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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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Optimizing deep learning models from multi-objective perspective via Bayesian optimization Mohamad Rom, Abdul Rahman; Jamil, Nursuriati; Ibrahim, Shafaf
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1420-1429

Abstract

Optimizing hyperparameters is crucial for enhancing the performance of deep learning (DL) models. The process of configuring optimal hyperparameters, known as hyperparameter tuning, can be performed using various methods. Traditional approaches like grid search and random search have significant limitations. In contrast, Bayesian optimization (BO) utilizes a surrogate model and an acquisition function to intelligently navigate the hyperparameter space, aiming to provide deeper insights into performance disparities between naïve and advanced methods. This study evaluates BO's efficacy compared to baseline methods such as random search, manual search, and grid search across multiple DL architectures, including multi-layer perceptron (MLP), convolutional neural network (CNN), and LeNet, applied to the Modified National Institute of Standards and Technology (MNIST) and CIFAR-10 datasets. The findings indicate that BO, employing the tree-structured parzen estimator (TPE) search method and expected improvement (EI) acquisition function, surpasses alternative methods in intricate DL architectures such as LeNet and CNN. However, grid search shows superior performance in smaller DL architectures like MLP. This study also adopts a multi-objective (MO) perspective, balancing conflicting performance objectives such as accuracy, F1 score, and model size (parameter count). This MO assessment offers a comprehensive understanding of how these performance metrics interact and influence each other, leading to more informed hyperparameter tuning decisions.
Event detection in soccer matches through audio classification using transfer learning Utsav Gadhia, Bijal; Modasiya, Shahid S.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1441-1449

Abstract

Addressing the complexities of generating sports summaries through machine learning, our research aims to bridge the gap in audio-based event detection, particularly in soccer games. We introduce an extended ResNet-50 deep learning approach for soccer audio, emphasizing key moments from large soccer content archives through the use of transfer learning. The proposed model accurately classifies soccer audio segments into two categories: i) events, representing crucial in-game occurrences and ii) no events, denoting less impactful moments. The model involves complete audio preprocessing, the implementation of proposed model using transfer learning and the classification of events. The model’s reliability is validated using the dataset soccer action dataset compilation (SADC), involves dataset creation by football fans. Comparative analysis with pre-trained models such as VGG19, DesNet121, and EfficientNetB7 demonstrates the superior performance of the extended ResNet-50 based approach. Results across different epochs reveal consistently high accuracy, precision, recall, and F1-score, emphasizing the proposed model's effectiveness in event detection through audio classification. The paper concludes that the proposed model offers a robust solution for detecting an event from audio of soccer sports providing valuable insights for fans, analysts, and content creators to identify interested moments from soccer game with low failure.
Real-time age-range recognition and gender identification system through facial recognition Cruz-Colan, Carlos; Lopez-Herrera, David; Paiva-Peredo, Ernesto; Acuna-Condori, Kevin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp992-999

Abstract

Facial recognition and age estimation are being implemented in apparel retailing which is undergoing significant changes due to fashion and technology. To improve interaction with customers and refine marketing strategies. The paper proposes an approach based on a Siamese neural network and the use of tools such as MediaPipe for face detection and DeepFace for age and gender estimation. In addition, the four stages of the research work, real-time image capture, ID assignment, facial feature extraction, and data storage, are described. Early approaches to age estimation were based on biometric features, such as eyes, nose, mouth, and chin, resulting in limited accuracy and low performance in older adults. To improve accuracy, additional elements, such as the presence of wrinkles, were considered and a diverse database of images was used. The proposed methodology achieves a positive result for real-time age classification and gender ID. The results include information on gender, age, ID, time and date for each person identified.
U-Net for wheel rim contour detection in robotic deburring Ait El Attar, Hicham; Samri, Hassan; Ech-Chhibat, Moulay El Houssine; Mansouri, Khalifa; Bahani, Abderrahim; Bahrar, Tarek
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1363-1376

Abstract

Automating robotic deburring in the automotive sector demands extreme precision in contour detection, particularly for complex components like wheel rims. This article presents the application of the U-Net architecture, a deep learning technique, for the precise segmentation of the outer contour of wheel rims. By integrating U-Net's capabilities with OpenCV, we have developed a robust system for wheel rim contour detection. This system is particularly well-suited for robotic deburring environments. Through training on a diverse dataset, the model demonstrates exceptional ability to identify wheel rim contours under various lighting and background conditions, ensuring sharp and accurate segmentation, crucial for automotive manufacturing processes. Our experiments indicate that our method surpasses conventional techniques in terms of precision and efficiency, representing a significant contribution to the incorporation of deep learning in industrial automation. Specifically, our method reduces segmentation errors and improves the efficiency of the deburring process, which is essential for maintaining quality and productivity in modern production lines.
Adaptive silicon synapse and CMOS neuron for neuromorphic VLSI computing El-Khatib, Ziad; Moussa, Sherif; Kamalov, Firuz; Yagoub, Mustapha C. E.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1000-1021

Abstract

The design of a fully integrated adaptive modified complementary metal-oxide-semiconductor (CMOS) synapse circuit is presented. By using multiple-gated transistor configuration in the modified CMOS synapse an additional branch provide control where the synaptic output current time-constant is tuned. The effect of changing the multiple-gated transistor bias voltage from 0.25 to 0.45 V tunes the spiking output current exponential time-constant range by 200 ms as shown in simulation results. Moreover, a fully-integrated adaptive quadratic integrate-and-fire (QIF) CMOS neuron circuit is presented as well. A differential pair with variable capacitor integrator and a tunable schmitt trigger threshold detector circuit are integrated in the CMOS neuron that can be tuned varying its spiking frequency. The proposed adaptive quadratic integrate-and-fire (AQIF) neuron has the ability to adjust the spiking frequency without changing the input current. The simulation results show the proposed CMOS neuron circuit spiking frequency can be tuned from 58.4 to 312.5 Hz and its spiking period from 17.1 to 3.2 ms with tuning the bias voltage of variable capacitor integrator. Having a peak voltage Vpeak=0.95 V, a reset voltage Vreset=-0.75 V and a voltage threshold of 0.35 V with a membrane potential range of 1.5 V. The proposed CMOS neuron circuit is designed in 130 nm process with a supply voltage of 1.8 V and a total power dissipation of 1.8 mW.
Boosting industrial internet of things intrusion detection: leveraging machine learning and feature selection techniques Idouglid, Lahcen; Tkatek, Said; Elfayq, Khalid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1232-1241

Abstract

The rapid integration of industrial internet of things (IIoT) technologies into Industry 4.0 has revolutionized industrial efficiency and automation, but it has also exposed critical vulnerabilities to cyber threats. This paper delves into a comprehensive evaluation of machine learning (ML) classifiers for detecting anomalies in IIoT environments. By strategically applying feature selection techniques, we demonstrate significant enhancements in both the accuracy and efficiency of these classifiers. Our findings reveal that feature selection not only boosts detection rates but also minimizes computational demands, making it a cornerstone for developing resilient intrusion detection systems (IDS) tailored for Industry 4.0. The insights garnered from this study pave the way for deploying more robust security frameworks, safeguarding the integrity and reliability of IIoT infrastructures in modern industrial settings.
A hybrid feature selection with data-driven approach for cardiovascular disease prediction using machine learning Shilpa, Thoutireddy; Debnath, Rajib
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1192-1200

Abstract

Affecting various disorders of heart and blood vessels mainly cardiovascular diseases (CVDs) is the leading cause of human mortality on the planet. A number of machine learning (ML) based supervised learning approaches existing in the literature have been found useful in the clinical decision support system (CDSS) for detecting CVDs automatically. The challenge, however, is that their performance tends to decline unless the training data is of a certain standard. Several approaches to solving this problem are known as feature selection techniques. Despite several notable advancements in the CVD modeling literature, a weak compendium of research exists in an area which supports the integration of the feature selection approach as a means of enhancing the training quality and thus the prediction accuracy. Against this background, in this paper, we proposed a framework called the cardiovascular disease prediction framework (CVDPF) that integrates ML methods. To support this, we designed and proposed a new hybrid feature selection (HFS) algorithm that aims to reduce the number of parameters. This algorithm adopts several filter methods in order to enhance its performance for the task of feature selection. To improve the prediction accuracy of CVDs, a number of ML tools using the HFS approach has been designed and is termed as machine learning based cardiovascular disease prediction (ML-CVDP). The validation of the framework and the algorithms discussed has been done on the basis of a CVD dataset. The experimental findings demonstrated that CVDPF in combination with HFS outperforms other methods of feature selection available.
Hybrid horned lizard optimization algorithm-aquila optimizer for DC motor Aribowo, Widi; Abualigah, Laith; Oliva, Diego; Mzili, Toufik; Sabo, Aliyu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1673-1682

Abstract

This research presents a modification of the horned lizard optimization (HLO) algorithm to optimize proportional integral derivative (PID) parameters in direct current (DC) motor control. This hybrid method is called horned lizard optimization algorithm-aquila optimizer (HLAO). The HLO algorithm models various escape tactics, including blood spraying, skin lightening or darkening, crypsis, and cellular defense systems, using mathematical techniques. HLO enhancement by modifying additional functions of aquila optimizer improves HLO performance. This research validates the performance of HLAO using performance tests on the CEC2017 benchmark function and DC motors. From the CEC2017 benchmark function simulation, it is known that HLAO's performance has promising capabilities. By simulating using 3 types of benchmark functions, HLOA has the best value. Tests on DC motors showed that the HLAO-PID method had the best integrated of time-weighted squared error (ITSE) value. The ITSE value of HLOA is 89.25 and 5.7143% better than PID and HLO-PID.
Per capita expenditure prediction using model stacking based on satellite imagery Kuswanto, Heri; Rouhan, Asva Abadila; Qori’atunnadyah, Marita; Hia, Supriadi; Fithriasari, Kartika; Widhianingsih, Tintrim Dwi Ary
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1220-1231

Abstract

One of the indicators for measuring poverty is per capita expenditure. However, collecting timely and reliable per capita expenditure data is quite challenging and expensive, as it requires collecting detailed household data directly. One way to deal with this issue is to use satellite image data processed by machine learning methods. This research proposes a method to predict the per capita expenditure of regencies or cities in Indonesia based on satellite imagery using machine learning techniques, such as k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM). The predictions are stacked to predict per capita expenditure using least absolute shrinkage and selection operator (LASSO) regression as the meta-learner. The model is trained on Google-Earth-based satellite imagery of Java Island, Indonesia, which provides more update field conditions compared to data collected from Statistics Indonesia (BPS). The research found that the stacked model outperforms the individual methods. However, the R2 criterion of the stacked method is comparable to that of RF, which is slightly higher than the others.
A novel approach to enhancing software quality assurance through early detection and prevention of software faults Rai, Deepti; Prashant, Jyothi Arcot
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp894-906

Abstract

The current manuscript presents a predictive mechanism towards analyzing software defects by developing a line-level fault prediction technique. Current methodologies rely on customized attributes and overlook the sophisticated structural and semantic characteristics inherent in programming languages. This oversight often led to suboptimal defect identification, as code defects are intricately scrambled with their contextual environment. Moreover, conventional software defect prediction (SDP) strategies, typically focusing on larger code units such as modules or classes, impede precise error localization. To address these challenges, this study proposes an automated scheme utilizing a recurrent neural network (RNN) with an attention layer to analyze line-level quantifiers within the code, such as the number of pairwise operations and single operand operators. The efficacy of this learning-driven scheme is validated through comprehensive experiments conducted on several C++ programs. The experimental results demonstrate a 95.8% recall, 83.12% precision, and 90.35% accuracy in identifying fault-prone lines within a testing dataset. These outcomes confirm the effectiveness of proposed SDP scheme in accurately identifying the defects and highlighting its inter-project capabilities, exhibiting the model's adaptability across different software projects.

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