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29,065 Article Results

Attitude and intention to use chatbots in e-commerce: the moderating role of personal innovativeness

10.11591/ijict.v14i3.pp760-771
Indah Oktaviani Hardi , Ahmad Maki , Evi Rinawati Simanjuntak
Internet-based retailers employ artificial intelligence (AI) chatbots to facilitate customer communication. This research endeavored to evaluate consumers' intentions regarding the utilization of chatbots for customer service interactions, building upon the technology acceptance model (TAM). TAM-based chatbot adoption is the subject of an abundance of research. Conversely, the extent to which users' perception of the chatbot's response quality influences their intention to adopt remains uncertain. In addition to investigating the potential influence of chatbot response accuracy and completeness on users' intention to adopt the system, this study explored the relationship between users' personal innovativeness and adoption intention. A total of 312 usable responses were analyzed with PLS-SEM from survey data collected via convenience sampling from e-commerce customers. Perceived usefulness, convenience of use, accuracy, and completeness all influenced attitudes toward chatbots, as shown by hypothesis testing result. Attitude formation toward chatbots is most strongly influenced by perceived completeness. Personal innovativeness has a negative influence, which contradicts the hypothesis despite the fact that its moderating effect is statistically significant. Further comprehension of the key determinants of attitude towards chatbots is enhanced by these findings. It is advisable for organizations to empower the chatbot with the capability to conduct thorough and precise responses to inquiries.
Volume: 14
Issue: 3
Page: 760-771
Publish at: 2025-12-01

Critical factors shaping project-based learning in mathematics education

10.11591/ijere.v14i6.35671
Chung Xuan Pham , Hang Thi My Nguyen , Giang Thi Châu Nguyen , Lam Thi Hong Thai , Dung Thi Truong
A total of 488 observations from secondary and high school teachers were randomly divided into two groups for conducting analysis: 244 for exploratory factor analysis (EFA) and 244 for confirmatory factor analysis (CFA). The experimental results from EFA revealed six latent factors, accumulating for over 68.43% of the variance in the data. The CFA reports validated the 6-factor model, demonstrating strong model fit indices and ensuring high reliability and validity. These factors discovered and named in this study are as: facilities and teaching support, capacity to organize project-based learning (PjBL), perception of PjBL, readiness for PjBL, teachers’ confidence in applying PjBL, and students’ characteristics. The findings validated these latent factors’ model as a comprehensive framework for understanding the essential aspects of organizing PjBL. By addressing both the identification and empirical validation of influencing factors, the study thoroughly responds to the research questions and strengthens the overall contribution to the field. This study is novel in its development and validation of a context-specific measurement model for PjBL in mathematics education, particularly within the Vietnamese educational context, where empirically grounded models of this nature remain scarce. Furthermore, it offers valuable practical information for teachers, educational administrators, and researchers to enhance and promote the effective application of PjBL in mathematics education.
Volume: 14
Issue: 6
Page: 4540-4554
Publish at: 2025-12-01

Quality of service optimization for 4G LTE upload and download throughput

10.11591/ijict.v14i3.pp1024-1033
Afrizal Yuhanef , Siska Aulia , Lefenia Indriani
Demand for mobile data services and people’s dependence on 4G LTE networks continue to increase. However, the quality of service (QoS) of this network still requires improvement, especially regarding the effect of QoS on throughput at specific frequencies. The research gap lies in the lack of indepth analysis of the impact of QoS parameters on network performance at frequencies of 2,100 MHz and 2,300 MHz. This study evaluates the effect of QoS parameters, such as delay, jitter, and packet loss, on throughput in 4G LTE networks at both frequencies. The research methodology uses an experimental approach with throughput, delay, jitter, and packet loss measurements in various network conditions. The results showed that delay (17.2174 ms to 37.0322 ms), jitter, and packet loss significantly influence throughput, ranging from 624.5 Kbps to 1,322.4 Kbps. The 2,100 MHz frequency tends to show better performance than 2,300 MHz. This study concludes that optimizing QoS parameters, especially delay and jitter, can significantly improve 4G LTE network performance. These findings provide practical contributions for mobile operators in improving network quality and customer satisfaction and open opportunities for further research on other frequencies or newer network technologies.
Volume: 14
Issue: 3
Page: 1024-1033
Publish at: 2025-12-01

Comparative analysis of u-net architectures and variants for hand gesture segmentation in parkinson’s patients

10.11591/ijict.v14i3.pp972-982
Avadhoot Ramgonda Telepatil , Jayashree Sathyanarayana Vaddin
U-Net is a well-known method for image segmentation, and has proven effective for a variety of segmentation challenges. A deep learning architecture for segmenting hand gestures in parkinson’s disease is explored in this paper. We prepared and compared four custom models: a simple U-Net, a three-layer U-Net, an auto encoder-decoder architecture, and a U-Net with dense skip pathways, using a custom dataset of 1,000 hand gesture images and their corresponding masks. Our primary goal was to achieve accurate segmentation of parkinsonian hand gestures, which is crucial for automated diagnosis and monitoring in healthcare. Using metrics including accuracy, precision, recall, intersection over union (IoU), and dice score, we demonstrated that our architectures were effective in delineating hand gestures under different conditions. We also compared the performance of our custom models against pretrained deep learning architectures such as ResNet and VGGNet. Our findings indicate that the custom models effectively address the segmentation task, showcasing promising potential for practical applications in medical diagnostics and healthcare. This work highlights the versatility of our architectures in tackling the unique segmentation challenges associated with parkinson’s disease research and clinical practice.
Volume: 14
Issue: 3
Page: 972-982
Publish at: 2025-12-01

Professional training of future primary school teachers in the context of Kazakh ethnopsychology and ethnopedagogy

10.11591/ijere.v14i6.32273
Serikkhan Zhuzeyev , Manat Zhailauova
Within the realm of primary education, the educational content and structure for the present youth generation are aligned with state regulations and rooted in the national values and traditions of the people. National education content should be integrated into primary school programs, with a focus on nurturing it through the ethnopsychological features and ethnopedagogical traditions of the nation. With that in mind, this study aims to enhance the ethnopedagogical and ethnopsychological professional qualifications of primary school teachers with focus on their ability to incorporate Kazakh national spiritual values and traditions in educational process. Research methodology includes theoretical instruments, consisting of analytical, and synthetic techniques, as well as pedagogical experiment, conducted via elective course “developmental and pedagogical psychology” among two groups of third-year university students from the department of primary education. To evaluate the effectiveness of the teacher training techniques, proposed in this study, we conducted and compared results of preliminary and final assessment of students’ professional competence. The research outcomes show the beneficial nature of training techniques and include an analysis of the content of the training carried out. These research findings can be applied in the further development of the fields of pedagogy and primary school methodology.
Volume: 14
Issue: 6
Page: 4700-4710
Publish at: 2025-12-01

A quantitative study of work values: perspectives of Vietnamese high school students

10.11591/ijere.v14i6.32951
Thanh-Thuy Ngo , Thang The Nguyen
In the context of Vietnam’s rapidly changing labor market, understanding the work values of youth has become essential for effective career guidance. By using a quantitative method to examine the work values of high school students in Vietnam, with a particular focus on the impact of gender differences. A sample of 544 students (327 female, 217 male) completing the work value questionnaire (WVQ), a validated instrument that underwent reliability testing and exploratory factor analysis (EFA) to ensure robustness. The WVQ, using a Likert scale from 1 to 5, assessed values including power (POW), benevolence (BEN), self-direction (SDI), tradition (TRA), and stimulation (STI). The results revealed that BEN and POW are dominated, with female students showing a greater inclination toward social values and self-assertive roles than male students do. BEN is received the highest mean score (4.09), followed by POW (4.03), while SDI, TRA, and STI are scored lower. These findings highlight the importance of aligning educational and career guidance programs with students’ core values, taking gender differences into account to create a supportive, inclusive environment for career decision-making. This research offers valuable insights for educators and policymakers, informing the development of vocational education strategies that are ensured by the diverse work value orientations of students.
Volume: 14
Issue: 6
Page: 4320-4328
Publish at: 2025-12-01

Language learning strategies in relation to advanced Chinese vocabulary and writing proficiency

10.11591/ijere.v14i6.31857
Xinqin Liu , Mohammed Y.M. Mai
The study investigated the relationship between the language learning strategies (LLSs) employed by international undergraduate students at universities in Qinghai Province, China, and their proficiency in advanced Chinese vocabulary and writing. Data was collected from 45 advanced-level students selected through purposive sampling, using Oxford’s strategy inventory for language learning (SILL), an advanced Chinese vocabulary knowledge test, and advanced Chinese writing test scores. The descriptive analysis revealed moderate language learning strategy usage, with a preference for speaking and listening development. This result indicates a limited strategy usage. The correlation analysis showed no significant relationship between strategy usage and advanced Chinese vocabulary or writing proficiency. However, a strong relationship was observed between advanced Chinese vocabulary and writing proficiency. The absent relationship between strategy usage and proficiency levels suggests insufficient Chinese language proficiency among the students. The significant relationship highlights the crucial role of vocabulary in enhancing Chinese writing skills. The results provide practical insights for enhancing the use of strategies and vocabulary teaching to improve advanced writing and Chinese proficiency among international undergraduate students.
Volume: 14
Issue: 6
Page: 4844-4853
Publish at: 2025-12-01

Real-time posture monitoring prediction for mitigating sedentary health risks using deep learning techniques

10.11591/ijict.v14i3.pp1126-1135
D. B. Shanmugam , J. Dhilipan
Sedentary behavior has become a pressing global public health issue. This study introduces an innovative method for monitoring and addressing posture changes during inactivity, offering real-time feedback to individuals. Unlike our prior research, which focused on post-analysis, this approach emphasizes real-time monitoring of upper body posture, including hands, shoulders, and head positioning. Image capture techniques document sedentary postures, followed by preprocessing with bandpass filters and morphological operations such as dilation, erosion, and opening to enhance image quality. Texture feature extraction is employed for comprehensive analysis, and deep neural networks (DNN) are used for precise predictions. A key innovation is a feedback system that alerts individuals through an alarm, enabling immediate posture adjustments. Implemented in MATLAB, the method achieved accuracy, sensitivity, and specificity rates of 98.2%, 90.7%, and 99.2%, respectively. Comparative analysis with established methods, including support vector machine (SVM), random forest, and K-nearest neighbors (KNN), demonstrate the superiority of our approach in accuracy and performance metrics. This real-time intervention strategy has the potential to mitigate the adverse effects of sedentary behavior, reducing risks associated with cardiovascular and musculoskeletal diseases. By providing immediate corrective feedback, the proposed system addresses a critical gap in sedentary behavior research and offers a practical solution for improving public health outcomes.
Volume: 14
Issue: 3
Page: 1126-1135
Publish at: 2025-12-01

AI-based federated learning for heart disease prediction: a collaborative and privacy-preserving approach

10.11591/ijict.v14i3.pp751-759
Stuti Bhatt , Surender Reddy Salkuti , Seong-Cheol Kim
People with symptoms like diabetes, high BP, and high cholesterol are at an increased risk for heart disease and stroke as they get older. To mitigate this threat, predictive fashions leveraging machine learning (ML) and artificial intelligence (AI) have emerged as a precious gear; however, heart disease prediction is a complicated task, and diagnosis outcomes are hardly ever accurate. Currently, the existing ML tech says it is necessary to have data in certain centralized locations to detect heart disease, as data can be found centrally and is easily accessible. This review introduces federated learning (FL) to answer data privacy challenges in heart disease prediction. FL, a collaborative technique pioneered by Google, trains algorithms across independent sessions using local datasets. This paper investigates recent ML methods and databases for predicting cardiovascular disease (heart attack). Previous research explores algorithms like region-based convolutional neural network (RCNN), convolutional neural network (CNN), and federated logistic regressions (FLRs) for heart and other disease prediction. FL allows the training of a collaborative model while keeping patient info spread out among various sites, ensuring privacy and security. This paper explores the efficacy of FL, a collaborative technique, in enhancing the accuracy of cardiovascular disease (CVD) prediction models while preserving data privacy across distributed datasets.
Volume: 14
Issue: 3
Page: 751-759
Publish at: 2025-12-01

Classification of breast cancer using a precise deep learning model architecture

10.11591/ijict.v14i3.pp933-940
Mohammed Ghazal , Murtadha Al-Ghadhanfari , Fajer Fadhil
Breast cancer is an important topic in medical image analysis because it is a high-risk disease and the leading cause of death in women. Early detection of breast cancer improves treatment outcomes, which can be achieved by identifying it using mammography images. Computer-aided diagnostic systems detect and classify medical images of breast lesions, allowing radiologists to make accurate diagnoses. Deep learning algorithms improved the performance of these diagnoses systems. We utilized efficient deep learning approaches to propose a system that can detect breast cancer in mammograms. The proposed approach adopted relies on two main elements: improving image contrast to enhance marginal information and extracting discriminatory features sufficient to improve overall classification quality, these improvements achieved based on a new model from scratch to focus on enhancing the accuracy and reliability of breast cancer detection. The model trained on the digital database for screening mammography (DDSM) dataset and compared with different convolutional neural network (CNN) models, namely EfficientNetB1, EfficientNetB5, ResNet-50, and ResNet101. Moreover, to enhance the feature selection process, we have integrated adam optimizer in our methodology. In evaluation, the proposed method achieved 96.5% accuracy across the dataset. These results show the effectiveness of this method in identifying breast cancer through images.
Volume: 14
Issue: 3
Page: 933-940
Publish at: 2025-12-01

Efficient design of approximate carry-based sum calculating full adders for error-tolerant applications

10.11591/ijict.v14i3.pp1189-1198
Badiganchela Shiva Kumar , Galiveeti Umamaheswara Reddy
Approximate computing is an innovative circuit design approach which can be applied in error-tolerant applications. This strategy introduces errors in computation to reduce an area and delay. The major power-consuming elements of full adder are XOR, AND, and OR operations. The sum computation in a conventional full adder is modified to produce an approximate sum which is calculated based on carry term. The major advantage of a proposed adder is the approximation error does not propagate to the next stages due to the error only in the sum term. The proposed adder was coded in verilog HDL and verified for different bit sizes. Results show that the proposed adder reduces hardware complexity with delay requirements.
Volume: 14
Issue: 3
Page: 1189-1198
Publish at: 2025-12-01

Advanced control techniques for performance improvement of axial flux machines

10.11591/ijict.v14i3.pp1095-1107
Kalpana Anumala , Ramesh Babu Veligatla
The topological advancements in twin rotor axial flux induction motors (TRAxFIMs) have spurred the interest in performance optimization and control strategies for electric vehicle (EV) applications in particular. This paper investigates for the enhanced performance of multi-level inverters (MLIs) fed TRAxFIMs with different advanced control techniques. The performance evaluation is done under variable speed conditions at constant torque and vice versa. The TRAxFIMs offer unique advantages like high power density, high efficiency and most suitable for EV applications. The performance analysis of MLIs fed TRAxFIM has been carried out with proportional-integral (PI), fuzzy controllers, and artificial neural network (ANN) controllers. The PI controller provides a conventional control approach, while the fuzzy and ANN controllers serve as advanced control strategies. The integration of MLIs and advanced control techniques with TRAxFIMs aims to enhance dynamic response, stability and efficiency. The proposed control strategies are evaluated through extensive MATLAB simulations and the potential of MLIs fed TRAxFIMs is emphasized for EV applications.
Volume: 14
Issue: 3
Page: 1095-1107
Publish at: 2025-12-01

Review of NLP in EMR: abbreviation, diagnosis, and ICD classification

10.11591/ijict.v14i3.pp881-891
Nurul Anis Balqis Iqbal Basheer , Sharifalillah Nordin , Sazzli Shahlan Kasim , Azliza Mohd Ali , Nurzeatul Hamimah Abdul Hamid
This review explores state-of-the-art natural language processing (NLP) methods applied to electronic medical records (EMRs) for key tasks such as expanding medical abbreviations, automated diagnosis generation, international classification of diseases (ICD) classification, and explaining model outcomes. With the growing digitization of healthcare data, the complexity of EMR analysis presents a significant challenge for accurate and interpretable results. This paper evaluates various methodologies, highlighting their strengths, limitations, and potential for improving clinical decision-making. Special attention is given to abbreviation expansion as a crucial step for disambiguating terms in the clinical text, followed by an exploration of auto-diagnosis models and ICD code assignment techniques. Finally, interpretability methods like integrated gradients and attention-based approaches are reviewed to understand model predictions and their applicability in healthcare. This review aims to provide a comprehensive guide for researchers and practitioners interested in leveraging NLP for clinical text analysis.
Volume: 14
Issue: 3
Page: 881-891
Publish at: 2025-12-01

Performance analysis of D2D network in heterogeneous multitier interference scenarios

10.11591/ijict.v14i3.pp811-821
Dhilipkumar Santhakumar , Arunachalaperumal Chellaperumal , Jenifer Suriya Lazer Jessie , Jerlin Arulpragasam
The trade-off between boosting network throughput and minimizing interference is a critical issue in fifth generation (5G) networks. Diverting the data traffic around the network access point in device-to-device (D2D) communication is an important step in realizing the vision of 5G. Though the D2D network improves the network performance, they complicate the interference management process. Interference is an invisible physical phenomenon occurring in wireless communication which happens when multiple transmissions happen simultaneously over a general wireless medium. Enormous growth in usage of mobile phone and other wireless gadgets in recent years has paved the way for significant role in Interference analysis over multi-tier network. Interference could affect communication systems performance and it might even prevent systems functioning properly. 3G and 4G wireless devices coexisted with reverse compatibility in a coverage area. Also, after their widespread adoption, 5G devices have become prevalent across the globe and this reaffirms interference coexistence as a significant field of research. Multiple systems operating in a region will cause severe interference and ultimately reduce the quality of received signal. A simulation environment for cellular standards coexistence considering real-time parameters is created and experimented. Various research works earlier addresses the interference mitigation techniques in multi-tier networks but none of them present the analysis of scenarios and interfering signal power levels in the respective contexts. In this paper various scenarios with different network interference coexistence were studied, simulated, and analyzed quantitatively.
Volume: 14
Issue: 3
Page: 811-821
Publish at: 2025-12-01

A hybrid approach using VGG16-EffcientNetV2B3-FCNets for accurate indoor vs outdoor and animated vs natural image classification

10.11591/ijict.v14i3.pp903-913
Meghana Deshmukh , Amit Gaikwad , Snehal Kuche
The paper introduces a hybrid approach that synergistically combines the strengths of VGG16, EfficientNetV2B3, and fully connected networks (FCNets) to achieve precise image classification. Specifically, our focus lies in discerning between basic indoor and outdoor scenes, further extended to distinguish between animated and natural images. Our proposed hybrid architecture harnesses the unique characteristics of each component to significantly enhance the model’s overall performance in fine-grained image categorization. In our methodology, we utilize VGG16 and EfficientNetV2B3 as the feature extractors. During evaluation, we examined various classification algorithms, such as VGG16, EfficientNet, Feature_Aggr_Avg, and Feature_Aggr_max, among others. Notably, our hybrid feature aggregation approach demonstrates a remarkable improvement of 0.5% in accuracy compared to existing solutions employing VGG16 and EfficientNet as feature extractors. Notably, for indoor versus outdoor image classification, feature_aggr_avgachieves an accuracy of 98.51%. Similarly, when distinguishing between animated and natural images, Feature_Aggr_Avgachieves an impressive accuracy of 99.20%. Our findings demonstrate improved accuracy with the hybrid model, proving its adaptability across diverse classification tasks. The model is promising for applications like automated surveillance, content filtering, and intelligent visual recognition, with its robustness and precision making it ideal for realworld scenarios requiring nuanced categorization.
Volume: 14
Issue: 3
Page: 903-913
Publish at: 2025-12-01
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