Pub Date : 2020-02-01DOI: 10.1109/AECT47998.2020.9194184
Sajida Akbar, U. Farooq, Ihsan Rabbi, K. Zia
Virtual worlds are unique 3D spaces, which are interactive, collaborative, coherent, persistent and social in nature. Users, an integral part of these environments are represented using digital characters, called avatars, and they are allowed to design and develop the content according to their own desires. This paper presents the virtual world presences, developed using OpenSimulator framework and blender, for the present and past of the University of Science and Technology Bannu - Pakistan. The developed spaces are explored through different navigation techniques and they are equipped with interactive maps and signboards for guidance and supporting advanced navigation technique called the teleporting. The developed environment not only imparts online education and helps youngsters to get acquainted with glorious past but also offers virtual tour of the campus to the prospective students of the University. The users were found more motivated when they explored the spaces using flying and teleporting - the navigation means not practical in the physical world. The virtual world presence, developed in this work, is compared with traditional techniques, currently in practice, for imparting education and preserving culture heritage, using a number of parameters.
{"title":"Linking the Present and Past in Virtual Worlds: A Case Study of Development and Navigation","authors":"Sajida Akbar, U. Farooq, Ihsan Rabbi, K. Zia","doi":"10.1109/AECT47998.2020.9194184","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194184","url":null,"abstract":"Virtual worlds are unique 3D spaces, which are interactive, collaborative, coherent, persistent and social in nature. Users, an integral part of these environments are represented using digital characters, called avatars, and they are allowed to design and develop the content according to their own desires. This paper presents the virtual world presences, developed using OpenSimulator framework and blender, for the present and past of the University of Science and Technology Bannu - Pakistan. The developed spaces are explored through different navigation techniques and they are equipped with interactive maps and signboards for guidance and supporting advanced navigation technique called the teleporting. The developed environment not only imparts online education and helps youngsters to get acquainted with glorious past but also offers virtual tour of the campus to the prospective students of the University. The users were found more motivated when they explored the spaces using flying and teleporting - the navigation means not practical in the physical world. The virtual world presence, developed in this work, is compared with traditional techniques, currently in practice, for imparting education and preserving culture heritage, using a number of parameters.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115696513","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194159
Dr. Abdullatif Baba
In this paper, we clarify how to recognize different levels of vigilance for vehicle drivers. In order to avoid the classical problems of crisp logic, we preferred to employ a fuzzy logicbased system that depends on two variables to make the final decision. Two iris segmentation techniques are well illustrated. A new technique for pupil position detection is also provided here with the possibility to correct the pupil detected position when dealing with some noisy cases.
{"title":"Iris segmentation techniques to recognize the behavior of a vigilant driver","authors":"Dr. Abdullatif Baba","doi":"10.1109/AECT47998.2020.9194159","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194159","url":null,"abstract":"In this paper, we clarify how to recognize different levels of vigilance for vehicle drivers. In order to avoid the classical problems of crisp logic, we preferred to employ a fuzzy logicbased system that depends on two variables to make the final decision. Two iris segmentation techniques are well illustrated. A new technique for pupil position detection is also provided here with the possibility to correct the pupil detected position when dealing with some noisy cases.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121036143","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194200
Mohd. Atir, Mark Haydoutov
This paper explores the use of machine learning for proactive invoice management through addressing the problem of predicting delinquent invoices and investigating the factors that correlate with delinquency. Unpaid or late-paid invoices lead to the writing-off of millions of dollars for large organizations globally. A key component in account receivables management is to proactively alleviate bad debts and accelerate payments, which considering the “time-value of money” has a significant impact on ultimate profitability. To achieve this dual goal, the focus is on tree-based ensemble models and use of various learning schemes on real-world invoice data from a Fortune 500 financial company made of several business units servicing several geographies. Our modeling scheme accounts for variations along several customer characteristics including agreed payment policies, type of business, and geo-locations. Our comparative results of Random Forest and LightGBM show that the LightGBM model gives better AUC and Lift across all Business Units.
{"title":"Tree-Based Bagging and Boosting Algorithms for Proactive Invoice Management","authors":"Mohd. Atir, Mark Haydoutov","doi":"10.1109/AECT47998.2020.9194200","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194200","url":null,"abstract":"This paper explores the use of machine learning for proactive invoice management through addressing the problem of predicting delinquent invoices and investigating the factors that correlate with delinquency. Unpaid or late-paid invoices lead to the writing-off of millions of dollars for large organizations globally. A key component in account receivables management is to proactively alleviate bad debts and accelerate payments, which considering the “time-value of money” has a significant impact on ultimate profitability. To achieve this dual goal, the focus is on tree-based ensemble models and use of various learning schemes on real-world invoice data from a Fortune 500 financial company made of several business units servicing several geographies. Our modeling scheme accounts for variations along several customer characteristics including agreed payment policies, type of business, and geo-locations. Our comparative results of Random Forest and LightGBM show that the LightGBM model gives better AUC and Lift across all Business Units.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121087804","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194161
Qurat-ul-ain Mastoi, A. Lakhan, F. Khan, Q. Abbasi
This paper study the aware failure task offloading in the dynamic mobile cloud environment. Task offloading is a method which allows resource-constraint mobile devices offload compute-intensive tasks of application to the precious resource cloud computing. However, the content of the wireless network (e.g., bandwidth, signal and noise) often change; therefore, communication failure usually occurs. To deal with the dynamic content and failure of the network, we devise the Dynamic Content Aware Task Offloading Algorithm (DCTOA) and Failure Aware Algorithm (FAA) schemes. DCTOA adopts dynamic changes of the network contents, and performance evaluation shows that it outperforms existing static task offloading schemes.
{"title":"Dynamic Content and Failure Aware Task Offloading in Heterogeneous Mobile Cloud Networks","authors":"Qurat-ul-ain Mastoi, A. Lakhan, F. Khan, Q. Abbasi","doi":"10.1109/AECT47998.2020.9194161","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194161","url":null,"abstract":"This paper study the aware failure task offloading in the dynamic mobile cloud environment. Task offloading is a method which allows resource-constraint mobile devices offload compute-intensive tasks of application to the precious resource cloud computing. However, the content of the wireless network (e.g., bandwidth, signal and noise) often change; therefore, communication failure usually occurs. To deal with the dynamic content and failure of the network, we devise the Dynamic Content Aware Task Offloading Algorithm (DCTOA) and Failure Aware Algorithm (FAA) schemes. DCTOA adopts dynamic changes of the network contents, and performance evaluation shows that it outperforms existing static task offloading schemes.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122665966","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194221
Shamini Raja Kumaran, M. Othman, L. M. Yusuf, Arda Yunianta
Educational business intelligence concerns the decision-making in the education sector and this article intends to analyse the student’s attributes’ contribution toward graduating within the duration. In this research, the framework identifies the best set of attributes and evaluates the performance of the model with the help of 22 input features. This article discussed the development of the business intelligence (BI) framework for the higher education that is able to explore, analyse and visualize the relevant data into information. This is to assist the top management in improving the methodologies in teaching and learning. In this case study, the framework used metaheuristic algorithm, Ant Colony Optimization (ACO) technique mainly to identify the best set of attributes, and the performance was validated using Support Vector Machine (SVM). The framework consists of four layers which are data source, data integration, analytics, and access layers. In this study, 46,658 input data were processed for the identification of postgraduate students who completed their studies within a specified period. The performance evaluation of the data achieved accuracy, sensitivity and precision of 87.44% for PhD dataset and t-test has been conducted to prove that the selected features are significant. Based on the findings, the results from the proposed educational business intelligence framework produced BI dashboard as an output from the framework is capable to act as a decision-making tool for education management and educational technology system.
{"title":"Educational Business Intelligence Framework Visualizing Significant Features using Metaheuristic Algorithm and Feature Selection","authors":"Shamini Raja Kumaran, M. Othman, L. M. Yusuf, Arda Yunianta","doi":"10.1109/AECT47998.2020.9194221","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194221","url":null,"abstract":"Educational business intelligence concerns the decision-making in the education sector and this article intends to analyse the student’s attributes’ contribution toward graduating within the duration. In this research, the framework identifies the best set of attributes and evaluates the performance of the model with the help of 22 input features. This article discussed the development of the business intelligence (BI) framework for the higher education that is able to explore, analyse and visualize the relevant data into information. This is to assist the top management in improving the methodologies in teaching and learning. In this case study, the framework used metaheuristic algorithm, Ant Colony Optimization (ACO) technique mainly to identify the best set of attributes, and the performance was validated using Support Vector Machine (SVM). The framework consists of four layers which are data source, data integration, analytics, and access layers. In this study, 46,658 input data were processed for the identification of postgraduate students who completed their studies within a specified period. The performance evaluation of the data achieved accuracy, sensitivity and precision of 87.44% for PhD dataset and t-test has been conducted to prove that the selected features are significant. Based on the findings, the results from the proposed educational business intelligence framework produced BI dashboard as an output from the framework is capable to act as a decision-making tool for education management and educational technology system.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114255383","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194157
Ali S. Sorour, A. Atkins, C. Stanier, Fawaz D. Alharbi
This paper aims to show how Business Intelligence is utilized in Higher Education Institutions for the purpose of monitoring Quality Assurance activities. This paper discusses Quality Assurance in Higher Education and investigates the challenging issues that institutions are facing. In addition, the paper discusses the role of Business Intelligence and Analytics in supporting decision making in the context of Higher Education. The paper outlines the link between Quality Assurance core elements and Business Intelligence systems. The paper outlines a proposed Business Intelligence solution for application to Higher Education in Saudi Arabia to address the main concerns for performance evaluation and monitoring in relation to Quality Assurance.
{"title":"The Role of Business Intelligence and Analytics in Higher Education Quality: A Proposed Architecture","authors":"Ali S. Sorour, A. Atkins, C. Stanier, Fawaz D. Alharbi","doi":"10.1109/AECT47998.2020.9194157","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194157","url":null,"abstract":"This paper aims to show how Business Intelligence is utilized in Higher Education Institutions for the purpose of monitoring Quality Assurance activities. This paper discusses Quality Assurance in Higher Education and investigates the challenging issues that institutions are facing. In addition, the paper discusses the role of Business Intelligence and Analytics in supporting decision making in the context of Higher Education. The paper outlines the link between Quality Assurance core elements and Business Intelligence systems. The paper outlines a proposed Business Intelligence solution for application to Higher Education in Saudi Arabia to address the main concerns for performance evaluation and monitoring in relation to Quality Assurance.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129061869","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194173
Sarah Kaleem, Sheeraz Ahmed, Fasee Ullah, M. Babar, Najia Sheeraz, F. Hadi
Requirement engineering (RE) plays a vital part for developing proficient systems and a major cause of system failure is due to the flaws in the RE. Understanding requirements for developing a system is an essential task and requires domain expertise. Internet of things (IoT) is the emerging field consists of smart devices and sensors connected to the Internet where they communicate with each other in order to exchange data and information. RE community gives very less attention towards IoT domain. This research proposes an improved RE framework for IoT-based smart applications using integrated approach. The proposed framework addresses the challenges faced in the requirement development of smart applications using existing RE methodologies. The proposed framework is an integrated approach which is equipped with the emerging RE techniques in the context of IoT-based smart applications. The proposed framework is comprised of five different steps of RE which are embedded with set of verified RE techniques for efficient requirement management. The proposed framework is validated with a real-time healthcare IoT-based case study to verify and realize the applicability of proposed framework. It is revealed that the projected framework provides precious impending into the requirement management of IoT based smart applications.
{"title":"An Improved RE Framewrok for IoT-Oriented Smart Applications Using Inetgrated Approach","authors":"Sarah Kaleem, Sheeraz Ahmed, Fasee Ullah, M. Babar, Najia Sheeraz, F. Hadi","doi":"10.1109/AECT47998.2020.9194173","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194173","url":null,"abstract":"Requirement engineering (RE) plays a vital part for developing proficient systems and a major cause of system failure is due to the flaws in the RE. Understanding requirements for developing a system is an essential task and requires domain expertise. Internet of things (IoT) is the emerging field consists of smart devices and sensors connected to the Internet where they communicate with each other in order to exchange data and information. RE community gives very less attention towards IoT domain. This research proposes an improved RE framework for IoT-based smart applications using integrated approach. The proposed framework addresses the challenges faced in the requirement development of smart applications using existing RE methodologies. The proposed framework is an integrated approach which is equipped with the emerging RE techniques in the context of IoT-based smart applications. The proposed framework is comprised of five different steps of RE which are embedded with set of verified RE techniques for efficient requirement management. The proposed framework is validated with a real-time healthcare IoT-based case study to verify and realize the applicability of proposed framework. It is revealed that the projected framework provides precious impending into the requirement management of IoT based smart applications.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124677907","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194198
Syed Aziz Shah, Syed Yaseen Shah, Syed Shah, Daniyal Haider, Ahsen Tahir, Jawad Ahmad
This paper presents remote monitoring of patients using non-invasive RF sensing to detect normal respiratory rates and abnormal breathing rates such as elevated patterns where person experiences heavy breathing and shallow rates where minimal air is inhaled and exhaled. In this context, a millimeter wave, frequency modulated continuous wave radar operating at 60 GHz is used to acquire data. A total of 10 volunteers participated in the experimental campaign and 300 observations were obtained represented in terms of micro-Doppler signatures. Time domain statistical features were obtained from features such as bandwidth and centroid of the corresponding signatures. Support vector machine (SVM), k-nearest neighbor (KNN) and decision tree algorithms were used to evaluate overall performance of the proposed model. It was observed that the SVM classifier provided best classification accuracy (96%).
{"title":"Identifying Elevated and Shallow Respiratory Rate using mmWave Radar leveraging Machine Learning Algorithms","authors":"Syed Aziz Shah, Syed Yaseen Shah, Syed Shah, Daniyal Haider, Ahsen Tahir, Jawad Ahmad","doi":"10.1109/AECT47998.2020.9194198","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194198","url":null,"abstract":"This paper presents remote monitoring of patients using non-invasive RF sensing to detect normal respiratory rates and abnormal breathing rates such as elevated patterns where person experiences heavy breathing and shallow rates where minimal air is inhaled and exhaled. In this context, a millimeter wave, frequency modulated continuous wave radar operating at 60 GHz is used to acquire data. A total of 10 volunteers participated in the experimental campaign and 300 observations were obtained represented in terms of micro-Doppler signatures. Time domain statistical features were obtained from features such as bandwidth and centroid of the corresponding signatures. Support vector machine (SVM), k-nearest neighbor (KNN) and decision tree algorithms were used to evaluate overall performance of the proposed model. It was observed that the SVM classifier provided best classification accuracy (96%).","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127010947","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194152
Junaid Masood, Sakeena Javaid, Sheeraz Ahmed, Sameeh Ullah, N. Javaid
In smart grids, one of the key issues is accurate forecasting of electricity load and price to reduce the gap between generation and consumption of electricity. To address this issue, a framework has been proposed, in which feature selection has been done by Random Forest (RF) technique in both datasets of load and price. For prediction, RF, Support Vector Machine (SVM) and SVM along with an enhanced linear kernel and tuned parameters are used. New York electricity market data for load (MWh) and price ($) has been taken for this purpose. Daily and weekly forecasting results have been taken by the proposed system. Several performance evaluation techniques have been used to evaluate prediction results. The results show that our proposed technique performed better (0.07% for load and 0.12% for price) than default linear-kernel SVR.
{"title":"An Optimized Linear-Kernel Support Vector Machine for Electricity Load and Price Forecasting in Smart Grids","authors":"Junaid Masood, Sakeena Javaid, Sheeraz Ahmed, Sameeh Ullah, N. Javaid","doi":"10.1109/AECT47998.2020.9194152","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194152","url":null,"abstract":"In smart grids, one of the key issues is accurate forecasting of electricity load and price to reduce the gap between generation and consumption of electricity. To address this issue, a framework has been proposed, in which feature selection has been done by Random Forest (RF) technique in both datasets of load and price. For prediction, RF, Support Vector Machine (SVM) and SVM along with an enhanced linear kernel and tuned parameters are used. New York electricity market data for load (MWh) and price ($) has been taken for this purpose. Daily and weekly forecasting results have been taken by the proposed system. Several performance evaluation techniques have been used to evaluate prediction results. The results show that our proposed technique performed better (0.07% for load and 0.12% for price) than default linear-kernel SVR.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130114184","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194219
A. Elmoselhy, E. Ramadan
Graph Databases have been used widely in different areas. Owing to the type of representation they offer, they have gained popularity in disciplines where the interconnection of the data is a substantial matter. With the amount of interconnected data that the era of omics has resulted in, analyzing this data is an important task in medicine, drug design, and many other related fields. This can be done with the help of graph databases. In this paper, a novel multi-bipartite heterogeneous biological graph model is provided. It has been implemented and stored in the graph database Neo4j. Moreover, a new modified version of degree centrality (hereafter ”Disease Degree Centrality”) is adapted to aid in extracting and mining for meaningful insights from the graph model in hand. We calculated the Disease Degree Centrality for the intended node and we reported the most important protein domains. Finally, we analysed our results on a case study of Menkes and Wilson diseases using DAVID and InterPro databases.
{"title":"Utilizing Graph Database for Inferring Domain-Disease Associations","authors":"A. Elmoselhy, E. Ramadan","doi":"10.1109/AECT47998.2020.9194219","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194219","url":null,"abstract":"Graph Databases have been used widely in different areas. Owing to the type of representation they offer, they have gained popularity in disciplines where the interconnection of the data is a substantial matter. With the amount of interconnected data that the era of omics has resulted in, analyzing this data is an important task in medicine, drug design, and many other related fields. This can be done with the help of graph databases. In this paper, a novel multi-bipartite heterogeneous biological graph model is provided. It has been implemented and stored in the graph database Neo4j. Moreover, a new modified version of degree centrality (hereafter ”Disease Degree Centrality”) is adapted to aid in extracting and mining for meaningful insights from the graph model in hand. We calculated the Disease Degree Centrality for the intended node and we reported the most important protein domains. Finally, we analysed our results on a case study of Menkes and Wilson diseases using DAVID and InterPro databases.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133831218","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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