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29,939 Article Results

Project QSUeVoto: distributed electronic voting system based on blockchain technology

10.11591/ijeecs.v38.i1.pp272-280
Winston G. Domingo , Manuel De Guzman , Charmaine Ruth G. Abella , Dennie T. Ruma , Rishelle B. Nucaza , Eduard P. Alip , Selino S. Malunao
Students' voting experience can be made far more secure, transparent, and effective with an electronic voting system based on blockchain. But for it to be implemented successfully, technological issues must be resolved, accessibility must be guaranteed, and student trust must be developed. Resilient security protocols, intuitive user interfaces, and unambiguous dissemination of the advantages and functionality of the system are vital for surmounting possible obstacles and optimizing favorable outcomes. System development techniques and a descriptive research design were used in this study. The developed systems are accepted and compliant as determined by the IT experts, as evidenced by the grand mean of 4.63 and the descriptive rating of conformity to a very high level. It can be deduced that the SG Advisor, SAS Director, students, and Canvasser Board from Maddela and Diffun Campus gave the generated application great approval and acceptance. This indicates that there is a notable discrepancy between the users' and IT specialists' perceptions of the system's adoption and compliance levels. This procedure can be made better with a safe voting system that has cutting-edge features. Blockchain technology is regarded as a disruptive breakthrough with substantial potential to improve the electronic voting system.
Volume: 38
Issue: 1
Page: 272-280
Publish at: 2025-04-01

An efficient frequent itemsets finding in distributed datasets with minimum communication overhead

10.11591/ijeecs.v38.i1.pp496-507
Houda Essalmi , Anass El Affar
Finding frequent itemsets is an essential researched technique and a challenging task of data mining. Traditional approaches for distributed frequent itemsets require massive communication overhead among different distributed datasets. In this paper, we adopt a new strategy for optimizing the time of communications/synchronizations from large datasets and, we present a novel algorithm for discovering frequent itemsets in different distributed datasets on the slave sites called finding efficient distributed frequent itemsets (FEDFI). The proposed algorithm is capable of generating the important frequent itemsets by applying an efficient technique for pruning the candidate itemsets. The experimental results confirm that our algorithm FEDFI performs better than Apriori and candidate distribution (CD) algorithms in terms of communication and computation costs.
Volume: 38
Issue: 1
Page: 496-507
Publish at: 2025-04-01

A multi-scale convolutional neural network and discrete wavelet transform based retinal image compression

10.11591/ijeecs.v38.i1.pp243-253
Dalila Chikhaoui , Mohammed Beladgham , Mohamed Benaissa , Abdelmalik Taleb-Ahmed
The different applications of medical images have contributed significantly to the growing amount of image data. As a result, compression techniques become essential to allow real-time transmission and storage within limited network bandwidth and storage space. Deep learning, particularly convolutional neural networks (CNN) have marked rapid advances in many computer vision tasks and have progressively drawn attention for being used in image compression. Therefore, we present a method for compressing retinal images based on deep CNN and discrete wavelet transform (DWT). To further enhance CNN capabilities, multi-scale convolutions are introduced into the network architecture. In this proposed method, multiscale CNNs are used to extract useful features to provide a compact representation at the encoding stage and guarantee a better reconstruction quality of the image at the decoding stage. Based on compression efficiency and reconstructed image quality, a wide range of experiments have been conducted to validate the proposed technique performance compared with popular image compression standards and existing deep learning-based methods. At a compression ratio (CR) of 80, the proposed method achieved an average peak signal-to-noise ratio (PSNR) value of 38.98 dB and 96.8% similarity in terms of multi-scale structural similarity (MS-SSIM), demonstrating its effectiveness.
Volume: 38
Issue: 1
Page: 243-253
Publish at: 2025-04-01

Hybrid horned lizard optimization algorithm-aquila optimizer for DC motor

10.11591/ijai.v14.i2.pp1673-1682
Widi Aribowo , Laith Abualigah , Diego Oliva , Toufik Mzili , Aliyu Sabo
This research presents a modification of the horned lizard optimization (HLO) algorithm to optimize proportional integral derivative (PID) parameters in direct current (DC) motor control. This hybrid method is called horned lizard optimization algorithm-aquila optimizer (HLAO). The HLO algorithm models various escape tactics, including blood spraying, skin lightening or darkening, crypsis, and cellular defense systems, using mathematical techniques. HLO enhancement by modifying additional functions of aquila optimizer improves HLO performance. This research validates the performance of HLAO using performance tests on the CEC2017 benchmark function and DC motors. From the CEC2017 benchmark function simulation, it is known that HLAO's performance has promising capabilities. By simulating using 3 types of benchmark functions, HLOA has the best value. Tests on DC motors showed that the HLAO-PID method had the best integrated of time-weighted squared error (ITSE) value. The ITSE value of HLOA is 89.25 and 5.7143% better than PID and HLO-PID.
Volume: 14
Issue: 2
Page: 1673-1682
Publish at: 2025-04-01

IDCCD: evaluation of deep learning for early detection caries based on ICDAS

10.11591/ijeecs.v38.i1.pp381-392
Rina Putri Noer Fadilah , Rasmi Rikmasari , Saiful Akbar , Arlette Suzy Setiawan
Dental caries is a common oral disease in children, influenced by environmental, psychological, behavioral, and biological factors. The American academy of pediatric dentistry recommends screening from the time the first tooth erupts or at one year of age to prevent caries, which mostly affects children from racial and ethnic minorities. In Indonesia, the 2023 health survey reported a caries prevalence of 84.8% in children aged 5-9 years. This research introduces early caries detection using three deep learning models: faster-RCNN, you only look once (YOLO) V8, and detection transformer (DETR), using Indonesian dental caries characteristic datasets (IDCCD) focused on Indonesian data with international caries detection and assessment system (ICDAS) classification D0 to D6. The results showed that YOLO V8-s and DETR gave good results, with mean average precision (mAP) of 41.8% and 41.3% for intersection over union (IoU) 50, and 24.3% and 26.2% for IoU 50:90. Precision-recall (PR) curves show that both models have high precision at low recall (0 to 0.2), but precision decreases sharply as recall increases. YOLO V8-s showed a slower and more regular decrease in precision, indicating a more stable performance compared to DETR.
Volume: 38
Issue: 1
Page: 381-392
Publish at: 2025-04-01

Development of clustering with Bayesian algorithm for optimal route formation in software-defined radio underwater WSN

10.11591/ijeecs.v38.i1.pp254-261
Anoop Sreeraj , Vijayalakshmi P , Velayutham Rajendran
Underwater wireless sensor networks (UWSNs) have recently offered chances to investigate oceans and thus enhance the underwater world. WSNs are imperative for discovering the ocean region. Software-defined networking (SDN) improves flexibility and uses the clustering method to improve lifespan. This article introduces the Development of a clustering process with a Bayesian algorithm (CPBA) for optimal route formation in software-defined radio UWSN. The clustering concept improves energy efficiency; however, cluster head (CH) selection is challenging. The present clustering mechanisms could be more successful in suitably assigning the node's energy. This mechanism utilizes a slap swarm optimization algorithm to pick out the optimal CH by node energy and distance among inter-cluster as well as intra-cluster. In addition, the Bayesian algorithm selects the best forwarder from sender to base station. Thus, enhances efficiency. The simulation results demonstrate that the UWSN improves both the 23% packet forward ratio and 0.014 joule energy. Furthermore, it minimizes the 30% network delay.
Volume: 38
Issue: 1
Page: 254-261
Publish at: 2025-04-01

TALOS: optimization of the CNN for the detection of the tomato leaf diseases

10.11591/ijeecs.v38.i1.pp292-302
Shruthi Kikkeri Subramanya , Naveen Bettahalli , Naveen Kalenahalli Bhoganna
Early detection of plant diseases using convolutional neural network (CNN)is crucial for maximizing crop yield and minimizing economic losses. Manual inspection, the frequent technique, is inefficient and error prone. While CNN’s offer potential for accurate and quick disease recognition, their performance is highly dependent on effective hyperparameter tuning. This process is time consuming, resource intensive, and needs significant expertise due to the vast hyperparameter space, since it can be hard to figure out which is ideal for optimal performance. An effective optimization tool, tunable automated hyperparameter learning optimization system (TALOS), is proposed, which automates the tuning of hyperparameters by systematically exploring the hyperparameter space and evaluates different combinations of parameters to find the optimal configuration that maximize the model’s performance. The performance of this approach is recognizable through its exploration of five different hyperparameters across a search space of 32 combinations, yielding optimal parameters by the second round. Using 3030 tomato leaf images from a benchmark data set, the model achieves a remarkable 94.7% validation accuracy with 33647 trainable parameters. Thus, automated hyperparameter tuning approach not only optimizes model performance but also reduces manual effort and resource requirements, paving the way for more effective and scalable solutions in agricultural technology.
Volume: 38
Issue: 1
Page: 292-302
Publish at: 2025-04-01

Experimental research on text CAPTCHA of fine-grained security features

10.11591/ijeecs.v38.i1.pp535-545
Qian Wang , Shafaf Ibrahim , Xing Wan , Zainura Idrus
CAPTCHA is a cybersecurity measure that distinguishes between humans and automated scripts. Researchers have employed various security features to thwart automated program identification by hackers. However, previous research on the attack resistance of CAPTCHAs has used roughly quantitative analysis instead of a fine-grain quantitative study. This study implemented comparative experiments based on CAPTCHA recognition algorithms to find the best-mixed security features. A multi-stage best parameter selection (MBPS) mechanism was proposed in this study. Experiment results indicated that mixed security features of “overlap + scale + rotate + bg (background)” were the best, with an average machine recognition accuracy of only 4.81%. The contrast experiment result illustrated that the anti-attack ability of mixed security features was better than adding adversarial noise, with machine recognition accuracy decreased by 2.2%. Moreover, by investigating the efficacy of security feature parameters, this study provides practical guidelines for designing robust CAPTCHAs. Furthermore, this study also presents valuable insights into the security of image generation technology.
Volume: 38
Issue: 1
Page: 535-545
Publish at: 2025-04-01

Diabetes detection and prediction through a multimodal artificial intelligence framework

10.11591/ijeecs.v38.i1.pp459-468
Gururaj N. Kulkarni , Kelapati Kelapati
Diabetes detection and prediction are crucial in modern healthcare, requiring advanced methodologies and comprehensive data analysis. This study aims to review the application of multi-parameters and artificial intelligence (AI) techniques in diabetes assessment, identify existing research limitations and gaps, and propose a novel multimodal framework for enhanced detection and prediction. The research objectives include evaluating current AI methodologies, analyzing multi-parameter integration, and addressing challenges in early detection and model evaluation. The study utilizes a systematic review approach, analyzing recent literature on AI-based diabetes detection and prediction, focusing on diverse data sources and machine learning (ML) techniques. Findings reveal a significant lack of integration of diverse data sources, limited focus on early detection strategies, and challenges in model evaluation. The study concludes with a proposed innovative framework for more accurate and personalized diabetes detection, contributing to the advancement of diabetes research and highlighting the potential of AI-driven healthcare interventions. This research underscores the importance of comprehensive data integration and robust evaluation methods in enhancing diabetes detection and prediction.
Volume: 38
Issue: 1
Page: 459-468
Publish at: 2025-04-01

Enhancing patient navigation and referral through tele-referral system with geographical information systems

10.11591/ijeecs.v38.i1.pp281-291
Winston G. Domingo , Virdi C. Gonzales , Jennifer A. Gamay
A tele-referral system with a geographic information system (GIS) integrates telehealth services with spatial data to enhance healthcare delivery. Resource constraints can significantly impact the effectiveness of a tele-referral system with GIS. Addressing delayed or missed referrals is critical to ensuring timely patient care and improving health outcomes. Implementing a tele-referral system with GIS can significantly enhance healthcare delivery by leveraging spatial data and telehealth technologies to improve access, efficiency, and outcomes. One major issue is the lack of access to specialists, particularly in underprivileged communities. Patients face accessing specialized care due to a cumbersome referral process or long wait times, as well as the lack of patient engagement. The results showed that the GIS-enabled tele-referral system significantly reduced patient waiting times and improved the coordination of care. By incorporating these functionalities and strategies, the tele-referral system with GIS can effectively address issues related to delayed or missed referrals, ensuring timely patient care and improving overall health outcomes. By incorporating these strategies and functionalities, the tele-referral system with GIS can effectively address limited access to specialists, ensuring timely patient care and optimal use of available resources.
Volume: 38
Issue: 1
Page: 281-291
Publish at: 2025-04-01

Flexible hybrid graphene-based NFC tag antenna for temperature monitoring application

10.11591/ijeecs.v38.i1.pp227-242
Najwa Mohd Faudzi , Ahmad Rashidy Razali , Asrulnizam Abd Manaf , Nurul Huda Abd Rahman , Ahmad Azlan Ab Aziz , Syed Muhammad Hafiz , Suraya Sulaiman , Nora’zah Abdul Rashid , Amirudin Ibrahim , Aiza Mahyuni Mozi
A hybrid graphene-based material, composed of reduced graphene oxide (rGO) and silver nanoparticle (AgNP), has been proposed for a near field communication (NFC) tag antenna with an integrated, flexible temperature monitoring circuit. The limited availability of high-conductivity graphene-based materials in the market has restricted the use of graphene in NFC tag applications. Therefore, this paper proposes a hybrid graphene-based composition featuring a high conductivity of 3.95×106 S/m. The feasibility of this material for NFC tags had not been validated previously, which is the main motivation for this research. The synthesis of the materials, along with the design, fabrication, and characterization of the NFC tag, is also presented. Results show that the inkjet-printed tag achieves a good reading range of up to 3 cm and demonstrates robustness against bending from 60⁰ to 190⁰, maintaining a maximum reading range of 1.3 cm. Performance on various materials, such as plastic, paper, and carton, also shows minimal impact on frequency shifting. Additionally, the graphene-based NFC tag integrates well with the temperature circuit, effectively monitoring temperatures in the 20-60 ⁰C range in real-time. This makes the developed tag suitable for applications such as food safety monitoring systems through NFC-integrated packaging.
Volume: 38
Issue: 1
Page: 227-242
Publish at: 2025-04-01

Secure financial application using homomorphic encryption

10.11591/ijeecs.v38.i1.pp595-602
Vijaykumar Bidve , Aruna Pavate , Rahul Raut , Shailesh Kediya , Pakiriswamy Sarasu , Koteswara Rao Anne , Aryani Gangadhara , Ashfaq Shaikh
In today’s digital age, the security and privacy of financial transactions are paramount. With the advent of technologies like homomorphic encryption, it is now possible to perform computations on encrypted data without the need to decrypt it first, offering a promising avenue for secure financial applications. This research paper explores the implementation and implications of utilizing homomorphic encryption in financial applications to safeguard sensitive data while maintaining computational integrity. By employing homomorphic encryption techniques, financial institutions can enhance the confidentiality of their clients’ information, protect against data breaches, and enable secure computations on encrypted data. The paper discusses the principles of homomorphic encryption, its applications in financial systems, challenges, and potential solutions. Additionally, it examines real-world examples and case studies where homomorphic encryption has been employed successfully, highlighting its effectiveness in ensuring the privacy and security of financial transactions. Overall, this paper aims to provide insights into the role of homomorphic encryption in creating secure financial applications and its potential to revolutionize the way sensitive financial data is handled and processed.
Volume: 38
Issue: 1
Page: 595-602
Publish at: 2025-04-01

Predicting peak demand for electricity consumption using time series data and machine learning model

10.11591/ijeecs.v38.i1.pp668-676
Suriya S. , Agusthiyar R.
Energy consumption is influenced by various factors, including the proliferation of electronic devices, technological advancements, economic growth, agricultural development, and population increase. Each of these factors contributes to the rising demand for energy. This paper addresses the challenge of predicting peak energy demand (ED) by utilizing historical time series data from the past five years, combined with temperature data from Tamil Nadu’s official sources. We employed feature engineering techniques to prepare the data for machine learning models, specifically XGBoost regressor, lasso, and ridge regression. The time series data was then analyzed using both univariate and multivariate models, including auto regressive integrated moving average (ARIMA) and vector autoregressive (VAR) models. The results show that our models can effectively forecast ED, providing critical insights for policymakers and stakeholders involved in energy planning and resource management.
Volume: 38
Issue: 1
Page: 668-676
Publish at: 2025-04-01

Tree-based models and hyperparameter optimization for assessing employee performance

10.11591/ijeecs.v38.i1.pp569-577
Rendra Gustriansyah , Shinta Puspasari , Ahmad Sanmorino , Nazori Suhandi , Dewi Sartika
The Palembang city fire and rescue service (FRS) is encountering challenges in adhering to national standards for fire response time. Hence, the Palembang city FRS is committed to enhancing employee performance through quarterly performance assessments based on various criteria such as attendance, work targets, behavior, education, and performance reports. This study proposes tree-based models in machine learning (ML) and hyperparameter optimization to assess the performance of Palembang city FRS employees. Tree-based models encompass decision trees (DT), random forests (RF), and extreme gradient boosting (XGB). The predictive performance of each model was evaluated using the confusion matrix (CM), the area under the receiver operating characteristic (AUROC), and the kappa coefficient (KC). The results indicate that RF performs better than DT and XGB in the sensitivity, AUROC, and KC metrics by 1.0000, 0.9874, and 0.8584, respectively.
Volume: 38
Issue: 1
Page: 569-577
Publish at: 2025-04-01

Ensemble learning weighted average meta-classifier for palm diseases identification

10.11591/ijeecs.v38.i1.pp303-311
Sofiane Abden , Mostefa Bendjima , Soumia Benkrama
Crop diseases lead to significant losses for farmers and threaten the global food supply. The date palm, valued for its nutritional benefits and drought resistance in desert climates, is a vital export crop for many countries in the Middle East and North Africa, second only to hydrocarbons. However, various diseases pose a threat to this important plant. Therefore, early disease prediction using deep learning (DL) is essential to prevent the deterioration of date palm crops. The aim of this paper is to apply a robust ensemble method (EL) combining tree transfer learning (TL) models Resnet50, DenseNet201, and InceptionV3, and compares its performance with the CNN-SVM model and the tree TL models mentioned previously. The models were applied to a date palm dataset containing three classes: White scale, brown spot, and healthy leaf. The training and validation sets were applied to a public dataset, while the testing set was applied to a local dataset captured manually to check the model’s performance. As a result, we considered that the ensemble method gave very satisfactory results compared to other methods. Our hybrid model reached a testing accuracy of 98% while achieving an amazing training and validation accuracy of 99.94% and 98.14%, respectively.
Volume: 38
Issue: 1
Page: 303-311
Publish at: 2025-04-01
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