Developing and Implementing A System for Shipping Companies Comparison

Authors(8) :-Rawan Al-Theeb, Hessa Al-Tami, Hadeel Al-Johani, Asalah Al-Mutairi, Ibrahim Al-Marashdeh, Mutasem K. Alsmadi, Muneerah Alshabanah, Daniah Alrajhi

Information-intensive Web services such as shipping comparison sites have recently been gaining popularity. However, most users including novice shoppers have difficulty in browsing such sites because of the massive amount of information gathered and the uncertainty surrounding Web environments. The aim of this research is to design a system which is called Shohnati to perform all procedures related to the order of shipment, and to store and process all information relating to customers or shipping companies in a database. Through this research, the customers will be able to order the shipment more easily by providing a complete comparison between the shipping companies, request the shipment from the preferred company's site, follow the shipment, and follow the latest offers of companies on our site. The proposed system was developed using the Unified Modeling Language (UML) and Visual Studio-ASP.NET programming language.

Authors and Affiliations

Rawan Al-Theeb
Department of Management Information Systems, College of Applied Studies and Community Service, Imam Abdurrahman Bin Faisal University, Al-Dammam, Saudi Arabia
Hessa Al-Tami
Department of Management Information Systems, College of Applied Studies and Community Service, Imam Abdurrahman Bin Faisal University, Al-Dammam, Saudi Arabia
Hadeel Al-Johani
Department of Management Information Systems, College of Applied Studies and Community Service, Imam Abdurrahman Bin Faisal University, Al-Dammam, Saudi Arabia
Asalah Al-Mutairi
Department of Management Information Systems, College of Applied Studies and Community Service, Imam Abdurrahman Bin Faisal University, Al-Dammam, Saudi Arabia
Ibrahim Al-Marashdeh
Department of Management Information Systems, College of Applied Studies and Community Service, Imam Abdurrahman Bin Faisal University, Al-Dammam, Saudi Arabia
Mutasem K. Alsmadi
Department of Management Information Systems, College of Applied Studies and Community Service, Imam Abdurrahman Bin Faisal University, Al-Dammam, Saudi Arabia
Muneerah Alshabanah
Department of Management Information Systems, College of Applied Studies and Community Service, Imam Abdurrahman Bin Faisal University, Al-Dammam, Saudi Arabia
Daniah Alrajhi
Department of Management Information Systems, College of Applied Studies and Community Service, Imam Abdurrahman Bin Faisal University, Al-Dammam, Saudi Arabia

Shipping Companies, Software Engineering and Unified Modeling Language

  1. Kim, J.W. and S.H. Ha, Price comparisons on the internet based on computational intelligence. PloS one, 2014. 9(9): p. e106946.
  2. Walther, J.B., C.T. Carr, and S.S.W. Choi, Interaction of interpersonal, peer, and media influence sources online: A research agenda for technology convergence, in A networked self2010, Routledge. p. 25-46.
  3. Zhang, Y.-Q. and T. Lin. Computational web intelligence (CWI): synergy of computational intelligence and web technology. in 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No. 02CH37291). 2002. IEEE.
  4. Hong, S.-Y., J.-W. Kim, and Y.-H. Hwang, Fuzzy-semantic information management system for dispersed information. Journal of Computer Information Systems, 2011. 52(1): p. 96-105.
  5. Castellano, G., A.M. Fanelli, and M.A. Torsello, Computational Intelligence techniques for Web personalization. Web Intelligence and Agent Systems: An International Journal, 2008. 6(3): p. 253-272.
  6. Baye, M.R., J. Morgan, and P. Scholten, Price dispersion in the small and in the large: Evidence from an internet price comparison site. The Journal of Industrial Economics, 2004. 52(4): p. 463-496.
  7. Haynes, M. and S. Thompson, Price, price dispersion and number of sellers at a low entry cost shopbot. International Journal of Industrial Organization, 2008. 26(2): p. 459-472.
  8. Lim, G.G., et al., Rule-based personalized comparison shopping including delivery cost. Electronic Commerce Research and Applications, 2011. 10(6): p. 637-649.
  9. Vachon, F., Can online decision aids support non-cognitive web shopping approaches? International Journal of Business and Management, 2011. 6(10): p. 16-27.
  10. Aramex, Aramex App. https://www.aramex.com/aramex-app, 2019.
  11. Preview, A.S., Naqel Express. https://apps.apple.com/sa/app/naqelexpress/id1297949272, 2019.
  12. SMSA-Express, http://www.smsaexpress.com/index.html#. 2019.
  13. Alsmadi, M.k., et al. Performance Comparison of Multi-layer Perceptron (Back Propagation, Delta Rule and Perceptron) algorithms in Neural Networks. in 2009 IEEE International Advance Computing Conference. 2009.
  14. Thalji, Z. and M. Alsmadi, Iris Recognition using robust algorithm for eyelid, eyelash and shadow avoiding. World Applied Sciences Journal, 2013. 25(6): p. 858-865.
  15. Alsmadi, M., U.A. Badawi, and H.E. Reffat, A High Performance Protocol for Fault Tolerant Distributed Shared Memory (FaTP). Journal of Applied Sciences, 2013. 13: p. 790-799.
  16. HADDAD, F., J. ALFARO, and M.K. ALSMADI, HOTELLING'S T² CHARTS USING WINSORIZED MODIFIED ONE STEP M-ESTIMATOR FOR INDIVIDUAL NON NORMAL DATA. Journal of Theoretical & Applied Information Technology, 2015. 72(2): p. 215-226.
  17. Haddad, F. and M.K. Alsmadi, Improvement of The Hotelling’s T2 Charts Using Robust Location Winsorized One Step M-Estimator (WMOM). Journal of Mathematics (ISSN 1016-2526), 2018. 50(1): p. 97-112.
  18. Alsmadi, M.K., U.A. Badawi, and H.M. Moharram, SERVER FAILURES ENABLED JAVASPACES SERVICE. Journal of Computer Science, 2014. 10(4): p. 671-679.
  19. Alsmadi, M., Apparatus and method for lesions segmentation, 2018, US Patent App. 15/614,893.
  20. Alsmadi, M.K., Facial expression recognition, 2018, Google Patents.
  21. Aldaej, R., et al., Analyzing, Designing and Implementing a Web-Based Auction online System. International Journal of Applied Engineering Research, 2018. 13(10): p. 8005-8013.
  22. Almaimoni, H., et al., Developing and Implementing WEB-based Online Destination Information Management System for Tourism. International Journal of Applied Engineering Research, 2018. 13(10): p. 7541-7550.
  23. Almrashdeh, I.A., et al., Requirement analysis for distance learning management system students in Malaysian universities. Journal of Theoretical and Applied Information Technology, 2011. 24(1): p. 17-27.
  24. Alsmadi, M.k., K.B. Omar, and S.A. Noah, Proposed method to decide the appropriate feature set for fish classification tasks using Artificial Neural Network and Decision Tree. IJCSNS 2009. 9(3): p. 297-301.
  25. Alsubaie, N., et al., Analyzing and Implementing an Online Metro Reservation System. International Journal of Applied Engineering Research, 2018. 13(11): p. 9198-9206.
  26. Daniyah Alkhaldi, D.A., Hajer Aldossary, Mutasem k. Alsmadi, Ibrahim Al-Marashdeh, Usama A Badawi, Muneerah Alshabanah, Daniah Alrajhi, Developing and Implementing Web-based Online University Facilities Reservation System. International Journal of Applied Engineering Research, 2018. 13(9): p. 6700-6708.
  27. Almarashdeh, i., et al., Real-Time Elderly Healthcare Monitoring Expert System Using Wireless Sensor Network International Journal of Applied Engineering Research, 2018. 13(6): p. 3517-3523.
  28. Al Smadi, M.K.S., Fish Classification Using Perceptron Neural Network, 2007, Centre for Graduate Studies, Universiti Utara Malaysia.
  29. Alsmadi, M.K. and U.A. Badawi, Pattern matching in Rotated Images Using Genetic Algorithm. Journal of King Abdulaziz University Computing and Information 2017. 5: p. 53 - 59.
  30. Aldossary, S., et al., ANALYZING, DESIGNING AND IMPLEMENTING A WEB-BASED COMMAND CENTER SYSTEM. International Research Journal of Engineering and Technology, 2019. 6(1): p. 1008-1019.
  31. Sheikh, R.A., et al., Developing and Implementing a Barcode Based Student Attendance System. International Research Journal of Engineering and Technology, 2019. 6(1): p. 497-506.
  32. Ali, S.A.S., et al., Determinants of deposit of commercial banks in Sudan: an empirical investigation (1970-2012). International Journal of Electronic Finance, 2019. 9(3): p. 230-255.
  33. Eljawad, L., et al., Arabic Voice Recognition Using Fuzzy Logic and Neural Network. International Journal of Applied Engineering Research, 2019. 14(3): p. 651-662.
  34. Al Smadi, A.M., et al., Accessing Social Network Sites Using Work Smartphone for Face Recognition and Authentication. Research Journal of Applied Sciences, Engineering and Technology, 2015. 11(1): p. 56-62.
  35. Alsmadi, M., Facial recognition under expression variations. Int. Arab J. Inf. Technol., 2016. 13(1A): p. 133-141.
  36. Alsmadi, M., K. Omar, and I. Almarashdeh, Fish Classification: Fish Classification Using Memetic Algorithms with Back Propagation Classifier2012: LAP LAMBERT Academic Publishing.
  37. Alsmadi, M., et al., A hybrid memetic algorithm with back-propagation classifier for fish classification based on robust features extraction from PLGF and shape measurements. Information Technology Journal, 2011. 10(5): p. 944-954.
  38. Alsmadi, M., et al., Fish Recognition Based on Robust Features Extraction from Size and Shape Measurements Using Neural Network Journal of Computer Science, 2010. 6(10): p. 1088-1094.
  39. Alsmadi, M.K., An efficient similarity measure for content based image retrieval using memetic algorithm. Egyptian Journal of Basic and Applied Sciences.
  40. Alsmadi, M.K., Query-sensitive similarity measure for content-based image retrieval using meta-heuristic algorithm. Journal of King Saud University - Computer and Information Sciences.
  41. Alsmadi, M.K., et al., FACE IMAGE RECOGNITION BASED ON PARTIAL FACE MATCHING USING GENETIC ALGORITHM. SUST Journal of Engineering and Computer Sciences (JECS), 2017. 18(1): p. 51-61.
  42. Alsmadi, M.K., et al., Fish recognition based on robust features extraction from color texture measurements using back-propagation classifier. Journal of Theoritical and Applied Information Technology, 2010. 18(1).
  43. Badawi, U.A. and M.K. Alsmadi, A GENERAL FISH CLASSIFICATION METHODOLOGY USING META-HEURISTIC ALGORITHM WITH BACK PROPAGATION CLASSIFIER. Journal of Theoretical & Applied Information Technology, 2014. 66(3): p. 803-812.
  44. Yousuf, M., et al., A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval. Mathematical Problems in Engineering, 2018. 2018.
  45. Saritha, R.R., V. Paul, and P.G. Kumar, Content based image retrieval using deep learning process. Cluster Computing, 2018: p. 1-14.
  46. Alsmadi, M.K., K.B. Omar, and S.A. Noah, Fish recognition based on robust features extraction from size and shape measurements using back-propagation classifier. International Review on Computers and Software, 2010. 5(4): p. 489-494.
  47. Alsmadi, M.K., et al., Fish recognition based on robust features extraction from size and shape measurements using neural network. Journal of Computer Science, 2010. 6(10): p. 1088.
  48. Alsmadi, M.K.S., et al., Fish recognition based on the combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree. arXiv preprint arXiv:0912.0986, 2009.
  49. Alsmadi, M., K.B. Omar, and S.A. Noah, Back propagation algorithm: the best algorithm among the multi-layer perceptron algorithm. International Journal of Computer Science and Network Security, 2009. 9(4): p. 378-383.
  50. Alsmadi, M., et al., Fish recognition based on robust features extraction from size and shape measurements using neural network. Information Technology Journal, 2009. 10(5): p. 427-434.
  51. Farag, T.H., et al., Extended Absolute Fuzzy Connectedness Segmentation Algorithm Utilizing Region and Boundary-Based Information. Arabian Journal for Science and Engineering, 2017: p. 1-11.
  52. Alsmadi, M.K., A hybrid Fuzzy C-Means and Neutrosophic for jaw lesions segmentation. Ain Shams Engineering Journal.
  53. Badawi, U.A. and M.K.S. Alsmadi, A Hybrid Memetic Algorithm (Genetic Algorithm and Great Deluge Local Search) With Back-Propagation Classifier for Fish Recognition International Journal of Computer Science Issues, 2013. 10(2): p. 348-356.
  54. M, A., O. K, and N. S, Back Propagation Algorithm : The Best Algorithm Among the Multi-layer Perceptron Algorithm. International Journal of Computer Science and Network Security, 2009. 9(9): p. 378-383.
  55. Sharma, M., G. Purohit, and S. Mukherjee, Information Retrieves from Brain MRI Images for Tumor Detection Using Hybrid Technique K-means and Artificial Neural Network (KMANN), in Networking Communication and Data Knowledge Engineering2018, Springer. p. 145-157.
  56. Gao, Y., et al., An enhanced artificial bee colony optimizer and its application to multi-level threshold image segmentation. Journal of Central South University, 2018. 25(1): p. 107-120.
  57. Alsmadi, M.K., A hybrid firefly algorithm with fuzzy-C mean algorithm for MRI brain segmentation. American Journal of Applied Sciences, 2014. 11(9): p. 1676-1691.
  58. Alsmadi, M.K., MRI brain segmentation using a hybrid artificial bee colony algorithm with fuzzy-c mean algorithm. Journal of Applied Sciences, 2015. 15(1): p. 100.
  59. Alsmadi, M.K., A hybrid Fuzzy C-Means and Neutrosophic for jaw lesions segmentation. Ain Shams Engineering Journal, 2017.
  60. Park, S.H. and K. Han, Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction. Radiology, 2018: p. 171920.
  61. Kermany, D.S., et al., Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell, 2018. 172(5): p. 1122-1131. e9.
  62. Almarashdeh, I., Sharing instructors experience of learning management system: A technology perspective of user satisfaction in distance learning course. Computers in Human Behavior, 2016. 63: p. 249-255.
  63. ALMARASHDEH, I., et al., AN OVERVIEW OF TECHNOLOGY EVOLUTION: INVESTIGATING THE FACTORS INFLUENCING NON-BITCOINS USERS TO ADOPT BITCOINS AS ONLINE PAYMENT TRANSACTION METHOD. Journal of Theoretical and Applied Information Technology, 2018. 96(13).
  64. Almarashdeh, I., The important of service quality and the trust in technology on users perspectives to continues use of mobile services. Journal of Theoretical & Applied Information Technology, 2018. 96(10).
  65. Almarashdeh, I. and M. Alsmadi. Investigating the acceptance of technology in distance learning program. in Information Science and Communications Technologies (ICISCT), International Conference on. 2016. Tashkent Uzbekistan IEEE.
  66. Almarashdeh, I. and M. Alsmadi. Heuristic evaluation of mobile government portal services: An experts' review. in 11th International Conference for Internet Technology and Secured Transactions (ICITST). 2016. IEEE.
  67. Almarashdeh, I. and M.K. Alsmadi, How to make them use it? Citizens acceptance of M-government. Applied Computing and Informatics, 2017. 13(2): p. 194-199.
  68. Almarashdeh, I. and M.K. Alsmadi, Applied Computing and Informatics. 2017.
  69. Almarashdeh, I., A. Althunibat, and N.F. Elias, Developing a Mobile Portal Prototype for E-government Services. Journal of Applied Sciences, 2014. 14(8): p. 791-797.
  70. Almarashdeh, I., et al., E-Government for mobile societies-stocktaking of current trends and initiatives. Journal of Applied Sciences, 2013. 14(8): p. 104-111.
  71. Almarashdeh, I., et al., Development of an interactive learning management system for malaysian distance learning institutions. Middle East Journal of Scientific Research, 2013. 14(11): p. 1471-1479.
  72. AlMarashdeh, I., et al., An Elite Pool-Based Big Bang-Big Crunch Metaheuristic for Data Clustering. Journal of Computer Science, 2018.
  73. Almarashdeh, I., N. Sahari, and N. Mat Zin, Heuristic evaluation of distance learning management system interface, in International Conference on Electrical Engineering and Informatics 2011, IEEE: Bandung, Indonesia p. 1-6.
  74. Almarashdeh, I., et al. Instructors acceptance of Distance Learning Management System. in International Symposium on Information Technology 2010 (ITSim 2010). 2010. Kuala Lumpur: IEEE.
  75. Almarashdeh, I.A., et al. Distance learners acceptance of learning management system. in 2nd International Conference on Data Mining and Intelligent Information Technology Applications (ICMIA2010). 2010. Seoul, Korea: IEEE.
  76. Almarashdeh, I.A., et al., Distance Learning Management System requirements From Student’s Perspective. The international Journal of Theoretical and Applied Information Technology, 2011. 24(1).
  77. Almarashdeh, I.A., N. Sahari, and N.A.M. Zin. Heuristic evaluation of distance learning management system interface. in Electrical Engineering and Informatics (ICEEI), 2011 International Conference on. 2011. IEEE.
  78. Almarashdeh, I.A., et al., The Success of Learning Management System Among Distance Learners in Malaysian Universitie. Journal of Theoretical and Applied Information Technology, 2010. 21 (2): p. 80-91.
  79. Almarashdeh, I.A., et al., THE SUCCESS OF LEARNING MANAGEMENT SYSTEM AMONG DISTANCE LEARNERS IN MALAYSIAN UNIVERSITIES. Journal of Theoretical & Applied Information Technology, 2010. 21(2).
  80. Almarashdeh, I.A., et al., Acceptance of learning management system: A comparison between distance learners and instructors. Advances in Information Sciences and Service Sciences, 2011. 3(5): p. 1-9.
  81. Almarashdeh, I.A.E., Study of the Usability of Learning Management System Tool (Learning Care) of Postgraduate Students in University Utara Malaysia (UUM), 2007, Graduate School, Universiti Utara Malaysia.
  82. Almrashdah, I.A., et al. Distance learners acceptance of learning management system. in Advanced Information Management and Service (IMS), 2010 6th International Conference on. 2010. IEEE.
  83. Almrashdah, I.A., et al. Instructors acceptance of distance learning management system. in Information Technology (ITSim), 2010 International Symposium in. 2010. IEEE.
  84. ALMRASHDEH, I.A., et al., DISTANCE LEARNING MANAGEMENT SYSTEM REQIUREMENTS FROM STUDENT'S PERSPECTIVE. Journal of Theoretical & Applied Information Technology, 2011. 24(1).
  85. Almrashdeh, I.A., et al. Instructor's success measures of Learning Management System. in Electrical Engineering and Informatics (ICEEI), 2011 International Conference on. 2011. IEEE.
  86. Ibrahim Almarashdeh, M.K.A., Ghaith Jaradat, Ahmad Althunibat, Sami Abdullah Albahussain, Yousef Qawqzeh, Usama A Badawi, Tamer Farag, Looking Inside and Outside the System: Examining the Factors Influencing Distance Learners Satisfaction in Learning Management System. Journal of Computer Science, 2018. 14(4): p. 453-465.
  87. Jaradat, G., M. Ayob, and I. Almarashdeh, The effect of elite pool in hybrid population-based meta-heuristics for solving combinatorial optimization problems. Applied Soft Computing, 2016. 44: p. 45-56.
  88. Jaradat, G.M., et al., Hybrid Elitist-Ant System for Nurse-Rostering Problem. Journal of King Saud University-Computer and Information Sciences, 2018.
  89. Rasmi, M., et al., Healthcare professionals’ acceptance Electronic Health Records system: Critical literature review (Jordan case study). International Journal of Healthcare Management, 2018: p. 1-13.
  90. Alsmadi, M.K., Forecasting River Flow in the USA Using a Hybrid Metaheuristic Algorithm with Back-Propagation Algorithm. Scientific Journal of King Faisal University (Basic and Applied Sciences), 2017. 18(1): p. 13-24.
  91. Adeyemo, J., O. Oyebode, and D. Stretch, River Flow Forecasting Using an Improved Artificial Neural Network, in EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI2018, Springer. p. 179-193.
  92. Ahani, A., M. Shourian, and P.R. Rad, Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting. Water Resources Management, 2018: p. 1-17.
  93. Poschmann, P., et al. Realization of ETA Predictions for Intermodal Logistics Networks Using Artificial Intelligence. in Interdisciplinary Conference on Production, Logistics and Traffic. 2019. Springer.
  94. Arendt, R., A. Kopczy?ski, and P. Spychalski. Centralized and Distributed Structures of Intelligent Systems for Aided Design of Ship Automation. in International Conference on Information Systems Architecture and Technology. 2017. Springer.
  95. Fontoura, M., W. Pree, and B. Rumpe. UML-F: A modeling language for object-oriented frameworks. in European Conference on Object-Oriented Programming. 2000. Springer.
  96. Teixeira, l., et al. Analysis and design of a project management information system: practical case in a consulting company. in CENTERIS/ProjMAN/HCis. 2016.
  97. Almarashdeh, I., et al., Development of an interactive learning management system for malaysian distance learning institutions. . Middle East Journal of Scientific Research, 14(11), . 10.5829/idosi.mejsr.2013.14.11.2339, 2013. 14(11): p. 1471-1479.
  98. Rajagopal, D. and K. Thilakavalli, A Study: UML for OOA and OOD. International Journal of Knowledge Content Development & Technology, 2017. 7(2): p. 5-20.
  99. Torchiano, M., et al., Do UML object diagrams affect design comprehensibility? Results from a family of four controlled experiments. Journal of Visual Languages & Computing, 2017. 41: p. 10-21.
  100. Dennis, A., B.H. Wixom, and D. Tegarden, Systems analysis and design: An object-oriented approach with UML2015: John wiley & sons.
  101. Bello, S.I., et al., A University Examination Web Application Based on Linear-Sequential Life Cycle Model. 2017.
  102. Dick, J., E. Hull, and K. Jackson, Requirements engineering2017: Springer.
  103. Bhuiyan, M., F. Haque, and L. Shabnam, Integration of organisational models and UML Use Case diagrams. Journal of Computers, 2018. 13(1): p. 1-18.
  104. Jurkiewicz, J. and J. Nawrocki, Automated events identification in use cases. Information and Software Technology, 2015. 58: p. 110-122.
  105. Almarashde, I., A. Althunibat, and N. Fazidah El, Developing a Mobile Portal Prototype for E-government Services. Journal of Applied Sciences, 2014. 14: p. 791-797.
  106. Al-Ghamdi, A., et al., Developing and Implementing a Web-Based Platform for Skills and Knowledge Exchange. International Journal of Scientific Research in Science and Technology (IJSRST), 2019. 6(3).
  107. Ibrahim, R., Formalization of the data flow diagram rules for consistency check. arXiv preprint arXiv:1011.0278, 2010.
  108. Begg, C. and T. Connolly, Database systems: A practical guide to design, implementation, and management, 2002, Addison-Wesley.
  109. Onuiri, E.E., et al., Intelligent Tourism Management System. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 2016. 18(1): p. 304-315.

Publication Details

Published in : Volume 6 | Issue 4 | July-August 2019
Date of Publication : 2019-07-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 57-70
Manuscript Number : IJSRST19649
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Rawan Al-Theeb, Hessa Al-Tami, Hadeel Al-Johani, Asalah Al-Mutairi, Ibrahim Al-Marashdeh, Mutasem K. Alsmadi, Muneerah Alshabanah, Daniah Alrajhi, " Developing and Implementing A System for Shipping Companies Comparison", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 6, Issue 4, pp.57-70, July-August-2019. Available at doi : https://doi.org/10.32628/IJSRST19649
Journal URL : http://ijsrst.com/IJSRST19649

Article Preview