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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

Enhancing precision medicine in neuroimaging: hybrid model for brain tumor analysis

10.11591/ijai.v14.i3.pp2196-2209
Ravikumar Sajjanar , Umesh D. Dixit
Brain tumors are a significant health challenge requiring precise diagnostic methods for optimal patient care. This study introduces a novel approach utilizing a convolutional neural network-based gated recurrent unit (CNN-GRU) for brain tumor detection. The method encompasses a rigorous preprocessing pipeline tailored for multi-modal magnetic resonance imaging (MRI) images, focusing on standardizing dimensions, normalizing pixel values, and enhancing contrast to facilitate robust tumor identification. Subsequently, temporal sequences of preprocessed images are analyzed by the CNN-GRU network to accurately pinpoint tumor regions. Evaluation on the BraTS2020 dataset, comprising diverse MRI scans with manual annotations, demonstrates the method's robust performance in tumor detection, reflecting real-world clinical complexities. Through meticulous preprocessing and model optimization, the approach achieves a remarkable accuracy rate of 99%, offering crucial insights for clinicians in treatment planning and prognosis prediction. Implemented using Python, the framework contributes to advancing brain tumor diagnosis and decision support systems, potentially enhancing personalized medicine and clinical practice. By improving diagnostic accuracy and patient outcomes, this research underscores the importance of integrating advanced computational techniques with medical imaging to address critical healthcare challenges effectively.
Volume: 14
Issue: 3
Page: 2196-2209
Publish at: 2025-06-01

Hypertensive patients: self-care experience in rural community

10.11591/ijphs.v14i2.24171
Joko Susanto , Makhfudli Makhfudli , Ilkafah Ilkafah , Anestasia Pangestu Mei Tyas , Ah. Yusuf , Iswatun Iswatun
Hypertension is a chronic disease that often occurs in the world. Individual involvement in self-care is very important, in controlling blood pressure. The inability to carry out treatment will increase the risk and complications. The aim study is to explore the experiences and behavior of self-management and self-care of hypertension patients. The study is qualitative research with a phenomenological approach. The technique for taking participants was a purposive sampling of ten hypertensive patients. Data was collected using semi-structured interviews and then analyzed thematically based on the methodological framework of constructivist basic theory. Participant statements describing patient experiences in self-care include six themes, namely the cause of recurrences, emotional response, social support, self-care behavior, barriers to self-care, access to health facilities. The support factors of social support from family, health workers, and affordability of health services greatly influence self-care. An adequate support system, especially from nurses, is needed by patients because it will increase active participation so that patients will try to improve their care and skills in self-care.
Volume: 14
Issue: 2
Page: 951-958
Publish at: 2025-06-01

Machine learning-based hybrid emotions recognition model using electroencephalogram signals

10.11591/ijece.v15i3.pp3180-3190
Tarun Kumar , Rajendra Kumar , Ram Chandra Singh
This paper uses Hindi video clips to propose an electroencephalogram (EEG) signal-based hybrid system for emotion identification. EEG signals cannot be altered, unlike other forms of expressiveness-like voice and facial emotion. The suggested approach uses a self-created dataset under the control environments. Accuracy is the main objective of the proposed model. This study used a self-created constructed using an 8-channel unicorn black hybrid EEG machine on 30 participants while they viewed Hindi movie video clips mimicking emotions: happy, fearful, sad, and neutral. The proposed model used a two-hybrid classifier support vector machine (SVM) and k-nearest neighbor (KNN), implemented using MATLAB R2017a. In the proposed implementation, the four emotion classification categories (happy, sad, fear, and neutral) observed an average accuracy of 60.832%. The results of the presented study were compared with two recent systems. It was found that the proposed system observed better accuracy for the category of NHP five classes and the category of HP Five Classes.
Volume: 15
Issue: 3
Page: 3180-3190
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

Robust deep learning approach for accurate detection of brain tumor and analysis

10.11591/ijece.v15i3.pp3226-3237
Lanke Pallavi , Thati Ramya , Singupurapu Sai Charan , Sirigadha Amith , Thodupunuri Akshay Kumar
Usually, one of the foremost predominant and intricate therapeutic conditions. As broadly perceived, brain tumors are among the foremost significantly harmful circumstances that can radically abbreviate a person’s life expectancy. Various methods are lacking for observing the assortment of tumor sizes, shapes, and areas. When merged with strategies of profound learning, generative adversarial networks (GANs) are competent of catching the measurements, areas, and structures of tumors. Profound learning frameworks will move forward upon the shortage of datasets. It can moreover progress photographs with determination. Classifying and partitioning brain tumors productively is significant. GANs are used in conjunction with an overarching learning handle. A profound learning design called NeuroNet19, could be an intercross of visual geometry group (VGG19) and inverted pyramid pooling module (IPPM) which is utilized to recognize brain tumors. It is clear that, NeuroNet19 employments the foremost exact technique in comparison to all models (DenseNet121, MobileNet, ResNet50, VGG16). The exactness examination gave a Cohen Kappa coefficient of 99% and a F1-score of 99.2%
Volume: 15
Issue: 3
Page: 3226-3237
Publish at: 2025-06-01

BioTapSync: revolutionizing data synchronization with human touch

10.11591/ijai.v14.i3.pp2528-2536
Sohil Shah , Harshal Shah
Human Tap introduces a new way to ensure secure data transmission synchronization by integrating advanced technologies. Motivated by the need for secure and efficient communication, it uses both near field communication (NFC) and human field communication (HFC) to provide a wide range of secure communication solutions. The aim is to create a system with detailed specifications, including maximum coverage range, frequency of operation, type of communication, and data rate for each protocol. These specifications are tailored for various applications, such as credit card payments, e-ticket bookings, E-ZPass systems, and item tracking. A notable contribution of Human Tap is its ability to achieve microsecond-level accuracy for distances up to 2 cm by thoroughly analyzing the relationship between distance and time. This innovative synchronization method not only ensures a secure data transmission environment but also shows remarkable flexibility, effectively addressing the challenges of modern communication systems. Human Tap sets a new benchmark for secure and adaptable data transmission technologies, paving the way for future advancements in the field. The objective is to establish a robust and versatile data transmission method that can be adapted to a wide range of modern applications.
Volume: 14
Issue: 3
Page: 2528-2536
Publish at: 2025-06-01

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

Impedance matching and power recovery in response to coil misalignment in wireless power transmission

10.11591/ijece.v15i3.pp2556-2566
Lunde Ardhenta , Ichijo Hodaka , Kazuya Yamaguchi , Takuya Hirata
The improper alignment of the coils between the transmitter and receiver has a significant impact on wireless power transfer. If designers carefully calculate the parameters of inductance, capacitance, coupling coefficient, and working frequency and precisely implement these parameters into actual components, the system can optimize power transfer. However, it is evident that such a precise realization is often unachievable. This paper proposes a symbolic condition to maintain significant power despite the misalignment of transmitter and receiver coils. These symbolic conditions constrain parameters by simplifying some variables. This matching condition develops in the inequality of coupling coefficient, working frequency and quality factor, which are a crucial reference for maintaining power transfer. This condition is considered an additional one to the well-known impedance matching condition.
Volume: 15
Issue: 3
Page: 2556-2566
Publish at: 2025-06-01

Rooftops detection with YOLOv8 from aerial imagery and a brief review on rooftop photovoltaic potential assessment

10.11591/ijai.v14.i3.pp2282-2290
Md. Sabbir Ahmed , Md. Shohel Arman , Nusrat Tasnim , Md Hafizul Imran , Musabbir Smmak , Touhid Bhuiyan
Recent years have seen significant advancements in the switch from fossil fuel-based energy systems to renewable energy. Decentralized solar photovoltaic (PV) is one of the most promising energy sources since there is a lot of rooftop space, it is easy to install, and the cost of the PV panels is low. The determination of rooftop locations for PV installation is crucial for energy planning. With this context, this study aimed to detect the suitable rooftops of different shapes. The dataset of 5,076 building roofs used in this study was gathered by us utilizing a drone. This study identified ten distinct roof shapes accurately, including triangle, square, penta, hexa, hepta, octa, nona, deca, gabled roof, and hipped roof, using the most recent version of you only live once (YOLO), known as YOLOv8. Recent research revealed, YOLOv8 is more accurate than earlier YOLO models which is the reason of utilizing YOLOv8. Accuracy of this work of rooftops detection is 93.6%. Also, the precision, recall, and F1-score confidence curve showed good performances too. Finally, a brief review of the most recent studies on the evaluation of rooftop PV potential was conducted to provide insight into the use of solar energy.
Volume: 14
Issue: 3
Page: 2282-2290
Publish at: 2025-06-01

Addressing the challenges of harmonizing law and artificial intelligence technology in modern society

10.11591/ijai.v14.i3.pp2471-2478
Lamprini Seremeti , Sofia Anastasiadou , Andreas Masouras , Stylianos Papalexandris
The invasion of artificial intelligence (AI) in all forms of human activity causes the sudden change of social cohesion into a new hybrid reality, where the static rule of law maybe is overthrown by the instant violations of fundamental human rights, including the general rights of personhood, in its image, honor, and privacy, as well as, of general principles of law, including the principle of the “abuse of rights”, the principle of contractual autonomy, principles of tort liability, and general principles of intellectual property law. In that sense, AI disrupts the acquis due to the poor regulatory quality indicators covering unforeseen occurrences. We call this instantiation AI legal “coup d’état”. This paper constitutes a philosophical thesis statement which is in accordance with the global efforts to legally embed AI into societal systems. As part of an ongoing research on AI and law synergy, this paper focuses on proposing a theoretical framework utilizing category theory to align AI functionalities with traditional legal principles.
Volume: 14
Issue: 3
Page: 2471-2478
Publish at: 2025-06-01

Relationship quality and its effects on college student’s psychological well-being

10.11591/ijphs.v14i2.24698
Afinnisa Rasyida , Maria Angelisa Siregar , Alifiah Nur'izza Rachmah , Raden Roro Fitria Rachmawati , Ivena Nathania Victor , Kezia Jeni Hindrawan
The field of psychological well-being is extensively studied globally, encompassing positive relationships as a notable facet. Presently, there is a discourse emphasizing the need for research on relationships to encompass both positive and negative aspects, along with their effects on an individual’s overall well-being. The study aimed to identify the impacts of relationship qualities of three sources (parental, friendship, and romantic relationships) on college students’ psychological well-being. The 251 college students in Surabaya (Indonesia) and 139 college students in Hangzhou (China), selected using purposive sampling technique, were involved in this research. All participants have at least three social networks/sources of relationship, i.e., parents, best friends, and romantic partners. The network of relationships inventory -relationship quality version (NRI-RQV) was given to identify the qualities of relationship participants have from the three sources. Participants’ psychological well-being was observed using the PERMA-Profiler. Data was analyzed using Statistical Package for the Social Sciences (SPSS). In both countries, students highlight that the quality of the relationship between parents and college students has the most substantial influence on their psychological well-being, with parental approval being one of the key components. This finding provides valuable insights for developing an effective support system for college students.
Volume: 14
Issue: 2
Page: 978-988
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

Techniques of Quran reciters recognition: a review

10.11591/ijai.v14.i3.pp1683-1695
Ibrahim Alomari , Asma Alshargabi , Mohammed Hadwan
The Quran is the holy book of the Islam. Reading and listening to the Quran is an important part of the daily life of Muslims. Muslims are keen to listen to recitations of Quran by skilled reciters to learn the correct recitation for the purpose of understanding and contemplating. Therefore, there are large variety of audio recitations for many skilled reciters. With the availability of this huge amount of recitations and also with the great progress in voice recognition technologies, many research efforts have been devoted to contribute making recitation better using artificial intelligence. One useful application in this area is identifying the reciters of the Quran. There are various solutions introduced by researchers; however, these solutions vary significantly in terms of accuracy, and efficiency. This research seeks to provide a review of these solutions. It also reviews available datasets using different criteria. Finally, some open issues and challenges were addressed.
Volume: 14
Issue: 3
Page: 1683-1695
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
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