2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)最新文献_第5页

Reliability Performances of Low Voltage & Medium Voltage Networks in Different Areas 不同地区低压和中压电网的可靠性性能

2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2020-12-21 DOI: 10.1109/ETCCE51779.2020.9350920

M. Ridzuan

{"title":"Reliability Performances of Low Voltage & Medium Voltage Networks in Different Areas","authors":"M. Ridzuan","doi":"10.1109/ETCCE51779.2020.9350920","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350920","url":null,"abstract":"Distribution network consists of low voltage (LV) and medium voltage (MV) networks. Each year, most distribution network operators (DNOs) report their network performance without separating LV and MV network performance, and also without separating urban and rural network performance. Since there is no separation of area and voltage level performances, there is no indicator that urban network is better than sub-urban and rural areas networks. In order to find the performance of distribution network, analytical method is applied to all types of networks; LV urban, LV sub-urban, LV rural, MV urban, MV sub-urban and MV rural networks. Analytical method involves mathematical solution to evaluate the reliability performance. It usually focuses only on mean value. The aim of this paper is to examine the performance of LV and MV networks in urban, sub- urban and rural areas. It is expected that urban network performance is better than sub-urban andrural networks, whether in LV and MV networks. Based on the result, certain network performance were not as expected due to several reasons; network configuration, values of mean fault rates and repair times, and number of components.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115791457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Implementation of Robot Operating System in Smart Garbage Bin Robot with Obstacle Avoidance System 具有避障系统的智能垃圾箱机器人中机器人操作系统的实现

2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2020-12-21 DOI: 10.1109/ETCCE51779.2020.9350912

Wong Seng Cheong, S. F. Kamarulzaman, M. A. Rahman

引用次数: 3

Students Online Exam Proctoring: A Case Study Using 360 Degree Security Cameras 学生在线考试监考:使用360度安全摄像头的案例研究

2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2020-12-21 DOI: 10.1109/ETCCE51779.2020.9350872

A. Turani, J. AlKhateeb, Abdulrahman A. Alsewari

{"title":"Students Online Exam Proctoring: A Case Study Using 360 Degree Security Cameras","authors":"A. Turani, J. AlKhateeb, Abdulrahman A. Alsewari","doi":"10.1109/ETCCE51779.2020.9350872","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350872","url":null,"abstract":"Online courses, online exams and online certificates are conducted by various universities and Information Technology (IT) institutes worldwide. Delivery tools have been created for conducting the exams from any place. Applying this will lead saving time and travelling cost. Nowadays, due to the COVID-19 pandemic, there is a big demand on the online courses and exams. This paper introduces a new approach for exam proctoring using 360-degree security camera. Mainly, online exams' security is a major concern. Thus, a delivery tools must not only ensure the identity of a test- taker but also the overall test integrity. In this paper, the usage of the 360-degree security camera over the traditional webcam was investigated in order to enhance the exam security and to minimize the stressful restrictions. To verify this goal, a case study on a group of volunteer students within the college of computer science and engineering was made. In addition, an automated proctoring model that will eliminate the need for a real-time proctoring and remove any scheduling constraints in order to prevent cheating is proposed in this paper. The machine learning algorithms is exploited to enrich the proposed system. A secure frame work using the biometric is applied in order to ensure authentication and running the online exam smoothly.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130001337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Early Identification of Parkinson's Disease from Hand-drawn Images using Histogram of Oriented Gradients and Machine Learning Techniques 利用定向梯度直方图和机器学习技术从手绘图像中早期识别帕金森病

2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2020-12-21 DOI: 10.1109/ETCCE51779.2020.9350870

Ferdib-Al-Islam, L. Akter

{"title":"Early Identification of Parkinson's Disease from Hand-drawn Images using Histogram of Oriented Gradients and Machine Learning Techniques","authors":"Ferdib-Al-Islam, L. Akter","doi":"10.1109/ETCCE51779.2020.9350870","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350870","url":null,"abstract":"Parkinson's disease is one of the supreme neurodegenerative problems of the human's vital nervous organism. It is a matter of sorrow that no specific clinical tests were introduced to detect Parkinson's disease correctly. As Parkinson's disease is non-communicable, early-stage detection of Parkinson's can prevent further damages in humans suffering from it. However, it has been observed that PD's presence in a human is related to its hand-writing as well as hand-drawn subjects. From that perspective, several techniques have been proposed by researchers to detect Parkinson's disease from hand-drawn images of suspected people. But, the previous methods have their constraints. In this investigation, an approach to predict Parkinson's disease from hand-drawn wave and spiral images using computer vision and machine learning techniques has been recommended. Decision Tree, Gradient Boosting, K-Nearest Neighbor, Random Forest, and some other classification algorithms with the HOG feature descriptor algorithm was applied. The proposed strategy with Gradient Boosting and K-Nearest Neighbors accomplished better execution in accuracy, sensitivity, and specificity as well as in system design flexibility. Gradient Boosting algorithm got 86.67%, 93.33%, and 80.33% for accuracy, sensitivity, specificity and KNN got 89.33%, and 91.67% for accuracy, and sensitivity respectively.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125821668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Learning-Based Conceptual framework for Threat Assessment of Multiple Vehicle Collision in Autonomous Driving 基于学习的自动驾驶多车碰撞威胁评估概念框架

2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2020-12-21 DOI: 10.1109/ETCCE51779.2020.9350869

Abu Jafar Md Muzahid, S. F. Kamarulzaman, M. Rahim

{"title":"Learning-Based Conceptual framework for Threat Assessment of Multiple Vehicle Collision in Autonomous Driving","authors":"Abu Jafar Md Muzahid, S. F. Kamarulzaman, M. Rahim","doi":"10.1109/ETCCE51779.2020.9350869","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350869","url":null,"abstract":"The autonomous driving is increasingly mounting, promoting, and promising the future of fully autonomous and, correspondingly presenting new challenges in the field of safety assurance. The unexpected and sudden lane change are extremely serious causes of traffic accident and, such an accident scheme leads the multiple vehicle collisions. Extensive evaluation of recent crash data we found a crucial indication that autonomous driving systems are most prone to rear-end collision, which is the leading factor of chain crash. Learning based self-developing assessment assists the operators in providing the necessary prediction operations or even replace them. Here we proposed a Reinforcement learning-based conceptual framework for threat assessment system and scrutinize critical situations that leads to multiple vehicle collisions in autonomous driving. This paper will encourage our transport community to rethink the existing autonomous driving models and reach out to other disciplines, particularly robotics and machine learning, to join forces to create a secure and effective system.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126928829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Bengali Abusive Speech Classification: A Transfer Learning Approach Using VGG-16 孟加拉语辱骂语分类:基于VGG-16的迁移学习方法

2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2020-12-21 DOI: 10.1109/ETCCE51779.2020.9350919

Shantanu Kumar Rahut, Riffat Sharmin, Ridma Tabassum

{"title":"Bengali Abusive Speech Classification: A Transfer Learning Approach Using VGG-16","authors":"Shantanu Kumar Rahut, Riffat Sharmin, Ridma Tabassum","doi":"10.1109/ETCCE51779.2020.9350919","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350919","url":null,"abstract":"Swear words used in speech to abuse someone is frowned upon in every society. Abusive speeches can destroy the victim's morale, mental strength, and the will to live. Abusing others through social media, video streaming sites, and over voice calls are becoming a common problem. There are laws to punish the offenders. However, without proper surveillance, stopping abusive speech is tough. Machine learning can help to create surveillance methods by detecting abusive speech from human conversation. There have been a few works in the relevant field to detect abusive speech. However, detecting abusive speech in the Bengali language remains an unexplored area. This paper aims at providing an approach towards the classification of abusive and non-abusive Bengali speech. The authors collected 960 voice recordings of native Bengali speakers. The authors used Transfer Learning for extracting features from the data. Then, the authors used different methods for classification. The proposed approach achieves high accuracy (98.61%) in classifying abusive and non-abusive Bengali speech.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130322555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Real-Time Motion Based Fuel Monitoring Technique For Vehicle Tracking Systems 基于实时运动的车辆跟踪系统燃油监测技术

2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2020-12-21 DOI: 10.1109/ETCCE51779.2020.9350860

Faizah Farzana, Md. Miraz Hossain, Mohammad Maruf Imtiaze, Md. Tafsir Hossain, Abu Shafin Mohammad Mahdee Jameel, Salekul Islam

{"title":"A Real-Time Motion Based Fuel Monitoring Technique For Vehicle Tracking Systems","authors":"Faizah Farzana, Md. Miraz Hossain, Mohammad Maruf Imtiaze, Md. Tafsir Hossain, Abu Shafin Mohammad Mahdee Jameel, Salekul Islam","doi":"10.1109/ETCCE51779.2020.9350860","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350860","url":null,"abstract":"Fuel monitoring is an integral part of a vehicle tracking system. It allows a user to track the fuel status of a vehicle, such as fuel level, refill, leak or theft, and analyze the consumption behavior. In this paper, we present a novel technique for improving the fuel monitoring service of vehicle tracking systems. We integrate the motion parameters of a vehicle (orientation, acceleration, and vibration) into the tracking device to increase the accuracy of the fuel level measurement. Additionally, we detect suspicious activities, such as tank leaks and fuel theft, and discern fuel refill by conducting a temporal analysis of the measured fuel level data. We design a hardware module for data collection and analyze the collected data to explore the relationship between fuel usage and motion parameters of a vehicle. We also develop a smartphone application for the real-time observation of the fuel data. Our experiments yield an 80 % success rate in determining the accurate fuel level value and a 100% success rate in detecting both refill and suspicious consumption activity. These findings can help reduce the errors in the fuel monitoring report and consequently improve the efficiency of the device as well as the benefit of the user.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128457684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An Integrated Grey Wolf Optimizer with Nelder-Mead Method for Workflow Scheduling Problem 基于Nelder-Mead方法的工作流调度集成灰狼优化器

2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2020-12-21 DOI: 10.1109/ETCCE51779.2020.9350893

N. Mohsin, R. S. Alhamdani, B. F. Al-Dulaimi

引用次数: 0

Entropy Based Frame Exclusion Framework for Video Transmission over Next Generation Networks 基于熵的下一代网络视频传输帧排除框架

2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2020-12-21 DOI: 10.1109/ETCCE51779.2020.9350901

Dalia El-Banna, Taufiq Asyhari

引用次数: 0

CNN-based Leaf Image Classification for Bangladeshi Medicinal Plant Recognition 基于cnn的孟加拉药用植物叶片图像分类

2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Pub Date : 2020-12-21 DOI: 10.1109/ETCCE51779.2020.9350900

Raisa Akter, Md. Imran Hosen

{"title":"CNN-based Leaf Image Classification for Bangladeshi Medicinal Plant Recognition","authors":"Raisa Akter, Md. Imran Hosen","doi":"10.1109/ETCCE51779.2020.9350900","DOIUrl":"https://doi.org/10.1109/ETCCE51779.2020.9350900","url":null,"abstract":"Classifying plant species has taken much attention in the research area to help people recognizing plants easily. In recent years, the convolutional neural networks (CNN) have achieved tremendous computer vision results, especially in image classification. Usually, humans find it difficult to recognize proper medicinal plants. It requires the intuition of an expert botanist, which is a time consuming manual task. In this research, we proposed an automated system for the medicinal plant classification, which will help people identify useful plant species quickly. A new dataset of 10 medicinal plants of Bangladesh is introduced, collected from different regions across the country, and some state-of-the images collected from different sources. After that, a three-layer convolutional neural network is employed to extract the high-level features for the classification trained with the data augmentation technique. The training process was done on 34123 images, and the experimental result on another 3570 images proved that this method is quite feasible and effective, which gave by a 71.3% accuracy rate.","PeriodicalId":234459,"journal":{"name":"2020 Emerging Technology in Computing, Communication and Electronics (ETCCE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133144383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10