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28,451 Article Results

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

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

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

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

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

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

Optimized reactive power management system for smart grid architecture

10.11591/ijece.v15i4.pp3707-3716
Manju Jayakumar Raghvin , Manjula R. Bharamagoudra , Ritesh Dash
The Indian power grid is an extensive and mature power system that transfers large amounts of electricity between two regions linked by a power corridor. The increased reliance on decentralized renewable energy sources (RESs), such as solar power, has led to power system instability and voltage variations. Power quality and dependability in a smart grid (SG) setting can be enhanced by the careful tracking and administration of solar energy generated by panels. This study proposes a number of reactive power regulation algorithms that take smart grids into account. When developing a kernel, debugging is a must in optimal reactive power management. In this research, a debugging primitive called physical memory protection (PMP), a security feature, is considered. Debugging in the kernel domain requires specialized tools, in contrast to the user space where we have kernel assistance. This research proposes an optimal reactive power management in smart grid using kernel debugging model (ORPM-SG-KDM) for managing the reactive power efficiently. This research achieved 98.5% accuracy in kernel debugging and 99.2% accuracy in optimal reactive power management. Kernel debugging accuracy is increased by 1.8% and 3% of reactive power management accuracy is increased.
Volume: 15
Issue: 4
Page: 3707-3716
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

A mixed-methods study on the impact of experiential financial education on Chinese students’ financial literacy and wellbeing

10.11591/ijere.v14i4.32906
Yani Zhou , Muhammad Hussin , Mohamad Zuber Abd Majid
This study examines the impact of experiential financial education on financial literacy and wellbeing among Chinese university students. In response to the growing demand for practical financial skills in China, where traditional financial education often focuses on theory, a mixed-methods approach with a sequential explanatory design was used. The quantitative phase employed a quasi-experimental method, involving an experimental group (EG) receiving experiential education and a control group (CG) following traditional methods, each with 60 students recruited through purposive sampling. Data on financial literacy and wellbeing were collected via pre- and post-intervention questionnaires. To gain deeper insights, qualitative interviews were conducted with nine students using voluntary sampling. The quantitative results showed that the EG significantly outperformed the CG in financial literacy (knowledge, behavior, and attitude) and financial wellbeing (security, resilience, control, and freedom of choice). Qualitative findings reinforced these results, revealing themes such as the practical application of financial knowledge, increased confidence, higher engagement, and the relevance of lessons to real-world financial challenges. Overall, the study underscores the importance of incorporating experiential learning into financial education to boost literacy and wellbeing, providing valuable insights for regions where practical financial education is still evolving.
Volume: 14
Issue: 4
Page: 2878-2890
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

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

Design and implementation of VR lato-lato STEAM in engineering education science

10.11591/ijere.v14i4.32206
Septian Rahman Hakim , Didik Hariyanto , Nadi Suprapto , Yulkifli Yulkifli , Hafizhuddin Zul Fahmi , Khoirun Nisa , Iqbal Ainur Rizki
This research focuses on integrating science, technology, engineering, art, and mathematics (STEAM) and virtual reality (VR) in engineering-science education through a lato-lato game (clackers ball). The research analyzes the development trends of VR, particularly the integration of the lato-lato game in STEAM education. Using a waterfall model, the research develops and tests VR-STEAM applications, involving analysis, design, development, and testing phases, with expert validation and student implementation. Data was collected from 60 Indonesian university students and analyzed using partial least squares structural equation modeling (PLS-SEM). Over the past 5 years, VR in education has surged, enhancing learning outcomes and student engagement. The study shows that integrating technology and pedagogy is crucial for effective STEAM education. The design of VR-STEAM is validated and ready for use. PLS-SEM analysis indicates that the indicators are valid and reliable for measuring university students’ experiences. The study reveals that integrating STEAM with VR significantly enhances engineering-science learning (ESL), focusing on understanding, personal goals, communication, and cooperation. VR serves as a mediator that fosters 21st-century skills. The research recommends developing assessment tools to measure VR-STEAM ethnoscience and evaluate effectiveness, highlighting the importance of technology in improving learning and connecting concepts to real-world phenomena like lato-lato.
Volume: 14
Issue: 4
Page: 2749-2761
Publish at: 2025-08-01

A routine immunization decision support system framework for vaccine demand forecasting in the city health office

10.11591/ijict.v14i2.pp625-635
Mariannie A. Rebortera , Jovito P. Bolacoy Jr. , Jovanne Alejandrino , Mark Ronald S. Manseguiao
“Immunization” has been documented as one of the most flourishing measures for community well-being ever devised. Management of “immunization” information will ensure that children and newborns receive immunization on schedule. However, managing this immunization information is done manually. Customary data processing method are timeintensive, lengthy, slow in progress and susceptible to inaccuracies during encoding, verification, and re-ordering. In this study, a web-based routine immunization decision support system (RIDSS) was conceptualized to address these challenges. The web-based system is an innovative tool designed to streamline vaccine demand forecasting within the city health office (CHO) of Panabo. This system uses time series analysis and machine learning models to output accurate predictions of future vaccination demand. Using historical data on the performance of routine immunization (RI), it allows identification and analysis of actionable signals to facilitate betterinformed decisions with respect to vaccine procurement, distribution and allocation. The system is a substantial improvement of the current basic vaccine supply management, making it possible for Panabo CHO to have an organized program in administering immunization. Key stakeholders identified were presented with the prototype of system to assure effectiveness and utility. An act of major recognition to the system and its relevance in community health.
Volume: 14
Issue: 2
Page: 625-635
Publish at: 2025-08-01

Assessing the knowledge and practices of internet of things security and privacy among higher education students

10.11591/ijece.v15i4.pp4074-4086
Aigul Adamova , Tamara Zhukabayeva , Makpal Zhartybayeva , Laula Zhumabayeva
When multiple internet of things (IoT) devices interact, there are risks of privacy breaches, personal data leaks, various attacks, and device manipulation. Security is one of the most important technological research problems that currently exist for the IoT. The main purpose of the present paper is to determine the level of awareness of university students about existing security issues when using IoT devices. The paper presented the methodology of the survey. A questionnaire was developed covering four areas, such as fact-finding about general concepts of the IoT, security measures when using IoT devices, security threats and the presence of vulnerabilities of IoT devices, general policies, practices and shared responsibilities. A methodology for calculating the Awareness Level Index is proposed. This study has potential limitations. The effect estimates in the model are based on a survey of undergraduate and master’s degree students in “Computer Science” and “Software Engineering” within several universities. A total of 370 undergraduate and master’s students participated in the survey. The data processing resulted in the development of recommendations and suggested measures. This study will be useful for both stakeholders and researchers to develop effective strategies and make informed decisions.
Volume: 15
Issue: 4
Page: 4074-4086
Publish at: 2025-08-01
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