2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)最新文献_第6页

Classifying Thai Occupation from Images using Deep Learning with Grayscale Feature Extractor 基于灰度特征提取器的深度学习泰国职业分类

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836300

Visaruth Punnium, Sitapa Rujikietgumjorn, Prapaporn Rattanatamrong

{"title":"Classifying Thai Occupation from Images using Deep Learning with Grayscale Feature Extractor","authors":"Visaruth Punnium, Sitapa Rujikietgumjorn, Prapaporn Rattanatamrong","doi":"10.1109/jcsse54890.2022.9836300","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836300","url":null,"abstract":"Religious, ethnicity, gender, and occupation are some examples of social characteristics that can accurately define and explain human social behavior. Being able to determine people's jobs based on their visual information in photographs can assist with better identifying people, determining social roles, offering personalized recommendations, and conducting security investi-gations. In this paper, our goal is to extract occupational data from human clothing in images. We collected a dataset called TH-UniformDB, which comprises 10,000 photos of a single individual wearing Thai uniforms from nine occupation classes and the other class; each class has 1,000 images. The dataset exhibits a significant level of intra-class variety as well as inter-class similarities, which pose challenges in occupation classification. To address these issues and improve classification performance, we propose an approach that performs visual occupation recognition by combining the strength of processing the color images along with that of the grayscale features of the same images. According to our experimental results, the combination of grayscale and RGB features of images can effectively improve the recognition accuracy of the traditional deep neural network model between 3.15 to 10.15 percent, resulting in less impact of the inter-class similarity and intra-class variance.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129527409","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

Efficiency of Time Series Clustering Method Based on Distribution of Difference Using Several Distances 基于多距离差分分布的时间序列聚类方法的有效性

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836279

Phudit Thanakulkairid, Tanupat Trakulthongchai, Naruesorn Prabpon, Pat Vatiwutipong

引用次数: 0

Understanding Relationships among Learning Styles, Learning Activities and Academic Performance: From a Computer Programming Course Perspective 了解学习方式、学习活动与学习成绩之间的关系:从计算机程序设计课程的角度

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836265

Phway Thant Thant Soe Lin, Chutiporn Anutariya, Piriya Utamachant

{"title":"Understanding Relationships among Learning Styles, Learning Activities and Academic Performance: From a Computer Programming Course Perspective","authors":"Phway Thant Thant Soe Lin, Chutiporn Anutariya, Piriya Utamachant","doi":"10.1109/jcsse54890.2022.9836265","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836265","url":null,"abstract":"Investigating factors that influence the learning process of students is important, especially in online education. It can help course instructors to design the learning environment that really fits the course requirements and students and to enhance the learning performance. This study focuses on identifying different learning styles and finding the relationships among learning styles, learning activities and performance of computer science students in a Java programming course. According to the results, students with balanced preference on receiving information obtained highest scores although most of them are visual learners in this aspect. It indicates that adding more visual presentations in teaching process will be helpful to enhance learning performance. As it is one of the very first programming courses, most students prefer to follow the instructors' steps in solving the problems and obviously they joined the class more regularly than others. Therefore, concrete examples that involve well-defined procedures, facts, data, and experimentation are essential to teaching programming language. Moreover, the results confirmed that the number of assignment attempts and solving the in-class problems could considerably improve the achievement of the learning outcomes and also related to the understanding of the programming in a sequential manner. Consequently, students should be encouraged to do more assignments and practice problems in learning the programming through online.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127173544","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

Improving the Movie Showtime Scheduling Problem by Integrated Artificial Intelligence Techniques 应用集成人工智能技术改进电影放映时间调度问题

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836305

Paknarat Lawitsanon, Kamonporn Hanthanunchai, Nattanan Chanachanchai, Sitthisak Mahanin, Jumpol Polvichai

{"title":"Improving the Movie Showtime Scheduling Problem by Integrated Artificial Intelligence Techniques","authors":"Paknarat Lawitsanon, Kamonporn Hanthanunchai, Nattanan Chanachanchai, Sitthisak Mahanin, Jumpol Polvichai","doi":"10.1109/jcsse54890.2022.9836305","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836305","url":null,"abstract":"This paper describes a development of an artificial intelligence system for efficiently scheduling movie showtimes. The strategy was to get the maximum amount of visitors by applying any appropriate artificial intelligence techniques to the problem of showtime schedule. The system consists of three key parts which are the movie showtime scheduling system, the predictive model of the total amount of visitors of each movie on selected days, and the web application. In this paper, five different branches of movie theaters were selected for examining the system. The total movie slots were calculated by the models and utilized to be used in the scheduling process following the criteria defined from exploratory data analysis (EDA). According to experiments, the final integrated system was evaluated with many appropriate test scenarios. The average number of visitors by the artificial intelligence system was greater than the average visitors normally reported by the movie theater company. Consequently, the developed system was showing ability to help the company increase the income and also decrease the staff's burden tasks. In addition, any mistakes caused by human errors were expected to alleviate as well.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127969527","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

Automatic Unit Testing-Based Assessments for Online C++ Programming Classroom 基于单元测试的在线c++编程课堂自动评估

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836289

W. Thamviset

{"title":"Automatic Unit Testing-Based Assessments for Online C++ Programming Classroom","authors":"W. Thamviset","doi":"10.1109/jcsse54890.2022.9836289","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836289","url":null,"abstract":"Usually, in programming courses, students can have good programming skills, only through intensive coding practices. This makes programming assignments to be the most important part of computer programming coursework. However, creating programming assignments with the traditional existing tools is not suitable for online learning classrooms, it cannot allow students to automatically tested and scored their assignment tasks. As the result, teachers must have an extensive workload for assessing the programming skill of their students. In this paper, we develop an online C++ web integrated development environment (Web-IDE), where teachers can prepare a set of unit testing-based programming task templates, in which the unique task descriptions and unit tests are specifically generated and assigned for each student, dynamically. The proposed system is implemented in the cloud so that it can be accessed via web browsers on both PC and mobile devices. This allows students to learn to write code, compile, run, test, and grade their programming assignments without installing additional software. We also confirm the effectiveness of the proposed system through our preliminary experiments with a real computer programming classroom.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128088720","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

MS-SRALAT: Multi-granularity SubStructure-aware Representation Learning Algorithm for Time-series MS-SRALAT:时间序列的多粒度子结构感知表示学习算法

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836269

Thapana Boonchoo

{"title":"MS-SRALAT: Multi-granularity SubStructure-aware Representation Learning Algorithm for Time-series","authors":"Thapana Boonchoo","doi":"10.1109/jcsse54890.2022.9836269","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836269","url":null,"abstract":"Time-series representation is essential in many data mining algorithms, such as clustering, classification, motif discovery, which have been used to discover knowledge from the time-series data. In this paper, we propose an algorithm to learn the semantic representation of a symbol sequence which is generated corresponding to a time-series by an approximation algorithm that can capture the structure of original data. However, the granularity of structure (coarse-to fine-grained) approximated by such an algorithm is defined by a parameter which affects the quality of resulting representation, and therefore impacts the performance of its subsequent tasks. We then propose a multi-granularity substructure-aware representation learning algorithm for time-series (MS-SRALAT) which is an ensemble model that incorporates the trained models with different granularity to produce more robust representations. The resulted experiments on the benchmark datasets showed the superiority of MS-SRALAT over single-granularity learning models, and comparable performances compared to the exact baseline methods while suggesting good scalability for the similar search task.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128469966","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

Improving of the Interference Classification Techniques under the Smart Farming Environment using iSVM 基于iSVM的智能农业环境下干扰分类技术改进

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836242

Natthanan Promsuk

{"title":"Improving of the Interference Classification Techniques under the Smart Farming Environment using iSVM","authors":"Natthanan Promsuk","doi":"10.1109/jcsse54890.2022.9836242","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836242","url":null,"abstract":"Smart farming is one of the recent concepts to increase the capability of the agriculture sector. This concept combines a set of algorithms, electronic sensors or devices, and technologies. The Internet of things (IoT), big data, and artificial intelligence (AI) play a significant role in providing and supporting the solution and optimization ways with the massive data inside the farm. Due to a large number of data inside the farm, smart farming needs to deploy the IoT tech-nology to communicate and transmit the data. However, the interference signals from the adjacent sensors or channels are a critical problem to reduce the reliability of the transmitted data. Therefore, we propose the i$S$VM experiment to observe and classify the interference signal from the received signal. The iSVM experiment compared the classical support vector machine (SVM), SVM with the radial basis function (RBF) kernel, and SVM with the different degrees of the polynomial kernel. Before implementing the i$S$VM experiment, this paper generated an IoT in smart farming with the effects of the actual environment, i.e., the path loss exponent, the additive white Gaussian noise (AWGN) noise, and the small scale fading. Next, this paper implemented the i$S$VM to classify and suppress the interference signal. Moreover, an i$S$VM was compared with the minimum mean square error (MMSE) filter and the received without the suppression technique. From our numerical results, SVM with the polynomial of degree 4 can perform with 80 percent (%) of the average accuracy.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130415975","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

AI-Assisted Diagnosis of Dyssynergic Defecation Using Deep Learning Approach on Abdominal Radiography and Symptom Questionnaire 基于腹部x线片和症状问卷深度学习的人工智能辅助排便障碍诊断

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836301

Sornsiri Poovongsaroj, P. Rattanachaisit, T. Patcharatrakul, S. Gonlachanvit, P. Vateekul

{"title":"AI-Assisted Diagnosis of Dyssynergic Defecation Using Deep Learning Approach on Abdominal Radiography and Symptom Questionnaire","authors":"Sornsiri Poovongsaroj, P. Rattanachaisit, T. Patcharatrakul, S. Gonlachanvit, P. Vateekul","doi":"10.1109/jcsse54890.2022.9836301","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836301","url":null,"abstract":"Patients are required to undergo specialized tests for dyssynergic defecation diagnosis. However, these tests are limited to tertiary healthcare centers. The aim of this paper is to prescreen potential patients from primary and secondary healthcare centers for further diagnostic tests by using easily obtainable data. We proposed an integrated model which utilizes symptom questionnaire and abdominal radiograph. First, we applied some of the most popular tree-based machine learning algorithms on symptom questionnaire. The best set of features was selected through feature selection. Second, a state-of-the-art image classification model, EfficientNet, was applied on abdominal radiograph with several image augmentation techniques for data preprocessing. Third, we combined the selected input features from symptom questionnaire with the image features extracted from the abdominal radiograph using a concatenate layer to imitate how human experts diagnose in real life. The combined data was used as input to the integrated model. The results demonstrate that our model outperforms the baseline models with a sensitivity of 73.08%, specificity of 57.33%, f1-score of 65.07%, and accuracy of 65.36%.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131750396","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

Sentiment Analysis of Thai Stock Reviews Using Transformer Models 用变压器模型分析泰国股票评论的情绪

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836278

Pongsatorn Harnmetta, T. Samanchuen

{"title":"Sentiment Analysis of Thai Stock Reviews Using Transformer Models","authors":"Pongsatorn Harnmetta, T. Samanchuen","doi":"10.1109/jcsse54890.2022.9836278","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836278","url":null,"abstract":"The stock market is typically affected by various factors for a long time, such as politics, economics, and finance. These are expressed through online media that people can easily access today. Moreover, in the digital era, data growth is an exponential trend, and a million records of data are generated through many online platforms over the internet. To utilize that information in time, a stock sentimental analysis system integrated with the transformer base model is proposed. This work applies the transformer base models that can break through NLP limitations from the past. Furthermore, we gather data as fundamental analysis in Thai financial content from a financial institution. However, to compare the result between embedding techniques with baseline, we use multinomial logistic regression in the form of a predictive model and apply the baseline, the term frequency-inverse document frequency (TF-IDF). Our experiment shows that WangchanBERTa and BERT can achieve high accuracy at 92.52% and 89.12%, respectively, and the baseline result is 85.03%. In conclusion, our proposed system can precisely predict stock sentiment in Thai with high accuracy.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131514965","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

Evaluation of the Use of Wi-Fi Probes to Produce A Human Detection System 评估使用Wi-Fi探针产生人体检测系统

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836280

Prapas Sarasalin, N. Kanyamee, Pinyo Taeprasartsit, Sirak Kaewjamnong

{"title":"Evaluation of the Use of Wi-Fi Probes to Produce A Human Detection System","authors":"Prapas Sarasalin, N. Kanyamee, Pinyo Taeprasartsit, Sirak Kaewjamnong","doi":"10.1109/jcsse54890.2022.9836280","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836280","url":null,"abstract":"There are many places of interest that do not have information on the number of people visiting, leaving, and the amount of time that they stayed for. In Thailand, there are over 5,000 registered historical sites, many of which have no guards or conservators. Because of this, records of visitors are not kept. Being able to collect and analyze such data would be a useful tool for planning, but these data are hard to automatically acquire in a cost-effective way. This paper evaluates the possibility of building a system from commodity hardware that is capable of collecting data from Wi-Fi probe signals that are sent by visitor's devices to determine the number of visitors and the amount of time they stayed at the destination. The evaluations focus on three parameters, signal range, signal strength, and power consumption. The results show that the Wi-Fi probe collection system has advantages in terms of coverage range, and low power consumption. In addition, location and direction estimation from signal strength with three sensors, and error reduction techniques are also evaluated. This system is suitable to be used as a budget preliminary data collection device.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"36 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132623925","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