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Customer segmentation using association rule mining on retail transaction data

10.11591/ijai.v14.i3.pp1919-1929
Siriwan Kajornkasirat , Pattarawan Gunglin , Kritsada Puangsuwan , Nawapon Kaewsuwan
This research aimed to investigate a suitable algorithm for customer segmentation using as customer behavior indicators the recency, frequency, and monetary (RFM) values of the customers. The clustering algorithms K-means, fuzzy C-means, and self-organizing neural network (SONN) were compared for finding the most appropriate algorithm. The customer segmentation was analyzed using association rule mining with the frequent pattern algorithm (FP-Growth). Data on retail transactions during January 2021 - May 2023 were obtained from Tuenjai Company, Thailand, with a total of 202,469 records. The results from the three algorithms were compared by the silhouette coefficient (SC), Calinski-Harabasz (CH) index, Davies-Bouldin (DB) index, iteration count, and execution time. The results showed that the K-means algorithm was the most suitable algorithm for customer segmentation in this study. K-means clustering grouped the customers into three groups here labeled as “important value”, “general development”, and “lost”, based on the RFM values. There were 38 rules for the important value segment, and two rules each for the general development and the lost groups. These results could be useful to the business organization for improving the customer experiences, increasing sales, preparing or promoting products, and stock management efficiency.
Volume: 14
Issue: 3
Page: 1919-1929
Publish at: 2025-06-01

Blockchain and machine learning driven agricultural transformation framework to enhance efficiency, transparency, and sustainability

10.11591/ijai.v14.i3.pp1976-1988
Shivashankar Shivashankar , Krishna Prasad Karani , Manjunath Rajgopal , Sarala Totad , Erappa Giddappa , Shivakumar Swamy
The agricultural sector is undergoing a transformative journey empowered by technological innovations. In this context, this research work endeavors to revolutionize the agricultural supply chain (ASC) by developing a comprehensive online platform that connects sellers, farmers, and customers. Through meticulous planning, design, and implementation, the system aims to streamline the process of buying and selling agricultural products, thereby fostering efficiency, transparency and accessibility. The key features include user registration, product management, order tracking, and blockchain-machine learning (ML) based transaction security. The proposed research work's success hinges on thorough testing and validation, ensuring its reliability and usability. By leveraging technology to bridge gaps in the agricultural ecosystem, this proposed work seeks to empower stakeholders and contribute to the sustainable growth of the agricultural industry. In the current agricultural landscape in India, traceability has been a significant challenge. The industry lacks a comprehensive system that provides visibility into the source and quality of produce. Our proposed system aims to address the shortcomings of the existing agricultural ecosystem by introducing a comprehensive solution powered by blockchain technology and advanced data processing techniques.
Volume: 14
Issue: 3
Page: 1976-1988
Publish at: 2025-06-01

Enhanced Arabic-language cyberbullying detection: deep embedding and transformer (BERT) approaches

10.11591/ijai.v14.i3.pp2258-2269
Ebtesam Jaber Aljohani , Wael M. S. Yafooz
Recent technological advances in smartphones and communications, including the growth of such online platforms as massive social media networks such as X (formerly known as Twitter) endangers young people and their emotional well-being by exposing them to cyberbullying, taunting, and bullying content. Most proposed approaches for automatically detecting cyberbullying have been developed around the English language, and methods for detecting Arabic-language cyberbullying are scarce. Methods for detecting Arabic-language cyberbullying are especially scarce. This paper aims to enhance the effectiveness of methods for detecting cyberbullying in Arabic-language content. We assembled a dataset of 10,662 X posts, pre-processed the data, and used the kappa tool to verify and enhance the quality of our annotations. We conducted four experiments to test numerous deep learning models for automatically detecting Arabic-language cyberbullying. We first tested a long short-term memory (LSTM) model and a bidirectional long short-term memory (Bi-LSTM) model with several experimental word embeddings. We also tested the LSTM and Bi-LSTM models with a novel pre-trained bidirectional encoder from representations (BERT) and then tested them on a different experimental models BERT again. LSTM-BERT and Bi-LSTM-BERT demonstrated a 97% accuracy. Bi-LSTM with FastText embedding word performed even better, achieving 98% accuracy. As a result, the outcomes are generalized.
Volume: 14
Issue: 3
Page: 2258-2269
Publish at: 2025-06-01

Early detection and classification of bone marrow changes in lumbar vertebrae using machine learning techniques

10.11591/ijai.v14.i3.pp2132-2145
Yasir Hussein Shakir , Tiong Sieh Kiong , Chai Phing Chen
Bone marrow changes in lumbar vertebrae (BMCLVB) have emerged as a significant correlation of chronic low back pain (CLBP) severity, especially in patients with comorbid conditions like HIV, osteoporosis, and cancer. Identifying these correlations not only aids governments and health insurance providers but also facilitates early treatment for those at risk. However, challenges lurk due to the unavailability and quality of healthcare data. The collaboration between data science and artificial intelligence, particularly machine learning (ML), has propelled biomedical research forward. So far, accessing and processing hospital and clinical data remains a hurdle. In doing so it aims to provide an opportunity for early intervention and treatment. In addition, the goal of the current study was to overcome data shortcomings using advanced ML techniques to unlock complex magnetic resonance imaging (MRI) features. We believe that extending the dataset with that obtained from an Iraqi hospital will not only assist in diagnosing BMCLVB but also fill the gap between data science and healthcare. Above all, the upgrade is intended to empower biomedical research and increase the chances of successful patient treatment.
Volume: 14
Issue: 3
Page: 2132-2145
Publish at: 2025-06-01

E-waste management in higher education institutions: insights and policy recommendations

10.11591/ijphs.v14i2.25020
Fredelino A. Galleto Jr. , Maricel G. Dayaday , Frediezel G. De Leon
Electronic waste disposal is a primary concern both on a national and global spectrum. The increased usage of information and communication technology and electronic devices among higher education institutions (HEIs) contributes to the annual generation of e-waste. HEIs are called to implement a sustainable scheme to curtail the environmental and public health threats brought by e-waste. Against this background, the recycling strategies of HEIs for discarded electronics were examined and evaluated as to its effectiveness and used as basis to develop policy recommendations for e-waste management. The interventions developed were met with general approval and positive response. The innovative products were perceived to be useful in serving their new purposes and reducing the problems of e-waste in HEIs. These findings provide valuable input for HEIs seeking to enhance their e-waste recycling practices. Existing legislation concerning e-waste was looked into, and policy recommendations that encouraged cooperation and shared accountability among key players and stakeholders were drawn up.
Volume: 14
Issue: 2
Page: 919-926
Publish at: 2025-06-01

Optimizing chest X-rays as a leading diagnostic modality for handling COVID-19: a diagnostic study

10.11591/ijphs.v14i2.24653
Utami Purbasari , Nurhayati Adnan Prihartono , Helda Helda , Budhi Antariksa , Fatira R. Audita
Recent studies have highlighted that chest CT scans are crucial for accurately diagnosing COVID-19. However, in rural areas of Indonesia, people may have difficulty assessing CT scans, leading to increased undetected cases. To address this issue, we investigated whether chest X-rays (CXR) could replace CT scans in diagnosing COVID-19 patients. A diagnostic cross-sectional-based study was conducted at Fatmawati General Hospital from January to September 2021. The study included suspected COVID-19 patients in isolation wards and ICU who were over 18, with or without comorbidities, and had complete clinical data and laboratory tests. We analyzed imaging data through reverse transcription-polymerase chain reaction (RT-PCR) tests, CXR, and chest CT scans. This study enrolled 150 eligible patients. With RT-PCR as the gold standard, we found that CXR had a sensitivity of 86.6% (95% CI: 78.9-92.3%) and chest CT scan had a sensitivity of 91.1% (95% CI: 84.2-95.6%). Similar performance was observed when detecting ground glass opacities (GGO), bilateral laterobasal, and influenza-like syndrome and dyspnea (ILI) between CXR and CT scans. Receiver operating characteristic (ROC) curves demonstrated that CXR is comparable to CT scan, especially in ground glass opacity (GGO) and consolidation (AUC=0.72; 95% CI: 0.61-0.83 and AUC=0.710; 95% CI: 0.64-0.78). The proposed CXR method can be a reliable primary imaging tool for diagnosing COVID-19 by considering ILI. However, chest CT scans remain the most effective diagnostic method for COVID-19. These findings may be useful for the utilization of CXR for diagnosing COVID-19 in areas with limited access to CT scans.
Volume: 14
Issue: 2
Page: 799-807
Publish at: 2025-06-01

Demographic determinants of patronage of medicine hawkers by commercial vehicle passengers in Ghana

10.11591/ijphs.v14i2.24606
Joy Ato Nyarko , Kofi Osei Akuoko , Jonathan Mensah Dapaah , Nana Yaa Serwaa Akuoko , Egwolo Perpetual Iyengunmwena
Medicine hawking is one of the major public health problems of the global south. This present study examined the demographic determinants of patronage of the services of medicine hawkers among commercial vehicle passengers in Kumasi, Ghana. A cross-sectional study was carried out from February 2022 to March 2022 at major bus terminals in Kumasi. Data were descriptively and inferentially analysed. The survey revealed that 55% of the respondents had bought medicines from medicine hawkers before. There was a significant relationship between having bought from a medicine hawker before and the intention to buy from them again in the future. Also, age, religion and education contributed significantly to patronising the services of medicine hawkers. We recommend that government intensifies its public health education on the implications of seeking health care services from these medicine hawkers.
Volume: 14
Issue: 2
Page: 912-918
Publish at: 2025-06-01

Stress, stressors, and stress management practices among public-school teachers

10.11591/ijphs.v14i2.24869
Jonathan Lapuz Mañas , Roel Sanchez Ang
Stress manifests differently among individuals in various circumstances, stemming from multiple sources. Teachers, in particular, encounter many stressors from personal and work-related domains. This study examines the stress levels of elementary, junior high school, and senior high school public school teachers within Congressional District IV of the Division Office of Nueva Ecija, focusing on everyday life stressors. Additionally, it investigates the stress management practices they employ for coping. The personal metrics of these teachers were analyzed to ascertain their significant relationship with stress levels. Data were randomly collected from 273 respondents through a questionnaire developed by Villamayor. The study unveils that public-school teacher experience slight stress levels and utilize diverse stress management techniques to tackle these stressors. However, the personal metrics of respondents were found to have an insignificant relationship with their stress levels. Nevertheless, the findings of this study pave the way for developing a comprehensive stress management plan to assist public school teachers.
Volume: 14
Issue: 2
Page: 818-826
Publish at: 2025-06-01

Hybrid method for optimizing emotion recognition models on electroencephalogram signals

10.11591/ijai.v14.i3.pp2302-2314
I Made Agus Wirawan , Kadek Yota Ernanda Aryanto , I N. Sukajaya , Ni Nyoman Mestri Agustini , Dewi Arum Widhiyanti Metra Putri
Two critical factors that need to be studied in emotion recognition are the differences in electroencephalogram (EEG) signal patterns caused by participant characteristics and EEG signals with spatial information. These factors significantly affect the resulting accuracy. The model proposed in this study can consider these factors. This model consists of the modified weighted mean filter method for the basic EEG signal smoothing process, the differential entropy method for the feature extraction process, the relative difference method for the baseline reduction, the 3D cube method for feature representation, and the continuous capsule network method for the classification process. Based on testing on three public datasets, this hybrid method can overcome factors affecting emotion recognition accuracy. This statement is based on the accuracy produced by this model, which outperformed the accuracy validated in previous studies.
Volume: 14
Issue: 3
Page: 2302-2314
Publish at: 2025-06-01

Changing perceptions on menstrual practices in Southern Bangladesh: a cross-generational study

10.11591/ijphs.v14i2.22409
Jannatul Bakia Jeni , Al Jamal Mustafa Shindaini , Md. Tanvir Mahtab , Shantanu Kumar Saha
Menstruation, a natural biological process, remains deeply intertwined with women's lives but is surrounded by superstitions and stigmas, posing health risks. This study explores generational shifts in menstrual perceptions, focusing on women's knowledge, rituals, social stigmas, and the evolving sources of socialization. Using a phenomenological approach, interviews were conducted with women from 25 households, spanning three generations. Thematic analysis has been used to analyze the data. Findings revealed that while the first and second generations understood menstruation primarily as a sign of fertility and bodily maturity, the third generation recognized it as a hormonal process. Hygiene practices also varied, with older generations relying on cloths and holy water for pain relief, whereas younger women opted for sanitary pads and were more aware of the dangers of unhygienic practices. Additionally, the study highlighted prevalent misconceptions and stigmas among the first and second generations, while the third generation was more informed, open, and vocal about menstruation. The process of socialization around menstruation has also evolved; earlier generations mainly learned from their mothers, with little involvement from male family members. In contrast, the third generation gained knowledge from both parents and media, reflecting a significant shift in the cultural dialogue surrounding menstruation.
Volume: 14
Issue: 2
Page: 808-817
Publish at: 2025-06-01

The influence of social media on Greek mothers’ attitudes and knowledge on breastfeeding

10.11591/ijphs.v14i2.24707
Katerina Mila , Eleni Tympa , Vasiliki Karavida
Recently, maternal breastfeeding in Greece has shown a positive trend. However, breastfeeding rates in Greece still lag behind those of other European countries. The participants of this quantitative study were recruited via social media and by personal connections. Data were gathered through questionnaire and analyzed utilizing descriptive statistics with SPSS 20. The study investigates: i) Trust levels among Greek mothers in social groups and their awareness of breastfeeding issues. ii) The impact of active participation in Greek parenting and breastfeeding support groups on Facebook on choosing and sustaining exclusive breastfeeding. iii) Whether engagement in social media groups positively influences the duration of exclusive breastfeeding and extends the overall breastfeeding period for infants. Out of the 776 participants, 727 were part of social media parenting groups. About 27.9% credited these groups for influencing their decision to breastfeed. Moreover, they perceived significant assistance from social media in staying well-informed about breastfeeding. The data collected confirms social media parenting groups positively influence breastfeeding mothers, enhancing knowledge, self-confidence, and deterring negative attitudes. This factor increases the likelihood of sustaining exclusive breastfeeding up to the recommended six months.
Volume: 14
Issue: 2
Page: 652-660
Publish at: 2025-06-01

Intravenous drug administration application for pediatric patients via augmented reality

10.11591/ijai.v14.i3.pp2412-2422
Kritsada Puangsuwan , Siriwan Kajornkasirat , Jaruphat Wongpanich , Chulalak Kaewsuk , Simaporn Puangsuwan
This research presents the development of the intravenous drug administration application for pediatric patients using augmented reality (AR) technology, with a primary focus on aiding nursing students in administering medications accurately to reduce the risk of errors. The system architecture encompasses two core components: the creation of medication preparation videos and detailed drug information, and the design of a mobile application featuring medication list display, drug dosage calculation, user satisfaction assessment, and intravenous drug information addition. The system classifies users into administrators and nursing students, allowing administrators to manage user information in the member database. the application seamlessly integrates Visual Studio Code, flutter, dart programming language, firebase database, and AR.js Studio for QR code-linked videos. Operating in four main parts, namely users, mobile application, member database, and results display, the IDA application enables users to log in, access detailed drug information, calculate dosages, and view AR-based medication preparation videos. Tested with 111 nursing students, the system demonstrated functionality, completeness, and accuracy. The Likert scale-based evaluation revealed high satisfaction levels in content, design, functionality, and benefits received, affirming the intravenous drug administration application's effectiveness in pediatric intravenous drug management through AR, offering an innovative solution for nursing education and error reduction.
Volume: 14
Issue: 3
Page: 2412-2422
Publish at: 2025-06-01

A new wrapper feature selection approach for binary ransomware detection

10.11591/ijai.v14.i3.pp2104-2112
Chaieb Omar , Kannouf Nabil , Mohammed Benabdellah
Concerns about ransomware attacks have heightened in recent years for both individuals and organizations. Detecting these malicious attacks poses considerable challenges for cybersecurity professionals, particularly due to their ever-evolving nature. Although behavior-based detection methods show promise in recognizing new ransomware variants, they face significant hurdles, especially in managing the massive volumes of data generated from real-time malware behavior monitoring, leading to high dimensionality. This paper introduces a new feature selection approach specifically for binary ransomware detection. Our method emphasizes assessing the impact of feature categories on the effectiveness and speed of detection algorithms. It involves two stages: the first stage selects the most relevant groups (categories) of features, while the second ranks and identifies the important features within those categories. Experimental results indicate that our approach surpasses similar studies regarding accuracy and ability to minimize the original features set. Moreover, both computation speed and accuracy are notably enhanced when using the selected subset compared to the original features.
Volume: 14
Issue: 3
Page: 2104-2112
Publish at: 2025-06-01

Comparative analysis of machine learning models for fake news detection in social media

10.11591/ijai.v14.i3.pp1951-1959
Bahaa Eddine Elbaghazaoui , Mohamed Amnai , Youssef Fakhri , Ali Choukri , Noreddine Gherabi
The rapid rise of information sharing on social media has amplified the spread of fake news, making its detection increasingly critical. As fake news continues to proliferate, the need for efficient detection mechanisms has become more urgent to protect users from misinformation and disinformation. This paper presents a comparative analysis of multiple machine learning models for detecting text based fake news on social media platforms. Using models such as gradient boosting, XGBoost, and linear support vector classifier (SVC) on the Infor mation Security and Object Technology (ISOT) fake news dataset, the study demonstrates that gradient boosting achieves the highest accuracy of 99.61%, while XGBoost provides a strong balance with 99.59% accuracy and a signifi cantly lower execution time, making it more suitable for real-time applications. These results offer valuable insights into the trade-offs between accuracy and computational efficiency, contributing to the development of more practical de tection systems and future research in the field.
Volume: 14
Issue: 3
Page: 1951-1959
Publish at: 2025-06-01

How ambidextrous entrepreneurial leaders react to the artificial intelligence boom

10.11591/ijai.v14.i3.pp1708-1718
Kandasamy Indravathy , Noorlizawati Abd Rahim
Artificial intelligence (AI) now plays a central role in enhancing business competitiveness by transforming systems, frameworks, and managerial strategies. This study employs a systematic literature review (SLR) approach, utilizing the 'Consensus AI' search platform to explore the characteristics and roles of ambidextrous entrepreneurial leaders in the AI era. Consensus AI is an AI-powered search engine that automates the processes of reviews, literature searches, screening, and data extraction. It also utilizes 'research question searches' within SLRs to avoid the challenges of ambiguity and irrelevant information associated with 'keyword searches,' delivering more directly relevant results and finding featured snippets that answer specific questions. A research gap exists concerning how ambidextrous leadership adapts to the AI boom, highlighting leadership dynamics in the digital age. The findings emphasize the critical role of ambidextrous entrepreneurial leadership (AEL) in guiding organizations through the AI boom, enabling them to leverage AI for innovation, agility, and competitiveness. Organizations that effectively implement AEL by integrating AI technologies can position themselves for long-term success. Key insights show the importance of AEL approaches, and future research may explore challenges that arise for ambidextrous entrepreneurial leaders in the era of AI, such as ethical considerations and organizational culture.
Volume: 14
Issue: 3
Page: 1708-1718
Publish at: 2025-06-01
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