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29,939 Article Results

Connected caregiving: investigating mothers in the era of digital access

10.11591/ijict.v14i1.pp347-354
Anissa Saidi , Wong Yee Von , Tirzah Zubeidah Zachariah@ Omar , Lim Seong Pek , Rita Wong Mee Mee , Khoo Kim Leng
Mothers have embraced and utilized digital access for nurturing and personal use to enhance their roles while balancing newfound demands. The Internet has provided mothers access to information on various topics, including pregnancy, childbirth, and infant care. Social media tools and platforms have also provided mothers with a space to connect with other mothers, share experiences, and seek support. This scoping review aims to identify the relationship of the focus skills among mothers in utilizing digital access. Four databases, including Scopus, web of science (WOS), education resources information centre (ERIC), and ScienceDirect, were used in this research, which found 36 articles for eligibility. Only 16 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. Based on the 16 publications examining mothers’ use of internet access, four essential skills have been identified. These included social, digital, cultural, and problem-solving skills and are acknowledged as being related to digital access mothering. The findings show these skills are offered to mothers through digital access, fostering diverse skill sets, contributing to their empowerment, and supporting sustainable development goal 5: gender equality, aiming to enhance women’s roles and ensure equal opportunities through digital inclusion.
Volume: 14
Issue: 1
Page: 347-354
Publish at: 2025-04-01

Efficient blockchain based solution for secure medical record management

10.11591/ijict.v14i1.pp59-67
Debani Prasad Mishra , B Rajeev , Soubhagya Ranjan Mallick , Rakesh Kumar Lenka , Surender Reddy Salkuti
Electronic medical records (EMRs) have become a key player in the healthcare ecosystem contributing to the assessment of ailments, the choice of the treatment avenue, and the delivery of services. However, there is consideration of EMR storage whereby centralized storage leads to increased security and privacy issues in the patient’s record. In this paper, we proposed a blockchain and interplanetary file system (IPFS) based prototype model for EMR management. It provides a smart contract-enabled decentralized storage platform where healthcare data security, availability, and access management are prioritized. This model also employs cryptographic techniques to protect sensitive healthcare data. Finally, the model is evaluated in a realistic scenario. The experimental results demonstrate that compared to the current systems, the proposed prototype model outperforms them in terms of efficiency, privacy, and security.
Volume: 14
Issue: 1
Page: 59-67
Publish at: 2025-04-01

Virtual exhibition systems using virtual reality technology

10.11591/ijeecs.v38.i1.pp367-380
P. M. Winarno , Wirawan Istiono , Rajendra Abhinaya
Exhibitions are an activity that can bring a lot of benefits to a company. By participating in an exhibition, a company can carry out promotions to increase their sales and improve their company image. However, there are several shortcomings that can be found with conventional exhibitions held in a face-to-face manner. These exhibitions cost a lot of money, run for only a relatively short period of time, and are limited by the location of the exhibition. Because of this, the idea came up to create a virtual exhibition system which could be used as an alternative to conventional exhibitions. The development of a virtual exhibition system for this research was carried out using the Unity game engine. At the virtual exhibition, users can choose which exhibition they want to visit and enter the exhibition room view products and find information about them. Evaluation is carried out using a user acceptance test with Likert scale questions. The evaluation results show a user satisfaction level of 92.7% among the 18 users who have tested the application. With this, it can be said that the virtual exhibition system based on virtual reality technology has been successfully built.
Volume: 38
Issue: 1
Page: 367-380
Publish at: 2025-04-01

Object retrieval analysis on plastic bottle waste recycling-based image control using convex hull algorithm and autoregressive integrated moving average prediction method

10.11591/ijece.v15i2.pp2055-2069
marisa Marisa , Azizul Azhar Ramli , Mohd Farhan Md Fudzee , Zubaile Abdullah
In Indonesia, plastic garbage bottles are the most common sort of waste. Given that waste is expected to grow annually, managing plastic waste is a major challenge. The results of the study were achieved by comparing the reference, which was a collection of manually created contour images, with 50 sets of vortex images with different forms and vortex areas as experimental objects. The results indicate that the suggested approach reports a mean error of 2.84%, a correlation coefficient of 0.9965, and a root mean square error of 0.2903 when compared to the manual extraction method. These findings imply that the extract area determined by the procedure outlined in this research is more accurate and nearer to the actual values. The proposed method can therefore be used in place of the traditional process for investigating cooling parameters through manual testing. With measurement values mean absolute percentage error (MAPE)=121,842, mean absolute deviation (MAD)=20,140, and mean squared deviation (MSD)=776,712, the trend analysis of plastic bottles for autoregressive integrated moving average (ARIMA) modeling leads to the conclusion that the waste from plastic bottles will continue to rise annually and that efforts must be made to address this trend with knowledge and waste recycling technology. Plastic that is advantageous to industry and society.
Volume: 15
Issue: 2
Page: 2055-2069
Publish at: 2025-04-01

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

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

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

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

Predictive model for converting leads into repeat order customer using machine learning

10.11591/ijict.v14i1.pp20-30
Deryan Everestha Maured , Gede Putra Kusuma
In the competitive business landscape, customer relationship management (CRM) is pivotal for managing customer relationships. Lead generation and customer retention are critical aspects of CRM as they contribute to sustaining business growth and profitability. Also, identifying and converting leads into repeat customers is essential for optimizing revenue and minimizing promotional costs. This study focuses on developing a predictive model using machine learning techniques to convert leads into repeat order customers in conventional businesses. Leveraging data from a motorcycle distribution company in Jakarta and Tangerang, the study compares the performance of various models for predicting repeat orders. This includes individual models like DeepFM, random forest, and gradient boosting decision tree models. Additionally, it explores the effectiveness of stacking these models using logistic regression as a meta-learner. Furthermore, the study implements backward feature elimination for feature selection and hyperband for hyperparameter tuning to enhance model performance. The results indicate that Stacking model using base model default configuration stands out as the most robust, achieving the highest scores in accuracy (0.95), area under the curve receiver-operating characteristic curve (AUC-ROC) (0.67), log loss (0.19), weighted average precision (0.95), weighted average recall (0.95), and weighted average F1- score (0.92), effectively handling the imbalanced dataset.
Volume: 14
Issue: 1
Page: 20-30
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

Boosting industrial internet of things intrusion detection: leveraging machine learning and feature selection techniques

10.11591/ijai.v14.i2.pp1232-1241
Lahcen Idouglid , Said Tkatek , Khalid Elfayq
The rapid integration of industrial internet of things (IIoT) technologies into Industry 4.0 has revolutionized industrial efficiency and automation, but it has also exposed critical vulnerabilities to cyber threats. This paper delves into a comprehensive evaluation of machine learning (ML) classifiers for detecting anomalies in IIoT environments. By strategically applying feature selection techniques, we demonstrate significant enhancements in both the accuracy and efficiency of these classifiers. Our findings reveal that feature selection not only boosts detection rates but also minimizes computational demands, making it a cornerstone for developing resilient intrusion detection systems (IDS) tailored for Industry 4.0. The insights garnered from this study pave the way for deploying more robust security frameworks, safeguarding the integrity and reliability of IIoT infrastructures in modern industrial settings.
Volume: 14
Issue: 2
Page: 1232-1241
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

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

Pilot study on deploying a wireless sensor-based virtual-key access and lock system for home and industrial frontiers

10.11591/ijict.v14i1.pp287-297
Andrew Okonji Eboka , Fidelis Obukohwo Aghware , Margaret Dumebi Okpor , Christopher Chukufunaya Odiakaose , Ejaita Abugor Okpako , Arnold Adimabua Ojugo , Rita Erhovwo Ako , Amaka Patience Binitie , Innocent Sunny Onyemenem , Patrick Ogholuwarami Ejeh , Victor Ochuko Geteloma
The rise in data processing activities vis-à-vis the consequent rise in adoption and adaptation of information and communication tech related approaches to resolve societal challenges has become both critical and imperative. Virtualization have become the order of the day to bridge various lapses of human mundane tasks and endeavors. Its positive impacts on society cannot be underestimated. This study advances a virtual wireless sensor-based key-card access system with cost-effective solution to manage access to restricted areas within a facility. We seek to integrate virtual key card access, web-access control, solenoid lock integration, and ESP32- controller to create a dependable internet of things (IoT)-enabled access control system. Results show system benefit includes improved security, improved convenience, privacy, efficiency with real-time control capabilities that will allows building administrators to track and manage access to the facility remotely.
Volume: 14
Issue: 1
Page: 287-297
Publish at: 2025-04-01

Conceptualization of IoT architectures

10.11591/ijict.v14i1.pp334-346
Gaetanino Paolone , Romolo Paesani , Jacopo Camplone , Andrea Piazza , Paolino Di Felice
Although there is a large interest about internet of things (IoT) architectures, still there is no consensus on their conceptualization in the extant literature. This lack of information in conceptualization is problematic because it hampers the deep understanding of the appeared proposals, as well as the adoption of a shared workflow by the involved architects of these systems. Thus, a concise and agreed-upon conceptualization of IoT architectures is called for. This paper aims at giving a contribution on the topic. We start by reviewing the available standards, then, in light of their suggestions, a workflow to be followed in the definition of the architecture descriptions (ADs) of IoT systems is detailed and, in addition, a sample case study, which implements that workflow, is proposed. The contributions are sufficiently abstract to be applicable also to the description of the architecture of artificial intelligence of things (AIoT) systems.
Volume: 14
Issue: 1
Page: 334-346
Publish at: 2025-04-01
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