2022 26th International Computer Science and Engineering Conference (ICSEC)最新文献_第7页

Forecasting Air Quality Index in Thailand Using Ensemble Method 用集合法预测泰国空气质量指数

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049373

Saksiri Lertnilkarn, Suphakant Phimoltares

引用次数: 0

Multi-resolution Coarse-to-fine Registration Approach for Liver Computed Tomography Image Analysis 肝脏计算机断层图像分析的多分辨率粗-细配准方法

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049333

Xuan Loc Pham, Quoc Anh Le, Duc Trinh Chu, Manh Ha Luu

{"title":"Multi-resolution Coarse-to-fine Registration Approach for Liver Computed Tomography Image Analysis","authors":"Xuan Loc Pham, Quoc Anh Le, Duc Trinh Chu, Manh Ha Luu","doi":"10.1109/ICSEC56337.2022.10049333","DOIUrl":"https://doi.org/10.1109/ICSEC56337.2022.10049333","url":null,"abstract":"Computed Tomography (CT) image contains vital medical information of patients and thus being irreplaceable in liver cancer treatment. Recently, computer-aided methods are increasingly applied into CT image processing, especially medical image segmentation and registration, and achieved promising results. However, performing non-rigid registration on liver CT images is challenging due to the large deformation caused by the big size of the liver organ. In this study, we propose a method for solving the liver registration problem, which utilizes convolutional neural network (CNN) with multi-resolution coarse-to-fine registration strategy to step-by-step deform the moving image to get closer the fixed shape. The proposed network is trained unsupervisedly for ease of expandability. We extensively evaluated the trained model on a variety of public liver datasets using dice (DSC), intersection over union (IoU) and landmark distance metrics, and compare to the performance of two well-known CNN-based registration methods. Experimental results show that the proposed method achieves promising results and proves its potential in the registration of CT images of diverse liver shapes.","PeriodicalId":430850,"journal":{"name":"2022 26th International Computer Science and Engineering Conference (ICSEC)","volume":"7 16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129271397","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 Tool for Automating IT Management in Small Schools 小型学校资讯科技管理自动化的工具

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049374

Jutamas Leanjay, P. Ratanaworabhan, Tarida Dalai

{"title":"A Tool for Automating IT Management in Small Schools","authors":"Jutamas Leanjay, P. Ratanaworabhan, Tarida Dalai","doi":"10.1109/ICSEC56337.2022.10049374","DOIUrl":"https://doi.org/10.1109/ICSEC56337.2022.10049374","url":null,"abstract":"The outbreak of COVID-2019 has resulted in the adaptation of the teaching and learning style in schools to become more online, conducting teaching and learning from any places without classroom meeting. Systems such as School Management, Online Meeting, and Online library, have been deployed to support all school members including students, teachers, parents, and administrators. These systems need to be properly managed. For business enterprises, this job falls on the shoulders of the IT department, which is usually well-staffed and well-equipped as companies realize their competitive edge depends on it. For educational institutions, especially in small schools, only 1 or 2 \"computer specialists\" assume the responsibility of the whole IT department. This can be overwhelming for them and, when IT tasks are poorly managed, dissatisfaction and productivity loss among school members ensue. This paper describes a system that we have designed and developed called Admin Task Management Center (ATMC). It aims to significantly reduce the manual workload of IT staff in small schools in document management, system monitoring, and other IT-related tasks. Our ATMC is currently being deployed at Satit Kaset IP (Kasetsart University Laboratory School, Center for Educational Research and Development, International Program). Our evaluation shows that the ATMC considerably raises the productivity level of IT staff, as well as other members of the school. We have released version 1 of our ATMC tool as open-source software. It is available on Github.","PeriodicalId":430850,"journal":{"name":"2022 26th International Computer Science and Engineering Conference (ICSEC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132500825","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

Information Systems in Medical Settings: A Covid-19 Detection System Using X-Ray Scans 医疗环境中的信息系统:使用x射线扫描的Covid-19检测系统

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049310

Ahmad AL Smadi, A. Abugabah, Shadrack Fred Mahenge, Farah Shahid

{"title":"Information Systems in Medical Settings: A Covid-19 Detection System Using X-Ray Scans","authors":"Ahmad AL Smadi, A. Abugabah, Shadrack Fred Mahenge, Farah Shahid","doi":"10.1109/ICSEC56337.2022.10049310","DOIUrl":"https://doi.org/10.1109/ICSEC56337.2022.10049310","url":null,"abstract":"Beginning in 2020, the new coronavirus began to expand globally. Due to Covid-19, millions of individuals are infected. Initially, the availability of corona test kits was problematic. Researchers examined the present scenario and developed the Covid-19 X-ray scan detection system. In terms of Covid-19 detection, artificial intelligence (AI)-based solutions give superior outcomes. Many AI-based models can not provide optimum results because of the issue of overfitting, which has a direct impact on model efficiency. In this work, we developed the CNN-based classification method based on the pre-trained Inception-v3 for normal, viral pneumonia, lung opacity, and Covid-19 samples. In the suggested model, we employed transfer learning to produce promising results for binary class classification. The presented model attained impressive outcomes with an accuracy of 99.42% for Covid-19 vs. Normal, 99.01% for Covid-19 vs. Lung Opacity, and 99.8% for Covid-19 vs. Viral Pneumonia, and 99.93% for Lung Opacity vs. Viral Pneumonia. Comparing the suggested model to existing deep learning-based systems indicated that ours was better.","PeriodicalId":430850,"journal":{"name":"2022 26th International Computer Science and Engineering Conference (ICSEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124298779","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

Network-based methods with heterogeneous data to identify severe COVID immune-related genes 基于异构数据的网络方法鉴定重症COVID免疫相关基因

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049313

Pakorn Sagulkoo, A. Suratanee, K. Plaimas

{"title":"Network-based methods with heterogeneous data to identify severe COVID immune-related genes","authors":"Pakorn Sagulkoo, A. Suratanee, K. Plaimas","doi":"10.1109/ICSEC56337.2022.10049313","DOIUrl":"https://doi.org/10.1109/ICSEC56337.2022.10049313","url":null,"abstract":"Bioinformatics and systems biology play a vital role in the computational prediction of disease-associated genes using multi-omics data. The network-based approach is one of the most potent tools in disease-associated gene prediction. The two commonly used methods are neighborhood-based and network diffusion techniques. However, there is still a lack of studies comparing the performance of these methods, especially in terms of functional pathway discovery. Thus, this study demonstrated the performance comparison of these two techniques in both numerical accuracies based on the area under the receiver operating characteristic curve (AUROC) and biological meaning efficiency based on functional pathway enrichment. In this study, we analyzed data of severe COVID-19 immune-related genes using heterogeneous data. The prediction results of the COVID-19 immune-related genes in the human protein-protein interaction (PPI) network showed that the network diffusion had better performance in both AUROC and pathway enrichment even though it provided a longer computational time than the neighborhood method.","PeriodicalId":430850,"journal":{"name":"2022 26th International Computer Science and Engineering Conference (ICSEC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116644830","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

Transforming Probabilistic Timed Automata to PRISM Model 概率时间自动机到PRISM模型的转换

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049340

Thirawat Sutalungka, P. Chariyathitipong, W. Vatanawood

引用次数: 0

Heterogeneous data analysis of hypertrophic cardiomyopathy to prioritize important genes 肥厚性心肌病的异质性数据分析优先考虑重要基因

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049332

Panisa Janyasupab, A. Suratanee, K. Plaimas

{"title":"Heterogeneous data analysis of hypertrophic cardiomyopathy to prioritize important genes","authors":"Panisa Janyasupab, A. Suratanee, K. Plaimas","doi":"10.1109/ICSEC56337.2022.10049332","DOIUrl":"https://doi.org/10.1109/ICSEC56337.2022.10049332","url":null,"abstract":"Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease that is often caused by abnormal genes in the heart muscle. The identification of HCM-related genes is one of the crucial tasks to prevent and treat the patient. Gene expression analysis is a direct approach to screen for a gene with a higher or lower expression level in the HCM cell than in the normal cell. Microarray and RNA-Seq technology are used for measuring transcription levels. Both techniques have different advantages to obtain gene expression data. The integration of microarray and RNA-Seq data has already been effectively used to identify disease biomarkers. The ranking method is an interesting technique and is mostly used for ranking players or teams in sports. Each method has different strengths and can be appropriately applied to integrate various data and used to prioritize the importance genes. In this work, six ranking techniques to integrate microarray and RNA-Seq data were applied to prioritize the HCM-related genes. The performance reveals that the ranking method is also a well-suited technique in this task, and it turns out that the PageRank technique yields the best performance.","PeriodicalId":430850,"journal":{"name":"2022 26th International Computer Science and Engineering Conference (ICSEC)","volume":" September","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113947160","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

Medical Image Segmentation using LeViT-UNet++: A Case Study on GI Tract Data 基于lewei - unet++的医学图像分割:以胃肠道数据为例

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-09-15 DOI: 10.1109/ICSEC56337.2022.10049343

Praneeth Nemani, Satyanarayana Vollala

引用次数: 5