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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

Body dissatisfaction and body weight status perception among overweight and obese children

10.11591/ijphs.v14i2.25523
Wan Mohd Nurussabah Abdul Karim , Hazizi Abu Saad
Children's perceptions of their weight, body image, and misjudgment of their actual body size can lead to body dissatisfaction, increasing health concerns related to childhood obesity. This cross-sectional study investigated the associations between body dissatisfaction and body weight status (BWS) perception among overweight and obese children aged 10 and 11 years. A total of 398 overweight and obese participants were selected and recruited from eleven primary schools in the state of Kedah, Malaysia. The questionnaire on body image perception was adapted from ‘The seven male and female child figure rating scale,’ and information on BWS perception was collected through a question on how respondents perceived their current BWS. The majority of children (89.4%) desired a smaller body size, and 55.5% did not perceive their BWS correctly; 50% were categorized as “underestimators,” and 5.5% as “overestimators.” There was no significant association between body size discrepancy score and age (p=0.110). However, a significant association was found between the perception of current body image and body mass index (BMI) (p<0.001). The high discrepancies between the perception of current body image and actual weight status need to be addressed, as this misperception could hinder overweight and obese children from successfully achieving a healthy body weight.
Volume: 14
Issue: 2
Page: 869-876
Publish at: 2025-06-01

Region based lossless compression for digital images using entropy coding

10.11591/ijeecs.v38.i3.pp1870-1879
Mangalapalli Vamsikrishna , Oggi Sudhakar , Bhagya Prasad Bugge , Asileti Suneel Kumar , Blessy Thankachan , K.B.V.S.R. Subrahmanyam , Natha Deepthi , Praveen Mande
Image compression is a method for reducing video and image storage space. Moreover, enhancing the performance of the transmission and storage processes is important. The region based coding technique is important for compressing and sending medical images. In the medical field, lossless compression can help telemedicine applications achieve high efficiency. It affects image quality and takes a long time to encode. As a result, this study proposes region-based lossless compression for digital images using entropy coding. The best performance is achieved by segmenting these areas. In this case, an integer wavelet transform (IWT) is utilized after the ROI of the image was manually generated. The IWT compression method is helpful for reversibly reconstructing the original image to the required quality. For enhancing the quality of compression, entropy coding is utilized. By passing images of varying sizes and formats, various quantitative metrics can be determined. The simulation results demonstrate that the region based lossless compression technique utilizing range blocks and iterations resulted in reduced encoding time and improved quality.
Volume: 38
Issue: 3
Page: 1870-1879
Publish at: 2025-06-01

Calibration of phased array antenna with the minimum point finding method of the array factor

10.11591/ijeecs.v38.i2.pp854-864
Nguyen Xuan Luong , Nguyen Trong Nhan , Tran Van Thanh , Dang Thi Thanh Thuy
The problem of phased array antenna calibration with the minimum point finding method of the array factor is investigated. A mathematical model of the minimum point finding method is presented. Then, the proposed method is applied to the phased array antenna and compared with the traditional rotating-element electric-field vectors method. Experimental verification of the mathematical model of the proposed method showed the following: the minimum point finding method determines the phase shift more accurately than the maximum point finding method of the array factor; the proposed method showed a better detection range per phase change corresponding to a 35 dB higher resolution. The error ranges of the minimum and the maximum point finding methods were 50 and 700 , respectively. The peak of the combined beam when using the minimum point finding method is higher than the maximum point finding method which is 3.7 ... 4.1 dB. One can use the research results in large-scale phased array antenna calibration systems during the production phase.
Volume: 38
Issue: 2
Page: 854-864
Publish at: 2025-05-01

Enhancing accessibility: deep learning-based image description for individuals with visual impairments

10.11591/ijeecs.v38.i2.pp1051-1060
Nidhi B. Shah , Amit P. Ganatra
Technological developments in artificial intelligence, namely in the area of deep learning, have created new avenues for enhancing accessibility for those with visual impairments. In order to improve the capacity of people who are blind or visually impaired to understand and interact with visual material, this research investigates the creation and use of deep learning-based image description systems. We provide a comprehensive method that uses recurrent neural networks (RNNs) to generate natural language descriptions and convolutional neural networks (CNNs) and Autoencoders for extracting picture features. Our technology automatically creates comprehensive, context-aware descriptions of photographs by incorporating these models, giving users a better knowledge of their surroundings. We show the accuracy and reliability of the system on a wide range of photos through comprehensive testing. According to our research, deep learning-based picture description systems and converting the description in audio and making a promise to empower people who are visually impaired and foster diversity in the digital sphere.
Volume: 38
Issue: 2
Page: 1051-1060
Publish at: 2025-05-01

Acute lymphoblastic leukemia diagnosis and subtype segmentation in blood smears using CNN and U-Net

10.11591/ijeecs.v38.i2.pp950-959
Hamim Reza , Nazrul Islam Tareq , M M Fazle Rabbi , Sharia Arfin Tanim , Rifat Al Mamun Rudro , Kamruddin Nur
Acute lymphoblastic leukaemia (ALL) is a severe disease requiring invasive, expensive, and time-consuming diagnostic tests for definitive diagnosis. Initial diagnosis using blood smear pictures (BSP) is crucial but challenging due to the similar indications and symptoms of ALL, often leading to misdiagnoses. This study presents a custom approach using Convolutional Neural Networks (CNNs) to detect all cases and categorize subtypes. Utilizing publicly available databases, the study includes 3562 blood smear images from 89 patients. The innovative combination of U-Net for segmentation and various CNN architectures (U-Net, MobileNetV2, InceptionV3, ResNet50, NASNet) for feature extraction, with DenseNet201 being the most effective, forms the core of this method. The U-Net model achieved a segmentation accuracy of 98% by recognizing patterns within blood smear images. Following segmentation, CNN architectures extracted high-level features, with DenseNet201 proving the most effective in diagnostic and classification tasks. Our proposed custom CNN model achieved a test accuracy of 98%, with a training accuracy of 99.31% and validation accuracy of 97.09%. This approach enables an accurate distinction between ALL and non-pathologic cases.
Volume: 38
Issue: 2
Page: 950-959
Publish at: 2025-05-01

Plant leaf disease detection and classification using artificial intelligence techniques: a review

10.11591/ijeecs.v38.i2.pp1308-1323
Kusuma R , R. Rajkumar
Agriculture is a cornerstone of human civilization, providing both food and economic stability. While not necessarily fatal, leaf diseases are a crucial threat to plant health. Accurate detection and classification of diseases in early stages are essential to minimize damage. Manual identification can be challenging, and delays in detection can lead to crop devastation. Fortunately, computer-aided image processing offers a solution. Researchers have explored several techniques for disease detection and classification by usage of affected leaf images, making significant progress over time. However, there's always room for improvement. Machine learning (ML), Deep learning (DL) techniques have shown hopeful results. ML, DL approaches act as black-box; eXplainable AI (XAI) provides clear explanations on decisions made by these black-boxes. This study aims to present a comprehensive review on plant leaf disease detection and classification by means of ML, DL and XAI methods with an overview of the outcomes of existing techniques, summarizes their performance, evaluation metrics, and analyses the challenges in existing systems, and offers the study's inferences.
Volume: 38
Issue: 2
Page: 1308-1323
Publish at: 2025-05-01

Temperature-dependent based optimal reactive power dispatch by chaotic equilibrium optimization algorithm

10.11591/ijeecs.v38.i2.pp698-712
Minh Trung Dao , Ngoc Dieu Vo
The optimal reactive power dispatch (ORPD) problem is considered as an important aspect in power system operation of the reactive power, which is vital to maintain network voltage within desirable limit for system reliability. In conventional ORPD problem, the resistance of components in power systems is considered to be independent to their temperature variations. Actually, there is a correlation between the branch resistance and temperature, thus the temperature should be taken into account when performing power flow analysis to improve the accuracy in the calculation of the power flow and power loss on branches. This paper proposes a new chaotic equilibrium optimization (CEO) method to solve the temperature-dependent based optimal reactive power dispatch (TDORPD) problem in power systems by optimizing the reactive power loss and voltage deviation. The proposed CEO algorithm is implemented for the conventional ORPD and TDORPD problems on the benchmark IEEE 30 bus testing network. Moreover, the effects of temperature variations on the considered TDORPD problem are also considered. The obtained results have demonstrated a better performance of the proposed CEO algorithm compared to the original EO and other methods in the literature review for the problem in terms of the solution quality, which confirms its efficacy to effectively resolve the ORPD and TDORPD problem.
Volume: 38
Issue: 2
Page: 698-712
Publish at: 2025-05-01

Indonesian sentiment analysis in natural environment topics

10.11591/ijeecs.v38.i2.pp1353-1366
Christofer Octovianto , Muhammad Okky Ibrohim , Indra Budi
Indonesia is one of the countries that is rich in biodiversity and has a high population growth. This condition can cause Indonesia to have problems related to the natural environment that are more complex than other countries. Hence, this has created a lot of discussions regarding natural environmental issues in Indonesia on social media platforms. In this case, stakeholders like the government in general can utilize sentiment analysis (SA) to comprehend the public’s views to allow them to better fit the public’s expectations when formulating a particular policy that related to the environmental sustainability (ES) issues. This paper built the first open dataset of Indonesian SA dataset in ES topics collected from Instagram. As the benchmark of our dataset, we used IndoBERT model variant for constructing the model and the experiment result shows that model based on IndoBERT-large-p2 obtained the best performance with 72.44% of F1-score.
Volume: 38
Issue: 2
Page: 1353-1366
Publish at: 2025-05-01

Multi-camera multi-person tracking with DeepSORT and MySQL

10.11591/ijeecs.v38.i2.pp997-1009
Shashank Horakodige Raghavendra , Yashasvi Sorapalli , Nehashri Poojar S. V. , Hrithik Maddirala , Ramakanth Kumar P. , Azra Nasreen , Neeta Trivedi , Ashish Agarwal , Sreelakshmi K.
Multi-camera multi-object tracking refers to the process of simultaneously tracking numerous objects using a network of connected cameras. Constructing an accurate depiction of an object’s movements requires the analysis of video data from many camera feeds, detection of items of interest, and their association across various camera perspectives. The objective is to accurately estimate the trajectories of the objects as they navigate through a monitored area. It has several uses, including surveillance, robotics, self-driving cars, and augmented reality. The current version of an object tracking algorithm, DeepSORT, doesn’t account for errors caused by occlusion or implementation of multiple cameras. In this paper, DeepSORT has been extended by introducing new states to improve the tracking performance in scenarios where objects are occluded in the presence of multiple cameras. The communication of track information across multiple cameras is achieved with the help of a database. The suggested system performs better in situations where objects are occluded, whether due to object occlusions or person occlusions.
Volume: 38
Issue: 2
Page: 997-1009
Publish at: 2025-05-01

Spatial-temporal data imputation for predictive modeling in intelligent transportation systems

10.11591/ijeecs.v38.i2.pp794-807
Yohanes Pracoyo Widi Prasetyo , Linawati Linawati , Dewa Made Wiharta , Nyoman Putra Sastra
Data imputation is necessary to overcome data loss in intelligent transportation systems (ITS) due to the many sensors used to monitor traffic conditions. Sensor malfunction, hardware limitations, and technical glitches can lead to incomplete data, potentially leading to errors in traffic data analysis. This analysis investigated spatial-temporal data imputation approaches applied for predictive modeling in ITS. Each approach's strengths, weaknesses, and applicability in the context of ITS are evaluated. We analyzed various imputation approaches involving statistical, machine learning, and combined methods. Statistical methods are more straightforward but could effectively handle modern traffic's complexity. On the other hand, machine learning and combined approaches, such as hybrid convolutional neural network (CNN)- long short-term memory (LSTM), offer more robust capabilities in capturing non-linear patterns present in spatio-temporal data. This research aims to investigate the effectiveness of each approach in overcoming data incompleteness and the accuracy of predicting future traffic conditions with the widespread adoption of IoT, electric vehicles, and autonomous vehicles. The results of this investigation provide an understanding of the most suitable approaches to address the challenges of spatio-temporal data imputation and provide practical guidance for predictive modeling in ITS.
Volume: 38
Issue: 2
Page: 794-807
Publish at: 2025-05-01

Enhance big data security based on HDFS using the hybrid approach

10.11591/ijeecs.v38.i2.pp1256-1264
Fayçal Zine-Dine , Sara Alcabnani , Ahmed Azouaoui , Jamal El Kafi
Hadoop has emerged as a prominent open-source framework for the storage, management, and processing of extensive big data through its distributed file system, known as Hadoop distributed file system (HDFS). This widespread adoption can be attributed to its capacity to provide reliable, scalable, and cost-effective solutions for managing large datasets across diverse sectors, including finance, healthcare, and social media. Nevertheless, as the significance and scale of big data applications continue to expand, the challenge of ensuring the security and safeguarding of sensitive data within Hadoop has become increasingly critical. In this study, the authors introduce a novel strategy aimed at bolstering data security within the Hadoop storage framework. This approach specifically employs a hybrid encryption technique that leverages the advantages of both advanced encryption standard (AES) and data encryption standard (DES) algorithms, whereby files are encrypted in HDFS and subsequently decrypted during the map task. To assess the efficacy of this method, the authors performed experiments with various file sizes, benchmarking the outcomes against other established security measures.
Volume: 38
Issue: 2
Page: 1256-1264
Publish at: 2025-05-01

A recurrent network technique for energy optimization in 6G networks with dynamic device-to-device communication

10.11591/ijeecs.v38.i2.pp897-903
Sonia Aneesh , Alam N. Shaikh
Energy efficiency has become a paramount concern in the design and deployment of 6G networks, driven by the exponential growth of connected devices and increasing traffic demands. For domain experts grappling with dynamic device-to-device (D2D) communication scenarios, optimizing energy consumption while maintaining reliable connectivity poses a significant challenge. To address this issue, we propose a novel recurrent network technique that dynamically configures D2D communication patterns, adaptively allocating temporary base stations among network nodes to enable efficient data transmission while minimizing energy expenditure. Our simulations demonstrate substantial energy savings, extended node lifetimes, and reliable performance, with a 37% reduction in overall network energy consumption and a 65% increase in average node lifetime compared to traditional cellular communication scenarios. In conclusion, this innovative approach paves the way for sustainable and energy efficient 6G communication systems, benefiting society by reducing operational costs, minimizing environmental impact, and prolonging the usability of mobile devices.
Volume: 38
Issue: 2
Page: 897-903
Publish at: 2025-05-01

Enhancing uncollateralized loan risk assessment accuracy through feature selection and advanced machine learning techniques

10.11591/ijeecs.v38.i2.pp1149-1161
Shahrul Nizam Salahudin , Yosza Dasril , Yosy Arisandy
Accuracy in evaluating the risk of credit applications is crucial for lenders, particularly when dealing with unsecured loans. Accuracy can be enhanced by selecting suitable features for a machine learning model. To better identify high-risk borrowers, this study applies an elaborate feature selection technique. This study uses the light gradient boosting machine (LGBM) Classifier model with boosting type gradient boosting decision tree (GBDT) algorithm and n_estimator value 100 for feature selection process. This work uses advanced machine learning techniques namely stacking to improve accuracy model perform. The dataset consists of 307,506 applicants from European lenders who have applied for loans in Southeast Asia. Each applicant is described by 126 different features. Using GDBT algorithm GBDT, 30 best features were selected based on their maximum accuracy compared to another feature. By employing a stacking technique that combines the LGBM, gradient boosting (GB), and random forest (RF) models, and utilizing logistic regression (LR) as the final estimator, an accuracy of 0.99637 was reached. This study demonstrates an improved the accuracy compared to previous research. This discovery indicates that utilizing feature selection and stacking method can provide one of the most precise choices for modelling the binary class classification among the current models.
Volume: 38
Issue: 2
Page: 1149-1161
Publish at: 2025-05-01

Artificial intelligence approaches for cardiovascular disease prediction: a systematic review

10.11591/ijeecs.v38.i2.pp1208-1218
Jasim Faraj Hammadi , Aliza Binti Abdul Latif , Zaihisma Binti Che Cob
Cardiovascular disease (CVD) remains a top global cause of mortality, highlighting the critical need for precise prediction models to improve patient outcomes and optimize healthcare resource allocation. Accurate prediction of CVD is paramount for early diagnosis and reducing mortality rates. Achieving efficient CVD detection and prediction requires a deep understanding of health history and the underlying causes of heart disease. Harnessing the power of data analytics proves advantageous in leveraging vast datasets to make informed predictions, aiding healthcare clinics in disease prognosis. By consistently maintaining comprehensive patient-related data, healthcare providers can anticipate the emergence of potential diseases. Our study conducts a meticulous comparative analysis of CVD prediction methods, focusing on various artificial intelligence (AI) algorithms, particularly classification and predictive algorithms. Scrutinizing approximately sixty papers on cardiovascular disease through the prism of AI techniques, this study carefully assesses the selected literature, uncovering gaps in existing research. The outcomes of this study are expected to empower medical practitioners in proactively predicting potential heart threats and facilitating the implementation of preventive measures.
Volume: 38
Issue: 2
Page: 1208-1218
Publish at: 2025-05-01
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