Proceedings of the 2nd International Conference on Computing Advancements最新文献

Machine Learning Method Based Industrial Risk Analysis and Prediction 基于机器学习方法的工业风险分析与预测

Proceedings of the 2nd International Conference on Computing Advancements Pub Date : 2022-03-10 DOI: 10.1145/3542954.3543003

Salim Khan, F. Hasan, M. O. Faruk, Anayet Ullah, Mohammad Woli Ullah, Abdul Gafur

引用次数: 0

A Constructive Review on Pedestrian Action Detection, Recognition and Prediction 行人动作检测、识别与预测研究综述

Proceedings of the 2nd International Conference on Computing Advancements Pub Date : 2022-03-10 DOI: 10.1145/3542954.3543007

Md. Akib Shahriar Khan, Md Jannatul Baki Showmik, Tanvir Ahmed, A. Saif

{"title":"A Constructive Review on Pedestrian Action Detection, Recognition and Prediction","authors":"Md. Akib Shahriar Khan, Md Jannatul Baki Showmik, Tanvir Ahmed, A. Saif","doi":"10.1145/3542954.3543007","DOIUrl":"https://doi.org/10.1145/3542954.3543007","url":null,"abstract":"Analysis of pedestrian activities in the video sequences is an intriguing domain that incorporates vast applications, such as autonomous driving systems, traffic control systems and interactions between people and computers. The primary focus of this research was on evaluating several strategies to analyse pedestrian activities effectively. The constructive comparison included three main steps, i.e. detection of the pedestrian, recognition of their actions and prediction about the activity of the pedestrian. Changes in activities of pedestrians, dynamic background, moving camera, view angle and processing time made it more challenging. Recent approaches were justified and compared based on precision accuracy, processing time and minimum resource allocation. The results were also compared by a series of state-of-the-art research datasets with provided significant observations in terms of greater accuracy which can lead to the construction of an extremely improvised system that would save pedestrian people from road accidents and assist autonomous driving systems. The purpose of this study is to discuss the current progress using different approaches.","PeriodicalId":104677,"journal":{"name":"Proceedings of the 2nd International Conference on Computing Advancements","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125592972","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: 0

Possible Side Effects after Getting COVID-19 Vaccine Based on Pre-Existing Disease: Asthma: Analysis of Side Effects after using Moderna, Pfizer and Janssen Vaccine Based on VAERS Dataset 基于已有疾病接种COVID-19疫苗后可能出现的副作用:哮喘:基于VAERS数据集的Moderna、辉瑞和杨森疫苗的副作用分析

Proceedings of the 2nd International Conference on Computing Advancements Pub Date : 2022-03-10 DOI: 10.1145/3542954.3543001

Md Musleh Uddin Hasan, M. Hossain, M. Hossain, K. I. Rushee

引用次数: 0

An overview of Mobile Edge Computing in Vehicle Platooning 车辆队列中移动边缘计算的概述

Proceedings of the 2nd International Conference on Computing Advancements Pub Date : 2022-03-10 DOI: 10.1145/3542954.3542975

Tareq Md Rabiul Hossain Chy, Nushrat Jahan

引用次数: 0

Phishing Website Detection using Deep Learning 使用深度学习的网络钓鱼网站检测

Proceedings of the 2nd International Conference on Computing Advancements Pub Date : 2022-03-10 DOI: 10.1145/3542954.3542967

Md. Abubakar Siddiq, Md. Arifuzzaman, M. S. Islam

引用次数: 2

An Enhanced CNN Model For Classifying Skin Cancer 一种用于皮肤癌分类的增强CNN模型

Proceedings of the 2nd International Conference on Computing Advancements Pub Date : 2022-03-10 DOI: 10.1145/3542954.3543019

Kazi Md Minhajul Haider, Mondira Dhar, Fahima Akter, Sadia Islam, Syed Ragib Shariar, Muhammad Iqbal Hossain

{"title":"An Enhanced CNN Model For Classifying Skin Cancer","authors":"Kazi Md Minhajul Haider, Mondira Dhar, Fahima Akter, Sadia Islam, Syed Ragib Shariar, Muhammad Iqbal Hossain","doi":"10.1145/3542954.3543019","DOIUrl":"https://doi.org/10.1145/3542954.3543019","url":null,"abstract":"Unrepaired deoxyribonucleic acid in skin cells causes skin cancer by generating genetic abnormalities or mutations, rising day by day. Detecting or diagnosing skin cancer in its initial stages is expensive and challenging, giving superior treatment options. Given the severity of these issues, researchers have generated a set of early classification techniques for skin cancer. Skin cancer is diagnosed and segregated from melanoma by looking at the symmetry, color, size, shape, and other features of lesions. While there are various computerized approaches for classifying skin lesions, convolutional neural networks (CNNs) have exceeded standard practices. In this paper, we have used multiple machine learning libraries. Also, we have used five pre-trained models such as VGG-16, VGG-19, Inception V3, Efficient Net B7, ResNet 50 models and presented our proposed model for skin cancer classification using the HAM10000 dataset, which is an enormous skin cancer dataset. This research reports a maximum accuracy of 85.25% for Inception V3 models within five pre-trained models and the highest accuracy of 90.55% for our proposed model. In terms of image detection, our experimental settings show that our suggested model achieved the best classification accuracy than the other five pre-trained models. Our findings are helpful in providing a comprehensive comparison with brief analysis of many deep neural networks in the categorization of skin cancer.","PeriodicalId":104677,"journal":{"name":"Proceedings of the 2nd International Conference on Computing Advancements","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126194518","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: 2

Banking Management System Architecture Using AI & Blockchain 基于AI和区块链的银行管理系统架构

Proceedings of the 2nd International Conference on Computing Advancements Pub Date : 2022-03-10 DOI: 10.1145/3542954.3542966

Meherab Mamun, B. Arifuzzaman, S. Mahmud

引用次数: 0

An Empirical Analysis of 5.76 Tbits/s SDM-PDM-Nyquist superchannel WDM hybrid multiplexing technique for channel capacity enhancement 5.76 Tbits/s SDM-PDM-Nyquist超信道WDM混合复用技术信道容量增强的实证分析

Proceedings of the 2nd International Conference on Computing Advancements Pub Date : 2022-03-10 DOI: 10.1145/3542954.3542971

Md. Redowan Mahmud Arnob, Sabiqun Nahar, Mohammad Nasir Uddin

引用次数: 0

Efficient Machine Learning Techniques to Predict Lung Cancer 预测肺癌的高效机器学习技术

Proceedings of the 2nd International Conference on Computing Advancements Pub Date : 2022-03-10 DOI: 10.1145/3542954.3543067

Rahul Deb Mohalder, Juliet Polok Sarkar, Khandkar Asif Hossain, Laboni Paul, M. Raihan

引用次数: 1

EMG-Based Hand Gesture Dataset to Control Electronic Wheelchair for SCI Patients 基于肌电图的手势数据集控制SCI患者电子轮椅

Proceedings of the 2nd International Conference on Computing Advancements Pub Date : 2022-03-10 DOI: 10.1145/3542954.3542979

S. Afrin, H. Mahmud, Md. Kamrul Hasan

{"title":"EMG-Based Hand Gesture Dataset to Control Electronic Wheelchair for SCI Patients","authors":"S. Afrin, H. Mahmud, Md. Kamrul Hasan","doi":"10.1145/3542954.3542979","DOIUrl":"https://doi.org/10.1145/3542954.3542979","url":null,"abstract":"This paper presents electromyography (EMG)-based hand gesture dataset to control electric wheelchair for the patient with spinal cord injury (SCI). We have recorded eight-channel surface EMG (sEMG) signals from EMG sensor placed at the forearm of the SCI patient. These signals were collected from six hand gesture-based wheelchair control movements (forward, backward, left, right, start and stop). We collected hand gesture data containing different EMG signals from 12 healthy subjects and 7 SCI subjects. Later on, The EMG signals were segmented and the time-domain feature extraction technique was applied to generate 18000 training samples and 10500 testing samples. We then classified the hand gestural EMG signals using 5 different classical machine learning models. We analyze the classification results in two ways. The first one is, training the models using only data of healthy subjects and cross-validated using data from 7 SCI patients. And the second one is by including six SCI patient’s data in the training process along with healthy subjects we performed leave one out cross-validation. From this analysis we were able to achieve highest 95.42% accuracy using decision tree (DT) and Random Forest(RF) algorithms.","PeriodicalId":104677,"journal":{"name":"Proceedings of the 2nd International Conference on Computing Advancements","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122574285","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: 2