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31,456 Article Results

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

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

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

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

Synthesis of nonlinear multilinked control systems of thermal power plants

10.11591/ijece.v15i5.pp4500-4507
Oksana Porubay , Isamiddin Siddikov
The paper addresses the synthesis of nonlinear control laws for the technological parameters of drum boiler steam generators in thermal power plants, based on a synergetic control approach. The controlled system is considered to be multidimensional and highly interconnected. The inherent nonlinearity and interdependence of the technological parameters in thermal power plants necessitate the use of nonlinear control laws to achieve effective regulation. This approach enables the expansion of the range of permissible variations in regulator parameters, thereby ensuring the desired dynamic behavior of the controlled variables. An analytical method for synthesizing nonlinear vector control laws for steam generators is proposed. A methodology is developed for designing dynamic regulators capable of compensating for uncertain disturbances while accounting for control constraints. A Lyapunov function is constructed to describe the internal state dynamics of the control object. The proposed method for constructing the dynamic regulator ensures the asymptotic stability of the control system and stabilization of the controlled parameters over a wide range of load variations.
Volume: 15
Issue: 5
Page: 4500-4507
Publish at: 2025-10-01

Design and analysis of reinforcement learning models for automated penetration testing

10.11591/ijai.v14.i5.pp4061-4073
Suresh Jaganathan , Mrithula Kesavan Latha , Krithika Dharanikota
Our paper proposes a framework to automate penetration testing by utilizing reinforcement learning (RL) capabilities. The framework aims to identify and prioritize vulnerable paths within a network by dynamically learning and adapting strategies for vulnerability assessment by acquiring the network data obtained from a comprehensive network scanner. The study evaluates three RL algorithms: deep Q-network (DQN), deep deterministic policy gradient (DDPG), and asynchronous episodic deep deterministic policy gradient (AE-DDPG) in order to compare their effectiveness for this task. DQN uses a learned model of the environment to make decisions and is hence called model-based RL, while DDPG and AE-DDPG learn directly from interactions with the network environment and are called model-free RL. By dynamically adapting its strategies, the framework can identify and focus on the most critical vulnerabilities within the network infrastructure. Our work is to check how well the RL technique picked security vulnerabilities. The identified vulnerable paths are tested using Metasploit, which also confirmed the accuracy of the RL approach's results. The tabulated findings show that RL promises to automate penetration testing tasks.
Volume: 14
Issue: 5
Page: 4061-4073
Publish at: 2025-10-01

Detection of chronic kidney disease based on ensemble approach with optimal feature selection using machine learning

10.11591/ijai.v14.i5.pp4017-4031
Deepika Amol Ajalkar , Jyoti Yogesh Deshmukh , Mayura Vishal Shelke , Shalini Vaibhav Wankhade , Shwetal Kishor Patil
Chronic kidney disease (CKD) poses a significant health risk globally, necessitating early and accurate detection to ensure timely intervention and effective treatment. This study presents an advanced ensemble machine learning (ML) approach combined with optimal feature selection to enhance the detection of CKD. Using five baseline ML classifiers like gradient boosting (GB), random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), and decision tree (DT), and utilizing grid search for hyperparameter tuning, the proposed ensemble model capitalizes on the strengths of each algorithm. Our approach was tested on a public benchmark CKD dataset from Kaggle. The experimental results demonstrate that the ensemble model consistently outperforms individual classifiers and existing methods, achieving 97.5% accuracy, precision, recall, and an F1-score of 97.4%. This superior performance underscores the ensemble model's potential as a reliable early CKD detection tool. Integrating ML into CKD diagnostics enhances accuracy. It facilitates the development of automated, scalable diagnostic tools, aiding healthcare professionals in making informed decisions and ultimately improving patient outcomes.
Volume: 14
Issue: 5
Page: 4017-4031
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

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

Artificial intelligence-driven integrated system for comprehensive email marketing automation

10.11591/ijece.v15i5.pp4875-4888
Soumaya Loukili , Lotfi Elaachak , Abderrahim Ghadi , Abdelhadi Fennan
Right in the context of digital marketing, this paper presents a comprehensive integrated system that combines the latest artificial intelligence advancement – large language models and diffusion models – to generate marketing email subjects and content that result in higher engagement. The system uses finetuned large language models for compelling email subject generation and finetuned Stable Diffusion model for visually appealing and convincing email content images creation. For the latter, both knowledge graphs and vector embeddings have been incorporated to improve contextual relevance. Experimental results demonstrated significant improvement in all engagement metrics that marketers rely on, including 46% growth in open rates, 56% higher click-through rates, and an 51% boost in conversion rates, all compared to human generated content. The unified approach presented by this paper outperforms standalone models and human-generated content in terms of engagement, as the comparative analysis shows. We also discuss the ethical considerations related to content bias and personalization boundaries, alongside challenges faced in this type of projects, such as computation demands and probable solutions. Finally, this paper proposes future directions to be taken, including expansion to other digital marketing channels, the use of other advanced artificial intelligence techniques, and the development of real-time content adaptation mechanisms based on user feedback.
Volume: 15
Issue: 5
Page: 4875-4888
Publish at: 2025-10-01

Classifying the suitability of educational videos for attention deficit hyperactivity disorder students with deep neural networks

10.11591/ijece.v15i5.pp4889-4898
Alshefaa Emam , Eman Karam Elsyed , Mai Kamel Galab
This paper presents a comprehensive deep learning-based system to evaluate the educational videos' suitability for students with attention deficit hyperactivity disorder (ADHD). Current methods frequently ignore important instructional elements that are necessary for improving learning experiences for students with ADHD, such as instructor hand movements, video length, object variety, and audio-visual quality. We emphasize two key issues for how to address these difficulties, first, we present the ADHD online instructor (AOI) dataset, a particular benchmark for assessing instructional hand movement in video suitability to solve the absence of a reference dataset for classifying educational videos relevant to ADHD. Second, the system includes creating an enhanced multitask deep learning model that increases classification accuracy by using task-specific enhancements and optimized architectures. This solves the requirement for a strong model that can distinguish between suitable and unsuitable instructional content. Comprehensive tests using pretrained convolutional neural network (CNN) models indicate that the enhanced VGG16 model outperforms baseline methods by achieving a highest accuracy of 97.84%. The results highlight the value of integrating deep learning methods with structured benchmark datasets, exposing up the path to more resilient and flexible instructional materials designed for students with ADHD.
Volume: 15
Issue: 5
Page: 4889-4898
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

Learning assistance module based on a small language model

10.11591/ijai.v14.i5.pp4202-4210
Marco Antonio Jinete , Robinson Jiménez-Moreno , Anny Astrid Espitia-Cubillos
This paper presents the development of a low-cost learning assistant embedded in an NVIDIA Jetson Xavier board that uses speech and gesture recognition, together with a long language model for offline work. Using the large language model (LLM) Phi-3 Mini (3.8B) model and the Whisper (model base) model for automatic speech recognition, a learning assistant is obtained under a compact and efficient design based on extensive language model architectures that give a general answer set of a topic. Average processing times of 0.108 seconds per character, a speech transcription efficiency of 94.75%, an average accuracy of 9.5/10 and 8.5/10 in the consistency of the responses generated by the learning assistant, a full recognition of the hand raising gesture when done for at least 2 seconds, even without fully extending the fingers, were obtained. The prototype is based on the design of a graphical interface capable of responding to voice commands and generating dynamic interactions in response to the user's gesture detection, representing a significant advance towards the creation of comprehensive and accessible human-machine interface solutions.
Volume: 14
Issue: 5
Page: 4202-4210
Publish at: 2025-10-01

An energy-efficient and secure framework for wireless sensor networks

10.11591/ijai.v14.i5.pp4151-4161
Maruthi Hanumanthappa Chandrappa , Poornima Govindaswamy
In wireless sensor networks (WSNs), achieving energy efficiency, security, and minimizing route change propagation time is essential for maintaining optimal performance. This paper introduces a new approach that combines Bray Jaccard Curtis-based Calinski Harabasz k-means (BJC-CHKMeans) for clustering and Karl Pearson correlation-based egret swarm optimization algorithm (KPC-ESOA) for selecting the best cluster head (CH) and path, along with classifying long short-term memory with gated recurrent units (CLE-GRU) for detecting harmful nodes. The methodology aims to enhance energy usage, improve routing efficiency, and strengthen security by identifying malicious nodes. Additionally, it integrates a secure routing table using elbow de-swinging k-anonymity (EDS-KA) and employs digital signature algorithm-based Zeta Bernoulli Merkle tree (DSA-ZBMT) to ensure secure communication with sink nodes. The WSN-DS dataset was used for training and testing, with rigorous preprocessing, feature extraction, and selection to maintain data integrity. Experimental results revealed that the proposed BJC-CHKMeans and CLE-GRU models outperform traditional methods in power consumption, latency, and accuracy. The system achieved a power consumption of 2.1 mW for clustering and 1.9 mW for classification, while also providing near-perfect accuracy in detecting harmful nodes. These findings demonstrate that the framework significantly enhances the energy efficiency and security of WSNs, making it a highly effective solution for large, dynamic sensor networks.
Volume: 14
Issue: 5
Page: 4151-4161
Publish at: 2025-10-01
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