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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 2 Documents
 1 
Search results for , issue "Vol 15, No 5: October 2025" : 2 Documents clear
Optimizing internet of things based gas sensors: deep learning and performance optimization strategies Abdellatif, Mariam M.; Çifçi, Mehmet Akif; Ibrahim, Asmaa A.; Harb, Hany M.; Desuky, Abeer S.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i5.pp4813-4828

Abstract

The rapid growth of industrialization and internet of things (IoT) driven advancements in Industry 5.0 necessitates efficient and user-friendly engineering solutions. Gas leakage incidents in coal mines, chemical enterprises, and households pose significant risks to ecosystems and human safety, emphasizing the need for automated and rapid gas-type detection. Traditional detection methods rely on single-source data and focus on isolated spatial or temporal features, limiting accuracy. This paper proposes a multimodal artificial intelligence (AI) fusion technique combining pre-trained convolutional neural networks (CNNs), such as VGG16, with a deep neural network (DNN) model. The particle swarm optimization (PSO) algorithm optimizes CNN hyperparameters, outperforming traditional trial-and-error methods. The system addresses challenges posed by gases being odorless, colorless, and tasteless, which limit conventional human detection methods. By leveraging sensor fusion, the late fusion technique integrates distinct network architectures for unified gas identification. Experimental results demonstrate 95% accuracy using DNN with gas sensor data, 96% with optimized VGG16 using thermal imaging, and 99.5% through multimodal late fusion. This IoT-enhanced solution outperforms single-sensor approaches, offering a robust and reliable gas leakage detection system suitable for industrial and smart city applications.
Strategic integration of social media in IT sector communication: designing effective practices Kesar, Benu; Joseph, Shaji
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i5.pp4653-4661

Abstract

This paper explores the transformative role of social media in enhancing communication and workflow efficiency within the information technology (IT) sector. We have introduced the adaptive social media for information technology collaboration (ASMIT) framework. Its goal is to provide a holistic strategy for digital transformation in the IT sector. Employing a mixed method approach, the research combines a systematic literature review with case study of HCL Technologies. Thematic analysis categorizes findings under five core pillars of the ASMIT framework. Results indicate that AI-driven tools, when embedded within collaborative social media platforms, significantly enhance organizational agility, project coordination, and security. The study contributes to IT scholarship by bridging technological integration with human-centered collaboration strategies.

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