Journal of Computer Science最新文献

{"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":"38 12","pages":""},"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}

{"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":"89 5","pages":""},"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}

{"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":"6 5","pages":""},"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}

{"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":"54 45","pages":""},"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}

{"title":"Enhancing Semantic Web Retrieval Through Ontology-Driven Feature Extraction: A Novel Proposition","authors":"Meer Hazar Khan, Muhammad Imran Sharif, Mehwish Mehmood, Fernaz Narin Nur, Md Palash Uddin, Zahid Akhtar, Kamran Siddique, Sadia Waheed Awan","doi":"10.3844/jcssp.2024.487.494","DOIUrl":"https://doi.org/10.3844/jcssp.2024.487.494","url":null,"abstract":": Web images represent unstructured data sets which often lead to challenges when users try to locate distinct images via text-based searches on the web. Such difficulties stem from different factors, e","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"2000 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141027461","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}

{"title":"Machine Learning Approaches for the Prediction of Gas Turbine Transients","authors":"Arnaud Nguembang Fadja, Giuseppe Cota, Francesco Bertasi, Fabrizio Riguzzi, E. Losi, L. Manservigi, M. Venturini, G. Bechini","doi":"10.3844/jcssp.2024.495.510","DOIUrl":"https://doi.org/10.3844/jcssp.2024.495.510","url":null,"abstract":": Gas Turbine (GT) emergency shutdowns can lead to energy production interruption and may also reduce the lifespan of a turbine. In order to remain competitive in the market, it is necessary to improve the reliability and availability of GTs by developing predictive maintenance systems that are able to predict future conditions of GTs within a certain time. Predicting such situations not only helps to take corrective measures to avoid service unavailability but also eases the process of maintenance and considerably reduces maintenance costs. Huge amounts of sensor data are collected from (GTs) making monitoring impossible for human operators even with the help of computers. Machine learning techniques could provide support for handling large amounts of sensor data and building decision models for predicting GT future conditions. The paper presents an application of machine learning based on decision trees and k-nearest neighbors for predicting the rotational speed of gas turbines. The aim is to distinguish steady states (e.g., GT operation at normal conditions) from transients (e.g., GT trip or shutdown). The different steps of a machine learning pipeline, starting from data extraction to model testing are implemented and analyzed. Experiments are performed by applying decision trees, extremely randomized trees, and k-nearest neighbors to sensor data collected from GTs located in different countries. The trained models were able to predict steady state and transient with more than 93% accuracy. This research advances predictive maintenance methods and suggests exploring advanced machine learning algorithms, real-time data integration, and explainable AI techniques to enhance gas turbine behavior understanding and develop more adaptable maintenance systems for industrial applications.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"2016 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141026970","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}

{"title":"Framework for the Adaptive Learning of Higher Education Students in Virtual Classes in Peru Using CRISP-DM and Machine Learning","authors":"Maryori Bautista, Sebastian Alfaro, Lenis Wong","doi":"10.3844/jcssp.2024.522.534","DOIUrl":"https://doi.org/10.3844/jcssp.2024.522.534","url":null,"abstract":": During the COVID-19 pandemic, virtual education played a significant role around the world. In post-pandemic Peru, higher education institutions did not entirely dismiss the online education modality. However, this virtual education system maintains a traditional teaching-learning model, where all students receive the same content material and are expected to learn in the same way; as a result, it has not been effective in meeting the individual needs of students, causing poor performance in many cases. For this reason, a framework is proposed for the adaptive learning of higher education students in virtual classes using the Cross-Industry Standard Process for Data Mining (CRISP-DM) and Machine Learning (ML) methodology in order to recommend individualized learning materials. This framework is made up of four stages: (i) Analysis of student aspects, (ii) Analysis of Learning Methodology (LM), (iii) ML development and (iv) Integration of LM and ML models. (i) evaluates the student-related factors to be considered in adapting their learning content material. (ii) Evaluate which LM is more effective in a virtual environment. In (iii), Four ML algorithms based on the CRISP-DM methodology are implemented. In (iv), The best ML model is integrated with the LM in a virtual class. Two experiments were carried out to compare the traditional teaching methodology (experiment I) and the proposed framework (experiment 2) with a sample of 68 students. The results showed that the framework was more effective in promoting progress and academic performance, obtaining an Improvement Percentage (IP) of 39.72%. This percentage was calculated by subtracting the grade average of the tests taken at the beginning and end of each experiment.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"18 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141043923","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}

{"title":"Papers Mentioning Things Board: A Systematic Mapping Study","authors":"Paolino Di Felice, Gaetanino Paolone","doi":"10.3844/jcssp.2024.574.584","DOIUrl":"https://doi.org/10.3844/jcssp.2024.574.584","url":null,"abstract":".","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141025618","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}

{"title":"Non-Hodgkin Lymphoma Risk Grading Through the Pathological Data by Using the Optimized Convolutional Lymphnet Model","authors":"Sivaranjini Nagarajan, Gomathi Muthuswamy","doi":"10.3844/jcssp.2024.511.521","DOIUrl":"https://doi.org/10.3844/jcssp.2024.511.521","url":null,"abstract":": Diagnosing Non-Hodgkin Lymphoma (NHL) is difficult and often requires specialised training and expertise as well as extensive morphological investigation and, in certain cases, costly immunohistological and genetic techniques. Computational approaches enabling morphological-based decision making are necessary for bridging the existing gaps. Histopathological images can be accurately classified using deep learning approaches, however data on NHL subtyping is limited. However, there is a lack of data about the categorization of lymph nodes affected by Non-Hodgkin Lymphoma. Here in this study, initially image preprocessing was done using the maximal Kalman filter which helps in removing the noise, data augmentation was done to improve the dataset, then the lymph nodal area was segmented using the sequential fuzzy YOLACT algorithm. Finally we trained and optimized an Convolutional Lymphnet model to classify and grade tumor level from tumor-free reference lymph nodes using the grey wolf optimized model by selecting the fitness parameters and optimize it for identifying the patient risk score. The overall experimentation was carried out under python framework. The findings demonstrate that the recommended strategy works better than the state-of-the-art techniques by having excellent detection and risk score prediction accuracy","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"35 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141024894","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}

{"title":"Application of Unimodular Hill Cipher and RSA Methods to Text Encryption Algorithms Using Python","authors":"Samsul Arifin, Dwi Wijonarko, Suwarno, Edwin K Sijabat","doi":"10.3844/jcssp.2024.548.563","DOIUrl":"https://doi.org/10.3844/jcssp.2024.548.563","url":null,"abstract":": Text encryption is one of the techniques used to maintain the confidentiality of information in digital communications. In this study, we propose to apply a combination of the Unimodular Hill Cipher and RSA methods to a text encryption algorithm using the Python programming language. The Unimodular Hill Cipher method uses an unimodular matrix to replace text characters with encrypted characters, while RSA (Rivest-Shamir-Adleman) is a public key encryption algorithm that relies on modulo arithmetic properties. The purpose of this research is to combine the strengths of the two methods and produce a more secure text encryption system. Unimodular Hill Cipher provides the advantage of randomizing text characters by using matrix modulo operations, while RSA provides a high level of security through the use of public and private key pairs. In this study, we explain in detail the basic theory and algorithms of the Unimodular Hill Cipher and RSA. We also describe the implementation steps of both methods in the Python programming language. The text data used in this study went through a preprocessing stage before being encrypted. We also analyze the results of the encryption using several statistical methods to measure how close the relationship between the original text and the result of the encryption is. In a comparative analysis with the previous paper, in this study, the use of the Unimodular Hill Cipher and RSA methods in the context of Python provides additional insight into the performance and level of security of both. The experimental results show that the combination of the Unimodular Hill Cipher and RSA methods can produce a higher level of security in text encryption. It is hoped that this research can contribute to the development of a more effective and secure text encryption algorithm.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":"107 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141050681","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}