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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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Advancing airway management for ventilation optimization in critical healthcare with cloud computing and deep learning Krishnamoorthi, Suresh Kumar; Karthi, Govindharaju; Radhika, Moorthy; Rathinam, Anantha Raman; Raju, Ayalapogu Ratna; Pinjarkar, Latika; Srinivasan, Chelliah
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1053-1063

Abstract

Improving patient outcomes in critical care settings is significantly connected to effective ventilation control. This research introduces a new method for improving ventilation methods in critical healthcare utilizing a long short-term memory (LSTM) network hosted in the cloud. Ventilators, pulse oximeters, and capnography are just a few examples of medical equipment that input data into the system, which then uploads the data to the cloud for analysis. The LSTM network can learn from data patterns and correlations, drawing on respiratory parameters' time dynamics, to provide real-time suggestions and predictions for ventilation settings. The system aims to improve clinical results and reduce the risk of ventilator-induced lung damage by tailoring ventilation techniques according to each patient's requirements and by forecasting potential issues. Due to remote monitoring technology, medical professionals can quickly analyze their patient's conditions and act accordingly. The system allows for continuous improvement using iterative learning of more data and feedback. With the ability to optimize breathing and enhance patient care in critical healthcare situations, a hopeful development in airway management is needed.
Systematic review for attack tactics, privacy, and safety models in big data systems Chaymae, Majdoubi; Youssef, Gahi; Saida, El Mendili
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1234-1250

Abstract

This systematic review explores cyberattack tactics, privacy concerns, and safety measures within big data systems, focusing on the critical challenges of maintaining data security in today's complex digital environments. The review begins by categorizing various cyberattacks, laying the groundwork for understanding the threats to big data. It identifies key vulnerabilities that compromise privacy and safety, and examines the ethical implications of these issues. The role of artificial intelligence in enhancing security defenses is highlighted as a crucial aspect of mitigating these threats. Additionally, a comparative assessment of regulatory frameworks such as GDPR, NIST, and ISO 27001 is provided, emphasizing the importance of legal and compliance considerations in data protection. The review concludes by proposing a structured approach to cyberattack detection and processing, advocating for strategies that address both technical vulnerabilities and regulatory requirements, followed by a critical discussion on the usability of previous methods for mobile security, highlighting adaptability and performance, discussing explainability and Gen AI adoption. This work offers valuable insights for researchers, practitioners, and policymakers, contributing to the ongoing discourse on cybersecurity in the big data era.
Integrating ELECTRA and BERT models in transformer-based mental healthcare chatbot Zeniarja, Junta; Paramita, Cinantya; Subhiyakto, Egia Rosi; Rakasiwi, Sindhu; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Savicevic, Anamarija Jurcev
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp315-324

Abstract

Over the last decade, the surge in mental health disorders has necessitated innovative support methods, notably artificial intelligent (AI) chatbots. These chatbots provide prompt, tailored conversations, becoming crucial in mental health support. This article delves into the use of sophisticated models like convolutional neural network (CNN), long-short term memory (LSTM), efficiently learning an encoder that classifies token replacements accurately (ELECTRA), and bidirectional encoder representation of transformers (BERT) in developing effective mental health chatbots. Despite their importance for emotional assistance, these chatbots struggle with precise and relevant responses to complex mental health issues. BERT, while strong in contextual understanding, lacks in response generation. Conversely, ELECTRA shows promise in text creation but is not fully exploited in mental health contexts. The article investigates merging ELECTRA and BERT to improve chatbot efficiency in mental health situations. By leveraging an extensive mental health dialogue dataset, this integration substantially enhanced chatbot precision, surpassing 99% accuracy in mental health responses. This development is a significant stride in advancing AI chatbot interactions and their contribution to mental health support.
Facial emotion recognition based on upper features and transfer learning Zohra, Ennaji Fatima; Hamada, El Kabtane
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp530-539

Abstract

Facial expression recognition (FER) in the upper face focuses on the analysis and recognition of emotions based on features extracted from the upper region of the face. This region typically affects the eyes, eyebrows, forehead, and sometimes the upper cheeks. Since these areas are often less affected by face masks or other facial coverings, FER algorithms can concentrate on capturing and interpreting the relevant facial cues, such as eye movements, eyebrow positions, and forehead wrinkles, to accurately recognize and classify different emotions. By focusing on the upper face, FER systems can mitigate the impact of occlusions caused by masks and still provide meaningful insights into the emotional states of individuals. In this work, a FER approach focusing on the upper region is proposed. Several experiments have been made using the CK+ dataset in addition to a comparison between the emotion recognition scores using the upper and the entire face in order to determine whether this area can reflect the real expressed emotion. The results of our approach are promising compared to previous studies with an accuracy up to 96%.
Hybrid resource optimization strategy in heterogeneous wireless networks Gadde, Nagaraja; Shivaswamy, Rashmi; Halasinanagenahalli Siddamal, Ramesh Babu; Gowrishankar, Gowrishankar; Thimma Raju, Govindaih; Suryakumar Prabhu Vijay, Subramani
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp829-838

Abstract

The future generation of heterogeneous wireless networks (HWNs) will combine various radio access technologies for connecting various mobile subscribers (MS) based on the quality of service (QoS) and wireless network parameters, connecting MS to the best possible wireless network (WN) has been a trending research topic in HWNs. Existing resource optimization methods are designed to meet the QoS of network criteria and user preferences are neglected. Very limited work is done for resource optimization considering user preferences. However, these models are designed considering multi-mode terminals (MMTs) running a single service at a time under a low-density network; as a result, cannot be adopted to run multiple services simultaneously and; thus, fail to meet current users’ service dynamics requirement. Further, fails to bring good tradeoffs between reducing interference and improving performance. In addressing the research problem this work introduced a hybrid resource optimization strategy (HROS) to reduce interference by establishing channel availability and enhancing resource utilization through game theory. The HROS proves the existence of nash equilibrium (NE) improves throughput by 16.32% and reduces collision by 26.16% over the existing resource optimization-based network selection (RONS) scheme.
Field-level sugarcane yield estimation utilizing Sentinel-2 time-series and machine learning B. U., Rekha; Desai, Veena V.; Kuri, Suresh; Ajawan, Pratijnya S; Jha, Sunil Kumar; Patil, V. C.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp475-487

Abstract

This work focused on developing a methodology for using machine learning (ML) approaches to establish a pre-harvest yield prediction model for sugarcane at field level by integrating time-series remote sensing imagery data with ML techniques. Ground truth agro data and thirty-one spectral vegetation indices were extracted from Sentinel-2 imagery and were considered for yield modeling. A two-level feature selection technique was used to determine the most significant variables that best correlated with sugarcane yield to predict yield in advance. Seven ML algorithms, including those based on regularization, decision trees, and ensemble methods like boosting, were used to predict yield. The approach achieved the highest R2 score of 0.73 and the lowest root mean squared error (RMSE) of 13.45 t/ha with random forest (RF) among the seven ML models tested. Furthermore, all feature selection procedures identified normalized difference red edge (NDRE), red edge chlorophyll index (RECI), and ratio vegetation index (RVI) as major yield-driving variables. The experiments during feature selection demonstrated the potential of red edge spectral bands in development of a reliable sugarcane-yield prediction approach. The RF model obtained using the proposed methodology outperforms the two baseline models developed using NDVI and GNDVI indices, with an improved RMSE of 16-18%.
Adversarially robust federated deep learning models for intrusion detection in IoT Ennaji, El Mahfoud; El Hajla, Salah; Maleh, Yassine; Mounir, Soufyane
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp937-947

Abstract

Ensuring the robustness, security, and privacy of machine learning is a pivotal objective, crucial for unlocking the complete potential of the internet of things (IoT). Deep neural networks have proven to be vulnerable to adversarial perturbations imperceptible to humans. These perturbations can give rise to adversarial attacks, leading to erroneous predictions by deep neural networks, particularly in intrusion detection within the IoT environment. This paper introduces a federated adversarial learning framework designed to protect both data privacy and deep neural network models. This framework consists of federated learning for data privacy and adversarial training on IoT devices to enhance model robustness. The experiments show that adversarial training at the Fog node devices significantly improves the robustness of a federated learning model against adversarial attacks when compared to normal training. Furthermore, the proposed adversarial deep federated learning model is validated using the Edge-IIoTset dataset, achieving an accuracy rate of 91.23% in the detection of attacks.
An evaluation model of website testing framework based on ISO 25010 performance efficiency Kurniasari, Dias Tri; Rochimah, Siti
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1130-1139

Abstract

Testing is an important aspect of software development. Automation testing is now widely used to achieve better and more efficient results. Various automation testing frameworks are available in the market. However, one of the major challenges is determining which automation testing framework is suitable for testing. This study proposes an evaluation model for evaluating web automation testing frameworks based on seven performance efficiency factors to address this issue. The model evaluates five types of transactions commonly used on the web; CRUD, Get Massive Data, search, file upload, and file download. In addition, the tested frameworks are categorized as good, medium, and low. To measure the success of the research, expert weighting was also used. Based on the results obtained for all types of transactions, almost all classifications between the experimental results and weighting were in the same class. Although the model was found to be effective with a 100% accuracy rate, it had an accuracy rate of 80% for upload transactions. The outcomes of this study serve as a valuable reference for choosing suitable software for both tested frameworks and other software applications. In future studies focus on narrowing the selection based on not only performance but also functionality and ease of use.
A novel model for detecting web defacement attacks transformer using plain text features Hoang, Xuan Dau; Nguyen, Trong Hung; Pham, Hoang Duy
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp232-240

Abstract

Over the last decade, web defacements and other types of web attacks have been considered serious security threats to web-based services and systems of many enterprises and organizations. A website defacement attack can bring severe repercussions to the website owner, such as immediate discontinuance of the website operations and damage to the owner’s reputation, which may lead to enormous monetary losses. Several solutions and tools for monitoring and detecting web defacements have been designed and developed. Some solutions and tools are limited to static web pages, while others can handle dynamic ones but demand significant computational power. The existing proposals’ other issues are relatively low detection rates and high false alarm rates because many crucial elements of web pages, including embedded code and images are not properly processed. This paper proposes a novel model for detecting web defacements to address these issues. The model is based on the bidirectional long-short term memory (Bi-LSTM) deep learning method using features of the plain text content extracted from web pages. Comprehensive testing on over 96,000 web pages dataset demonstrates that the proposed Bi-LSTM-based web defacement detection model outperforms earlier methods, achieving a 96.04% overall accuracy and a 2.03% false positive rate.
A deep learning model with an inductive transfer learning for forgery image detection Bevinamarad, Prabhu; H. Unki, Prakash; Bhandage, Venkatesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp801-810

Abstract

Due to the availability of affordable electronic devices and several advanced online and offline multimedia content editing applications, the frequency of image manipulation has increased. In addition, the manipulated images are presented as evidence in courtrooms, circulated on social media and uploaded upon authentication to deceive the situation. This study implements a deep learning (DL) framework with inductive transfer learning (ITL) by using a pre-trained network to benefit from the discovered feature maps rather than starting from scratch and fine-tuning the process to check and classify whether the suspected image is authenticated or forged effectively. To experiment with the proposed model, we used both Columbian uncompressed image splicing detection (CUISD) and the CoMoFoD dataset for training and testing. We measured the model’s performance by changing hyperparameters and confirmed the better selection of values for the hyperparameter to yield compromised results. As per the evaluation results, our model showed improved results by classifying new instances of images with an average precision of 89.00%, recall of 86.43%, F1-score of 87.32, and accuracy of 87.72% and consistently performed better compared to other methods currently in use.

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