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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 65 Documents
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Search results for , issue "Vol 35, No 1: July 2024" : 65 Documents clear
Enhancing machine learning algorithm performance through feature selection for driver behavior classification Bouhsissin, Soukaina; Sael, Nawal; Benabbou, Faouzia; Soultana, Abdelfettah
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp354-365

Abstract

Machine learning (ML) techniques empower computers to learn from data and make predictions or decisions in various domains, while preprocessing methods assist in cleaning and transforming data before it can be effectively utilized by ML. Feature selection in ML is a critical process that significantly influences the performance and effectiveness of models. By carefully choosing the most relevant and informative attributes from the dataset, feature selection enhances model accuracy, reduces overfitting, and minimizes computational complexity. In this study, we leverage the UAH-DriveSet dataset to classify driver behavior, employing Filter, embedded, and wrapper methods encompassing 10 distinct feature selection techniques. Through the utilization of diverse ML algorithms, we effectively categorize driver behavior into normal, drowsy, and aggressive classes. The second objective is to employ feature selection techniques to pinpoint the most influential features impacting driver behavior. As a results, random forest emerges as the top-performing classifier, achieving an impressive accuracy of 96.4% and an F1-score of 96.36% using backward feature selection in 7.43 s, while K-nearest neighbour (K-NN) attains an accuracy of 96.29% with forward feature selection in 0.05 s. Following our comprehensive results, we deduce that the primary influential features for studying driver behavior include speed (km/h), course, yaw, impact time, road width, distance to the ahead vehicle, vehicle position, and number of detected vehicles.
Melanoma image synthesis: a review using generative adversarial networks Ahmed, Mohammed Altaf; Qureshi, Mohammad Naved; Umar, Mohammad Sarosh; Bedoui, Mouna
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp551-569

Abstract

Melanoma is a highly malignant skin cancer that may be fatal if not promptly detected and treated. The limited availability of high-quality melanoma images, which are needed for training machine learning models, is one of the obstacles to detecting melanoma. Generative adversarial networks (GANs) have grown in popularity as a strong technique for image synthesis. This research is also targeted at the sustainable development goal (SDG) for health care. In this study, we survey existing GAN-based melanoma image synthesis methods. In this work, we briefly introduce GANs and how they may be used for generating synthetic images. Ensuring healthy lifestyles and promoting well-being for everyone, regardless of age, is the main aim. A comparative study is carried out on how GANs are used in current research to generate melanoma images and how they improve the classification performance of neural networks. Various public and proprietary datasets for training GANs in melanoma image synthesis are also discussed. Lastly, we assess the examined studies' performance using measures like the Frechet Inception distance (FID), Inception score, structural similarity ındex (SSIM), and various classification performance metrics. We compare the evaluated findings and suggest further GAN-based melanoma image-creation research.
Feature fusion-based video summarization using SegNetSN Girase, Sheetal Pravin; Bedekar, Mangesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp274-283

Abstract

This paper addresses the video summarization problem. For the given video goal is to find the subset of frames that capture the important events of the input video and produce a small concise summary. We formulate video summarization as a sequence labeling problem, where for a given input video a subset of frames are selected as a summary video. Based on the principle of semantic segmentation, here each pixel within a frame is assigned to one of the labels, where each frame is assigned a binary label indicating whether it will be included in the summary video or not. We propose a SegNet sequence network (SegNetSN) for video summarization and further extend the work by applying various feature fusion techniques to enhance the input. We performed experiments on the benchmark dataset TVSum.
Performance evaluation of multiclass classification models for ToN-IoT network device datasets Soni, Soni; Remli, Muhammad Akmal; Daud, Kauthar Mohd; Al Amien, Januar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp485-493

Abstract

Internet of things (IoT) technology has empowered tangible objects to establish internet connections, facilitating data exchange with computational capabilities. With significant potential across sectors like healthcare, environmental monitoring, and industrial control, IoT represents a promising technological advancement. This study explores datasets from ToN-IoT’s IoT devices, focusing on multi-class classification, including normal and attack classes, with an additional aim of identifying potential attack sub-classes. Datasets comprise various IoT devices, such as refrigerators, garage doors, global positioning systems (GPS) sensors, motion lights, modbus devices, thermostats, and weather sensors. Comparative analysis is conducted between two prominent multiclass classification models, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), utilizing accuracy and computational time metrics as evaluation criteria. Research findings highlight that the LightGBM model achieves superior accuracy at 78%, surpassing XGBoost 74.31%. However, XGBoost demonstrates an advantage with a shorter computational time of 1.23 seconds, compared to LightGBM 6.79 seconds. This study not only provides insights into multiclass classification model selection but also underscores the crucial consideration of the trade-off between accuracy and computational efficiency in decision-making. Research contributes to advancing our understanding of IoT security through effective classification methodologies. The findings offer valuable information for researchers and practitioners, emphasizing the nuanced decisions needed when selecting models based on specific priorities like accuracy and computational efficiency.
PQ enhancement in grid connected EV charging station using novel GVCR control algorithm for AUPQC device Dharavatu, Anil Kumar; Ramavathu, Srinu Naik
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp1-13

Abstract

The rapid increase of environmental impacts together global warming is conquered by substantial selection of electric-vehicles (EV’s) over the internal-combustion engine (ICE) vehicles. The replacement of these vehicles in transportation industry has led to reducing the running cost, ecological emissions, vehicle maintenance. The EV’s are operated by available battery energy and energized through utility-grid integrated EV charging stations. It is noted that, such charging stations may introduce power-quality issues, highly impacting the electric-grid due to presence of power electronic conversion devices in EV charging stations. The primary emphasis of power-quality impacts on electrical distribution grid are counteracted by employing active universal power-quality conditioner (AUPQC) device. The main role of AUPQC has been selected for mitigation of various PQ problems on both electric-grid side and charging station by using feasible control objective. In this work, a novel generalized voltage-current reference (GVCR) control objective has been proposed for extraction of fundamental reference voltage-current signals. The key findings are simple mathematical notations, no transformations, fast response, low dv/dt switch stress, low switching loss and maximum efficiency. The main goal is design, operation and performance of proposed GVCR controlled AUPQC device has been validated under integration of various EV chargers to electric-grid by using MATLAB/Simulink computing tool, simulation results are presented for analysis and interpretation.
Reviewing chronic ailments: predicting diseases with a multi-symptom approach Oussous, Aicha; Ez-Zahout, Abderrahmane; Ziti, Soumia
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp418-427

Abstract

The integration of machine learning (ML) techniques is now indispensable in healthcare, especially in addressing the challenges posed by chronic illnesses, which present a significant global health concern due to their unpredictable nature. This study compares ML techniques employed in the diagnosis and treatment of chronic conditions such as diabetes, liver disease, thyroid disease, breast cancer, heart disease, Alzheimer’s disease, and others. Two primary criteria guided the selection of diseases under investigation. Firstly, those extensively studied with ML methods, and secondly, those leveraging ML models to resolve issues or yield promising results. The research concludes that in real-time clinical practice, there is no universally proven method for selecting the optimal course of action due to each method’s unique advantages and disadvantages. While a hybrid technique may exhibit slightly slower speed growth, it holds the potential to enhance the accuracy and performance of a model.
Method level static source code analysis on behavioral change impact analysis in software regression testing Muthengi, Fredrick Mugambi; Mugo, David Muchangi; Mutua, Stephen Makau; Musyoka, Faith Mueni
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp665-672

Abstract

Though a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons of software modifications. In assessing the impact of the changes made in software, static source code analysis plays a key role. However, static source code analysis can be a little complex depending on the reason for the expedition. Despite the work done so far, little focus has been made on the potential of changed methods analysis during static source code analysis in assessing the impact of the changes made in a software system. We propose and investigate a static source code analysis technique that would generate information on the modified methods in the source code. This study analyzes four aThough a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons for software modifications. In assessing the impact of the changes made in the software, static source code analysis can be a little complex depending on the reason for the expedition. Despite the works done so far, little focus has been directed on the potential of changed methods during static source code analysis, in assessing the impact of the changes made in software. This study investigates a method-level static source code analysis technique that would generate information on the methods affected by changes made in the software. The work analyzed three Java projects. The results indicate an improvement in leveraging on the knowledge of edited methods in change impact assessment during regression testing. The approach enhances code review efforts in light of assessing operational behavior impacted by the changes made.Java projects and shows that an analysis of the changed methods reveals the level of regression testing that ought to be conducted for the changes made.
Smart distance alert system with Blynk integration for safer gadget use Ismail, Syila Izawana; Ismail, Nuraiza; Mohd Zaki, Aisyah Hannah; Omar, Suziana; Mohamed, Syazilawati
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp70-77

Abstract

Gadgets have certainly become an integral part of our daily lives. From smartphones and tablets to laptops and smartwatches, we rely on these devices to stay connected, entertained, and productive throughout the day. Excessive usage of gadgets for a long time and unhealthy habits will lead to health problems such as myopia. Using gadgets at a close distance is one of the most common unhealthy habits among gadget users, especially children. This study, called "smart distance alert system" is developed to address the unhealthy habit of using gadgets at a close distance. The developed prototype operates by measuring the distance between the user and the gadget screen using an ultrasonic sensor. The buzzer and vibration motor work as an alert system, activating when the distance is less than 50 cm. Parents or guardians will get notifications through the Blynk application. The entire prototype is controlled by NodeMicrocontroller unit.
Hardware-realized secure transceiver for human body communication in wireless body area networks Nataraju, Chaitra Soppinahally; Sreekantha, Desai Karanam; Sairam, Kanduri VSSSS
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp601-609

Abstract

Wireless body area networks (WBANs), featuring wearable and implantable devices for collecting physiological data are increasingly critical in healthcare for enabling continuous remote monitoring, diagnostic improvements, and treatment optimization. Secure communication within WBANs is essential to protect sensitive health data from unauthorized access and manipulation. This paper introduces a novel secure digital (SD)- human body communication (HBC) Transceiver (TR) system, tailored for WBAN applications, that prioritizes security and offers significant enhancements in size, power efficiency, speed, and data transmission efficiency over current solutions. Leveraging a combination of frequency-selective (FS) digital transmission with walsh codes (WCs) or quadrature amplitude modulation (QAM), and incorporating one-round encryption and decryption modules, the system complies with the IEEE 802.15.6 standard, ensuring broad compatibility. Specifically, the QAM-based SD-HBC TR system exhibits a 4% reduction in chip area, a 7.6% increase in operating frequency, a 3.4% decrease in power consumption, a 27.5% reduction in latency, and improvements of 33% in throughput and 35.5% in efficiency. Importantly, it achieves a bit error rate (BER) of up to 10-8 , demonstrating high reliability across communication methods. This research significantly advances secure communication in WBANs, offering a promising approach for enhancing the reliability, efficiency, and security of healthcare monitoring technologies.
A data-driven analysis to determine the optimal number of topics 'K' for latent Dirichlet allocation model Goyal, Astha; Kashyap, Indu
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp310-322

Abstract

Topic modeling is an unsupervised machine learning technique successfully used to classify and retrieve textual data. However, the performance of topic models is sensitive to selecting optimal hyperparameters, the number of topics 'K' and Dirichlet priors 'α' and 'β.' This data-driven analysis aims to determine the optimum number of topics, 'K,' within the latent Dirichlet allocation (LDA) model. This work utilizes three datasets, namely 20-Newsgroups news articles, Wikipedia articles, and Web of Science containing science articles, to assess and compare various 'K' values through the grid search approach. The grid search approach finds the best combination of hyperparameter values by trying all possible combinations to see which performs best. This research seeks to identify the 'K' that optimizes topic relevance, coherence, and model performance by leveraging statistical metrics, such as coherence scores, perplexity, and topic distribution quality. Through empirical analysis and rigorous evaluation, this work provides valuable insights for determining the ideal 'K' for LDA models.

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