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Comparative analysis of YOLOv8 techniques: OpenCV and coordinate attention weighting for distance perception in blind navigation systems

10.11591/ijece.v15i3.pp3267-3278
Ema Utami , Erwin Syahrudin , Anggit Dwi Hartanto
Blindness is a very important issue to consider in research aimed at assisting vision. This condition requires further study to provide solutions for the blind. This study evaluates and compares the effectiveness of the you only look once v8 (YOLOv8) model integrated with OpenCV and the coordinate attention weighting (CAW) technique for distance estimation in a blind navigation system. Initially, YOLOv8 integrated with OpenCV produced less than optimal results, prompting further improvement efforts to surpass the performance of CAW. The goal is to enhance the accuracy and efficiency of distance perception without the need for additional sensors. The materials used include a variety of datasets annotated with distance information to train and evaluate the model. The methods employed include integrating YOLOv8 with OpenCV for baseline comparison and applying CAW to improve distance perception through enhanced feature attention. The results show that YOLOv8+OpenCV Improved achieves the lowest mean squared error (MSE) across the entire distance range: 0-1 m (0.44), 1-2 m (0.50), 2-3 m (0.58), 3-4 m (0.64), and 4-5 m (0.71). YOLOv8+CAW also outperforms YOLOv8+OpenCV original, demonstrating a notable enhancement in accuracy. The model achieves a detection accuracy of 95.7%, showcasing the effectiveness of computer vision techniques in supporting blind navigation systems, offering precise distance estimation capabilities and reducing the reliance on external sensors. The implications include improved real-time performance and accessibility for the blind, paving the way for more efficient and reliable navigation assistance technologies.
Volume: 15
Issue: 3
Page: 3267-3278
Publish at: 2025-06-01

The practical reality of learning assessment in initial teacher training from the perspective of students vs teachers

10.11591/ijere.v14i3.32957
Francisco Gallardo-Fuentes , Bastian Carter-Thuillier , Sebastian Peña-Troncoso , Luis Añazco-Martínez , Jorge Gallardo-Fuentes
A central issue in the initial training of physical education teachers lies in the dominance of traditional assessment systems that fail to fully support student learning and engagement. This study addresses this issue by identifying the assessment systems used in four university campuses in the southernmost region of Chile, comparing the perspectives of teachers and students. A sample of 538 students (M=21.8, SD=2.9) and 60 teachers (M=42.9, SD=12.3) was surveyed using the “Questionnaire for the study of the assessment system in the initial training of physical education teachers”. The results revealed significant differences between students and teachers in their perception of the importance of cognitive abilities and the coherence of syllabus elements. Traditional assessment tools were used more frequently, and students attributed failed assessments to issues with teaching methods. Additionally, students perceived having less influence on grading processes. These findings suggest a need for reform in assessment practices, emphasizing more formative and participatory approaches to better align with student needs and improve the learning process in physical education teacher training. The practical applications of the study facilitate implementing formative assessment in physical education with active feedback and training teachers in shared assessment.
Volume: 14
Issue: 3
Page: 2409-2418
Publish at: 2025-06-01

Cuckoo search algorithm approach for optimal placement and sizing of distribution generation in radial distribution networks

10.11591/ijece.v15i3.pp2681-2696
Kayode Ojo , Seyi Fanifosi , Awelewa Ayokunle , Isaac Samuel
Radial distribution networks (RDNs) often experience power loss due to improper distribution generation (DG) allocation. Strategic DG placement can reduce power loss, minimize costs, and improve voltage profiles and stability. This research optimizes DG placement and sizing in RDNs using the cuckoo search algorithm (CSA). The objective function considers losses across all network branches, and CSA identifies optimal DG locations and sizes. Tested on IEEE 33-bus, IEEE 69-bus, and Nigeria's Imalefalafia 32-bus RDN, the Cuckoo Search technique results in optimal DG locations at buses 6, 50, and 18 with corresponding sizes of 2.4576, 1.852, and 2.718 MW, respectively. Voltage improvements are 0.9509, 0.9817, and 0.9821 p.u, while total active and reactive power losses for IEEE 33-bus are reduced by 49.03% and 45.00%, and for IEEE 69-bus by 63.67% and 61.14%. The CSA approach significantly enhances voltage profiles and reduces power losses in these networks.
Volume: 15
Issue: 3
Page: 2681-2696
Publish at: 2025-06-01

Generation of business process modeling notation diagrams from textual functional requirements in Indonesian

10.11591/ijece.v15i3.pp2938-2950
Sholiq Sholiq , Muhammad Ainul Yaqin , Apol Pribadi Subriadi , Bambang Setiawan
This study proposes a method for converting textual functional requirements in Indonesian to business process modeling notation (BPMN) diagrams. has not been found in previous studies. The use of BPMN diagrams to present software functional requirements has the advantage of being better in terms of presenting sequential activities than using use case diagrams. On the other hand, the requirements obtained from clients in the requirements collection session are more in the form of user stories, namely text in natural language. The method used in this study is to integrate natural language processing and a set of mapping rules and BPMN diagram generation rules. The proposed method is tested with 15 functional requirement cases from three applications, namely mini hospital software, employee cooperatives, and stores. Then, the results are compared with diagrams made by experts for the same cases. The test results show an accurate level of the proposed method of 94.4%.
Volume: 15
Issue: 3
Page: 2938-2950
Publish at: 2025-06-01

Unraveling the predictors of research utilization among Thai educators: evidence from PLS-SEM analysis

10.11591/ijere.v14i3.31468
Phuchit Laowang , Suntonrapot Damrongpanit
This groundbreaking study unveils critical factors driving research utilization (RU) among Thai educators, offering vital insights for educational policymakers and administrators. Employing an advanced partial least squares structural equation modeling (PLS-SEM) approach, we examined data from 688 teachers under the office of the basic education commission. Our findings reveal a complex interplay of factors influencing RU, with organizational support (SUPP) emerging as the most potent driver (beta=0.570), followed by knowledge and research skills (KNOWS) (beta=0.539), organizational leadership (LEAD) (beta=0.472), and attributes of research (ATTR) (beta=0.391). Interestingly, ATTR showed the highest direct effect (DE) (beta=0.391), while LEAD had the strongest indirect impact (beta=0.429). Surprisingly, organizational climate (ORGA) showed no significant effect, challenging conventional wisdom. The study explains 52.5% of the variance in RU, providing a robust foundation for evidence-based educational reforms. Delve into our analysis to discover how these relationships between knowledge, leadership, and organizational dynamics shape educational RU in Thailand, and explore our recommendations for enhancing research integration in educational practices.
Volume: 14
Issue: 3
Page: 1684-1694
Publish at: 2025-06-01

Impact of immersive learning environments on the development of technological competencies in students

10.11591/ijere.v14i3.33034
Marily Yenifer Mamani-Choque , Javier Ignacio Machaca-Casani , Benjamín Maraza-Quispe
This research identifies the need to enhance students’ technological competencies in designing and building technological solutions through immersive learning environments. The study proposes the use of immersive technologies, such as virtual and augmented reality (AR), to foster creativity and improve students’ ability to develop effective solutions. The aim was to evaluate the impact of immersive learning on the competence of “designing and building technological solutions” in higher education students, focusing on four specific capacities: determining, designing, implementing, and validating solutions, as well as promoting innovation. Using a quantitative, descriptive cross-sectional experimental design, structured questionnaires were administered to 35 students engaged in an immersive technology program. Data were analyzed using descriptive and comparative techniques, revealing a significant positive impact on technological competencies. Students found these experiences to be relevant, engaging, and conducive to creativity, improving their ability to design and test solutions. However, challenges such as resource availability and the need for continuous teacher training were noted. The study concludes that immersive technologies are effective in enhancing academic performance and practical skills, emphasizing their integration in various educational contexts to strengthen key competencies essential for the 21st century.
Volume: 14
Issue: 3
Page: 2249-2262
Publish at: 2025-06-01

Enhancing cyberbullying detection with advanced text preprocessing and machine learning

10.11591/ijece.v15i3.pp3139-3148
Rakesh Bapu Dhumale , Ajay Kumar Dass , Amit Umbrajkaar , Pradeep Mane
The use of social media and the internet has been increasing dramatically in recent years. Cyber-bullying is the term used to describe the misuse of social media by some people who make threatening comments. This has a devastating influence on people's lives, especially those of children and teenagers, and can lead to feelings of depression and suicidal thoughts. The methodology proposed in this paper includes four steps for identifying cyberbullying: preprocessing, feature extraction, classification, and evaluation. The first step is to create a labeled, varied dataset. Word2Vec and term frequency-inverse document frequency are used in feature extraction to transform text into high-dimensional vectors. Word2Vec creates word embeddings using the skip-gram and continuous bag-of-words models, while term frequency-inverse document frequency assesses the text's term relevancy. Support vector machine classifiers are used in the model, and their effectiveness is compared to that of other techniques like logistic regression and naïve Bayes. The classifiers support vector machine, naïve Bayes, and logistic regression were assessed. The maximum accuracy was 95% for the support vector classifier with skip-gram and 93% for continuous bag-of-words. For sentiment categories, F1-scores, recall, and precision were computed. The average precision and recall were 0.77 and 0.79, respectively.
Volume: 15
Issue: 3
Page: 3139-3148
Publish at: 2025-06-01

Local knowledge in inclusive education: a systematic literature review

10.11591/ijere.v14i3.30218
Dwitya Sobat Ady Dharma , Mumpuniarti Mumpuniarti , Ariefa Efianingrum , Ibnu Syamsi
This article presents a comprehensive literature review on the role of local knowledge in inclusive education. Employing a systematic review methodology, the study involved goal setting, article selection through abstract and keyword analysis, thorough reading, data abstraction, and subsequent analysis using Publish or Perish 8, Mendeley, and VOSviewer. The review focused on articles published in Scopus-indexed journals between 2020 and 2023. Initial searches identified 259 articles, which were refined to 68 based on their relevance to the research questions. The analysis of these 68 articles revealed three principal findings: i) the diversity of local knowledge in the implementation of inclusive education; ii) global support for integrating local knowledge within inclusive education frameworks; and iii) strategies for incorporating local knowledge into educational practices. These findings underscore the potential of local knowledge to enhance inclusive education through culturally relevant and contextually sensitive approaches, fostering more holistic and responsive educational practices. The review emphasizes the necessity of adapting educational strategies to local contexts to better meet the needs of diverse student populations. It advocates for further research to explore local knowledge in greater depth, aiming to develop more effective and contextually appropriate strategies to improve inclusivity and responsiveness in education globally.
Volume: 14
Issue: 3
Page: 1651-1660
Publish at: 2025-06-01

Investigation of the satellite internet of things and reinforcement learning via complex software defined network modeling

10.11591/ijece.v15i3.pp3506-3518
Arun Kumar , Sumit Chakravarty , Aziz Nanthaamornphong
The satellite internet of things (SIoT) has emerged as a transformative technology, enabling global connectivity and extending IoT infrastructure to remote and underserved regions. This paper explores the integration of SIoT with advanced reinforcement learning (RL) techniques through sophisticated software-defined networking (SDN) modeling. The study emphasizes SDN’s capability to offer flexible, dynamic, and efficient management of satellite-based IoT networks, addressing unique challenges such as high latency, limited bandwidth, and frequent mobility. To address these challenges, we propose an RL based approach for optimizing network resource allocation, routing, and communication strategies. The RL algorithm enables autonomous adaptation to real-time network conditions, tackling critical concerns such as spectrum management, energy efficiency, and load balancing, ensuring reliable connectivity while minimizing congestion and power consumption. Furthermore, SDN facilitates network programmability, enabling centralized control and streamlined management of SIoT systems. The proposed RL-driven SDN model is validated through simulation experiments, demonstrating significant improvements in throughput, network efficiency, and quality of service (QoS) metrics compared to traditional network models. This work advances the development of satellite IoT networks by providing a robust, scalable framework that integrates RL and SDN technologies, offering intelligent and efficient connectivity solutions to meet the growing demands of next-generation SIoT systems.
Volume: 15
Issue: 3
Page: 3506-3518
Publish at: 2025-06-01

Review on optimal planning and operation of charging stations for electric vehicles

10.11591/ijape.v14.i2.pp359-372
M. S. Arjun , N. Mohan , K. R. Satish , Arunkumar Patil , D. P. Somashekar
Several factors need to be taken into account while planning the locations of electric vehicle charging stations. The thoughtful design and arrangement of charging stations, as a crucial component of the infrastructure supporting electric vehicles, is essential for the advancement of these kinds of vehicles. However, a number of intricate aspects, including policy economics, charging demand, user comfort when charging, and traffic circumstances, influence the design and arrangement of charging stations. With the goal to uncover competing interests and opportunities for collaboration in the operation and development of charging infrastructure, this study intends to assist researchers and technology developers in investigating cutting-edge techniques from the viewpoint of each constituent. Additionally, only a strong electric vehicle charging station (EVCS) infrastructure may provide some of the answers to the most basic EV concerns, like EV cost and range. The literature claims that several sorts of techniques, objective functions, and constraints for issue formulation have been used by the scholars. In addition, sensitivity analysis, vehicle to grid strategy, integration of distributed generation, charging kinds, objective functions, restrictions, EV load modelling, uncertainty, and optimization methodologies are examined for the most recent research publications. Discussions occur as well regarding the effects of the EV load on the distribution network, the environment, and the economy.
Volume: 14
Issue: 2
Page: 359-372
Publish at: 2025-06-01

Butterfly optimization-based ensemble learning strategy for advanced intrusion detection in internet of things networks

10.11591/ijece.v15i3.pp3494-3505
Mouad Choukhairi , Sara Tahiri , Ouail Choukhairi , Youssef Fakhri , Mohamed Amnai
The massive growth in internet of things (IoT) devices has led to enhanced functionalities through their interconnections with other devices, smart infrastructures, and networks. However, increased connectivity also increases the risk of cyberattacks. To protect IoT systems from these threats, intrusion detection systems (IDS) employing machine learning (ML) techniques have been developed to identify cybersecurity threats. This paper introduces a novel ensemble IDS framework called butterfly optimization-based ensemble learning (BOEL). This framework integrates the butterfly optimization algorithm (BOA) with ensemble learning techniques to improve IDS detection performance in IoT networks. BOEL is designed to accurately detect various types of attacks in IoT networks by dynamically optimizing the weights of base learners, which are the four sophisticated ML gradient-boosting algorithms (GBM, CatBoost, XGBoost, and LightGBM) for each attack category, and identifying the best weight combination for ensemble models. Experiments conducted on two public IoT security datasets, CICIDS2017 and Bot-IoT, demonstrate the robustness of the proposed BOEL in intrusion detection across diverse IoT environments, achieving 99.795% accuracy on CICIDS2017 and 99.966% accuracy on Bot-IoT. These results outline the successful application of diverse learning approaches and highlight the framework’s potential to enhance IDS in addressing IoT cyber threats.
Volume: 15
Issue: 3
Page: 3494-3505
Publish at: 2025-06-01

Hardware implementation of safety smart password based GSM module controlling circuit breaker

10.11591/ijape.v14.i2.pp441-448
Rakesh G. Shriwastava , Pawan C. Tapre , Rajendra M. Rewatkar , Swapna M. Choudhary , Ramesh K. Rathod , Sham H. Mankar , Hemant R. Bhagat Patil , Salim A. Chavan
This research work highlights the hardware implementation of safety smart password-based GSM module controlling circuit breaker. Safety is the major concern in daily life for domestic activities. In current scenario, accidental death of a lineman are the major issues and to protect operators for the same. To control circuit breakers, passwords security is essential for lineman. Due to that electrical accident’s ratio is increased day to day life at the time of repairing the lines. It is also done due to lack of communication and coordination between maintenance and substation. For safety of lineman, on and off line turning operation is proposed. Secure password is for breaker operation and maintenance. In the proposed system, password is sent to the line operator's mobile phone and GSM module by automatic voltage regulator (AVR) microcontroller. Entered password and password received by the GSM receiver is match so circuit breaker will be smoothly operated. If password is incorrect, message will appear on the LCD display for security purposed and message sent to control room regarding unauthorized access to the system.
Volume: 14
Issue: 2
Page: 441-448
Publish at: 2025-06-01

Particle swarm optimization tuned controllers for capacitor voltage balancing and harmonic suppression in modular multilevel converters

10.11591/ijece.v15i3.pp2616-2630
Mbarek Outazkrit , Faicel El aamri , Essaid Jaoide , Abdelhadi Radouane , Azeddine Mouhsen
The modular multilevel converter (MMC) has become a highly attractive power converter topology for various applications due to its modularity and scalability. However, it faces significant challenges, such as capacitor voltage balancing and circulating current, which can lead to instability and high-power losses. While the sorting algorithm is commonly used to balance capacitor voltages, this paper uses an individual balancing control method as an alternative. Additionally, a proportional resonant controller is employed to suppress the second and fourth harmonics in the circulating current. This paper presents a method for tuning the parameters of both the circulating current controller and the individual balancing control using the particle swarm optimization (PSO) algorithm, which represents the main contribution of this work. The MMC system, connected to a grid with a low number of submodules, is modeled and evaluated using the PLECS and MATLAB/Simulink environments. The results demonstrate the effectiveness of the proposed PSO-based tuning method in improving the performance and stability of the MMC.
Volume: 15
Issue: 3
Page: 2616-2630
Publish at: 2025-06-01

Advancement in driver drowsiness and alcohol detection system using internet of things and machine learning

10.11591/ijece.v15i3.pp3477-3493
Avenaish Sivaprakasam , Sumendra Yogarayan , Jashila Nair Mogan , Siti Fatimah Abdul Razak , Mohd. Fikri Azli Abdullah , Afizan Azman , Kavilan Raman
Globally traffic accidents are influenced by factors such as drowsiness and alcohol consumption. Consequently, there has been a considerable focus on the development of detection systems as part of ongoing efforts to mitigate these risks. This review paper aims to offer a comprehensive analysis of various drowsiness and alcohol detection methods. The paper particularly emphasizes drowsiness and alcohol detection methods, including those centered on sensor-based approaches, physiological-based techniques, and visual analysis of the eye and mouth state. The aim is to evaluate their method, effectiveness and highlight recent advancements within this domain. Additionally, this review paper evaluates the research gaps of these detection methods, considering factors such as precision, sensitivity, specificity, and adaptability to different environmental conditions.
Volume: 15
Issue: 3
Page: 3477-3493
Publish at: 2025-06-01

Personalized learning recommendations based on graph neural networks

10.11591/ijece.v15i3.pp3246-3256
Ismail Chetoui , Essaid El Bachari
This paper presents a novel graph neural network (GNN)-based model for personalized learning with advanced graph neural networks, incorporating both graph convolutional networks (GCN) and graph attention Networks (GAT). Our model leverages GCN, which consists of multiple layers embedding deep learning models, to aggregate data from neighboring nodes and capture the intricate relationships between students and courses. The GAT layers refine these embeddings by dynamically assigning importance weights to connections, prioritizing relationships critical for personalized course recommendations. This dual-layered approach enables the model to account for both global structural patterns and locally significant interactions within the student-course graph. We evaluated the performance of our model using the open university learning analytics dataset (OULAD), a rich dataset encompassing student demographic information, interaction data, and course performance metrics. Experimental results achieved 78.9% F1-score, 78.3% precision, and 76.2% recall in personalized recommendations, outperforming single-layer GCN implementations by approximately 15 percentage points. These results demonstrate the model's ability to handle complex, dynamic relationships in educational data, ensuring more relevant and effective recommendations. By addressing key challenges in recommendation systems, such as the need for dynamic weighting of relationships and the handling of sparsity in educational data, our study underscores the transformative potential of GNNs in advancing personalized education. This work sets the stage for further exploration of GNN applications in e-learning, paving the way for adaptive and intelligent course recommendation systems that align with individual learning needs and preferences.
Volume: 15
Issue: 3
Page: 3246-3256
Publish at: 2025-06-01
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