.2025.010105