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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 2,143 Documents
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An efficient computational framework for abstracting program slices from Java code Aparna Kudloor Siddalingaiah; Rajkumar Kulkarni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4969-4979

Abstract

Over the past decades, Java-based applications have formed the backbone of enterprise, web, and mobile software systems, often containing complex business logic and domain-specific requirements embedded in older codebases (Java SE 1.2-8). Maintaining and migrating such systems is difficult due to unstructured code, missing documentation, and outdated design artifacts. To address these challenges, the paper introduces a computationally efficient static-backward slicing framework for Java programs. The framework restructures the source code, abstracts control and data flow information, computes functional dependencies using a data flow table (DFT), and extracts precise program slices. Extracted slices are further represented using object-Z notation to formalize software specifications. Furthermore, the extracted backward slices and object-Z specifications can be leveraged with artificial intelligence (AI) and large language models (LLMs) to automatically generate human-readable software requirements. This integration also ensures structured, accurate, and minimal requirements outputs, facilitating AI-assisted software modernization. Experimental evaluations on Java programs such as automated teller machine (ATM), student grading, and quadratic root calculators exhibit significant reductions in extracted code size while preserving functional dependencies. In comparison with traditional dynamic and machine learning (ML)-based slicing approaches, the proposed framework offers improved precision, computational efficiency, and scalability, making it suitable for modernizing legacy Java systems and supporting AI-driven software engineering tasks.
Optimization of medical check-up patient segmentation for personalized healthcare services Ferdiansyah Ferdiansyah; Hendra Tjahyadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4356-4365

Abstract

Medical check-up (MCU) services commonly apply uniform examination pathways that do not reflect individual variations in demographic and lifestyle risk factors. This study aims to develop an unsupervised learning model that enables early personalization of MCU recommendations using non-laboratory pre-screening data. A dataset of 11,061 participants was processed following the cross-industry standard process for data mining (CRISP-DM) framework, with median imputation, outlier removal, and min–max scaling applied to enhance data quality. K-means and agglomerative hierarchical clustering (AHC) were evaluated using Silhouette score and Davies–Bouldin index, and cluster characteristics were examined based on age, body mass index (BMI), sex, smoking, alcohol consumption, exercise, and family medical history. K-means with four clusters provided the most stable segmentation (Silhouette 0.366; Davies–Bouldin index 1.21), producing clinically interpretable profiles ranging from high-risk groups marked by smoking and elevated BMI to lower-risk individuals with active lifestyles. These findings demonstrate that simple pre-screening variables can support meaningful early risk stratification and guide personalized MCU pathways. The study also outlines an integration approach for embedding the clustering model into electronic medical record (EMR) systems to enable real-time deployment within clinical workflows.
Text mining in biomedical literature: identifying chemical and disease relationships Nurul Mutiah; Dian Prawira
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4830-4838

Abstract

Text mining is an essential method for extracting valuable information from biomedical literature, especially concerning chemical-disease relationships (CDR). This study integrates named entity recognition (NER), relation extraction (RE), classification, and topic modeling to analyze kratom-related research articles from PubMed. Using the biocreative V chemical-disease relation (BC5CDR) dataset, NER achieved high recall and accuracy, demonstrating its ability to identify diverse entities. However, low precision revealed challenges with false positives due to ambiguous contexts and term overgeneralization. Network analysis of extracted relationships identified “Neuropathic” as the most central entity, underscoring its importance in linking chemicals like “mitragynine” to clinical conditions. Classification with bidirectional encoder representations from transformers for biomedical text mining (BioBERT) yielded moderate results, with an F1-score of 0.48, precision of 0.43, and recall of 0.38, highlighting the need for larger, curated datasets to improve accuracy. Bidirectional encoder representations topic modeling (BERTopic) uncovered five main topics with moderate coherence (C_V = 0.5567), emphasizing themes related to kratom’s toxicity and clinical implications, although capturing semantic relationships remained challenging. This research demonstrates the potential of text mining in biomedical knowledge discovery, revealing key insights and thematic patterns in scientific literature. Improved data preprocessing, model fine-tuning, and manual validation are recommended to enhance the reliability and application of these methods in future studies.

Page 215 of 215 | Total Record : 2143

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