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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 1,722 Documents
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A smart grid fault detection using neuro-fuzzy deep learning algorithm Mouckomey, Etienne Francois; Bikai, Jacques; Mbey, Camille Franklin; Boum, Alexandre Teplaira; Souhe, Felix Ghislain Yem; Kakeu, Vinny Junior Foba
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5096-5105

Abstract

This paper proposes a novel data analysis framework that integrates deep learning with a binary neuro-fuzzy algorithm to address the problem of fault localization in smart power grids. In the first stage, a long short-term memory (LSTM) network is employed to train data samples collected from smart meters. The resulting learned features are subsequently utilized by an adaptive neuro-fuzzy inference system (ANFIS) for accurate fault detection and classification. Through this intelligent hybrid approach, multi-phase faults can be efficiently identified using a limited amount of data. The proposed method distinguishes itself by its capacity to rapidly train and test large datasets while maintaining high computational efficiency. To evaluate the performance of the model, an advanced simulation of the IEEE 123-node test feeder is conducted. The robustness and effectiveness of the proposed framework are validated using multiple performance metrics, including precision, recall, accuracy, F1-score, computational complexity, and the ROC curve. The results demonstrate that the proposed deep learning–based model significantly outperforms existing approaches in the literature, achieving a fault detection and classification precision of 99.99%.
AI-driven creativity in software development using services information Fahmi, Faisal; Wang, Feng-Jian; Subramaniam, Hema
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4474-4483

Abstract

In competitive markets, organizations that do not innovate risk becoming outdated. At it is core, innovation depends on creativity, with creative outcomes typically characterized by novelty, usefulness, and surprisingness. In software development, creative solutions are often generated through brainstorming sessions. However, brainstorming is constrained by the knowledge of the participant and facilitator. In this paper, we present an artificial intelligence (AI)-based method to generate creative solutions in software by leveraging service information. The presented method includes two phases, where the first phase involves constructing creativity resources through text clustering (TF-IDF, K-medoid) and capability extraction (dependency parsing), and the second phase employs semantic similarity along with structured creativity techniques (exploration, transformation, and combination) to generate creative solutions in software. Besides, experimental results showed that the AI-based method achieved comparable creativity scores to traditional brainstorming with more limited time, demonstrating fast and strong performance in generating novel and useful solutions, although participants perceived some results as less surprising due to overlap with brainstorming outcomes.

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