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28,451 Article Results

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

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

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

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

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

Revolutionizing internet of things intrusion detection using machine learning with unidirectional, bidirectional, and packet features

10.11591/ijai.v14.i4.pp3047-3062
Zulhipni Reno Saputra Elsi , Deris Stiawan , Bhakti Yudho Suprapto , M. Agus Syamsul Arifin , Mohd. Yazid Idris , Rahmat Budiarto
Detection of attacks on internet of things (IoT) networks is an important challenge that requires effective and efficient solutions. This study proposes the use of various machine learning (ML) techniques in classifying attacks using unidirectional, bidirectional, and packet features. The proposed methods that implement decision tree (DT), random forest (RF), extreme gradient boosting classifier (XGBC), AdaBoost (AB) and linear discriminant analysis (LDA) work perfectly with all kinds of datasets and includes. It also works very well with data type-based feature selection (DTBFS) and correlation-based feature selection (CBFS). The experiment results show a significant improvement compared to previous studies and reveals that unidirectional and bidirectional features provide higher accuracy compared to packet features. Furthermore, ML models, particularly DT, and RF, have faster computing times compared to more complex deep learning models. This analysis also shows potential overfitting in some models, which requires further validation with different datasets. Based on these findings, we recommend the use of RF and DT for scenarios with unidirectional and bidirectional features, while AB and LDA for packet features. The study concludes that using the right ML techniques along with features that work in both directions can make an intrusion detection system for IoT networks becomes very accurate.
Volume: 14
Issue: 4
Page: 3047-3062
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

Contract-based federated learning framework for intrusion detection system in internet of things networks

10.11591/ijai.v14.i4.pp3324-3333
Yuris Mulya Saputra , Divi Galih Prasetyo Putri , Jimmy Trio Putra , Budi Bayu Murti , Wahyono Wahyono
A plethora of national vital infrastructures connected to internet of things (IoT) networks may trigger serious data security vulnerabilities. To address the issue, intrusion detection systems (IDS) were investigated where the behavior and traffic of IoT networks are monitored to determine whether malicious attacks or not occur through centralized learning on a cloud. Nonetheless, such a method requires IoT devices to transmit their local network traffic data to the cloud, thereby leading to data breaches. This paper proposes a federated learning (FL)-based IDS on IoT networks aiming at improving the intrusion detection accuracy without privacy leakage from the IoT devices. Specifically, an IoT service provider can first motivate IoT devices to participate in the FL process via a contract-based incentive mechanism according to their local data. Then, the FL process is executed to predict IoT network traffic types without sending IoT devices’ local data to the cloud. Here, each IoT device performs the learning process locally and only sends the trained model to the cloud for the model update. The proposed FL-based system achieves a higher utility (up to 44%) than that of a non-contract-based incentive mechanism and a higher prediction accuracy (up to 3%) than that of the local learning method using a real-world IoT network traffic dataset.
Volume: 14
Issue: 4
Page: 3324-3333
Publish at: 2025-08-01

Development and evaluation of a smart home energy management system using internet of things and real-time monitoring

10.11591/ijece.v15i4.pp3977-3985
Mohamed Imran Mohamed Ariff , Nur Anim Abdul Halim , Mohammad Nasir Abdullah , Samsiah Ahmad , Masurah Mohamad , Anis Zafirah Azmi
This project presents the design and implementation of a smart home energy management system using internet of things (IoT) technology to optimize household energy consumption. The system integrates various sensors, including passive infrared (PIR), light dependent resistor (LDR), and DHT11, to collect real-time environmental data, which is processed by a NodeMCU microcontroller. The microcontroller controls home appliances using relays, while the Blynk mobile app and Streamlit web platform provide users with remote monitoring and control capabilities. Despite successfully optimizing energy usage, the system faces limitations such as high sensor sensitivity and potential hazards during high-load power demonstrations. To address these issues, future work proposes integrating additional sensors for improved accuracy and incorporating renewable energy sources for increased sustainability. This project aims to enhance energy efficiency, provide users with greater control over their energy consumption, and contribute to smart home automation by utilizing real-time data, IoT integration, and user-friendly interfaces.
Volume: 15
Issue: 4
Page: 3977-3985
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

Challenges of recommender systems in finance and banking: a systematic review

10.11591/ijai.v14.i4.pp2559-2567
Lossan Bonde , Abdoul Karim Bichanga
Recommender systems are widely applied in various domains, including e-commerce, marketing, and education. Despite their popularity, recommender systems are not widely used in finance and banking. This paper aims to identify the challenges associated with using recommender systems in finance and banking and recommend directions for future research. Using a systematic literature review (SLR) method, 52 papers were selected and analyzed. A three-step process was used to make the selection. First, a keyword search was made to identify a seed list of sources. A snowball technique with specific inclusion and exclusion criteria was applied to expand the list. Finally, a quick study was made to produce the final list of sources to consider. Through the study of the 52 relevant papers, three main challenges: i) transparency, ethics, and data privacy; ii) handling complex content information and accounting for multiple user behaviors; and iii) explainability of AI models were identified. This study has established the barriers to adopting recommender systems in the finance and banking industry. Specific subjects of concern identified include cold-start problems, personalization, fraud detection, transparency, and data privacy. The study recommends further research leveraging advanced machine learning models and emerging technologies to fill the gap.
Volume: 14
Issue: 4
Page: 2559-2567
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

Design strategies for solar photovoltaic integration in rural areas

10.11591/ijece.v15i4.pp3603-3612
Intan Mastura Saadon , Emy Zairah Ahmad , Nurbahirah Norddin , Norain Idris
This study explores the optimization of photovoltaic (PV) systems in the Sungai Tiang Camp region, Malaysia, with a focus on determining the ideal tilt angles to maximize energy generation in a tropical environment while incorporating a cost analysis. While existing studies optimize tilt angles for energy maximization in temperate regions, this study addresses the unique climatic and socio-economic conditions of rural Malaysia. Unlike fixed-tilt assumptions common in prior work, this research explores cost-effective, manually adjustable systems tailored for local weather patterns and rural affordability. To address this, the study examines the relationship between tilt angle, solar irradiance, temperature and output power. The results are analyzed to identify optimal configurations. Results reveal that tilt angles between 5° and 10° deliver the highest energy output, with slight seasonal adjustments for efficiency improvement. These findings align with Malaysia's tropical solar profile, offering practical insights for micro-scale solar deployments in similar climates. By addressing the unique needs of remote areas, this research contributes to bridging the gap in localized PV studies. Its outcomes not only enhance the understanding of solar PV performance in tropical conditions but also provide valuable guidelines for rural electrification and sustainable energy solutions in equatorial regions worldwide.
Volume: 15
Issue: 4
Page: 3603-3612
Publish at: 2025-08-01
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