2022 26th International Computer Science and Engineering Conference (ICSEC)最新文献_第4页

Deep learning for sex determination of the clavicle: A blind study on a Thai population 锁骨性别决定的深度学习:一项泰国人口的盲法研究

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049361

Kewalee Pichetpan, Phruksachat Singsuwan, A. Sinthubua, Patison Palee, P. Mahakkanukrauh

{"title":"Deep learning for sex determination of the clavicle: A blind study on a Thai population","authors":"Kewalee Pichetpan, Phruksachat Singsuwan, A. Sinthubua, Patison Palee, P. Mahakkanukrauh","doi":"10.1109/ICSEC56337.2022.10049361","DOIUrl":"https://doi.org/10.1109/ICSEC56337.2022.10049361","url":null,"abstract":"Sex determination from bone is a primary step in biological identification. Clavicles are helpful in autopsies and identification that can lead to sex determination. We employed a previous deep learning method for the sex determination of the clavicle. We trained the model using a deep network designer of the GoogLeNet (a subset of the convolutional neural network) and received the best training model for the study results. This study's goal was to bring the optimal training model of each side view of the clavicle for a blind test and obtain an accurate blind test set on a Thai population. The total sample consisted of 50 pairs of clavicles as a test group (25 females, 25 males). For the deep learning approach, the clavicle was photographed, and each clavicle image was submitted to the training model for sex determination. Test groups of 50 samples were made. Images of the same size were input to test for blind study. The percentage of blind test accuracy was included in the statistical analysis using descriptive statistics. After training the model from GoogLeNet, we discovered the training model to test a blind dataset accuracy by picking the best of the training model from all experiments and bringing the model to test a blind dataset and get the result of blind test set accuracy. The results of this study found accuracies for a blind test set with the highest overall left inferior view of the clavicle with an accuracy of 92%. Accuracy from the test set of each view of the clavicle can demonstrate the forensic value of sex determination. Deep learning using a clavicle can determine the sex and is user friendly for forensic anthropology specialists.","PeriodicalId":430850,"journal":{"name":"2022 26th International Computer Science and Engineering Conference (ICSEC)","volume":"117 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":"128278396","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

Leveraging Feature Headers to Learn Sparse and Semantically Pertinent Linear Models 利用特征头来学习稀疏和语义相关的线性模型

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049377

Sasin Janpuangtong

{"title":"Leveraging Feature Headers to Learn Sparse and Semantically Pertinent Linear Models","authors":"Sasin Janpuangtong","doi":"10.1109/ICSEC56337.2022.10049377","DOIUrl":"https://doi.org/10.1109/ICSEC56337.2022.10049377","url":null,"abstract":"Readily available data and software tools have turned \"analytics\" into a game anyone can play. But genuine, serious modeling demands prudence: domain experts routinely use their knowledge to assess the relevance of various input features and to be judicious with model selection criteria. While engaged in analysis, they marshal knowledge to consider the meaning of the data involved. Seeking to automate and reproduce such aspects, the present paper proposes a framework that makes use of semantics latent within given feature headers to help produce sparse and semantically pertinent linear models, rather than exploiting mere correlations or (potentially spurious) patterns. This framework enables a model builder to employ both features’ data and certain semantic information derived from their headers to search for an optimal feature subset in order to improve generalization of a linear model being built. To do so, a characteristic called \"semantic inconsistency\" is formulated in order to quantify the degree of conflict between weights learned from data and the amount of relationship between a set of input features and the output being predicted in the semantic space. Using this quantity, semantic information can be incorporated into a regularization procedure in a manner that is quite general and may be computed from any form of background knowledge. The results obtained from validating the framework with four datasets indicate that taking the semantics of features into account can improve model generalization: the approach is shown to perform better than classic linear regression and regularization techniques that consider only complexity of learned models.","PeriodicalId":430850,"journal":{"name":"2022 26th International Computer Science and Engineering Conference (ICSEC)","volume":"210 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":"132074470","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

Fingerprint Database Enhancement using Spatial Interpolation for IoT-based Indoor Localization 基于物联网室内定位的指纹数据库空间插值增强

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049367

Farid Yuli Martin Adiyatma, Dwi Joko Suroso, P. Cherntanomwong

{"title":"Fingerprint Database Enhancement using Spatial Interpolation for IoT-based Indoor Localization","authors":"Farid Yuli Martin Adiyatma, Dwi Joko Suroso, P. Cherntanomwong","doi":"10.1109/ICSEC56337.2022.10049367","DOIUrl":"https://doi.org/10.1109/ICSEC56337.2022.10049367","url":null,"abstract":"The widespread adoption of the internet of things (IoT) drives indoor location-based service (ILBS) applications forward. The core parameter of ILBS is indoor localization. Generally, indoor localization is divided into two techniques, distance-based, i.e., triangulation, and distance-free, i.e., fingerprint technique. This paper discusses the fingerprint technique because of some advantages, i.e., higher accuracy performance compared to the distance-based technique. However, the fingerprint technique has drawbacks in offline database construction: extraordinarily time-consuming and labor-intensive, which hinders its application in the real world. Furthermore, the fingerprint database needs to be updated regularly in a dynamic environment. Therefore, we propose fingerprint database enhancement based on various spatial interpolations to tackle the issues of fingerprint database construction. We apply Inverse Distance Weighted (IDW), Quadratic Spline, Cubic Spline, and Ordinary Kriging Interpolation methods to generate the synthetic database. We have conducted a measurement campaign to obtain Received Signal Strength Indicator (RSSI) as the fingerprint-based localization parameter. From our results, the interpolation methods show that the generated synthetic RSSI can provide a lower prediction error. Our proposed methods can have similar accuracy performance compared to manual fingerprints using actual data. Moreover, the synthetic RSSI data has a 0 dBm error for the best prediction and not more than 6 dBm for the worst prediction. Thus, we conclude that our proposed methods can enhance the fingerprint database and have proven to increase localization performance.","PeriodicalId":430850,"journal":{"name":"2022 26th International Computer Science and Engineering Conference (ICSEC)","volume":"12 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":"114917903","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

Advanced Monitoring System for Streetlights with Theft Identification Features (AMoSSTIF) Using Raspberry Pi 使用树莓派的具有盗窃识别功能的路灯高级监控系统(AMoSSTIF)

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049375

Arjay R. Alba, N. Linsangan

{"title":"Advanced Monitoring System for Streetlights with Theft Identification Features (AMoSSTIF) Using Raspberry Pi","authors":"Arjay R. Alba, N. Linsangan","doi":"10.1109/ICSEC56337.2022.10049375","DOIUrl":"https://doi.org/10.1109/ICSEC56337.2022.10049375","url":null,"abstract":"Street lighting systems are vital for rural and urban development as they provide safety for motorists and pedestrians. It is also forecasted the emergence of intelligent street lighting as part of the development of smart cities. However, street lightings are prone to infrastructure theft and electricity pilferage as they are exposed to the public, especially in poor urban regions where cabled streetlights are commonly used. To protect it from such activities, the researchers developed a prototype device called AMoSSTIF based on Raspberry Pi, which can monitor streetlight electrical parameters and detect electricity and streetlight infrastructure theft. Specifically, the AMoSSTIF can detect events such as: (a) power cable theft and its location; (b) lamp theft and its location; (c) burnt-out lamp and its location; and (d) electricity theft. Moreover, the system is equipped with an alarm feature when such activities are detected. GSM module is also installed in the device so that it can text streetlight administrators responsible for streetlight monitoring when strange incidents occur. In addition, a website is developed for viewing the gathered electrical parameters data of the AMoSSTIF and the video recording of the attached IP camera of the system. Finally, the researchers tested the device through a prototype street lighting system. The result shows that the AMoSSTIF is 100% accurate in detecting the aforementioned illegal activities.","PeriodicalId":430850,"journal":{"name":"2022 26th International Computer Science and Engineering Conference (ICSEC)","volume":"8 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":"115116582","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

Finding Picking Point Using Straight Line Detection of Label Attachment Machine 用直线检测方法寻找贴标机的拾取点

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049335

Lipheng Prum, Rutchanee Gullayanon, John Morris

引用次数: 0

Robust Adaptive Backstepping Control Design for MIMO Electrically Driven Robot Manipulators Using RBF Neural Network With Disturbance 带干扰RBF神经网络的MIMO电驱动机器人鲁棒自适应反步控制设计

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049342

M. Jamil, Irfan Ahmad, Uraiwan Buatoom

{"title":"Robust Adaptive Backstepping Control Design for MIMO Electrically Driven Robot Manipulators Using RBF Neural Network With Disturbance","authors":"M. Jamil, Irfan Ahmad, Uraiwan Buatoom","doi":"10.1109/ICSEC56337.2022.10049342","DOIUrl":"https://doi.org/10.1109/ICSEC56337.2022.10049342","url":null,"abstract":"In this paper, a robust adaptive (RA) radial basis function (RBF) neural network (NN) based backstepping control design is proposed for multi-input multi-output (MIMO) electrically driven robot manipulators (EDRM) with completely unmodeled dynamics, unknown nonlinearities, disturbance, and virtual control inputs. The proposed research methodology guarantees the controller’s resilience even in the presence of parameter changes. The main idea of the backstepping control design is to reduce the error to zero via the use of parameter adjustment rules and a virtual control technique. The recursive backstepping design method treats specific system signals as virtual inputs to smaller subsystems. This novel control strategy guarantees the boundedness of the trajectory tracking error and also weight updates of NN. The key advantage of our control strategy is that it eliminates the requirement for regression matrices, the linear in parameter (LIP) assumption, and an offline learning phase. The results of proposed robust adaptive control methodology with unknown disturbance are compared with conventional proportional-derivative (PD) control scheme, i.e., without backstepping.","PeriodicalId":430850,"journal":{"name":"2022 26th International Computer Science and Engineering Conference (ICSEC)","volume":"30 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":"125516647","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

Face recognition using Skin Color Segment and Modified Binary Particle Swarm Optimization 基于肤色分割和改进二值粒子群算法的人脸识别

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049354

Titiwat Kuarkamphun, Chiabwoot Ratanavilisagul

引用次数: 1

Elderly Job Opportunity Analytic and Prediction System for Thai Aging Society 泰国老龄化社会老年就业机会分析与预测系统

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049325

Taweewat Luangwiriya, A. Siriphorn, Somying Saithanu, Tiraphap. Fakthong, Praneet Pensri, A. Leckcivilize, Nongyao Mongkhonittivech, R. Kongkachandra

引用次数: 0

Using compression tables to improve HiveQL Performance with Spark A Case study on NVMe Storage Devices 使用压缩表提升HiveQL性能—以NVMe存储设备为例

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049309

Youppadee Intasorn, Kritwara Rattanaopas, Yanapat Chuchuen

引用次数: 0

Remote Moderated Usability Testing of a Web-Based Translation: A Case Study 网络翻译的远程调节可用性测试:一个案例研究

2022 26th International Computer Science and Engineering Conference (ICSEC) Pub Date : 2022-12-21 DOI: 10.1109/ICSEC56337.2022.10049371

T. Lim

引用次数: 0