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29,393 Article Results

Autonomous mobile robot implementation for final assembly material delivery system

10.11591/ijece.v16i1.pp158-173
Ahmad Riyad Firdaus , Imam Sholihuddin , Fania Putri Hutasoit , Agus Naba , Ika Karlina Laila Nur Suciningtyas
This study presents the development and implementation of an autonomous mobile robot (AMR) system for material delivery in a final assembly environment. The AMR replaces conventional transport methods by autonomously moving trolleys between the warehouse, production stations, and recycling areas, thereby reducing human intervention in repetitive logistics tasks. The proposed system integrates a laser-SLAM navigation approach, customized trolley design, RoboShop programming, and robot dispatch system coordination, enabling real-time route planning, obstacle detection, and material scheduling. Experimental validation demonstrated high accuracy in path following, with root mean square error values ranging between 0.001 to 0.020 meters. The AMR achieved an average travel distance of 118.81 meters and a cycle time of 566.90 seconds across three final assembly stations. The overall efficiency reached 57%, primarily due to reduced idle time and optimized material replenishment. These results confirm the feasibility of AMR deployment as a scalable and flexible intralogistics solution, supporting the transition toward Industry 4.0 smart manufacturing systems.
Volume: 16
Issue: 1
Page: 158-173
Publish at: 2026-02-01

Deep learning architecture for detection of fetal heart anomalies

10.11591/ijece.v16i1.pp414-422
Nusrat Jawed Iqbal Ansari , Maniroja M. Edinburgh , Nikita Nikita
Research has demonstrated that artificial intelligence (AI) techniques have shown tremendous potential over the past decade for analyzing and detecting anomalies in the fetal heart during ultrasound tests. Despite their potential, the adoption of these algorithms remains limited due to concerns over patient privacy, the scarcity of large well-annotated datasets and challenges in achieving high accuracy. This research aims to overcome these limitations by proposing an optimal solution. Two methods such as deterministic image augmentation techniques and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) showcase the framework's capacity to seamlessly and effectively expand original datasets to 14 times and 17 times respectively, thereby effectively tackling the problem of data scarcity. It uses an annotation tool to precisely categorize anomalies identified in the echocardiogram dataset. Segmentation of the annotated data is done to highlight region of interest. Nine distinct fetal heart anomalies are identified with respect to the fewer covered in existing research. This study also investigates the state-of-the-art architectures and optimization techniques used in deep learning models. The results clearly indicate that the ResNet-101 model demonstrated superior precision accuracy of 99.15%. To ensure the reliability of the proposed model, its performance underwent thorough evaluation and validation by certified gynecologists and fetal medicine specialists.
Volume: 16
Issue: 1
Page: 414-422
Publish at: 2026-02-01

Evaluating plant growth performance in a greenhouse hydroponic salad system using the internet of things

10.11591/ijece.v16i1.pp505-517
Chonthisa Rattanachu , Wiyuda Phetjirachotkul , Isara Chaopisit , Kronsirinut Rothjanawan
Hydroponic salad cultivation is becoming increasingly popular. However, a common challenge is the lack of time to maintain hydroponic vegetables due to other responsibilities. This study presents a hydroponic system based on the internet of things (IoT) technique, designed to save time by enabling remote control through a mobile application connected to a NodeMCU microcontroller. Various sensors are integrated with the NodeMCU for real-time monitoring and automation. The study also explores the use of RGB LEDs, which significantly accelerated plant growth and reduced cultivation time. A comparative experimental design was employed to evaluate the growth rate of green oak salad vegetables under two different greenhouse systems. The primary factor compared was the greenhouse system type, with plant growth rate as the outcome variable. Each treatment was replicated 10 times. F-tests were used to statistically determine significant differences in growth rates between the two systems across measured intervals. Results showed that the automated greenhouse system produced the highest leaf width and plant weight values. The use of RGB LEDs reduced the cultivation period from 45 days to 30 days, enabling more planting cycles and ultimately increasing overall yield.
Volume: 16
Issue: 1
Page: 505-517
Publish at: 2026-02-01

Efficiency enhancement of off-grid solar system

10.11591/ijece.v16i1.pp111-120
Satish Kumar , Asif Jamil Ansari , Anil Kumar Singh , Deepak Gangwar
This paper presents the design and implementation of a sensor-enabled off-grid solar charge controller aimed at maximizing the utilization of renewable energy. The proposed system integrates solar and load power sensors to minimize solar energy wastage. A microcontroller is employed to efficiently monitor and regulate battery voltage, solar power generation, and load demand. This system is designed to optimize solar energy usage, reduce dependency on the electrical grid, and lower electricity bills. Additionally, a main supply controller board with a display is introduced, along with a smart scheduler for appliance management. Prior to deployment, total solar power wastage was recorded at 93.1 watts per day. After implementing the proposed solution, wastage was reduced to 13.1 watts per day—reflecting an 85.92% reduction. These results confirm the system’s effectiveness in reducing energy loss, increasing self-consumption, and promoting energy sustainability in off-grid environments. It is important to note that this value may vary based on factors such as temperature, cloud cover, fog, and irradiation levels.
Volume: 16
Issue: 1
Page: 111-120
Publish at: 2026-02-01

An information retrieval system for Indian legal documents

10.11591/ijece.v16i1.pp246-255
Rasmi Rani Dhala , A V S Pavan Kumar , Soumya Priyadarsini Panda
In this work, a legal document retrieval system is presented that estimates the significance of the user queries to appropriate legal sub-domains and extracts the key documents containing required information quickly. In order to develop such a system, a document repository is prepared comprising the documents and case study reports of different Indian legal matters of last five years. A legal sub-domain classification technique using deep neural network (DNN) model is used to obtain the relevance of the user queries with respective legal sub-domains for quick information retrieval. A query-document relevance (QDR) score-based technique is presented to rank the output documents in relation to the query terms. The presented model is evaluated by performing several experiments under different context and the performance of the presented model is analyzed. The presented model achieves an average precision score of 0.98 and recall score of 0.97 in the experiments performed. The retrieval model is assessed with other retrieval models and the presented model achieves 13% and 12% increase average accuracy with respect to precision scores and recall measures respectively compared to the traditional models showing the strength of the presented model.
Volume: 16
Issue: 1
Page: 246-255
Publish at: 2026-02-01

Students performance clustering for future personalized in learning virtual reality

10.11591/ijece.v16i1.pp297-310
Ghalia Mdaghri Alaoui , Abdelhamid Zouhair , Ilhame Khabbachi
This study investigates five clustering algorithms—K-Means, Gaussian mixture model (GMM), hierarchical clustering (HC), k-medoids, and spectral clustering—applied to student performance in mathematics, reading, and writing to support the development of virtual reality (VR)-based adaptive learning systems. Cluster quality was assessed using Davies-Bouldin and Calinski-Harabasz indices. Spectral clustering achieved the best results (DBI = 0.75, CHI = 1322), followed by K-Means (DBI = 0.79, CHI = 1398), while HC demonstrated superior robustness to outliers. Three distinct student profiles—beginner, intermediate, and advanced—emerged, enabling targeted adaptive interventions. Supervised classifiers trained on these clusters reached up to 99% accuracy (logistic regression) and 97.5% (support vector machine (SVM)), validating the discovered groupings. This work introduces a novel, data-driven methodology integrating unsupervised clustering with supervised prediction, providing a practical framework for designing immersive VR learning environments.
Volume: 16
Issue: 1
Page: 297-310
Publish at: 2026-02-01

AI SWLM: artificial intelligence-based system for wildlife monitoring

10.11591/ijece.v16i1.pp216-229
Arun Govindan Krishnan , Jayaraman Bhuvana , Mirnalinee Thanga Nadar Thanga Thai , Bharathkumar Azhagiya Manavala Ramanujam
Detection and recognition of wild animals are essential for animal surveillance, behavior monitoring and species counting. Intrusion of animals and the disaster to be caused can be averted by the timely recognition of intruding animals. An artificial intelligence-based system for wildlife monitoring (AI SWLM) is designed and implemented on the camera trap images. The challenges such as detecting and recognizing animals of different sizes, shape, angles and scale, recognizing the animals of same and different species, detecting them under various illumination conditions, with pose variants and occlusion are addressed by identifying the optimal weights of the deep learning architecture, AI SWLM. Models were trained using Gold Standard Snapshot Serengeti dataset with random weights and the best weights of model were used as initial weights for training the augmented data. This has doubled the performance in terms of mean average precision, which can be interpreted.
Volume: 16
Issue: 1
Page: 216-229
Publish at: 2026-02-01

Dengue case forecasting using multi-step deep learning models with attention layers

10.11591/ijeecs.v41.i2.pp546-554
Anibal Flores , Hugo Tito Chura , Victor Yana Mamani , Charles Rosado Chavez
Dengue is a viral infection that is transmitted from mosquitoes to people. It is more common in regions with tropical and subtropical climates. Accurate dengue forecasting is important to make the right decisions on time. In this sense, in this study, deep learning models with attention mechanisms such as long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) were implemented, and to improve the accuracy of model results they were linearly interpolated. According to the results, in most cases, linear interpolation improved the implemented deep learning models with attention mechanisms in terms of mean squared error (RMSE), mean absolute percentage error (MAPE) and R2. For one-step predictions, improvements occurred between 0.08% and 0.13%, for two-step predictions between 8.55% and 22.81%, for three-step predictions between 0.26% and 23.88%, for four-steps between 0.15% and 4.79%, and between 0.11% and 0.19% for five-step predictions. Based on the obtained results, it is possible to experiment with other types of interpolations such as polynomial, spline, and inverse distance weighting (IDW).
Volume: 41
Issue: 2
Page: 546-554
Publish at: 2026-02-01

Development of interactive e-content to enhance listening skill and language comprehension among secondary school students

10.11591/ijere.v15i1.33599
Nitha Varghese , Kennedy Andrew Thomas
The present study aimed to develop interactive e-content, conduct expert validation, and examine the appropriate level. The researchers used a purposive sampling technique to select the sample of 100 secondary school students and 35 teachers from the Kerala state scheme. The researchers adopted the analysis, design, development, implementation, and evaluation (ADDIE) model to develop interactive e-content. The study employed two quantitative methods. Firstly, the study administered expert validation sheets to three content and two media experts to validate developed interactive e-content. The study utilized the percentage analysis to evaluate the results of the expert validation sheets. Secondly, the study administered a survey questionnaire to 100 secondary school students and 35 teachers to examine the appropriate level of interactive e-content. The study employed the correlation method to analyze the questionnaire results, examining the strength and direction of relationships between variables. The average score of content expert validation is 95.5% and media expert validation is 91.5% confirm that the developed interactive e-content is highly valid and appropriate. A major challenge for the researchers was the insufficient internet speed in rural areas of Kerala. The study recommends that teachers have to develop interactive multimedia teaching-learning aids to improve listening, speaking, reading, and writing (LSRW) among students.
Volume: 15
Issue: 1
Page: 670-679
Publish at: 2026-02-01

Saudi female EFL learners’ task engagement: the role of agency and self-regulation

10.11591/ijere.v15i1.36140
Hajar Khalifa Al Sultan
Despite governmental reforms promoting independent learning, Saudi English as a foreign language (EFL) classrooms continue to use teacher-centered methods that limit learners’ autonomy and ability to self-regulate. This study uses a sociocultural framework and a qualitative design that includes semi-structured interviews and classroom observations of first-year Saudi female students enrolled in a university listening and speaking course. It aims to address the lack of qualitative evidence on how learner agency and self-regulation influence task engagement, and to examine how these factors affect student engagement in university-level language classrooms. Thematic analysis (TA) revealed that scaffolded autonomy and emotionally supportive classroom environments were especially influential in reducing anxiety and fostering confidence and independence. Findings show that engagement increases when students are offered structured opportunities to make choices, self-regulate, and learn in emotionally safe environments. Learners demonstrated agency through task choice, role negotiation, and alignment of assignments with personal preferences. Simultaneously, self-regulation was enacted through planning, emotional control, self-monitoring, and adaptive strategies such as peer observation and anxiety management. These findings illustrate that agency and self-regulation are socially constructed processes that flourish through interaction, guided support, and student-centered pedagogy. Addressing gaps in experimental research, the study presents how agency and self-regulation develop in real Saudi EFL classrooms
Volume: 15
Issue: 1
Page: 848-859
Publish at: 2026-02-01

The effect of rapid automatized naming on foreign language anxiety among dyslexic students

10.11591/ijere.v15i1.35729
Abdelaziz Abdelfattah Elfeky , Abdulhamid Fathi Alholah , Afaf Saeed Al-Budaiwi , Radwa Hassan Yacoub , Ashraf Ragab Ibrahim , Mohamed Ali Nemt-allah
This study examined whether rapid automatized naming (RAN) training—a cognitive intervention focused on improving the speed and accuracy of naming visual stimuli—could effectively reduce foreign language anxiety (FLA) among dyslexic learners. Using a single-group pretest-posttest design with follow-up assessment, a 10-week RAN training intervention was implemented with 30 dyslexic students (18 males, 12 females; aged 13-14 years) from Egyptian preparatory institutes. The intervention consisted of individual 30-minute sessions conducted 3 times per week, systematically progressing from basic single-category naming tasks to complex mixed-category combinations designed to enhance processing fluency and automaticity. Using the foreign language classroom anxiety scale (FLCAS), anxiety levels were measured before intervention, immediately after, and at 8-week follow-up. Results revealed significant reductions in overall FLA (partial η²=.32), with particularly notable improvements in communication apprehension (partial η²=.33) and anxiety in the English classroom (partial η²=.29). Test anxiety showed initial improvement but returned to near-baseline levels at follow-up, while fear of negative evaluation remained largely unchanged. Results suggest that RAN training may be associated with reductions in FLA among dyslexic students, though causal relationships cannot be established without a control group.
Volume: 15
Issue: 1
Page: 815-825
Publish at: 2026-02-01

Motivation and generative artificial intelligence: perceived benefits among advertising and multimedia students

10.11591/ijere.v15i1.36429
Ygnacio Tomaylla-Quispe , Fanny Paredes-Quispe , María del Pilar Ponce , Luis Melgar-Amado
This study aimed to examine how intrinsic and extrinsic motivation, along with the use of and exposure to generative artificial intelligence (GenAI), influence the educational benefits perceived by students in disciplines linked to digital creativity and visual communication, namely graphic design, advertising, and multimedia. A quantitative, correlational and non-experimental design was used. The data was collected online during the 2024 academic year through a validated survey administered to 203 college students selected by convenience sampling. An instrument based on and adapted from previous studies was used to measure intrinsic and extrinsic motivation, perceived benefits, and creative use of GenAI tools. The answers were collected with a five-item Likert scale. The relationships between the variables were analyzed with the partial least squares structural equation modeling (PLS-SEM) procedure using the SmartPLS 4. The results indicate that both intrinsic and extrinsic motivation positively influence perceived benefits. These findings highlight the educational impact of GenAI on creative disciplines and highlight the need for academic programs and education policy directors to promote its responsible adoption, ensuring that students gain the skills and confidence to use these technologies effectively.
Volume: 15
Issue: 1
Page: 544-554
Publish at: 2026-02-01

The effectiveness of problem-based learning approach in science subject

10.11591/ijere.v15i1.29983
Nurul Hazwani Shamsudin , Izzati Azmi , Shahrizal Idzuan Wahab Abdul Rahman , Nik Hanis Zuraihan Rahimi , Yuvenitha A/P Subramaniam
The study was conducted to investigate the effectiveness of problem-based learning (PBL) on the achievements of Form one science students in Klang. Malaysia’s low ranking in scientific knowledge, as highlighted by the Programme for International Student Assessment (PISA), underscores the need for innovative teaching methods. Therefore, this study investigates the impact of PBL intervention on science subjects among 60 Form one students consisting of 30 treatment group and 30 control group students. The research instruments used were pre-test and post-test designed by the researcher. The pre-test was conducted using both treatment and control group students. The treatment group was exposed to PBL intervention for eight weeks before post-tests were given to both groups. Data was analyzed by using the independent t-test and paired t-test for pre-test and post-test. The research findings showed that the treatment group demonstrated significantly higher post-test scores than the control group. The findings suggest that the PBL approach positively impacts students’ learning achievements. Therefore, the study supports the adoption of PBL to enhance science education among Form one students.
Volume: 15
Issue: 1
Page: 278-285
Publish at: 2026-02-01

Evaluating perceptions of Arabic teaching and curriculum integration of dialect

10.11591/ijere.v15i1.34541
Tariq Mohammed Farghal , Mohammad Abed Latif Mohammad Smadi , Hadieh Yousef , Sara Tamimi , Sireen Afara
This study investigates the attitudes of foreign students at Amman Arab University (AAU) in Jordan toward learning Arabic, shedding light on the teaching approaches of Arabic dialect, particularly Jordanian Arabic (JA), while exploring their perspective on Arabic diglossia. The study examines faculty member’s academic views specifically those of the English department and basic sciences, incorporating linguistics, translation scholars, and Arabic lecturers. The findings show that while students stigmatize the Jordanian dialect, recognizing its grammar inferiority to modern standard Arabic (MSA), they contradictorily express a keen interest in learning the dialect due to its cultural and practical relevance to Arabic-speaking societies. Contrarily and strikingly, faculty members, despite their tolerance of the vernacular in informal contexts, hold an opposing opinion that prevents the inclusion of an Arabic curriculum for non-native speakers. Moreover, the study affirms the divergence in perspectives which showcases the tension between the traditional view of H variety being the “proper” variety of the language and the practical demand of the dialect for effective communications in the Arab world. While emphasizing the significance of presenting these contradictions in Arabic language pedagogy, the study introduces the integration of dialect teaching alongside MSA to simply align with students’ linguistic needs.
Volume: 15
Issue: 1
Page: 457-468
Publish at: 2026-02-01

An integrated FSM-BABER-SROA framework for secure and energy-efficient internet of things networks using blockchain consensus

10.11591/ijece.v16i1.pp518-534
Achyut Yaragal , Kirankumar Bendigeri
The rapid expansion of the internet of things (IoT) and wireless sensor networks (WSNs) has intensified the demand for energy-efficient, reliable, and secure data transmission. Traditional clustering and static sleep scheduling approaches often fail to ensure long-term sustainability and tamper-resistant communication. This paper presents BABER-SROAChain, a hybrid optimization and security framework that integrates four core modules: i) Fuzzy similarity matrix (FSM)-based clustering for spatial-energy-aware node grouping, ii) Binary Al-Biruni earth radius (BABER) optimization for intelligent cluster head (CH) selection, iii) ship rescue optimization algorithm (SROA) for adaptive sleep scheduling, and iv) a lightweight blockchain protocol with modified practical byzantine fault tolerance (PBFT) consensus for secure inter-cluster communication. The unified objective function incorporates cluster efficiency, redundancy minimization, latency reduction, and packet delivery ratio maximization. Simulation experiments on large-scale WSNs (100–300 nodes) demonstrate that BABER-SROAChain achieves up to 20% improvement in network lifetime, 18% lower energy consumption, and 15% higher packet delivery ratio compared to state-of-the-art models. Additionally, it minimizes blockchain consensus latency while ensuring high data integrity. The proposed framework offers a scalable, secure, and energy-aware solution suitable for real-time IoT applications, including smart cities, healthcare monitoring, and industrial automation, while addressing the dual challenges of performance optimization and blockchain-based security.
Volume: 16
Issue: 1
Page: 518-534
Publish at: 2026-02-01
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