Pub Date : 2023-09-30DOI: 10.5626/jcse.2023.17.3.135
Sachnev Vasily, B. S. Mahanand
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.
{"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":"27 1","pages":"0"},"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}
Pub Date : 2023-09-30DOI: 10.5626/jcse.2023.17.3.93
Xiong Wei
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.
{"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":"5 1","pages":"0"},"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}
Pub Date : 2023-09-30DOI: 10.5626/jcse.2023.17.3.127
Sachnev Vasily, B. S. Mahanand
In this work, an efficient machine learning technique for autism diagnosis using structural magnetic resonance imaging (MRI) is proposed. The proposed technique employs the voxel-based morphometry (VBM) approach to extract a set of 989 relevant features from MRI. These features are used to train an efficient extreme learning machine (ELM) classifier to identify autism spectrum disorder (ASD) and healthy controls. The proposed selective binary coded genetic algorithm (sBCGA) found a subset of significant VBM features. The selected subset of features was used to build a final ELM classifier with maximum overall accuracy. The proposed sBCGA uses a selective sample-balanced crossover designed to improve the classification of ASD and healthy controls. The proposed sBCGA has been extensively tested, and the experimental results clearly indicated better accuracy than existing methods.
{"title":"An Efficient Autism Detection Using Structural Magnetic Resonance Imaging Based on Selective Binary Coded Genetic Algorithm","authors":"Sachnev Vasily, B. S. Mahanand","doi":"10.5626/jcse.2023.17.3.127","DOIUrl":"https://doi.org/10.5626/jcse.2023.17.3.127","url":null,"abstract":"In this work, an efficient machine learning technique for autism diagnosis using structural magnetic resonance imaging (MRI) is proposed. The proposed technique employs the voxel-based morphometry (VBM) approach to extract a set of 989 relevant features from MRI. These features are used to train an efficient extreme learning machine (ELM) classifier to identify autism spectrum disorder (ASD) and healthy controls. The proposed selective binary coded genetic algorithm (sBCGA) found a subset of significant VBM features. The selected subset of features was used to build a final ELM classifier with maximum overall accuracy. The proposed sBCGA uses a selective sample-balanced crossover designed to improve the classification of ASD and healthy controls. The proposed sBCGA has been extensively tested, and the experimental results clearly indicated better accuracy than existing methods.","PeriodicalId":37773,"journal":{"name":"Journal of Computing Science and Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135132222","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}
Pub Date : 2023-09-30DOI: 10.5626/jcse.2023.17.3.100
Gwanghee Lee, Sangjun Moon, Dasom Choi, Gayeon Kim, Kyoungson Jhang
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.
{"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":"56 1","pages":"0"},"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}
Pub Date : 2023-09-30DOI: 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
This study presents a method for diagnosing fatty liver disease by using time-difference liver computed tomography (CT) images of the same patient to perform segmentation and rigid registration on liver regions, excluding the vascular regions. The proposed method comprises three main steps. First, the liver region is segmented in the precontrast phase, and the liver and liver vessel regions are segmented in the portal phase. Second, rigid registration is performed between the liver regions to align the liver positions affected by the patient
{"title":"Segmentation and Rigid Registration of Liver Dynamic Computed Tomography Images for Diagnostic Assessment of Fatty Liver Disease","authors":"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","doi":"10.5626/jcse.2023.17.3.117","DOIUrl":"https://doi.org/10.5626/jcse.2023.17.3.117","url":null,"abstract":"This study presents a method for diagnosing fatty liver disease by using time-difference liver computed tomography (CT) images of the same patient to perform segmentation and rigid registration on liver regions, excluding the vascular regions. The proposed method comprises three main steps. First, the liver region is segmented in the precontrast phase, and the liver and liver vessel regions are segmented in the portal phase. Second, rigid registration is performed between the liver regions to align the liver positions affected by the patient","PeriodicalId":37773,"journal":{"name":"Journal of Computing Science and Engineering","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135132223","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}
Pub Date : 2023-09-30DOI: 10.5626/jcse.2023.17.3.109
Sang-Won Bae
In this paper, we study the problem of counting the number of monotone polygons in a given set S of n points in general position in the plane. A simple polygon is said to be monotone when any vertical line intersects its boundary at most twice. To our best knowledge, this counting problem remains unsolved and no nontrivial algorithm is known so far. As a research step forward to tackle the problem, we define a subclass of monotone polygons and present the first efficient algorithms that exactly count them.
{"title":"On Counting Monotone Polygons and Holes in a Point Set","authors":"Sang-Won Bae","doi":"10.5626/jcse.2023.17.3.109","DOIUrl":"https://doi.org/10.5626/jcse.2023.17.3.109","url":null,"abstract":"In this paper, we study the problem of counting the number of monotone polygons in a given set S of n points in general position in the plane. A simple polygon is said to be monotone when any vertical line intersects its boundary at most twice. To our best knowledge, this counting problem remains unsolved and no nontrivial algorithm is known so far. As a research step forward to tackle the problem, we define a subclass of monotone polygons and present the first efficient algorithms that exactly count them.","PeriodicalId":37773,"journal":{"name":"Journal of Computing Science and Engineering","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135132224","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}
Pub Date : 2007-09-03DOI: 10.5626/jcse.2007.1.2.195
Y. Bao, Fangling Leng, Daling Wang, Ge Yu
Dwarf is a highly compressed structure, which compresses the cube by eliminating the semantic redundancies while computing a data cube. Although it has high compression ratio, Dwarf is slower in querying and more difficult in updating due to its structure characteristics. We all know that the original intention of data cube is to speed up the query performance, so we propose two novel clustering methods for query optimization: the recursion clustering method which clusters the nodes in a recursive manner to speed up point queries and the hierarchical clustering method which clusters the nodes of the same dimension to speed up range queries. To facilitate the implementation, we design a partition strategy and a logical clustering mechanism. Experimental results show our methods can effectively improve the query performance on data cubes, and the recursion clustering method is suitable for both point queries and range queries.
{"title":"A Clustered Dwarf Structure to Speed up Queries on Data Cubes","authors":"Y. Bao, Fangling Leng, Daling Wang, Ge Yu","doi":"10.5626/jcse.2007.1.2.195","DOIUrl":"https://doi.org/10.5626/jcse.2007.1.2.195","url":null,"abstract":"Dwarf is a highly compressed structure, which compresses the cube by eliminating the semantic redundancies while computing a data cube. Although it has high compression ratio, Dwarf is slower in querying and more difficult in updating due to its structure characteristics. We all know that the original intention of data cube is to speed up the query performance, so we propose two novel clustering methods for query optimization: the recursion clustering method which clusters the nodes in a recursive manner to speed up point queries and the hierarchical clustering method which clusters the nodes of the same dimension to speed up range queries. To facilitate the implementation, we design a partition strategy and a logical clustering mechanism. Experimental results show our methods can effectively improve the query performance on data cubes, and the recursion clustering method is suitable for both point queries and range queries.","PeriodicalId":37773,"journal":{"name":"Journal of Computing Science and Engineering","volume":"584 1","pages":"170-180"},"PeriodicalIF":0.0,"publicationDate":"2007-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77286602","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}
Pub Date : 2007-07-16DOI: 10.5626/jcse.2009.3.1.027
T. Tran, B. Lee
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.
{"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":"29 1","pages":"330-347"},"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：