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31,456 Article Results

Efficiency search: application of nature-inspired algorithms in artificial intelligence forecasting models

10.11591/ijai.v14.i5.pp3528-3541
José Rolando Neira Villar , Miguel Angel Cano Lengua
This study reviews how nature-inspired optimization algorithms (NIOAs) have been applied to artificial intelligence-based demand forecasting, using preferred reporting items for systematic reviews and meta-analyses (PRISMA) and clustering analysis to examine 36 selected articles. The findings reveal that NIOAs, particularly genetic algorithms and swarm intelligence methods, including their hybrids, have been frequently applied to long short-term memory (LSTM) and other backpropagation neural network models (BPNN). A key insight is the differentiated application of NIOAs depending on network depth: In shallow networks, they have been effectively used to optimize trainable parameters, whereas in deep networks, their role has focused primarily on hyperparameter optimization due to the prohibitive dimensionality of trainable weights. In all studies, NIOA-optimized models consistently outperform conventional baselines based on backpropagation. However, persistent challenges such as excessive execution times and slow convergence have led to the development of more efficient hybrid strategies and adaptive mechanisms for automated exploration-exploitation control. By mapping explored and unexplored pathways, summarizing key outcomes and techniques, and identifying promising methodologies, this review offers a practical foundation to guide future experiments and implementations involving NIOA-based optimization strategies in neural network models. As a conceptual contribution, it also proposes an innovative use of multispace optimization to address one of the most critical challenges identified: the optimization of trainable parameters in deep neural networks.
Volume: 14
Issue: 5
Page: 3528-3541
Publish at: 2025-10-01

Two-steps feature selection for detection variant distributed denial of services attack in cloud environment

10.11591/ijai.v14.i5.pp3945-3957
Kurniabudi Kurniabudi , Eko Arip Winanto , Sharipuddin Sharipuddin
The prevalence of cloud computing among organizations poses a significant problem in ensuring security. Specifically, distributed denial of services (DDoS) attacks targeting cloud computing networks can lead to financial losses for consumers of cloud computing services. This assault has the potential to render cloud services inaccessible. The detection system serves as a remedy to prevent more substantial losses. This research aims to enhance the efficacy of the system detection model by integrating feature selection with three machine learning algorithms: decision tree (DT), random forest (RF), and naïve Bayes (NB). Therefore, our study suggests combining two phases of feature selection into the DDoS attack detection procedure. The first phase uses the information gain (IG) feature selection technique approach, and the second phase uses the principal component analysis (PCA) feature extraction approach. The technique is referred to as two-step feature selection. The test findings indicate that the implementation of two-step feature selection can enhance the performance of the DT and RF detection models by around 9%.
Volume: 14
Issue: 5
Page: 3945-3957
Publish at: 2025-10-01

Ensemble reverse knowledge distillation: training robust model using weak models

10.11591/ijai.v14.i5.pp4162-4170
Christopher Gavra Reswara , Tjeng Wawan Cenggoro
To ensure that artificial intelligence (AI) can be aligned with humans, AI models need to be developed and supervised by humans. Unfortunately, it is possible for an AI to exceed human capabilities, which is commonly referred to as superalignment models. Thus, it raised the question of whether humans can still supervise a superalignment model, which is encapsulated in a concept called weak-to-strong generalization. To address this issue, we introduce ensemble reverse knowledge distillation (ERKD), which leverages two weaker models to supervise a more robust model. This technique is a potential solution for humans to manage a super-alignment of models. ERKD enables a more robust model to achieve optimal performance with the assistance of two weaker models. We tried to train a more robust EfficientNet model with weaker convolutional neural network (CNN) models in a supervised fashion. With this method, the EfficientNet model performed better than the model trained with the standard transfer learning (STL) method. It also performed better than a model that was supervised by a single weaker model. Finally, ERKD-trained EfficientNet models can perform better than EfficientNet models that are one or even two levels stronger.
Volume: 14
Issue: 5
Page: 4162-4170
Publish at: 2025-10-01

BonoNet: a deep convolutional neural network for recognizing bangla compound characters

10.11591/ijai.v14.i5.pp4171-4180
Kazi Rifat Ahmed , Nusrat Jahan , Adiba Masud , Nusrat Tasnim , Sazia Sharmin , Nusrat Jahan Mim , Imran Mahmud
The bangla alphabet includes vowels, consonants, and compound symbols. The compound nature of bangla is a product of combining two or more root bangla characters into one graph. They are difficult to differentiate because they have a sophisticated geometric shape and an immense variety of scripts used by different places and individuals. This is one of the greatest challenges in creating effective optical character recognition (OCR) systems for bangla. In this paper, a deep convolutional neural network (DCNN)-based system is presented to identify bangla compound characters with high precision. The model was trained using the AIBangla dataset. It has about 171 classes of bangla compound characters. A DCNN system, BonoNet, was designed to classify compound characters. BonoNet outperformed all other state-of-the-art architecture on the test set and improved over current state-of-the-art architecture methods. BonoNet will greatly improve the automation and analysis of the bangla language by accurately identifying these compound complex characters.
Volume: 14
Issue: 5
Page: 4171-4180
Publish at: 2025-10-01

Object detection for indoor mobile robot: deep learning approaches review

10.11591/ijai.v14.i5.pp3520-3527
Hind Messbah , Mohamed Emharraf , Mohamed Saber
Efficient object detection is crucial for enabling autonomous indoor robot navigation. This paper reviews current methodologies and challenges in the field, with a focus on deep learning-based techniques. Methods like you only look once (YOLO), region-based convolutional neural networks (R-CNN), and Faster R-CNN are explored for their suitability in real-time detection in dynamic indoor environments. Deep learning models are emphasized for their ability to improve detection accuracy and adaptability to varying conditions. Key performance metrics such as accuracy, speed, and scalability across different object types and environmental scenarios are discussed. Additionally, the integration of object detection with navigation systems is examined, highlighting the importance of accurate perception for safe and effective robot movement. This study provides insights into future research directions aimed at advancing the capabilities of indoor robot navigation through enhanced deep learning-based object detection techniques.
Volume: 14
Issue: 5
Page: 3520-3527
Publish at: 2025-10-01

Remote sensing applied to cocoa crop identification, a thematic review

10.11591/ijece.v15i5.pp4848-4855
Luisa Fernanda Cuellar-Escobar , Vladimir Henao-Céspedes
This article presents a thematic review of 25 publications related to the use of remote sensing techniques for the identification of cocoa crops from 2000 to 2023. Although the use of remote sensing techniques is widely used for mapping different covers because it is very useful in discriminating them, the generation of maps of cocoa crops presents challenges due to their spectral behavior similar to that of forests. This is because cocoa cultivation, being an agroforestry system that is developed in association with timber trees, causes the classification algorithms used to fail to differentiate between forest cover and cocoa crops. For this reason, this study seeks to investigate the different remote sensing techniques used in the mapping of cocoa crops, as well as an analysis of the structure of the publications highlighting the connections between countries and the factors that motivated the authors to research this crop.
Volume: 15
Issue: 5
Page: 4848-4855
Publish at: 2025-10-01

Arithmetic artificial bee colony optimization algorithm with flexible manipulator system

10.11591/ijai.v14.i5.pp3790-3801
Mohd Ruzaini Hashim , Ahmad Fitri Mazlan , Mohammad Osman Tokhi
The artificial bee colony (ABC) algorithm, a well-known swarm intelligence-based metaheuristic inspired by the food foraging behavior of honeybees, has been widely applied to solve complex optimization problems. Despite its effectiveness, the standard ABC algorithm suffers from drawbacks such as slow convergence rates, limited balance between exploration and exploitation, and a tendency to get stuck in local optima, thereby hindering its overall performance. This study introduces an enhanced variant of the ABC algorithm, integrating the exploration strategy of the arithmetic optimization algorithm (AOA) to overcome these limitations. The enhanced algorithm is thoroughly tested on a set of benchmark functions as well as a flexible manipulator system model. Comprehensive statistical analyses are employed to evaluate and compare the performance of the improved algorithm against the original ABC. The results demonstrate that the enhanced ABC algorithm delivers superior performance in both benchmark scenarios and the flexible manipulator application.
Volume: 14
Issue: 5
Page: 3790-3801
Publish at: 2025-10-01

Comparative analysis of convolutional neural network architectures for poultry meat classification

10.11591/ijai.v14.i5.pp3715-3723
Sekhra Salma , Mohammed Habib , Adil Tannouche , Youssef Ounejjar
The increasing demand for standardized food quality assurance, particularly in regions like Morocco, emphasizes the need for accurate classification of poultry meat. This study evaluates and compares ten convolutional neural network (CNN) architectures—VGG19, VGG16, ResNet50, GoogleNet, MobileNetV1, MobileNetV2, DenseNet, NasNet, EfficientNet, and AlexNet—for classifying commonly consumed poultry meat types in Moroccan markets, including chicken, turkey, fayoumi, and farmer’s chicken. A labeled image dataset was used to train and test each model, with performance assessed using metrics such as accuracy, precision, recall, training time, and computational complexity. Additionally, the study investigates how dataset size influences model performance, addressing challenges like limited data availability and scalability. The results highlight DenseNet as the top-performing architecture, achieving 98% classification accuracy while also demonstrating superior computational efficiency. These findings are valuable for improving food quality control, offering data-driven support for stakeholders in poultry production, distribution, and regulatory bodies. By identifying optimal deep learning models for poultry meat classification, the study contributes to enhancing food authentication and safety in Morocco and similar regions. It also encourages the integration of AI-driven systems in food inspection processes, providing scalable, accurate, and efficient solutions for ensuring standardized quality in the poultry supply chain.
Volume: 14
Issue: 5
Page: 3715-3723
Publish at: 2025-10-01

Analysis of partial discharge characteristics in transformer oil insulation media using needle-plane and plane-plane electrode systems

10.11591/ijece.v15i5.pp4445-4453
Teuku Khairul Murad , Abdul Syakur , Iwan Setiawan
Insulation failure is a common issue in electric power transmission. Insulation is necessary to separate two or more live conductors to prevent electrical arcing or sparking between them. Partial discharge (PD) is a phenomenon that can also occur in high-voltage equipment under pre-breakdown conditions. This PD activity can take place in liquid insulation, such as transformer oil, leading to a decrease in the quality and reliability of the transformer. This study aims to detect PD under various conditions and investigate its characteristics. Although various studies have been conducted on PD in liquid insulation, most of them focus on PD characterization under specific conditions without considering variations in electrode configurations that may influence the PD phenomenon. Therefore, this research is necessary to fill this gap by analyzing PD characteristics using a needle-plane and plane-plane electrode system. This study introduces the use of castor oil as an alternative liquid insulating material. In this study, PD testing will be conducted in a laboratory environment, and it is expected to produce reliable data regarding the capability of liquid insulation to withstand PD. The results obtained indicate that the PD phenomenon occurs more quickly in the needle-plane electrode configuration compared to the plane-plane configuration. PD in the needle-plane electrode occurs at an average voltage of 10.96 kV, while PD in the plane-plane electrode occurs at an average voltage of 12.5 kV.
Volume: 15
Issue: 5
Page: 4445-4453
Publish at: 2025-10-01

The effectiveness of ChatGPT in extracting architectural patterns and tactics

10.11591/ijai.v14.i5.pp4363-4370
Hind Milhem , Naderah Al-Jawabrah , Raghad Abu Wadi
This work investigates the potential of ChatGPT, a cutting-edge large language model (LLM), for software design analysis specifically in detecting architectural patterns and tactics. The evaluation involves comparing ChatGPT’s performance with that of Archie, a traditional Eclipse plugin designed for architectural analysis. The study uses the source code of five open-source software systems as the testing ground. Results reveal that ChatGPT achieves noteworthy performance in both pattern and tactic detection tasks. Specifically, for pattern detection, ChatGPT demonstrates an accuracy of up to 47.06%, while for tactic detection, it achieves a precision of 28.25%. While ChatGPT’s current capabilities are not yet a replacement for specialized tools like Archie, it offers significant potential as a complementary tool in architectural analysis workflows. By bridging the gap between natural language understanding and software engineering, ChatGPT could pave the way for more intelligent and automated solutions in the field. However, a key limitation is its difficulties in handling foundational or traditional tactics, resulting in a lower detection rate in certain areas. This research contributes valuable insights into the application of LLMs in software engineering, highlighting both the strengths and the limitations of ChatGPT in addressing complex architectural tasks.
Volume: 14
Issue: 5
Page: 4363-4370
Publish at: 2025-10-01

Comparison of HSV-color and ANN-HSV-color segmentation for detecting soybean adulteration

10.11591/ijai.v14.i5.pp3734-3743
Farid Rahmat Abadi , Rudiati Evi Masithoh , Lilik Sutiarso , Sri Rahayoe
Soybeans are an important food crop, but their quality is often compromised by contamination with other materials, a process known as adulteration. Conventional methods for detecting adulteration are slow; therefore, there is a need for rapid and non-invasive alternatives. This study aimed to assess the capability of hue-saturation-value (HSV) color segmentation and its combination with artificial neural networks (ANN) to identify adulteration in soybean samples. This research employed image processing and machine learning to segment soybeans mixed with adulterants at concentrations of 5%, 10%, 15%, 20%, and 25%. The HSV method successfully distinguished soybeans and other materials, but some challenges were observed in shadow regions and areas with similar colors. The HSV-ANN model with six hidden layers performed well with a calibration accuracy of R² value of 0.97 and root-mean-square error (RMSE) of 2.16%, which provided more detailed segmentation, although it still had some problems in shadow regions and undetected corn embryo parts. The validation results indicated that the HSV model had an R² value of 0.98 and RMSE of 4.48%, while the HSV-ANN model had an R² value of 0.96 and RMSE of 1.3%. Both models were capable of predicting the levels of adulteration, and the HSV-ANN model proved to be more accurate. It is concluded that both methods are efficient; however, there is a need for more work on modeling and sampling to increase the segmentation precision and decrease the biases, especially in the shadow and overlapped color.
Volume: 14
Issue: 5
Page: 3734-3743
Publish at: 2025-10-01

Optimizing nitik batik classification through comparative analysis of image augmentation

10.11591/ijai.v14.i5.pp3970-3981
Suprapto Suprapto , Meilany Nonsi Tentua , Ahmad Rizki Maulana
Nitik batik is one of the most intricate and culturally significant motifs in Yogyakarta's batik tradition, characterized by its complex, geometric dot-based patterns. The unique challenges of automatically classifying nitik batik motifs stem from the high variability within the class and the limited availability of training data. This study investigates how different image data augmentation techniques can enhance the performance of a random forest classifier for nitik batik motifs. Techniques such as geometric transformations (flip, rotate, and scaling), intensity transformations (cut-out, grid mask, and random erasing), non-instance level augmentation (pairing samples), and unconditional image generation (deep convolutional generative adversarial network (DCGAN)) were used to expand the dataset and improve the model's ability to generalize. The results show that specific techniques, notably flip, cut-out, and DCGAN, significantly improved classification accuracy, with flip achieving the highest accuracy improvement of 20.20%, followed by cut-out at 19.27% and DCGAN at 16.25%. Moreover, DCGAN demonstrated the lowest standard deviation (0.78%), indicating high stability and robustness in classification performance across multiple validation folds. These findings suggest that augmentation techniques effectively improve classification accuracy and enhance the model's ability to generalize from limited and complex datasets.
Volume: 14
Issue: 5
Page: 3970-3981
Publish at: 2025-10-01

Residual edge dense enhanced module network: a deep learning approach with multi-class SVM for lung tumor stage classification

10.11591/ijai.v14.i5.pp4032-4042
Prabakaran Jayaraman , Pandiaraj Selvaraj , Ashwini Elango
Lung cancer segmentation with positron emission tomography (PET) and computed tomography (CT) images plays a critical role to accurately detect lung cancer. Nevertheless, lung tumor segmentation in PET/CT images were extremely difficult due to the movement caused by respiration. Despite this fact, the lung tumor images shown large number of variations mostly in PET images and CT images. As PET-CT images are acquired concurrently the shape and size of lung tumor varies according to modality. To address these issues, we developed a residual edge dense enhanced module network (REDEM-NET) framework for lung tumor stage classification. The proposed REDEM-NET can process PET and CT images as inputs. In addition, the dense residual convolutional network (DRCN) collects both inputs and extracts high-dimensional features concurrently. The extracted features from both imaging modalities were fed into UNet+++ to obtain multi-level decoded features. The extracted decoded features are concurrently supplied to the pixel level learning module (PELM) and edge level learning module (E2LM) which resulting in two outputs for subsequent learning. The outputs were merged to provide a very precise lung tumor segmentation. Furthermore, segmented tumor was fed to multi-class support vector machine (MC-SVM) for lung tumor stage classification. Moreover, it was able to identify three stages and its substages namely primary tumor, region lymph node and distant metastasis.
Volume: 14
Issue: 5
Page: 4032-4042
Publish at: 2025-10-01

Experimental validation of a dual-band printed antenna array operating at 2.45/5.8 GHz with a high efficiency for wireless power transmission applications

10.11591/ijece.v15i5.pp4662-4670
Walid En-Naghma , Mohamed Latrach , Hanan Halaq , Abdelghani El Ougli
This paper presents a simple dual-band antenna element with a rectangular patch designed for the industrial, scientific, and medical bands at 2.45/5.8 GHz. The antenna achieves satisfactory simulated performance at both resonant frequencies, including a reflection coefficient below -10 dB, a voltage standing wave ratio (VSWR) not exceeding 1.2, radiation efficiencies of approximately 90% at 2.45 GHz and 95.55% at 5.8 GHz, and bandwidths of 125.80 MHz around 2.45 GHz and 308.60 MHz around 5.8 GHz. Building on this single-element design, an antenna array configuration comprising two elements etched on a Taconic TLY-5 substrate is developed. The two rectangular patches are connected via a T-junction to a 50 Ω excitation port. The proposed array's effectiveness is validated through simulations using three electromagnetic solvers and experimental measurements. The fabricated antenna array demonstrates improved performance, including a measured return loss of -16.78 dB at 2.45 GHz and -20.61 dB at 5.8 GHz, a VSWR not exceeding 1.5 (1.34 and 1.22 at 2.45 and 5.8 GHz, respectively), input impedance close to 50 Ω, high gain exceeding 8 dBi, bandwidths of 179.50 MHz at 2.45 GHz and 462.90 MHz at 5.8 GHz, and high radiation efficiencies of 96.54% at 2.45 GHz and 98.65% at 5.8 GHz. With only two patches, the proposed antenna array offers a compact, efficient, and practical solution for wireless power transmission applications, particularly for small wireless devices like rectenna systems, due to its simplicity, compact design, and excellent radiation efficiency.
Volume: 15
Issue: 5
Page: 4662-4670
Publish at: 2025-10-01

Unveiling critical features for failure prediction in green internet of things applications

10.11591/ijai.v14.i5.pp4308-4318
Ouiam Khattach , Omar Moussaoui , Mohammed Hassine
The rapid growth of the green internet of things (GIoT) in recent years signifies a transformative shift in internet of things (IoT) solution development. This evolution is driven by technological advancements, heightened environmental awareness, and a global imperative to combat climate change. Ensuring the reliability of GIoT applications is crucial for their success. This study identifies critical features for predicting IoT device failures, enabling early detection and intervention. Using datasets from industry, energy, and agriculture sectors, we employ a feature selection strategy to analyze extensive data from diverse GIoT deployments. Our analysis identifies significant features and integrates key insights from existing literature. Our findings support enhanced predictive maintenance strategies, reduced downtime, and improved overall performance of sustainable IoT solutions.
Volume: 14
Issue: 5
Page: 4308-4318
Publish at: 2025-10-01
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