2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)最新文献_第8页

An Efficient Image Processing Technique for Brain Tumor Detection from MRI Images 一种有效的MRI脑肿瘤检测图像处理技术

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162361

S. Alam, M. Abdullah, Fairoz Nower Khan, A. K. M. A. Ullah, Md. Muzahidul Islam Rahi, Dr. Md. Ashraful Alam

{"title":"An Efficient Image Processing Technique for Brain Tumor Detection from MRI Images","authors":"S. Alam, M. Abdullah, Fairoz Nower Khan, A. K. M. A. Ullah, Md. Muzahidul Islam Rahi, Dr. Md. Ashraful Alam","doi":"10.1109/CSDE48274.2019.9162361","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162361","url":null,"abstract":"Brain tumor, a type of cancer, is caused by a genetic mutation of abnormal neuronal cells. However, it cannot be easily diagnosed and depends on the patient’s symptoms which range from hemialgia, seizure, irregular vision, mental shift and many more. The symptoms may vary depending on the region of the tumor. Currently, Magnetic Resonance Imaging (MRI) scan is the foremost means for tumor detection as well as identifying the position and extent of the tumor for surgical procedure. However, the MRIs are needed to be manually checked by a professional to determine the results. We propose a system which is an effective image processing algorithm for detecting and recognizing the tumor from MRI images to obtain the image segmentation of brain bleeding more accurately, making immediate medical treatment possible. In this system, the same problem with different procedures and methods is tested to find the best conjunction through trial and error. Through the combination, the effectiveness and error ratio of the results is engrossed on. This approach is for early detection of brain tumors with high correctness which gives an adequate result by detecting brain tumor properly.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131709256","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

Ransomware Noise Identification and Eviction Through Machine Learning Fundamental Filters 基于机器学习基本滤波器的勒索软件噪声识别与剔除

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162376

Priynka Sharma, K. Chaudhary, Mgm Khan, Michael Wagner

{"title":"Ransomware Noise Identification and Eviction Through Machine Learning Fundamental Filters","authors":"Priynka Sharma, K. Chaudhary, Mgm Khan, Michael Wagner","doi":"10.1109/CSDE48274.2019.9162376","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162376","url":null,"abstract":"The existence of noise in a Ransomware dataset can negatively affect the classification model constructed. More explicitly, the noisy examples in the dataset can antagonistically influence the learnt hypothesis. Eviction of noisy occurrences will improve the hypothesis; thus, improving the classification precision of the model. This paper acquaints a novel strategy through upgraded inferiority of training ransomware data with a noisy dependent variable for multiclass classification problems. Noise diminishes classification accuracy by disturbing the informational training index and setting off the classifier to assemble erroneous models. Our methodology uses a Machine Learning Fundamental Filters (MLFF) to arrange suspicious noisy examples and prototype selection (PS) in order to recognise the set of real noisy occurrences in ransomware dataset. This paper shows that the tuning of MLFF with prototype selection improves the nature of noisy training data collections; thus, increases the classification precision of the model trained with the training dataset without noise.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117110870","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

Analysing Computer Science Course Using Learning Analytics Techniques 使用学习分析技术分析计算机科学课程

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162369

Sanjay Jha, Meena Jha, L. O'Brien

{"title":"Analysing Computer Science Course Using Learning Analytics Techniques","authors":"Sanjay Jha, Meena Jha, L. O'Brien","doi":"10.1109/CSDE48274.2019.9162369","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162369","url":null,"abstract":"The academic community has identified some critical issues facing the sector and are trying to Figure out how these can be addressed. One of these issues is that Science, Technology, Engineering, and Mathematics (STEM) fields have notoriously low retention rates. According to 2016 report on Australia’s STEM workforce only 32% are university qualified. LA has become an emerging research field where knowledge is extracted and patterns are discovered from e-learning systems. LA provides a set of techniques which can help improve the retention rate by identifying students’ needs, and analysing students’ learning behaviour patterns. The objective of our research is to introduce a data-driven investigation to determine students’ learning behaviour in a computer science course. Our research uses a variety of techniques such as Clustering (K-Means), and Classification (Decision Tree Induction) in order to achieve the goal to discover data-driven knowledge from the Moodle Learning Management System (LMS) and the Student Information System (SIS). The analysis was carried out using log data obtained from Moodle and student’s information obtained from the SIS in a computer science course. The study collected demographic profiles of students studying a course in Information Systems and Analysis and compared them to find answers to questions such as how student’s engagement to online activities and accessing learning resources impact students’ success rate and hence improving retention rate by identifying students need on time. For this work, Rapid Miner data mining tool was used to mine and analyse data from the SIS and the Moodle system.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122562646","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

Faculty Orientation Online Tool For First Year Science Students: Transitioning For Success 面向一年级理科学生的教师指导在线工具:向成功过渡

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162380

B. Kumar, B. Sharma, Ajendra Prasad

{"title":"Faculty Orientation Online Tool For First Year Science Students: Transitioning For Success","authors":"B. Kumar, B. Sharma, Ajendra Prasad","doi":"10.1109/CSDE48274.2019.9162380","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162380","url":null,"abstract":"This paper investigates the design implementation of a discipline specific online orientation tool for new students of higher education. Many incoming students face difficulties because of the sudden change in the eduscape, the transition from high schools to higher education institutions made more difficult due to attributes such as different learning styles, lifestyles and traditional and cultural practices. In general, the lower pass rates in first year courses are especially noticeable for students who register late and those based in peripheral campuses and centres. This is also the case for many students who arrive to a new campus away from their countries for programmes not offered in their local campuses. This ICT enabled tool is designed to address the new students’ anxieties and fears, and ease the adaptation to Science Programmes in higher education. The tool also allows students to understand the dynamic environment, be informed about the academic and social issues and also have the ownership of the University. Based on the findings, the paper provides recommendations on the design, development and implementation of the Faculty Orientation Online tool (FOOT).","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123006581","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

Analysis of Pressure Gauge Reliability in Highly Caustic Alumina Precipitation Process – A Case Study 高碱性氧化铝沉淀过程中压力表可靠性分析——以实例为例

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162405

Md. Gyas. Uddin, S. Azad, Subhash C. Sharma

{"title":"Analysis of Pressure Gauge Reliability in Highly Caustic Alumina Precipitation Process – A Case Study","authors":"Md. Gyas. Uddin, S. Azad, Subhash C. Sharma","doi":"10.1109/CSDE48274.2019.9162405","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162405","url":null,"abstract":"Alumina refineries are asset and energy intensive process facilities that reform bauxite ores into alumina. Commonly used process to produce alumina is Bayer Process. In each phase of this process reliable pressure monitoring plays a significant role to ensure energy and resource efficient safe and sustainable operation. In this research, a case study from a world class alumina refinery’s alumina precipitation area is presented to highlight the failure of a reliable pressure monitoring system and how it contributes to high asset maintenance costs, production loss, and health, safety and environmental issues. A success story of successful enhancement of service life of economically non-repairable pressure gauge is discussed in this paper. The paper demonstrates how an innovative robust approach of gauge installation significantly eliminated the failure of a critical component of the pressure gauge, pressure sensing diaphragm seal, which led to a service life improvement of multiple times i.e. from few weeks to years. The research applied Root Cause Analysis methodology to identify the root causes of the failure and developed a key action list to solve the problem. In addition, life data analysis technique was used to evaluate the improvement in service life.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126879943","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

Student perceptions of the Safety and Security Risk of On-campus Student Housing Facilities 学生对校园学生住宿设施安全及保安风险的认知

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162378

S. Adisa, F. Simpeh

{"title":"Student perceptions of the Safety and Security Risk of On-campus Student Housing Facilities","authors":"S. Adisa, F. Simpeh","doi":"10.1109/CSDE48274.2019.9162378","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162378","url":null,"abstract":"In the recent times, studies conducted on the provision of student housing facility in universities in South Africa, indicate that security and safety are major issues across the universities. Hence, this paper aims to present the result of an assessment of the risk associated with the absence/lack of security and fire safety measures in the student housing facilities (SHFs) in two universities in South Africa from the students’ perspective. This was done with the aim of addressing the security and safety challenges which has become problematic in most of the on-campus university SHFs in South Africa. The study adopted quantitative survey approach with questionnaire used as instrument for data collection from the students who are registered members of the SHFs from both universities. Analysis of the data was done through relevant descriptive and inferential statistics using a four-step analysis approach. The study found that the majority of SHFs at university A lack security and fire safety measures such as: CCTV, access control with functional smart-card, security alarm, smoke detectors, emergency help line, and water sprinkler system. Although the SHFs at university B had a better provision of security measures, it become evident that some important security and fire safety measures such as; access control with functional smart-card, CCTV, water sprinkler system and weapon detectors were found missing in some of the SHFs. The majority of respondent were of the opinion that absence of those security and fire safety measures pose a very high risk in the SHFs. There is no significant difference in the level of risk students attached to the absence/lack of security and fire safety measures in the SHFs across both universities. The recommendations can guide university health and safety officers (SHE unit), hostel and facility managers to make provision for adequate security and fire safety measures in order to mitigate the security and safety challenges in the on-campus SHFs.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128697023","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

Optimal Harvest date Prediction by Integrating Past and Future Feature Variables 整合过去和未来特征变量的最佳收获日期预测

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162374

JongMoon Choi, N. Koshizuka

{"title":"Optimal Harvest date Prediction by Integrating Past and Future Feature Variables","authors":"JongMoon Choi, N. Koshizuka","doi":"10.1109/CSDE48274.2019.9162374","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162374","url":null,"abstract":"Agriculture, especially horticultural farming is one of the most important issues in the world. On the business side, rural area has a disadvantage in the competitiveness with the region near the huge market by the transportation cost. To increase competitiveness, the countryside has adopted greenhouse cultivation to maximize the winter crop yield when there is a low yield near the metropolitan area. In addition, most of the farming environments are set up and manipulated by farmers for themselves. Consequently, they need not only knowledge about farming but also Information and Communication Technology (ICT) ability to be able to understand information. However, it is inefficient for new farmers to acquire both ICT skills and agricultural knowledge, so recent smart farms use AI technology to analyze obtained data. As a first step, to provide expertise and support for new farmers, we propose a method to predict the optimal harvest date of eggplant combining past pattern and future feature variables. That is because estimating accurate harvest date is important for crops that have a short period of optimum size, weight or quality. The proposed model is composed of pattern analysis and solar radiation prediction models, and it forecasts the growth rate as a crop’s response. We evaluated several methods and the result shows that the proposed method is an efficient tool for predicting the optimal harvest date in IoT-enabled smart greenhouse. This method can contribute to providing specialized and useful information for inexperienced farmers. Moreover, farmers advantage business contract, as estimating early optimal and more accurate harvest date.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126049114","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: 5

Self-motivated Information Sharing in Communities for Promoting Regional Revitalization 社区自主信息共享促进区域振兴

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162394

Ryo Shioya, Mizuki Tanaka, Yurika Shiozu, Katsuhiko Yonezaki, I. Tanev, K. Shimohara

{"title":"Self-motivated Information Sharing in Communities for Promoting Regional Revitalization","authors":"Ryo Shioya, Mizuki Tanaka, Yurika Shiozu, Katsuhiko Yonezaki, I. Tanev, K. Shimohara","doi":"10.1109/CSDE48274.2019.9162394","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162394","url":null,"abstract":"A community is a system composed of “Hito,” “Mono,” and “Koto,” and their relationships, and it cannot be established without the voluntary involvement of people. In order to revitalize the local community, we have worked on a mechanism through which residents can be involved in creating better and richer relationships. For this purpose, we have built a system for collecting, quantifying, and visualizing relationships created by residents in their daily lives, and the system has been in operation in the field so far. In addition, we have introduced a mechanism for people to transmit and share awareness about regional factors and problems. As a result, we can confirm the behavior modification of residents through the said mechanism. In this study, we propose a new mechanism to promote self-motivated information sharing, which results in more effective behavior modification among people in a community. The mechanism incorporates peoples’ self-centered behavior, such as walking and exercise, into an information-sharing mechanism based on the map application, with the aim to investigate whether people’s self-centered behavior can have altruistic effects.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114946711","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