Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096823
F. Alhaidari, Sarah Alwarthan, Abrar Alamoudi
Due to the huge number of information on the internet, users use search engines to fetch the relevant pages, which include the information that meet users' needs. Search engines encountered some challenges in the process of retrieving pages matching user queries. To improve search results and how the user navigates the results of pages, search engines applied ranking method on the obtained search results. In this paper, we discussed the main Page Ranking algorithms including PageRank, Weighted Page Rank and Hyperlink- Induced Topic Search algorithms. we presented a comparative study of the latest improvements on the page ranking algorithms focusing on the algorithms that are related to user preference and user behavior. The main contribution of this paper is the proposal of an algorithm called User Preference Based Weighted Page Ranking Algorithm (UPWPR) which is an enhancement for existing ranking algorithms. UPWPR algorithm uses web content mining and web usage mining in order to rank the search results based on user preferences. A numerical case study was used to validate and compare UPWPR proposed algorithm. Results showed better ranking output based on different parameters such as the Content Weight, the User Activities Time, Page Reading Time, and the number of visits.
{"title":"User Preference Based Weighted Page Ranking Algorithm","authors":"F. Alhaidari, Sarah Alwarthan, Abrar Alamoudi","doi":"10.1109/ICCAIS48893.2020.9096823","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096823","url":null,"abstract":"Due to the huge number of information on the internet, users use search engines to fetch the relevant pages, which include the information that meet users' needs. Search engines encountered some challenges in the process of retrieving pages matching user queries. To improve search results and how the user navigates the results of pages, search engines applied ranking method on the obtained search results. In this paper, we discussed the main Page Ranking algorithms including PageRank, Weighted Page Rank and Hyperlink- Induced Topic Search algorithms. we presented a comparative study of the latest improvements on the page ranking algorithms focusing on the algorithms that are related to user preference and user behavior. The main contribution of this paper is the proposal of an algorithm called User Preference Based Weighted Page Ranking Algorithm (UPWPR) which is an enhancement for existing ranking algorithms. UPWPR algorithm uses web content mining and web usage mining in order to rank the search results based on user preferences. A numerical case study was used to validate and compare UPWPR proposed algorithm. Results showed better ranking output based on different parameters such as the Content Weight, the User Activities Time, Page Reading Time, and the number of visits.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130834558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096747
Shrooq A. Alsenan, Isra M. Al-Turaiki, Alaaeldin M. Hafez
The recent advances in Machine Learning tools and algorithms have influenced fields including drug discovery. Nowadays, research conducted via trial- and-error experiments have been replaced by computational approaches. This growth prompted an undeniable development in synthesizing chemical data to support chemoinformatics research. One of the widely used tools to model chemoinformatics problems is Quantitative Structure-Activity Relationships (QSAR). Previous QSAR models were dealing with small datasets and limited number of features. Current QSAR datasets suffer from the problem of high dimensionality, where the number of features exceeds the number of records. Over the years, the curse of high dimensionality posed a major shortcoming in QSAR classification models. Linear Principle Component Analysis is a popular feature extraction method used to reduce the high dimensioanlity of QSAR datasets. However, QSAR datasets are highly complex and require deep understanding of features representation. Autoencoder is a type of neural networks that is not fully explored in QSAR modeling for dimensionality reduction purposes. In this research, we investigate the impact of autoencoder on a high dimensional QSAR dataset. The autoencoder performance is compared with PCA on the over all accuracy measure. Our preliminary analysis demonstrated that the proposed technique outperforms PCA.
{"title":"Autoencoder-based Dimensionality Reduction for QSAR Modeling","authors":"Shrooq A. Alsenan, Isra M. Al-Turaiki, Alaaeldin M. Hafez","doi":"10.1109/ICCAIS48893.2020.9096747","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096747","url":null,"abstract":"The recent advances in Machine Learning tools and algorithms have influenced fields including drug discovery. Nowadays, research conducted via trial- and-error experiments have been replaced by computational approaches. This growth prompted an undeniable development in synthesizing chemical data to support chemoinformatics research. One of the widely used tools to model chemoinformatics problems is Quantitative Structure-Activity Relationships (QSAR). Previous QSAR models were dealing with small datasets and limited number of features. Current QSAR datasets suffer from the problem of high dimensionality, where the number of features exceeds the number of records. Over the years, the curse of high dimensionality posed a major shortcoming in QSAR classification models. Linear Principle Component Analysis is a popular feature extraction method used to reduce the high dimensioanlity of QSAR datasets. However, QSAR datasets are highly complex and require deep understanding of features representation. Autoencoder is a type of neural networks that is not fully explored in QSAR modeling for dimensionality reduction purposes. In this research, we investigate the impact of autoencoder on a high dimensional QSAR dataset. The autoencoder performance is compared with PCA on the over all accuracy measure. Our preliminary analysis demonstrated that the proposed technique outperforms PCA.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129333713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096718
A. Gazdar, M. Kefi
A little attention has been made to Recommender Systems (RS) for TV services broadcast through satellites due to the one-way data flow nature of the linear satellite TV. Nevertheless, the emergence of new Internet enabled TV Set-Top-Box (STB) with an embedded open source operating system has partially overcame this shortage and has encouraged implementing new kinds of applications for those STBs. In this context, we discuss in this paper the feasability of a Recommender System for the linear satellite based TV which suggests a dynamic list of services that may interest the TV viewer based on his profile (age, gender, etc) with regards to the ongoing audience rate of users having the same profile as him.
{"title":"A Recommender System for Linear Satellite TV: Is It Possible?","authors":"A. Gazdar, M. Kefi","doi":"10.1109/ICCAIS48893.2020.9096718","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096718","url":null,"abstract":"A little attention has been made to Recommender Systems (RS) for TV services broadcast through satellites due to the one-way data flow nature of the linear satellite TV. Nevertheless, the emergence of new Internet enabled TV Set-Top-Box (STB) with an embedded open source operating system has partially overcame this shortage and has encouraged implementing new kinds of applications for those STBs. In this context, we discuss in this paper the feasability of a Recommender System for the linear satellite based TV which suggests a dynamic list of services that may interest the TV viewer based on his profile (age, gender, etc) with regards to the ongoing audience rate of users having the same profile as him.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114342811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096841
Alejandra De Luna Pámanes, Jorge Antonio Ayala Urbina, Francisco J. Cantú Ortiz, Héctor Gibrán Ceballos Cancino
Data analytics has opened the possibility of exploring great amounts of information and finding patterns which helps us make predictions or give an explanation to certain behaviours and phenomena. Research analytics helps us uncover trends and relationships in different academic fields, and implement metrics that assess the quality of researchers and educational institutions. In this work we take the World University Rankings by Times Higher Education and their indicators from 2011 to 2019 to validate their evaluation model, assess the predicting potential of their rankings and uncover potential relationships between the ranking’s indicators. We found out a good prediction model and that some of the indicators carry relationships worth exploring and explaining.
{"title":"The World University Rankings Model Validation and a Top 50 Universities Predictive Model","authors":"Alejandra De Luna Pámanes, Jorge Antonio Ayala Urbina, Francisco J. Cantú Ortiz, Héctor Gibrán Ceballos Cancino","doi":"10.1109/ICCAIS48893.2020.9096841","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096841","url":null,"abstract":"Data analytics has opened the possibility of exploring great amounts of information and finding patterns which helps us make predictions or give an explanation to certain behaviours and phenomena. Research analytics helps us uncover trends and relationships in different academic fields, and implement metrics that assess the quality of researchers and educational institutions. In this work we take the World University Rankings by Times Higher Education and their indicators from 2011 to 2019 to validate their evaluation model, assess the predicting potential of their rankings and uncover potential relationships between the ranking’s indicators. We found out a good prediction model and that some of the indicators carry relationships worth exploring and explaining.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122429988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096684
Albandari L. Alanazi, Almetwally M. Mostafa, A. Alnuaim
Nowadays, most software systems manage large amount of data. The clients depend heavily on these data and expect them to be available at all times. To use and manage these data in an efficient way and to ensure availability, the data replication technique is applied. So far, three basic models for replication exist with their variants. This paper reviews these three basic models of replication techniques and their variants with regard to how the load is distributed among replicas, what the total throughput is for these set of replicas, and which type of consistency models is supported by them.
{"title":"A Systematic Literature Review of Recent Trends in Replication Techniques","authors":"Albandari L. Alanazi, Almetwally M. Mostafa, A. Alnuaim","doi":"10.1109/ICCAIS48893.2020.9096684","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096684","url":null,"abstract":"Nowadays, most software systems manage large amount of data. The clients depend heavily on these data and expect them to be available at all times. To use and manage these data in an efficient way and to ensure availability, the data replication technique is applied. So far, three basic models for replication exist with their variants. This paper reviews these three basic models of replication techniques and their variants with regard to how the load is distributed among replicas, what the total throughput is for these set of replicas, and which type of consistency models is supported by them.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122647837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096676
H. Mrabet
In this paper, an investigation of system performance is addressed for new emergent air interface waveforms for 5G communication systems and beyond applications. Therefore, filter-bank multi-carrier (FBMC) and universal filtered multi-carrier (UBMC) are compared to the classical OFDM waveform in the context of the cloud radio access network (CRAN) architecture. In addition, additive white Gaussian noise (AWGN) and chromatic dispersion due to single-mode-fiber (SMF) are the main system limitations that are considered in the system performance study. Likewise, the system performance is carried out through power spectral density and constellation diagrams for both pulse amplitude modulation (PAM) and quadrature amplitude modulation (QAM) high order format. Finally, it is demonstrated that FBMC-PAM waveform outperforms both UBMC-QAM and OFDM- QAM in terms of power spectral density and robustness against AWGN and chromatic dispersion.
{"title":"Performance Investigation of New Waveforms in CRAN Architecture for 5G Communication Systems","authors":"H. Mrabet","doi":"10.1109/ICCAIS48893.2020.9096676","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096676","url":null,"abstract":"In this paper, an investigation of system performance is addressed for new emergent air interface waveforms for 5G communication systems and beyond applications. Therefore, filter-bank multi-carrier (FBMC) and universal filtered multi-carrier (UBMC) are compared to the classical OFDM waveform in the context of the cloud radio access network (CRAN) architecture. In addition, additive white Gaussian noise (AWGN) and chromatic dispersion due to single-mode-fiber (SMF) are the main system limitations that are considered in the system performance study. Likewise, the system performance is carried out through power spectral density and constellation diagrams for both pulse amplitude modulation (PAM) and quadrature amplitude modulation (QAM) high order format. Finally, it is demonstrated that FBMC-PAM waveform outperforms both UBMC-QAM and OFDM- QAM in terms of power spectral density and robustness against AWGN and chromatic dispersion.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115875690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096731
Freeh Alenezi, C. Tsokos
Information security is everyone’s concern. Computer systems are used to store sensitive data. Any weakness in their reliability and security makes them vulnerable. The Common Vulnerability Scoring System (CVSS) is a commonly used scoring system, which helps in knowing the severity of a software vulnerability. In this research, we show the effectiveness of common machine learning algorithms in predicting the computer operating systems security using the published vulnerability data in Common Vulnerabilities and Exposures and National Vulnerability Database repositories. The Random Forest algorithm has the best performance, compared to other algorithms, in predicting the computer operating system vulnerability severity levels based on precision, recall, and F-measure evaluation metrics. In addition, a predictive model was developed to predict whether a newly discovered computer operating system vulnerability would allow attackers to cause denial of service to the subject system.
{"title":"Machine Learning Approach to Predict Computer Operating Systems Vulnerabilities","authors":"Freeh Alenezi, C. Tsokos","doi":"10.1109/ICCAIS48893.2020.9096731","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096731","url":null,"abstract":"Information security is everyone’s concern. Computer systems are used to store sensitive data. Any weakness in their reliability and security makes them vulnerable. The Common Vulnerability Scoring System (CVSS) is a commonly used scoring system, which helps in knowing the severity of a software vulnerability. In this research, we show the effectiveness of common machine learning algorithms in predicting the computer operating systems security using the published vulnerability data in Common Vulnerabilities and Exposures and National Vulnerability Database repositories. The Random Forest algorithm has the best performance, compared to other algorithms, in predicting the computer operating system vulnerability severity levels based on precision, recall, and F-measure evaluation metrics. In addition, a predictive model was developed to predict whether a newly discovered computer operating system vulnerability would allow attackers to cause denial of service to the subject system.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"514 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116087254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096732
Abeer A. Alzahrani, J. Feki
The number of organizations adopting the Data Warehouse (DW) technology along with data analytics in order to improve the effectiveness of their decision–making processes is permanently increasing. Despite the efforts invested, the DW design remains a great challenge research domain. More accurately, the design quality of the DW depends on several aspects; among them, the requirement-gathering phase is a critical and complex task. In this context, we propose a Natural language (NL) NL-template based design approach, which is twofold; firstly, it facilitates the involvement of decision-makers in the early step of the DW design; indeed, using NL is a good and natural means to encourage the decision-makers to express their requirements as query-like English sentences. Secondly, our approach aims to generate a DW multidimensional schema from a set of gathered requirements (as OLAP: On-Line-Analytical-Processing queries, written according to the NL suggested templates). This approach articulates around: (i) two NL-templates for specifying multidimensional components, and (ii) a set of five heuristic rules for extracting the multidimensional concepts from requirements. Really, we are developing a software prototype that accepts the decision-makers' requirements then automatically identifies the multidimensional components of the DW model.
{"title":"Toward a Natural Language-Based Approach for the Specification of Decisional-Users Requirements","authors":"Abeer A. Alzahrani, J. Feki","doi":"10.1109/ICCAIS48893.2020.9096732","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096732","url":null,"abstract":"The number of organizations adopting the Data Warehouse (DW) technology along with data analytics in order to improve the effectiveness of their decision–making processes is permanently increasing. Despite the efforts invested, the DW design remains a great challenge research domain. More accurately, the design quality of the DW depends on several aspects; among them, the requirement-gathering phase is a critical and complex task. In this context, we propose a Natural language (NL) NL-template based design approach, which is twofold; firstly, it facilitates the involvement of decision-makers in the early step of the DW design; indeed, using NL is a good and natural means to encourage the decision-makers to express their requirements as query-like English sentences. Secondly, our approach aims to generate a DW multidimensional schema from a set of gathered requirements (as OLAP: On-Line-Analytical-Processing queries, written according to the NL suggested templates). This approach articulates around: (i) two NL-templates for specifying multidimensional components, and (ii) a set of five heuristic rules for extracting the multidimensional concepts from requirements. Really, we are developing a software prototype that accepts the decision-makers' requirements then automatically identifies the multidimensional components of the DW model.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116840752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096736
Noura Alosaimi, N. M. Elzein, Afaf Mohammed Tukka
In line with Vision 2030 KSA, to enable women economically by promoting and marketing family-made products at the lowest-cost. Moreover, Saudi productive families' projects considered as one of the main sources promote employment opportunities especially for low-income families. our project promotes self-employment by manufacturing products variety. The research aims to create a suitable-safe working environment for low-income families capable of producing goods. Furthermore, it to improve product quality through customer interaction. This search was carried out by building an Android application written in java, android studio and Firebase. In fact, we aspire to manage productive family projects with a one-application which ensures that risks are reduced while saving consumer effort with multiple options for the same product. As a result of the project's implementation low-income families market their products professionally and effectively. Also, the application allows consumers to evaluate items after receiving the service to be used for optimizations process.
{"title":"Mobile Application for Productive Families Business","authors":"Noura Alosaimi, N. M. Elzein, Afaf Mohammed Tukka","doi":"10.1109/ICCAIS48893.2020.9096736","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096736","url":null,"abstract":"In line with Vision 2030 KSA, to enable women economically by promoting and marketing family-made products at the lowest-cost. Moreover, Saudi productive families' projects considered as one of the main sources promote employment opportunities especially for low-income families. our project promotes self-employment by manufacturing products variety. The research aims to create a suitable-safe working environment for low-income families capable of producing goods. Furthermore, it to improve product quality through customer interaction. This search was carried out by building an Android application written in java, android studio and Firebase. In fact, we aspire to manage productive family projects with a one-application which ensures that risks are reduced while saving consumer effort with multiple options for the same product. As a result of the project's implementation low-income families market their products professionally and effectively. Also, the application allows consumers to evaluate items after receiving the service to be used for optimizations process.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115492665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096751
Abdulmajeed Aljuhani, Abdulaziz Alhubaishy
According to the literature, traditional desktop-based application development processes profit from the adoption of Multi-Criteria Decision-Making (MCDM) approaches under specific conditions. However, agile methodology often shuns this method, instead encouraging a cooperative decision support structure. Within the domain of mobile-based applications, an optimized structure is constitutionally introduced, thus, the presentation of a powerful decision-making approach in agile mobile application development should prompt a more significant level of contentment with mobile applications built in this way. The present paper introduces an approach to adopting the MCDM method for the agile mobile application development process. The paper consists of a framework for investigating insertion points for decision-making method - namely, the Best- Worst Method (BWM) - and a practical example of the application of the Best-Worst decision support method in the development process of agile mobile applications.
{"title":"Incorporating a Decision Support Approach within the Agile Mobile Application Development Process","authors":"Abdulmajeed Aljuhani, Abdulaziz Alhubaishy","doi":"10.1109/ICCAIS48893.2020.9096751","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096751","url":null,"abstract":"According to the literature, traditional desktop-based application development processes profit from the adoption of Multi-Criteria Decision-Making (MCDM) approaches under specific conditions. However, agile methodology often shuns this method, instead encouraging a cooperative decision support structure. Within the domain of mobile-based applications, an optimized structure is constitutionally introduced, thus, the presentation of a powerful decision-making approach in agile mobile application development should prompt a more significant level of contentment with mobile applications built in this way. The present paper introduces an approach to adopting the MCDM method for the agile mobile application development process. The paper consists of a framework for investigating insertion points for decision-making method - namely, the Best- Worst Method (BWM) - and a practical example of the application of the Best-Worst decision support method in the development process of agile mobile applications.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125122732","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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