Bulletin of Electrical Engineering and Informatics publishes original papers in the field of electrical, computer and informatics engineering which covers, but not limited to, the following scope:    Computer Science, Computer Engineering and Informatics[...]  Electronics[...]    Electrical and Power Engineering[...]    Telecommunication and Information Technology[...]Instrumentation and Control Engineering[...]

The Bulletin of Electrical Engineering and Informatics publishes original papers in the fields of electrical, computer, and informatics engineering. The scope includes, but is not limited to: Computer Science, Computer Engineering, and Informatics; Electronics; Electrical and Power Engineering; Telecommunication and Information Technology; Instrumentation and Control Engineering.

《Bulletin of Electrical Engineering and Informatics》期刊发表电气、计算机和信息工程领域的原创论文，涵盖但不限于以下范围：计算机科学、计算机工程和信息学；电子学；电气与电力工程；电信与信息技术；仪器与控制工程等。

Bulletin of Electrical Engineering and Informatics

An excellent navigation, guidance, and control (NGC) system had a high impact on trajectory tracking and the following scenarios. Both scenarios will include the heading, tangent, and velocity parameters in the computation. However, the control system design problem is not a new issue in the unmanned surface vehicle (USV) and autonomous ground vehivle (AGV) due to this constraint faced by many researchers since early these autonomy developments. Hence, this paper listed and emphasizing the techniques, including techniques implementation, strength, and the algorithm's constraints, a fusion of several techniques implemented for vehicle's stability, a turning ahead, and heading estimation. This paper concerns the similar algorithm used in the USV and AGV. Most of the selected techniques are basic algorithms and have been frequently implemented to control both vehicles' systems. Previous research shows pure pursuit guidance is the most popular technique in AGV to control the degree-of-freedom (DOF) velocity and the dynamic rate (sway, surge, and yaw). Simultaneously, the line of sight (LOS) controller is very compatible with controlling the movement of the USV. In conclusion, the technique's simulation test needs further research that will expose in the actual situation.

Putri Nur Farhanah Mohd Shamsuddin

Roshahliza M. Ramli

Muhamad Arifpin Mansor

Navigation and motion control techniques for surface unmanned vehicle and autonomous ground vehicle: a review

水面无人艇与地面自主车辆导航与运动控制技术综述

Classifying skin lesions poses a significant challenge due to the distinctive characteristics and diverse shapes they can exhibit, particularly in identifying early-stage melanoma. To address the shortcomings of the prior method, a neural network-driven strategy was introduced to differentiate between two types of skin lesions based on dermoscopic images. This new approach comprises four key stages: i) initial image processing, ii) skin lesion segmentation, iii) feature extraction, and iv) classification using deep neural networks (DNNs). Computers can also provide more accurate diagnosis results. In the review process, the articles are analyzed and summarized to contribute to developing methods or application development in skin lesion diagnosis. The stages include defining the relevant theory, input data, methods used (architecture and modules), training process, and model evaluation. This review also explores information based on trends and users, emphasizing the skin lesion segmentation process, skin lesion classification process, and minimal datasets as recommendations for future research.

Arief Kelik Nugroho

Retantyo Wardoyo

Moh Edi Wibowo

Hardyanto Soebono

Bulletin EEI

Image dermoscopy skin lesion classification using deep learning method: systematic literature review

使用深度学习方法的图像皮肤镜皮肤病变分类：系统性文献综述

Crowdfunding is a concept that emerged due to difficulties in raising funds for community business projects, social activities, micro-enterprises, and start-ups conventionally. Crowdfunding uses internet technology as a bridge between the donor and the recipient of funds so that it can reach a wider range of donors. This study aims to compare the performance of machine learning approaches in predicting crowdfunding campaign success. Three machine learning algorithms were employed to predict crowdfunding campaign success, namely logistic regression, random forest, and extreme gradient boosting (XGBoost). The dataset used in this study contains data about all projects posted on Kickstarter from January 2020 to September 2022. To improve the prediction model's performance, experiments using principal component analysis (PCA) feature reduction and log transformation were conducted. The results show that the implementation of log transformation on the dataset can increase the prediction model's performance. Meanwhile, XGBoost algorithm performs better than linear regression and random forest.

Sarifah Putri Raflesia

Dinda Lestarini

Rizka Dhini Kurnia

Dinna Yunika Hardiyanti

Using machine learning approach towards successful crowdfunding prediction

利用机器学习方法进行成功众筹预测

Road defect inspection is a crucial task in maintaining a good transportation infrastructure as road surface distress can impact user’s comfortability, reduce the lifetime of vehicles’ parts, and cause road casualties. In recent years, machine learning has been adapted widely in various fields, including object detection, thanks to its superior performance and the availability of high computing power which is generally needed for its model training. Many works have reported using machine-learning-based object detection algorithms to detect defects, such as cracks in buildings and roads. In this work, YOLOv5, YOLOv6 and YOLOv7 models have been implemented and trained using a custom dataset of road cracks and potholes and their performances have been evaluated and compared. Experiments on the dataset show that YOLOv7 has the highest performance with mAP@0.5 score of 79.0% and an inference speed of 0.47 m for 255 test images.

Najiha ‘Izzaty Mohd Yusof

Ali Sophian

Hasan Firdaus Mohd Zaki

Ali Aryo Bawono

Abd Halim Embong

Arselan Ashraf

Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study

评估YOLOv5、YOLOv6和YOLOv7在道路缺陷检测与分类中的性能：一项比较研究

Cardiovascular diseases engender serious public health concerns in developing nations since access to specialized medical equipment is often limited and standard treatment expenses can be prohibitive. This study proposes an efficient and relatively affordable electrocardiogram (ECG) monitoring system that reads and analyzes a person's electrocardiogram data to provide affordable and quality healthcare solutions. The device initially extracts features from electrocardiogram records by reading electrical signals in the heart. Extracted data are then analyzed by a trained deep learning model to determine precisely if the heart is in a healthy state or undergoing complexities. Experimental results showed that the fine-tuned ANN architecture outperformed the state-of-the-art architectures in this field with an accuracy of 98.95%. The data can also be sent to specialists through an MQTT server if necessary, allowing for remote diagnosis and treatment. The system is intended to be deployed in countries where rural regions lack access to specialized healthcare equipment and professionals. Additionally, the device is inexpensive and, hence can be made accessible to people with limited affordability.

Md Obaidur Rahaman

Mohammod Abul Kashem

A cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions

农村地区经济高效的ECG监测：利用人工神经网络实现高效医疗服务

Information systems are currently developing very rapidly, and this is inseparable from the role of internet of things (IoT) technology, especially in the world of telemedicine. MitApp is an open-source application that can be used to monitor electrocardiogram (ECG) signals in real-time. The aim of this study is to develop an IoT-based ECG signal monitoring system that utilizes the MitApp application to detect abnormal ECG signals that are characterized by symptoms of cardiac arrhythmias. To process ECG signal data obtained from lead electrode results, the research method utilizes Arduino Uno as a microcontroller. The result is then displayed on the thin film transistor (TFT) layer using the Nextion module. The ESP32 module is used as a Wi-Fi module to send data to the MitApp app on a smartphone. The results showed that the results of the comparison test of ECG signal module data with ECG simulator tools with beats per minute values of 60, 80, 100, 120, and 140 obtained an error rate of 0.05. Based on these results, there is potential to develop this feature and integrate the system with the patient management system to improve the effectiveness of remote monitoring.

Bedjo Utomo

Triwiyanto Triwiyanto

Sari Luthfiyah

Wahyu Caesarendra

Vijay Anant Athavale

IoT-based health information system using MitApp for  abnormal electrocardiogram signal monitoring

基于物联网的健康信息系统使用MitApp进行异常心电图信号监测

The adoption of biomedical signals such as the electrocardiogram (ECG) for biometric is rising in tandem with the increased attention to wearable devices. However, despite its potential benefits, ECG is rarely implemented as a biometric mechanism in real-life wearable applications. Therefore, this research aims to analyse the ECG signals extracted from wearable Hexoskin Proshirt for biometric authentication in different physiological conditions. A total of 11 subjects participated in this study, where the ECG signals were recorded while standing, sitting, walking, and uncontrolled activity. The raw ECG signal is first pre-processed using noise-removal butterworth filters in the time domain, followed by an efficient QRS segmented feature extraction approach. Finally, around 854 datasets were generated for training and validation, while the remaining 300 were used to test the proposed recognition method with a quadratic support vector machine (QSVM). The results show that the proposed method achieved a reliable accuracy above 98% with false acceptance rate (FAR) of 0.93%, false rejection rate (FRR) of 3.64%, and true positive rate (TPR) above 96% on the in-house datasets. This researchs findings confirm the possibility of using ECG biometrics for authentication purposes in various real-life settings with varying physiological parameters using a smart textile shirt.

Muhammad Muizz Mohd Nawawi

Khairul Azami Sidek

Amelia Wong Azman

ECG biometric in real-life settings: analysing different physiological conditions with wearable smart textiles shirts

现实生活环境中的心电生物识别：分析可穿戴智能纺织衬衫在不同生理条件下的表现

ISSN	2089-3191, 2302-9285
h-index	25

Publication Information	Publisher: Institute of Advanced Engineering and Science (IAES)，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2017，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%

Bulletin of Electrical Engineering and Informatics

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Navigation and motion control techniques for surface unmanned vehicle and autonomous ground vehicle: a review

Image dermoscopy skin lesion classification using deep learning method: systematic literature review

Using machine learning approach towards successful crowdfunding prediction

Using machine learning approach towards successful crowdfunding prediction

Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study

A cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions

Image dermoscopy skin lesion classification using deep learning method: systematic literature review

IoT-based health information system using MitApp for abnormal electrocardiogram signal monitoring

Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study

ECG biometric in real-life settings: analysing different physiological conditions with wearable smart textiles shirts