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28,451 Article Results

A novel fuzzy logic based sliding mode control scheme for non-linear systems

10.11591/ijai.v14.i4.pp2676-2688
Abdul Kareem , Varuna Kumara
Sliding mode control (SMC) has been widely used in the control of non-linear systems due to many inherent properties like superposition, multiple isolated equilibrium points, finite escape time, limit cycle, bifurcation. This research proposes super-twisting controller architecture with a varying sliding surface; the sliding surface being adjusted by a simple single input-single output (SISO) fuzzy logic inference system. The proposed super-twisting controller utilizes a varying sliding surface with an online slope update using a SISO fuzzy logic inference system. This rotates sliding surface in the direction of enhancing the dynamic performance of the system without compromising steady state performance and stability. The performance of the proposed controller is compared to that of the basic super-twisting sliding mode (STSM) controller with a fixed sliding surface through simulations for a benchmark non-linear system control system model with parametric uncertainties and disturbances. The simulation results have confirmed that the proposed approach has the improved dynamic performance in terms of faster response than the typical STSM controller with a fixed sliding surface. This improved dynamic performance is achieved without affecting robustness, system stability and level of accuracy in tracking. The proposed control approach is straightforward to implement since the sliding surface slope is regulated by a SISO fuzzy logic inference system. The MATLAB/Simulink is used to display the efficiency of proposed system over conventional system.
Volume: 14
Issue: 4
Page: 2676-2688
Publish at: 2025-08-01

Optimized pap-smear image enhancement: hybrid Perona-Malik diffusion filter-CLAHE using spider monkey optimization

10.11591/ijai.v14.i4.pp2765-2775
Ach Khozaimi , Isnani Darti , Wuryansari Muharini Kusumawinahyu , Syaiful Anam
Pap-smear image quality is crucial for cervical cancer detection. This study introduces an optimized hybrid approach that combines the Perona-Malik diffusion (PMD) filter with contrast-limited adaptive histogram equalization (CLAHE) to enhance pap-smear image quality. The PMD filter reduces the image noise, whereas CLAHE improves the image contrast. The hybrid method was optimized using spider monkey optimization (SMO PMD-CLAHE). Blind/reference-less image spatial quality evaluator (BRISQUE) and contrast enhancement-based image quality (CEIQ) are the new objective functions for the PMD filter and CLAHE optimization, respectively. The simulations were conducted using the SIPaKMeD dataset. The results indicate that SMO outperforms state-of-the-art methods in optimizing the PMD filter and CLAHE. The proposed method achieved an average effective measure of enhancement (EME) of 5.45, root mean square (RMS) contrast of 60.45, Michelson’s contrast (MC) of 0.995, and entropy of 6.80. This approach offers a new perspective for improving pap-smear image quality.
Volume: 14
Issue: 4
Page: 2765-2775
Publish at: 2025-08-01

Imagery based plant disease detection using conventional neural networks and transfer learning

10.11591/ijai.v14.i4.pp2701-2712
Ali Mhaned , Salma Mouatassim , Mounia El Haji , Jamal Benhra
Ensuring the sustainability of global food production requires efficient plant disease detection, challenge conventional methods struggle to address promptly. This study explores advanced techniques, including convolutional neural networks (CNNs) and transfer learning models (ResNet and VGG), to improve plant disease identification accuracy. Using a plant disease dataset with 65 classes of healthy and diseased leaves, the research evaluates these models' effectiveness in automating disease recognition. Preprocessing techniques, such as size normalization and data augmentation, are employed to enhance model reliability, and the dataset is divided into training, testing, and validation sets. The CNN model achieved accuracies of 95.45 and 94.52% for 128×128 and 256×256 image sizes, respectively. ResNet50 proved the best performer, reaching 98.38 and 98.63% accuracy, while VGG16 achieved 97.99 and 98.34%. These results highlight ResNet50's superior ability to capture intricate features, making it a robust tool for precision agriculture. This research provides practical solutions for early and accurate disease identification, helping to improve crop management and food security.
Volume: 14
Issue: 4
Page: 2701-2712
Publish at: 2025-08-01

Unpacking the drivers of artificial intelligence regulation: driving forces and critical controls in artificial intelligence governance

10.11591/ijai.v14.i4.pp2655-2666
Ibrahim Atoum , Salahiddin Altahat
The burgeoning field of artificial intelligence (AI) necessitates a nuanced approach to governance that integrates technological advancement, ethical considerations, and regulatory oversight. As various AI governance frameworks emerge, a fragmented landscape hinders effective implementation. This article examines the driving forces behind AI regulation and the essential control mechanisms that underpin these frameworks. We analyze market-driven, state-driven, and rights-driven regulatory approaches, focusing on their underlying motivations. Furthermore, critical regulatory controls such as data governance, risk management, and human oversight are highlighted to demonstrate their roles in establishing effective governance structures. Additionally, the importance of international cooperation and stakeholder collaboration in addressing the challenges posed by rapid technological change is emphasized. By providing insights into the strengths, weaknesses, and potential synergies of different governance models, this study contributes to the development of equitable and effective AI regulatory frameworks that encourage innovation while safeguarding societal interests. Ultimately, the findings aim to inform policymakers, industry leaders, and civil society organizations in their efforts to foster a future where AI is utilized responsibly and equitably for the betterment of humanity.
Volume: 14
Issue: 4
Page: 2655-2666
Publish at: 2025-08-01

Optimizing convolutional neural network hyperparameters to enhance liver segmentation accuracy in medical imaging

10.11591/ijece.v15i4.pp3876-3887
Iwan Purnama , Agus Perdana Windarto , Solikhun Solikhun
Liver segmentation in medical imaging is a crucial step in various clinical applications, such as disease diagnosis, surgical planning, and evaluation of response to therapy, which require a high degree of precision for accurate results. This research focuses on increasing the accuracy of liver segmentation by optimizing hyperparameters in the convolutional neural network (CNN) model using the developed ResNet architecture. The uniqueness of this research lies in the application of hyperparameter optimization methods such as random search and Bayesian optimization, which allow broader and more efficient exploration than conventional approaches. The results show that the DeepLabV3Plus model (the proposed model) significantly outperforms the standard ResNet in the image segmentation task. DeepLabV3Plus shows excellent performance with an MIoU score of 0.965, a PA Score of 0.929, and a meager loss value of 0.011. These results show that DeepLabV3Plus is able to recognize and predict segmentation areas very accurately and consistently and minimize prediction errors effectively. In conclusion, the results of this study show a significant improvement in segmentation accuracy, with the optimized model providing better performance in the evaluation.
Volume: 15
Issue: 4
Page: 3876-3887
Publish at: 2025-08-01

Deep transfer learning for classification of ECG signals and lip images in multimodal biometric authentication systems

10.11591/ijai.v14.i4.pp3160-3171
Latha Krishnamoorthy , Ammasandra Sadashivaiah Raju
Authentication plays an essential role in diverse kinds of application that requires security. Several authentication methods have been developed, but biometric authentication has gained huge attention from the research community and industries due to its reliability and robustness. This study investigates multimodal authentication techniques utilizing electrocardiogram (ECG) signals and face lip images. Leveraging transfer learning from pre-trained ResNet and VGG16 models, ECG signals and photos of the lip area of the face are used to extract characteristics. Subsequently, a convolutional neural network (CNN) classifier is employed for classification based on the extracted features. The dataset used in this study comprises ECG signals and face lip images, representing distinct biometric modalities. Through the integration of transfer learning and CNN classification, improving the reliability and precision of multimodal authentication systems is the primary objective of the study. Verification results show that the suggested method is successful in producing trustworthy authentication using multimodal biometric traits. The experimental analysis shows that the proposed deep transfer learning-based model has reported the average accuracy, F1-score, precision, and recall as 0.962, 0.970, 0.965, and 0.966, respectively.
Volume: 14
Issue: 4
Page: 3160-3171
Publish at: 2025-08-01

Revolutionizing autism diagnosis using hybrid model for autism spectrum disorder phenotyping

10.11591/ijece.v15i4.pp3904-3912
Vijayalaxmi N. Rathod , Rayangouda H. Goudar
The growing prevalence of autism spectrum disorder (ASD) necessitates efficient data-driven screening solutions to complement traditional diagnostic methods, which often suffer from subjectivity and limited scalability. This study introduces a hybrid ensemble model combining logistic regression (LR) and naive Bayes (NB) for ASD classification across four age groups (toddlers, children, adolescents, and adults) using behavioral screening datasets. By integrating statistical learning and probabilistic inference, the proposed model effectively captured behavioral markers, ensuring a higher classification accuracy and improved generalization. The experimental evaluation demonstrated its superior performance, achieving 94.24% accuracy and 99.40% area under the receiver operating characteristic curve (AUROC), surpassing those of individual classifiers and existing approaches. This artificial intelligence (AI)-driven framework offers a scalable, cost-effective, and accessible solution for both clinical and telemedicine-based ASD screening, facilitating early intervention and risk assessment. This study underscores the transformative potential of AI in neurodevelopmental diagnostics, paving the way for more efficient and widely deployable autistic screening technologies.
Volume: 15
Issue: 4
Page: 3904-3912
Publish at: 2025-08-01

Blockchain as a digital governance tool: A systematic review

10.11591/ijece.v15i4.pp3986-3995
Cesar Patricio-Peralta , Jimmy Ramirez Villacorta , Milton Amache Sánchez , Jacker Paredes Meneses , Jesús Zamora Mondragon , Luis Segura Terrones , Paul Torres Santos , César Veliz Manrique , Walter Patricio Peralta
This systematic review explores the implementation of blockchain technology as a digital governance tool, focusing specifically on the Peruvian context. In the digital transformation era, blockchain has established itself as an innovative solution to manage and authenticate information. This research focuses on optimizing administrative and governmental processes in Peru, a country where document verification is crucial in legal, financial, educational, and medical procedures. The methodology used follows the problem/population, intervention, comparison, outcome, context (PICOC) model. 56 high-impact articles were selected in Scopus, prioritizing those in the areas of engineering, computer science, and business, and published between 2022 and 2025. The objective was to define the scope and structure of the research questions. These questions address the implementation of blockchain and its applications in digital governance to ensure security and reliability in administrative procedures. Through a comprehensive literature review, we seek to provide a comprehensive view of how blockchain could transform the interaction between citizens and the Peruvian government by automating document verification. In addition, successful cases from other countries and similar sectors will be analyzed, evaluating their feasibility and applicability in the Peruvian context. This approach will allow us to identify both the potential benefits and the challenges and implications associated with the integration of blockchain into government processes in Perú.
Volume: 15
Issue: 4
Page: 3986-3995
Publish at: 2025-08-01

Federated deep learning intrusion detection system on software defined-network based internet of things

10.11591/ijai.v14.i4.pp3109-3120
Heba Dhirar , Ali H. Hamad
The internet of things (IoT) and software-defined networks (SDN) play a significant role in enhancing efficiency and productivity. However, they encounter possible risks. Artificial intelligence (AI) has recently been employed in intrusion detection systems (IDSs), serving as an important instrument for improving security. Nevertheless, the necessity to store data on a centralized server poses a potential threat. Federated learning (FL) addresses this problem by training models locally. In this work, a network intrusion detection system (NIDS) is implemented on multi-controller SDN-based IoT networks. The interplanetary file system (IPFS) FL has been employed to share and train deep learning (DL) models. Several clients participated in the training process using custom generated dataset IoT-SDN by training the model locally and sharing the parameters in an encrypted format, improving the overall effectiveness, safety, and security of the network. The model has successfully identified several types of attacks, including distributed denial of service (DDoS), denial of service (DoS), botnet, brute force, exploitation, malware, probe, web-based, spoofing, recon, and achieving an accuracy of 99.89% and a loss of 0.005.
Volume: 14
Issue: 4
Page: 3109-3120
Publish at: 2025-08-01

The growth and trends information technology endangered language revitalization research: Insight from a bibliometric study

10.11591/ijece.v15i4.pp3888-3903
Leonardi Paris Hasugian , Syifaul Fuada , Triana Mugia Rahayu , Apridio Edward Katili , Feby Artwodini Muqtadiroh , Nur Aini Rakhmawati
Since United Nations Educational, Scientific and Cultural Organization (UNESCO) declared endangered languages, researchers have revitalized endangered languages in many fields. This study discusses a bibliometric analysis conducted to investigate research on the topic of revitalization of endangered languages in information technology. The study's aim is to assess research topics by identifying authors, institutions, and countries that influence research collaboration. The Scopus dataset (from 2002-2024) was obtained from journal articles (n=62) and conference papers (n=76) and visualized using VOSviewer 1.6.20. The analysis outcomes reveal a fluctuating trend with an increasing pattern. The United States, Canada, and China were identified as the top three countries in terms of publications. Meanwhile, the University of Alberta, Université du Québec à Montréal, University of Auckland, and University of Hawaiʻi at Mānoa are the most prolific institutions on this topic, with two authors from the Université du Québec à Montréal, Sadat and Le, being the most productive. The dominant research is related to computational linguistics. Meanwhile, topics such as phonetic posteriograms, integrated frameworks, and artificial intelligence are some of the potential research areas that can be explored in the future. Its implications for exposing the extent to which the development of endangered language revitalization can be accommodated in the field of information technology.
Volume: 15
Issue: 4
Page: 3888-3903
Publish at: 2025-08-01

Integrating time-frequency features with deep learning for lung sound classification

10.11591/ijece.v15i4.pp3737-3747
Su Yuan Chang , Marni Azira Markom , Zhi Sheng Choong , Arni Munira Markom , Latifah Munirah Kamaruddin , Erdy Sulino Mohd Muslim Tan
Deep learning has transformed medical diagnostics, especially in analyzing lung sounds to assess respiratory conditions. Traditional methods like CT scans and X-rays are impractical in resource-limited settings due to radiation exposure and time consumption, while conventional stethoscopes often lead to misdiagnosis due to subjective interpretation and environmental noise. This study evaluates deep learning models for lung sound classification using the International Conference on Biomedical Health Informatics 2017 dataset, comprising 920 annotated samples from 126 subjects. Pre-processing includes down sampling, segmentation, normalization, and audio clipping, with feature extraction techniques like spectrogram and Mel-frequency cepstral coefficients (MFCC). The adopted automatic lung sound diagnosis network (ASLD-Net) model with triple feature input (time domain, spectrogram, and MFCC) achieved the highest accuracy at 97.25%, followed by the dual feature model (spectrogram and MFCC) at 95.65%. Single-input models with spectrogram and MFCC performed well, while the time domain input alone had the lowest accuracy.
Volume: 15
Issue: 4
Page: 3737-3747
Publish at: 2025-08-01

Evaluation of the dynamic performance and practical limitations of a two-wheeled self-balancing robot

10.11591/ijece.v15i4.pp3613-3620
Rupasinghe Arachchige Don Dhanushka Dharmasiri , Malagalage Kithsiri Jayananda
Two-wheeled self-balancing robots (TWSBR) are statically unstable. However, using closed-loop controllers can stabilize. In this work, the proportional-integral-derivative (PID) controller was designed to maintain the TWSBR stability by adding two zeros and a pole at the origin to the loop gain and by determining the parameter K via root-locus analysis. Then using the K value Kp, Ki, and Kd parameters were calculated. By applying an impulse response to the system, it was found that the system is able to reach a dynamic balance in less than 1.2 seconds with minimum steady-state error. The dynamic performance and limitations of the developed system were investigated. The highest disturbance angle that can be applied to the system while keeping the motor input voltage below 12 V, in order to create counterbalancing torque and achieve dynamic balance, is determined to be θ = 0.0524 rad. Additionally, it was found that the TWSBR system managed to retain stability in a significantly large range of sudden payload changes with the same PID controller.
Volume: 15
Issue: 4
Page: 3613-3620
Publish at: 2025-08-01

Navigating cyber investigations: strategies and tools for forensic data acquisition

10.11591/ijece.v15i4.pp4022-4030
Srinivas Kanakala , Vempaty Prashanthi , K. V. Sharada
The rapid proliferation of cybercrimes has underscored the critical importance of robust data acquisition methodologies in the field of digital forensics. This research publication explores various aspects of forensic data acquisition, focusing on techniques, tools, and best practices employed by forensic investigators to collect and preserve digital evidence effectively. Beginning with an overview of the escalating cyber threat landscape and the consequential need for forensic investigations, the publication delves into the fundamental concepts of data acquisition, emphasizing the significance of ensuring data integrity and admissibility in legal proceedings. It examines the process of acquiring both volatile and non-volatile data from diverse sources, including hard drives, RAM, and other digital storage media. Furthermore, evaluates a range of forensic imaging and validation methods, encompassing tools such as Belkasoft live RAM capturer, AccessData FTK Imager, and ProDiscover, alongside validation techniques using PowerShell utility and commercial forensic software. Through comprehensive analysis and discussion, this study serves as a valuable resource for forensic practitioners, researchers, and legal professionals seeking to enhance their understanding of forensic data acquisition methodologies in the ever-evolving landscape of cybercrime investigation.
Volume: 15
Issue: 4
Page: 4022-4030
Publish at: 2025-08-01

Optimization model of vehicle routing problem with heterogenous time windows

10.11591/ijece.v15i4.pp4043-4057
Herman Mawengkang , Muhammad Romi Syahputra , Sutarman Sutarman , Gerhard Wilhelm Weber
This study proposes a novel optimization framework for the vehicle routing problem with heterogeneous time windows, a critical aspect in logistics and supply chain operations. Unlike conventional vehicle routing problem (VRP) models that assume uniform service schedules and fleet capacities, our approach acknowledges the diverse time constraints and vehicle specifications often encountered in real-world scenarios. By formulating the problem as a mixed integer linear programming model, we incorporate constraints related to time windows, vehicle load capacities, and travel distances. To tackle the NP-hard complexity, we employ a hybrid strategy combining metaheuristic algorithms with exact methods, thus ensuring both solution quality and computational efficiency. Extensive computational experiments, conducted on benchmark datasets and real-world logistics data, confirm the superiority of our model in terms of solution quality, runtime, and adaptability. These findings underscore the model’s practicality for industries facing dynamic routing requirements and tight service windows. Furthermore, the proposed framework equips decision-makers with a robust tool for optimizing route planning, ultimately enhancing service quality, reducing operational costs, and promoting more reliable delivery outcomes.
Volume: 15
Issue: 4
Page: 4043-4057
Publish at: 2025-08-01

An analysis between the Welsh-Powell and DSatur algorithms for coloring of sparse graphs

10.11591/ijece.v15i4.pp3867-3875
Radoslava Kraleva , Velin Kralev , Toma Katsarski
In this research an analysis between the Welsh-Powell and DSatur algorithms for the graph vertex coloring problem was presented. Both algorithms were implemented and analyzed as well. The method of the experiment was discussed and the 46 test graphs, which were divided into two sets, were presented. The results show that for sparse graphs with a smaller number of vertices and edges, both algorithms can be used for solving the problem. The results show that in 50% of the cases the Welsh-Powell algorithm found better solutions (23 in total). So, the DSatur algorithm found better solutions in only 19.6% of cases (9 in total). In the remaining 30.4% of cases, both algorithms found identical solutions. For graphs with a larger number of vertices, the usage of the Welsh-Powell algorithm is recommended as it finds better solutions. The execution time of the DSatur algorithm is greater than the execution time of the Welsh-Powell algorithm, reaching up to a minute for graphs with a larger number of vertices. For graphs with fewer vertices and edges, the execution times of both algorithms are shorter, but the time is still greater for the DSatur algorithm.
Volume: 15
Issue: 4
Page: 3867-3875
Publish at: 2025-08-01
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