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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 1,974 Documents
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Stacking ensemble techniques for automated peripheral blood cell classification using Inception v3 features Marwa Mawfaq Mohamedsheet Al-Hatab; Maysaloon Abed Qasim; Nawar A. Sultan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2247-2259

Abstract

Robust distinction of blood cells is crucial in clinical evaluation. Manual examination is slow and exposed to errors. This work investigates using machine learning (ML) techniques for automated classification of eight categories of peripheral blood cell types from multi-color images. The Inception v3 network was used to extract features, a split of 66%/34% were used to evaluate the model along with 20-fold cross-validation. To reduce computational complexity, principal component analysis (PCA) was used to reduce the 2048-dimensional feature vectors to 100 components. Among all classifiers used, the highest performance without using PCA was achieved using the support vector machine (SVM) with an accuracy equal to 93.4% and an area under the curve (AUC) of 0.996. Using PCA, affected monocytes and immature granulocytes most due to the slight reduction in the accuracy and AUC which became 90.1% and to 0.991 respectively. Results were further enhanced when a stacked ensemble of neural network (NN), logistic regression (LR), and SVM were used, achieving an accuracy of 95.2% and an AUC of 0.998. The obtained findings confirmed the effectiveness of using stacked ensembles in providing a robust, high accuracy framework for automated blood cell classification, while PCA efficiently reduced dimensions with minimal performance loss.
Multilingual signs recognition using recurrent neural network Thouseef Ulla Khan; Dileep Marichi Ramachandra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2494-2510

Abstract

Recognition of sign language is a crucial step towards providing individuals with hearing and speech impairment meaningful communication, but the fact that there are a number of distinct sign languages and gestures remain complex makes it a challenge to the current automated systems. The present paper describes a real-time multilingual sign language recognition system that is based on a recurrent neural network with long short-term memory (RNN-LSTM) with hand landmark MediaPipe-based hand landmark detection to successfully receive spatial and temporal gesture features. The proposed system was trained and tested over a self-collected set of alphabet gestures of the Chinese, American, and Indian sign language, including one hand and two-hand gestures, and was run with Keras with extensive performance evaluation metrics. The strength and generalization abilities of the suggested approach as part of different gesture patterns and variations in users are confirmed by experimental outcomes that indicate high recognition rates of 99.58%, 99.62%, and 99.63% of the Chinese, American, and Indian sign languages, respectively. These results demonstrate the promise of the given framework as a dedicated assistive system of communication and give it a solid base to continue its development to the point of the system of the continuous sign language recognition (CSLR) and multimodal translators.
Deep learning-based integrated XAI for photovoltaic power forecasting considering actual power production period Promphak Boonraksa; Warunee Srisongkram; Kedsara Palachai; Boonruang Marungsri; Terapong Boonraksa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2970-2984

Abstract

This paper proposes a deep learning (DL)-based integrated explainable artificial intelligence (XAI) framework for photovoltaic (PV) power forecasting, explicitly considering the actual power production period to improve operational reliability. The framework uses solar irradiance, ambient temperature, and relative humidity as input features and evaluates nine DL architectures, including artificial neural networks (ANN), recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), CNN-LSTM, CNN-BiLSTM, RNN-LSTM, and RNN-BiLSTM. Model performance is evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results show that the residual-based RNN-LSTM model provides highest forecasting accuracy, achieving MAE of 1.21 kW, MAPE of 5.12%, and RMSE of 2.24 kW. In comparison, the LSTM and BiLSTM models exhibit substantially higher prediction errors, with MAPEs exceeding 21%, while hybrid convolutional models show moderate improvements but remain inferior. To enhance model transparency, XAI techniques are integrated to interpret feature contributions. The analysis confirms that solar irradiance is the dominant influencing factor, while temperature and humidity introduce secondary nonlinear effects captured effectively by recurrent architectures. The proposed framework provides a high-accuracy and interpretable solution for PV power forecasting, supporting reliable energy management and smart grid applications.
From audio to image: gunshot classification using Mel spectrogram convolutional neural networks Peerapol Khunarsa; Pafan Doungpaisan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2166-2180

Abstract

Accurate identification of firearm types from acoustic signals is essential for modern public safety and forensic applications. Traditional gunshot analysis methods often rely on physical evidence or handcrafted audio features, which can be unreliable under noisy and reverberant conditions. This study presents a systematic investigation of gunshot sound classification using Mel spectrogram representations and convolutional neural networks (CNNs). Raw audio signals are transformed into Mel spectrogram images, enabling firearm classification to be formulated as an image recognition problem. Thirteen CNN architectures, ranging from lightweight to deep models, are evaluated under a unified experimental protocol to analyze both classification performance and computational efficiency. Experiments are conducted on a publicly available multi-firearm dataset recorded in semi-controlled real-world environments. The results demonstrate that Mel spectrogram–based CNN models achieve classification accuracy exceeding 94%, while moderate-complexity architectures provide a favorable balance between accuracy and efficiency. The findings highlight the importance of representation–architecture alignment and offer practical design guidelines for selecting deployable CNN models in real-time gunshot detection systems.

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