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2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)最新文献

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Incorporating Prior Scientific Knowledge Into Deep Learning for Precipitation Nowcasting on Radar Images 基于先验科学知识的雷达降水临近预报深度学习
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493821
Pattarapong Danpoonkij, Nutnaree Kleawsirikul, Patamawadee Leepaisomboon, Natnapat Gaviphatt, Hidetomo Sakaino, P. Vateekul
{"title":"Incorporating Prior Scientific Knowledge Into Deep Learning for Precipitation Nowcasting on Radar Images","authors":"Pattarapong Danpoonkij, Nutnaree Kleawsirikul, Patamawadee Leepaisomboon, Natnapat Gaviphatt, Hidetomo Sakaino, P. Vateekul","doi":"10.1109/JCSSE53117.2021.9493821","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493821","url":null,"abstract":"Precipitation nowcasting aims to precisely predict the rainfall intensity in the near future that can be applied in various applications. A common approach is to simulate the complex physical processes or extrapolate the rainfall from the current stage. The existing deep learning model for this task uses an end-to-end network to forecast, but this approach has often met with limited success due to the complexities of the problem. Therefore, this paper proposes a novel hybrid model that combines the scientific method from meteorology and the deep learning method from computer science. We experimented with the model on both simulated data and radar images. Also, we have created the simulated data to imitate important features from radar images. The results show that our hybrid modeling approach outperforms all baselines on almost all datasets (both simulated and the radar data).","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"34 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":"134276871","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: 5
Topic Modeling Enhancement using Word Embeddings 使用词嵌入增强主题建模
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493816
Siriwat Limwattana, S. Prom-on
{"title":"Topic Modeling Enhancement using Word Embeddings","authors":"Siriwat Limwattana, S. Prom-on","doi":"10.1109/JCSSE53117.2021.9493816","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493816","url":null,"abstract":"Latent Dirichlet Allocation(LDA) is one of the powerful techniques in extracting topics from a document. The original LDA takes the Bag-of-Word representation as the input and produces topic distributions in documents as output. The drawback of Bag-of-Word is that it represents each word with a plain one-hot encoding which does not encode the word level information. Later research in Natural Language Processing(NLP) demonstrate that word embeddings technique such as Skipgram model can provide a good representation in capturing the relationship and semantic information between words. In recent studies, many NLP tasks could gain better performance by applying the word embedding as the representation of words. In this paper, we propose Deep Word-Topic Latent Dirichlet Allocation(DWT-LDA), a new process for training LDA with word embedding. A neural network with word embedding is applied to the Collapsed Gibbs Sampling process as another choice for word topic assignment. To quantitatively evaluate our model, the topic coherence framework and topic diversity are the metrics used to compare between our approach and the original LDA. The experimental result shows that our method generates more coherent and diverse topics.","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":"115746876","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: 4
COVID-19 Classification using DCNNs and Exploration Correlation using Canonical Correlation Analysis 使用DCNNs进行COVID-19分类和使用典型相关分析探索相关性
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493846
Rujira Jullapak, Tongjai Yampaka
{"title":"COVID-19 Classification using DCNNs and Exploration Correlation using Canonical Correlation Analysis","authors":"Rujira Jullapak, Tongjai Yampaka","doi":"10.1109/JCSSE53117.2021.9493846","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493846","url":null,"abstract":"Coronavirus disease (COVID-19) has rapidly spread among people living in many countries. Chest radiography (CXR) image is an alternative diagnosis option to observe COVID-19. However, CXR usually requires an expert radiologist to distinguish the lesion from viral pneumonia and COVID-19 because the symptoms of COVID-19 pneumonia may be similar to other types of viral pneumonia. In this study, three different convolutional neural network based models (VGG19, ResNet50, and InceptionV3) have been proposed for the detection of coronavirus pneumonia infected patient using chest X-ray. In addition, this studies can potentially find the correlation between COVID-19 pneumonia and viral pneumonia using canonical correlation analysis. Considering the performance results obtained the best performance as an accuracy of 0.97, sensitivity of 0.97, specificity of 0.93, and F1-score value of 0.97 for VGG19 pre-trained model. The experiment results also show that the viral lesion of Viral pneumonia and COVID-19 is less similarity.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"49 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":"126670925","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
Implementing Machine Learning Methods for Ballpoint Pen Ink Classification based on Mass Spectrometry Data: Toward a Forensic Application 实现基于质谱数据的圆珠笔墨水分类的机器学习方法:走向法医应用
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493823
Pirada Boonna, Chawanya Chaiwan, S. Deepaisarn, N. Simanon, O. Reamtong, C. Butkinaree
{"title":"Implementing Machine Learning Methods for Ballpoint Pen Ink Classification based on Mass Spectrometry Data: Toward a Forensic Application","authors":"Pirada Boonna, Chawanya Chaiwan, S. Deepaisarn, N. Simanon, O. Reamtong, C. Butkinaree","doi":"10.1109/JCSSE53117.2021.9493823","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493823","url":null,"abstract":"Mass spectrometry (MS) is widely used for material analysis in various applications including forensic science. This work explores computational techniques and develops an application called \"MSpec\" using suitable algorithms for extracting informative parts of the MS dataset that aims towards pen ink classification. The system is intended as a tool that is capable of giving preliminary answers for such forensic analyses of documentary evidence involving different pen-ink types on writing. Support Vector Machine (SVM) was implemented and compared with other machine learning techniques via systematic performance assessments. They were trained and tested using MS data acquired from 10 blue-ink ballpoint pen samples, which were pre-processed using optimized steps. The results show that the tested models performed well in classifying the pen ink samples, with the SVM cubic kernel model giving the highest accuracy of 96.0%. Furthermore, dimensionality reduction of the dataset through peak detection helps improve the classification accuracy.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"104 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":"134046009","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
Smart Inventory Access Monitoring System (SIAMS) using Embedded System with Face Recognition 基于人脸识别嵌入式系统的智能库存监控系统(SIAMS)
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493815
Kanjana Eiamsaard, P. Bamrungthai, Songchai Jitpakdeebodin
{"title":"Smart Inventory Access Monitoring System (SIAMS) using Embedded System with Face Recognition","authors":"Kanjana Eiamsaard, P. Bamrungthai, Songchai Jitpakdeebodin","doi":"10.1109/JCSSE53117.2021.9493815","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493815","url":null,"abstract":"In this paper, we present a system called Smart Inventory Access Monitoring System (SIAMS) that integrates an embedded system with face recognition into an inventory system. It is developed to prevent theft in warehouses from authorized staff. The embedded system is attached with an RGB camera and deployed three software modules: image capturing, face detection, and face recognition. The face detection module sends detected face images to the face recognition module to identify a person as the person’s name or unknown class using a deep learning approach. The system achieved competitive accuracy by performing standard evaluation metrics for face detection and recognition. The inventory system that was developed will receive data via TCP/IP socket communication to log access history. The retrieved information can be used to investigate an unusual situation. The system can be improved with object detection and person tracking system to detect theft in real-time.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"72 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":"121194100","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 based e-Testing as a common yardstick for measuring human abilities 基于人工智能的电子测试作为衡量人类能力的通用标准
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493810
M. Ueno
{"title":"AI based e-Testing as a common yardstick for measuring human abilities","authors":"M. Ueno","doi":"10.1109/JCSSE53117.2021.9493810","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493810","url":null,"abstract":"It is difficult to evaluate a person’s ability or performances because there is no common yardstick for measuring individual human abilities. For example, in general, paper based tests have different characteristics (difficulties, accuracies, and so on) because they depend on items of each test. Recently, e-testing technologies from AI approach, for which each test form has equivalent measurement accuracy but with a different set of items, have become popular. Even if different examinees with the same ability take different tests, their test scores should be guaranteed to be equivalent. We introduce state-of the-art e-testing technologies from AI approach.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"20 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":"128724788","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
JCSSE 2021 Cover Page JCSSE 2021封面
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/jcsse53117.2021.9493835
{"title":"JCSSE 2021 Cover Page","authors":"","doi":"10.1109/jcsse53117.2021.9493835","DOIUrl":"https://doi.org/10.1109/jcsse53117.2021.9493835","url":null,"abstract":"","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"86 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":"124815746","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
Maximizing the Generative Performance of Echo State Networks Using the Particle Swarm Optimization 基于粒子群优化的回声状态网络生成性能最大化
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493824
Kristsana Seepanomwan
{"title":"Maximizing the Generative Performance of Echo State Networks Using the Particle Swarm Optimization","authors":"Kristsana Seepanomwan","doi":"10.1109/JCSSE53117.2021.9493824","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493824","url":null,"abstract":"This work reveals the hidden potential of two Echo State Networks (ESNs) in time-series generation tasks. The first system is a typical ESN with all-to-all connection weights. The latter possess sparse reservoir's connectivity and a limited number of input-output connections, or an Economy ESN (EcoESN). The standard Particle Swarm Optimization (PSO) is adopted to adjust the connection weight between the input and the reservoir. Two experiments regard the variation of the PSO training, and the rate of the input-output connection is conducted. The results confirm that optimizing the input connection of the ESN can boost the generative performance of the two networks. However, the EcoESN gains better benefit from the optimization and can surpass the fully connected in most of the tests. Furthermore, EcoESN with a low input-output rate of 0.1 can outperform the use of higher ones. This finding could shed insight into the construction of a lightweight and accurate generative model.","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":"126360900","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
Predicting Football Match Result Using Fusion-based Classification Models 基于融合的分类模型预测足球比赛结果
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493837
Chananyu Pipatchatchawal, Suphakant Phimoltares
{"title":"Predicting Football Match Result Using Fusion-based Classification Models","authors":"Chananyu Pipatchatchawal, Suphakant Phimoltares","doi":"10.1109/JCSSE53117.2021.9493837","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493837","url":null,"abstract":"In recent decades, many researchers attempted to predict football match outcome. To forecast future match results, most papers relied on using in-game match statistics, such as number of shots on target, yellow cards, red cards, etc. In this paper, fusion-based classification model was constructed for future matches, using none of in-game statistics. The model used video games’ ratings of players and teams to help in prediction. Two types of fusion-based models, which are hierarchical model and ensemble model, were proposed in this paper. In the experiment, the proposed models were compared with different simple classification models in terms of accuracy using a dataset of English Premier League (EPL) season 2010/2011 to 2014/2015. Additionally, each model was also tested on the whole 2015/2016 EPL season as the selected season contains several unexpected results. Both proposed models yielded the accurate rates at 56.5332% and 56.8002%, which are higher than those of the other models.","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":"131310256","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
Mass-ratio-variance based Outlier Factor 基于质量比方差的离群因子
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493811
Phichapop Changsakul, Somjai Boonsiri, K. Sinapiromsaran
{"title":"Mass-ratio-variance based Outlier Factor","authors":"Phichapop Changsakul, Somjai Boonsiri, K. Sinapiromsaran","doi":"10.1109/JCSSE53117.2021.9493811","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493811","url":null,"abstract":"An outlier of a finite dataset in statistics is defined as a data point that differs significantly from others. It is normally surrounded by a few data points while normal ones are engulfed by others. This behavior leads to the proposed outlier factor called Mass-ratio-variance Outlier Factor (MOF). A score is assigned to a data point from the variance of the mass-ratio distribution from the rest of data points. Within a sphere of an outlier, there will be few data points compared with a normal one. So, the mass-ratio of an outlier will be different from that of a normal data point. The algorithm to generate MOF requires no parameter and embraces the density concept. Experimental results show that top-10 highest scores from MOF could identify all outliers from synthesized datasets similar to those scores from the state-of-the-art outlier scoring methods such as LOF and FastABOD. Moreover, it could retrieve more outliers from three real World datasets.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"65 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":"116980622","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: 4
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