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30,376 Article Results

A survey on convolutional neural network hardware acceleration through approximate computing multiple and accumulates unit

10.11591/ijra.v14i3.pp366-375
Suvitha Pathiyadan Sudhakaran , Aathmanesan Thangakalai
Convolutional neural networks (CNNs) are applied to a different range of real-world complex tasks to provide effective solutions with high accuracy. Based on the application's complexity, CNN demands a lot of processing units and memory spaces for its effective implementation. Bringing this computational task to hardware for processing the data to enhance the acceleration helps in achieving real-time performance improvement. Recent studies focused on approximation methodology to overcome this problem. This proposed survey analyzes various recent methods involved in implementing approximating computing-based processing elements and their usage in CNNs. Primarily, the survey focuses on multiple and accumulates (MAC) unit and their various approximation methods, which acts as a fundamental block as a processing element in the CNN layers. Secondly, it focuses on various CNN hardware acceleration architectures and their layers designed using different methods and their wide range of applications. Some of the recent design methods applied to various ranges of applications are also analyzed in the proposed survey. This detailed analysis gives an outlook on effective approximation blocks and the CNN architecture to be effectively used in various designs, with a scope of area in which future improvement can be made.
Volume: 14
Issue: 3
Page: 366-375
Publish at: 2025-12-01

Mobile robot replacement in multi-robot fault-tolerant formation

10.11591/ijra.v14i3.pp311-319
Ahmed M. Elsayed , Mohamed Elshalakani , Sherif Ali Hammad , Shady Ahmed Maged
Formation control in multi-robot systems (MRS) is essential for collaborative transport, environmental surveillance, material handling, and distributed monitoring. A major challenge in MRS is maintaining predefined formations or cooperative task execution when individual robots experience operational faults, potentially isolating them from the group. In mission-critical scenarios, preserving the number of operational robots is crucial for task success. To address this, we propose a Robot Replacement approach framework for differential wheeled mobile robots. This approach isolates faulty robots and dynamically replaces them with pre-deployed spares, ensuring uninterrupted formation tasks. A graph theory-based framework models inter-robot communication and formation topology, enabling decentralized coordination. The proposed techniques were implemented in a MATLAB/Simulink simulation environment. The simulated robots are equipped with LiDAR, an inertial measurement unit (IMU), and wheel encoders for navigation. Simulation results demonstrate that the framework successfully maintains the target formation and task continuity during robot failures by dynamically integrating replacements with minimal disruption.
Volume: 14
Issue: 3
Page: 311-319
Publish at: 2025-12-01

Forecasting business exceptions in robotic process automation with machine learning

10.11591/ijra.v14i3.pp450-458
Igor Saez , Sara Segura , Mónica Gago
Business exceptions interrupt robotic process automation (RPA) workflows and oblige costly human intervention. This paper explores the application of machine learning (ML) time series forecasting techniques to predict business exceptions in RPA. Using RPA robot logs from a financial service company, we employ ARIMA, SARIMAX, and Prophet statistical models, comparing their performance with ML models such as XGBoost and LightGBM. Furthermore, we explore hybrid approaches that combine the strengths of statistical models with ML techniques, specifically integrating Prophet with XGBoost and LightGBM. Our findings reveal that a hybrid LightGBM model substantially outperforms traditional methods, achieving a 40% reduction in the weighted absolute percentage error (WAPE) when compared to the top-performing statistical model. These results suggest the potential of ML forecasting in optimizing RPA operations through the analysis of log-generated data.
Volume: 14
Issue: 3
Page: 450-458
Publish at: 2025-12-01

Design and development of a modular magnetic wheeled robot for out-pipe inspection

10.11591/ijra.v14i3.pp331-344
Sugin Elankavi Rajendran , Kuppan Chetty Ramanathan , Harish Kumar Guasekaran , Arun Kumar Pinagapani , Dinakaran Devaraj , Ramya Mathanagopal
This paper presents the design of a modular mobile robot capable of climbing and inspecting vertical ferromagnetic pipes using magnetic wheels. Mobile robots used for climbing ferromagnetic surfaces employ magnetic tracks, wheels, and magnets attached to the robot’s body. When it comes to ferromagnetic pipes, magnetic wheels and magnets attached to the body can be used. Among them, magnetic wheels are commonly used for inspecting ferromagnetic pipes. While current robots are suitable for large pipes, they are not practical for smaller ones. To address this gap, a small-sized robot equipped with a magnetic wheel system that ensures both strong attachment and smooth movement along vertical ferromagnetic surfaces is developed. The robot’s magnetic adhesion performance was analyzed through simulations using finite element method magnetics and validated through laboratory experiments. The results show an average error of only 8.25% between simulation and real-world tests, confirming the system’s reliability for external pipe inspection.
Volume: 14
Issue: 3
Page: 331-344
Publish at: 2025-12-01

Design of low-power, high-speed approximate 4:2 compressors for efficient partial product reduction in multipliers

10.11591/ijra.v14i3.pp459-467
Jabez Daniel Vincent David Michael , Anusha Gorantla , Ahilan Appathurai , Dinesh Ramachandran
Partial product reduction becomes the main task in the multiplication process. Therefore, the partial product stages of multipliers are reduced with the usage of compressors, by using compressors in the multiplier. Using compressors in the multiplier circuit significantly impacts multiplier performance. Approximate compressors are crucial for achieving better design metrics in parallel multipliers. This paper proposes to create various new approximate 4:2 compressor circuits. A trade-off is made between the performance and accuracy of this approximate circuit design approach. The proposed designs have been implemented using XOR-XNOR gates with a 2-to-1 multiplexer, and also XOR-XNOR gates with transmission gates. All these circuits have been simulated using Cadence in different technological nodes. Compared with the existing technique, the proposed 4:2 approximation compressor provides 51.4% power reduction and 26.45% delay reduction for 45 nm equipment.
Volume: 14
Issue: 3
Page: 459-467
Publish at: 2025-12-01

Culture-based teaching practices of teachers in a Philippine-Chinese school: narratives and insights

10.11591/ijere.v14i6.33611
Remedios C. Bacus , Rivika C. Alda , Helen B. Boholano
Philippine-Chinese schools have been in existence for several decades. This study explores the influence of Chinese culture on Filipino teachers’ pedagogical and content delivery, language and social interaction, and their practices and constructions while teaching in a Chinese school. Through a descriptive–qualitative approach to explore the experiences, 12 non-Chinese teachers were purposively chosen to participate in the study. Guided by the validated interview guide and the qualitative data analysis steps by Braun and Clarke, six themes emerged: bilingual pedagogy, cultural values integration, language immersion, technology in education, thriving through diversity, support and collegiality. The study revealed the enriching interaction between Chinese cultural influences and Filipino teaching practices, suggesting the need for ongoing professional development that enhances cultural competence, innovative pedagogical strategies, and supportive teaching communities.
Volume: 14
Issue: 6
Page: 5080-5093
Publish at: 2025-12-01

Determinants of integrated teaching capacity among teachers in ethnic minority primary schools in northern Vietnam

10.11591/ijere.v14i6.30087
Hang Nguyen Thi Thu , Chuyen T. H. Nguyen
This study explores factors affecting the integrated teaching capacity of primary school teachers in ethnic minority schools in the Northern mountainous regions of Vietnam. Given the challenges of linguistic and cultural diversity in this context, the research aims to address gaps in current practices and propose measures for improvement. A quantitative approach was adopted, surveying 280 teachers and administrators using exploratory factor analysis (EFA) and multivariate regression. The results identify four primary factors influencing teaching capacity: i) language, culture, and parent coordination; ii) teacher capacity and community participation; iii) teaching materials, equipment, and teacher attitudes; and iv) policies and support from management agencies. Among these, language, culture, and parent coordination are the most impactful. The study underscores the need for targeted teacher training programs and improved collaboration with local communities to enhance teaching outcomes. These findings provide actionable insights for policymakers and educators to improve integrated teaching in ethnically diverse and economically challenged regions.
Volume: 14
Issue: 6
Page: 4295-4306
Publish at: 2025-12-01

Factors affecting career orientation: indigenous ethnic minority students in Vietnam’s Central Highlands

10.11591/ijere.v14i6.34954
Phung Viet Hai , Tran Thi Huong Xuan , Phung Thi To Loan , Nguyen Thi Thanh Phuong
Career orientation competency (COC) plays a crucial role in preparing students for lifelong learning and labor market adaptability. However, existing research has largely overlooked how this competency develops among indigenous ethnic minority students in culturally diverse and educationally disadvantaged contexts such as Vietnam’s Central Highlands. Addressing this gap, the present study adopts the social cognitive career theory (SCCT) to examine how personal, contextual, and behavioral factors interact to shape COC in this population. SCCT serves not only as a conceptual lens but also informs the development of the research model and interpretation of findings. A quantitative approach was employed using cross-sectional survey data collected from 669 ethnic minority students. Analytical techniques included reliability analysis (Cronbach’s alpha), exploratory factor analysis (EFA) for construct validation, and multiple linear regression to test predictive relationships. Results revealed six key determinants of COC: self-awareness (SA), expectations for results (ER), personal goals (PG), community connection (CC), career exploration (CE), and cultural identity (CI). Notably, CI had the most significant effect (β=0.308), suggesting its central role in guiding career-related behaviors. These findings have important implications for both theory and practice. They extend SCCT by integrating culturally specific constructs relevant to marginalized communities and they highlight the need for context-responsive career guidance programs that recognize and leverage students’ cultural identities. The study contributes to the empirical foundation for inclusive education policy reforms targeting ethnic minority youth in Vietnam.
Volume: 14
Issue: 6
Page: 4711-4723
Publish at: 2025-12-01

ADC-LIO: A direct LiDAR-inertial odometry method based on adaptive distortion covariance

10.11591/ijra.v14i3.pp399-408
Lixiao Yang , Youbing Feng
Focusing on the localization challenges for robots in dynamic navigation environments, this study proposes a direct LiDAR-inertial odometry (LIO) system named ADC-LIO, which achieves robust pose estimation and accurate map reconstruction using adaptive distortion covariance. ADC-LIO is engineered to address uncertain motion patterns in autonomous mobile robots, effectively integrating LiDAR scan undistortion within the Kalman filtering update process by embedding an iterative smoothing process and a backpropagation strategy. The ADC-LIO architecture enhances point cloud accuracy, improving the system's overall performance and robustness. In addition, an adaptive covariance processing method is developed to resolve motion-induced sensing uncertainties, which calculates different covariances according to the error characteristics of the point cloud. This method enhances the constraints of high-quality point clouds, reduces the limitations on low-quality point clouds, and utilizes information more effectively. Experiments on the publicly available NTU-VIRAL dataset validate the effectiveness of ADC-LIO, which improves pose estimation accuracy and reduces absolute position errors compared to other state-of-the-art methods, including FAST-LIO, Faster-LIO, FR-LIO, and Point-LIO. The proposed ADC-LIO is an appealing odometry method that delivers accurate, real-time, and reliable tracking and map-building results, posing a practical solution for robotic applications in structured indoor and GPS-denied outdoor environments.
Volume: 14
Issue: 3
Page: 399-408
Publish at: 2025-12-01

Predicting Emirati student academic outcomes: school tracks and standardized tests

10.11591/ijere.v14i6.33951
Fatima Al-Ali , John Rice
Global education systems apply grouping strategies to enhance academic outcomes. The United Arab Emirates (UAE) has developed school tracks to address performance gaps by offering more varied high-school tracks while also creating a local Emirates Standardized Tests (EmSAT) for measurement. This study examines the impact of educational tracks in Emirati schools and EmSAT scores on UAE university students’ academic performance. A quantitative multivariate analysis of 3,190 University of Sharjah students compared the outcomes across different high school tracks and analyzed the predictive power of EmSAT scores on university cumulative grade point average (CGPA). EmSAT scores vary significantly by tracks, with elite students performing best, followed by those in the advanced and scientific tracks. Arabic and mathematics EmSAT scores predict CGPA more strongly than English, which has a moderate effect. General track students achieve higher CGPAs compared to other tracks, even after controlling EmSAT performance and gender, suggesting a complex relationship between high school experiences and university success. The findings highlight the track model’s effectiveness, with the elite fostering strong academic pathways. However, the overlap in university achievement between the general and advanced warrants further research. The study provides insights for policymakers to refine educational strategies and enhance student outcomes.
Volume: 14
Issue: 6
Page: 4592-4603
Publish at: 2025-12-01

FIND-ROUTE: Fourier series integrated deep learning model for energy efficient routing in Internet of Things-wireless sensor network

10.11591/ijra.v14i3.pp468-478
Shobanbabu Ramaswamy Jaganathan , Sathya Rajendran , Karthikeyan Ramamoorthy
The Internet of Things (IoT) relies on wireless sensor networks (WSNs) to transmit data across a wide range of applications. However, the commonly encountered primary challenges in IoT-enabled WSNs are high energy consumption during data transmission, which insists energy optimized routing to prolong the network lifetime. To address these challenges, a novel Fourier series integrated deep learning-based routing (FIND-ROUTE) framework has been proposed for energy-aware communication among IoT nodes in WSN. Initially, a hybrid clustering approach forms an adaptive cluster for efficient data aggregation with reduced energy consumption. After clustering, stable cluster heads (CHs) are elected by a Fourier series-based metaheuristic optimization algorithm for balancing the energy usage with extended network lifetime. Finally, an Intelligent neural network dynamically selects the optimal path and transmits the data efficiently with reduced latency for reliable communication in IoT-WSN. The FIND-ROUTE framework is simulated by using MATLAB, and it is validated by using the WSN-DS dataset. The proposed FIND-ROUTE framework is evaluated based on several parameters, including energy consumption, packet delivery ratio (PDR), network lifetime (NL), time complexity, throughput, number of alive nodes, packet loss ratio (PLR), and space complexity. In comparison, the proposed FIND-ROUTE framework achieves a PDR of 90%, whereas MLBDARP, LQEER, and NBSHO-DRNN achieve 70%, 60%, and 67% respectively.
Volume: 14
Issue: 3
Page: 468-478
Publish at: 2025-12-01

Antecedents and consequences of memorable experience in the airline industry: service robots versus human staff

10.11591/ijra.v14i3.pp409-417
Jinsoo Hwang , Ja Young (Jacey) Choe , Kyuhyeon Joo , Jinkyung Jenny Kim
The study aims to examine the type of service providers, such as service robots and human staff, as a potential moderator in the relationship between SERVQUAL and memorable experience in the airport industry. In order to verify 15 hypotheses, data were collected from 313 travelers who acquired information from service robots and 313 travelers who acquired information from human staff at the airport. The results of data analysis revealed that the five sub-dimensions of SERVQUAL, including tangibles, reliability, responsiveness, assurance, and empathy, enhance memorable experience. In addition, a memorable experience has a positive effect on customer satisfaction, which subsequently influences attitude and intention to use. In addition, the type of service providers moderated the links between i) responsiveness and memorable experience and ii) empathy and memorable experience.
Volume: 14
Issue: 3
Page: 409-417
Publish at: 2025-12-01

Unveiling the emotional labor of overseas Filipino international teachers

10.11591/ijere.v14i6.34579
Leomar O. Baylosis , Ivy F. Amante , Rovy M. Banguis , Aldin Paul S. Genovia , Shem A. Cedeño
Emotional labor at work typically manifests through surface acting and deep acting. This phenomenological study examines the emotional labor experienced by 15 international Filipino teachers working in the United States, Saudi Arabia, and Thailand. Guided by self-determination theory (SDT), the research explores their reasons for teaching abroad, as well as the challenges they face and how they navigate them emotionally. A qualitative design was employed using semi-structured interviews and framed narratives. Each participant engaged in one individual interview and one focus group discussion. Data were analyzed using interpretative phenomenological analysis (IPA) to generate key themes. Findings show that deep acting involves emotional control, display of positive emotions, emotional exhaustion, experience of negative emotions, and emotional indifference toward self. In contrast, surface acting includes masking emotions, projecting artificial feelings, and withdrawal behaviors. The five major themes emerged as contributing factors to emotional labor: cultural adjustment, language barrier, professional challenges, limited support networks, and work-life balance. Coping strategies identified include emotional regulation, positive cognitive response, support from family and peers, and participation in recreational activities. These nuanced findings offer important insights for international teacher preparation, emotional well-being, and future research on cross-cultural educational contexts.
Volume: 14
Issue: 6
Page: 4628-4637
Publish at: 2025-12-01

Enhancing informatics teacher training in Kazakhstan through dual education and specialized educational platforms

10.11591/ijere.v14i6.34236
Alima Seitaliyeva , Nurzhan Shyndaliyev , Dinara Kalmanova , Assemgul Kaipova , Kaussar Mukhtarkyzy
This study addresses the gap between traditional informatics teacher training in Kazakhstan and the practical demands of modern classrooms. It explores the integration of dual education and the informaticedu.kz digital platform as a means to enhance methodological and practical competencies among future teachers. A mixed-methods design was used, involving 24 students from Pavlodar Pedagogical University. Data were collected through structured questionnaires and qualitative interviews. Quantitative responses were analyzed using descriptive statistics, t-tests, and correlation analysis, while qualitative data underwent thematic analysis. The findings showed that the platform significantly supported lesson planning and methodological development, particularly among 4th-year students who rated the tool more positively than 3rd-year students. High correlations were found between understanding lesson structure and effective planning. However, participants reported a lack of interactive content such as case studies and problem-solving tasks. The results suggest that integrating dual education with specialized digital platforms enhances informatics teacher training. Still, to maintain relevance and effectiveness, platforms must evolve to include more interactive and adaptive features tailored to different training stages.
Volume: 14
Issue: 6
Page: 5003-5013
Publish at: 2025-12-01

Explore activities management to support the care and education of public preschools

10.11591/ijere.v14i6.33235
Thuan Van Pham , LongAn Dang Nguyen
This study aims to explore the contents of the management of support activities for care and education, as well as the impacts of these activities on improving the quality of support and care for children in public preschools in Thu Duc city, Vietnam. To achieve this purpose, qualitative and quantitative research methods were used through the evaluation of relevant documents. At the same time, a survey of 175 people, including managers, teachers, and parents of students, was conducted. The research results show that although the management of support activities for care and education for children in public preschools has achieved some good results, it still reveals many limitations that need to be identified and addressed with appropriate solutions. Based on the assessment of the current situation and identification of the causes, this study has proposed suitable solutions to improve the quality of support activities for care and education for children in public schools in Thu Duc city.
Volume: 14
Issue: 6
Page: 4555-4566
Publish at: 2025-12-01
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