2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)最新文献_第2页

Improvement of the lighting fixtures based indoor localization method CEPHEID 基于室内定位方法CEPHEID的照明灯具改进

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2020-12-16 DOI: 10.1109/CSDE50874.2020.9411387

Hiroyuki Kobayashi

引用次数: 0

Student Background: An Invaluable Player for Academic Success 学生背景:学业成功的无价球员

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2020-12-16 DOI: 10.1109/CSDE50874.2020.9411526

Sarnesh Deo, K. Chaudhary, B. Sharma

引用次数: 1

Towards an Analytical Probe for Twitter Information Flow Micro-structure 微博信息流微观结构的分析探讨

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2020-12-16 DOI: 10.1109/CSDE50874.2020.9411379

Monaheng Ramokhoro, Pheello Maboea, Tresia Holtzhausen, Phumlani N Khoza

{"title":"Towards an Analytical Probe for Twitter Information Flow Micro-structure","authors":"Monaheng Ramokhoro, Pheello Maboea, Tresia Holtzhausen, Phumlani N Khoza","doi":"10.1109/CSDE50874.2020.9411379","DOIUrl":"https://doi.org/10.1109/CSDE50874.2020.9411379","url":null,"abstract":"This article reviews the concepts presented with the conceptual framework of the network society, both in historical and present considerations, and extends them towards creating an analytical probe for information flow micro-structure on Twitter. By considering the effects of societal interconnectedness as it relates to individuals, the philosophical foundations of the network society are predicated on network interconnectedness that goes beyond institutional integration. Although the manifestations are progressively becoming apparent, the literature is still largely lacking in empirical results to facilitate further theoretical developments. Specifically; we present arguments that support the notion that society is becoming highly interconnected, and that these developments warrant study. As a technical contribution, we present the foundations for an analytical tool that is formulated from the joint application of machine learning and network science. Technically, we construct a multilayer network composed of: user interaction patterns, topics that characterize the discourse, and the entities that are referenced in the discourse. The multilayer network then forms the mathematical object on which analysis can be conducted, and allows us to study information flow micro-structure. The set of multilayer networks, that is generated by varying time, serves as a technical underpinning on which to characterize information flow micro-structure on Twitter. As an overarching theme, we argue that developing an understanding of such information flows is of significance to: commercial entities, governments, and the general populace.","PeriodicalId":445708,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131237662","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

Analyzing students’ online presence in undergraduate courses using Clustering 运用聚类分析大学生在线课程的存在感

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2020-12-16 DOI: 10.1109/CSDE50874.2020.9411534

Ravneil Nand, Ashneel Chand, M. Naseem

{"title":"Analyzing students’ online presence in undergraduate courses using Clustering","authors":"Ravneil Nand, Ashneel Chand, M. Naseem","doi":"10.1109/CSDE50874.2020.9411534","DOIUrl":"https://doi.org/10.1109/CSDE50874.2020.9411534","url":null,"abstract":"The Higher Education Institute (HEI) are experiencing a major paradigm shift due to recent global pandemic. A sudden shift from face-to-face (F2F) and blended modes of study to completely online mode of delivery has introduced hidden challenges to facilitators and students alike. Student’s online engagement has become even more important for their academic success as F2F component is not there in most cases. Therefore, there is a need to investigate the effects of the various indicators of students’ online presence towards their academic performance. This paper explores the effectiveness of online presence in HEI where Covid-19 has shifted the course deliveries to fully online mode. Previously, Online Measurable Presence Model (OMPM) was used to find students effectiveness in a blended learning environment where two indicators used were Frequency and Duration. The chosen indicator in this research is frequency, which will be adequately used to quantify the effectiveness of the online presence in two mathematics courses in the Pacific. Clustering technique is used to create clusters of Frequency and see their relation to OMPM model. Prediction is made using neural network to see the accuracy based on model. The clusters would allow to build predictive models to predict future outcomes or occurrences and student performances, with a major focus on mathematics courses.","PeriodicalId":445708,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133403879","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

Breast Cancer Risk Prediction based on Six Machine Learning Algorithms 基于六种机器学习算法的乳腺癌风险预测

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2020-12-16 DOI: 10.1109/CSDE50874.2020.9411572

M. Razu Ahmed, Md. Asraf Ali, Joy Roy, Shakil Ahmed, N. Ahmed

{"title":"Breast Cancer Risk Prediction based on Six Machine Learning Algorithms","authors":"M. Razu Ahmed, Md. Asraf Ali, Joy Roy, Shakil Ahmed, N. Ahmed","doi":"10.1109/CSDE50874.2020.9411572","DOIUrl":"https://doi.org/10.1109/CSDE50874.2020.9411572","url":null,"abstract":"Breast Cancer is the second most important cause of death among women. As per the clinical expert, breast cancer is one of prominent cancers after lung cancer. However, early detection of this type of cancer in its initial stage helps to save lifes and increases lifespan. The survival chance of a patient can increase if there is a classifier that helps with a quick prediction of breast cancer. Therefore, a smart framework is required that can effectively detect and predict with high accuracy early stage of breast cancer. In this article, six machine learning classification algorithms, namely Logistic Regression (LR), K-Nearest Neighbours (kNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF) are implemented in order to evaluate the performance and the prediction power of the model. The main target of this work is to compare these algorithm performances using the Wisconsin Breast Cancer (original) dataset. The number of performance metrics such as accuracy, precision, recall, f-1 score, and specificity are taken into consideration Our analysis of the results shows that the Support Vector Machine achieved the highest accuracy of 97.07% with the least error rate and Naive Bayes gives the lowest accuracy of 96%. All these experiments were carried out using SciKit.","PeriodicalId":445708,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115100520","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 Machine Learning Approach to detect Depression and Anxiety using Supervised Learning 使用监督学习检测抑郁和焦虑的机器学习方法

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2020-12-16 DOI: 10.1109/CSDE50874.2020.9411642

Tahmidur Rahman Ullas, M. Begom, Anamika Ahmed, Raihan Sultana

{"title":"A Machine Learning Approach to detect Depression and Anxiety using Supervised Learning","authors":"Tahmidur Rahman Ullas, M. Begom, Anamika Ahmed, Raihan Sultana","doi":"10.1109/CSDE50874.2020.9411642","DOIUrl":"https://doi.org/10.1109/CSDE50874.2020.9411642","url":null,"abstract":"Depression and anxiety are among the leading causes of substantial disability in developing countries. According to a study of World Health Organization (WHO) South East Region, Bangladesh ranks highest in anxiety disorders with women being affected twice severely as men. Intervening these orders at an early stage would be cheaper and more effective than later treatment, and thus, we have proposed a model that uses a standard psychological assessment and machine learning algorithms to diagnose the different levels of such mental disorders. In our proposed model we have found the usage and effectiveness of the five different types of AI algorithms: Convolutional Neural Network, Support vector machine, Linear discriminant analysis, K Nearest Neighbor Classifier and Linear Regression on the two datasets of anxiety and depression. Comparing the results on the basis of different measurement metrics (accuracy, recall and precision), our model achieves the highest accuracy of 96% for anxiety and 96.8% for depression using the CNN algorithm. Additionally, our analysis shows that among Bangladeshi women of age 18-35, 7.4% suffers from profound levels of anxiety and 15.6% undergoes chronic depression.","PeriodicalId":445708,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125953867","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

New and Simple Offline Authentication Approach using Time-based One-time Password with Biometric for Car Sharing Vehicles 基于时间的一次性密码与生物特征的汽车共享车辆简单离线认证新方法

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2020-12-16 DOI: 10.1109/CSDE50874.2020.9411569

Haqi Khalid, S. Hashim, S. M. S. Ahmad, F. Hashim, M. A. Chaudary

{"title":"New and Simple Offline Authentication Approach using Time-based One-time Password with Biometric for Car Sharing Vehicles","authors":"Haqi Khalid, S. Hashim, S. M. S. Ahmad, F. Hashim, M. A. Chaudary","doi":"10.1109/CSDE50874.2020.9411569","DOIUrl":"https://doi.org/10.1109/CSDE50874.2020.9411569","url":null,"abstract":"Car sharing provides consumers a flexible peer-to-peer service or station service. However, the connectivity problems are pervasive in remote areas and places with multi-path obstructions with no clear line-of-sight (LoS). In this scenario, availability of the network can be intermittent and is not always guaranteed, especially for untethered wireless networks consisting of mobile vehicles. A conventional online authentication scheme; therefore, is not an effective solution when it comes to securing the vehicles. Also, the malicious attackers could gain access to the vehicles using a replay of the user signal, that is known as a “replay attack” In order to provide an effective authentication approach, we propose an offline authentication approach based on a Time-based One-time Password (TOTP) algorithm. OTP is chosen due to its protection against the notorious replay attack that is popular against keyless start vehicles. It also utilized an additional security biometric factor to enhance the security of the driver’s authentication. The new proposed scheme is divided into online and offline schemes to provide a secure solution. The novelty is that it can enable the authorized drivers to securely start and operate during offline duration just by using their mobile devices. The other car-sharing maintenance operations including registration, booking, telematics monitoring, and location tracking can be performed or synchronized whenever the network is back in connection and reachable within the wireless coverage area.","PeriodicalId":445708,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129512627","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

A Case Study of User Privacy based on WiFi Data in a Campus Setting 校园环境下基于WiFi数据的用户隐私案例研究

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2020-12-16 DOI: 10.1109/CSDE50874.2020.9411524

Shangyu Chen, Wai Yan Wong, Xiang Ci, R. Sinnott

{"title":"A Case Study of User Privacy based on WiFi Data in a Campus Setting","authors":"Shangyu Chen, Wai Yan Wong, Xiang Ci, R. Sinnott","doi":"10.1109/CSDE50874.2020.9411524","DOIUrl":"https://doi.org/10.1109/CSDE50874.2020.9411524","url":null,"abstract":"In many walks of life, data recording/capture and its subsequent use can be of benefit to society and businesses alike. In many cases, users are fully willing to share data about themselves as part of getting access to some perceived benefit through this sharing, e.g. using the geo-location of their mobile device to obtain local information for route planning. However in other cases, information is captured on individuals where they have little choice. This might be CCTV images of pedestrians walking down the streets, or as presented in this paper: WiFi data from individuals at a given educational organisation. Individuals at the University of Melbourne often depend on having access to University wireless to undertake their courses/degrees or their jobs respectively. The terms and conditions that they agree to when they sign up to WiFi access include a description of how the data can be used by the University, e.g. to assist in understanding the use of the WiFi network and/or the physical University campus for space/infrastructure management. The University also has stringent privacy policies that prevent the tracking of individuals. In this context, the need to provide services that can use WiFi data for business purposes, but protect the information so that individuals cannot be re-identified is paramount. There are also many researchers that wish to access safe versions of this data for research purposes, e.g. way-finding. This paper explores case studies exploring this data based on WiFi data analytics. We show how (consenting) individuals can be re-identified with minimal external and seemingly innocuous (i.e. non-identity-revealing) data.","PeriodicalId":445708,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130996557","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

Attribute-driven Topical Influential Users Detection in Online Social Networks 在线社交网络中属性驱动的话题影响力用户检测

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2020-12-16 DOI: 10.1109/CSDE50874.2020.9411637

Aditi Dhali, Sarmistha Sarna Gomasta, M. Anwar, Iqbal H. Sarker

{"title":"Attribute-driven Topical Influential Users Detection in Online Social Networks","authors":"Aditi Dhali, Sarmistha Sarna Gomasta, M. Anwar, Iqbal H. Sarker","doi":"10.1109/CSDE50874.2020.9411637","DOIUrl":"https://doi.org/10.1109/CSDE50874.2020.9411637","url":null,"abstract":"At present, online social influencers are guiding the recognition and behaviors of their connections by becoming a voice of moulding opinions. As a consequence, influential user detection has become unavoidable to explore the dynamic evolution of Online Social Networks (OSNs) for any new procedure either for viral marketing applications or administrating the propagation of producing information. Existing methods pay less concentration on the temporal factor of the users’ interests. Our intent is to detect influential users who express their interest towards a particular query on multiple topics at various time periods by spotlighting more on users’ latest activities. The suggested temporal TwitterRank based topical influential users detection in multi hop neighbors network (TIUDMNN) method is based on the addition of PageRank algorithm. We also estimate the outcome of indirect influence i.e. focusing both on users’ influence to their direct neighbors and considering neighbors who are multi hops (2 or 3 hops) away. We conduct experiments on real datasets to illustrate the potency and performance of the proposed approach.","PeriodicalId":445708,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131005685","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

A Comparison Based Analysis on the Performance of Deep Neural Network Models in Terms of Classifying Pneumonia from Chest X-ray Images 基于对比分析的深度神经网络模型在胸部x线图像肺炎分类中的性能

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2020-12-16 DOI: 10.1109/CSDE50874.2020.9411560

N. Akter, Md. Tanzim Reza, Md. Ashraful Alam

引用次数: 0