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31,456 Article Results

Enhancing cybersecurity awareness strategies in organization using Delphi technique

10.11591/ijece.v15i3.pp2986-2997
Anawin Kaewsa-Ard , Nattavee Utakrit
Cybersecurity concerns were once primarily perceived as technical issues, prompting many organizations to prioritize investments in security technologies. However, it has become increasingly evident that cybersecurity is not solely a technical matter. In fact, a significant number of cybersecurity breaches arise from users' lack of awareness about secure technological practices. This research aims to develop a cybersecurity awareness strategy using the Delphi technique over three rounds, involving 15 cybersecurity experts. The findings indicate a consensus among experts that cybersecurity awareness training is an effective strategy to enhance an organization's overall cybersecurity posture. However, the true essence of cybersecurity lies in fostering secure technology usage practices among all users within the organization. To address this, the researcher developed systematic training content for cybersecurity awareness, which was evaluated and refined by experts using the Delphi technique to ensure its effectiveness in promoting genuine cybersecurity awareness.
Volume: 15
Issue: 3
Page: 2986-2997
Publish at: 2025-06-01

Exploring patient-patient interactions graphs by network analysis

10.11591/ijai.v14.i3.pp1752-1762
Zaher Salah , Esraa Abu Elsoud , Kamal Salah
Understanding how patient demographics and shared experiences impact interactions is essential for strengthening pa/tient support networks and optimizing health outcomes as personalized healthcare becomes more and more important. To this end, this study explores the patient-patient interactions (PPIs) graph as a network and applies selected network analysis approaches to examine the PPIs network of accutane drug. Two main research questions are addressed by gaining deeper insight at the hidden patterns of reactivity and connectivity among interchanging nodes. There was a negative response to the first research question, which asked if patients react to others that have similar gender and/or age profiles in a consistent way. Patients tended to interact with people of different genders and ages, indicating a high degree of heterogeneity in the network. Negative responses were likewise given to the second research question, which asked if communities inside the network could identify patients based on gender or age profile. Network analysis approaches for community detection failed to distinguish between groups with similar demographic characteristics. Rather, groups seemed to emerge based on other factors, like similarity in patient opinions. The results imply that gender and age do not have a major influence on community membership. Future research will concentrate on applying more sophisticated graph mining techniques to expand these approaches to cover more and larger PPIs networks.
Volume: 14
Issue: 3
Page: 1752-1762
Publish at: 2025-06-01

LMS bot: enhanced learning management systems for improved student learning experiences using robotic process automation

10.11591/ijai.v14.i3.pp2044-2054
Mamidyala Durga Prasad , Nandini Balusu
In this paper, a workflow for bot is designed using robotic process automation (RPA) that is used to enhance learning management systems (LMS) by providing content from external sources along with educator made course content for better student learning experiences. Many students prefer to watch YouTube videos for learning, even if they have been taught the same content by an educator. YouTube is a dynamic platform where video rankings change based on viewer engagement, relevance, and newly included videos. This variability poses a challenge for educators seeking to include external videos, as the content environment within the LMS platform is unpredictable and can change significantly. The bot addresses the challenge by conducting periodic searches for related courses and topics on YouTube. It retrieves top-ranked videos based on relevance, which are then seamlessly integrated into external links within LMS. The LMS external links option enhances accessibility by offering videos sorted by popularity, ensuring students receive updated and relevant information seamlessly. The bot efficiently retrieves details of 750 videos from YouTube in just 17 seconds, showcasing its exceptional performance. Moreover, its capability to autonomously update LMS external links content weekly represents an added advantage. The bot is designed and tested using UiPath tool.
Volume: 14
Issue: 3
Page: 2044-2054
Publish at: 2025-06-01

A systematic review on software code smells

10.11591/ijece.v15i3.pp3010-3027
Mohammed Ghazi Al-Obeidallah , Dimah Al-Fraihat
This paper provides a systematic review of code smell detection studies published from 2001 to 2023, addressing their significance in identifying underlying issues in software systems. Through stringent inclusion criteria, 116 primary studies were analyzed, focusing on various aspects such as publication venue, code smell categories, subject systems, supported programming languages, evaluation criteria, and detection techniques. The analysis reveals that 50% of the papers were conference proceedings, with 80% utilizing Java-supported techniques and commonly used subject systems like Apache Xerces, GanttProject, and ArgoUML. Metrics-based methods (33%) and search-based approaches (32%) were predominantly employed, with machine learning emerging in 20% and rule-based methods in 15% of the studies. Notably, recent studies have shown an increased adoption of machine learning techniques. The identified code smells include god class, feature envy, long method, and data class, with precision and recall being the most commonly used evaluation metrics. This review aims to inform future research directions and aid the software engineering community in developing novel detection techniques to enhance code quality and system reliability.
Volume: 15
Issue: 3
Page: 3010-3027
Publish at: 2025-06-01

Camera-based advanced driver assistance with integrated YOLOv4for real-time detection

10.11591/ijai.v14.i3.pp2236-2245
Keerthi Jayan , Balakrishnan Muruganantham
Testing object detection in adverse weather conditions poses significant chal lenges. This paper presents a framework for a camera-based advanced driver assistance system (ADAS) using the YOLOv4 model, supported by an electronic control unit (ECU). The ADAS-based ECU identifies object classes from real-time video, with detection efficiency validated against the YOLOv4 model. Performance is analysed using three testing methods: projection, video injection, and real vehicle testing. Each method is evaluated for accuracy in object detection, synchronization rate, correlated outcomes, and computational complexity. Results show that the projection method achieves highest accuracy with minimal frame deviation (1-2 frames) and up to 90% correlated outcomes, at approximately 30% computational complexity. The video injection method shows moderate accuracy and complexity, with frame deviation of 3-4 frames and 75%correlated outcomes. The real vehicle testing method, though demand ing higher computational resources and showing a lower synchronization rate (> 5 frames deviation), provides critical insights under realistic weather condi tions despite higher misclassification rates. The study highlights the importance of choosing appropriate method based on testing conditions and objectives, bal ancing computational efficiency, synchronization accuracy, and robustness in various weather scenarios. This research significantly advances autonomous ve hicle technology, particularly in enhancing ADAS object detection capabilities in diverse environmental conditions.
Volume: 14
Issue: 3
Page: 2236-2245
Publish at: 2025-06-01

Support group model for Vietnamese people affected by substance abuse and HIV/AIDS

10.11591/ijphs.v14i2.25129
Loan Hoi Nguyen , Minh Thanh Bui , Trang Thu Nguyen
As commonly seen in East and Southeast Asia, one of the major responses of the Vietnamese government to substance abuse is compulsory drug rehabilitation, which was found not very effective with a high relapse rate. Since 2000s, many people affected by substance abuse (PASA) in Vietnam started actively seeking help from group models in Buddhist temples for their substance abuse and HIV/AIDS problems. This case study aimed to examine the philosophical foundation, theoretical mechanism, implementation, and tentative outcomes of the group work model with the PASA at a Buddhist temple in the North of Vietnam. This qualitative approach study collected data from participation observation, reports collected from the temple, and 14 in-depth interviews with two monks, two rehabilitation therapists, and 10 PASA, mostly narcotic addicts, who participated in the group model in the temple. Results of the study show that PASA were self-motivated to voluntarily participate and maintain their membership in the group model. The head monk/master and his fellow monks played the role of the para-interventionists to provide comprehensive care for PASA members using Buddhist core values and philosophy in its approach. Both the para-interventionists and group members perceived their relationship with each other as equal, respectful, supportive, and therapeutic. The results provide implications for the development of new intervention models that are culturally appropriate in the unique sociocultural context of Vietnam and other countries that shared similar religious influences.
Volume: 14
Issue: 2
Page: 729-739
Publish at: 2025-06-01

Optimizing rice leaf disease classification through convolutional neural network architectural modification and augmentation techniques

10.11591/ijece.v15i3.pp3429-3438
Mohamad Firdaus , Kusrini Kusrini , I Made Artha Agastya , Rodrigo Martínez-Béjar
This research focuses on advancing the accuracy of rice leaf disease classification through the integration of convolutional neural network (CNN) and deep learning models. With Indonesia ranking third in global rice production, effective crop management is crucial for sustaining agricultural output. This study employs innovative data augmentation techniques, including random zoom and others, to enhance model training robustness. The experimentation involves eight scenarios with varied architectural configurations applied to a residual network-50 (ResNet50) layers model, aiming to optimize disease classification performance. Featuring random zoom without the multilayer perceptron (MLP) component, emerges as the most effective, demonstrating superior accuracy and performance metrics. A grid search is conducted to optimize MLP layers, revealing a three-layer configuration as most effective. We found that the data augmentation and MLP layer can increase the accuracy of the disease classification task. The method proposed in this study is likely to have a much higher proportion of correct disease classification by combining MLP and zoom augmentation. Specifically, the model with three MLP layers and zoom augmentation demonstrated significantly higher accuracy, achieving a test accuracy, precision, recall, and F1-score of 0.92, 0.94, 0.92, and 0.92, respectively.
Volume: 15
Issue: 3
Page: 3429-3438
Publish at: 2025-06-01

Millet consumption in type 2 diabetics in urban slums of India: a pilot study

10.11591/ijphs.v14i2.25422
Pooja Sohil , Sudhanshu Mahajan , Rupeshkumar Deshmukh , Jayashree Gothankar , Saibal Adhya
Diabetes is a leading cause of death and disability worldwide, affecting people regardless of their country, age group, or sex. Considering the growing prevalence of diabetes among low socioeconomic groups in developing countries like India, the present study aims to determine the prevalence of millet consumption in urban slums in Pune, India. A pilot cross-sectional study was conducted at a private medical college’s field practice area in Pune from January to March 2024. The study focused on individuals with type 2 diabetes mellitus. A pre-designed, pretested semi-structured paperless questionnaire in the Kobo tool app was used to collect information on socio-demographic information, consumption of millet, and diabetes-related information. A total of 30 type 2 diabetics were interviewed as a pilot study. Of these, 53.57% were females, and 46.43% were males. The prevalence of millet consumption was 93% in type 2 diabetics. Sorghum was the most consumed millet amongst all other millet. Age >50 years, education >10th pass, and non-alcoholics were significantly associated with satisfactory consumption of millet. Diversifying diets with nutritious foods like millet can help reduce health-related burdens, including type 2 diabetes. This is important for policy-making and prioritizing diabetes self-care interventions.
Volume: 14
Issue: 2
Page: 904-911
Publish at: 2025-06-01

The future of healthcare: exploring internet of things and artificial intelligence applications, challenges, and opportunities

10.11591/ijece.v15i3.pp3075-3083
Kamal Elhattab , Driss Naji , Abdelouahed Ait ider , Karim Abouelmehdi
The internet of things (IoT) refers to a network of physical devices embedded with sensors, software, and communication tools, which allow for seamless exchange and collection of data. This technology enables automation, continuous monitoring, and data-driven decision-making across a variety of fields. In the healthcare sector, the integration of IoT with artificial intelligence (AI) is transforming how patient care is delivered, providing real-time health monitoring, personalized treatment options, and more efficient management of healthcare resources. This study investigates the significant influence of the IoT and AI on the healthcare system, focusing on how these technologies improve patient outcomes and streamline healthcare operations. It also highlights emerging challenges in the adoption of these technologies and suggests potential solutions to address these obstacles and enhance healthcare delivery. The research is based on an in-depth review of AI and IoT applications in healthcare, uncovering advancements in patient monitoring, disease management, and operational efficiency, while also identifying key challenges such as data privacy concerns and issues with system interoperability.
Volume: 15
Issue: 3
Page: 3075-3083
Publish at: 2025-06-01

The association of sociodemographic and social capital with self-rated health: a microdata analysis of North Sumatera 2021

10.11591/ijphs.v14i2.25188
Sri Novita Lubis , Sudarto Ronoatmodjo , Evi Martha , Besral Besral
Current research suggests a possible connection between social capital and its effect on self-reported health. However, this relationship has not been extensively studied in urban and rural regions of Indonesia. This research aimed to evaluate the differences in sociodemographic factors, social capital, and self-rated health between rural and urban populations and to identify sociodemographic and social capital elements related to self-rated health in the urban and rural areas of Sumatra Utara Province, Indonesia. This study makes use of secondary data from the Happiness Level Measurement Survey Report 2021. A two-stage, one-phase sampling method was employed for sample selection. The Chi-square test was utilized to examine the distribution of factors related to respondents in both urban and rural settings. Multivariable Cox regression analysis was conducted to determine associations with self-rated health. Our findings indicate that rural inhabitants exhibit a higher rate of poor self-rated health compared to their urban counterparts (p-value<0.001). Significant differences were observed between urban and rural areas in terms of sex, education, trust, social participation, and feelings of safety. The multivariable Cox regression analysis revealed that being younger, having higher education, possessing greater trust, and engaging in high social participation was associated with a lower risk of poor self-rated health for both urban and rural residents. Consequently, trust and social participation could reduce the occurrence of poor self-rated health.
Volume: 14
Issue: 2
Page: 1068-1075
Publish at: 2025-06-01

Trend analysis of machine learning techniques for traffic control based on bibliometrics

10.11591/ijai.v14.i3.pp2402-2411
Hilda Luthfiyah , Eko Syamsuddin Hasrito , Tri Widodo , Sofwan Hidayat , Okghi Adam Qowiy
Machine learning in traffic control for intelligent transportation systems (ML-ITSTC) aims to enhance user coordination and safety within transportation networks, ultimately improving overall traffic system performance. ML-ITSTC is achieved by leveraging data to execute machine learning algorithms in intelligent transportation management and optimizing traffic flow to prevent or reduce congestion. This paper conducts bibliometric analysis to explain the research status, development trajectory, and challenges of ML-ITSTC, drawing insights from literature in the Scopus database literature covering 2013 to November 2023. The bibliometric analysis of ML-ITSTC includes: performance analysis, science mapping analysis, and citation analysis. The evaluation of ML algorithm trends over the 10-year span indicates that traffic prediction (TP), neural networks, and deep learning are frequently used keywords. Further, an examination of keywords used over the entire period and in 2023 (up to November) shows that reinforcement learning (RL) is the latest popular approach for traffic control in transportation. The results provide a comprehensive view of the opportunities and challenges in ML-ITSTC, covering data, models, and applications, offering researchers insights into the current and future directions of ML-ITSTC research.
Volume: 14
Issue: 3
Page: 2402-2411
Publish at: 2025-06-01

Epidemiological analysis of the incidence of endometrial hyperplasia in a large city of Kazakhstan

10.11591/ijphs.v14i2.24971
Imasheva Bayan Imashkyzy , Kamaliev Maksut Adilkhanovich , Lokshin Vyacheslav Notanovich , Kiselyova Marina Viktorovna , Turekhanova Aizhan Dzhambylbayevna , Jexembekova Alfiya Ernazarovna , Uteshova Malika , Saparaliyeva Aizhan Mukhtarkyzy
Endometrial hyperplasia (EH) is an abnormality of uterus, characterized by excessive proliferation of endometrium, and in case of lack of timely diagnostics and treatment, it may rapidly progress to endometrial cancer (EC). According to the World Cancer Research Fund, EC ranks 6th among all female malignancies in the world. EC takes the 3rd place in the Republic of Kazakhstan according to the state statistics. However, there is no determining statistical data on dynamics of EH incidence in our region, which will show the extent of exposure of the population to this disease. Therefore, this research provides assessment and analysis of all registered cases of EH from the Republican Center for Electronic Healtcare for the period from 2012 to 2022 for presentation of accurate and correct information on the trends of EH incidence and its histologic types, taking into account age differences of the female population of our city. The study has showed the increase of indicators of incidence of EH, especially, non-atypical endometrial hyperplasia (NAEH) and atypical endometrial hyperplasia (AEH). It was found that the peak incidence of EH occurred in the age group of 65-69 years, where the main increase was due to an increase in number of cases of NAEH. The age group of 45-49 years is at the peak of the incidence of AEH. The conducted research has identified the trends of EH incidence, reflecting population changes in the EH risk factors, and that requires their comprehensive study for development of strategies of treatment and prevention measures.
Volume: 14
Issue: 2
Page: 586-593
Publish at: 2025-06-01

Enhancing training performance for small models using data-centric approaches

10.11591/ijece.v15i3.pp2951-2964
Reda A. El-Khoribi , Eid Emary , Amr Essam Hassan
In this work, we propose a new system to improve the performance of classification models by applying data-centric principles. The system optimizes datasets by removing poor-quality samples and generating high-quality synthetic data. We tested the system on various classification models and datasets, measuring its performance with accuracy, precision, recall, and F1-score. The results showed significant improvements in classification performance, highlighting the effectiveness of this data-centric approach. While the scalability to large-scale datasets is still an open question, it offers great potential for future research. This approach could be valuable in critical areas like healthcare, finance, and autonomous systems, where high-quality data is crucial. Future work could explore advanced data augmentation, adapting the system for different data types like text and time-series, and extending it to semi-supervised and unsupervised learning. Our findings emphasize the importance of data quality in achieving better model performance, often overlooked in favor of model architecture. By advancing data-centric artificial intelligence (AI), this work offers a practical framework for researchers and practitioners to optimize datasets and improve machine learning systems.
Volume: 15
Issue: 3
Page: 2951-2964
Publish at: 2025-06-01

Do gender, age, and emotional intelligence affect the emotional regulation of adolescents involved in cyberbullying?

10.11591/ijphs.v14i2.24335
Nia Agustiningsih , Ah Yusuf , Ahsan Ahsan , Dwi Indah Iswanti , I Made Moh. Yanuar Saifudin
The objective of this study was to explore how age, gender, and emotional intelligence impact emotion regulation in adolescents participating in cyberbullying. A cross-sectional study was conducted in January 2023, involving 108 teenagers selected through purposive sampling. Data, gathered through a demographic questionnaire, Revised Cyberbullying Inventory II, and an emotional intelligence questionnaire, were analyzed using descriptive and multiple regression methods. The findings revealed that age, gender, and emotional intelligence collectively influence situation modification, accounting for a 2.52% impact (p-value=0.024). Emotional intelligence demonstrated effects on both situation modification and attentional deployment in individuals playing the roles of victims and perpetrators (p-value=0.018). In the case of adolescents acting as perpetrators, age, gender, and emotional intelligence collectively exhibited significant influence on attention deployment, contributing to a 9.83% impact (p-value=0.01). For adolescents who abstain from participating, the modulation response is simultaneously affected by age, gender, and emotional intelligence (p-value<0.001), resulting in a 4.44% influence. Notably, age, gender, and emotional intelligence were identified as factors influencing emotion regulation at various stages, depending on whether adolescents played the roles of victims, victims-perpetrators, or perpetrators. it is recommended that mental health nurses implement tailored emotion regulation interventions for adolescents involved in cyberbullying.
Volume: 14
Issue: 2
Page: 692-700
Publish at: 2025-06-01

Skin cancer diagnosis using hybrid deep pre-trained convolutional neural networks

10.11591/ijai.v14.i3.pp2291-2301
Maha Ibrahim Khaleel , Amir Lakizadeh
As a variant of skin cancer, melanoma represents a substantial menace to the health and overall well-being of individuals. Statistics reveal that 55% of skin cancer patients succumb to this particular disease. However, early detection plays a crucial role in reducing mortality rates and saving lives. In the past several decades, there has been a rise in the adoption of deep learning algorithms, capturing the interest of researchers working in this field. One popular method involves utilizing pre-trained deep neural networks. In this study, a hybrid approach is employed to extract features from melanoma images. This approach integrates the utilization of pre-trained architectures, including AlexNet, ResNet-50, and GoogleNet. During the transfer training phase, these networks are fine-tuned to detect skin cancer by adjusting the learning rate. Subsequently, the maximum relevance minimum redundancy (MRMR) algorithm is employed to select optimal features based on the concepts of minimum redundancy and maximum relevance in order to minimize feature redundancy and enhance classification accuracy. The bagging technique is employed for the classification of various skin cancer types. The experimental results demonstrate the success of the suggested approach, yielding 98.9% accuracy. Furthermore, the results indicate the superiority of this method according to precision, recall, and F1-score in comparison with existing algorithms.
Volume: 14
Issue: 3
Page: 2291-2301
Publish at: 2025-06-01
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