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Analysis of bibliometrics in studying the influence of the environment on preschool children’s psychological development

10.11591/ijere.v14i4.30817
Nguyen Thi Ut Sau , Tran Thi Nhung , Pham Thi Kieu Oanh , Vu Thi Thuy , Le Thi Thuong Thuong , Nguyen Thi Hoa
This article aims to provide an overall picture of research on the environment’s influence on preschool children’s psychological development. The researchers used the preferred reporting items for systematic reviews and meta-analysis (PRISMA) method to collect data and VOSviewer software to analyze 119 articles from the Scopus database from 2000 to 2023. The results showed that since 2006, i) the environmental influence on the psychological development of preschool children has received much attention; ii) the United States and the United Kingdom are the two leading countries in terms of the number of publications; iii) Leve, Neiderhiser, Reiss, and Shaw are the four leading authors; iv) 16 out of 20 influential journals in this field are Q1 journals, most of which belong to educational psychology. The two main concerns of the authors in these 119 articles are “parenting” and “development.” In the past five years, researchers have focused on topics such as “autism,” “preschoolers,” “environment,” “COVID-19”, and “externalizing problems”.
Volume: 14
Issue: 4
Page: 3051-3064
Publish at: 2025-08-01

Exploring gender bias, belonging, and prejudice among IT students in higher education

10.11591/ijere.v14i4.33291
Ruth G. Luciano
This study investigates gender discrimination, inclusion, and belonging among information technology (IT) students in higher education, with specific objectives to assess their sense of belonging, classroom participation, and perceptions of gender bias. The research aims to identify persistent issues related to gender equity and propose actionable solutions to foster inclusivity. Using a descriptive research design, the study surveyed 180 student-leaders, purposively selected to represent various year levels and provide diverse insights into academic and extracurricular experiences. Data were collected through a structured questionnaire adapted and modified to fit the study’s context. The instrument included Likert-scale items focusing on dimensions such as belonging, teaching environment, gender discrimination, and prejudice. Findings reveal that while most students feel respected by peers and professors, subtle forms of gender bias continue to affect female students, limiting role models and occasionally undermining their confidence and engagement. Practical implications highlight the need for gender-sensitive curricula, mentorship programs, and policy adjustments to create a more inclusive academic environment. Recommendations include increasing female representation in leadership roles, conducting gender sensitivity training, and diversifying course materials. These strategies aim to enhance gender equity, support student success, and establish a culture of inclusion within IT education.
Volume: 14
Issue: 4
Page: 3224-3233
Publish at: 2025-08-01

Benefits and challenges of graduate start-up and academic spin-off model integration: a systematic review

10.11591/ijere.v14i4.30288
Fakhrul Anwar Zainol , Wan Norhayate Wan Daud , Syamsul Azri Abdul Rahman , Safrul Izani Mohd Salleh , Balogun Daud Ishola
Government representatives and university administrators must comprehend the reasons behind academics’ desire to start their own businesses to create laws that effectively encourage academics to take up entrepreneurship. One may understand how seemingly difficult it might be to foster creativity and entrepreneurship in a varied community, considering how difficult it can be to teach entrepreneurship to university students. Consequently, the goal of this systematic review was to summarize the challenges and benefits of integration of graduate start-up and academic-spin off model. Three internet databases were searched for articles between 2010 and 2023 (i.e., a cumulative index using Scopus, the Web of Science, and Emerald to provide a summary of the challenges and benefits of graduate start-up and academic spin-off models). The study adds to a thorough understanding of the complex nature of business models by highlighting the models’ dynamic evolution over time, the value of global collaboration, the necessity of carefully examining individual models, and the strategic diversity that comes from exploring several business models simultaneously. When taken as a whole, these observations offer insightful information that decision-makers, business owners, and academics may use to better understand, traverse, and navigate the terrain of innovation and entrepreneurial processes.
Volume: 14
Issue: 4
Page: 2945-2955
Publish at: 2025-08-01

Enhancing logo security: VGG19, autoencoder, and sequential fusion for fake logo detection

10.11591/ijict.v14i2.pp506-515
Debani Prasad Mishra , Prajna Jeet Ojha , Arul Kumar Dash , Sai Kanha Sethy , Sandip Ranjan Behera , Surender Reddy Salkuti
This paper deals with a way of detecting fake logos through the integration of visual geometry group-19 (VGG19), an autoencoder, and a sequential model. The approach consists of applying the method to a variety of datasets that have gone through resizing and augmentation, using VGG19 for extracting features effectively and autoencoder for abstracting them in a subtle manner. The combination of these elements in a sequential model account for the improved performance levels as far as accuracy, precision, recall, and F1-score are concerned when compared to existing approaches. This article assesses the strengths and limitations of the method and its adapted comprehension of brand identity symbols. Comparative analysis of these competing approaches reveals the benefits resulting from such fusion. To sum up, this paper is not only a major contribution to the domain of counterfeit logo detection but also suggests prospects for enhancing brand security in the digital world.
Volume: 14
Issue: 2
Page: 506-515
Publish at: 2025-08-01

Comparative evaluation of left ventricle segmentation using improved pyramid scene parsing network in echocardiography

10.11591/ijai.v14.i4.pp3214-3227
Jin Wang , Sharifah Aliman , Shafaf Ibrahim
Automatic segmentation of the left ventricle is a challenging task due to the presence of artifacts and speckle noise in echocardiography. This paper studies the ability of a fully supervised network based on pyramid scene parsing network (PSPNet) to implement echocardiographic left ventricular segmentation. First, the lightweight MobileNetv2 was selected to replace ResNet to adjust the coding structure of the neural network, reduce the computational complexity, and integrate the pyramid scene analysis module to construct the PSPNet; secondly, introduce dilated convolution and feature fusion to propose an improved PSPNet model, and study the impact of pre-training and transfer learning on model segmentation performance; finally, the public data set challenge on endocardial three-dimensional ultrasound segmentation (CETUS) was used to train and test different backbone and initialized PSPNet models. The results demonstrate that the improved PSPNet model has strong segmentation advantages in terms of accuracy and running speed. Compared with the two classic algorithms VGG and Unet, the dice similarity coefficient (DSC) index is increased by an average of 7.6%, Hausdorff distance (HD) is reduced by 2.9%, and the mean intersection over union (mIoU) is improved by 8.8%. Additionally, the running time is greatly shortened, indicating good clinical application potential.
Volume: 14
Issue: 4
Page: 3214-3227
Publish at: 2025-08-01

Enhancing touchless smart locker systems through advanced facial recognition technology: a convolutional neural network model approach

10.11591/ijai.v14.i4.pp3262-3273
Abdul Haris Rangkuti , Evawaty Tanuar , Febriant Yapson , Felix Octavio Sijoatmodjo , Varyl Hasbi Athala
As the world recovers from COVID-19, demand for contactless systems is increasing, promising safety and convenience. Touchless technology, particularly public locker security systems that use facial recognition and hand detection, is advancing rapidly. The system minimizes physical contact, increasing user safety. It uses advanced models such as multi-task cascaded convolutional networks (MTCNN) and RetinaFace, FaceNet512, ArcFace, and visual geometry group (VGG)-Face for face detection and recognition, with a combination of RetinaFace, ArcFace, and L2 norm Euclidean or cosine as the most effective distance metric method, where the accuracy reaches 96 and 90%. 'Yourvault', an application demonstrating this efficient security feature, provides notifications for mask detection, facial authenticity and locker status, offering a solution to the problem of convenience and security of public spaces. Future research could investigate the impact of photo age on facial recognition accuracy, potentially making touchless systems more efficient. In general, the application of this technology is an important step towards a safer and more comfortable world after the pandemic. This model approach can be followed up with more optimal facial recognition.
Volume: 14
Issue: 4
Page: 3262-3273
Publish at: 2025-08-01

Urban incident detection based on hybrid convolutional neural networks and bidirectional long short-term memory

10.11591/ijai.v14.i4.pp3153-3159
Meryem Ayou , Jaouad Boumhidi
Real-time incident detection is a major challenge in urban roads. This paper proposes an innovative hybrid method for incident detection, combining convolutional neural networks (CNN) and bidirectional-long short-term memory (Bi-LSTM). CNN extracts complex spatial features from raw data, while Bi-LSTMs are used for incident detection by capturing long-term temporal dependencies present in data. The proposed algorithm is evaluated using simulated data from the open-source software simulation of urban mobility (SUMO). This combination improves incident detection's accuracy and robustness by exploiting spatial and temporal information. Experimental results show that our hybrid approach outperforms the support vector machine (SVM), random forest (RF), and Bi-LSTM algorithms, with a substantial decrease in false positives and the speed of detecting urgent situations.
Volume: 14
Issue: 4
Page: 3153-3159
Publish at: 2025-08-01

The degree of exercise of academic freedom and its relationship to job satisfaction: ‎Al-Yarmouk University model

10.11591/ijere.v14i4.31445
Khawla Mahmoud Al Alawneh , Nidaa Izhiman , Shaima Mokhemer Yahya , Sara Mohammad El-Freihat
Academic freedom in Jordanian universities is the subject of debate and research, due to the policies of university education imposed within the country. However, academic freedom has been researched in Jordan for more than two years. This study aimed to find out the level of academic freedom practiced by faculty members at Yarmouk University and its relationship to job satisfaction from a point of view, as there was recently a difference in views on the exercise of academic freedom or the imposition of educational policies in Jordanian universities according to the views of academics. About 317 members of the university were selected in a simple random way. The correlational descriptive approach and a two-part questionnaire were used: the first part measures the degree of academic freedom exercised by faculty members in three areas (freedom of expression, research, and teaching). The second part measures the level of job satisfaction. The results indicated that the degree of academic freedom exercised by faculty members was high, as well as the level of job satisfaction, as it was found that faculty members were significantly satisfied with their jobs. There was also a correlation between the degree of academic freedom exercised by faculty members and the level of job satisfaction of faculty members. These results shed positive light on the policies adopted in the country to educate university students in order to continue to keep pace with global developments and global trends in university education and work to amend some university education policies with the concerned authorities.
Volume: 14
Issue: 4
Page: 2822-2831
Publish at: 2025-08-01

An automatic social engagement measurement during human-robot interaction

10.11591/ijai.v14.i4.pp2805-2814
Wael Hasan Ali Almohammed , Sinan Adnan Muhisn , Zahraa AbedAljasim Muhisn
Social engagement refers the expressions of existing interpersonal relationships during the interaction which represents the actual interesting of human in the interaction. However, social engagement measurement is a significant concern in social human-robot interaction (HRI) because of its role in understanding the interaction’s trend and adapt robot’s behavior accordingly. Hence, we achieved the two main objectives of this study. Firstly, enrichment the theoretical literature and related concepts. Secondly, proposed a robust neural network model which is multilayer perceptron (MLP) classifier to measure social engagement state during interaction. PInSoRo dataset was used for training and testing purpose. In particular, the parameters of MLP model were meticulously crafted to recognize the social engagement accurately. We evaluated the model’s performance by several metrics and the result showed an interesting accuracy reached 94.85%. Given that, it supports the robot to has adaptive and responsive behavior in real time applications which is improving HRI eventually.
Volume: 14
Issue: 4
Page: 2805-2814
Publish at: 2025-08-01

Gender differences in motivation and problem-solving in a physics course online problem-based learning

10.11591/ijere.v14i4.31105
Elnetthra Folly Eldy , Fauziah Sulaiman , Mohd Zaki Ishak , Lorna Uden , Jo-Ann Netto-Shek
Online learning has been crucial since COVID-19, yet its effectiveness, particularly in physics education, remains debated. Understanding students’ motivation and problem-solving abilities in online environments is critical. This paper examined and presented the gender difference in motivation and problem-solving skills using an integrated online problem-based learning (iON-PBL) in a physics course. Developed using analysis, design, development, implementation, and evaluation (ADDIE) mode, iON-PBL module of physics guided students through problem-solving activities over 13 weeks. A post-test–delayed post-test design was used to assess retention of motivation and problem-solving skills. The study involved 116 pre-university students from Universiti Malaysia Sabah (88 females, 28 males). Motivation was measured using the motivated strategies for learning questionnaire (MSLQ) (four components), and problem-solving skills were assessed with the problem-solving inventory (PSI) (three components). Data analysis was conducted using SPSS version 28. Findings showed a significant gender difference in the ‘cognitive strategy’ component of motivation at the post-test, favoring female students. However, this difference was not sustained in the delayed post-test. In contrast, no gender difference was found in problem-solving at the post-test, but females scored significantly higher in ‘personal control’ in the delayed post-test. These findings suggest that female students are more likely to maintain cognitive strategies and personal control in online learning. Educators should consider targeted strategies to support male students’ motivation and problem-solving development in virtual environments to foster gender equity. Educators should consider targeted strategies to support male students’ motivation and problem-solving development in virtual environments to foster gender equity.
Volume: 14
Issue: 4
Page: 2832-2845
Publish at: 2025-08-01

Leveraging machine learning for column generation in the dial-a-ride problem with driver preferences

10.11591/ijai.v14.i4.pp2826-2838
Sana Ouasaid , Mohammed Saddoune
The dial-a-ride problem (DARP) is a significant challenge in door-to-door transportation, requiring the development of feasible schedules for transportation requests while respecting various constraints. This paper addresses a variant of DARP with time windows and drivers’ preferences (DARPDP). We introduce a solution methodology integrating machine learning (ML) into a column generation (CG) algorithm framework. The problem is reformulated into a master problem and a pricing subproblem. Initially, a clustering-based approach generates the initial columns, followed by a customized ML-based heuristic to solve each pricing subproblem. Experimental results demonstrate the efficiency of our approach: it reduces the number of the new generated columns by up to 25%, accelerating the convergence of the CG algorithm. Furthermore, it achieves a solution cost gap of only 1.08% compared to the best-known solution for large instances, while significantly reducing computation time.
Volume: 14
Issue: 4
Page: 2826-2838
Publish at: 2025-08-01

Data-driven support vector regression-genetic algorithm model for predicting the diphtheria distribution

10.11591/ijai.v14.i4.pp2909-2921
Wiwik Anggraeni , Yeyen Sudiarti , Muhammad Ilham Perdana , Edwin Riksakomara , Adri Gabriel Sooai
Indonesia is one of the countries with the largest number of diphtheria sufferers in the world. Diphtheria is a case of re-emerging disease, especially in Indonesia. Diphtheria can be prevented by immunization. Diphtheria immunization has drastically reduced mortality and susceptibility to diphtheria, but it is still a significant childhood health problem. This study predicted the number of diphtheria patients in several regions using support vector regression (SVR) combined with the genetic algorithm (GA) for parameter optimization. The area is grouped into 3 clusters based on the number of cases. The proposed method is proven to overcome overfitting and avoid local optima. Model robustness tests were carried out in several other regions in each cluster. Based on the experiments in three scenarios and 12 areas, the hybrid model shows good forecasting results with an average mean squared error (MSE) of 0.036 and a symmetric mean absolute percentage error (SMAPE) of 41.2% with a standard deviation of 0.075 and 0.442, respectively. Based on experiments in various scenarios, the SVR-GA model shows better performance than others. Compares two- means tests on MSE and SMAPE were given to prove that SVR-GA models have better performance. The results of this forecasting can be used as a basis for policy-making to minimize the spread of diphtheria cases.
Volume: 14
Issue: 4
Page: 2909-2921
Publish at: 2025-08-01

Contract-based federated learning framework for intrusion detection system in internet of things networks

10.11591/ijai.v14.i4.pp3324-3333
Yuris Mulya Saputra , Divi Galih Prasetyo Putri , Jimmy Trio Putra , Budi Bayu Murti , Wahyono Wahyono
A plethora of national vital infrastructures connected to internet of things (IoT) networks may trigger serious data security vulnerabilities. To address the issue, intrusion detection systems (IDS) were investigated where the behavior and traffic of IoT networks are monitored to determine whether malicious attacks or not occur through centralized learning on a cloud. Nonetheless, such a method requires IoT devices to transmit their local network traffic data to the cloud, thereby leading to data breaches. This paper proposes a federated learning (FL)-based IDS on IoT networks aiming at improving the intrusion detection accuracy without privacy leakage from the IoT devices. Specifically, an IoT service provider can first motivate IoT devices to participate in the FL process via a contract-based incentive mechanism according to their local data. Then, the FL process is executed to predict IoT network traffic types without sending IoT devices’ local data to the cloud. Here, each IoT device performs the learning process locally and only sends the trained model to the cloud for the model update. The proposed FL-based system achieves a higher utility (up to 44%) than that of a non-contract-based incentive mechanism and a higher prediction accuracy (up to 3%) than that of the local learning method using a real-world IoT network traffic dataset.
Volume: 14
Issue: 4
Page: 3324-3333
Publish at: 2025-08-01

Deep learning for grape leaf disease detection

10.11591/ijict.v14i2.pp653-662
Pragati Patil , Priyanka Jadhav , Nandini Chaudhari , Nitesh Sureja , Umesh Pawar
Agriculture is crucial to India's economy. Agriculture supports almost 75% of the world's population and much of its gross domestic product (GDP). Climate and environmental changes pose a threat to agriculture. India is recognized for its grapes, a commercially important fruit. Diseases reduce grape yields by 10-30%. If not recognized and treated early, grape diseases can cost farmers a lot. The main grape diseases include downy and powdery mildew, leaf blight, esca, and black rot. This work creates an Android grape disease detection app which uses machine learning. When a farmer submits a snapshot of a diseased grape leaf, the smartphone app identifies the ailment and offers grape plant disease prevention tips. In this research, an android app that detects grape plant illnesses use convolutional neural network (CNN) and AlexNet machine learning architectures. We investigated and compared CNN and AlexNet architecture's efficacy for grape disease detection using accuracy and other metrics. The dataset used comes from Kaggle. CNN and AlexNet architectures yielded 98.04% and 99.03% accuracy. AlexNet was more accurate than CNN in the final result.
Volume: 14
Issue: 2
Page: 653-662
Publish at: 2025-08-01

Enhancing face mask detection performance with comprehensive dataset and YOLOv8

10.11591/ijai.v14.i4.pp2634-2645
Trong Thua Huynh , Hoang Thanh Nguyen
In the context of the COVID-19 pandemic and the risk of similar infectious diseases, monitoring and promoting public health measures like wearing face masks have become crucial in controlling virus transmission. Deep learning-based mask recognition systems play an important role, but their effectiveness depends on the quality and diversity of training datasets. This study proposes the diverse and robust dataset for face mask detection (DRFMD), designed to address limitations of existing datasets and enhance mask recognition models' performance. DRFMD integrates data from sources such as AIZOO, face mask detector by Karan-Malik (KFMD), masked faces (MAFA), MOXA3K, properly wearing masked face detection dataset (PWMFD), and the Zalo AI challenge 2022, comprising 14,727 images with 29,846 instances, divided into training, validation, and testing sets. The dataset's scale and diversity ensure higher accuracy and better generalization for mask recognition models. Experiments with variations of the YOLOv8 model (n, s, m, l, x), an advanced object detection algorithm, on the DRFMD dataset, demonstrate superior performance through metrics like precision, recall, and mAP@50. Additionally, comparisons with previous dataset like FMMD show that models trained on DRFMD maintain strong generalization capabilities and higher performance. This study significantly contributes to improving accuracy of public health monitoring systems, aiding in the prevention of hazards from infectious diseases and air pollution.
Volume: 14
Issue: 4
Page: 2634-2645
Publish at: 2025-08-01
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