Pub Date : 2021-08-30DOI: 10.15282/ijsecs.7.2.2021.2.0085
Maria Belen Bonino, A. Garis, D. Riesco
Formal methods provide multiple benefits when applied in the software development process. For instance, they enable engineers to verify and validate models before working on their implementation, leading to earlier detection of design defects. However, most of them lack flexibility to be applied in agile software development projects. Alloy is a lightweight formal modeling language with a friendly tool that facilitates the agile approaches application. Unfortunately, its industrial adoption is hampered by the lack of methods and tools for current software development frameworks, such as Entity Framework. This platform is usually chosen by agile projects following the code-first approach that allows automatic generation of a database from domain classes coded in the C# language. We present a new method and tool for the formal specification and analysis of Entity Framework projects with Alloy. The proposal allows engineers to start the software development using Alloy for modeling, validation and verification, automatically translate Alloy specifications to C# domain classes and then generate the corresponding database with Entity Framework. We validate our approach with a real case study: an application required by a gas supplier company.
{"title":"FORMAL MODELING AND ANALYSIS OF ENTITY FRAMEWORK USING ALLOY","authors":"Maria Belen Bonino, A. Garis, D. Riesco","doi":"10.15282/ijsecs.7.2.2021.2.0085","DOIUrl":"https://doi.org/10.15282/ijsecs.7.2.2021.2.0085","url":null,"abstract":"Formal methods provide multiple benefits when applied in the software development process. For instance, they enable engineers to verify and validate models before working on their implementation, leading to earlier detection of design defects. However, most of them lack flexibility to be applied in agile software development projects. Alloy is a lightweight formal modeling language with a friendly tool that facilitates the agile approaches application. Unfortunately, its industrial adoption is hampered by the lack of methods and tools for current software development frameworks, such as Entity Framework. This platform is usually chosen by agile projects following the code-first approach that allows automatic generation of a database from domain classes coded in the C# language. We present a new method and tool for the formal specification and analysis of Entity Framework projects with Alloy. The proposal allows engineers to start the software development using Alloy for modeling, validation and verification, automatically translate Alloy specifications to C# domain classes and then generate the corresponding database with Entity Framework. We validate our approach with a real case study: an application required by a gas supplier company.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91355499","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}
Pub Date : 2021-06-19DOI: 10.15344/2456-4451/2021/165
Kaiyu Suzuki, Tomofumi Matsuzawa
Out-of-distribution (OOD) detection, the classification of samples not included in the training data, is essential to improve the reliability of deep learning. Recently, the accuracy of OOD detection through unsupervised representation learning is high; however, the accuracy of in-distribution classification (IND) is reduced. This is due to the cross entropy, which trains the network to predict shifting transformations (such as angles) for OOD detection. Cross entropy loss conflicts with the consistency in representation learning; that is, samples with different data augmentations applied to the same sample should share the same representation. To avoid this problem, we add the Jensen–Shannon divergence (JSD) consistency loss. To demonstrate its effectiveness for both OOD detection and IN-D classification, we apply it to contrasting shifted instances (CSI) based on the latest representation learning. Our experiments demonstrate that JSD consistency loss outperforms existing methods in both OOD detection and IN-D classification for unlabeled multi-class datasets. Improving Accuracy of Out-of-Distribution Detection and In-Distribution Classification by Incorporating JSD Consistency Loss Publication History: Received: June 07, 2021 Accepted: June 23, 2021 Published: June 25, 2021
{"title":"Improving Accuracy of Out-of-Distribution Detection and In-Distribution Classification by Incorporating JSD Consistency Loss","authors":"Kaiyu Suzuki, Tomofumi Matsuzawa","doi":"10.15344/2456-4451/2021/165","DOIUrl":"https://doi.org/10.15344/2456-4451/2021/165","url":null,"abstract":"Out-of-distribution (OOD) detection, the classification of samples not included in the training data, is essential to improve the reliability of deep learning. Recently, the accuracy of OOD detection through unsupervised representation learning is high; however, the accuracy of in-distribution classification (IND) is reduced. This is due to the cross entropy, which trains the network to predict shifting transformations (such as angles) for OOD detection. Cross entropy loss conflicts with the consistency in representation learning; that is, samples with different data augmentations applied to the same sample should share the same representation. To avoid this problem, we add the Jensen–Shannon divergence (JSD) consistency loss. To demonstrate its effectiveness for both OOD detection and IN-D classification, we apply it to contrasting shifted instances (CSI) based on the latest representation learning. Our experiments demonstrate that JSD consistency loss outperforms existing methods in both OOD detection and IN-D classification for unlabeled multi-class datasets. Improving Accuracy of Out-of-Distribution Detection and In-Distribution Classification by Incorporating JSD Consistency Loss Publication History: Received: June 07, 2021 Accepted: June 23, 2021 Published: June 25, 2021","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90616385","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}
Pub Date : 2021-02-28DOI: 10.15282/ijsecs.7.1.2021.2.0078
Norita B. Ahmad, Aziman Abdullah
Web engagement is a user experience related to emotional, cognitive and behavioural interaction that connect to the goals and interests of the customers. There are seven (7) engagement metrics listed that used to measure web engagement. However, not all of these engagement metrics will help to achieve the objectives because each website have different purpose. There are various web engagement strategies used in business, government and education sectors that has their own way of engaging the web to ensure their success. In business sector, positive communication and experience with customers can make the customers stay long with the brand. In government, most of the services provided are to facilitate the people and to maintain the trust and support of the people. The E-Government 2.0 model was presented with an additional element of Web 2.0 technology. In education, Learning Management System (LMS) and Massive Open Online Courses (MOOCs) are some of the approach in online learning. There are four (4) main features of online learning that are distance learning, connectivity and involvement, support and flexibility control to ensure the continuity of online learning. This reviewing will lead having in attention the aim of developing high-performance in online engagement.
{"title":"Web Engagement Strategies In Business, Government And Education Sector: Review","authors":"Norita B. Ahmad, Aziman Abdullah","doi":"10.15282/ijsecs.7.1.2021.2.0078","DOIUrl":"https://doi.org/10.15282/ijsecs.7.1.2021.2.0078","url":null,"abstract":"Web engagement is a user experience related to emotional, cognitive and behavioural interaction that connect to the goals and interests of the customers. There are seven (7) engagement metrics listed that used to measure web engagement. However, not all of these engagement metrics will help to achieve the objectives because each website have different purpose. There are various web engagement strategies used in business, government and education sectors that has their own way of engaging the web to ensure their success. In business sector, positive communication and experience with customers can make the customers stay long with the brand. In government, most of the services provided are to facilitate the people and to maintain the trust and support of the people. The E-Government 2.0 model was presented with an additional element of Web 2.0 technology. In education, Learning Management System (LMS) and Massive Open Online Courses (MOOCs) are some of the approach in online learning. There are four (4) main features of online learning that are distance learning, connectivity and involvement, support and flexibility control to ensure the continuity of online learning. This reviewing will lead having in attention the aim of developing high-performance in online engagement.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87853823","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}
Pub Date : 2021-02-28DOI: 10.15282/ijsecs.7.1.2021.6.0082
Yusuff Shakirat, A. Bajeh, T. O. Aro, K. Adewole
Change is an inevitable phenomenon of life. This inevitability of change in the real world has made a software change an indispensable characteristic of software systems and a fundamental task of software maintenance and evolution. The continuous evolution process of software systems can greatly affect the systems’ quality and reliability if proper mechanisms to manage them are not adequately provided. Therefore, there is a need for automated techniques to effectively make an assessment of proposed software changes that may arise due to bug fixes, technological advancements, changing user requirements etc., before their implementation. Software Change Impact Analysis (CIA) is an essential activity for comprehending and identifying potential change impacts of software changes that can help prevent the system from entering into an erroneous state. Despite the emergence of different CIA techniques, they are yet to reach an optimal level of accuracy desired by software engineers. Consequently, researchers in recent years have come up with hybrid CIA techniques which are a blend of multiple CIA approaches, as a way of improving the accuracy of change impacts analysis techniques. This study presents these hybrid CIA techniques and how they improve accuracy. They are also compared and areas for further research are identified.
{"title":"Improving the Accuracy of Static Source Code Based Software Change Impact Analysis Through Hybrid Techniques: A Review","authors":"Yusuff Shakirat, A. Bajeh, T. O. Aro, K. Adewole","doi":"10.15282/ijsecs.7.1.2021.6.0082","DOIUrl":"https://doi.org/10.15282/ijsecs.7.1.2021.6.0082","url":null,"abstract":"Change is an inevitable phenomenon of life. This inevitability of change in the real world has made a software change an indispensable characteristic of software systems and a fundamental task of software maintenance and evolution. The continuous evolution process of software systems can greatly affect the systems’ quality and reliability if proper mechanisms to manage them are not adequately provided. Therefore, there is a need for automated techniques to effectively make an assessment of proposed software changes that may arise due to bug fixes, technological advancements, changing user requirements etc., before their implementation. Software Change Impact Analysis (CIA) is an essential activity for comprehending and identifying potential change impacts of software changes that can help prevent the system from entering into an erroneous state. Despite the emergence of different CIA techniques, they are yet to reach an optimal level of accuracy desired by software engineers. Consequently, researchers in recent years have come up with hybrid CIA techniques which are a blend of multiple CIA approaches, as a way of improving the accuracy of change impacts analysis techniques. This study presents these hybrid CIA techniques and how they improve accuracy. They are also compared and areas for further research are identified.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"82 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81058308","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}
Pub Date : 2021-02-28DOI: 10.15282/ijsecs.7.1.2021.3.0079
S. Dutta, A. Banerjee
Optimization methods, frequently used in several image and video processing algorithms for the attainment of optimal solutions, pose severe hurdle in case of real-time processing. For catering to the needs of real-time operations in a cost-effective way, dedicated hardware is inevitable. The huge computational load of any optimization method strikes down its feasibility of being realized in terms of dedicated hardware. The computational complexities of meta-heuristic optimization methods are even more than any other conventional optimization methods. So, in spite of having the capability of providing global solution by dodging local optima, meta-heuristic optimizations are avoided in real-time systems. To overcome the bottleneck, in this article, a modified GWOA (modified grey wolf optimization algorithm)is formulated by blending the advantages of CS (cuckoo search), Levy fly (LV), and MA (Mantegna algorithm). This modified GWOA is articulated to be computationally efficient and precise, so that, it can easily be realized in terms of dedicated VLSI architecture while maintaining the accuracy at a high level. The proposed method helps to diminish the cost and power requirement of high end and costly real-time imaging/ video processing systems while upholding its precision. The proposed method is tested by using MATLAB R2018b. The high-level synthesis (HLS) tool of Xilinx Vivado18.2softwareisusedtosynthesizethisMGWOA, thus establishing the viability of the proposed algorithm to be implemented on FPGA/ ASIC.
{"title":"An Efficient Modification of Grey Wolf Optimization Using Cuckoo Search, Levy Fly and Mantegna Algorithm for Real-Time Image Processing Applications","authors":"S. Dutta, A. Banerjee","doi":"10.15282/ijsecs.7.1.2021.3.0079","DOIUrl":"https://doi.org/10.15282/ijsecs.7.1.2021.3.0079","url":null,"abstract":"Optimization methods, frequently used in several image and video processing algorithms for the attainment of optimal solutions, pose severe hurdle in case of real-time processing. For catering to the needs of real-time operations in a cost-effective way, dedicated hardware is inevitable. The huge computational load of any optimization method strikes down its feasibility of being realized in terms of dedicated hardware. The computational complexities of meta-heuristic optimization methods are even more than any other conventional optimization methods. So, in spite of having the capability of providing global solution by dodging local optima, meta-heuristic optimizations are avoided in real-time systems. To overcome the bottleneck, in this article, a modified GWOA (modified grey wolf optimization algorithm)is formulated by blending the advantages of CS (cuckoo search), Levy fly (LV), and MA (Mantegna algorithm). This modified GWOA is articulated to be computationally efficient and precise, so that, it can easily be realized in terms of dedicated VLSI architecture while maintaining the accuracy at a high level. The proposed method helps to diminish the cost and power requirement of high end and costly real-time imaging/ video processing systems while upholding its precision. The proposed method is tested by using MATLAB R2018b. The high-level synthesis (HLS) tool of Xilinx Vivado18.2softwareisusedtosynthesizethisMGWOA, thus establishing the viability of the proposed algorithm to be implemented on FPGA/ ASIC.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"14 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72466222","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}
Pub Date : 2021-02-28DOI: 10.15282/ijsecs.7.1.2021.4.0080
O. J. Falana, Ife Olalekan Ebo, Ifeanyi Shadrach Odom
One of the research topics that focus on Information Communication Technology in Education is Learning Management System (LMS). LMS is a web-based software application developed to create, manage, and delivered e-learning courses. Many research works have been conducted on different learning options in LMS. However, the increased use of LMS has brought with it the security issues such as the denial of service attack, malware and privacy. In order to protect the different actors of LMS such as students, instructors and controlling authorities, this paper proposes a multi-factor authentication and identity management for securing LMS. Se-LMS is capable of dynamically authenticating users using different methods such as a seamless combination of Oauth2.0 and 2FA or Username/Password and 2FA as proposed. Also, the paper explains the situation and existing research relating to security in Learning Management Systems in smart school. The proposed framework has been applied to cloud-based LMS to show the ability to mitigate an attack.
{"title":"Se-LMS: Secured learning management systems for smart school","authors":"O. J. Falana, Ife Olalekan Ebo, Ifeanyi Shadrach Odom","doi":"10.15282/ijsecs.7.1.2021.4.0080","DOIUrl":"https://doi.org/10.15282/ijsecs.7.1.2021.4.0080","url":null,"abstract":"One of the research topics that focus on Information Communication Technology in Education is Learning Management System (LMS). LMS is a web-based software application developed to create, manage, and delivered e-learning courses. Many research works have been conducted on different learning options in LMS. However, the increased use of LMS has brought with it the security issues such as the denial of service attack, malware and privacy. In order to protect the different actors of LMS such as students, instructors and controlling authorities, this paper proposes a multi-factor authentication and identity management for securing LMS. Se-LMS is capable of dynamically authenticating users using different methods such as a seamless combination of Oauth2.0 and 2FA or Username/Password and 2FA as proposed. Also, the paper explains the situation and existing research relating to security in Learning Management Systems in smart school. The proposed framework has been applied to cloud-based LMS to show the ability to mitigate an attack.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88658006","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}
Pub Date : 2021-02-28DOI: 10.15282/ijsecs.7.1.2021.1.0077
Aminah Abdul Malek, N. A. Abdul Rahim, Nor Farah Nabilah Mushtafa, Nadhirah Afiqah Zailan, Norlyda Mohamed
Early detection of breast cancer can be detected through screening mammography. However, the potential abnormality such as microcalcification can hardly be differentiated by the radiologists due to the tiny size, which sometimes be hidden behind the density of breast tissue. Therefore, image segmentation technique is required. This paper proposes the potential use of Parametric Kernel Graph Cut Algorithm in segmenting microcalcification. The performances of this method were measured based on accuracy, sensitivity, Dice and Jaccard coefficient. All the experimental results generated satisfying results, whereby all images produced the average of 91.67% for Dice coefficient and 84.72% for Jaccard coefficient. Meanwhile, both accuracy and sensitivity results acquired 97.84% and 96%, respectively. Therefore, Parametric Kernel Graph Cut algorithm had proved its ability to segment the microcalcification robustly and efficiently.
{"title":"Multiregion segmentation of microcalcificationin mammogram images by using Parametric Kernel Graph Cut algorithm","authors":"Aminah Abdul Malek, N. A. Abdul Rahim, Nor Farah Nabilah Mushtafa, Nadhirah Afiqah Zailan, Norlyda Mohamed","doi":"10.15282/ijsecs.7.1.2021.1.0077","DOIUrl":"https://doi.org/10.15282/ijsecs.7.1.2021.1.0077","url":null,"abstract":"Early detection of breast cancer can be detected through screening mammography. However, the potential abnormality such as microcalcification can hardly be differentiated by the radiologists due to the tiny size, which sometimes be hidden behind the density of breast tissue. Therefore, image segmentation technique is required. This paper proposes the potential use of Parametric Kernel Graph Cut Algorithm in segmenting microcalcification. The performances of this method were measured based on accuracy, sensitivity, Dice and Jaccard coefficient. All the experimental results generated satisfying results, whereby all images produced the average of 91.67% for Dice coefficient and 84.72% for Jaccard coefficient. Meanwhile, both accuracy and sensitivity results acquired 97.84% and 96%, respectively. Therefore, Parametric Kernel Graph Cut algorithm had proved its ability to segment the microcalcification robustly and efficiently.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88637617","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}
Pub Date : 2021-02-28DOI: 10.15282/ijsecs.7.1.2021.5.0081
Muhamad idaham Umar Ong, M. A. Ameedeen
The role of Information System Design Theory in software development is enormous. With the help of design theory, developers could estimate how the system will be and the reasons why the system reacts that way. The focus of the article is to revised and re-establish the design theory of a self-service university management system. The objectives of this publication are to identify the most relevant publication that will act as a guideline to generate the design theory artifacts and to establish the artifacts which will consist of three components named kernel theory, meta-requirements, and meta-design for the stated system. The system scope will be focused on enabling self-service functions. Validation will be done through FEDS framework with the inclusion of a traceability matrix between the meta-requirement against the real-world user requirement. The significance of this article is to provide an initial set of design theory artifacts to be utilized by software developers in the development or evaluation of a University Management System.
{"title":"An Information System Design Product Theory for Student Academic Self-Service University Management System","authors":"Muhamad idaham Umar Ong, M. A. Ameedeen","doi":"10.15282/ijsecs.7.1.2021.5.0081","DOIUrl":"https://doi.org/10.15282/ijsecs.7.1.2021.5.0081","url":null,"abstract":"The role of Information System Design Theory in software development is enormous. With the help of design theory, developers could estimate how the system will be and the reasons why the system reacts that way. The focus of the article is to revised and re-establish the design theory of a self-service university management system. The objectives of this publication are to identify the most relevant publication that will act as a guideline to generate the design theory artifacts and to establish the artifacts which will consist of three components named kernel theory, meta-requirements, and meta-design for the stated system. The system scope will be focused on enabling self-service functions. Validation will be done through FEDS framework with the inclusion of a traceability matrix between the meta-requirement against the real-world user requirement. The significance of this article is to provide an initial set of design theory artifacts to be utilized by software developers in the development or evaluation of a University Management System.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"298 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76852671","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}
Pub Date : 2021-02-28DOI: 10.15282/ijsecs.7.1.2021.7.0083
Edward N. Udo, Etebong B. Isong, Emmanuel E. Nyoho
As many countries around the world are trying to live with the deadly coronavirus by adhering to the safety measures put in place by their government as regulated by World Health Organization (WHO), it becomes very vital to continuously trace patients with COVID-19 symptoms for isolation, quarantine and treatment. In this work, an intelligent software-aided contact tracing for real-time model-driven prediction of COVID-19 cases is proposed utilizing COVID-19 dataset from kaggle.com. The dataset is preprocessed using One-Hot encoding and Principal Component Analysis. Isolation Forest algorithm is used to train and predict COVID-19 cases. The performance of the model is evaluated using Accuracy, Precision, Recall and F1-Score. The intelligent software-aided contact tracing framework has four layers: symptoms, modeling/prediction, cloud storage/contact routing and contact tracers. The contact tracing system is an android application that receives symptom values, predict it and automatically send the prediction result together with user’s contact and location details to the closest contact tracer via the Firebase real-time database. The closest contact tracer is determined by employing a dynamic routing algorithm (contact routing algorithm) that uses Open Shortest Path First (OSPF) protocol to compute the distance between two geographic locations (user and contact tracer) and chooses a contact tracer with shortest distance to the patient utilizing a unicast routing technique (routing a patient to a contact tracer in a one-to-one relationship). The predictive model along with the android application for software-aided contact tracing is implemented using the python, and Java programming language on Pycharm and Android Studio IDE respectively. This Framework is capable of predicting COVID-19 patients, notifying contact tracers of positive cases for proper follow-up which can subsequently curtail the spread of the virus.
世界上许多国家都在遵守世界卫生组织(WHO)规定的政府安全措施，努力与新冠病毒共存，因此，持续追踪新冠病毒症状患者，进行隔离、检疫和治疗变得至关重要。本文利用kaggle.com的COVID-19数据集，提出了一种智能软件辅助接触追踪方法，用于COVID-19病例的实时模型驱动预测。使用One-Hot编码和主成分分析对数据集进行预处理。隔离森林算法用于训练和预测COVID-19病例。模型的性能使用准确性、精度、召回率和F1-Score进行评估。智能软件辅助接触者追踪框架有四层:症状、建模/预测、云存储/接触者路由和接触者追踪器。接触追踪系统是一个android应用程序，它接收症状值，预测它，并自动将预测结果连同用户的联系人和位置详细信息通过Firebase实时数据库发送给最近的接触追踪器。最亲密的接触跟踪器采用动态路由算法(接触路由算法)确定，该算法使用开放最短路径优先(OSPF)协议计算两个地理位置(用户和接触跟踪器)之间的距离，并利用单播路由技术选择距离患者最近的接触跟踪器(将患者路由到一对一关系的接触跟踪器)。预测模型以及用于软件辅助接触跟踪的android应用程序分别在Pycharm和android Studio IDE上使用python和Java编程语言实现。该框架能够预测COVID-19患者，向接触追踪者通报阳性病例，以便进行适当随访，从而遏制病毒的传播。
{"title":"Intelligent Software-Aided Contact Tracing Framework: Towards Real-Time Model-Driven Prediction of Covid-19 Cases in Nigeria","authors":"Edward N. Udo, Etebong B. Isong, Emmanuel E. Nyoho","doi":"10.15282/ijsecs.7.1.2021.7.0083","DOIUrl":"https://doi.org/10.15282/ijsecs.7.1.2021.7.0083","url":null,"abstract":"As many countries around the world are trying to live with the deadly coronavirus by adhering to the safety measures put in place by their government as regulated by World Health Organization (WHO), it becomes very vital to continuously trace patients with COVID-19 symptoms for isolation, quarantine and treatment. In this work, an intelligent software-aided contact tracing for real-time model-driven prediction of COVID-19 cases is proposed utilizing COVID-19 dataset from kaggle.com. The dataset is preprocessed using One-Hot encoding and Principal Component Analysis. Isolation Forest algorithm is used to train and predict COVID-19 cases. The performance of the model is evaluated using Accuracy, Precision, Recall and F1-Score. The intelligent software-aided contact tracing framework has four layers: symptoms, modeling/prediction, cloud storage/contact routing and contact tracers. The contact tracing system is an android application that receives symptom values, predict it and automatically send the prediction result together with user’s contact and location details to the closest contact tracer via the Firebase real-time database. The closest contact tracer is determined by employing a dynamic routing algorithm (contact routing algorithm) that uses Open Shortest Path First (OSPF) protocol to compute the distance between two geographic locations (user and contact tracer) and chooses a contact tracer with shortest distance to the patient utilizing a unicast routing technique (routing a patient to a contact tracer in a one-to-one relationship). The predictive model along with the android application for software-aided contact tracing is implemented using the python, and Java programming language on Pycharm and Android Studio IDE respectively. This Framework is capable of predicting COVID-19 patients, notifying contact tracers of positive cases for proper follow-up which can subsequently curtail the spread of the virus.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79922180","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}
Pub Date : 2020-12-30DOI: 10.15344/2456-4451/2020/160
Ma Chenguang, Srishti Kulshrestha, Shih-Hsuan Wei, Kosuke Kaneko, Akira Haga, Y. Okada, R. Bose
This paper treats one of the activities of ICER (Innovation Centre for Educational Resources) in Kyushu University Library of Kyushu University, Japan. It is the development of attractive educational materials using recent ICT (Information Communication Technologies) like 3D CG (Computer Graphics) and VR (Virtual Reality) for enhancing the educational efficiency in the university. The authors have already proposed a framework dedicated for the development of web-based interactive 3D educational materials. Firstly, this paper introduces this development framework and its educational material example of Japanese history education. For developing attractive educational materials, such materials need multimedia contents like images and videos related to the teaching target. If the multimedia contents can be retrieved from the Internet, the development cost can be reduced. To do so, the authors focused on Linked Data and have already proposed an authoring tool of educational materials based on Linked Data. Furthermore, the authors integrated the authoring tool into the development framework. Secondly, this paper introduces the integrated development framework based on Lined Data and its educational materials of two teaching targets, i.e., IoT security education and Japanese history education. Furthermore, the authors have proposed another educational material development framework that supports 360VR images/videos and integrated the authoring tool with it. Finally, this paper introduces this integrated development framework based on Linked Data and its example educational material for IoT security education. Integrated Development Frameworks Based on Linked Data for WebBased Interactive Educational Materials Publication History: Received: December 03, 2020 Accepted: December 28, 2020 Published: December 30, 2020
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