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International Journal of Artificial Intelligence and Science
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : -     EISSN : 30642728     DOI : https://doi.org/10.63158/IJAIS
Core Subject : Science,
Computer Science & IT
The International Journal of Artificial Intelligence and Science (IJAIS) is independently organized and managed by the Asosiasi Doktor Sistem Informasi Indonesia (ADSII). IJAIS is an open-access journal designed for researchers, lecturers, and students to publish their findings in the fields of Artificial Intelligence and Science. IJAIS serves as a platform for sharing innovative and original research, showcasing the latest advancements and technological developments in Artificial Intelligence and Science.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 12 Documents
 1   2 
Artificial Neural Network for Investigating the Impact of EMF on Ignition of Flammable Vapors in Gas Stations Umoren, Imeh; Inyang, Saviour; Etuk, Ubong; Akpanobong , Aloysius; James, Gabriel
International Journal of Artificial Intelligence and Science Vol. 2 No. 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2.i1.19

Abstract

The inadvertent ignition of flammable vapors by radio frequency (RF) radiation poses a significant safety risk in mega gas stations, necessitating the development of an intelligent predictive model for hazard prevention. This study proposes Artificial Neural Networks (ANN) Model to classify and predict ignition risks based on structured datasets obtained from smart sensing devices. The model formulation is based on the perceptron architecture, incorporating threshold logic units (TLUs) and multi-layer perceptron’s (MLPs) with backpropagation learning for enhanced predictive accuracy. The dataset, preprocessed to remove noise and redundancy, was divided into an 80:20 training-to-testing ratio and evaluated using cross-validation techniques. The experimental results show that the ANN-based model achieved an accuracy of 86%, demonstrating its effectiveness in identifying the impact of hazardous conditions. These findings underscore the robustness of the proposed approach, offering a reliable solution for mitigating ignition hazards in industrial environments. This research contributes to advancing safety protocols by leveraging on machine learning for predictive hazard assessment in flammable vapor-prone areas.
Personalized Energy Optimization in Smart Homes Using Adaptive Machine Learning Models: A Feature-Driven Approach Oyeniran, Matthew; J.D., Adekunle; H.S., Sule; O., Folorunso; S.A, Alagbe; T. J., Anifowoshe; C. O., Robert; B. N., Ebonyem; E. G., Ideh; S. O., Oyelakin; C. K., Ogu
International Journal of Artificial Intelligence and Science Vol. 2 No. 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2.i1.20

Abstract

The increase in demand for efficient energy smart homes has necessitates the personalized optimization strategies to have a reduction in energy consumption while maintaining user comfort. This research develops a Personalized Energy Optimization System using adaptive machine learning models to analyze household energy patterns and predict consumption in real time. Leveraging the Appliances Energy Prediction Dataset from the UCI repository, we applied supervised learning algorithms such as Gradient Boosting, XGBoost, CatBoost, LightGBM, and Random Forest to identify key factors influencing energy use, including occupancy patterns, appliance usage, and environmental conditions. Through feature engineering, normalization, and one-hot encoding, we enhanced model performance and interpretability. Among the evaluated models, LightGBM achieved the highest accuracy (R²: 0.999573, RMSE: 0.013526), outperforming others in predicting energy consumption. The findings offer data-driven insights for dynamic energy management, optimizing household efficiency, and promoting sustainability.

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