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Co-Authors

Mahmoud Bakr
Maryam Hazman
Esraa Afify
Mostafa Sayed
Essam Shaaban
Mohamed Saied
Soha Ahmed
Ahmed Ibrahim El Seddawy
Khaled ElMalah
Amira Hassan Abed
Basant Ali Sayed
Walaa Saber
Omar Farouk
Ahmed Mohamedeen
Ali Elrafie
Marwan Bedeir
Ali Khaled
Rana Osama
Hussein Ayman
Nouran Mosaad
Nourhan Ebrahim
Adriana mounir
Andrew karam
Mina Atef
Kirollos Boles
Kirollos Samir
Mario Raouf
Ebrahem Said
Naglaa Saeed Shehata
Laila El Fangary
Laila Abd EL Hamid

Journals

International Journal of Advanced Networking and Applications

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Nasr, Mona

Cooperative Spatial Decision Support System for Controlling Animal Diseases Outbreaks in Egypt

Abstract Views :157 | PDF Views:1

Authors

Mahmoud Bakr ¹, Mona Nasr ², Maryam Hazman ³

Affiliations
1 Department of Expert System Tools, Central Lab. For Agricultural Expert Systems, ARC, EG
2 Department of Information Systems, Helwan University, EG
3 Department of Testing and Training, Central Lab. For Agricultural Expert Systems, ARC, EG

Source

International Journal of Advanced Networking and Applications, Vol 6, No 6 (2015), Pagination: 2533-2541

Abstract

Decision Support System (DSS) aims to help decision maker in the process of making decision, a Spatial Decision Support System (SDSS) is a DSS deals with spatial problem or use spatial data in solving a problem. Animal Diseases Spatial Decision Support System (ADSDSS) utilizes the capabilities of Data warehouse, Online Analytical Processing (OLAP), Geographic Information System (GIS), data mining techniques and knowledge base systems to provide decision makers with their needed information about the infected animals, infected places and diseases outbreaks. This information is displayed as reports or charts or allocated on a map which illustrates the most and the least affected places in an easy and fast way. So decision makers can take the right decision to control the spread of diseases outbreaks. For building ADSDSS the following steps are done (a) Animal diagnosis data from different data bases with climate database collected into a repository data warehouse for generating diagnosis data mart, (b) OLAP capabilities integrated with the diagnosis data mart for analysis and aggregation of data, (c) One of data mining techniques was applied and integrated into the system (association rules) to discover the relationships between different data items, (d) GIS spatial analysis and visualization capabilities integrated with the system to analyze diagnosis data and generate maps of diseases and outbreaks, (e) decisions suggestion capability integrated into the system to provide decision makers with suggestions and solutions to deal with diseases outbreaks. The experimental results show that the proposed system can provide the decision makers with their needed information in a fast and easy way.

Keywords

Animal Diseases, Data Mining, Data Warehouse, Decision Suggestion, Geographic Information System (GIS), Online Analytical Processing (OLAP), Spatial Analysis, Spatial Decision Support System (SDSS).

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A Proposed Model for a Web-Based Academic Advising System

Abstract Views :187 | PDF Views:0

Authors

Esraa Afify ¹, Mona Nasr ²

Affiliations
1 Modern Academy for Computer Science & Management Technology, Cairo, EG
2 Helwan University, Cairo, EG

Source

International Journal of Advanced Networking and Applications, Vol 9, No 2 (2017), Pagination: 3345-3361

Abstract

Student advising is an important and time-consuming effort in academic life. Academic advising has been implemented in order to fill the gap between student and the academic routine, by moving advising, complaining, evaluating, suggesting system from the traditional ways to an automated way. The researcher surveyed the existing literature; as utilized that many institutions have implemented computerized solutions in order to enhance their overall advising experience. In this paper the researcher innovates an automated mechanism for academic advising in the university system. The paper presents an overview of the development and implementation of a new model of e-Academic Advising System as a web-based application. The proposed model attempts to develop a model that the staff and advisor can access to follow-up the student complaints and suggestions. Also, the students who registered can through complain, evaluate & suggest in any subject. Finally, the head of the department can receive a KPIs reports to follow-up his department. Therefore, a need for a system that could detect student’s problems and provide them with suitable feedback is raised. The aim of this paper is to implement a system which facilitates and assists academic advisors in their efforts to providing quality, accurate and consistent advising services to their students; also, to explore the design and implementation of a computerized tool to facilitate this process. This paper discussed the required methodologies used in the development of the Academic Advising System, it has been shown that Academic Advising is a Process more than a Final Product or system, a technical vision for Academic Advising System has been provided. The e-Academic Advising web-based developed and implemented by "Ruby on Rails" as a Web framework which runs via the Ruby programming language and "PostgreSQL" as a Database Engine.

Keywords

Academic Advising System, Complaint System, Evaluating System, Suggesting System.

Full Text

References

Afify, E., A., E., A., E., Hegazy, A., E.,A.,& ElSayed, M., A., K., “A Model For Customer Complaint Management System using SOA.” in Proceedings of the 15th WSEAS International Conference on Computers, World Scientific and Engineering Academy and Society (WSEAS), pp. (291-296), 2011.

Henderson, L., K., & Goodridge, W., " AdviseMe: An Intelligent Web-Based Application for Academic Advising", International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 6, No. 8, 2015.

Laghari, M., S., Al Habsi, S., A., A., Maaz, N., A., & Al Naqbi, M., A., A., A., " A One-Semester Course Planner for EE Students", International Research Journal of Electronics & Computer Engineering, Vol. 1, No.1, 2015.

Daramola, O., Emebo, O., Afolabi, I., T., & Ayo, C., K.,"Implementation of an Intelligent Course Advisory Expert System", International Journal of Advanced Research in Artificial Intelligence (IJARAI), Vol. 3, No.5, pp. (6–12), 2014.

Shatnawi, R., Althebyan, Q., Ghalib, B., & Al-Maolegi, M., "Building a Smart Academic Advising System Using Association Rule Mining", arXiv preprint arXiv:1407.1807, 2014.

Laghari, M., S., "Automated course advising system", International Journal of Machine Learning and Computing (IJMLC), Vol. 4, No. 1, pp. (47–51), 2014.

Engin, G., Aksoyer, B., Avdagic, M., Bozanlı, D., Hanay, U., Maden, D., & Ertek, G., "Rule-based expert systems for supporting university students", 2nd International Conference on Information Technology and Quantitative Management (ITQM), pp. (22-31), 2014.

Lightfoot, J., M., " A web-based knowledge management tool utilizing concept maps for on-line student advising", Journal of International Technology and Information Management, Vol. 23, No. 1, pp. (41-57), 2014.

Hingorani, K., & Askari-Danesh, N., "Design and Development of an Academic Advising System for Improving Retention and Graduation", Issues in Information Systems, Vol. 15, No. 2, pp. (344-349), 2014.

Al-Nory, M., T., "Simple Decision Support Tool for university academic advising", In Information Technology in Medicine and Education (ITME), 2012 International Symposium, IEEE, Vol. 1, pp. (53-57), 2012.

Al-Ghamdi, A., Al-Ghuribi, S., Fadel, A., & AL-Ruhaili, F., "An Expert System for Advising Postgraduate Students", International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 3, No.3 , pp.(4529-4532), 2012.

Nwelih, E., 1., & Chiemeke, S., C., "Framework for a Web-Based Spatial Decision Support System for Academic Advising", African Journal of Computing & ICT, Vol. 5, No. 4, pp. (121-126), 2012.

Ishak, I., B., & Lehat, M., L., B., "A conceptual framework of web-based academic advisory information system.", In Humanities, Science and Engineering Research (SHUSER), IEEE , (pp. 957-961), 2012.

Feghali, T., Zbib, I., & Hallal, S., "A web-based decision support tool for academic advising", Educational Technology & Society, Vol. 14, No. 1, pp. (82–94), 2011.

Al Ahmar, M., A., "A Prototype Student Advising Expert System Supported with an Object-Oriented Database", International Journal of Advanced Computer Science and Applications (IJACSA), Special Issue on Artificial Intelligence, Vol. 1, No. 3, pp.(100-105), 2011.

Hwang, G., J., Chen, C., Y., Tsai, P., S., & Tsai, C., C., "An expert system for improving web-based problem-solving ability of students", Expert Systems with Applications, Vol. 38, No. 7, pp. (8664-8672), 2011.

Aslam, M., Z., & Khan, A., R.,"A Proposed Decision Support System/Expert System for Guiding Fresh Students in Selecting a Faculty in Gomal University, Pakistan", Industrial Engineering Letters, Vol. 1, No.4, pp. (33-40), 2011.

Nambiar, A., N., & Dutta, A., K., "Expert system for student advising using JESS", In International Conference on Educational and Information Technology (ICEIT), IEEE, Vol. 1, pp. (V1-312-V1315), .2010.

Deorah, S., Sridharan, S., & Goel, S., "SAES-expert system for advising academic major", In: 2nd International Advanced Computing Conference (IACC), IEEE, pp.(331-336), 2010.

Albalooshi, F., & Shatnawi, S., "HE-Advisor: A multidisciplinary web-based higher education advisory system", Global Journal of Computer Science & Technology, Vol. 10, No. 7, pp. (37-49), 2010.

Albalooshi, F., & Shatnawi, S., “Online Academic Advising Support,” In Technological Developments in Networking, Education and Automation, Springer, pp. (25–29), 2010.

McMahan, B., "An Automatic Dialog System for Student Advising", Journal of Undergraduate Research, Minnesota State University, Mankato, Vol. 10, No. 1, 2010.

Martínez-Argüelles, M., J., Ruiz-Dotras, E., & Rimbau-Gilabert, E., "The Academic Advising System in a Virtual University", In Technology Enhanced Learning. Quality of Teaching and Educational Reform, pp. (345-350), Springer, 2010.

Cline, B., E., Brewster, C., C., & Fell, R., D., "A rulebased system for automatically evaluating student concept maps", Expert systems with applications, Vol. 37, No.3, pp. (2282-2291), 2010.

Werghi, N., & Kamoun, F., K., "A decision-treebased system for student academic advising and planning in information systems programmes", International Journal of Business Information Systems, Vol. 5, No. 1, pp. (1-18), 2009.

Fong, S., & Biuk-Aghai, R., P.,"An Automated University Admission Recommender System for Secondary School Students", In: The 6th International Conference on Information Technology and Applications (ICITA), 2009.

Hung, T., "IU–ADVISE: A Web-Based Advising Tool for Academic Advisors and Students", Master Thesis, May 18, 2009.

Binh, N., Duong, H., Hieu, T., Nhuan, N., & Son, N., "An integrated approach for an academic advising system in adaptive credit-based learning environment", VNU Journal of Science, Natural Sciences and Technology, Vol. 24, pp. (110-121), 2008.

Lin, F., Leung, S., Wen, D., Zhang, F., & Kinshuk, M., “e-Advisor: A multi-agent System for Academic Advising”, International Transactions on Systems Science and Applications, Vol. 4, No. 2, pp. (89–98), 2008.

Thanh Binh, N., Anh Duong, H., Hieu, T., Duc Nhuan, N., & Hong Son, N., "An integrated approach for an academic advising system in adaptive creditbased learning environment", VNU Journal of Science, Natural Sciences and Technology 24, pp. (110-121), 2008.

A Proposed Transformation Model for Integration Between E-Justice Applications and E-Commerce Services

Abstract Views :146 | PDF Views:0

Authors

Mona Nasr ¹, Mostafa Sayed ²

Affiliations
1 Information Systems Department, Helwan University, EG
2 Information Systems Department, Beni-Suef University, EG

Source

International Journal of Advanced Networking and Applications, Vol 9, No 2 (2017), Pagination: 3396-3399

Abstract

The electronic services become an important integral part of the Information Systems which supported by the term e-government. Many traditional business systems are now shifting to electronic systems and that in the midst of tremendous information, which is stored inside these systems. There are many researches in business information systems and their importance and advantages. Transforming business information systems to gain profit especially in government services is more difficult. This paper discusses the factors effects on the transformation of business information system represented in the State Council of Egypt information systems as a case study to an electronic inquiries system.

Keywords

Bis, Electronic Inquiries System, E-Government, E-Justice.

Full Text

References

(Shipsey, 2010) R. Shipsey, Information systems: foundations of e-business, Volume 1, university of London , 2010.

(Vidgen, 2002) Richard Vidgen, Constructing a web information system development methodology, Info Systems volume 12, p 247–261, 2002.

Roberto De Virgilio, Riccardo Torlone,Geert-Jan Houben, Rule-based Adaptation of Web Information Systems, Springer, 2007.

Francesco Virili , Maddalena Sorrentino, The enabling role of Web services in information system development practices: a grounded theory study, Springer, 2008.

Jesper Holck, 4 Perspectives on Web Information Systems, IEEE, p.1–8, 2003.

Hohenberg, H.E. and Rufera, S., 2004. Das Mobiltelefon als Geldbörse der Zukunft – Chancen und Potentiale des Mobile Payment (M-Payment). In der markt: Zeitschrift für Absatzwirtschaft und Marketing, Vol. 43, No. 168, 2004/1, pp. 33-40, Vienna.

Brown, M. (2000), "Advancing E-Commerce in Egypt: legal and Regulatory Recommendations" Report submitted to the Ministers of Economy and Foreign Trade and Communications and Information Technology.

Mahesha Kapurubandara (2009) A FRAMEWORK TO E-TRANSFORM SMES IN DEVELOPING

COUNTRIES , EJISDC, 39, 3, 1-24

El-Nawawy, M.A. & Ismail, M.M. (1999) Overcoming Deterrents and Impediments to Electronic Commerce in Light of Globalisation: The Case of Egypt. Paper presented at the 9th Annual Conference of the Internet Society, INET 99 San Jose, USA,.

Lawal Mohammed Ma’aruf & Khadija Abdulkadir , An overview of e-commerce implementation in developed and developing country; A case study of United State and Nigeria , Vol.2, Issue.5, Sep.-Oct.. 2012 pp-3068-3080

Bianchi, C. and Andrews, L. (2012). "Risk, trust, and consumer online purchasing behaviour: a Chilean perspective", International Marketing Review, Vol. 29 Iss: 3 pp. 253 – 275

Otlobmall. (2010). Online. Available on: www.otlobmall.com

Arab Advisors Group: A member of the Arab Jordan Investment bank Group. (2012) Internet Users In Egypt Report. Online. Available on http://www.arabadvisors.com/arabic/Pressers/presser 110412.htm

El Gawady, Z. (2005). The Impact of E-Commerce on Developed and Developing Countries: Case Study: Egypt and United States. Presented at the international conference of Globalization, Technology and Sustainable Development, United Arab Emirates University, World Association for sustainable Development, Al Ain , UAE.

Ayman E.Khedr,2008. Adoption of new technologies in a highly uncertain environment, PHD thesis, Leiden, The Netherlands, 2008.

Optimized Approach for Collaborative eLearning using Real-Time Social Networks

Abstract Views :174 | PDF Views:0

Authors

Essam Shaaban ¹, Mona Nasr ²

Affiliations
1 Canadian International College, Cairo, EG
2 Helwan University, Cairo, EG

Source

International Journal of Advanced Networking and Applications, Vol 10, No 2 (2018), Pagination: 3765-3769

Abstract

With the new era of Information and Communication Technology; collaborative learning is considered to be an elearning approach where learners are able to socially interact to the others, as well as instructors. In essence, learners work together in order to expand their knowledge of a particular subject or skill. Nowadays, Social Networks could be used as an e-learning platform. One would typically log in and collaborate with other learners on a specific topic using the social network as the common working space. In this paper, a new approach is presented for collaborative learning throughout social networks. The behavior the proposed approach is presented. The proposed collaborative e-learning approach consists of three modules. The first one is mobile application which is used for collecting some data about the user and his/her interests that may be considered as a data entry process. The second module is getting some location information using the GPS of the mobile device. The third module here is matching algorithm, which will do two main functions. The first one is matching the interests of the users and displaying the results based on these interests. The second one is displaying the results and sorting it based on the nearest user.in addition to routing information.

Keywords

Collaborative, Social Networks, E-learning, Approach, Location-based Learning.

Full Text

References

C. Gütl, & V. Chang. (2008). The use of Web 2.0 Technologies and Services to support E-Learning Ecosystem to develop more effective Learning Environments. In Proceedings

Q. Tan, Y. L. Jeng, & Y. M. Huang. (2010, July). A collaborative mobile virtual campus system based on location-based dynamic grouping. In Advanced Learning Technologies

P. Pocatilu, F. Alecu, & M. Vetrici. (2010). Measuring the efficiency of cloud computing for elearning systems. WSEAS Transactions on Computers, 9(1), 42–51

N. A. Christakis, & J. H. Fowler. (2009). Connected: The surprising power of our social networks and how they shape our lives. Little, Brown.

D. Boyd. 2004. “The characteristics of successful online students”, New Horizons in Adult Education, vol.18, no.2, pp.31-3

E. Fredericksen, A. Pickett, P. Shea, W. Pelz, and K. Swan. (2000) “Learner satisfaction and perceived learning with on-line courses: principles and examples from the SUNY

L. Y. Muilenburg and Z. L. Berge. (2005) “Learner barriers to online learning: a factor analytic study,” Distance Education, vol. 26, no. 1, pp. 29–48

G. Zurita, M. Nussbaum, & R. Salinas (2005). Dynamic Grouping in Collaborative Learning Supported by Wireless Handhelds. Educational Technology & Society, 8(3), 149-161.

B. Patten, I. A. Sa´nchez, & B. Tangney (2006). Designing collaborative, constructionist and contextual applications for hand held devices. Computers & Education, 46, 294–308

M. Michie (1998). Factors influencing secondary science teachers to organize and conduct field trips.

Australian Sci. Teacher’s J. 44(4)

S. Y. Choi and J. M. Kang. (2012). Adaptive System Supporting Collaborative Learning Based on a Location-Based Social Network and Semantic User Modeling, Volume 2012,

Y. M. Huang, Y. L. Jeng, & T. C. Huang. (2009). An educational mobile blogging system for supporting collaborative learning. Journal of Educational Technology & Society, 12(2).

R.M. Felder & L.K. Silverman (1988). Learning and Teaching Styles in Engineering Education. Engr.

Education, 78 (7), 674 681

R. Wegerif. (1998) “The social dimension of asynchronous learning Networks,” Journal of ALN, vol. 2, no. 1, pp. 34–49

S. R. Hiltz, N. Coppola, N. Rotter, and M. Turoff. (2000) “Measuring the importance of collaborative learning for the effectiveness of ALN: a multimeasure, multi-method

Blended Learning Model Supported by Recommender System and up-to-date Technologies

Abstract Views :166 | PDF Views:0

Authors

Mohamed Saied ¹, Mona Nasr ²

Affiliations
1 Canadian International College, Cairo, EG
2 Helwan University, Cairo, EG

Source

International Journal of Advanced Networking and Applications, Vol 10, No 2 (2018), Pagination: 3829-3832

Abstract

This paper is describing how to add recommendation resources in Blended learning systems. The Blended learning system is a term increasingly used to describe the way e-learning is being combined with traditional classroom methods and independent study to create a new, which called hybrid teaching methodology. It represents a much greater change in basic technique than simply adding computers to classrooms; it represents, in many cases, a fundamental change in the way teachers and students approach the learning experience. This paper describes how an algorithm can be used to make recommendation of resources in the Blended Learning field.

Keywords

Blended Learning, Recommender System, Recommendation Engine, Adaptive Learning System, Virtual learning Environment (VLE).

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References

Lothridge, Karen; et al. (2013). "Blended learning: efficient, timely, and cost effective.". Journal for Forensic Sciences.

Jump up to: a b c d e "Five benefits of blended learning - DreamBox Learning". DreamBox Learning. Retrieved 2016-01-28.

S. Alexander (2010). "Flexible Learning in Higher Education". In Penelope Peterson; Eva Baker; Barry McGaws. International Encyclopedia of Education (Third ed.). Oxford: Elsevier. pp. 441–447. ISBN 9780080448947. doi:10.1016/B978-0-08-044894-7.00868-X.

Oliver M, Trigwell K (2005). "Can 'Blended Learning' Be Redeemed?" (PDF). E-Learning. 2 (1): 17–26. doi:10.2304/elea.2005.2.1.17.

Francesco Ricci and LiorRokach and Bracha Shapira, Introduction to Recommender Systems Handbook, Recommender Systems Handbook, Springer, 2011, pp. 1-35

Montaner, M.; Lopez, B.; de la Rosa, J. L. (June 2003). "A Taxonomy of Recommender Agents on the Internet". Artificial Intelligence Review. 19 (4): 285–330.

Sultan,T., Nasr, M., Saied, M., Recommender System Role in e-Learning Environment, In the Proceeding of the 2nd International Conference in learning and Distance Education, Riyadh, Saudi Arabia, 21-24 Feb., 2011.

Nasr, M., Saied, M., A Proposed Framework for an Automatic Recommendation System for eLearning, In the Proceeding of the Sixth International Conference "Education and Scientific Research in the project of the Arab Renaissance of the knowledge society problems and prospects", Organized by the Arab Center for Education and Development with cooperation with the Center of Future Studies of the Egyptian Cabinet Information & Decision Support and the Secretariat of the League of Arab States, Cairo, Egypt, 5 – 7 July, 2011

Sultan,T., Nasr, M., Saied, M., Smart Recommendation Paradigm for VLE, in the Proceeding of the 2nd IEEE International Conference on e-Education, e-Business, eManagement and E-Learning, IC4E 2011, Catalog Number: CFP1102J-PRT, ISBN: 978-14244-9213-8, Mumbai, India, 7-9 Jan., 2011.

ElSayed, M., Nasr, M., Sultan,T., Enhancing e-Learning Environment with Embedded Recommender Systems, book entitled, “Information Systems Applications in the Arab Education Sector”, IGI Global Release Date: August, 2012, Copyright © 2013. 398 pages., DOI: 10.4018/978-1-4666-1984-5, ISBN13: 9781466619845, ISBN10: 1466619848, EISBN13: 9781466619852.

Schein, A., Popescul, A., Ungar, L. and Pennock, D. “Methods and metrics for cold-start recommendations.” In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’02), Tampere, Finland, August 2002.

A Proposed Framework for Detecting and Predicting Diseases through Business Intelligence Applications

Abstract Views :161 | PDF Views:0

Authors

Soha Ahmed ¹, Ahmed Ibrahim El Seddawy ², Mona Nasr ³

Affiliations
1 Helwan University, EG
2 Business Information System Department, College of Management & Information Technology, Arab Academy for Science, Technology& Maritime Transport, EG
3 Department of Information Systems, Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 10, No 4 (2019), Pagination: 3951-3957

Abstract

The Demand for healthcare IT and its analytics increases in the last few years. To improve quality of care (e.g., ensuring that patients receive the correct medication) which will help to improve the efficiency of clinical quality and safety, operations.

The Nature of the medical field is rich with information where there’s a variety and abundance of data but untapped in a correct and effective manner to get the right knowledge. and therefore, the most serious challenge facing this area is the quality of service provided which means to make the diagnose in a proper manner at a timely manner and provide appropriate medications to patients because Poor diagnosing can lead to serious consequences which are unacceptable. And because there is a lack of effective analysis tools to discover hidden relationships and trends in data, so Health information technology has emerged as a new technology in health care sector in a short period by utilizing Business Intelligence ‘BI’ which is a data-driven Decision Support System. Which Was developed from 1990s to now, and gradually become one of the most important information systems applied in any sector. BI enables to deal with huge amount of data and extract useful knowledge to support decision making. Data mining ‘DM’ is a kind of data processing technology which can be regarded as a part of the BI system, but it can be also considered as an independent and integrated technology which can treat mass data and extract hidden relationships from it.

This introduction highlights the main importance of how to apply the business intelligence applications using data mining techniques to help medical professionals in healthcare sector rapidly diagnosing and predicting diseases of any patients not only this but also detecting the disease complications on the patient which will decrease the overall cost of expenditure that the country paid, briefly this is the central research idea which address the motivation for doing this research.

Keywords

Business Intelligence, HealthCare, Data Mining, Data-Driven Decision Support System.

Full Text

References

Abd Elaziz, L., Sharaf, A., Nasr, M. (2016), Personal Integrated Electronic Health Record, International Journal of Computer Applications (IJCA), Volume 150 – No.12, September 2016, pp 44-47.

Ashrafi, N. et al (2014), The impact of Business Intelligence on Healthcare Delivery in the USA, Interdisciplinary Journal of Information Knowledge and Management, 117-130.

Anand, R et al (2013), A Data Mining Framework for Building Health Care Management System, International Journal of Engineering Research & Technology, 1639-1648.

Bonney W. (2013), Applicability of Business Intelligence in Electronic Health Record, The 2ndInternational Conference on Integrated Information, 257-262.

Bhuvaneswari K. (2015), A Comparative Analysis of Clustering Techniques using Genetic Algorithm, International Journal of Computer Science and Mobile Computing, 80-86.

Fayyad, U. et al (1996), From Data Mining to Knowledge discovery in Database, American Association of Artificial Intelligence Magazine, 37-54.

Garets, D., Davis, M. (2006), Electronic Medical Record vs. Electronic Health Record: Yes, There Is a Difference, A HIMSS Analytics White Paper, 1-14.

Helmi, A., Nasr, M., Farhan, M., (2015) The Pivotal Role of Geospatial Information Systems based on Hybrid Cloud Computing for the Health Sector in Egypt, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 4, Issue 5(2), Page 99-103, September-October- 2015.

Helmi, A., Farhan, M., Nasr, M., (2018) A Framework for Integrating Geospatial Information Systems and Hybrid Cloud Computing, Computers & Electrical Engineering Journal, Elsevier, Volume 67, April 2018, pp. 145-158.

Helmi, A., Nasr, M., Mohamed, R. (2017)., Cloud based Spatial Analysis for the Health Sector: A Case Study of Egypt, International Journal of Advanced Networking and Applications (IJANA), Volume 09, Issue 02, Sep - Oct 2017 issue, pp. 3387-3390.

Hoyt, R. (2009), Medical Informatics (Practical Guide for Healthcare Professional), Florida: Lulu.com, Electronic Health Record. (n.d.), Retrieved from HIMSS: http://www.himss.org/library/ehr/?navItemNumber=13261

Jensen, P., et al (2012), Mining Electronic Health Record: Towards better research applications and clinical care, Nature Reviews, 395-405.

Kavitha, S. (2012), Monitoring of Diabetes with Data Mining via CART Method, International Journal of Emerging Technology and Advanced Engineering, 157-162.

Khedr, A et al (2016), A proposed Electronic Health Record Content Structure based on Clinical organizational survey, 1-15.

Lyer, A., et al (2015), Diagnosis of Diabetes using Classification Mining Technique, International journal of Data Mining & Knowledge Management Process, 1-14.

Milovic, B. et al (2012), Prediction and Decision Making in Health Care using Data Mining, International Journal of Public Health Science, 69-78.

Rajesh, S. (2012), Application of Data Mining Methods and Techniques for Diabetes Diagnosis, International Journal of Engineering and Innovative Technology, 224-229.

Rajkumar, R. (2010), Diagnosis of heart disease using data mining algorithm, Global Journal of Computer Science & Technology, 38-43.

Stackowiak, R. a. (2007), Oracle Data warehouse and Business Intelligence Solutions, Wiley Publishing.

Soni, Y. et al (2011), Predictive Data Mining for Medical Diagnosis: An overview of heart disease Prediction, International Journal of Computer Application, 43-48.

Srinivas, R. (2010), Application of Data Mining techniques in healthcare & Prediction of heart attacks, International Journal on computer science and engineering, 250-255.

Sudhakar, K. et al (2014), Study of Heart Disease Prediction Using Data Mining, International Journal of Advanced Research in Computer Science and Software Engineering, 1157-1160.

Sultan, T., Nasr, M., Khedr, A., Abdou, R., (2013) A Proposed Integrated Approach for BI and GIS in Health Sector to support Decision Makers (BIGIS-DSS), In the International Journal of Advanced Computer Science and Applications (IJACSA), January 2013, Volume 4, No.1, pp. 170-176.

Tvrdikova, M. (2007), Support of Decision Making by Business Intelligence Tools, 6^th International Conference (p. 368), Computer information system and industrial management application.

Wang, Y. (2010), Business Intelligence and Data Mining in MBS Carbon management.

Self-Protected Mobile Agent Paradigm for DDoS Threats using Block Chain Technology

Abstract Views :221 | PDF Views:0

Authors

Mona Nasr ¹

Affiliations
1 Department of Information Systems, Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 10, No 6 (2019), Pagination: 4070-4075

Abstract

This paper describes Mobile Agents paradigm for tracking and tracing the effects of Denial of Service security threat in Mobile Agent System, an implementation of this paradigm has been entirely developed in java programming language. The proposed paradigm considers a range of techniques that provide high degree of security during the mobile agent system life cycle in its environment.

This paper highlights the spot to two main design objectives: The importance of including various supportive types of agents within a system e.g., police agents, service agents, …etc. Second: Evaluation analysis and number of checks to be done to trace the Mobile Agents if denial of the provided services during its path. Evaluation analysis for detecting tolerance differences for the calculated agent’s route before and during its journey, storing agent transactions, storing snapshots of agent state information, checking from time to time agent status and task completeness and lastly guard agent checks the changed variables of migrated agent. During tracing and monitoring Mobile Agents, the initiator node may destroy it and continue with another. In this paper a new paradigm is presented that detects and eliminate with high probability, any degree of tampering within a reasonable amount of time, also provide the ability of scalability of security administration.

Keywords

Mobile Agents, Denial of Service DDoS, Security Threats, Block Chain Technology BCT, Trust.

Full Text

References

Ahila, S. Sobitha, and K. L. Shunmuganathan. “Overview of mobile agent security issues— Solutions.” In Information Communication and Embedded Systems (ICICES), International Conference on, pp. 1-6. IEEE, 2014.

Aloui, Imene, Okba Kazar, Laid Kahloul, and Sylvie Servigne. “A new Itinerary planning approach among multiple mobile agents in wireless sensor networks (WSN) to reduce energy consumption.” International Journal of Communication Networks and Information Security Vol.7, no.2, 2015.

Bagga, Pallavi, and Rahul Hans. “Applications of mobile agents in healthcare domain: a literature survey.” International Journal of Grid Distribution Computing Vol.8, no. 5, pp.55-72, 2015.

Bhaskar, B., Kumar T. Jagadish., Kamal, M.V., “A Security Determination-Reaction Architecture for Heterogeneous Distributed Network.”, IJSRCSE, Vol.5, Issue.5, pp.22-34, 2017.

Calvaresi, D., et al., Multi-agent systems and blockchain: Results from a systematic literature review, Conference: Swiss eHealth Summit 2018.

Chowhan, R., Mobile Agent Programming Paradigm and its Application Scenarios, International Journal of Current Microbiology and Applied Sciences, Volume 7 Number 05, 2018.

Chowhan, R., Dayya, P., Itinerary and Mobile Code Patterns for Emerging Mobile Agent Systems in Large Scale Distributed Environments, International Journal of Computer Sciences and Engineering, Volume 6, Issue 5, May 2018.

Dinh, Hoang T., Chonho Lee, Dusit Niyato, and Ping Wang. “A survey of mobile cloud computing: architecture, applications, and approaches.” Wireless communications and mobile computing Vol.13, no.18 pp.1587-1611, 2013.

Dounya, K., et al., A new approach based mobile agent system for ensuring secure big data transmission and storage, 2017 International Conference on Mathematics and Information Technology (ICMIT).

Gavalas, Damianos, Ioannis E. Venetis, Charalampos Konstantopoulos, and Grammati Pantziou. “Mobile agent itinerary planning for WSN data fusion: considering multiple sinks and heterogeneous networks” International Journal of Communication Systems Vol.30, no.8, 2017.

Hasan, Ragib, Md Mahmud Hossain, and Rasib Khan. “Aura: An iot based cloud infrastructure for localized mobile computation outsourcing.” In Mobile Cloud Computing, Services, and Engineering (MobileCloud), 3rd IEEE International Conference on, pp. 183-188. IEEE, 2015.

Karim, M., Security for Mobile Agents and Platforms: Securing the Code and Protecting its Integrity, Journal of Information Technology & Software Engineering, Volume 8, Issue 1, 2018.

Kilari, N., Sridaran, R., An Overview of DDoS Attacks in Cloud Environment, International Journal of Advanced Networking and Applications IJANA, Special Issue pp: 124-126.

Miller, Naomi Liora, Harold Roy Miller, and Warren Stableford. “Translation of user requests into itinerary solutions.” U.S. Patent 9,659,099, issued May 23, 2017.

Rahul Singh Chowhan and Rajesh Purohit, “Study of mobile agent server architectures for homogeneous and heterogeneous distributed systems.” International Journal of Computer Applications Vol.156, No. 4, pp.32-37, 2016.

Rahul Singh Chowhan, Amit Mishra, and Ajay Mathur, “Aglet and kerrighed as a tool for load balancing and scheduling in distributed environment.” In Recent Advances and Innovations in Engineering (ICRAIE), International Conference on, pp.1-6. IEEE, 2016.

Shermin, V.: Disrupting governance with blockchains and smart. Strategic Change Vol 26, Issue 5, 2017. pp499–509.

Shiraz, Muhammad, Abdullah Gani, Rashid Hafeez Khokhar, and Rajkumar Buyya. “A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing.” IEEE Communications Surveys & Tutorials Vol.15, no. 3, pp.1294-1313, 2013.

Singla, Annie., Jain, Kamal., Gairola, Ajay., “Delving into Security of Networks-Time’s Need” IJSRNSC, Vol.2, Issue.3, pp.1-8, 2014.

Tapscott, D., Tapscott, A.: Blockchain Revolution: How the technology behind Bitcoin is changing money, business, and the world. Penguin, 2016.

Singla, Annie., Jain, Kamal., Gairola, Ajay., “Delving into Security of Networks-Time’s Need” IJSRNSC, Vol.2, Issue.3, pp.1-8, 2014.

Cloud Business Intelligence

Abstract Views :197 | PDF Views:0

Authors

Khaled ElMalah ¹, Mona Nasr ²

Affiliations
1 Inforce Engineering Consultant, EG
2 Department of Information Systems, Faculty of Computers & Information, Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 10, No 6 (2019), Pagination: 4120-4124

Abstract

Using Business Intelligence in the cloud is considered a key factor for success in various fields in 2018, about 66 percent of successful organizations in BI already using cloud. 86% of Cloud BI adopters choose Amazon AWS as their first choice, 82% choose Microsoft Azure, 66% choose Google Cloud, and 36% identify IBM Bluemix as their preferred provider of cloud BI services. In recent years, both Business Intelligence and cloud computing have undergone dramatic changes and advancements. The newest capabilities that these recent developments bring forth are introduced. In this paper the latest technologies in the field of Cloud (SaaS) BI is introduced. The paper shows also that many of the current problems in Cloud (SaaS) BI can be solved by enhance the performance and increase the use and acceptance of this technology. Many of the key characteristics of Business Intelligence systems tend to complement those of cloud computing systems and vice versa. Therefore, when integrated properly, these two technologies can be made to strengthen each other’s advantages and eliminate each other’s weaknesses.

Keywords

Business Intelligence, Software as A Service, Infrastructure as A Service, Platform as A Service, Enterprise Information Integration, Cloud Computing.

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References

Brist Inc. Why Cloud BI? The 9 Substantial Benefits of Software-as-a-Service Business Intelligence, White paper, 2010

Gartner RAS Core Research, Data in the Cloud: Changing Nature of Managing Data Accessibility, 2009.

Gartner, Gartner Says Emerging Technologies will marginalize its Role in Business Intelligence, 2008.

SAP Business Objects Portfolio, Strategic Alignment, Predictable Performance, and Confident Decisions, www.sap.com/solutions/businessobjects.

Inmon, W. H., Building the data warehouse. John Wiley Sons, Inc., New York (NY, USA), 2005.

Moss, S.T. and Atre, S., Business Intelligence Roadmap – The Complete Project Lifecycle forDecision Support Applications. Wiley, Boston, 2005.

Baars, H. and Kemper, H.G., Management Support with Structured and Unstructured Data – An Integrated Business Intelligence Framework. Information Systems Management, 25 (2), 132-148, 2008.

Bose, R., Advanced analytics: opportunities and challenges. Industrial Management and Data Systems, 109 (2), 55-172, 2009.

Baars, H., Horakh, T.A. and Kemper, H.G., Business Intelligence Outsourcing – A Framework. Proceedings of the 15th European Conference on Information Systems (ECIS2007), St. Gallen (Switzerland), 2007.

Hayes, B., Cloud Computing. Communications of the ACM, 51 (7), 9-11. 2008.

Kern, T., Lacity, M.C. and Willcocks, M.P. Netsourcing: renting business applications and services over a network. Prentice Hall, Upper Saddle River (NJ, USA), 2002.

McKnight, W. Introducing the Data Warehouse Appliance, Part I. DMReview, March 2005, 16., 2005.

Shimaa Ouf and Mona Nasr, The Cloud Computing: The Future of BI in the Cloud, International Journal of Computer Theory and Engineering, Vol. 3, No. 6, December 2011.

R. Buyya, C. S. Yeo, and S. Venugopal, “Market-Oriented Cloud Computing: Vision, Hype and Reality for Delivering IT Services as Computing Utilities.” Future Generation Computer Systems, 25, 599-616, 2009.

Horakh,Thomas, et al “Service-Based Approach as a Prerequisite for BI Governance,” Proceedings of the 14th Americas Conference on Information Systems (AMCIS), Toronto, 2008.

Shimaa Ouf and Mona Nasr, Business Intelligence in The Cloud, In the Proceeding of the 2011 International Conference on Computer and Network Engineering (ICCNE 2011), indexed by the Ei-Compendex, ISI Proceeding and other indexing services, Zhengzhou, China, 17 – 19 June 2011.

Columbus Louis, The State Of Cloud Business Intelligence, www.Forbes.com, 2018; https://www.forbes.com/sites/louiscolumbus/2018/04/08/the-state-of-cloud-business-intelligence2018/#6730f8622180

Singh et el, Load Balancing in Cloud Computing Environment: A Comparative Study of Service Models and Scheduling Algorithms, International Journal of Advanced Networking and Applications IJANA, Volume: 08 Issue: 06 Pages: 3246-3252, 2017.

Diabetes Disease Detection through Data Mining Techniques

Abstract Views :170 | PDF Views:0

Authors

Amira Hassan Abed ¹, Mona Nasr ²

Affiliations
1 Department of Information Systems center, Egyptian Organization for Standardization & Quality, EG
2 Department of Information Systems, Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 11, No 1 (2019), Pagination: 4142-4149

Abstract

Diabetes is a inveterate defect and disturbance resulted from metabolic conk out in carbohydrate metabolism thus it has occupied a globally serious health problem. In general, the detection of diabetes in early stages can greatly has significant impact on the diabetic patients treatment in which lead to drive out its relevant side effects. Machine learning is an emerging technology that providing high importance prognosis and a deeper understanding for different clustering of diseases such as diabetes. And because there is a lack of effective analysis tools to discover hidden relationships and trends in data, so Health information technology has emerged as a new technology in health care sector in a short period by utilizing Business Intelligence ‘BI’ which is a data-driven Decision Support System.

In this study, we proposed a high precision diagnostic analysis by using k-means clustering technique. In the first stage, noisy, uncertain and inconsistent data was detected and removed from data set through the preprocessing to prepare date to implement a clustering model. Then, we apply k-means technique on community health diabetes related indicators data set to cluster diabetic patients from healthy one with high accuracy and reliability results.

Keywords

Business Intelligence, Health Care, Data Mining, Data-Driven Decision Support System.

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References

https://www.diabetesdaily.com/learn-about-diabetes/ what-is-diabetes/how-manypeople-have-diabetes/.

Ashrafi, N. et al (2014), The impact of Business Intelligence on Healthcare Delivery in the USA, Interdisciplinary Journal of Information Knowledge and Management, vol. 11, no. 2, pp117-130.

Stackowiak, R. a. (2007), Oracle Data warehouse and Business Intelligence Solutions, Wiley Publishing.

Tvrdikova, M. (2007), Support of Decision Making by Business Intelligence Tools, 6^th International Conference (p. 368), Computer information system and industrial management application.

Wang, Y. (2010), Business Intelligence and Data Mining in MBS Carbon management.

Ahmed, S., Seddawy, A. Nasr, A Proposed Framework for Detecting and Predicting Diseases through Business Intelligence Applications, International Journal of Advanced Networking and Applications (IJANA), Vol. 10, Issue 04, Jan - Feb 2019 issue, pp 3951-3957.

Soni, Y. et al (2011), Predictive Data Mining for Medical Diagnosis: An overview of heart disease Prediction, International Journal of Computer Application, pp.43-48.

Srinivas, R. (2010), Application of Data Mining techniques in healthcare & Prediction of heart attacks, International Journal on computer science and engineering, pp 250-255.

Sudhakar, K. et al (2014), Study of Heart Disease Prediction Using Data Mining, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 14, no. 3, pp.1157-1160.

Jain A., Murty M., and Flynn P. (1999), “Data Clustering: A Review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264-323.

Abbas O., “Comparisons Between Data Clustering Algorithms,” The International Arab Journal of Information Technology, vol. 5, no. 3, pp. 320-325, 2008.

Khandegar Anjali. Khushbu Pawar diagnosis of diabetes mellitus using PCA, neural Network and cultural algorithm. International Journal of Digital Application Contemp Res 2017; vol.5, no.6, pp. 115-125.

Patil BM, Joshi RC, Durga Toshniwal. Hybrid prediction model for Type-2 diabetic patients. Expert Systems Applications 2010; Vol.37, no.8.pp:8102–8115.

Iyer A, Jeyalatha S, Sumbaly R. Diagnosis of diabetes using classification mining techniques. Int J Data Min Knowl Manag Process (IJDKP) 2015; Vol.5, no.1.

Gowda Karegowda Asha, Jayaram MA, Manjunath AS. Cascading K-means clustering and K-nearest neighbor classifier for categorization of diabetic patients. International Journal of Eng Adv Technology 2012; Vol.1, no.3. ISSN: 2249 – 8958.

Novakovic J, Rankov S. Classification performance using principal component analysis and different value of the ratio R. International Journal of Computer Communication Control 2011;Vol. VI, no.2, pp. :317–27. ISSN 1841-9836, E-ISSN 1841-9844.

Han J, Kamber M, Pei J. Data mining concepts and techniques. 3rd USA: Morgan Kaufmann Publishers; 2012.

Applying Data Mining Techniques for Predicting Diseases

Abstract Views :153 | PDF Views:0

Authors

Basant Ali Sayed ¹, Mona Nasr ²

Affiliations
1 Teaching Assistant, Department of Information System Higher Institute of Qualitative Studies, EG
2 Department of Information Systems, Faculty of Computers & Information Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 11, No 2 (2019), Pagination: 4231-4235

Abstract

The techniques of data mining are very popular of Diseases. The advancement in health analysis has been improved by technical advances in computation, automation and data mining. Nowadays, data mining is getting used in a vast area .The Nature of the medical field is made with the knowledge wherever there’s a spread of data but untapped during a correct. and thus, the foremost serious challenge facing this area is the quality of service provided which suggests tocreate the diagnose during a correct manner in a timely manner and supply acceptable medications to patients. Thus Health information technology has emerged as a replacement technology within the health care sector in a shortamount by utilizing Business Intelligence ‘BI’ that could be a data-driven Decision Support System. The various techniques of data mining are used and compared during this analysis.

Keywords

Business Intelligence, Data Mining, Data-Driven Decision Support System, Knowledge Discovery in Database (KDD).

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The Future of Internet of Things for Anomalies Detection using Thermography

Abstract Views :140 | PDF Views:0

Authors

Amira Hassan Abed ¹, Mona Nasr , Walaa Saber ²

Affiliations
1 Egyptian Organization for Standardization &Quality, EG
2 Emirates College of Technology, Abu Dhabi, AE

Source

International Journal of Advanced Networking and Applications, Vol 11, No 3 (2019), Pagination: 4298-4304

Abstract

Abnormal temperature of human body is a natural extensive indicator of illness. Infrared thermography (IRT) is a fast, non-invasive, non-contact and passive substitution to ordinary medical thermometers for monitoring and observation human body temperature. Aside from, IRT is able to chart body surface heat remotely. Last five decades testified a stationary development in thermal imaging cameras utilization to obtain relations between the thermal physiology and surface temperature. IRT has effectively used in diagnosis and detection of breast cancer, diabetes neuropathy and peripheral vascular disorders. It has been employed to detect issues related to gynecology, dermatology, heart, neonatal physiology, and brain imaging. With the advent of modern infrared cameras, data acquisition and processing techniques, it is now possible to have real time high resolution thermographic images, which is likely to surge further research in this field. The emergent technology known as the Internet of Things (IoT) has guided practitioners, physicians and researchers to design innovative solutions in different environments, particularly in medical and healthcare using smart sensors, computer networks and a remote server. This paper aims to propose IoT-enabled medical system enables diagnostics and detection for several medical anomalies remotely; in real-time and simultaneous depend on combination of IoT and Thermal Infrared imaging techniques. It will detect and diagnostics any abnormal and alert the user through IoT remotely and in real-time.

Keywords

Thermography, Anomaly Detection, Infrared Thermography, Imaging Techniques, Medical Systems.

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Benchmarking Meta-Heuristic Optimization

Abstract Views :154 | PDF Views:0

Authors

Mona Nasr ¹, Omar Farouk ², Ahmed Mohamedeen ², Ali Elrafie ², Marwan Bedeir ², Ali Khaled ²

Affiliations
1 Department of Information Systems, Helwan University - Cairo, EG
2 Department of Computer Science, Helwan University - Cairo, EG

Source

International Journal of Advanced Networking and Applications, Vol 11, No 6 (2020), Pagination: 4451-4457

Abstract

Solving an optimization task in any domain is a very challenging problem, especially when dealing with nonlinear problems and non-convex functions. Many meta-heuristic algorithms are very efficient when solving nonlinear functions. A meta-heuristic algorithm is a problem-independent technique that can be applied to a broad range of problems. In this experiment, some of the evolutionary algorithms will be tested, evaluated, and compared with each other. We will go through the Genetic Algorithm, Differential Evolution, Particle Swarm Optimization Algorithm, Grey Wolf Optimizer, and Simulated Annealing. They will be evaluated against the performance from many points of view like how the algorithm performs throughout generations and how the algorithm’s result is close to the optimal result. Other points of evaluation are discussed in depth in later sections.

Keywords

Optimization, Algorithms, Benchmark.

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References

Hoos H., Stützle, T., Stochastic local search: Foundations and applications. Elsevier, 2004.

Dixon L., Szegö G.,Towards global optimisation. University of Cagliari, Italy, October 1974. Amsterdam-Oxford. North-Holland Publ. Co. 1975. X. 472 S”. In: ZeitschriftAngewandteMathematik und Mechanik59 (1979), pp. 137–138.

Beal, P. “A Study of Genetic Algorithm Techniques for the Design of Metamaterial Ferrites”. PhD thesis. Queen Mary University of London, 2019.

Abuiziah I., Shakarneh N. “A Review of Genetic Algorithm Optimization: Operations and Applications to Water Pipeline Systems”. In: International Journal of Physical, Natural Science and Engineering 7 (Dec. 2013).

Kang-Ping Wang et al. “Particle swarm optimization for traveling salesman problem”. In: Proceedings of the 2003 international conference on machine learning and cybernetics (IEEE cat. no. 03ex693). Vol. 3. IEEE. 2003, pp. 1583–1585.

Eberhart R.,Shi. Y. “Comparison between genetic algorithms and particle swarm optimization”. In: International conference on evolutionary programming. Springer. 1998, pp. 611–616.

Mahmud Iwan et al. “Performance comparison of differential evolution and particle swarm optimization in constrained optimization”. In: Procedia Engineering 41 (2012), pp. 1323–1328.

Mirjalili S., Lewis A. “Grey wolf optimizer”. In: Advances in engineering software 69 (2014), pp. 46–61.

Panda M., Das. B., “Grey Wolf Optimizer and Its Applications: A Survey”. In: Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems. Springer. 2019, pp. 179–194.

Lampinen J.,Storn R., “Differential evolution”. In: New optimization techniques in engineering. Springer, 2004, pp. 123–166.

Kirkpatrick S., Gelatt, Vecchi M., “Optimization by simulated annealing”. In: science 220.4598 (1983), pp. 671–680.

Vanderbilt D., Louie S., “A Monte Carlo simulated annealing approach to optimization over continuous variables”. In: Journal of computational physics 56.2 (1984), pp. 259–271.

Mahdi W., Medjahed S., and Ouali M., “Performance analysis of simulated annealing cooling schedules in the context of dense image matching”. In: Computación y Sistemas21.3 (2017), pp. 493–501.

Chong E., and Zak S. An introduction to optimization. John Wiley & Sons, 2004.

Eberhart R., and Shi Y., “Comparison between Genetic Algorithms and Particle Swarm Optimization.” In: vol. 1447. Mar. 1998, pp. 611– 616.

Singh D., Khare A., Different Aspects of Evolutionary Algorithms, Multi-Objective Optimization Algorithms and Application Domain, Int. J. Advanced Networking and Applications, Volume: 02, Issue: 04, Pages: 770-775 (2011).

Realtime Multi-Person 2D Pose Estimation

Abstract Views :147 | PDF Views:0

Authors

Mona Nasr ¹, Rana Osama ², Hussein Ayman ², Nouran Mosaad ², Nourhan Ebrahim ², Adriana mounir ¹

Affiliations
1 Department of Information systems, Helwan University – Cairo, EG
2 Department of Computer science, Helwan University – Cairo, EG

Source

International Journal of Advanced Networking and Applications, Vol 11, No 6 (2020), Pagination: 4501-4508

Abstract

This paper explains how to detect the 2D pose of multiple people in an image. We use in this paper Part Affinity Fields for Part Association (It is non-parametric representation), Confidence Maps for Part Detection, Multi-Person Parsing using PAFs, Simultaneous Detection and Association, this method achieve high accuracy and performance regardless the number of people in the image. This architecture placed first within the inaugural COCO 2016 key points challenge. Also, this architecture exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.

Keywords

Real Time Performance, Part Affinity Fields, Part Detection, Multi-person Parsing, Confidence Maps.

Full Text

References

. A. Newell, K. Yang, and J. Deng. Stacked hourglass networks for human pose estimation. In ECCV, 2016. 1 [18] W. Ouyang, X. Chu, and X. Wang. Multi-source deep l.

. W. Ouyang, X. Chu, and X. Wang. Multi-source deep learning for human pose estimation. In CVPR, 2014. 1

. J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler. Efficient object localization using convolutional networks. In CVPR, 2015. 1

. J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler. Joint training of a convolutional network and a graphical model for human pose estimation. In NIPS, 2014

. X. Chen and A. Yuille. Articulated pose estimation by a graphical model with image dependent pairwise relations. In NIPS, 2014

. A. Toshev and C. Szegedy. Deeppose: Human pose estimation via deep neural networks. In CVPR, 2014

. V. Belagiannis and A. Zisserman. Recurrent human pose estimation. In 12th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2017.

. T. Pfister, J. Charles, and A. Zisserman. Flowing convnets for human pose estimation in videos. In ICCV, 2015.

. V. Ramakrishna, D. Munoz, M. Hebert, J. A. Bagnell, and Y. Sheikh. Pose machines: Articulated pose estimation via inference machines. In ECCV, 2014. 1.

. A. Bulat and G. Tzimiropoulos. Human pose estimation via convolutional part heatmap regression. In ECCV, 2016. 1

. D. Ramanan, D. A. Forsyth, and A. Zisserman. Strike a Pose: Tracking people by finding stylized poses. In CVPR, 2005. 1.

. D. Ramanan, D. A. Forsyth, and A. Zisserman. Strike a Pose: Tracking people by finding stylized poses. In CVPR, 2005. 1.

. Y. Yang and D. Ramanan. Articulated human detection with flexible mixtures of parts. In TPAMI, 2013. 1

. L. Pishchulin, M. Andriluka, P. Gehler, and B. Schiele. Poselet conditioned pictorial structures. In CVPR, 2013. 1.

. P. F. Felzenszwalb and D. P. Huttenlocher. Pictorial structures for object recognition. In IJCV, 2005. 1.

. S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh. Convolutional pose machines. In CVPR, 2016. 1, 2, 3, 6.

. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. Reed. Ssd: Single shot multibox detector. In ECCV, 2016. 6.

. M. Andriluka, S. Roth, and B. Schiele. Monocular 3D pose estimation and tracking by detection. In CVPR, 2010. 1.

. M. Andriluka, S. Roth, and B. Schiele. Pictorial structures revisited: people detection and articulated pose estimation. In CVPR, 2009. 1.

Natural Language Processing: Text Categorization and Classifications

Abstract Views :133 | PDF Views:0

Authors

Mona Nasr ¹, Andrew karam ¹, Mina Atef ¹, Kirollos Boles ¹, Kirollos Samir ¹, Mario Raouf ¹

Affiliations
1 Department of Computer science,Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 12, No 2 (2020), Pagination: 4542-4548

Abstract

There are huge data from unstructured text obtained daily from various resources like emails, tweets, social media posts, customer comments, reviews, and reports in many different fields, etc. Unstructured text data can be analyzed to obtain useful information that will be used according to the purpose of the analysis also the domain that the data was obtained from it. Because of the huge amount of the data the human manually analysis of these texts is not possible, so we have to automatic analysis. The topic analysis is the Natural Language Processing (NLP) technology that organizes and understands large collections of text data, by identifying the topics, finding patterns and semantic. There two common approaches for topic analysis, topic modeling, and topic classification each approach has different algorithms to apply that will be discussed.

Keywords

Natural Language Processing, Topic Classification, Topic Modeling, Text Categorization.

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Face Recognition System

Abstract Views :133 | PDF Views:0

Authors

Ebrahem Said ¹, Mona Nasr ¹

Affiliations
1 Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 12, No 2 (2020), Pagination: 4567-4574

Abstract

One of the modern technological techniques in practical application will be discussed in this paper in broaden manner will be discussed on a large scale.This technique, which is considered a means of technological security to keep personal data away from the hands of snoopers and spies, this technology has occupied the minds of developers in recent years who have ensured its development continuously is a facial recognition technology.

Keywords

Face Recognition, Artificial Neural Network, Facial Expression recognition, Recognition Algorithms.

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Algorithms of Deep Learning:Convolutional Neural Network Role with Colon Cancer Disease

Abstract Views :142 | PDF Views:1

Authors

Naglaa Saeed Shehata ¹, Mona Nasr ², Laila El Fangary ¹, Laila Abd EL Hamid ¹

Affiliations
1 Information Systems Department ,Faculty of Computers and Artificial Intelligence, Helwan University, EG
2 Information Systems Department, Faculty of Computers and Artificial Intelligence, Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 13, No 1 (2021), Pagination: 4827-4832

Abstract

The world's third most serious and lethal cancer rankings are colon cancer. Like cancer, the most important stage of early diagnosis is. Deep learning has become a leading learning tool for object detection and its successes in advancing the analysis of medical images have attracted attention. Convolutionary neural networks (CNNs), which play an indispensable role in the detection and potential early diagnose of colon cancer, are the most popular method of deep learning algorithms for this purpose. In this article we hope to take a look at the progress of colonic cancer analysis by studying profound learning practices. This study provides an overview of popular profound study algorithms used in analysis of colon cancer. All studies in the fields of colon cancer, including detection, classification as well as segmentation and survival prediction, will then be collected. Finally, we will conclude the work by summarizing the latest deep learning practices in analysis of colon cancer, a critical examination of the challenges and proposals for future research.

Keywords

Deep Learning, Colon Cancer, Medical Image Analysis, Convolutional Neural Networks.

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References

Home, American Cancer Society, Cancerfacts & statistics, https://cancerstatisticscenter.cancer.org/?_ga 2.191090925.69837.1589031645-278983135.158031645#!/.accessed June 9, 2021

J.D. Schiffman, P.G. Fisher, P. Gibbs, "Early detection of cancer: past, present, and future," Am. Soc. Clin. Oncol. Educ. B (2015) 57–65.

V. Pamudurthy, N. Lodhia, V.J.A. Konda, "Advances in endoscopy for colorectal polyp detection and classification," Baylor Univ. Med. Cent. Proc. 33 (2020) 28–35,

A. S. N. N.Mona Mohamed, H.LAILA, “A Comparative Study for Methodologies and Algorithms Used In Colon Cancer Diagnoses and Detection A Comparative Study for Methodologies and Algorithms Used In Colon Cancer Diagnoses and Detection,” Futur. Comput. Informatics J., vol. 4, no. 2, 2019.

G.S. Lodwick, T.E. Keats, J.P. Dorst, "The coding of roentgen images for computer analysis as applied to lung cancer, Radiology" 81 (1963) 185–200.

I. Pacal, D. Karaboga, A. Basturk, B. Akay, and U. Nalbantoglu, “A comprehensive review of deep learning in colon cancer,” Comput. Biol. Med., vol. 126, no. April, 2020.

S.A.R. Korsuk Sirinukuonwattana, N.R. YeeWiah Tsang, David Snead, , "A spatially constrained deep learning framework for detection of epithelial tumor nuclei in histology images," Lect. Notes Comput. Sci. 9467 (2015), https://doi.org/10.1007/978-3-319-28194-0, 0–8.

T. Oziawa, S. Ishiihara, Y. Kumagai, S. Shiichijo, T. Tada, "Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks," Therap. Adv. Gastroenterol. 13 (2020) 1–13,

Y. Zeang, S. Xu, W.C. Chapmman, S. Li, Z. Alipoiur, H. Abdelal, D. Chatterjee, M. Mutch, Q. Zhu, "Real-time colorectal cancer diagnosis using PR-OCT with deep learning," Theranostics 10 (2020) 2587–2596.

R. Wang, T.M., J.R. Glissen Brown, S. Bharadwaj, A, P. Song, D. Zhang, Y. Li, G. Xu, Real-time "automatic detection system increases colonoscopic polyp and adenoma detection rates:" a prospective randomised controlled study, Gut 68 (2019) 1813–1819.

S. Brryman, K. Malthews, J.H. Lee, S.P. Duffy, H. Ma, "Image-based Cell Phenotyping Using Deep Learning", BioRXiv, 2019, p. 817544.

K. Sirinaukunwattana, D. Snead, I. Mujeeb, Y., I. Caree, N. Rajpoot, "Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer", Sci. Rep. 8 (2018) 1–13.

M. Shaopcott, K.J., N. Raijpoot, "Deep learning with sampling in colon cancer histology", Front. Bioeng. Biotechnol. 7 (2019).

S. Zhang, F. Han, J. Tan,, Y. Gao, M. Pomeroy, K. Ng, W. Hou, "An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets", Comput. Med. Imag. Graph. 77 (2019) 101645.

S. Bruno Korbar, Andrea M. Olofson, Catherine M. Nicka, Matthew A. Suriawinata, Lorenzo Torresani, A. Arief, Suriawinata, Hassanpour, "Deep learning for classification of colorectal polyps on whole-slide images",J. Pathol. Inf. 9 (2017) 1–9.

F. Ciompi, O. Geessink, B.E. Bejnordi, G.S. De Souza, A. Baiodoshvili, I. Nagtegaal, J. Van Der Laak, "The importance of stain normalization in colorectal tissue classification with convolutional networks" , Proc. Int. Symp. Biomed. Imaging (2017) 160–163.

Al-Bahrrani, A. Agrawal, A. Choudhary, "Survivability prediction of colon cancer patients using neural networks", Health Inf. J. 25 (2019) 878–891.

F.D. SobeIranis-MuLkul, M. Kayser, A.-M. Zvereva, N. Navab, S. Albarqouni, "A Learning without Forgetting Approach to Incorporate Artifact Knowledge in Polyp Localization Tasks", 2020.

M. Abdellatif, M. Salah, and N. Saeed, “ScienceDirect Overcoming business process reengineering obstacles using ontology-based knowledge map methodology,” Futur. Comput. Informatics J., vol. 3, no. 1, pp. 7–28, 2018.

N.Shehata and A.Abed, “Big Data with Column Oriented NOSQL Database to Overcome the Drawbacks of Relational Databases,” Int. J. Adv. Netw. Appl., vol. 4428, pp. 4423–4428, 2020.

Business Intelligence (BI) Significant Role in Electronic Health Records - Cancer Surgeries Prediction: Case Study

Abstract Views :145 | PDF Views:1

Authors

Amira Hassan Abed ¹, Mona Nasr ²

Affiliations
1 Department of Information Systems center Egyptian Organization for Standardization & Quality, EG
2 Department of Information Systems, Faculty of Computers & Information Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 13, No 6 (2022), Pagination: 5220-5228

Abstract

Medical datasets reflect a great environment as they integrate analyses of structured and unstructured data that holds several benefits for medical sector. With a continues demand for implementing Electronic Health Records (EHRs), there is a relative requirement for utilizing data mining (DM) techniques to find out useful data, unknown patterns and inference rules from data stored in EHRs which help in a real-time decisions making process and prove-based practice for medical providers and experts. Business Intelligence (BI) is a technology able to process the huge data inside EHRs repository for enhancing the quality of medical delivery. DM is data processing techniques that considered a critical part of the BI platform. In this paper, we highlight significance of the BI integration with the EHRs to aid medical providers and professionals in real- time detection and prediction for several diseases. For more explanation, we apply BI technology with support of clustering technique as one of DM methods, for cancer surgeries prediction to prove the power of cooperating BI and EHRs in medical area.

Keywords

Business Intelligence (BI), Electronic Health Records, Data Mining, Cancer Surgeries Prediction.

Full Text

References

W. Bonney (2013). Applicability of Business Intelligence in Electronic Health Record. Procedia - Social and Behavioral Sciences. Vol. 25. pp: 257–262. 10.1016/j.sbspro.2013.02.050. The 2nd International Conference on Integrated Information.

H. Baars & H. Kemper (2008). Management support with structured and unstructured data - an integrated business intelligence framework. Information Systems Management, Vol. 25. NO. 2. pp: 132-148.

A. Noushin, K. Lori & K. jean-pierre. (2014). The Impact of Business Intelligence on Healthcare Delivery in the USA. Interdisciplinary Journal of Information, Knowledge and Management. Vol.9. pp: 117-130. 10.28945/1993.

N. Brannon (2010). Business intelligence and Ediscovery. Intellectual Property & Technology Law Journal. Vol. 22. NO. 7. pp: 1-5.

S. Chaudhuri, U. Dayal & V. Narasayya (2011). An overview of business intelligence technology. Communications of the ACM. Vol. 54. NO.8, pp: 88-98.

L. De Voe, & K. Neal (2005). When business intelligence equals business value. Business Intelligence Journal. Vol. 10. NO. 8. pp: 57-63.

S. Kudyba & M. Rader (2010). Conceptual factors to leverage business intelligence in healthcare (Electronic medical records, six sigma and workflow management). Proceedings of the Northeast Business & Economics Association, pp: 428-430.

J. Glaser & J. Stone (2008). Effective use of business intelligence. Healthcare Financial Management. Vol. 62. NO. 2. pp: 68-72.

A. Hassan Abed (2020). Recovery and concurrency challenging in Big Data and NoSQL database systems. International Journal of Advanced Networking and Applications (IJANA), Vol. 11. NO. 4. pp: 4321-4329.

J. Reinschmidt, & A. Françoise (2000). Business intelligence certification guide. http://www.redbooks.

ibm.com/pubs/ pdfs/ redbooks/ sg245747.pdf

A. Hassan Abed & M. Nasr (2019). “Diabetes disease detection through data mining techniques”, International Journal of Advanced Networking and Applications (IJANA), Vol.: 11(1). pp: 4142-4149.

M. Salah, M. Abd-Ellatif, A. Hassan Abed (2017). The success implementation CRM model for examining the critical success factors using statistical data mining techniques. International Journal of Computer Science and Information Security (IJCSIS). Vol.15. NO. 1. pp: 455 – 475.

Y. Soni (2011). Predictive Data Mining for Medical Diagnosis: An overview of heart disease Prediction.

International Journal of Computer Application. Vol.12. NO. 5. pp: 43-48.

R. Srinivas (2010). “Application of Data Mining techniques in healthcare & Prediction of heart attacks”. International Journal on computer science and engineering. Vol.8. NO. 3. pp: 250-255.

K. Sudhakarm (2014). Study of heart disease prediction using Data Mining. International Journal of Advanced Research in Computer Science and Software Engineering. Vol.14. NO. 3. pp: 1157- 1160.

International Organization for Standardization (ISO/TC 215). (2005). Health informatics — electronic health record — definition, scope, and context. Geneva, Switzerland: ISO. Retrieved from http://www.openehr.org/downloads/standards/iso/isotc215wg3_N202_ISOTR__Final_%5B2005-01-31%5D.pdf

R. Agrawal, T. Grandison, C. Johnson, & J. Kiernan, (2007). Enabling the 21st century health care information technology revolution. Communication of the ACM. Vol.50. NO. 2. pp: 34-42.

T. Watkins, R. Haskell, C. Lundberg, J. Brokel, M. Wilson, & N. Hardiker (2009). Terminology use in electronic health records: Basic principles. Urologic Nursing. Vol.29. NO. 5. pp: 321-327.

R. Hoyt (2009). “Medical Informatics (Practical Guide for Healthcare Professional), Florida: Lulu.com, Electronic Health Record”. (n.d.), Retrieved from HIMSS: http://www.himss.org/library/ehr/?navItemNumber=1326

A. Hassan Abed, M. Nasr & B. Sayed (2020). “The Principle Internet of Things (IoT) Security Techniques Framework Based on Seven Levels IoT’s Reference Model”. Proceedings of Internet of Things: Applications and Future ITAF 2019. Springer Publisher: Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 114).

A. Khedr, & K. Fahad (2014). A proposed Electronic Health Record Content Structure based on Clinical organizational survey. International journal of computers & technology. Vol. 13. NO. 12. pp: 5233 -5246.

Electronic Health Records Overview. (2006, April 1). National Institutes of Health. Retrieved February 20, 2012, from ncrr.nih.gov/publications/informatics/EHR.pdf

K. Fickenscher (2005). The new frontier of data mining. Health Management Technology journal. Vol. 26. NO. 10. pp: 26-30.

E. Giniat (2011). Using business intelligence for competitive advantage. Healthcare Financial Management, Vol. 65. NO. 9. pp: 142-146.

M. Salah, A. Hassan Abed & M. Abd-ellatif. (2018) “A systematic review for the determination and classification of the CRM critical success factors supporting with their metrics”. Future Computing and Informatics Journal. Vol. 3. pp: 398-416.

A. Hassan, M. Nasr, W. Saber (2019). “The Future of Internet of Things for Anomalies Detection using

Thermography”. International Journal of Advanced Networking and Applications (IJANA), Vol.: 11(1), pp:

-4149.

S. Ahmed, S. Ahmed, & M. Nasr, (2019). A proposed framework for detecting and predicting diseases through business intelligence applications. International Journal of Advanced Networking and Applications (IJANA), Vol. 10. NO. 4. pp: 3951-3957.

A. Hassan Abed, and Essam M. Shaaban. (2021) “Modeling Deep Neural Networks For Breast Cancer

Thermography Classification: A Review Study, ”International Journal of Advanced Networking and

Applications. Vol.: 13(2), pp: 4939-4946.

A.H. Abed, Essam M. Shaaban, Jena, O.P., & Elngar,A.A. (2022). A Comprehensive Survey on Breast Cancer Thermography Classification Using Deep Neural Network , Machine Learning and Deep Learning in Medical Data Analysis and Healthcare Applications.

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