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Computer Science and Information Technologies
Published by Institute of Advanced Engineering and Science
ISSN : 2722323X     EISSN : 27223221     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Computer Science and Information Technologies ISSN 2722-323X, e-ISSN 2722-3221 is an open access, peer-reviewed international journal that publish original research article, review papers, short communications that will have an immediate impact on the ongoing research in all areas of Computer Science/Informatics, Electronics, Communication and Information Technologies. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. The journal is published four-monthly (March, July and November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 13 Documents
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Search results for , issue "Vol 7, No 1: March 2026" : 13 Documents clear
Cloud-based predictive analytics for pension fund performance optimization Garaba, Beauty; Mutandavari, Mainford; Chibhabha, Jerita
Computer Science and Information Technologies Vol 7, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i1.p46-55

Abstract

This study introduces a novel, cloud-based predictive analytics framework tailored for pension fund performance management in Zimbabwe. Addressing limitations in traditional actuarial models, the proposed system leverages real-time data pipelines and explainable artificial intelligence (XAI) techniques to enhance forecasting accuracy and transparency. Using regression, classification, and deep learning models, it forecasts member contributions, identifies risks of contribution drops, and predicts member churn. The system’s cloud deployment ensures scalability and interactive integration with tools like Power BI for decision support. This solution significantly advances sustainable pension fund management for emerging economies.
Bridging archaeological visibility analysis and real-time 3D visualization Malaperdas, George; Delli, Georgia
Computer Science and Information Technologies Vol 7, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i1.p93-101

Abstract

This paper investigates the integration of geographic information systems (GIS)-based visibility analysis—commonly known as viewshed analysis—with real-time 3D rendering in unreal engine, specifically within the context of archaeological and cultural heritage applications. Visibility maps are an essential tool in archaeological research, helping scholars understand the spatial relationships, sightlines, and symbolic visibility between structures, monuments, and landscapes. However, traditional GIS viewshed analysis is often static and limited to 2D environments. This project proposes a method to bring visibility analysis into immersive 3D environments by visualizing GIS-generated data within unreal engine. The methodology involves generating a viewshed from a given digital elevation model (DEM) using established GIS software. The resulting raster is then exported and processed into a texture or material mask compatible with unreal engine. Once imported, the data is mapped onto a 3D landscape model, allowing users to explore visibility dynamically, including first-person or VR-based navigation. This interdisciplinary approach contributes to the field of digital archaeology by enhancing spatial interpretation and audience engagement through immersive geovisualization. It also outlines a flexible pipeline for integrating geospatial datasets into 3D environments, potentially applicable to site management, public education, and digital preservation efforts.
Advances in Parkinson’s disease diagnosis and treatment using artificial intelligence: a review Qasimi, Mehr Ali; Acar, Züleyha Yılmaz
Computer Science and Information Technologies Vol 7, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i1.p121-130

Abstract

Parkinson’s disease (PD) diagnosis and monitoring have significantly improved because to current advancements in artificial intelligence (AI), particularly in the areas of deep learning (DL) and machine learning (ML). Early-stage insensitivity of traditional diagnostic techniques necessitates the use of clever, data-driven alternatives. AI-powered noninvasive diagnostic methods like speech recognition, handwriting analysis, and neuroimaging categorization are the main topic of this technical review. We provide a summary of comparative performance measures from recent models, highlighting their practical usefulness, data modality, and accuracy. Also covered are important issues like data variability, real-world implementation, and model interpretability. Unlike prior surveys that primarily report accuracy metrics, this review explicitly focuses on identifying the gap between experimental AI performance and real-world clinical deployment, emphasizing interpretability, validation, and scalability challenges in PD diagnosis. The purpose of this letter is to provide guidance for researchers creating deployable and clinically valid AI systems for PD detection.

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