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30,411 Article Results

The design of an electronic load for mitigating transient overvoltage in the track circuits of railway signaling systems

10.11591/ijeecs.v39.i2.pp807-820
Ukrit Kornkanok , Sansak Deeon , Chuthong Summatta , Saktanong Wongcharoen
The research presented the design of safety electronic load suppression (SELS) for mitigating transient overvoltage in the track circuits of railway signaling systems while changing the track occupancy in the track circuits of the signaling system that caused damage to the BR966F2 relay. The analysis of the average failure of the electronic devices, the failure modes and effect analysis (FMEA), and the performance test of electronic devices were conducted. and the performance test of electronic devices were conducted. which can control the operation with 2oo3 processing mode (two out of three voting) under the series circuits pattern to resolve the damage caused by the application. Results illustrated that the mean operating time of the SELS between failures was 9,399 hours. In addition, regarding the performance of the electronic load for mitigating transient overvoltage of 1 kV at 31.4 V and overvoltage 50 VDC at 178.6 °C within 83 seconds at 35.4 V. Additionally, the SELS could function adequately without failure or causing any damage. Therefore, the SELS was more reliable.
Volume: 39
Issue: 2
Page: 807-820
Publish at: 2025-08-01

Ethics in human-robot interaction research

10.11591/ijeecs.v39.i2.pp1005-1012
Robinson Jimenez Moreno , Anny Astrid Espitia Cubillos , Javier Eduardo Martinez Baquero
This paper explores the basic ethical and bioethical considerations necessary to mediate interaction with various everyday robots, analyzing several stateof-the-art reports and own research, considering advances in human-robot interaction (HRI) and artificial intelligence (AI). It is important to indicate that the adoption of robotic assistance systems is limited by users' nervousness about the enforcement of ethics, security and privacy of their information, in addition to the regular threats of Internet use, considering that HRI must reason its social and ethical impacts by including specific issues associated with HRI such as autonomy, transparency, deception and policies. In this way, it is relevant both to evaluate how robotic architectures influence people's daily lives and to study how to avoid possible negative impacts. Finally, it is significant to establish the ethical considerations required to enable the development of AI algorithms that help HRI in a natural way.
Volume: 39
Issue: 2
Page: 1005-1012
Publish at: 2025-08-01

Efficient object detection for augmented reality based english learning with YOLOv8 optimization

10.11591/ijeecs.v39.i2.pp1189-1197
Arya Krisna Putra , Fiqri Ramadhan Tambunan , Samson Ndruru , Andry Chowanda
This study develops a mobile-based augmented reality (AR) application with machine learning for elementary school students to enhance basic English vocabulary learning. The application integrates an optimized YOLOv8 object detection model, designed to recognize 20 common classroom objects in real-time. The model optimization involves replacing standard Conv layers with GhostConv and the C2f block with the C2fCIB block that has significantly improved computational efficiency. Evaluation results show the optimized model reduces the parameters by 22.003% and decreases the file size from 6.2 MB to 4.9 MB. The model performance improved by achieving precision of 83.7%, recall of 73.5% and a mean Average Precision (mAP) of 81.4%. The model was integrated into the Unity platform via the Barracuda library, enabling real-time detection and interactive display of 3D objects. This aplication also complete with English text, translations, example sentences also audio pronunciation. 3D objects representing classroom vocabulary were specifically created to support AR-based learning. Performance testing on a Samsung A14 showed an improved frame rate of 6–12 FPS compared to the original model’s 5–10 FPS. These results demonstrate that the optimized YOLO model effectively integrates with AR technology, creating a more interactive and enjoyable vocabulary learning experience.
Volume: 39
Issue: 2
Page: 1189-1197
Publish at: 2025-08-01

A framework for security risk assessment of blockchain-based applications

10.11591/ijeecs.v39.i2.pp952-962
Mohammad Qatawneh
Blockchain technology has revolutionized various industries by enabling decentralized, transparent, and tamper-resistant digital transactions. However, despite its benefits, blockchain-based applications are vulnerable to security threats such as smart contract exploits, 51% attacks, Sybil attacks, and private key compromises, posing significant risks to their integrity and reliability. Traditional security frameworks lack a comprehensive approach to systematically assess and mitigate these risks across different blockchain layers. To address this challenge, this paper proposes the blockchain cybersecurity risk assessment model (BCRAM), a structured framework designed to identify, analyze, evaluate, and mitigate security risks in blockchain systems. The methodology involves categorizing threats, assessing risks using quantitative and qualitative techniques, and validating the model through a case study on Ethereum. Results demonstrate that implementing BCRAM led to a 65% reduction in smart contract exploits, a 70% decrease in phishing incidents, and an 85% improvement in distributed denial of service (DDoS) resilience, proving its effectiveness. This research offers a standardized risk assessment approach, providing valuable insights for developers, security analysts to enhance blockchain security.
Volume: 39
Issue: 2
Page: 952-962
Publish at: 2025-08-01

Human detection in CCTV screenshot using fine-tuning VGG-19

10.11591/ijict.v14i2.pp645-652
Firdaus Angga Dewangga , Abba Suganda Girsang
Closed-circuit television (CCTV) systems have generated a vast amount of visual data crucial for security and surveillance purposes. Effectively categorizing security level types is vital for maintaining asset security effectively. This study proposes a practical approach for classifying CCTV screenshot images using visual geometry group (VGG-19) transfer learning, a convolutional neural network (CNN) classification model that works really well in image classification. The task in classification compromise of categorizing screenshots into two classes: “humans present” and “no humans present.” Fine-tuning VGG-19 model attained 98% training accuracy, 98% validation accuracy, and 85% test accuracy for this classification. To evaluate its performance, we compared fine-tuning VGG-19 model with another method. The VGG-19-based fine-tuning model demonstrates effectiveness in handling image screenshots, presenting a valuable tool for CCTV image classification and contributing to the enhancement of asset security strategies.
Volume: 14
Issue: 2
Page: 645-652
Publish at: 2025-08-01

Binary white shark optimization algorithm with Z-shaped transfer function for feature selection problems

10.11591/ijeecs.v39.i2.pp1269-1279
Avinash Nagaraja Rao , Sitesh Kumar Sinha , Shivamurthaiah Mallaiah
Feature selection is critical for improving model performance and managing high-dimensional data, yet existing methods often face limitations such as inefficiency and suboptimal results. This study addresses these challenges by introducing a novel approach using the white shark optimization (WSO) algorithm and its binary variants to enhance feature selection. The proposed methods are evaluated on various datasets, including “Dorothea,” “Breast Cancer,” and “Arrhythmia,” focusing on classification accuracy, the number of features selected, and fitness values. Results demonstrate that the WSO algorithms significantly outperform traditional methods, offering notable improvements in accuracy and efficiency. Specifically, the WSO variants consistently achieve higher accuracy and better fitness values while effectively reducing the number of selected features. This research contributes to the field by providing a more effective optimization approach for feature selection, addressing existing inefficiencies, and suggesting future directions for further refinement and broader application. The findings highlight the potential of advanced optimization techniques in enhancing data analysis and model performance, offering valuable insights for practitioners and researchers.
Volume: 39
Issue: 2
Page: 1269-1279
Publish at: 2025-08-01

Prediction of side effects of drug resistant tuberculosis drugs using multi-label random forest

10.11591/ijai.v14.i4.pp2899-2908
Siti Syahidatul Helma , Wisnu Ananta Kusuma , Mushthofa Mushthofa , Diah Handayani
Drug-resistant tuberculosis (DR-TB) has become a concern because anti-tuberculosis drugs (ATD) used to treat it can cause side effects in patients. This study aimed to predict the potential side effects of ATD using a multi-label classification approach with a random forest (RF) algorithm. This study used 660 medical record data, including the 14 ATD treatments prescribed to the patients and the six side effects experienced by patients. The model was trained using the best parameters based on the hyperparameter tuning process. The results show that the RF multi-label algorithm can be an alternative for building ATD side effect prediction models because it produces the most optimal performance value compared to the decision tree (DT) and extreme gradient boosting (XGBoost). The area under the curve (AUC) score of all RF multi-label models is above 0.8, which means that all RF multi-label models are considered acceptable and applicable for ATD side effect prediction. In addition, eight features influenced the models based on the average feature importance score of the RF models. This study is expected to help predict the side effects of ATD used to treat DR-TB based on ATD treatment and determine the most promising tree-based machine learning algorithm for predicting ATD side effects.
Volume: 14
Issue: 4
Page: 2899-2908
Publish at: 2025-08-01

IoT-enabled smart healthcare system with machine learning for real-time vital sign monitoring and anomaly detection

10.11591/ijeecs.v39.i2.pp1155-1163
Sanjay Deshmukh , Shrey Shah , Asim Wahedna , Nimish Sabnis
This paper presents an innovative IoT-enabled smart healthcare system that combines real-time vital sign monitoring with machine learning-based anomaly detection. The system utilizes a MAX30102 photoplethysmography sensor interfaced with an ESP-32 microcontroller to collect heart rate and blood oxygen saturation (SpO2) data. MQTT protocol ensures efficient data transmission to a cloud database. A long short-term memory (LSTM) neural network architecture is employed for time-series prediction of vital signs and anomaly detection. The system demonstrates high accuracy, with mean squared errors of 0.3% in offline testing and over 90% accuracy in real-time prediction. This affordable and scalable solution offers continuous monitoring capabilities, making it viable for widespread adoption in healthcare settings. The integration of IoT and machine learning techniques provides a robust framework for early detection of health anomalies, potentially improving patient care and outcomes in various medical scenarios.
Volume: 39
Issue: 2
Page: 1155-1163
Publish at: 2025-08-01

Load forecasting of electrical parameters: an effective approach towards optimization of electric load

10.11591/ijict.v14i2.pp708-716
Debani Prasad Mishra , Rudranarayan Pradhan , Ananya Priyadarshini , Subha Ranjan Das , Surender Reddy Salkuti
The increasing need for energy and the increasing cost of electricity have prompted the development of smart energy optimization systems that can help consumers reduce their electricity consumption and minimize costs. These systems are developed on the concept of a “smart grid” which is a digitalized and intelligent energy network that provides help in the efficient distribution of energy. Load forecasting plays a crucial role in the precise prediction of uncontrollable electrical load. Long-term load analysis predicts a load of more than one year and helps in the planning of power systems whereas short-term and medium-term load forecasting helps in the supply and distribution of load, maintenance of load system, ensuring safety, continuous electricity generation, and cost management. Machine learning (ML) focuses on the development of smart energy optimization systems by enabling intuitive decision-making and reciprocation to sudden variations in consumer energy demands. This study focuses on the consumption of consumer electricity and provides a solution regarding the optimized methods that will predict future consumption based on previous data and help in reducing costs and preserving renewable energy. This research promotes sustainable energy usage. The use of ML models enables intelligent decision-making and accurate predictions, making the system an effective tool for managing electricity consumption.
Volume: 14
Issue: 2
Page: 708-716
Publish at: 2025-08-01

Categorizing hyperspectral imagery using convolutional neural networks for land cover analysis

10.11591/ijict.v14i2.pp393-404
Assia Nouna , Soumaya Nouna , Mohamed Mansouri , Achchab Boujamaa
Categorizing hyperspectral imagery (HSI) is crucial in various remote sensing applications, including environmental monitoring, agriculture, and urban planning. Recently, numerous approaches have emerged, with convolutional neural network (CNN)-based algorithms demonstrating remarkable performance in HSI classification due to their ability to learn complex spatial-spectral features. However, these algorithms often require significant computational resources and storage capacity, which can be limiting in practical applications. In this study, we propose a novel CNN architecture tailored for HSI classification within the spectral domain, focusing on optimizing computational efficiency without compromising accuracy. The architecture leverages advanced spectral feature extraction techniques to enhance classification performance. Experimental evaluations on multiple benchmark hyperspectral datasets reveal that the proposed approach not only improves classification accuracy but also achieves a superior balance between performance and computational demand compared to traditional methods like K-nearest neighbors (KNN) and other deep learning-based techniques. Our results demonstrate the potential of the proposed CNN model in advancing the field of HSI classification, offering a viable solution for real-world applications with constrained computational resources.
Volume: 14
Issue: 2
Page: 393-404
Publish at: 2025-08-01

Revolutionizing recommendations a survey: a comprehensive exploration of modern recommender systems

10.11591/ijai.v14.i4.pp2579-2589
Prithvi Ram Vinayababu , Pushpa Sothenahalli Krishna Raju
The rapid proliferation of digital information and online services has fundamentally reshaped user interactions with websites, necessitating the evolution of recommender systems. These systems, crucial in domains such as e-commerce, education, and scientific research, serve to enhance user engagement and satisfaction through personalized recommendations. However, it comes up with new challenges, including information overload, prompting the development of recommender systems that can efficiently navigate this vast group to offer more personalized and relevant suggestions. This survey paper explores the dynamic opinion of recommendation systems, addressing the limitations of traditional approaches, the emergence of deep learning models, and the extended potential for additional data. By investigating various recommendation systems and the evolving role of deep learning, this paper illuminates the path toward more accurate, personalized, and effective recommender systems, considering challenges like sparse data and improved context-based recommendations. The study encompasses three primary recommendation approaches: collaborative filtering, content-based filtering, and hybrid systems. It further investigates into the transformation brought about by deep learning models, showcasing how these models intricate user-item interactions. This survey offers a comprehensive exploration of recommendation systems and their advancements in the digital era, providing insights into the future of personalized content delivery.
Volume: 14
Issue: 4
Page: 2579-2589
Publish at: 2025-08-01

Enhancing database query interpretation: a comparative analysis of semantic parsing models

10.11591/ijict.v14i2.pp467-477
Gunjan Keswani , Manoj B. Chandak
The rapid proliferation of NoSQL databases in various domains necessitates effective parsing models for interpreting NoSQL queries, a fundamental aspect often overlooked in database management research. This paper addresses the critical need for a comprehensive understanding of existing semantic parsing models tailored for NoSQL query interpretation. We identify inherent issues in current models, such as limitations in precision, accuracy, and scalability, alongside challenges in deployment complexity and processing delays. This review is pivotal, shedding light on the intricacies and inefficiencies of existing systems, thereby guiding future advancements in NoSQL database querying. This methodical comparison of these models across key performance metrics-precision, accuracy, recall, delay, deployment complexity, and scalability-reveals significant disparities and areas for improvement. By evaluating these models against both individual and combined parameters, we identify the most effective methods currently available. The impact of this work is far-reaching, providing a foundational framework for developing more robust, efficient, and scalable parsing models. This, in turn, has the potential to revolutionize the way NoSQL databases are queried and managed, offering significant improvements in data retrieval and analysis. Through this paper, we aim to bridge the gap between theoretical model development and practical database management, paving the way for enhanced data processing capabilities in diverse NoSQL database applications.
Volume: 14
Issue: 2
Page: 467-477
Publish at: 2025-08-01

Techniques of deep learning neural network-based building feature extraction from remote sensing images: a survey

10.11591/ijict.v14i2.pp614-624
Shrinivas B. Khandare , Manoj B. Chandak
Recently, due to earthquake disaster, many people have lost their lives and homes, and not able to settle to new locations immediately. Therefore, a framework or a plan should be ready to immediately relocate the people to different locations or do resettlement. Much research has been done in this field but still there are problems of identifying clear building boundaries, rectangular houses, due to the problem of different shapes of the buildings. These techniques were explored for identification of clear building boundaries, rectangular houses, buildings which are more highlighted and smaller size buildings for pre-disaster and post-disaster building extraction scenarios. In this survey of building extraction techniques, most of the approach is training the network, second approach is refining the trained output features, running the trained samples on the predefined models of neural network. Several issues and their assessment are studied in these techniques. These are beneficial to the various researchers for different building extractions.
Volume: 14
Issue: 2
Page: 614-624
Publish at: 2025-08-01

Consumer behavior switching from human agents to chatbots in the health service industry

10.11591/ijict.v14i2.pp355-365
Dwi Fajar Yulianto , Titik Pratiwi , Fatih Akbarul Irsan , Faranita Mustikasari
Artificial intelligence (AI) technology is used in organizations to replace human services with technology, altering customer service experiences. Only a limited number of studies have explored how consumers change their behavior from human-assisted to technology-assisted services when using AI in frontline and specialty healthcare services. This study examined the elements that impact consumers’ transition from human agents to AI-based conversational agents using the push-pull mooring framework. Data from 147 healthcare users was evaluated using structural equation modeling. The data indicates that push effects, specifically adaptability, have a negative impact on switching behavior, while pull effects, such as responsiveness and accessibility, have a positive impact on the switching behavior of customers.
Volume: 14
Issue: 2
Page: 355-365
Publish at: 2025-08-01

Automatic identification of native trees using MobileNetV2 model

10.11591/ijict.v14i2.pp416-426
Melidiossa V. Pagudpud , Reynold A. Rustia , Wilyn S. Marzo , Joel G. Carig
In protecting our biodiversity, knowledge of tree species is vital. However, not all people are familiar with the trees present in the community which can affect their ability to fully protect the trees. In this premise that the researchers decided to conduct this study to support the sustainable forest management project in the Province of Quirino through the creation of a model of automatic identification of native trees, using the leaves of the trees, found within the Quirino Forest landscape. The model aims to help residents with accessible tools for tree identification which can be used in the conservation efforts within the province. Transfer learning for deep learning, one of the latest advancements in image processing, shows potential for tree identification because the method dodges the labor intensive feature engineering. Using the Quirino Province native trees leaf/leaflet images dataset, which was annotated by foresters, the MobileNetV2 convolutional neural network was evaluated systemically in this paper. The result shows that the best model version to classify the native trees based on their leaves or leaflets is the one produced using 800 training steps which yields an overall accuracy of 89.61%. The result attained for the tree identification indicates that the proposed technique might be an appropriate tool to assist humans in the identification of native trees found within the landscape of Quirino and can provide reliable technical support for sustainable forest management.
Volume: 14
Issue: 2
Page: 416-426
Publish at: 2025-08-01
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