2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)最新文献_第7页

Fall Detection and Prediction Based on IMU and EMG Sensors for Elders 基于IMU和EMG传感器的老年人跌倒检测与预测

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836284

Wigran Siwadamrongpong, J. Chinrungrueng, Shoichi Hasegawa, E. Nantajeewarawat

{"title":"Fall Detection and Prediction Based on IMU and EMG Sensors for Elders","authors":"Wigran Siwadamrongpong, J. Chinrungrueng, Shoichi Hasegawa, E. Nantajeewarawat","doi":"10.1109/jcsse54890.2022.9836284","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836284","url":null,"abstract":"One of the most crucial changes in the future of social structure is the increase of the aging population. Accidents in the elderly are often caused by degeneration and worsening of their bodies. The most common accidents in the elderly are falls. This research proposes fall prediction and detection methods based on the Inertial Measurement Unit (IMU) sensor and Electromyogram (EMG). This method used features from EMG signal to adjust and co-verify with IMU sensor and then used machine learning technicians to create the model for abnormal classification gait, normal gait, and fall event. The results show that the EMG signal based on the Random forest model gained the average accuracy values of 3-class classifications (Abnormal gait, Normal gait, and Fall event) is 71.91%. For 4-class classifications (Abnormal left leg, Abnormal right leg, Normal gait, and Fall event) is 67.76%. The IMU sensor base on the Random Forest (RF) model got the best performance on both accuracies at 3-class and 4-class classification; the average accuracy value of 3-class classification is 94.72%. For the 4-class classification is 87.70%, respectively.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"137 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":"133885803","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

Player Recommendation System for Fantasy Premier League using Machine Learning 基于机器学习的梦幻超级联赛球员推荐系统

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836260

V. Rajesh, P. Arjun, Kunal Ravikumar Jagtap, C. M. Suneera, J. Prakash

引用次数: 0

The Design and Development of an Adaptive Intelligent Tutoring System Based on Constructive Alignment and Cognitive Theories 基于建构一致性和认知理论的自适应智能辅导系统的设计与开发

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836290

P. Nguyen, Preecha Tangworakitthaworn, L. Gilbert

引用次数: 1

Automatic Music Transcription for the Thai Xylophone played with Soft Mallets 用软木槌演奏的泰国木琴的自动音乐转录

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836266

Apichai Huaysrijan, S. Pongpinigpinyo

引用次数: 3

Question Generation in the Thai Language Using MT5 使用MT5的泰语问题生成

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836271

Nutthanit Wiwatbutsiri, A. Suchato, P. Punyabukkana, Nuengwong Tuaycharoen

引用次数: 0

Thai Preschooler Speech Recognition for Voice Enabled Interactive Counting Exercises 泰国学龄前儿童语音识别语音启用互动计数练习

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836310

Prapaporn Rattanatamrong, Onanong Kongmeesub, Tanakorn Dittaporn, Natphitchayuk Siwahansaphan, Sirapop Chatarupa, Vataya Chunwijitra, Sumonmas Thatphithakkul

{"title":"Thai Preschooler Speech Recognition for Voice Enabled Interactive Counting Exercises","authors":"Prapaporn Rattanatamrong, Onanong Kongmeesub, Tanakorn Dittaporn, Natphitchayuk Siwahansaphan, Sirapop Chatarupa, Vataya Chunwijitra, Sumonmas Thatphithakkul","doi":"10.1109/jcsse54890.2022.9836310","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836310","url":null,"abstract":"Over time, voice recognition technology has in-creased its capacity to understand the intricacy of children's speech, which has distinct pitches and vocalizations than adults'. However, obtaining outstanding results in children voice recog-nition, particularly in Thai, is hampered by a lack of sufficient dataset for children to train on. This paper describes our first steps in developing a speech recognition model for Thai children and its viability to be integrated with SmartMath, a Web-based interactive numerical skill practice application for preschoolers. In order to build an adequate recognizer for Thai children, two methodologies were investigated: spectrogram classification and GMM-HMM based ASR. The experimental results show that the GMM-HMM based ASR has the best WER, with a 4.23 percent reduction in error on the individual counting task when compared to the speech image categorization. For the incremental counting task, the best WER achieved by the ASR model is 6.81 percent. Further data analysis suggests potential ways for improving children's ASR, which could lead to the use of children's ASR to close the learning gap.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"25 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":"122382654","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

Image classification based on multi-granularity convolutional Neural network model 基于多粒度卷积神经网络模型的图像分类

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836281

Xiaogang Wu, T. Tanprasert, Wang Jing

{"title":"Image classification based on multi-granularity convolutional Neural network model","authors":"Xiaogang Wu, T. Tanprasert, Wang Jing","doi":"10.1109/jcsse54890.2022.9836281","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836281","url":null,"abstract":"In the field of image classification, traditional feature extraction algorithms, such as texture feature, local feature and global feature, more or less lose some important image classification information, leading to the reduction of the classification effect. Deep learning based on feature pyramid can identify objects of different scales, but it will greatly increase the density of computation and storage. Therefore, we propose a convolutional neural network image classification method based on multi-granularity features. The convolutional neural network model consists of three different channels, each channel uses different granularity convolution kernels to extract multi-granularity feature information, and then uses feature fusion technology for processing. Finally, three granularities of feature information are introduced into the weight parameters to improve the model, and experimental comparisons are made with a variety of single-channel CNN models in CIFAR10 dataset image classification. The experimental results show that the classification accuracy of the model is significantly improved.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"9 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":"128124854","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

Improved Particle Swarm Optimization using Evolutionary Algorithm 基于进化算法的改进粒子群优化

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836238

Sukanya Chansamorn, Wichaya Somgiat

引用次数: 0

CareerVio: A Platform for Personalized Collaborative and Gamified Software Engineering MOOCs CareerVio:一个个性化协作和游戏化软件工程mooc平台

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836240

Krissada Chalermsook, Chutiporn Anutariya

{"title":"CareerVio: A Platform for Personalized Collaborative and Gamified Software Engineering MOOCs","authors":"Krissada Chalermsook, Chutiporn Anutariya","doi":"10.1109/jcsse54890.2022.9836240","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836240","url":null,"abstract":"Software engineer is one of the highest in-demand manpower jobs in the world. As the prolonged COVID19 pandemic crisis, more and more people throughout the world became unemployed. There arises an incoming demand for reskilling and career shifting to software engineer. Online learning through MOOCs is, therefore, a viable and practical solution. However, reskilling to be a software engineer requires specific platform support that is able to develop both technical and non-technical skills, including management, communication, and teamwork, that traditional MOOCs cannot fulfill the challenges. This research aims to augment the traditional MOOCs with the additional use of personalization, collaboration, and gamification theory in the name of “CareerVio MOOCs Platform”. The distinct supports and services provided by the platform comprise team composition, team recomposition, team learning, and team assessments. CareerVio enables learners to master the necessary skills required in software engineer career, enhances the interaction between learners, and allows them to take what they have learned off the page and put it into practice.","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":"131040523","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

Developing Autopilot Agent Transparency for Collaborative Driving 开发协同驾驶的自动驾驶代理透明度

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836249

Rinta Kridalukmana, D. Eridani, Risma Septiana, A. F. Rochim, Charisma T. Setyobudhi

{"title":"Developing Autopilot Agent Transparency for Collaborative Driving","authors":"Rinta Kridalukmana, D. Eridani, Risma Septiana, A. F. Rochim, Charisma T. Setyobudhi","doi":"10.1109/jcsse54890.2022.9836249","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836249","url":null,"abstract":"Collaborative driving is considered as a form of human-autonomy teaming (HAT) in which the advanced driving assistance system (ADAS) with an autopilot feature plays a role as the human driver counterpart, not merely as an automation tool. However, such a collaborative driving raises a problem for the human driver's situational awareness development, particularly because of the lack of mechanisms to comprehend the autopilot agent's behaviours. The human driver becomes overly trust to the agent and is vulnerable to distractions. As a result, many road incidents occur because of such mental model. It is believed that the transparency of the autopilot agent can help its human counterpart to calibrate their trust in this agent. However, a lack of studies investigating how such transparency is delivered to the human driver. Hence, this study aims to develop autopilot agent transparency for collaborative driving. The developed transparency is implemented and simulated using open-source software for autonomous driving called Carla simulator. The findings show that the transparency can help the human driver to understand and predict the autopilot agent's behaviours better. Such transparency is critical to enhance human-machine interaction, particularly in a collaborative driving context.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"25 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120915999","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