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Deep Index Price Forecasting in Steel Industry 钢铁行业的深度指数价格预测
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493843
Thittaporn Ganokratanaa, M. Ketcham
{"title":"Deep Index Price Forecasting in Steel Industry","authors":"Thittaporn Ganokratanaa, M. Ketcham","doi":"10.1109/JCSSE53117.2021.9493843","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493843","url":null,"abstract":"Steel is one of the most expensive materials in the construction industry. Currently, Thailand imports steel from abroad, facing a price fluctuation due to the economy, production capacity, and consumption in domestic and international markets. The cost control of the steel price can also be unstable and risky to purchase. To handle these issues, there is a need for good management of the quantity and procurement of steel at the right price. Thus, we propose a prediction of the steel price index in construction using deep learning neuron networks. Our experimental results show good performance as our mean square error equals 2.34. Our proposed method can be applied for decision-making support and used as a reliable system for steel purchases in construction projects.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125354996","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
Ontology for Blood Group Phenotyping and ABO Discrepancy Screening* 血型表型和ABO差异筛选本体*
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493808
Areerat Trongratsameethong, S. Somhom, Jakarin Chawachat, Ratsameetip Wita, N. Anukul, Chonticha Sirikul
{"title":"Ontology for Blood Group Phenotyping and ABO Discrepancy Screening*","authors":"Areerat Trongratsameethong, S. Somhom, Jakarin Chawachat, Ratsameetip Wita, N. Anukul, Chonticha Sirikul","doi":"10.1109/JCSSE53117.2021.9493808","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493808","url":null,"abstract":"This paper proposed a knowledge base of blood group phenotyping. The knowledge base was designed to identify blood groups and ABO discrepancy screening. These procedures are very important for blood transfusion. The incompatible blood group in blood transfusion may result in haemolytic transfusion reactions. The severity can range from no symptom to death. In the study, knowledge on blood group phenotyping and ABO discrepancy was gathered from trust sources. The knowledge base was developed using ontology engineering and the knowledge concepts are represented on a Resource Description Framework (RDF) graph. The knowledge structure and data, or ontology schema and data, are stored in the Turtle Ontology Web Language (OWL) format. This valuable knowledge is very beneficial for blood banks in blood transfusion, especially when there are new blood groups in use.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128651046","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
IoT System Design for Agro-Tourism 农业旅游物联网系统设计
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493826
Thittaporn Ganokratanaa, Patiyuth Pramkeaw, M. Ketcham, Narumol Chumuang, Worawut Yimyam, Parinya Timted
{"title":"IoT System Design for Agro-Tourism","authors":"Thittaporn Ganokratanaa, Patiyuth Pramkeaw, M. Ketcham, Narumol Chumuang, Worawut Yimyam, Parinya Timted","doi":"10.1109/JCSSE53117.2021.9493826","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493826","url":null,"abstract":"Agro-Tourism is another form of tourism in which the visitors can gain both enjoyment and knowledge. It covers various types of agriculture, including garden, herb, livestock, aquaculture facility, and pet farm. In modern technology, Agro-Tourism can be improved as a smart farm with the intelligent system design using the Internet of Things (IoT) to help care for crops. It also can be applied with educational institutions for research and agricultural production and development. Our proposed IoTs system helps to increase the convenience of farmers as the experimental results show that we achieve good performance with a 98% accuracy rate.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122057411","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: 10
ILM and Fovea Detection using Standard Deviation Profiling Method 用标准偏差轮廓法检测光阑和中央凹
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493813
Mohamed Shahud Hussain, S. Deepaisarn, P. Aimmanee
{"title":"ILM and Fovea Detection using Standard Deviation Profiling Method","authors":"Mohamed Shahud Hussain, S. Deepaisarn, P. Aimmanee","doi":"10.1109/JCSSE53117.2021.9493813","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493813","url":null,"abstract":"Fovea located in the Macular region of the retina is the important location of the eye that is responsible for vision. Fovea can be observed from optical coherence tomography (OCT) images. This type of medical image is actively being used in the medical field to detect ocular diseases such as Age-related Macular Degeneration and Diabetic Retinopathy. However, it is often challenging to spot the fovea in abnormal OCT images for diagnostic purposes. In this paper, we proposed a method called standard deviation profiling to detect the Inner Limiting Membrane (ILM). Features extracted from the ILM layer were used in the decision tree for case classification. The fovea was detected from the ILM layer based on a rule-based method. For the ILM detection, the results show that it can significantly reduce the root mean square error compared with the CASEREL and Canny edge detection methods. For fovea detection, we achieve an overall accuracy of 94%.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126634142","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
Guideline of Personalized Facial Makeup Using a Hierarchical Cascade Classifier 使用层次级联分类器的个性化面部化妆指南
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493828
Piyapat Ponlawan, Namintorn Kaewsaitiam, Suphakant Phimoltares, Sasipa Panthuwadeethorn
{"title":"Guideline of Personalized Facial Makeup Using a Hierarchical Cascade Classifier","authors":"Piyapat Ponlawan, Namintorn Kaewsaitiam, Suphakant Phimoltares, Sasipa Panthuwadeethorn","doi":"10.1109/JCSSE53117.2021.9493828","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493828","url":null,"abstract":"By considering skin color, occasion and dress color as input data, this paper proposes a hierarchical cascade classifier to develop a guideline of personalized facial makeup. Although the makeup recommendation system was previously studied in many researches, but the suggestion cannot be applied for a person accurately in real situation. Color tone based on color wheel theory for facial makeup and color selection from individual skin tone were employed in this study. There were two stages of the hierarchical cascade classifier. The first stage was relied on a rule-based classification procedure, in which rules can be generated by investigating input data within the scope of this research together with the data from a professional makeup artist and 250 face images with makeup originated by makeup experts, resulting in primary color of eye shadow, cheek blush color, and lipstick color. Next, machine learning concept was used as the second stage of the hierarchical cascade classifier to indicate secondary color of eye shadow and alternative lipstick color corresponding to a feature vector. Six classification models, which are Multi-Layer Perceptron, Logistic Regression classifier, Support Vector Machine, Decision Tree, k-nearest neighbor classifier, and Naïve Bayes classifier were selected in this study. From the experimental results, the mixture of rule-based classifier and Multi-Layer Perceptron was suitable to be used as a guideline of personalized facial makeup.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132105125","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
Evolving Compact Prediction Model for PM2.5 level of Chiang Mai Using Multiobjective Multigene Symbolic Regression 基于多目标多基因符号回归的清迈PM2.5水平演化紧凑预测模型
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493833
P. Unachak, Prayat Puangjaktha
{"title":"Evolving Compact Prediction Model for PM2.5 level of Chiang Mai Using Multiobjective Multigene Symbolic Regression","authors":"P. Unachak, Prayat Puangjaktha","doi":"10.1109/JCSSE53117.2021.9493833","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493833","url":null,"abstract":"In recent years, fine particulate matter (PM2.5) has caused economic and health-related adversities to people of Northern Thailand. An accurate predictive model would allow residents to take precautions for their safeties. Also, a human-readable predictive model can lead to better understandings of the issues. In this paper, we use multigene symbolic regression, a genetic programming (GP) approach, to create predictive models for PM2.5 levels in the next 3 hours. This approach creates mathematical models consists of multiple simpler trees for equivalent expressiveness to conventional GP. We also used Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a multiobjective optimization technique, to ensure accurate yet compact models. Using pollutants and meteorological data from Yupparaj Wittayalai monitoring station, combined with satellite-based fire hotspots data from Fire Information of Resource Management System (FIRMS), our approach has created compact human-readable models with better or comparable accuracies to benchmark approaches, as well as identifies possible nonlinear relationships in the dataset.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131330457","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
Hybrid Input-type Recurrent Neural Network Language Modeling for End-to-end Speech Recognition 端到端语音识别的混合输入型递归神经网络语言建模
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493812
P. Sertsi, P. Lamsrichan, Vataya Chunwijitra, M. Okumura
{"title":"Hybrid Input-type Recurrent Neural Network Language Modeling for End-to-end Speech Recognition","authors":"P. Sertsi, P. Lamsrichan, Vataya Chunwijitra, M. Okumura","doi":"10.1109/JCSSE53117.2021.9493812","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493812","url":null,"abstract":"The out-of-vocabulary (OOV) words is a problem that impacts recognition accuracy, whether it is the HMM model, DNN model, or end-to-end speech recognition. This paper proposes a hybrid input-type recurrent neural network language model (RNNLM) for end-to-end speech recognition, which uses word and pseudo-morpheme (PM) as a sub-lexical unit during training. The advantage of PM is a new vocabulary, or unseen vocabulary can be reconstructed from sub-lexical units. The results showed that the accuracy of using the proposed method could reduce the error rate by 1.28% compared to the conventional end-to-end technique.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132215112","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
Classification of Abusive Thai Language Content in Social Media Using Deep Learning 使用深度学习对社交媒体中滥用泰语内容进行分类
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493829
Ruangsung Wanasukapunt, Suphakant Phimoltares
{"title":"Classification of Abusive Thai Language Content in Social Media Using Deep Learning","authors":"Ruangsung Wanasukapunt, Suphakant Phimoltares","doi":"10.1109/JCSSE53117.2021.9493829","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493829","url":null,"abstract":"This paper presents binomial and multinomial models for Thai language abusive speech classification in social media. While previous similar research focused on using traditional machine learning models for binomial classification, we showed that deep learning models have better performance. Our binomial and multinomial models achieved F1 scores of 0.8510 and 0.9067, respectively. These scores were significantly better than the machine learning models’ respective best F1 scores of 0.7452 and 0.8090. While the bidirectional LSTM performed well, the DistilBERT had higher accuracy and recall. Moreover, the recall was especially higher for the “figurative” class where certain words were more likely to have different meanings depending on context.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133165859","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
Next Generation Cloud Computing: Security, Privacy and Trust Issues from the System View 下一代云计算:系统视角下的安全、隐私和信任问题
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493842
Ronel Bester, M. A. Khan
{"title":"Next Generation Cloud Computing: Security, Privacy and Trust Issues from the System View","authors":"Ronel Bester, M. A. Khan","doi":"10.1109/JCSSE53117.2021.9493842","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493842","url":null,"abstract":"Cloud Computing has become the key resource providing the computing needs. The inclusion of Internet of Things (IoT) along with the emergence of Artificial Intelligence (AI) including Machine Learning (ML) provide endless opportunities with a new extended reach into all facets of human life. This platform brings new and unprecedented challenges, particularly the combination of end user devices and service provided systems; as such, there is a need to develop an end-to-end architecture that is modular whilst addresses four key parameters being, system architecture, security, privacy, and trust. Within this work in progress article, we identify the new and emerging security, privacy, and trust issues along with the gaps within Next Generation Cloud Computing when viewed in entirety, the system view; propose a novel modular architecture for use when consuming these services that reduces identified security risks; and develop a new framework for mitigating issues utilising Machine Learning.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130028614","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
Location-based Daily Human Activity Recognition using Hybrid Deep Learning Network 基于位置的基于混合深度学习网络的日常人类活动识别
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493807
S. Mekruksavanich, C. Promsakon, A. Jitpattanakul
{"title":"Location-based Daily Human Activity Recognition using Hybrid Deep Learning Network","authors":"S. Mekruksavanich, C. Promsakon, A. Jitpattanakul","doi":"10.1109/JCSSE53117.2021.9493807","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493807","url":null,"abstract":"Human activity recognition (HAR) is an interesting and challenging subject of study. HAR provides useful information regarding human movement and activity in ordinary life. A number of HAR-based solutions such as wellness tracking and biometric identification systems have been introduced over the past decade. A number of deep learning algorithms have recently been employed to resolve the complication of handcrafted features in traditional machine learning approaches. The novel deep learning framework to solve the HAR effect on overall accuracy is proposed in this study. The framework is a location-based CNN-LSTM hybrid model. The framework is validated using evaluation measures such as accuracy and other effective measures on a public dataset of wristwatch accelerometer data named the DHA dataset. When comparing the accuracy of alternative deep learning approaches, the proposed location-based CNN-LSTM ranked highest with an accuracy of 96.75%.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122452091","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: 8
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