Pub Date : 2024-07-01DOI: 10.3844/jcssp.2024.751.757
C. M. Bentaouza
: This study focuses on the detection of wearing mask errors after machine learning by a Multi-Layer Perceptron Mixer (MLP Mixer) applied to protect masks from COVID-19. To combat the spread of the COVID-19 pandemic, facemasks have become an essential accessory, so it's necessary to identify individuals who follow this health protection. In this case, the most successful face-detection method Viola-Jones was used combining different techniques, each in one step. To make decisions, image classification aims to detect the presence of masks in images using mathematical methods. The classy design involves partitioning the parameter space based on representative attributes for each class. For this purpose, we used MLP mixer which is a convolutional neural network, also known as CNNs or ConvNets, they constitute deep learning because it is much better at detecting similarities than by an integrated image-to-image comparison. The classification ratio is satisfactory to achieve maximum accuracy in detecting. However, the learning time for network convergence is prolonged due to changes in parameters.
{"title":"Improving the Detection of Mask-Wearing Mistakes by Deep Learning","authors":"C. M. Bentaouza","doi":"10.3844/jcssp.2024.751.757","DOIUrl":"https://doi.org/10.3844/jcssp.2024.751.757","url":null,"abstract":": This study focuses on the detection of wearing mask errors after machine learning by a Multi-Layer Perceptron Mixer (MLP Mixer) applied to protect masks from COVID-19. To combat the spread of the COVID-19 pandemic, facemasks have become an essential accessory, so it's necessary to identify individuals who follow this health protection. In this case, the most successful face-detection method Viola-Jones was used combining different techniques, each in one step. To make decisions, image classification aims to detect the presence of masks in images using mathematical methods. The classy design involves partitioning the parameter space based on representative attributes for each class. For this purpose, we used MLP mixer which is a convolutional neural network, also known as CNNs or ConvNets, they constitute deep learning because it is much better at detecting similarities than by an integrated image-to-image comparison. The classification ratio is satisfactory to achieve maximum accuracy in detecting. However, the learning time for network convergence is prolonged due to changes in parameters.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691242","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 : 2024-07-01DOI: 10.3844/jcssp.2024.708.721
Alaa Mohamed Youssef, A. Youssif, W. E. Behaidy
{"title":"Challenges of Breast Cancer Detection Based on Histopathology Images","authors":"Alaa Mohamed Youssef, A. Youssif, W. E. Behaidy","doi":"10.3844/jcssp.2024.708.721","DOIUrl":"https://doi.org/10.3844/jcssp.2024.708.721","url":null,"abstract":"","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141692848","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 : 2024-06-01DOI: 10.3844/jcssp.2024.628.640
A. W. Muzaffar
: The rise of online learning platforms and the growing demand for remote education emphasize the importance of online exam-proctoring tools. Online proctoring tools presented in the literature require high internet speed and specialized hardware support, posing accessibility challenges for individuals in developing countries. This study aims to develop a solution that relies on something other than high internet speed and high-end hardware components. The proposed solution extracts data generated from keystroke logs, browser history, and applications opened during the assessment to predict online exam cheating. This data is compared to the words in the test using Term Frequency (TF) and Inverse Document Frequency (IDF) to predict cheating. To evaluate the effectiveness of the proposed solution, an experiment was conducted with sixteen undergraduate Software Engineering students divided into two groups of eight students. The groups were given 20-minute-long software engineering and database exams, each comprising 30 MCQS. These exams were conducted with the proposed proctoring tool and only one group was allowed to cheat. Results indicated that the proposed tool effectively detects cheating during exams. This approach can mitigate the digital divide, particularly for individuals lacking high-speed internet access and costly hardware. Consequently, the study proposes an inclusive solution designed to cater to users from diverse demographic backgrounds.
{"title":"An Open-Source Online Examination System to Meet the Integrity Demands of E-Learning","authors":"A. W. Muzaffar","doi":"10.3844/jcssp.2024.628.640","DOIUrl":"https://doi.org/10.3844/jcssp.2024.628.640","url":null,"abstract":": The rise of online learning platforms and the growing demand for remote education emphasize the importance of online exam-proctoring tools. Online proctoring tools presented in the literature require high internet speed and specialized hardware support, posing accessibility challenges for individuals in developing countries. This study aims to develop a solution that relies on something other than high internet speed and high-end hardware components. The proposed solution extracts data generated from keystroke logs, browser history, and applications opened during the assessment to predict online exam cheating. This data is compared to the words in the test using Term Frequency (TF) and Inverse Document Frequency (IDF) to predict cheating. To evaluate the effectiveness of the proposed solution, an experiment was conducted with sixteen undergraduate Software Engineering students divided into two groups of eight students. The groups were given 20-minute-long software engineering and database exams, each comprising 30 MCQS. These exams were conducted with the proposed proctoring tool and only one group was allowed to cheat. Results indicated that the proposed tool effectively detects cheating during exams. This approach can mitigate the digital divide, particularly for individuals lacking high-speed internet access and costly hardware. Consequently, the study proposes an inclusive solution designed to cater to users from diverse demographic backgrounds.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230728","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 : 2024-06-01DOI: 10.3844/jcssp.2024.670.681
Shreya Priyadarshini Roy, N. Maheswari, M. Sivagami, Angelin Beulah S
.
.
{"title":"Denoising of Underwater Images with Regulated Autoencoders","authors":"Shreya Priyadarshini Roy, N. Maheswari, M. Sivagami, Angelin Beulah S","doi":"10.3844/jcssp.2024.670.681","DOIUrl":"https://doi.org/10.3844/jcssp.2024.670.681","url":null,"abstract":".","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141235601","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 : 2024-06-01DOI: 10.3844/jcssp.2024.602.609
Sulaiman Abdullah Alateyah
: The entire educational model has undergone a significant change as a result of the Internet of Things (IoT)'s introduction to the educational sector. Universities have a significant chance to take advantage of the IoT technology in a variety of ways, including data collection and analysis to improve the educational experience, promote the achievement of learning objectives, and enhance overall school operations. Since the IoT promises, among other things, to improve education process and quality of life, as well as increase resource efficiency and management, the IoT has emerged as a highly debated research topic. Meanwhile, the Internet of things have temptation of using mobile devices and other technology while virtual session start for non-educational purpose, strength is always a reason for weakness. Therefore, student monitoring is a key part of many educational services. Especially during the virtual session. It can reduce the number of students who not completing the course, while increasing the access to education to those who really need these monitoring. In a variety of settings, wireless and mobile technologies are being used to monitor students: Courses and online sessions. The quality and dependability of student monitoring, however, have not been particularly satisfactory due to a number of restrictions, such as Wireless Networks (WN) unforeseen and fragmented coverage of users. In this study, we describe a method for monitoring students that makes use of ad-hoc Wireless Networks (ad-hoc-WN) that may be created on an ongoing basis between wearable and mobile devices. This enables the vital signs’ transmission in both routine and emergency circumstances. In order to make student monitoring through ad-hoc-WN a reality, we clarify a wireless architecture, go over emergency message routing, and bring together numerous relevant technical and non-technical difficulties. The suggested student monitoring method is made with the intention of being trustworthy and doable in the near future.
{"title":"Using Wireless Networks and Internet of Things for Enhanced Monitoring of Students During Virtual Class","authors":"Sulaiman Abdullah Alateyah","doi":"10.3844/jcssp.2024.602.609","DOIUrl":"https://doi.org/10.3844/jcssp.2024.602.609","url":null,"abstract":": The entire educational model has undergone a significant change as a result of the Internet of Things (IoT)'s introduction to the educational sector. Universities have a significant chance to take advantage of the IoT technology in a variety of ways, including data collection and analysis to improve the educational experience, promote the achievement of learning objectives, and enhance overall school operations. Since the IoT promises, among other things, to improve education process and quality of life, as well as increase resource efficiency and management, the IoT has emerged as a highly debated research topic. Meanwhile, the Internet of things have temptation of using mobile devices and other technology while virtual session start for non-educational purpose, strength is always a reason for weakness. Therefore, student monitoring is a key part of many educational services. Especially during the virtual session. It can reduce the number of students who not completing the course, while increasing the access to education to those who really need these monitoring. In a variety of settings, wireless and mobile technologies are being used to monitor students: Courses and online sessions. The quality and dependability of student monitoring, however, have not been particularly satisfactory due to a number of restrictions, such as Wireless Networks (WN) unforeseen and fragmented coverage of users. In this study, we describe a method for monitoring students that makes use of ad-hoc Wireless Networks (ad-hoc-WN) that may be created on an ongoing basis between wearable and mobile devices. This enables the vital signs’ transmission in both routine and emergency circumstances. In order to make student monitoring through ad-hoc-WN a reality, we clarify a wireless architecture, go over emergency message routing, and bring together numerous relevant technical and non-technical difficulties. The suggested student monitoring method is made with the intention of being trustworthy and doable in the near future.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229553","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 : 2024-06-01DOI: 10.3844/jcssp.2024.649.657
Kamal ElDahshan, H. Hefny, Iman Ahmed ElSayed
: Respiratory genetic diseases are considered a major participant in the reasons of death worldwide nowadays and were one of the major participants in helping in increasing the numbers of COVID-19 patients. It is considered one of the most alarming diseases affecting in particular the respiratory system. The journey of early detection of respiratory genetic diseases is considered to be very challenging today to assist in lessening the percentage rate of death since people with these diseases are more vulnerable to being infected by COVID-19 and other dangerous diseases than others. Also, it is considered a very difficult mission for medical practitioners because of the high requirement for expertise and knowledgeable practitioners. While, predicting or detecting respiratory genetic disease in an early phase has many gaps and lacks accuracy accommodated with speed as well; as a result any slight update in the accuracy accommodating speed will be considered of great improvement and importance which will later result in the reduction of the increasing number of genetically diseased patients as the well-known diseases of Alpha-1 antitrypsin deficiency, Cystic fibrosis, Kartagener syndrome and many other respiratory genetic diseases. In this study we will introduce a new hybrid-model approach (HMGD) based on merging two outstanding soft computing optimization algorithms which weren’t used before in neither detection nor prediction of diseases which are Extended Compact Genetic Algorithm (ECGA) and Compact Co-Evolutionary Algorithm (CCoEA); one for which ECGA will act for the feature selection phase and output will be fed to the CCoEA for feature optimization resulting in the certainty factor of the detected/predicted respiratory genetic disease. The model will be used through a graphical user-friendly interface built up especially for the model to analyze data, learn from that output data, and result in a tactile and touchable prediction/detection for the respiratory genetic disease. The HMGD model proved its reliability and outstanding performance over other known computational models by an accuracy of 98.27% for respiratory genetic diseases’ prediction in 1.03 sec, while an accuracy of 97.89% for respiratory genetic diseases’ detection in 1.4 sec. The model proved to achieve a higher level of accuracy in the detection or prediction of respiratory genetic diseases than other machine learning models.
{"title":"HMGD: A High-Accuracy Model for Detection and Prediction of Respiratory Genetic Diseases","authors":"Kamal ElDahshan, H. Hefny, Iman Ahmed ElSayed","doi":"10.3844/jcssp.2024.649.657","DOIUrl":"https://doi.org/10.3844/jcssp.2024.649.657","url":null,"abstract":": Respiratory genetic diseases are considered a major participant in the reasons of death worldwide nowadays and were one of the major participants in helping in increasing the numbers of COVID-19 patients. It is considered one of the most alarming diseases affecting in particular the respiratory system. The journey of early detection of respiratory genetic diseases is considered to be very challenging today to assist in lessening the percentage rate of death since people with these diseases are more vulnerable to being infected by COVID-19 and other dangerous diseases than others. Also, it is considered a very difficult mission for medical practitioners because of the high requirement for expertise and knowledgeable practitioners. While, predicting or detecting respiratory genetic disease in an early phase has many gaps and lacks accuracy accommodated with speed as well; as a result any slight update in the accuracy accommodating speed will be considered of great improvement and importance which will later result in the reduction of the increasing number of genetically diseased patients as the well-known diseases of Alpha-1 antitrypsin deficiency, Cystic fibrosis, Kartagener syndrome and many other respiratory genetic diseases. In this study we will introduce a new hybrid-model approach (HMGD) based on merging two outstanding soft computing optimization algorithms which weren’t used before in neither detection nor prediction of diseases which are Extended Compact Genetic Algorithm (ECGA) and Compact Co-Evolutionary Algorithm (CCoEA); one for which ECGA will act for the feature selection phase and output will be fed to the CCoEA for feature optimization resulting in the certainty factor of the detected/predicted respiratory genetic disease. The model will be used through a graphical user-friendly interface built up especially for the model to analyze data, learn from that output data, and result in a tactile and touchable prediction/detection for the respiratory genetic disease. The HMGD model proved its reliability and outstanding performance over other known computational models by an accuracy of 98.27% for respiratory genetic diseases’ prediction in 1.03 sec, while an accuracy of 97.89% for respiratory genetic diseases’ detection in 1.4 sec. The model proved to achieve a higher level of accuracy in the detection or prediction of respiratory genetic diseases than other machine learning models.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232904","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 : 2024-06-01DOI: 10.3844/jcssp.2024.690.699
Jordan, J. Andry, Fransiskus Adikara, Yemima Monica Geasela, Francka Sakti Lee
: The development of IT has become a crucial and essential necessity for fostering innovation within companies. Recent technological advancements have resulted in Information Systems (IS) involving the processes of identification, evaluation, and decision-making related to a company's strategies. In its application, IS has been used not only within companies but has also extended into the realm of education. The significance of information systems in the education sector has become crucial for more modern education, especially in this era of education 4.0, where education tends toward digitalization. The high school is an educational organization located in Jakarta, Indonesia. These organizations have diverse business processes, ranging from the enrollment of new students, school data management such as student and teacher records, facility management, and the learning process, to routine divisional reporting, these high schools have not optimally and integratively implemented technology. They acknowledge the importance of integrating business processes with IS. Therefore, the design of enterprise architecture is needed to align business processes with information systems to ensure their mutual integration. The enterprise architecture undertaken in this study will assist in designing advancements in business processes, information systems, and organizational infrastructure, enabling high schools to better prepare for future challenges and move toward a more modern learning environment. This research employs The Open Group Architecture Framework (TOGAF ADM) combined with the application of the Ward Peppard method. TOGAF provides methods related to building, managing, and implementing enterprise architecture and information systems. The findings of this research consist of a blueprint recommendation comprising detailed proposals for integrated applications to enhance business processes and cater to the needs of the company. These recommendations can serve as a reference for designing information systems and making decisions in developing high school information systems.
{"title":"Implementation of Information System Architecture Using TOGAF and Ward Peppard Analysis for High School","authors":"Jordan, J. Andry, Fransiskus Adikara, Yemima Monica Geasela, Francka Sakti Lee","doi":"10.3844/jcssp.2024.690.699","DOIUrl":"https://doi.org/10.3844/jcssp.2024.690.699","url":null,"abstract":": The development of IT has become a crucial and essential necessity for fostering innovation within companies. Recent technological advancements have resulted in Information Systems (IS) involving the processes of identification, evaluation, and decision-making related to a company's strategies. In its application, IS has been used not only within companies but has also extended into the realm of education. The significance of information systems in the education sector has become crucial for more modern education, especially in this era of education 4.0, where education tends toward digitalization. The high school is an educational organization located in Jakarta, Indonesia. These organizations have diverse business processes, ranging from the enrollment of new students, school data management such as student and teacher records, facility management, and the learning process, to routine divisional reporting, these high schools have not optimally and integratively implemented technology. They acknowledge the importance of integrating business processes with IS. Therefore, the design of enterprise architecture is needed to align business processes with information systems to ensure their mutual integration. The enterprise architecture undertaken in this study will assist in designing advancements in business processes, information systems, and organizational infrastructure, enabling high schools to better prepare for future challenges and move toward a more modern learning environment. This research employs The Open Group Architecture Framework (TOGAF ADM) combined with the application of the Ward Peppard method. TOGAF provides methods related to building, managing, and implementing enterprise architecture and information systems. The findings of this research consist of a blueprint recommendation comprising detailed proposals for integrated applications to enhance business processes and cater to the needs of the company. These recommendations can serve as a reference for designing information systems and making decisions in developing high school information systems.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141278004","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 : 2024-06-01DOI: 10.3844/jcssp.2024.594.601
Rand Marwan Khalil Ibrahim, Samer Zein
: The mobile app market is still in continual growth. People are migrating to smartphone mobile devices to accomplish their daily activities while working, playing, and communicating with others. From the developers' perspective, there exists a wide variety of platforms, technologies, and architecture choices for developing and testing mobile apps. However, because of the constant changes in software applications and the great technological development, developers are supposed to speed up the development process to satisfy the customer's needs and provide robust applications within a short period of time. Cross-platform mobile app development technology, such as react Native, aims to overcome these difficulties, where instead of building separate applications for each platform, a single code base that can be run on multiple platforms is developed, which accelerates the development process. Model-based testing is one of the techniques that are used to test cross-platform applications and identify and find defects and bugs. This study proposes a React Native Abstract Syntax Tree pruning (RN-AST pruning) framework, which aims to facilitate the mobile app testing process by pruning the original GUI model of the application and reducing the number of test cases by keeping only the test cases that cover the impacted regions from internal code changes. The pruning process to keep the GUI elements is applied to the abstract syntax tree, which is the result of doing the static analysis on the last two versions of the source code. After that the two pruned AST will be compared to keep only the affected and updated GUI elements. The affected files will be listed as paths to prevent any other file from being tested, consequently reducing the number of test cases. According to our knowledge, no comprehensive work was dedicated to use the static analysis approach in keeping only the impacted GUI elements by the internal code changes in cross-platform software, thus reducing the run test cases and increasing productivity by accelerating the development life cycle. Preliminary experimentation was done on our framework with the help of six developers and test engineers in cross-platform development. The experiment was carried out in a systematic process with clear steps on a proof of concept mobile application. Results show that the RN-AST Pruning framework is useful and provides test engineers with affected files and paths that need to be tested, thus reducing the test cases and minimizing the testing time and effort. Moreover, it identifies exactly the changes that occurred in each file and categorizes them into updates, placements, and deletions based on the differences between the original version and the updated version of the source code. The authors confirm that this study is original and its contents are unpublished. Moreover, no specific grant from any funding agency was received.
{"title":"Testing React Native Mobile Apps: Pruning GUI Model Approach","authors":"Rand Marwan Khalil Ibrahim, Samer Zein","doi":"10.3844/jcssp.2024.594.601","DOIUrl":"https://doi.org/10.3844/jcssp.2024.594.601","url":null,"abstract":": The mobile app market is still in continual growth. People are migrating to smartphone mobile devices to accomplish their daily activities while working, playing, and communicating with others. From the developers' perspective, there exists a wide variety of platforms, technologies, and architecture choices for developing and testing mobile apps. However, because of the constant changes in software applications and the great technological development, developers are supposed to speed up the development process to satisfy the customer's needs and provide robust applications within a short period of time. Cross-platform mobile app development technology, such as react Native, aims to overcome these difficulties, where instead of building separate applications for each platform, a single code base that can be run on multiple platforms is developed, which accelerates the development process. Model-based testing is one of the techniques that are used to test cross-platform applications and identify and find defects and bugs. This study proposes a React Native Abstract Syntax Tree pruning (RN-AST pruning) framework, which aims to facilitate the mobile app testing process by pruning the original GUI model of the application and reducing the number of test cases by keeping only the test cases that cover the impacted regions from internal code changes. The pruning process to keep the GUI elements is applied to the abstract syntax tree, which is the result of doing the static analysis on the last two versions of the source code. After that the two pruned AST will be compared to keep only the affected and updated GUI elements. The affected files will be listed as paths to prevent any other file from being tested, consequently reducing the number of test cases. According to our knowledge, no comprehensive work was dedicated to use the static analysis approach in keeping only the impacted GUI elements by the internal code changes in cross-platform software, thus reducing the run test cases and increasing productivity by accelerating the development life cycle. Preliminary experimentation was done on our framework with the help of six developers and test engineers in cross-platform development. The experiment was carried out in a systematic process with clear steps on a proof of concept mobile application. Results show that the RN-AST Pruning framework is useful and provides test engineers with affected files and paths that need to be tested, thus reducing the test cases and minimizing the testing time and effort. Moreover, it identifies exactly the changes that occurred in each file and categorizes them into updates, placements, and deletions based on the differences between the original version and the updated version of the source code. The authors confirm that this study is original and its contents are unpublished. Moreover, no specific grant from any funding agency was received.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230005","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 : 2024-06-01DOI: 10.3844/jcssp.2024.682.689
Neelam Gupta, Sarvesh Tanwar, Sumit Badotra
: Software Networking (SDN) is growing in popularity due to its benefits, which include portability, mobility, analytics, and ease of creation. But it needs to be adequately shielded from security risks. One of the main vulnerabilities to the SDN network is the Distributed Denial-of-Service (DDoS) attack. To fulfill the demands of the complex and demanding security concerns of today, a new network philosophy is needed. Because SDN relies on a central controller, it has a single point of attack and failure. The present research monitors and analyses network traffic coming from switches, host computers, emulators, and wireless access points using a multi-vendor packet sampling technique using sFlow. In the process, we have clarified the usefulness and efficiency of the recommended strategy, which makes use of SDN controllers for the detection and mitigation of DDoS flooding attacks. The outcomes also demonstrate that the ODL controller outperforms the other remaining controllers in terms of load-shedding efficiency and flow setup latency. According to TCP bandwidth measurements, the ODL controller performs better in terms of processing power and jitter than the remaining controllers due to its higher computational complexity. These test results indicate that the jitter performance of all controllers is comparable. Overall analysis indicates that ODL is more reliable than other controllers in our scenario.
:软件联网（SDN）因其便携性、移动性、分析性和易于创建等优点而越来越受欢迎。但它需要充分防范安全风险。SDN 网络的主要漏洞之一是分布式拒绝服务（DDoS）攻击。为了满足当今复杂而苛刻的安全要求，需要一种新的网络理念。由于 SDN 依赖于中央控制器，因此存在单点攻击和故障。本研究采用 sFlow 多厂商数据包采样技术，对来自交换机、主机、模拟器和无线接入点的网络流量进行监控和分析。在此过程中，我们明确了所推荐策略的实用性和效率，该策略利用 SDN 控制器来检测和缓解 DDoS 泛洪攻击。研究结果还表明，ODL 控制器在负载平衡效率和流量设置延迟方面优于其他控制器。根据 TCP 带宽测量结果，由于 ODL 控制器的计算复杂度更高，因此它在处理能力和抖动方面的表现优于其他控制器。这些测试结果表明，所有控制器的抖动性能相当。总体分析表明，在我们的方案中，ODL 比其他控制器更可靠。
{"title":"Efficiency Assessment of Software-Defined Networking for Real-Time Network Systems","authors":"Neelam Gupta, Sarvesh Tanwar, Sumit Badotra","doi":"10.3844/jcssp.2024.682.689","DOIUrl":"https://doi.org/10.3844/jcssp.2024.682.689","url":null,"abstract":": Software Networking (SDN) is growing in popularity due to its benefits, which include portability, mobility, analytics, and ease of creation. But it needs to be adequately shielded from security risks. One of the main vulnerabilities to the SDN network is the Distributed Denial-of-Service (DDoS) attack. To fulfill the demands of the complex and demanding security concerns of today, a new network philosophy is needed. Because SDN relies on a central controller, it has a single point of attack and failure. The present research monitors and analyses network traffic coming from switches, host computers, emulators, and wireless access points using a multi-vendor packet sampling technique using sFlow. In the process, we have clarified the usefulness and efficiency of the recommended strategy, which makes use of SDN controllers for the detection and mitigation of DDoS flooding attacks. The outcomes also demonstrate that the ODL controller outperforms the other remaining controllers in terms of load-shedding efficiency and flow setup latency. According to TCP bandwidth measurements, the ODL controller performs better in terms of processing power and jitter than the remaining controllers due to its higher computational complexity. These test results indicate that the jitter performance of all controllers is comparable. Overall analysis indicates that ODL is more reliable than other controllers in our scenario.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232162","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}
: The research aimed to develop software and hardware to control the water quality of hydroponic plants; create custom software for the ESP8266, turning it into an IoT client capable of seamless integration with AWS IoT cloud-based system; initiate targets integrating and enhancing AWS IoT modules, facilitating automated water quality control functions and building intuitive dashboards for real-time water quality monitoring. The research started with the preparation of hardware and software focusing on aspects of hydroponics, including monitoring water quality, plant response, and the hydroponic system as a whole. Apart from that, TCP/IP networks were built by using Wi-Fi routers, LAN networks, and access to the internet. Then, System development involving hardware modules, software development, and user interface design were conducted. The final research result is the development of an integrated system to control and monitor water quality which is vital for the growth of hydroponic plants. Finally, by developing a hydroponic planting system supporting device that is integrated into a cloud-based system, it is expected to create a digital system that can work automatically to regulate water quality in maintaining the growth of hydroponic plants which is equipped with easy access at any time remotely and from anywhere.
{"title":"Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System","authors":"Cahya Lukito, Rony Baskoro Lukito, Endang Ernawati","doi":"10.3844/jcssp.2024.658.669","DOIUrl":"https://doi.org/10.3844/jcssp.2024.658.669","url":null,"abstract":": The research aimed to develop software and hardware to control the water quality of hydroponic plants; create custom software for the ESP8266, turning it into an IoT client capable of seamless integration with AWS IoT cloud-based system; initiate targets integrating and enhancing AWS IoT modules, facilitating automated water quality control functions and building intuitive dashboards for real-time water quality monitoring. The research started with the preparation of hardware and software focusing on aspects of hydroponics, including monitoring water quality, plant response, and the hydroponic system as a whole. Apart from that, TCP/IP networks were built by using Wi-Fi routers, LAN networks, and access to the internet. Then, System development involving hardware modules, software development, and user interface design were conducted. The final research result is the development of an integrated system to control and monitor water quality which is vital for the growth of hydroponic plants. Finally, by developing a hydroponic planting system supporting device that is integrated into a cloud-based system, it is expected to create a digital system that can work automatically to regulate water quality in maintaining the growth of hydroponic plants which is equipped with easy access at any time remotely and from anywhere.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232292","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}
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