Journal of Computing Science and Engineering最新文献

An Efficient Attention Deficit Hyperactivity Disorder (ADHD) Diagnostic Technique Based on Multi-Regional Brain Magnetic Resonance Imaging 基于多区域脑磁共振成像的注意力缺陷多动障碍(ADHD)高效诊断技术

Journal of Computing Science and Engineering Pub Date : 2023-09-30 DOI: 10.5626/jcse.2023.17.3.135

Sachnev Vasily, B. S. Mahanand

{"title":"An Efficient Attention Deficit Hyperactivity Disorder (ADHD) Diagnostic Technique Based on Multi-Regional Brain Magnetic Resonance Imaging","authors":"Sachnev Vasily, B. S. Mahanand","doi":"10.5626/jcse.2023.17.3.135","DOIUrl":"https://doi.org/10.5626/jcse.2023.17.3.135","url":null,"abstract":"In this paper, an efficient technique for the diagnosis of attention deficit hyperactivity disorder (ADHD) was proposed. The proposed method used features/voxels extracted from structural magnetic resonance imaging (MRI) scans of seven brain regions and efficiently classified three subtypes of ADHD: ADHD-C, ADHD-H, and ADHD-I, as well as the typically developing control (TDC). Training and testing data for experiments were obtained from ADHD-200 database, and 41,721 features/voxels were extracted from sMRI by using region-of-interest (ROI). The proposed ADHD diagnostic technique built an efficient ADHD classifier in two steps. In the first step, a proposed regional voxels selection method (rVSM) selected an optimal set of features/voxels from seven brain regions available in ADHD-200, i.e., the Amygdala, Caudate, Cerebellar Vermis, Corpus Callosum, Hippocampus, Striatum, and Thalamus. In the second step, voxels/features selected by rVSM were used together to form a unified set of voxels. The unified set of voxels was used by a multi-region voxels selection method to train an efficient classifier using the extreme learning machine (ELM). Finally, the proposed method selected a unique set of voxels from the seven brain regions and built a final ELM classifier with maximum accuracy. Experiments clearly indicated that the proposed method produced better results than existing methods.","PeriodicalId":37773,"journal":{"name":"Journal of Computing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135132051","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

A Study on the Recognition of English Pronunciation Features in Teaching by Machine Learning Algorithms 基于机器学习算法的英语语音特征识别研究

Journal of Computing Science and Engineering Pub Date : 2023-09-30 DOI: 10.5626/jcse.2023.17.3.93

Xiong Wei

{"title":"A Study on the Recognition of English Pronunciation Features in Teaching by Machine Learning Algorithms","authors":"Xiong Wei","doi":"10.5626/jcse.2023.17.3.93","DOIUrl":"https://doi.org/10.5626/jcse.2023.17.3.93","url":null,"abstract":"A better understanding of students\" English pronunciation features would be a useful guide for teaching spoken English. This paper first analyzed the English pronunciation features and extracted Mel-frequency cepstral coefficients (MFCC) features from the pronunciation signal. Then, the support vector machine (SVM) method was used to identify the cases of incorrect and correct pronunciation. To further improve the recognition effect, deep features were extracted using deep brief network (DBN) as the input of the SVM, and the parameters of both DBN and SVM were optimized by the sparrow search algorithm (SSA). Experiments were conducted on the dataset. The results showed that the MFCC-SSA-SVM algorithm had better recognition performance than the MFCC-SVM algorithm. The DBN-SVM algorithm had higher recognition correctness and accuracy than the MFCC-SSA-SVM algorithm, while the SSA-DBN-SVM method had 88.07% correctness and 85.49% accuracy, indicating the best performance. The results demonstrated the reliability of the proposed method for English pronunciation feature recognition; therefore, it can be applied in practical spoken language teaching.","PeriodicalId":37773,"journal":{"name":"Journal of Computing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135132220","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

An Efficient Autism Detection Using Structural Magnetic Resonance Imaging Based on Selective Binary Coded Genetic Algorithm 基于选择性二值编码遗传算法的结构磁共振成像自闭症检测

Journal of Computing Science and Engineering Pub Date : 2023-09-30 DOI: 10.5626/jcse.2023.17.3.127

Sachnev Vasily, B. S. Mahanand

引用次数: 0

Exploration of Key Point Localization Neural Network Architectures for Y-Maze Behavior Test Automation y迷宫行为测试自动化中关键点定位神经网络体系结构的探索

Journal of Computing Science and Engineering Pub Date : 2023-09-30 DOI: 10.5626/jcse.2023.17.3.100

Gwanghee Lee, Sangjun Moon, Dasom Choi, Gayeon Kim, Kyoungson Jhang

{"title":"Exploration of Key Point Localization Neural Network Architectures for Y-Maze Behavior Test Automation","authors":"Gwanghee Lee, Sangjun Moon, Dasom Choi, Gayeon Kim, Kyoungson Jhang","doi":"10.5626/jcse.2023.17.3.100","DOIUrl":"https://doi.org/10.5626/jcse.2023.17.3.100","url":null,"abstract":"The Y-maze behavioral test is a pivotal tool for assessing the memory and exploratory tendencies of mice in novel environments. A significant aspect of this test involves the continuous tracking and pinpointing of the mouse’s location, a task that can be labor-intensive for human researchers. This study introduced an automated solution to this challenge through camera-based image processing. We argued that key point localization techniques are more effective than object detection methods, given that only a single mouse is involved in the test. Through an experimental comparison of eight distinct neural network architectures, we identified the most effective structures for localizing key points such as the mouse’s nose, body center, and tail base. Our models were designed to predict not only the mouse key points but also the reference points of the Y-maze device, aiming to streamline the analysis process and minimize human intervention. The approach involves the generation of a heatmap using a deep learning neural network structure, followed by the extraction of the key points’ central location from the heatmap using a soft argmax function. The findings of this study provide a practical guide for experimenters in the selection and application of neural network architectures for Y-maze behavioral testing.","PeriodicalId":37773,"journal":{"name":"Journal of Computing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135132221","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

Segmentation and Rigid Registration of Liver Dynamic Computed Tomography Images for Diagnostic Assessment of Fatty Liver Disease 肝动态ct图像分割与刚性配准在脂肪肝诊断评估中的应用

Journal of Computing Science and Engineering Pub Date : 2023-09-30 DOI: 10.5626/jcse.2023.17.3.117

Kyoyeong Koo, Sunyoung Lee, Kyoung Won Kim, Kyung Won Kim, Jeongjin Lee, Jiwon Hwang, Taeyong Park, Heeryeol Jeong, Seungwoo Khang, Jongmyoung Lee, Hyuk Kwon, Seungwon Na

引用次数: 0

On Counting Monotone Polygons and Holes in a Point Set 关于点集中单调多边形和孔的计数

Journal of Computing Science and Engineering Pub Date : 2023-09-30 DOI: 10.5626/jcse.2023.17.3.109

Sang-Won Bae

引用次数: 0

Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System. 集体经验:数据库驱动、跨学科团队领导的学习系统。

Journal of Computing Science and Engineering Pub Date : 2012-03-01 DOI: 10.5626/JCSE.2012.6.1.51

Leo A Celi, Roger G Mark, Joon Lee, Daniel J Scott, Trishan Panch

{"title":"Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System.","authors":"Leo A Celi, Roger G Mark, Joon Lee, Daniel J Scott, Trishan Panch","doi":"10.5626/JCSE.2012.6.1.51","DOIUrl":"https://doi.org/10.5626/JCSE.2012.6.1.51","url":null,"abstract":"We describe the framework of a data-fuelled, interdisciplinary team-led learning system. The idea is to build models using patients from one's own institution whose features are similar to an index patient as regards an outcome of interest, in order to predict the utility of diagnostic tests and interventions, as well as inform prognosis. The Laboratory of Computational Physiology at the Massachusetts Institute of Technology developed and maintains MIMIC-II, a public deidentified high- resolution database of patients admitted to Beth Israel Deaconess Medical Center. It hosts of teams of clinicians (nurses, doctors, pharmacists) and scientists (database engineers, modelers, epidemiologists) who translate the day-to-day questions during rounds that have no clear answers in the current medical literature into study designs, perform the modeling and the analysis and publish their findings. The studies fall into the following broad categories: identification and interrogation of practice variation, predictive modeling of clinical outcomes within patient subsets and comparative effectiveness research on diagnostic tests and therapeutic interventions. Clinical databases such as MIMIC-II, where recorded health care transactions - clinical decisions linked with patient outcomes - are constantly uploaded, become the centerpiece of a learning system.","PeriodicalId":37773,"journal":{"name":"Journal of Computing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3678291/pdf/nihms-391025.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31598658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Clustered Dwarf Structure to Speed up Queries on Data Cubes 一种提高数据立方体查询速度的聚类矮结构

Journal of Computing Science and Engineering Pub Date : 2007-09-03 DOI: 10.5626/jcse.2007.1.2.195

Y. Bao, Fangling Leng, Daling Wang, Ge Yu

引用次数: 4

Transformation of Continuous Aggregation Join Queries over Data Streams 数据流上连续聚合连接查询的转换

Journal of Computing Science and Engineering Pub Date : 2007-07-16 DOI: 10.5626/jcse.2009.3.1.027

T. Tran, B. Lee

{"title":"Transformation of Continuous Aggregation Join Queries over Data Streams","authors":"T. Tran, B. Lee","doi":"10.5626/jcse.2009.3.1.027","DOIUrl":"https://doi.org/10.5626/jcse.2009.3.1.027","url":null,"abstract":"We address continuously processing an aggregation join query over data streams. Queries of this type involve both join and aggregation operations, with windows specified on join input streams. To our knowledge, the existing researches address join query optimization and aggregation query optimization as separate problems. Our observation, however, is that by putting them within the same scope of query optimization we can generate more efficient query execution plans. This is through more versatile query transformations, the key idea of which is to perform aggregation before join so join execution time may be reduced. This idea itself is not new (already proposed in the database area), but developing the query transformation rules faces a completely new set of challenges. In this paper, we first propose a query processing model of an aggregation join query with two key stream operators: (1) aggregation set update, which produces an aggregation set of tuples (one tuple per group) and updates it incrementally as new tuples arrive, and (2) aggregation set join, i.e., join between a stream and an aggregation set of tuples. Then, we introduce the concrete query transformation rules specialized to work with streams. The rules are far more compact and yet more general than the rules proposed in the database area. Then, we present a query processing algorithm generic to all alternative query execution plans that can be generated through the transformations, and study the performances of alternative query execution plans through extensive experiments.","PeriodicalId":37773,"journal":{"name":"Journal of Computing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89424962","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