Cloud computing is rapidly expanding because it allows users to save the development and implementation time on their work. It also reduces the maintenance and operational costs of the used systems. Furthermore, it enables the elastic use of any resource rather than estimating workload, which may be inaccurate, as database systems can benefit from such a trend. In this paper, we propose an algorithm that allocates the materialized view over cloudbased replica sets to enhance the database system's performance in stock market using a Peerto-Peer architecture. The results show that the proposed model (MVCRS) improves the query processing time and network transfer cost by distributing the materialized views over cloudbased replica sets. Also, it has a significant effect on decision-making and achieving economic returns.
{"title":"Enhancing query processing on stock market cloud-based database","authors":"Hagger Essam, Ahmed Gamal, Essam M. Shaaban","doi":"10.54623/fue.fcij.7.2.2","DOIUrl":"https://doi.org/10.54623/fue.fcij.7.2.2","url":null,"abstract":"Cloud computing is rapidly expanding because it allows users to save the development and implementation time on their work. It also reduces the maintenance and operational costs of the used systems. Furthermore, it enables the elastic use of any resource rather than estimating workload, which may be inaccurate, as database systems can benefit from such a trend. In this paper, we propose an algorithm that allocates the materialized view over cloudbased replica sets to enhance the database system's performance in stock market using a Peerto-Peer architecture. The results show that the proposed model (MVCRS) improves the query processing time and network transfer cost by distributing the materialized views over cloudbased replica sets. Also, it has a significant effect on decision-making and achieving economic returns.","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74813163","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 main objective of any educational institution is to provide its students with the best educational knowledge and experience so, they can be employed to meet the labor market demands. Due to the rapidly changing in the technology industry and the expanding need for information technology (IT) professionals. The mismatch between IT graduates and the needs of the labor market leads to their inability to employ and job misplacement. Therefore, this paper aims to identify the most significant factors affecting IT graduates' employability and their ability to compete in the local, regional, and international labor markets through a detailed literature review and by conducting two surveys, one for IT graduates and the other for IT employers. Then, data were collected and analyzed by Statistical Package for Social Sciences (SPSS) 28.0 to build our proposed framework which will integrate all factors and parties involved to enhance graduates’ employability to match the labor market demands. The proposed model will assist all parties in improving their plans for producing graduates who are skilled, knowledgeable, and meet the labor market demands.
{"title":"A Framework to Enhance the International Competitive Advantage of Information Technology Graduates","authors":"G. Elsharkawy, Y. Helmy, Engy Yehia","doi":"10.54623/fue.fcij.7.2.6","DOIUrl":"https://doi.org/10.54623/fue.fcij.7.2.6","url":null,"abstract":"The main objective of any educational institution is to provide its students with the best educational knowledge and experience so, they can be employed to meet the labor market demands. Due to the rapidly changing in the technology industry and the expanding need for information technology (IT) professionals. The mismatch between IT graduates and the needs of the labor market leads to their inability to employ and job misplacement. Therefore, this paper aims to identify the most significant factors affecting IT graduates' employability and their ability to compete in the local, regional, and international labor markets through a detailed literature review and by conducting two surveys, one for IT graduates and the other for IT employers. Then, data were collected and analyzed by Statistical Package for Social Sciences (SPSS) 28.0 to build our proposed framework which will integrate all factors and parties involved to enhance graduates’ employability to match the labor market demands. The proposed model will assist all parties in improving their plans for producing graduates who are skilled, knowledgeable, and meet the labor market demands.","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84879938","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}
M. Abdullahi, A. Adamu, Ibrahm Hayatu, Abdulrazaq Abdulrahim
Feature Selection (FS) is an efficient technique use to get rid of irrelevant, redundant and noisy attributes in high dimensional datasets while increasing the efficacy of machine learning classification. The CSA is a modest and efficient metaheuristic algorithm which has been used to overcome several FS issues. The flight length (fl) parameter in CSA governs crows' search ability. In CSA, fl is set to a fixed value. As a result, the CSA is plagued by the problem of being hoodwinked in local minimum. This article suggests a remedy to this issue by bringing five new concepts of time dependent fl in CSA for feature selection methods including linearly decreasing flight length, sigmoid decreasing flight length, chaotic decreasing flight length, simulated annealing decreasing flight length, and logarithm decreasing flight length. The proposed approaches' performance is assessed using 13 standard UCI datasets. The simulation result portrays that the suggested feature selection approaches overtake the original CSA, with the chaotic-CSA approach beating the original CSA and the other four proposed approaches for the FS task.
{"title":"Crow search algorithm with time varying flight length Strategies for feature selection","authors":"M. Abdullahi, A. Adamu, Ibrahm Hayatu, Abdulrazaq Abdulrahim","doi":"10.54623/fue.fcij.7.2.1","DOIUrl":"https://doi.org/10.54623/fue.fcij.7.2.1","url":null,"abstract":"Feature Selection (FS) is an efficient technique use to get rid of irrelevant, redundant and noisy attributes in high dimensional datasets while increasing the efficacy of machine learning classification. The CSA is a modest and efficient metaheuristic algorithm which has been used to overcome several FS issues. The flight length (fl) parameter in CSA governs crows' search ability. In CSA, fl is set to a fixed value. As a result, the CSA is plagued by the problem of being hoodwinked in local minimum. This article suggests a remedy to this issue by bringing five new concepts of time dependent fl in CSA for feature selection methods including linearly decreasing flight length, sigmoid decreasing flight length, chaotic decreasing flight length, simulated annealing decreasing flight length, and logarithm decreasing flight length. The proposed approaches' performance is assessed using 13 standard UCI datasets. The simulation result portrays that the suggested feature selection approaches overtake the original CSA, with the chaotic-CSA approach beating the original CSA and the other four proposed approaches for the FS task.","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77176363","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}
One of the most prosperous domains that Data mining accomplished a great progress is Food Security and safety. Some of Data mining techniques studies applied several machine learning algorithms to enhance and traceability of food supply chain safety procedures and some of them applying machine learning methodologies with several feature selection methods for detecting and predicting the most significant key performance indicators affect food safety. In this research we proposed an adaptive data mining model applying nine machine learning algorithms (Naive Bayes, Bayes Net Key -Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), J48, Hoeffding tree, Logistic Model Tree) with feature selection wrapper methods (forward and backward techniques) for detecting food deterioration’s key performance indicators. Therefore, results before and after applying wrapper feature selection methods have been compared, analyzed, and interpreted. In conclusion the proposed model applied effectively and successfully detected the most significant indicators for meat safety and quality with the aim of helping farmers and suppliers for being sure of delivering safety meat for consumer and diminishing the cost of monitoring meat safety.
{"title":"Proposed framework for applying data mining techniques to detect key performance indicators for food deterioration","authors":"Fatma Abogabal, Shimaa M. Ouf, Amira M. Idrees","doi":"10.54623/fue.fcij.7.2.4","DOIUrl":"https://doi.org/10.54623/fue.fcij.7.2.4","url":null,"abstract":"One of the most prosperous domains that Data mining accomplished a great progress is Food Security and safety. Some of Data mining techniques studies applied several machine learning algorithms to enhance and traceability of food supply chain safety procedures and some of them applying machine learning methodologies with several feature selection methods for detecting and predicting the most significant key performance indicators affect food safety. In this research we proposed an adaptive data mining model applying nine machine learning algorithms (Naive Bayes, Bayes Net Key -Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), J48, Hoeffding tree, Logistic Model Tree) with feature selection wrapper methods (forward and backward techniques) for detecting food deterioration’s key performance indicators. Therefore, results before and after applying wrapper feature selection methods have been compared, analyzed, and interpreted. In conclusion the proposed model applied effectively and successfully detected the most significant indicators for meat safety and quality with the aim of helping farmers and suppliers for being sure of delivering safety meat for consumer and diminishing the cost of monitoring meat safety.","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89207855","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}
E-CRM strives to enhance customer service, build relationships with customers, and keep key clients. E-CRM deals with technology, people, and processes and with the goal of fostering customer loyalty. This paper aims to investigate the relationship between E-CRM, service quality, customer satisfaction, trust, and loyalty in banking industry. In order to gather sufficient reviews, a literature review was carried out utilizing a number of corresponding publications that were indexed in reliable databases. A model that highlights the relation between E-CRM and customer satisfaction, service quality, trust, and loyalty is also shown in this study. The supervisors of administrative organizations can utilize this research's insights into E-CRM to build client loyalty and increase the revenue and profitability of their firm
{"title":"Relationship between E-CRM, Service Quality, Customer Satisfaction, Trust, and Loyalty in banking Industry","authors":"Shymaa Mohamed Mohamed, Engy Yehia, M. Marie","doi":"10.54623/fue.fcij.7.2.5","DOIUrl":"https://doi.org/10.54623/fue.fcij.7.2.5","url":null,"abstract":"E-CRM strives to enhance customer service, build relationships with customers, and keep key clients. E-CRM deals with technology, people, and processes and with the goal of fostering customer loyalty. This paper aims to investigate the relationship between E-CRM, service quality, customer satisfaction, trust, and loyalty in banking industry. In order to gather sufficient reviews, a literature review was carried out utilizing a number of corresponding publications that were indexed in reliable databases. A model that highlights the relation between E-CRM and customer satisfaction, service quality, trust, and loyalty is also shown in this study. The supervisors of administrative organizations can utilize this research's insights into E-CRM to build client loyalty and increase the revenue and profitability of their firm","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"86 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74200946","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}
Agile methodologies have become one of the most applied methods in the software development industry. However, agile methodologies face some challenges such as less documentation and wasting time considering changes. This review presents how the previous studies attempted to cover issues of agile methodologies and the modifications in the performance of agile methodologies. The paper also highlights unresolved issues to get the attention of developers, researchers, and software practitioners
{"title":"A Literature Review on Agile Methodologies Quality, eXtreme Programming and SCRUM","authors":"Naglaa A. Eldanasory, Engy Yehia, Amira M. Idrees","doi":"10.54623/fue.fcij.7.2.3","DOIUrl":"https://doi.org/10.54623/fue.fcij.7.2.3","url":null,"abstract":"Agile methodologies have become one of the most applied methods in the software development industry. However, agile methodologies face some challenges such as less documentation and wasting time considering changes. This review presents how the previous studies attempted to cover issues of agile methodologies and the modifications in the performance of agile methodologies. The paper also highlights unresolved issues to get the attention of developers, researchers, and software practitioners","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86020858","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 is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today's banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary losses, not just for financial institutions but also for individuals. as technology and usage patterns evolve, making credit card fraud detection a particularly difficult task. Traditional statistical approaches for identifying credit card fraud take much more time, and the result accuracy cannot be guaranteed. Machine learning algorithms have been widely employed in the detection of credit card fraud. The main goal of this review intends to present the previous research studies accomplished on Credit Card Fraud Detection (CCFD), and how they dealt with this problem by using different machine learning techniques.
{"title":"Credit Card Fraud Detection Using Machine Learning Techniques","authors":"Nermine Samy, Shimaa mohamed mohamed","doi":"10.54623/fue.fcij.7.1.2","DOIUrl":"https://doi.org/10.54623/fue.fcij.7.1.2","url":null,"abstract":"This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today's banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary losses, not just for financial institutions but also for individuals. as technology and usage patterns evolve, making credit card fraud detection a particularly difficult task. Traditional statistical approaches for identifying credit card fraud take much more time, and the result accuracy cannot be guaranteed. Machine learning algorithms have been widely employed in the detection of credit card fraud. The main goal of this review intends to present the previous research studies accomplished on Credit Card Fraud Detection (CCFD), and how they dealt with this problem by using different machine learning techniques.","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83778432","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 e-learning and assessment systems became a dominant technology nowadays and distribute across the globe. With severe consequences of COVID19-like crises, the key importance of such technology appeared in which courses, quizzes and questionnaires have to be conducted remotely. Moreover, the use of Learning Management Systems (LMSs), such as blackboard, eCollege, and Moodle, has been sanctioned in all respects of education. This paper presents an open-source interactive Quiz Maker and Management System (QMMS) that suits the research, education (under-grad, grad, or post-grad), and industrial organizations to perform distant quizzes, training and questionnaires with an integration facility with other LMS tools such as Moodle. The proposed system supports three basic levels: 1) administration, 2) instructors, and 3) learners at the micro-level teaching. The proposed system is adopted using .Net framework integrated with SQL-Server database engine that compromise between performance, security and stability. The proposed QMMS is described through different phases of Software Development Life Cycle (SDLC) including detailed analysis, design, implementation, testing, verification, and maintenance in order to exploit the importance of the analysis and design of LMS from the software engineering point of view. A comparative analysis, among the proposed system and a recent list of challenging ones, is presented in different aspects that shows the effectiveness, reliability and validity of proposed tool. Moreover, the proposed QMMS shows an enhancement ratio of up to 42.19% in response time perspective as compared to Moodle system in the case of massive concurrent transactions.
{"title":"The Development Of QMMS: A Case Study for Reliable Online Quiz Maker and Management System","authors":"Mohamed Abdelmoneim Elshafey, Tarek Said Ghoniemy","doi":"10.54623/fue.fcij.6.2.3","DOIUrl":"https://doi.org/10.54623/fue.fcij.6.2.3","url":null,"abstract":"The e-learning and assessment systems became a dominant technology nowadays and distribute across the globe. With severe consequences of COVID19-like crises, the key importance of such technology appeared in which courses, quizzes and questionnaires have to be conducted remotely. Moreover, the use of Learning Management Systems (LMSs), such as blackboard, eCollege, and Moodle, has been sanctioned in all respects of education. This paper presents an open-source interactive Quiz Maker and Management System (QMMS) that suits the research, education (under-grad, grad, or post-grad), and industrial organizations to perform distant quizzes, training and questionnaires with an integration facility with other LMS tools such as Moodle. The proposed system supports three basic levels: 1) administration, 2) instructors, and 3) learners at the micro-level teaching. The proposed system is adopted using .Net framework integrated with SQL-Server database engine that compromise between performance, security and stability. The proposed QMMS is described through different phases of Software Development Life Cycle (SDLC) including detailed analysis, design, implementation, testing, verification, and maintenance in order to exploit the importance of the analysis and design of LMS from the software engineering point of view. A comparative analysis, among the proposed system and a recent list of challenging ones, is presented in different aspects that shows the effectiveness, reliability and validity of proposed tool. Moreover, the proposed QMMS shows an enhancement ratio of up to 42.19% in response time perspective as compared to Moodle system in the case of massive concurrent transactions.","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76633184","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}
Alaa Salah ElDin Ghoneim, Salah ElDin Ismail Salah ElDin, Mohamed Sameh Hassanein
Academic advising plays a vital role in achieving higher educational institution’s purposes. Academic advising is a process where an academic advisor decides to select a certain number of courses for a student to register in each semester to fulfil the graduation requirements. This paper presents an Academic Advising Decision Support System (AADSS) to enhance advisors make better decisions regarding their students’ cases. AADSS framework divided into four layers, data preparation layer, data layer, processing layer and decision layer. The testing results from those participating academic advisors and students considered are that AADSS beneficial in enhancing their decision for selecting courses.
{"title":"Enhancing Academic Advising In Credit Hours System Using Dss","authors":"Alaa Salah ElDin Ghoneim, Salah ElDin Ismail Salah ElDin, Mohamed Sameh Hassanein","doi":"10.54623/fue.fcij.6.2.4","DOIUrl":"https://doi.org/10.54623/fue.fcij.6.2.4","url":null,"abstract":"Academic advising plays a vital role in achieving higher educational institution’s purposes. Academic advising is a process where an academic advisor decides to select a certain number of courses for a student to register in each semester to fulfil the graduation requirements. This paper presents an Academic Advising Decision Support System (AADSS) to enhance advisors make better decisions regarding their students’ cases. AADSS framework divided into four layers, data preparation layer, data layer, processing layer and decision layer. The testing results from those participating academic advisors and students considered are that AADSS beneficial in enhancing their decision for selecting courses.","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78326784","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}
Howida A. Shedeed, H. M. Ebied, Berry-Maryam Al, Ahmed El
Steganography is one of the most important tools in the data security field as there is a huge amount of data transferred each moment over the internet. Hiding secret messages in an image has been widely used because the images are mostly used in social media applications. The proposed algorithm is a simple algorithm for hiding an image in another image. The proposed technique uses QR factorization to conceal the secret image. The technique successfully hid a gray and color image in another one and the performance of the algorithm was measured by PSNR, SSIM and NCC. The PSNR for the cover image was in the range of 41 to 51 dB. DWT was added to increase the security of the method and this enhanced technique increased the cover PSNR to 48 t0 56 dB. The SSIM is 100% and the NCC is 1 for both implementations. Which improves that the imperceptibility of the algorithm is very high. The comparative analysis showed that the performance of the algorithm is better than other state-of-the-art algorithms
{"title":"Image Hiding Using QR Factorization And Discrete Wavelet Transform Techniques","authors":"Howida A. Shedeed, H. M. Ebied, Berry-Maryam Al, Ahmed El","doi":"10.54623/fue.fcij.6.2.2","DOIUrl":"https://doi.org/10.54623/fue.fcij.6.2.2","url":null,"abstract":"Steganography is one of the most important tools in the data security field as there is a huge amount of data transferred each moment over the internet. Hiding secret messages in an image has been widely used because the images are mostly used in social media applications. The proposed algorithm is a simple algorithm for hiding an image in another image. The proposed technique uses QR factorization to conceal the secret image. The technique successfully hid a gray and color image in another one and the performance of the algorithm was measured by PSNR, SSIM and NCC. The PSNR for the cover image was in the range of 41 to 51 dB. DWT was added to increase the security of the method and this enhanced technique increased the cover PSNR to 48 t0 56 dB. The SSIM is 100% and the NCC is 1 for both implementations. Which improves that the imperceptibility of the algorithm is very high. The comparative analysis showed that the performance of the algorithm is better than other state-of-the-art algorithms","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87662026","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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