Articles

Comparative analysis of metaheuristic algorithms (genetic algorithm, artificial bee colony, differential evolution) in the design of substrate integrated waveguide dual bandpass filter

10.11591/ijece.v15i5.pp4682-4691

Souad Akkader , Abdennaceur Baghdad , Hamid Bouyghf , Aziz Dkiouak , Yassine Gmih

A well-optimized substrate integrated waveguide (SIW) filter can significantly enhance the performance of modern technologies, including wireless communication systems, radar, and sensors. The frequencies of 5 and 6 GHz play a crucial role in these applications. Metaheuristic algorithms such as genetic algorithm (GA), artificial bee colony (ABC), and differential evolution (DE) are effective for designing SIW filters specifically tailored to these needs. This paper evaluates the performance of evolutionary optimization techniques in the design of substrate integrated waveguide filters. The optimization focuses on achieving optimal impedance matching within the frequency range of 4 to 8 GHz. The attenuation constant serves as the cost function, guiding the optimization process to ensure reliable and accurate results from each algorithm. The filter parameters derived from the most efficient algorithm are verified using ANSYS HFSS, resulting in two bands with S11=-45 dB and S21=-0.2 dB in the first band, and S11=-28 dB and S21=-0.5 dB in the second band. Additionally, two transmission zeros with rejections of -23 and -12 dB are achieved at 6.4 and 7.08 GHz, respectively. These results highlight the practicality of SIW technologies in designing microwave circuits, particularly for internet of things (IoT) applications.

Volume: 15

Issue: 5

Page: 4682-4691

Publish at: 2025-10-01

Stock market liquidity: hybrid deep learning approaches for prediction

10.11591/ijai.v14.i5.pp3624-3633

Mariam Ait Al , Said Achchab , Younes Lahrichi

Predicting stock market liquidity especially in emerging or frontier financial markets, such as the Casablanca stock exchange (CSE), presents significant challenges given the relative narrowness and volatility of these markets. In this paper, we conduct a comprehensive study to address the predictions accuracy gaps between five main deep learning models: convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and two hybrid architectures, CNN-LSTM and CNN-BiLSTM. The proposed methodology focused on training and testing these models on historical data from the CSE, with precision on capturing both spatial and temporal market dynamics. The models were fine-tuned using key hyperparameters and validated on 20% of the dataset to ensure reliable results. The evaluation of performance was conducted using error metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The study demonstrates that the hybrid CNN-biLSTM model consistently outperformed all standalone and other hybrid models in predictive accuracy. This underscores the considerable promise of hybrid deep learning architectures for addressing the unique challenges of predicting stock market liquidity in volatile and emerging financial markets.

Volume: 14

Issue: 5

Page: 3624-3633

Publish at: 2025-10-01

Bone-Net: a parallel deep convolutional neural network-based bone fracture recognition

10.11591/ijece.v15i5.pp4692-4704

Md. Hasan Imam Bijoy , Nusrat Islam Kohinoor , Syeda Zarin Tasnim , Md Saidur Rahman Kohinoor

Many people suffer from bone fractures, which can result from minor accidents, forceful blows, or even diseases like osteoporosis or bone cancer. In the medical realm, accurately identifying bone fractures from X-ray images is paramount for effective diagnosis and treatment. To address this, a comparative study is conducted utilizing three distinct models: a traditional convolutional neural network (CNN), MobileNet-V2, and a newly developed parallel deep convolutional neural network (PDCNN). The primary aim is to evaluate and contrast these models in terms of precision, sensitivity, and specificity for diagnosing bone fractures. X-ray images of fractured and non-fractured bones are sourced from Kaggle and subjected to various image processing techniques to rectify anomalies. Techniques such as cropping, resizing, contrast enhancement, filtering, and augmentation are applied, culminating in canny edge detection. These processed images are then used to train and test models. The results showcased the superior performance of the newly developed PDCNN model, achieving an impressive accuracy of 92.89%, surpassing both the traditional CNN and pretrained MobileNet-V2 models. A series of ablation studies are conducted to fine-tune the hyperparameters of the PDCNN model, further validating its efficacy. Throughout the investigation, PDCNN consistently outperformed MobileNet-V2 and traditional CNN, underscoring its potential as an advanced tool for streamlining bone fracture identification.

Volume: 15

Issue: 5

Page: 4692-4704

Publish at: 2025-10-01

Translation-based image steganography system utilizing autoencoder and CycleGAN

10.11591/ijai.v14.i5.pp3958-3969

Thakwan Akram Jawad , Jamshid Bagherzadeh Mohasefi , Mohammed Salah Reda Abdelghany

Traditional image steganography involves embedding secret information into a cover image, a process that requires modification of the carrier and potentially leaves detectable marks. This paper proposes a novel method of coverless image steganography based on generative models. Initially, a CycleGAN model is constructed and trained to learn the features of different image domains. Subsequently, an Autoencoder model is trained using two sets of images to achieve a precise one-to-one mapping. Once the models are trained, the autoencoder is used on both the sender and receiver sides to convert the cover image (also known as the stego image) into the secret image and vice versa. The CycleGAN model is then utilized to enhance the visual quality of the images generated by the autoencoder. Experimental results demonstrate that this method not only effectively secures secret information transmission but also improves efficiency and increases the capacity for information hiding compared to similar methods.

Volume: 14

Issue: 5

Page: 3958-3969

Publish at: 2025-10-01

Transforming campus mobility: the DigiSticker system in digital parking solutions

10.11591/ijai.v14.i5.pp3667-3680

Rahman Md. Mojnur , Md. Tanjil Sarker , Wan-Noorshahida Mohd-Isa , Fahmid Al Farid , Md. Rakibul Hassan Sheam , Hezerul Abdul Karim , Gobbi Ramasamy , Aziah Ali

University digital parking systems have several benefits and solve many problems with traditional parking. Universities without a digital parking system face restricted parking, traffic congestion, inefficient space utilization, security issues, limited decision-making data, and diminished sustainability initiatives. This study paper discusses the benefits of digital parking systems and the drawbacks of traditional methods. By using technology to streamline university parking administration, the DigiSticker system offers an innovative solution. The DigiSticker system improves parking efficiency, convenience, and security for students, professors, and staff by delivering real-time parking information, assistance, and automated payments. This system has a user-friendly website and mobile app, fast registration, gate access management, security, user experience enhancement, and sustainability. Universities can improve student and staff parking experiences while improving parking management efficiency, security, and sustainability by meeting these requirements.

Volume: 14

Issue: 5

Page: 3667-3680

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

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

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

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

Improving multilayer perceptron on rainfall data using modified genetics algorithm

10.11591/ijai.v14.i5.pp3994-4005

Marji Marji , Wayan Firdaus Mahmudi , Endang Wahyu Handamari , Edy Santoso , Maulana Muhamad Arifin

Rainfall prediction is essential for managing water resources, agriculture, and disaster response, particularly in regions affected by climate variability. This study introduces a modified genetic algorithm (MGA) to optimize hyperparameters of a multilayer perceptron (MLP) for rainfall forecasting. The MGA incorporates elitism to retain top-performing solutions and adaptive selection based on model accuracy. The proposed MGA–MLP model was tested on rainfall datasets from Australia and Indonesia (BMKG). Experimental results show that configurations with two hidden layers, rectified linear unit (ReLU) activation and limited-memory Broyden Fletcher Goldfarb Shannon (LBFGS) optimizer, a learning rate of 0.001 and 1000 epochs consistently delivered strong performance. The model achieved accuracies of 86.02% and 79.05%, respectively. These findings indicate that MGA significantly improves MLP performance and provides a reliable, generalizable method for rainfall prediction across diverse climatic conditions.

Volume: 14

Issue: 5

Page: 3994-4005

Publish at: 2025-10-01

Recommendation system for football player recruitment using k-nearest neighbor

10.11591/ijai.v14.i5.pp3847-3857

Maukar Maukar , Rodiah Rodiah

In modern professional football, achieving a competitive edge depends not only on on-field performance but also on effective off-field strategies, particularly in player recruitment. This study proposes a machine learning-based recommendation system to support talent identification and optimal player placement using statistical performance data. The model analyzes a wide range of features, including shots, expected goals, expected assists, pass types, offensive contributions, and defensive actions across field zones. The dataset undergoes preprocessing steps such as normalization (per 90 minutes) and dimensionality reduction. A key innovation of this research is the use of principal component analysis (PCA) to reduce feature dimensionality, minimizing redundancy while retaining essential information, which improves model efficiency and scalability. The refined data is then processed using the k-nearest neighbors (KNN) algorithm with cosine similarity, allowing the system to identify players with similar performance profiles based on directional similarity in a high-dimensional space. This combination enhances recommendation accuracy by focusing on performance structure rather than raw values. The resulting system provides actionable insights into player suitability and potential, offering clubs a data-driven tool for informed scouting and recruitment decisions. The approach demonstrates the effectiveness of combining PCA and KNN in optimizing football player recommendation systems.

Volume: 14

Issue: 5

Page: 3847-3857

Publish at: 2025-10-01

Optimized ensemble framework for predicting hydroponic stock and sales using machine learning

10.11591/ijai.v14.i5.pp3879-3886

Viktor Handrianus Pranatawijaya , Ressa Priskila , Putu Bagus Adidyana Anugrah Putra , Nova Noor Kamala Sari , Efrans Christian , Septian Geges , Novera Kristianti

The increasing global demand for food necessitates the adoption of sustainable agricultural practices. Hydroponic farming, while efficient in resource utilization, faces challenges in accurately predicting stock levels and sales due to dynamic, ever-changing factors. This research presents an optimized ensemble framework for forecasting hydroponic stock levels and sales by integrating linear regression (LR), random forest (RF), and XGBoost, further enhanced through an evolutionary algorithm (EA). The proposed framework is evaluated using root mean square error (RMSE) and mean absolute error (MAE), demonstrating significant accuracy improvements over individual models. The ensemble model achieves an RMSE reduction of 43.82% for stock prediction and 55.3% for sales forecasting compared to the best-performing individual model. Additionally, local interpretable model-agnostic explanations (LIME) are employed to offer stakeholders clear insights into decision-making processes, such as identifying "number of harvested crops" and "sales data" as key drivers of prediction outcomes. This framework supports sustainable development goals (SDGs) 9.3, 12.3, and 12.C by promoting resource efficiency, reducing food waste, and improving small-scale farmer market access. Future research will explore real-time data integration for dynamic adaptation and further model enhancements.

Volume: 14

Issue: 5

Page: 3879-3886

Publish at: 2025-10-01

Backpropagation neural networks for solving gas flow equations in porous media

10.11591/ijai.v14.i5.pp3744-3756

Adrianto Adrianto , Zuher Syihab , Sutopo Sutopo , Taufan Marhaendrajana

This study proposes a backpropagation neural network (BPNN) as an alternative solver for nonlinear equations in gas flow simulation through porous media. Conventional solvers like the Newton-Raphson (N-R) method are accurate but may become inefficient for large-scale or heterogeneous systems. We develop a feedforward BPNN architecture with adaptive learning rates to solve discretized residual equations from the one-dimensional gas flow model. The methodology includes finite difference discretization and mapping the nonlinear algebraic system into a four-layer neural network. The BPNN solver is validated against the Newton method across various grid sizes and heterogeneous permeability-porosity distributions. Results show that BPNN achieves high accuracy, with maximum absolute errors (MAE) of only 0.241 psi in the homogeneous model and 0.0418 psi in the heterogeneous model. While the BPNN requires more iterations and longer computation time, especially for finer grids, it exhibits the ability to learn pressure patterns and improve efficiency over time. This approach demonstrates that BPNN can serve as a viable nonlinear solver in reservoir simulation, offering flexibility in handling nonlinearities while maintaining accuracy.

Volume: 14

Issue: 5

Page: 3744-3756

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

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

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31,456 Article Results

Comparative analysis of metaheuristic algorithms (genetic algorithm, artificial bee colony, differential evolution) in the design of substrate integrated waveguide dual bandpass filter

Stock market liquidity: hybrid deep learning approaches for prediction

Bone-Net: a parallel deep convolutional neural network-based bone fracture recognition

Translation-based image steganography system utilizing autoencoder and CycleGAN

Transforming campus mobility: the DigiSticker system in digital parking solutions

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

Comparative analysis of convolutional neural network architectures for poultry meat classification

Ensemble reverse knowledge distillation: training robust model using weak models

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

Improving multilayer perceptron on rainfall data using modified genetics algorithm

Recommendation system for football player recruitment using k-nearest neighbor

Optimized ensemble framework for predicting hydroponic stock and sales using machine learning

Backpropagation neural networks for solving gas flow equations in porous media

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

Object detection for indoor mobile robot: deep learning approaches review

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