Pub Date : 2024-07-22DOI: 10.3844/jcssp.2024.986.996
Leonid Chervyakov, Tagirbek Aslanov, Dmitry Polezhaev, Viktor Lysenko
: Global computer networks have enabled ordinary users, companies, organizations and medical institutions to gain virtually unlimited access to data arrays. Therefore, developing systems capable of ensuring the good performance and secure operation of a standard Computer Telecommunication Network (CTN) has become one of the most pressing tasks demanded in the medical industry. Consequently, this study aims to create an ordered chain of operations that can perform information encryption to enhance data transmission and exchange security. This study examines the existing ordered chains of encryption operations and assesses their strengths and weaknesses. In addition, a framework for implementing cryptographic algorithms is proposed. This algorithm structure enables verification of the existence of the correct key along the specified path, thereby enhancing the overall security of the system. The study results indicate that the optimal variant of encryption is the ordered chains of encoding operations that rely on cryptography. The results of the testing demonstrated that the developed ordered chain of operations exhibited several advantages compared to its analogs, with an efficiency that exceeded that of the analogs by more than fourfold. The implementation of the proposed ordered chain of operations would provide a significantly safer operation of a standard CTN in a typical Medical Institution (MI).
{"title":"Features of the Security System Development of a Computer Telecommunication Network","authors":"Leonid Chervyakov, Tagirbek Aslanov, Dmitry Polezhaev, Viktor Lysenko","doi":"10.3844/jcssp.2024.986.996","DOIUrl":"https://doi.org/10.3844/jcssp.2024.986.996","url":null,"abstract":": Global computer networks have enabled ordinary users, companies, organizations and medical institutions to gain virtually unlimited access to data arrays. Therefore, developing systems capable of ensuring the good performance and secure operation of a standard Computer Telecommunication Network (CTN) has become one of the most pressing tasks demanded in the medical industry. Consequently, this study aims to create an ordered chain of operations that can perform information encryption to enhance data transmission and exchange security. This study examines the existing ordered chains of encryption operations and assesses their strengths and weaknesses. In addition, a framework for implementing cryptographic algorithms is proposed. This algorithm structure enables verification of the existence of the correct key along the specified path, thereby enhancing the overall security of the system. The study results indicate that the optimal variant of encryption is the ordered chains of encoding operations that rely on cryptography. The results of the testing demonstrated that the developed ordered chain of operations exhibited several advantages compared to its analogs, with an efficiency that exceeded that of the analogs by more than fourfold. The implementation of the proposed ordered chain of operations would provide a significantly safer operation of a standard CTN in a typical Medical Institution (MI).","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815628","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}
: This study presents an extensive examination of CPU scheduling algorithms, focusing on the First-Come, First-Served (FCFS), Round-Robin (RR), and Shortest-Job-First (SJF) strategies through a carefully designed scenario-based approach. By deploying a Java-based simulation to dynamically generate random process arrival and burst times, this study simulates a variety of operational conditions to test these scheduling algorithms’ adaptability and performance in environments that closely resemble real-world computing scenarios. The research aims to explore the effects of dynamic quantum size allocation on RR scheduling and assess its impact on system performance metrics such as response time and context switching overhead. Through a detailed analysis, this study seeks to provide new insights into the operational efficiency of the FCFS, RR, and SJF scheduling strategies, highlighting their strengths, limitations, and applicability across different computing environments.
{"title":"Performance Assessment of CPU Scheduling Algorithms: A Scenario-Based Approach with FCFS, RR, and SJF","authors":"Olaa Hajjar, Escelle Mekhallalati, Nada Annwty, Faisal Alghayadh, Ismail Keshta, Mohammed Algabri","doi":"10.3844/jcssp.2024.972.985","DOIUrl":"https://doi.org/10.3844/jcssp.2024.972.985","url":null,"abstract":": This study presents an extensive examination of CPU scheduling algorithms, focusing on the First-Come, First-Served (FCFS), Round-Robin (RR), and Shortest-Job-First (SJF) strategies through a carefully designed scenario-based approach. By deploying a Java-based simulation to dynamically generate random process arrival and burst times, this study simulates a variety of operational conditions to test these scheduling algorithms’ adaptability and performance in environments that closely resemble real-world computing scenarios. The research aims to explore the effects of dynamic quantum size allocation on RR scheduling and assess its impact on system performance metrics such as response time and context switching overhead. Through a detailed analysis, this study seeks to provide new insights into the operational efficiency of the FCFS, RR, and SJF scheduling strategies, highlighting their strengths, limitations, and applicability across different computing environments.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815904","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.2024.730.741
K. R. Rohini, P. S. Rajakumar, S. Geetha
: Innovation for Electronic Medical Records (EMRs) has been hindered by years of excessive regulation and inefficient bureaucracy. As data science and personalization encourage individuals to take an active role in their healthcare and regain control of their own medical records, there is an urgent need for new approaches. The ability to exchange electronic health records is fundamental in contemporary healthcare systems for facilitating a wider range of health services and delivering high-quality treatment. Despite the requirement for utilizing medical information for various reasons, most patients still authorize paper forms with minimal participation. The present methods of managing patient consent and medical data exchange are laborious, expensive, and prone to failures, even with quality assurance measures in effect. Because of this, there may not be enough patient empowerment, which can lead to inefficiencies in the process and a lack of trust and transparency. A shortage of resources makes it harder to acquire individual consent, which is necessary for health data exchange. Healthcare organizations also grapple with patient consent. Blockchain-based platforms enable data exchange by developing a trusted user network. Users can share their data without relying on health service providers for time and resources. Blockchain-based systems necessitate data governance frameworks to specify and monitor data exchange and use. This research article aims to establish a system that healthcare organizations may use to easily gain patient consent for various objectives, while also giving patients more flexibility in managing their consent. In this study, a novel electronic consent model namely ‘Smart Consent Blockchain Based System (SCBCS)”, is built on the hyper ledger fabric Blockchain that employs a purpose-based access control method. Distributed ledger technology (blockchain) ensures that all metadata pertaining to patient records, permissions, and data access cannot be altered once written. Additionally, Blockchain chain code is developed to handle patient consent-related business logic. A prototype is constructed and verified business logic with the chain code, validating the requestor's data access and patient permission saved in the Blockchain. The proposed SCBCS acts as a consent management system for patients and healthcare organizations. The proposed method is compared with other existing methods 'MedRec’, Consent Management System (CMS). The results demonstrate this system manages medical staff data access requests effectively. The proposed method outperforms other methods compared with low latency, less gas consumption, and low access time. This Blockchain-based proposed SCBCS technology offers great dependability, transparency, and traceability for patient data sharing in hospitals and research.
{"title":"Smart Patient Consent Management Model for Health Information Exchange Based on Blockchain Technology","authors":"K. R. Rohini, P. S. Rajakumar, S. Geetha","doi":"10.3844/jcssp.2024.730.741","DOIUrl":"https://doi.org/10.3844/jcssp.2024.730.741","url":null,"abstract":": Innovation for Electronic Medical Records (EMRs) has been hindered by years of excessive regulation and inefficient bureaucracy. As data science and personalization encourage individuals to take an active role in their healthcare and regain control of their own medical records, there is an urgent need for new approaches. The ability to exchange electronic health records is fundamental in contemporary healthcare systems for facilitating a wider range of health services and delivering high-quality treatment. Despite the requirement for utilizing medical information for various reasons, most patients still authorize paper forms with minimal participation. The present methods of managing patient consent and medical data exchange are laborious, expensive, and prone to failures, even with quality assurance measures in effect. Because of this, there may not be enough patient empowerment, which can lead to inefficiencies in the process and a lack of trust and transparency. A shortage of resources makes it harder to acquire individual consent, which is necessary for health data exchange. Healthcare organizations also grapple with patient consent. Blockchain-based platforms enable data exchange by developing a trusted user network. Users can share their data without relying on health service providers for time and resources. Blockchain-based systems necessitate data governance frameworks to specify and monitor data exchange and use. This research article aims to establish a system that healthcare organizations may use to easily gain patient consent for various objectives, while also giving patients more flexibility in managing their consent. In this study, a novel electronic consent model namely ‘Smart Consent Blockchain Based System (SCBCS)”, is built on the hyper ledger fabric Blockchain that employs a purpose-based access control method. Distributed ledger technology (blockchain) ensures that all metadata pertaining to patient records, permissions, and data access cannot be altered once written. Additionally, Blockchain chain code is developed to handle patient consent-related business logic. A prototype is constructed and verified business logic with the chain code, validating the requestor's data access and patient permission saved in the Blockchain. The proposed SCBCS acts as a consent management system for patients and healthcare organizations. The proposed method is compared with other existing methods 'MedRec’, Consent Management System (CMS). The results demonstrate this system manages medical staff data access requests effectively. The proposed method outperforms other methods compared with low latency, less gas consumption, and low access time. This Blockchain-based proposed SCBCS technology offers great dependability, transparency, and traceability for patient data sharing in hospitals and research.","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":"141709625","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}
: In the current era, information technology systems have rapidly developed and are commonly utilized to address company requirements. Implementation of this technology significantly impacts business processes. MSME development collaborator company is actively engaged in helping MSMEs to survive and thrive through collaborative initiatives. Currently, the company does not offer educational support to MSMEs. This research aims to develop a web-based educational application to tackle current challenges. The extreme programming application development method, a five-step process including planning, design, coding, testing, and software increment, will be utilized. The study produced a website-based education application designed to provide educational support to MSME actors.
{"title":"Website-Based Educational Application to Help MSMEs in Indonesia Develop","authors":"David Freggy Dinata, Francka Sakti Lee, Yemima Monica Geasela, Shierly Everlin, Yunianto Purnomo","doi":"10.3844/jcssp.2024.742.750","DOIUrl":"https://doi.org/10.3844/jcssp.2024.742.750","url":null,"abstract":": In the current era, information technology systems have rapidly developed and are commonly utilized to address company requirements. Implementation of this technology significantly impacts business processes. MSME development collaborator company is actively engaged in helping MSMEs to survive and thrive through collaborative initiatives. Currently, the company does not offer educational support to MSMEs. This research aims to develop a web-based educational application to tackle current challenges. The extreme programming application development method, a five-step process including planning, design, coding, testing, and software increment, will be utilized. The study produced a website-based education application designed to provide educational support to MSME actors.","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":"141691002","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.2024.722.729
Kevin Christianto, A. Chakir, J. Andry, Fransiskus Adikara, Lydia Liliana, Jennifer Felicia
: Technological developments bring comfort and ease to users in accessing applications. Companies need to design systems that are centered on human needs to create technology that is competitive in the market, especially in the field of expeditions. The problem that arises in the sea cargo expedition company is that master data recording is still done manually, which has an impact on reducing data accuracy. Apart from that, the process of recording shipping transactions and status changes is not updated in real time. Things like this are the reason Sea Cargo Expedition hopes to design a goods delivery application that is centered on user needs. This research aims to design a prototype and test the level of user satisfaction with the goods delivery application. Based on the background of this problem, the research focuses on implementing the design thinking method to develop a goods delivery application because it is able to collect user needs directly. Design thinking is divided into five stages, namely, empathize (understanding the problem), define (analyzing the problem), ideate (creating a wireframe), prototype (designing an application design), and test (trial using System Usability Testing (SUS)). This research produces a prototype goods delivery application based on the user's user experience assessment so that it can provide
{"title":"Modeling User Experience in Delivery Applications Using the Design Thinking Method and System Usability Scale","authors":"Kevin Christianto, A. Chakir, J. Andry, Fransiskus Adikara, Lydia Liliana, Jennifer Felicia","doi":"10.3844/jcssp.2024.722.729","DOIUrl":"https://doi.org/10.3844/jcssp.2024.722.729","url":null,"abstract":": Technological developments bring comfort and ease to users in accessing applications. Companies need to design systems that are centered on human needs to create technology that is competitive in the market, especially in the field of expeditions. The problem that arises in the sea cargo expedition company is that master data recording is still done manually, which has an impact on reducing data accuracy. Apart from that, the process of recording shipping transactions and status changes is not updated in real time. Things like this are the reason Sea Cargo Expedition hopes to design a goods delivery application that is centered on user needs. This research aims to design a prototype and test the level of user satisfaction with the goods delivery application. Based on the background of this problem, the research focuses on implementing the design thinking method to develop a goods delivery application because it is able to collect user needs directly. Design thinking is divided into five stages, namely, empathize (understanding the problem), define (analyzing the problem), ideate (creating a wireframe), prototype (designing an application design), and test (trial using System Usability Testing (SUS)). This research produces a prototype goods delivery application based on the user's user experience assessment so that it can provide","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":"141715097","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.2024.758.767
Damayanti, Sutyarso, Akmal Junaidi, F. R. Lumbanraja
: Post Translational Modification (PTM) is an important mechanism involved in regulating protein function. Post-translational modification refers to the addition of covalent and enzymatic modifications of proteins in protein biosynthesis, which has an important role in modifying protein function and regulating gene expression. One of the post-translational modifications is glycosylation. Glycosylation is the addition of a sugar group to a protein structure. One type of glycosylation is glycosylation, which occurs in sequence O. Glycosylation has been linked to several illnesses, including diabetes, cancer, and the flu. Therefore, it is important to anticipate the occurrence of glycosylation by carrying out predicted glycosylated or non-glycosylated data. Glycosylation prediction has been widely done using manual laboratory techniques, which results in the prediction process being long and expensive for lab equipment. To overcome this, computerized data is needed that can predict glycosylation more quickly. The data used is glycosylation data on sequence O obtained from the UniProt website, which can be openly accessed. This study aimed to improve the accuracy of post-translational modification glycosylation in sequence O prediction using the method of extreme gradient boosting as a framework for gradient enhancement that tends to be faster. This accuracy is increased by conducting feature extraction experiments with the following types: AAIndex, hydrophobicity, sable, composition, CTD, and PseAAC. Feature selection uses the MRMR approach. Evaluation using k-fold cross-validation. The results of this study indicate the prediction performance of post-translational modification glycosylation in sequence O with an accuracy value of 100%. The study's findings indicate that the XGBoost algorithm performs better than other research that has been conducted.
:翻译后修饰（PTM）是调节蛋白质功能的重要机制。翻译后修饰是指在蛋白质生物合成过程中对蛋白质添加共价修饰和酶修饰，在改变蛋白质功能和调控基因表达方面具有重要作用。糖基化是翻译后修饰之一。糖基化是在蛋白质结构上添加糖基。糖基化与多种疾病有关，包括糖尿病、癌症和流感。因此，通过预测糖基化或非糖基化数据来预测糖基化的发生非常重要。糖基化预测已广泛使用人工实验室技术，这导致预测过程漫长且实验室设备昂贵。为了克服这一问题，需要能更快预测糖基化的计算机化数据。所使用的数据是从 UniProt 网站获取的序列 O 的糖基化数据，该网站可以公开访问。本研究旨在提高序列 O 预测翻译后修饰糖基化的准确性，使用的方法是极端梯度提升法，作为梯度增强的框架，这种方法往往更快。通过对以下类型进行特征提取实验，提高了准确性：AAIndex、疏水性、sable、成分、CTD 和 PseAAC。特征选择采用 MRMR 方法。使用 k 倍交叉验证进行评估。研究结果表明，序列 O 中翻译后修饰糖基化的预测准确率为 100%。研究结果表明，XGBoost 算法的性能优于其他已开展的研究。
{"title":"Model Classification for Predicting the Post-Translational Modification (PTM) Glycosylation in Sequence O Using an Extreme Gradient Boosting Algorithm","authors":"Damayanti, Sutyarso, Akmal Junaidi, F. R. Lumbanraja","doi":"10.3844/jcssp.2024.758.767","DOIUrl":"https://doi.org/10.3844/jcssp.2024.758.767","url":null,"abstract":": Post Translational Modification (PTM) is an important mechanism involved in regulating protein function. Post-translational modification refers to the addition of covalent and enzymatic modifications of proteins in protein biosynthesis, which has an important role in modifying protein function and regulating gene expression. One of the post-translational modifications is glycosylation. Glycosylation is the addition of a sugar group to a protein structure. One type of glycosylation is glycosylation, which occurs in sequence O. Glycosylation has been linked to several illnesses, including diabetes, cancer, and the flu. Therefore, it is important to anticipate the occurrence of glycosylation by carrying out predicted glycosylated or non-glycosylated data. Glycosylation prediction has been widely done using manual laboratory techniques, which results in the prediction process being long and expensive for lab equipment. To overcome this, computerized data is needed that can predict glycosylation more quickly. The data used is glycosylation data on sequence O obtained from the UniProt website, which can be openly accessed. This study aimed to improve the accuracy of post-translational modification glycosylation in sequence O prediction using the method of extreme gradient boosting as a framework for gradient enhancement that tends to be faster. This accuracy is increased by conducting feature extraction experiments with the following types: AAIndex, hydrophobicity, sable, composition, CTD, and PseAAC. Feature selection uses the MRMR approach. Evaluation using k-fold cross-validation. The results of this study indicate the prediction performance of post-translational modification glycosylation in sequence O with an accuracy value of 100%. The study's findings indicate that the XGBoost algorithm performs better than other research that has been conducted.","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":"141706867","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.2024.793.800
Saif Addeen Alrababah, Nawaf O. Alsrehin
: Decision-making methodologies can differentiate between several types of criterion weights. The subjective weights of decision-makers are prone to be influenced by various factors, including their level of knowledge, experience and competency. This may result in the wrong evaluation of the criteria due to the inherent ambiguity of human judgments, leading to unavoidable assessment errors. Beyond that, while assessing the decision alternatives, the majority of Multiple Criteria Decision Making (MCDM) take the evaluation criteria into consideration separately. However, in actual application, most of the criteria are not mutually exclusive. In the context of online customer reviews, it is essential to prioritize product aspects in order to facilitate the purchasing process for potential consumers. Selecting the appropriate product aspects is a difficult task due to the vast quantity of product reviews. This research develops an MCDM solution through the integration of the Preference Selection Index (PSI) with The approach for Order Preference by Similarity to an Ideal Solution (TOPSIS) method for decision-making. The contribution of this study is to enhance the TOPSIS ranking technique by incorporating PSI objective weights as an alternative to subjective weights. PSI offers the benefit of focusing on the convergence of the criteria involved rather than their divergence. This approach will improve the ranking process of TOPSIS by taking into account the interconnectedness of the criteria, hence facilitating the prioritization of significant aspects of a product based on online reviews. A dataset comprising four electronic products was utilized as a reference for conducting a statistical analysis. Through the examination of the outcomes utilizing the discount cumulative gain metric, it becomes apparent that the combination of the TOPSIS approach alongside PSI weights facilitates the identification of the suitable product aspects that effectively differentiate the one that aligns with consumer expectations.
{"title":"Application of Preference Selection Index and TOPSIS in Product Aspect Extraction and Ranking","authors":"Saif Addeen Alrababah, Nawaf O. Alsrehin","doi":"10.3844/jcssp.2024.793.800","DOIUrl":"https://doi.org/10.3844/jcssp.2024.793.800","url":null,"abstract":": Decision-making methodologies can differentiate between several types of criterion weights. The subjective weights of decision-makers are prone to be influenced by various factors, including their level of knowledge, experience and competency. This may result in the wrong evaluation of the criteria due to the inherent ambiguity of human judgments, leading to unavoidable assessment errors. Beyond that, while assessing the decision alternatives, the majority of Multiple Criteria Decision Making (MCDM) take the evaluation criteria into consideration separately. However, in actual application, most of the criteria are not mutually exclusive. In the context of online customer reviews, it is essential to prioritize product aspects in order to facilitate the purchasing process for potential consumers. Selecting the appropriate product aspects is a difficult task due to the vast quantity of product reviews. This research develops an MCDM solution through the integration of the Preference Selection Index (PSI) with The approach for Order Preference by Similarity to an Ideal Solution (TOPSIS) method for decision-making. The contribution of this study is to enhance the TOPSIS ranking technique by incorporating PSI objective weights as an alternative to subjective weights. PSI offers the benefit of focusing on the convergence of the criteria involved rather than their divergence. This approach will improve the ranking process of TOPSIS by taking into account the interconnectedness of the criteria, hence facilitating the prioritization of significant aspects of a product based on online reviews. A dataset comprising four electronic products was utilized as a reference for conducting a statistical analysis. Through the examination of the outcomes utilizing the discount cumulative gain metric, it becomes apparent that the combination of the TOPSIS approach alongside PSI weights facilitates the identification of the suitable product aspects that effectively differentiate the one that aligns with consumer expectations.","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":"141695063","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.2024.768.782
Chahid Abdelilah, S. Ahriz, Kamal El Guemmat, Khalifa Mansouri
: In this research, we delve into the implementation and impact of Information Technology Governance (ITG) in the dynamic landscape of university settings, where information technology is rapidly evolving. The study's primary aim is to investigate the various contingency factors that play a pivotal role in the effectiveness of ITG frameworks in academic environments. Utilizing a comprehensive approach, we conducted a systematic review of 72 scholarly articles sourced from online databases, analyzing the global application of ITG in universities through a blend of qualitative and quantitative research methods. Our findings underscore an increasing focus on ITG within the realm of higher education, a trend that has gained momentum in the aftermath of COVID-19. Notably, significant contributions to this field have emerged from Asia and Europe. Central to our study is the development of a novel ITG model, which is grounded in contingency theory and derived from a detailed case study conducted in five Moroccan universities. This model underscores the necessity for higher education institutions to adopt ITG strategies that are not only flexible but also specifically tailored to meet their individual needs and circumstances.
{"title":"Building a Specialized IT Governance Strategy for Higher Education: A Strategic Model","authors":"Chahid Abdelilah, S. Ahriz, Kamal El Guemmat, Khalifa Mansouri","doi":"10.3844/jcssp.2024.768.782","DOIUrl":"https://doi.org/10.3844/jcssp.2024.768.782","url":null,"abstract":": In this research, we delve into the implementation and impact of Information Technology Governance (ITG) in the dynamic landscape of university settings, where information technology is rapidly evolving. The study's primary aim is to investigate the various contingency factors that play a pivotal role in the effectiveness of ITG frameworks in academic environments. Utilizing a comprehensive approach, we conducted a systematic review of 72 scholarly articles sourced from online databases, analyzing the global application of ITG in universities through a blend of qualitative and quantitative research methods. Our findings underscore an increasing focus on ITG within the realm of higher education, a trend that has gained momentum in the aftermath of COVID-19. Notably, significant contributions to this field have emerged from Asia and Europe. Central to our study is the development of a novel ITG model, which is grounded in contingency theory and derived from a detailed case study conducted in five Moroccan universities. This model underscores the necessity for higher education institutions to adopt ITG strategies that are not only flexible but also specifically tailored to meet their individual needs and circumstances.","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":"141703649","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.2024.783.792
Ujwala Kolte, Sachin Naik, V. Kumbhar
: The task of recognizing symbols poses a significant challenge owing to the wide variability in human handwriting. Complexity in terms of the structural representation of symbols used in physics expressions is a major challenge in the recognition process The emergence of online handwriting, fueled by the widespread adoption of handheld digital devices, particularly in educational contexts, highlights the critical importance of precise symbol recognition, especially in the teaching and learning process. In contemporary literature, there is a notable emphasis on LaTex sequencing, symbol recognition and parsing. However, deep learning continues to yield promising results in this domain. The convenience of user input provides benefits to e-learning applications. In this study, we propose three approaches for the recognition of physics symbols within physics expressions (1) A proposed Java user interface for taking input from the user, as convenience of user input provides benefits to e-learning applications. (2) Contour-based bounding box segmentation algorithm, which deals with broken symbols within physics expressions. (3) For recognition, we propose a Convolution Neural Network-K-Nearest Neighbor (CNN-KNN) recognition model, as CNN plays an important role in extracting features, which are further provided as input to the K-NN classifier using the dropout method. Combining these three approaches into a symbol recognition model provides state-of-arts results. Handwritten physics symbols were collected from 20 different writers and each writer has written 5 types of physics expressions under different categories like electric flux, Maxwell’s equations, inductance and pointing vector and moment of Interia. There were 25 classes identified from the 780 samples collected from the users. The recognition rate is identified using (1) Using CNN model, which shows an accuracy of 91.48 and (2) Using the proposed hybrid CNN-KNN model the accuracy reported is 98.06.
{"title":"A CNN-KNN Based Recognition of Online Handwritten Symbols within Physics Expressions Using Contour-Based Bounding Box (CBBS) Segmentation Technique","authors":"Ujwala Kolte, Sachin Naik, V. Kumbhar","doi":"10.3844/jcssp.2024.783.792","DOIUrl":"https://doi.org/10.3844/jcssp.2024.783.792","url":null,"abstract":": The task of recognizing symbols poses a significant challenge owing to the wide variability in human handwriting. Complexity in terms of the structural representation of symbols used in physics expressions is a major challenge in the recognition process The emergence of online handwriting, fueled by the widespread adoption of handheld digital devices, particularly in educational contexts, highlights the critical importance of precise symbol recognition, especially in the teaching and learning process. In contemporary literature, there is a notable emphasis on LaTex sequencing, symbol recognition and parsing. However, deep learning continues to yield promising results in this domain. The convenience of user input provides benefits to e-learning applications. In this study, we propose three approaches for the recognition of physics symbols within physics expressions (1) A proposed Java user interface for taking input from the user, as convenience of user input provides benefits to e-learning applications. (2) Contour-based bounding box segmentation algorithm, which deals with broken symbols within physics expressions. (3) For recognition, we propose a Convolution Neural Network-K-Nearest Neighbor (CNN-KNN) recognition model, as CNN plays an important role in extracting features, which are further provided as input to the K-NN classifier using the dropout method. Combining these three approaches into a symbol recognition model provides state-of-arts results. Handwritten physics symbols were collected from 20 different writers and each writer has written 5 types of physics expressions under different categories like electric flux, Maxwell’s equations, inductance and pointing vector and moment of Interia. There were 25 classes identified from the 780 samples collected from the users. The recognition rate is identified using (1) Using CNN model, which shows an accuracy of 91.48 and (2) Using the proposed hybrid CNN-KNN model the accuracy reported is 98.06.","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":"141692430","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.2024.700.707
Abdelfattah Abassi, Brahim Bakkas, Moustapha El Jai, Ahmed Arid, Hussain Benazza
: In this study, we present a Multi-Split Cross-Strategy (MSC-Strategy) designed to leverage synthetic tabular data generated by a Conditional Generative Adversarial Network (CGAN). Our study aims to investigate the potential of synthetic data in comparison to real-world data for improving machine learning predictive results. Firstly, we develop a CGAN architecture tailored to generate synthetic tabular data, trained on a comprehensive real-world dataset. Secondly, we validate the synthetic data generated by the CGAN to ensure its statistical fidelity and resemblance to the distribution of real data. Finally, we selectively leverage a subset of the generated data and apply our strategy to create a new combined training set comprising the training set of real data and the chosen subset of generated data. To validate our approach, we employ six diverse regression models: Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), XGB Regressor (XGB), and Support Vector Regressor (SVR). Each model is trained and tested using a training set of real data, generated data, combined data (training set of real data and generated data), and data formed by our MSC strategy. Our findings indicate that the training set formed by our MSC strategy demonstrates remarkable predictive performance compared to real-world data and generated data, highlighting its ability to enhance the prediction of machine learning models using only a subset of generated data.
{"title":"A Multi-Split Cross-Strategy for Enhancing Machine Learning Algorithms Prediction Results with Data Generated by Conditional Generative Adversarial Network","authors":"Abdelfattah Abassi, Brahim Bakkas, Moustapha El Jai, Ahmed Arid, Hussain Benazza","doi":"10.3844/jcssp.2024.700.707","DOIUrl":"https://doi.org/10.3844/jcssp.2024.700.707","url":null,"abstract":": In this study, we present a Multi-Split Cross-Strategy (MSC-Strategy) designed to leverage synthetic tabular data generated by a Conditional Generative Adversarial Network (CGAN). Our study aims to investigate the potential of synthetic data in comparison to real-world data for improving machine learning predictive results. Firstly, we develop a CGAN architecture tailored to generate synthetic tabular data, trained on a comprehensive real-world dataset. Secondly, we validate the synthetic data generated by the CGAN to ensure its statistical fidelity and resemblance to the distribution of real data. Finally, we selectively leverage a subset of the generated data and apply our strategy to create a new combined training set comprising the training set of real data and the chosen subset of generated data. To validate our approach, we employ six diverse regression models: Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), XGB Regressor (XGB), and Support Vector Regressor (SVR). Each model is trained and tested using a training set of real data, generated data, combined data (training set of real data and generated data), and data formed by our MSC strategy. Our findings indicate that the training set formed by our MSC strategy demonstrates remarkable predictive performance compared to real-world data and generated data, highlighting its ability to enhance the prediction of machine learning models using only a subset of generated data.","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":"141691020","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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