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31,456 Article Results

Ensemble model-based arrhythmia classification with local interpretable model-agnostic explanations

10.11591/ijai.v14.i3.pp2012-2025
Md. Rabiul Islam , Tapan Kumar Godder , Ahsan Ul-Ambia , Ferdib Al-Islam , Anindya Nag , Bulbul Ahamed , Nujhut Tanzim , Md. Estiak Ahmed
Arrhythmia can lead to heart failure, stroke, and sudden cardiac arrest. Prompt diagnosis of arrhythmia is crucial for appropriate treatment. This analysis utilized four databases. We utilized seven machine learning (ML) algorithms in our work. These algorithms include logistic regression (LR), decision tree (DT), extreme gradient boosting (XGB), K-nearest neighbors (KNN), naïve Bayes (NB), multilayer perceptron (MLP), AdaBoost, and a bagging ensemble of these approaches. In addition, we conducted an analysis on a stacking ensemble consisting of XGB and bagging XGB. This study examines various arrhythmia detection techniques using both a single base dataset and a composite dataset. The objective is to identify the optimal model for the combined dataset. This study aims to evaluate the efficacy of these models in accurately categorizing normal (N) and abnormal (A) heartbeats as binary classes. The empirical findings demonstrated that the stacking ensemble approach exhibited superior accuracy when used with the combined dataset. Arrhythmia classification models rely on this as a crucial component. The binary classification achieved an accuracy of 98.61%, a recall of 97.66%, and a precision of 97.77%. Subsequently, the local interpretable model-agnostic explanations (LIME) technique is employed to assess the prediction capability of the model.
Volume: 14
Issue: 3
Page: 2012-2025
Publish at: 2025-06-01

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

Knowledge, attitude, and practice among the Indian population regarding COVID-19 using LASI-DAD COVID-19 data

10.11591/ijphs.v14i2.23836
Ala Saritha , Rohit Jangra , Lekha Bhat , Neena Elezebeth Philip
During the COVID-19 pandemic, India was one of the worst-hit countries in terms of the number of cases and deaths. Knowledge, attitude, and practice play an important role in reducing disease transmission. This study uses a nationally represented large data set to understand the knowledge, attitude, and practice (KAP) of COVID-19 among the Indian population. This cross-sectional study utilized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) COVID-19 data which provides information on respondents’ demographics, socioeconomic effects, health status, behaviour, perceptions, and attitudes toward COVID-19. Descriptive statistics, correlation, and regression were performed to find the results. Out of 3,797 respondents, 1,929 (50.8%) accounted for males and 1,868 (49.2%) for females; rural respondents were 40.3% and urban were 59.7%. The findings show that the respondent’s knowledge about COVID-19 was improved from rounds 1 to 4 and 7, but attitude from round 2 to round 8 and behaviour from round 1 to round 9 were poor. In multivariate analysis, males (AOR=1.855; CI=1.129-3.048; p=0.015) and people residing in urban areas (AOR=1.698; CI=1.050-2.745; p=0.031) had a good level of knowledge towards COVID-19 when compared to their females, and rural counterparts. Despite a good level of knowledge about COVID-19 among the Indian population, attitudes and practices towards COVID-19 were poor. There is a need to establish and implement effective policies and interventions to improve people’s behaviour towards COVID-19 and similar pandemics that the world might encounter in the future.
Volume: 14
Issue: 2
Page: 634-642
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

Enhancing plagiarism detection using data pre-processing and machine learning approach

10.11591/ijai.v14.i3.pp1940-1950
Vrushali Bhuyar , Sachin N. Deshmukh
Modern technology and the internet have enhanced academic information accessibility, but this has led to a rising global concern about plagiarism. Researchers are actively exploring machine learning as a promising solution for detection. This study underscores the importance of robust data preprocessing for optimal machine learning algorithm performance. Using a dataset of 67 research papers, big five factors (OCEAN), and plagiarism rates, the study employed machine learning to detect plagiarism. The training process involved exposing algorithms to an 80% training subset, followed by evaluating their performance on the remaining 20% in the testing phase, assessing generalization capabilities. For the random forest regressor, bagging regressor, gradient boosting regressor, XGB regressor, and AdaBoost regressor, corresponding root mean squared error (RMSE) are 9.48, 10.66, 11.79, 12.53, and 12.79, respectively. This research contributes novel insights to existing literature by introducing a plagiarism detection model that innovatively integrates outlier detection, normalization, missing value imputation, and feature selection. The unique aspect lies in the effective combination of feature selection and missing value imputation, surpassing previous benchmarks and optimizing precision and efficiency. The approach is metaphorically likened to assembling puzzle pieces, highlighting the distinctive methodology employed in enhancing the performance of the plagiarism detection model using data preprocessing.
Volume: 14
Issue: 3
Page: 1940-1950
Publish at: 2025-06-01

Enhancing Alzheimer’s disease diagnosis through metaheuristic feature selection and advanced classification techniques

10.11591/ijece.v15i3.pp3382-3395
Arar Al-Tawil , Worood Al-Muhtaseb , Laiali Almazaydeh , Hanaa Fathi
A diverse array of diagnostic and detection methods has been developed as a result of the advent of Alzheimer’s disease (AD) as a significant global health issue. This study employs bio-inspired algorithms, such as the parrot optimization algorithm (POA), grey wolf optimizer (GWO), and differential evolution (DE), to identify the most effective feature selection techniques for AD diagnosis. The predictive accuracy of these algorithms was improved by the simple keywords: Alzheimer’s disease optimization classification machine learning metaheuristic mentation of the Alzheimer’s disease Dataset. This was achieved by integrating a personalized fitness function and optimizing parameter settings with decision tree classifiers. To evaluate the algorithms’ effectiveness in machine learning models with population sizes of 30 and 60, precision, recall, accuracy, and F1-score were evaluated at 5, 15, and 30 iterations. The gradient boosting and XGBoost classifiers consistently obtained the highest results, while DE, GWO, and parrot optimization (PO) achieved maximal accuracy rates of 0.94, 0.93, and 0.94, respectively. These findings underscore the efficacy of integrating metaheuristic algorithms with robust classifiers to enhance the predictive accuracy of AD diagnosis. Furthermore, they illustrate that artificial intelligence (AI) algorithms that are operated by biological processes can accurately forecast AD, with the success rates and stability of the proposed methods serving as metrics for evaluating their efficacy.
Volume: 15
Issue: 3
Page: 3382-3395
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

Effectiveness of artificial intelligence-driven chatbot responses in diabetes knowledge: a readability and reliability assessment

10.11591/ijai.v14.i3.pp2379-2388
Muhammad Thesa Ghozali , Woro Supadmi , Fadya Bella Suci Maharani , Aloina Jean Rassyifa
Patient education is vital in diabetes management, empowering patients with necessary knowledge and skills to manage condition effectively. However, traditional educational methods often face challenges such as limited access to healthcare professionals and variability in information quality. This study aimed to assess the reliability and readability of artificial intelligence (AI)-driven chatbot responses in disseminating diabetes knowledge. Technically, the diabetes knowledge questionnaire (DKQ-24) was administered to evaluate the effectiveness of AI-driven chatbot in disseminating diabetes-related information. Responses were evaluated for reliability and quality applying the modified DISCERN (mDISCERN) scale and global quality scale (GQS), and readability was assessed using the Flesch reading ease (FRE) score, Flesch-Kincaid grade level (FKGL), gunning fog index (GFI), Coleman-Liau index (CLI), and simple measure of gobbledygook (SMOG). The mean mDISCERN score was 31.50±2.89, indicating generally reliable responses. The median GQS score was 4, reflecting the high overall quality. The readability assessment revealed a mean FRE score of 66.30, indicating that the text was fairly easy to read. FKGL mean score was 6.54±3.19, suggesting the text was suitable for readers at a sixth-grade level. In conclusion, AI-driven chatbot provides reliable and high-quality information on the diabetes self-management, but it requires improvements to enhance accessibility.
Volume: 14
Issue: 3
Page: 2379-2388
Publish at: 2025-06-01

The influence of sentiment analysis in enhancing early warning system model for credit risk mitigation

10.11591/ijai.v14.i3.pp1829-1838
Angel Karentia , Derwin Suhartono
One important source of bank income is interest income from credit activities, another part of which is obtained from fee-based income. Rapid credit growth is directly proportional to an increase in potential credit risk (counterparty default). In addition to comprehensive credit assessment at the initial stage of credit initiation, banks need to monitor the condition of existing debtors. Empirically, difficulties in handling non-performing loans often occur due to delays in detection and preparation of action plans. In this case, losses due to non-performing loans can have implications for the bank's reputation and worsen its financial performance. This research aims to determine the effect of sentiment analysis (external sentiment prediction model [positive, neutral, and negative] with certain keywords) on the level of accuracy of the early warning system (EWS) model in predicting the credit quality of bank debtors in the coming months. This study found that upgrading EWS with sentiment analysis will give better accuracy levels compared to traditional EWS models. In addition, the predictive power of EWS (traditional and upgraded) is inversely proportional to the prediction period, the longer the target prediction time, and the less predictive power of the EWS model.
Volume: 14
Issue: 3
Page: 1829-1838
Publish at: 2025-06-01

Investigation of microwave absorption performance of anti-radiation plastic cement brick

10.11591/ijece.v15i3.pp2900-2910
Linda Mohd Kasim , Hasnain Abdullah Idris , Mohd Nasir Taib , Norhayati Mohamad Noor , Azizah Ahmad , Noor Azila Ismail , Nazirah Mohamat Kasim , Nur Qaisarah Anuar
The increasing demand for effective anti-microwave radiation materials motivates the exploration of sustainable and eco-friendly alternatives. This research investigates the microwave absorption properties of various brick compositions, including commercial brick (CB) and solid bricks (SB1, SB2 and SB3) incorporating recycled materials, polyethylene terephthalate (PET) and palm oil fuel ash (POFA). The dimension of the developed brick is 200×100×60 mm (length×width×height). The absorption performance of the bricks was measured in 100 mm and 60 mm thickness across the frequency range of 1 to 12 GHz using the naval research laboratory (NRL) free space arch method. At 100 mm thickness, SB3 shows the highest absorption up to-32.2061 dB at 1.98 GHz. At 60 mm thickness, SB1 achieved the maximum absorption at -57.6511 dB at 2.505 GHz. SB2 shows consistent average absorption performance at 15.2064 dB at 100 mm thickness and -19.5 dB at 60 mm thickness respectively. The compressive strength of the brick was measured, and it was shown that SB2 exhibited the highest average compressive strength of 7.17 MPa. Considering the standard wall thickness and brick strength, SB2 shows the most effective performance due to its enhanced composition and consistent performance across frequencies.
Volume: 15
Issue: 3
Page: 2900-2910
Publish at: 2025-06-01

Artificial intelligence model for the prediction of cannabis addiction

10.11591/ijphs.v14i2.25786
Abdelilah Elhachimi , Mohamed Eddabbah , Abdelhafid Benksim , Ibanni Hamid , Mohamed Cherkaoui
A novel approach for predicting cannabis addiction has been introduced by integrating combined machine learning (ML) algorithms, specifically K-means clustering and linear regression (LR). The study, conducted in Marrakech, Morocco, at a center linked to the National Association for drug-risk reduction (DRR), involved 146 participants. Among those with prior cannabis use, one subgroup included passive users, while another exhibited cannabis dependence. The research utilised features derived from patient data, emphasising psycho-cognitive state, addiction status, and socio-demographic factors. The goal was to evaluate the effectiveness of the combined ML algorithms (K-means + LR) in distinguishing between addicted and non-addicted individuals using real-world data from a primary care addiction center. The findings indicate that the proposed method delivers balanced results, achieving an overall accuracy of 70%, a sensitivity of 65%, and a specificity of 86%. These results are particularly noteworthy when compared to other ML studies in addiction research. The combined algorithm demonstrates promising potential with competitive accuracy and high specificity. Further efforts to improve sensitivity and validate the model in diverse settings will be essential for advancing predictive modeling in this field. Our findings contribute to existing research by developing simple and effective tools for early detection of cannabis addiction, potentially aiding in the creation of preventive and therapeutic strategies to reduce its prevalence.
Volume: 14
Issue: 2
Page: 1076-1087
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

Energy consumption prediction methods in a cyber-physical system

10.11591/ijece.v15i3.pp3054-3063
Kenzhegali Nurgaliyev , Akylbek Tokhmetov
In recent decades, cyber-physical systems (CPS) have become an essential part of modern industry and daily life. These systems integrate physical processes with computer and network components, allowing them to interact with their environment and manage their components autonomously. One of the most significant aspects of CPS efficiency is managing energy consumption, which significantly affects their reliability, efficiency, and economic performance. CPS devices generate vast amounts of diverse data, which is crucial to accurately model. Researchers use predictive analysis to develop models that forecast trends and simulate real-world conditions, enabling them to make better-informed decisions. This article presents a comparative analysis of different predictive models for CPS data analytics, focusing on energy consumption in smart buildings. Short-term models include gradient-boosted regressor (XGBoost), random forest (RF) and long short-term memory (LSTM). The comparative results have been studied in terms of prediction errors to determine accuracy.
Volume: 15
Issue: 3
Page: 3054-3063
Publish at: 2025-06-01

Smart chatbot for surveys by convolutional networks speech recognition

10.11591/ijece.v15i3.pp3410-3417
Robinson Jimenez-Moreno , Javier Eduardo Martínez Baquero , Luis Alfredo Rodriguez Umaña
This paper details the development of an innovative voice chatbot interface specifically designed for evaluating user options using a Likert scale by color. The core of this interface is designing a convolutional neural network architecture, which has been trained with MEL spectrogram inputs from seven possible words for each answer. These spectrograms are crucial in capturing the audio features necessary for effective voice recognition and establishing the interactions that occur between the chatbot and the user, allowing the convolutional network to learn and distinguish between different types of user responses accurately. During the training phase, the convolutional neural network achieved an accuracy rate of 91.4%, indicating its robust performance in processing and interpreting voice commands. The interface was tested in a controlled environment, with a group of ten users and a survey of 5 questions, where it achieved a perfect detection accuracy of 100%. The results demonstrate the system's capacity for natural user interaction by voice and employing a free text to speech (TTS) algorithm for the chatbot voice.
Volume: 15
Issue: 3
Page: 3410-3417
Publish at: 2025-06-01

Augmented reality learning media for electrical motor: case study in electrical engineering education

10.11591/ijece.v15i3.pp2545-2555
Bima Mustaqim , Abdul Muin Sibuea , Sahat Siagian , Agus Junaidi , Muhammad Amin , Sriadhi Sriadhi , Wan Ahmad Jaafar Wan Yahaya , Harun Sitompul
The impact of augmented reality (AR) learning tools and students' critical thinking abilities on learning outcomes in electrical engineering education is the focus of this study. The study explores the ways in which these factors, both independently and in combination, influence student performance. Findings reveal that AR-based learning materials significantly enhance understanding and retention across self-directed and guided learning models. Critical thinking skills emerge as a key determinant of success, with students exhibiting strong critical thinking consistently outperforming peers with lower-level skills, regardless of the instructional model. The study also highlights variations in AR tools' effectiveness depending on the learning model and students’ critical thinking abilities. Guided learning with AR tools benefits all students, while self-directed AR tools prove most effective for those with advanced critical thinking skills. Students with lower critical thinking abilities face challenges in navigating less structured AR environments. These results underscore the importance of fostering critical thinking and adopting tailored strategies when integrating AR technology into engineering education. By considering both the learning model and critical thinking levels, educators can optimize AR’s potential to enhance student learning outcomes in technical fields.
Volume: 15
Issue: 3
Page: 2545-2555
Publish at: 2025-06-01
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