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

A Simplified Convolutional Neural Network Design for COVID-19 Classification on Chest X-ray Images 基于简化卷积神经网络的胸部x线图像COVID-19分类设计

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836299

Wannipa Sae-Lim, R. Suwannanon, P. Aiyarak

引用次数: 0

Determining Natural Rubber Humidity Level using Rubber Color 用橡胶颜色测定天然橡胶的湿度水平

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836244

Boonsit Yimwadsana, Pichamon Chanthapeth

引用次数: 0

COVID-19 and Respiratory Diseases Classification using Deep Convolution Neuron Network 基于深度卷积神经元网络的COVID-19和呼吸系统疾病分类

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836259

Orawan Chunhapran, S. Vonganansup, Tongjai Yampaka, Rath Burirat

{"title":"COVID-19 and Respiratory Diseases Classification using Deep Convolution Neuron Network","authors":"Orawan Chunhapran, S. Vonganansup, Tongjai Yampaka, Rath Burirat","doi":"10.1109/jcsse54890.2022.9836259","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836259","url":null,"abstract":"This study proposes COVID-19 and Respiratory Diseases Classification using Deep Convolution Neuron Network. ICBHI 2017 Respiratory Sound Database including COVID-19 from Coswara databased were used in our experiments. The potential results show that the left side model performances are 0.85 accuracy, 0.76 sensitivity, and 0.90 specificity. The right side model performances are 0.86 accuracy, 0.76 sensitivity, and 0.93 specificity. No side set model performances are 0.83 accuracy, 0.71 sensitivity, and 0.93 specificity. In addition, the lung characteristics and lung functions are different among left and right. Therefore, the breathing sound from left and right lung are difference. For this reason, the cross-model performances were evaluated to test this assumption. The cross-model performance results show that the left data is consistent with the left model. As same as the right data is consistent with the right model. Furthermore, the experiment found that mixing training data built the no side set model is the lowest performance. In addition, the proposed framework tends to achieve high performance when compared with a recent study.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"76 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":"121198917","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

Recipe Recommendations for Toddlers Using Integrated Nutritional and Ingredient Similarity Measures 使用综合营养和成分相似性措施的幼儿食谱建议

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836248

Nantaporn Ratisoontorn

{"title":"Recipe Recommendations for Toddlers Using Integrated Nutritional and Ingredient Similarity Measures","authors":"Nantaporn Ratisoontorn","doi":"10.1109/jcsse54890.2022.9836248","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836248","url":null,"abstract":"Nutritious and healthy diets in early childhood are critical determinants of children's health, growth and development. An extensive selection of cookery books and recipe sharing websites, containing toddler's recipes, have been provided. The overload of recipe data available makes the recommendation system become indispensable in assisting individuals when making food decisions for their young children. This paper presents a framework that utilizes a combination of nutrient-based and weighted ingredient-based similarity measures to make recipe recommendations. The method is divided into three processes: nutrient analysis, ingredient extraction and integration of similarity measures. The dataset from a reliable source, containing the total of 35 recipes and 87 ingredients, is used in the study. The experimental results show that the proposed technique can effectively generate similarity-based recipe recommendations. The recipe results share both high nutritional and ingredient similarity scores. The comparison results further suggest that the presented approach offers a promising balance between nutrients and ingredients. It is shown that the existing nutrient-based similarity measure tends to overproduce the outputs, while the weighted ingredient-based similarity plays a role to mitigate the shortcomings by removing insignificant recipe pairs with tf-idf weights.","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":"132234210","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

Enabling Semantic Interoperability in Bhutan's E-Government: An Ontology-based Framework 在不丹的电子政务中实现语义互操作性:基于本体的框架

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836286

Younten Tshering, Chutiporn Anutariya

{"title":"Enabling Semantic Interoperability in Bhutan's E-Government: An Ontology-based Framework","authors":"Younten Tshering, Chutiporn Anutariya","doi":"10.1109/jcsse54890.2022.9836286","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836286","url":null,"abstract":"Interoperability in e-Government is important to improve public services by enabling systems to exchange, integrate, and use information in a meaningful manner. However, e-government systems along with heterogeneous databases have created difficulties in interoperability. These heterogeneous databases lead to data redundancy, data inconsistency, and difficulty to integrate data and process data among agencies. The representation of heterogeneous systems through common standards and vocabularies is considered a valuable solution for interoperability. In this paper, an ontology-based framework for Bhutan's e-government is proposed to support semantic interoperability. To evaluate the framework, the domain ontology for Bhutan's Administrative Division, namely BTNAD is developed and discussed. BTNAD Ontology has been implemented and verified using Protégé, and its practical usage has been validated by checking against the defined competency questions and the corresponding SP ARQL query implementation. The presented study can clearly demonstrate the Single Source of Truth (SSOT), hence enabling the data reusability and collaboration, and interoperability within and across the agencies. Finally, the research discusses some recommendations from the proposed framework and interoperability challenges.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"10 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":"134374502","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 Algorithms for Online and On-Site Classes 在线和现场课程的人脸识别算法

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836309

Thitinan Kliangsuwan, A. Heednacram, Kittasil Silanon

{"title":"Face Recognition Algorithms for Online and On-Site Classes","authors":"Thitinan Kliangsuwan, A. Heednacram, Kittasil Silanon","doi":"10.1109/jcsse54890.2022.9836309","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836309","url":null,"abstract":"Face recognition is used in a wide variety of applications such as surveillance systems, human-computer interaction, automatic door access control systems, and network security. One of the policies of the smart university is to adopt technology to help with teaching and learning, especially during the Covid-19 pandemic. In this paper, a smart attendance system using face recognition algorithms with deep learning is proposed and used in the university's classroom. Instead of calling names to confirm the identity of students, our system does it automatically. The system was tested in 3 scenarios, namely, in online classes, in on-site classes, and in problematic cases using a standard dataset. The performances of the 3 scenarios were compared in the experiment in terms of precision, recall, F1 score, and percentage accuracy. Our result revealed that in online classes the recognition accuracy is as high as 100%. The implemented system is inexpensive and practical. The application can be used on any portable device such as tablets or smartphones. History viewing, multiple subjects handling, and file exporting features are also incorporated into the system.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"91 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":"133593167","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

A Novel Deep BiGRU-ResNet Model for Human Activity Recognition using Smartphone Sensors 基于智能手机传感器的人类活动识别深度BiGRU-ResNet模型

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836276

S. Mekruksavanich, Ponnipa Jantawong, Narit Hnoohom, A. Jitpattanakul

{"title":"A Novel Deep BiGRU-ResNet Model for Human Activity Recognition using Smartphone Sensors","authors":"S. Mekruksavanich, Ponnipa Jantawong, Narit Hnoohom, A. Jitpattanakul","doi":"10.1109/jcsse54890.2022.9836276","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836276","url":null,"abstract":"Human activity recognition (HAR) employing wearable sensors is utilized in several implementations, including remote health monitoring and exercise performance. The most widely used HAR research is inspired by traditional machine learning and developing methodologies using deep learning. Whereas machine learning techniques have proven effective in resolving HAR, these require human feature extraction. Consequently, deep learning methods have been designed to circumvent this constraint autonomously rather than manually extracting information. This paper provides an innovative deep residual learning approach based on LSTM-CNN and deep residual modeling techniques. The objective of the proposed model, BiGRUResNet, was to increase accuracy while decreasing the number of parameters. Two BiGRU layers, three residual layers, one global average pooling layer, and one softmax layer are present. Utilizing a publicly recognized UCI-HAR dataset, the proposed model was analyzed. Results of the experiment indicate that the proposed model outperforms previous deep learning-based models in 5-fold cross-validation, with a 99.09% accuracy and 99.15% F1 score.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"11 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":"133099656","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

Memory-Efficient Adjoints via Graph Partitioning 基于图分区的内存效率共轭

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836288

Ekkapot Charoenwanit

{"title":"Memory-Efficient Adjoints via Graph Partitioning","authors":"Ekkapot Charoenwanit","doi":"10.1109/jcsse54890.2022.9836288","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836288","url":null,"abstract":"Derivative information plays a crucial role in the correctness and performance of scientific computing in a wide variety of scientific domains such as computational fluid dynamics (CFD), finance engineering and so on. The reverse mode of Algorithmic Differentiation is particularly efficient for the computation of derivatives of multivariate vector functions $F: R^{n}mapsto R^{m}$, where the number of inputs $n$ far exceeds the number of outputs $m$, frequently appearing as cost functions in numerical optimization kernels. In particular, reverse-mode AD is at the heart of the back propagation algorithm widely used in machine learning. The reverse mode of AD requires that the control flow of the derivative program be reversed, meaning that the results of intermediate computations (in our case, the computational graph of $F$) must be stored either in memory or secondary storage. As a result, this requirement leads to the memory wall problem, especially for large-scale numerical problems, where the results of intermediate computations cannot fit entirely in memory. In this paper, we present an algorithm called Memory-Efficient Adjoints (ME-Adjoints) for solving the memory wall problem by dynamically applying a simple partitioning scheme to the computational graph of the function $F$ at runtime. Our approach employs operator overloading in C++ to achieve a fully automatic adjoining process, whereby derivative programs require only trivial changes to the code as opposed to the use of checkpointing techniques, which require substantial changes to the code.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"115 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":"133752740","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

Categorize Level of Crystal Sugar Making with Recurrent Neural Network 用递归神经网络对结晶糖生产水平进行分类

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836272

Pimolrat Ounsrimuang, S. Nootyaskool

引用次数: 0

Deep Learning Models for Daily Living Activity Recognition based on Wearable Inertial Sensors 基于可穿戴惯性传感器的日常生活活动识别深度学习模型

2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-06-22 DOI: 10.1109/jcsse54890.2022.9836239

S. Mekruksavanich, Ponnipa Jantawong, Narit Hnoohom, A. Jitpattanakul

{"title":"Deep Learning Models for Daily Living Activity Recognition based on Wearable Inertial Sensors","authors":"S. Mekruksavanich, Ponnipa Jantawong, Narit Hnoohom, A. Jitpattanakul","doi":"10.1109/jcsse54890.2022.9836239","DOIUrl":"https://doi.org/10.1109/jcsse54890.2022.9836239","url":null,"abstract":"Due to the breadth of its application domains, Hu-man Activity Recognition (HAR) is a problematic area of human-computer interaction. HAR can be used in remote monitoring of senior healthcare and concern situations in intelligent man-ufacturing, among other applications. HAR based on wearable inertial sensors has been researched identification efficiency in various kinds of human actions considerably more than vision-based HAR. The sensor-based HAR is generally applicable to indoor and outdoor locations without privacy considerations of implementation. In this research, we explore the recognition performance of multiple deep learning (DL) models to recognize everyday living human activities. We developed a deep residual neural network that employed aggregated multi-branch transformation to boost identification performance. The proposed model is called the ResNeXt model. To evaluate its performance, three standard DL models (CNN, LSTM, and CNN-LSTM) are investigated and compared to our proposed model using a standard HAR dataset called Daily Living Activity dataset. These datasets gathered mobility signal data from multimodal sensors (accelerometer, gyroscope, and magnetometer) in three distinct body areas (wrist, hip, and ankle). The experimental findings reveal that the proposed model surpasses other benchmark DL models with maximum accuracy and F1-scores. Furthermore, the findings show that the ResNeXt model is more resistant than other models with fewer training parameters.","PeriodicalId":284735,"journal":{"name":"2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"105 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":"124756146","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