DOCSLIB.ORG

Implementation of a String Matching Sms Based System for Academic Registration

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Implementation of a String Matching Sms Based System for Academic Registration BINDURA UNIVERSITY OF SCIENCE EDUCATION FACULTY OF SCIENCE DEPARTMENT OF COMPUTER SCIENCE NAME: MATOMBO WILLIE REGNUMBER: B1335487 SUPERVISOR: MR CHAKA PROJECT TITLE: IMPLEMENTATION OF A STRING MATCHING SMS BASED SYSTEM FOR ACADEMIC REGISTRATION APPROVAL FORM The undersigned certify that they have supervised the student MATOMBO WILLIE (B1335487) dissertation entitled, “IMPLEMENTATION OF A STRING MATCHING SMS BASED SYSTEM FOR ACADEMIC REGISTRATION” submitted in Partial fulfillment of the requirements for a Bachelor of Computer Science Honors Degree at Bindura University of Science Education. …………………………………… …………………………….. STUDENT DATE …….………………………………… ………………………….. SUPERVISOR DATE i Abstract The implementation of a string matching SMS based system at Bindura University of Science Education focuses on improving accessibility to academic registrations and viewing results to all students, despite geographical location and internet connectivity. The proposed system bases its operations on SMS technologies which is also supported by string matching techniques. The system user requirements were obtained by interacting with various university stakeholders and experts in the fields of academic registrations. Based on the stipulated requirements, the system was tested using blackbox testing techniques and the results show that the system satisfies user requirements. The system accuracy was also tested and it proved that the implementation of a string matching SMS based system was viable for the institution to adopt since, it proved that all students are now able to do academic registrations in a cost effective way as well as reliable and fast using SMS. Keywords Short Message Service, String matching, Algorithm, Academic registration, fuzzy string matching technique. ii Dedications This paper is dedicated to my late dad Mr. Henry Matombo and my mother Elizabeth Kandemiri for they did not only raise and nurture me but also sacrificed their finances over the years for my education and intellectual development. If I ever do anything right in my life it is because you taught me how to and you have believed in me since birth and for that reason I am proud to be your son. iii Acknowledgements Firstly I would like to give more credit to the almighty God for guiding me through my final year project. I would also want to thank my supervisor Mr. Chaka for his time and patience in supervising me through the project, I value your contribution and time sir. I also want to extend my gratitude to Bindura University of Science Education for all the infrastructural support and all the academic provisions that fostered the completion of this study. I would never forget mentioning my family and colleagues Madenyika Darlington, Tendai Mavhunga, Ali Leo and Tafadzwa Madimutsa for their supportive role contributing positively to my welfare. iv Table of Contents APPROVAL FORM ...................................................................................................................................... i Abstract ......................................................................................................................................................... ii Dedications .................................................................................................................................................. iii Acknowledgements ...................................................................................................................................... iv CHAPTER ONE PROBLEM IDENTIFICATION ................................................................................ 1 1.1 Introduction ...................................................................................................................................... 1 1.2 Background of Study ............................................................................................................................. 2 1.3 Problem Statement ................................................................................................................................. 2 1.4 Research Aim ......................................................................................................................................... 3 1.5 Research Objectives ............................................................................................................................... 3 1.6 Research questions ................................................................................................................................. 3 1.7 Research propositions ............................................................................................................................ 3 1.8 Research Justification ............................................................................................................................ 3 1.9 Assumptions ..................................................................................................................................... 4 1.10 Limitations ........................................................................................................................................ 4 1.11 Scope of research ................................................................................................................................. 5 1.12 Definition of terms ............................................................................................................................... 5 CHAPTER TWO LITERATURE REVIEW ......................................................................................... 6 2.1.0 Introduction ......................................................................................................................................... 6 2.2.0 GSM theory ......................................................................................................................................... 6 2.2.1 SMS Theory ..................................................................................................................................... 6 2.2.2 Analyzing GSM services ................................................................................................................. 7 2.3.0 String matching techniques ................................................................................................................ 8 2.3.1.0 Exact string matching technique ................................................................................................. 8 2.3.1.1 Brute force algorithm (Naïve algorithm) ................................................................................ 8 2.3.1.2 Booyer Moore Algorithm ......................................................................................................... 9 2.3.1.3 Knuth Morris-Pratt (KMP) algorithm ................................................................................... 10 2.4.1.0 Fuzzy matching technique ......................................................................................................... 10 2.4.1.1 The Bitap Algorithm .............................................................................................................. 10 2.5.0 Existing SMS Based Systems ............................................................................................................ 11 v 2.5.1 SMS User Interface Result Checking System .............................................................................. 11 Strength of NeXS ............................................................................................................................... 12 Weakness of NeXS ............................................................................................................................ 12 2.5.3 Enhancing Students' Academic Records Management Systems Using Short Message Service Gateway .................................................................................................................................................. 13 2.5.4 User Acceptance of SMS-Based eGovernment Services .............................................................. 13 2.6.0 Conclusion ..................................................................................................................................... 14 CHAPTER THREE RESEARCH METHODOLOGY ............................................................................. 15 3.0.0 Introduction ............................................................................................................................ 15 3.1.0 Research Design ............................................................................................................................ 15 3.1.1 Requirements Analysis .............................................................................................................. 15 3.1.4 Technical Requirements ........................................................................................................... 16 3.2.0 System Development............................................................................................................... 17 3.2.1 System development tools ................................................................................................... 17 3.2.2 Waterfall Model ......................................................................................................................... 17 3.3.0 Summary on how the system works ....................................................................................... 18 3.4.0 System design ...........................................................................................................................
Load more

Recommended publications
  • The Problem of Fuzzy Duplicate Detection of Large Texts
    The problem of fuzzy duplicate detection of large texts E V Sharapova1 and R V Sharapov1 1Vladimir State University, Orlovskaya street 23, Murom, Russia, 602264 Abstract. In the paper, considered the problem of fuzzy duplicate detection. There are given the basic approaches to detection of text duplicates–distance between strings, fuzzy search algorithms without indexing data, fuzzy search algorithms with indexing data. Thereview of existing methods for the fuzzy duplicate detection is given. The algorithm of fuzzy duplicate detection is present. The algorithm of fuzzy duplicate texts detection was implemented in the system AVTOR.NET. The paper presents the results of testing of the system.The use of filtering text, stemming and character replacement, allow the algorithm to found duplicates even in minor modified texts. 1. Introduction Today computer technology and the World Wide Web have become part of our lives. Billions of users use the Internet every day. The Internet contains a huge number of documents. Many books are transformed in a digital form. Users can read books, articles, newspapers directly from computers and electronic gadgets. Today, in the Internet there are many studying materials: lectures, textbooks, methodical literature, etc. There are big collections of essays, course and diploma projects and scientific dissertations. It leads to problem un-controlling coping of information. The text of document may be a part of another document, formed from several documents, may be modified part of another document (with a change of words to synonyms, endings, times, etc.). In this way, the document is fuzzy duplicate of other documents. There is the problem of text checking to presence of fussy duplicates.
    [Show full text]
  • Headprint: Detecting Anomalous Communications Through Header-Based Application Fingerprinting
    HeadPrint: Detecting Anomalous Communications through Header-based Application Fingerprinting Riccardo Bortolameotti Thijs van Ede Andrea Continella University of Twente University of Twente UC Santa Barbara [email protected] [email protected] [email protected] Thomas Hupperich Maarten H. Everts Reza Rafati University of Muenster University of Twente Bitdefender [email protected] [email protected] [email protected] Willem Jonker Pieter Hartel Andreas Peter University of Twente Delft University of Technology University of Twente [email protected] [email protected] [email protected] ABSTRACT 1 INTRODUCTION Passive application fingerprinting is a technique to detect anoma- Data breaches are a major concern for enterprises across all indus- lous outgoing connections. By monitoring the network traffic, a tries due to the severe financial damage they cause [25]. Although security monitor passively learns the network characteristics of the many enterprises have full visibility of their network traffic (e.g., applications installed on each machine, and uses them to detect the using TLS-proxies) and they stop many cyber attacks by inspect- presence of new applications (e.g., malware infection). ing traffic with different security solutions (e.g., IDS, IPS, Next- In this work, we propose HeadPrint, a novel passive fingerprint- Generation Firewalls), attackers are still capable of successfully ing approach that relies only on two orthogonal network header exfiltrating data. Data exfiltration often occurs over network covert characteristics to distinguish applications, namely the order of the channels, which are communication channels established by attack- headers and their associated values. Our approach automatically ers to hide the transfer of data from a security monitor.
    [Show full text]
  • Author Guidelines for 8
    I.J. Information Technology and Computer Science, 2014, 01, 68-75 Published Online December 2013 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2014.01.08 FSM Circuits Design for Approximate String Matching in Hardware Based Network Intrusion Detection Systems Dejan Georgiev Faculty of Electrical Engineering and Information Technologies , Skopje, Macedonia E-mail: [email protected] Aristotel Tentov Faculty of Electrical Engineering and Information Technologies, Skopje, Macedonia E-mail: [email protected] Abstract— In this paper we present a logical circuits to perform a check to a possible variations of the design for approximate content matching implemented content because of so named "evasion" techniques. as finite state machines (FSM). As network speed Software systems have processing limitations in that increases the software based network intrusion regards. On the other hand hardware platforms, like detection and prevention systems (NIDPS) are lagging FPGA offer efficient implementation for high speed behind requirements in throughput of so called deep network traffic. package inspection - the most exhaustive process of Excluding the regular expressions as special case ,the finding a pattern in package payloads. Therefore, there problem of approximate content matching basically is a is a demand for hardware implementation. k-differentiate problem of finding a content Approximate content matching is a special case of content finding and variations detection used by C c1c2.....cm in a string T t1t2.......tn such as the "evasion" techniques. In this research we will enhance distance D(C,X) of content C and all the occurrences of the k-differentiate problem with "ability" to detect a a substring X is less or equal to k , where k m n .
    [Show full text]
  • Comparison of Levenshtein Distance Algorithm and Needleman-Wunsch Distance Algorithm for String Matching
    COMPARISON OF LEVENSHTEIN DISTANCE ALGORITHM AND NEEDLEMAN-WUNSCH DISTANCE ALGORITHM FOR STRING MATCHING KHIN MOE MYINT AUNG M.C.Sc. JANUARY2019 COMPARISON OF LEVENSHTEIN DISTANCE ALGORITHM AND NEEDLEMAN-WUNSCH DISTANCE ALGORITHM FOR STRING MATCHING BY KHIN MOE MYINT AUNG B.C.Sc. (Hons:) A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Computer Science (M.C.Sc.) University of Computer Studies, Yangon JANUARY2019 ii ACKNOWLEDGEMENTS I would like to take this opportunity to express my sincere thanks to those who helped me with various aspects of conducting research and writing this thesis. Firstly, I would like to express my gratitude and my sincere thanks to Prof. Dr. Mie Mie Thet Thwin, Rector of the University of Computer Studies, Yangon, for allowing me to develop this thesis. Secondly, my heartfelt thanks and respect go to Dr. Thi Thi Soe Nyunt, Professor and Head of Faculty of Computer Science, University of Computer Studies, Yangon, for her invaluable guidance and administrative support, throughout the development of the thesis. I am deeply thankful to my supervisor, Dr. Ah Nge Htwe, Associate Professor, Faculty of Computer Science, University of Computer Studies, Yangon, for her invaluable guidance, encouragement, superior suggestion and supervision on the accomplishment of this thesis. I would like to thank Daw Ni Ni San, Lecturer, Language Department of the University of Computer Studies, Yangon, for editing my thesis from the language point of view. I thank all my teachers who taught and helped me during the period of studies in the University of Computer Studies, Yangon. Finally, I am grateful for all my teachers who have kindly taught me and my friends from the University of Computer Studies, Yangon, for their advice, their co- operation and help for the completion of thesis.
    [Show full text]
  • Algorithms for Approximate String Matching Petro Protsyk (28 October 2016) Agenda
    Algorithms for approximate string matching Petro Protsyk (28 October 2016) Agenda • About ZyLAB • Overview • Algorithms • Brute-force recursive Algorithm • Wagner and Fischer Algorithm • Algorithms based on Automaton • Bitap • Q&A About ZyLAB ZyLAB is a software company headquartered in Amsterdam. In 1983 ZyLAB was the first company providing a full-text search program for MS-DOS called ZyINDEX In 1991 ZyLAB released ZyIMAGE software bundle that included a fuzzy string search algorithm to overcome scanning and OCR errors. Currently ZyLAB develops eDiscovery and Information Governance solutions for On Premises, SaaS and Cloud installations. ZyLAB continues to develop new versions of its proprietary full-text search engine. 3 About ZyLAB • We develop for Windows environments only, including Azure • Software developed in C++, .Net, SQL • Typical clients are from law enforcement, intelligence, fraud investigators, law firms, regulators etc. • FTC (Federal Trade Commission), est. >50 TB (peak 2TB per day) • US White House (all email of presidential administration), est. >20 TB • Dutch National Police, est. >20 TB 4 ZyLAB Search Engine Full-text search engine • Boolean and Proximity search operators • Support for Wildcards, Regular Expressions and Fuzzy search • Support for numeric and date searches • Search in document fields • Near duplicate search 5 ZyLAB Search Engine • Highly parallel indexing and searching • Can index Terabytes of data and millions of documents • Can be distributed • Supports 100+ languages, Unicode characters 6 Overview Approximate string matching or fuzzy search is the technique of finding strings in text or dictionary that match given pattern approximately with respect to chosen edit distance. The edit distance is the number of primitive operations necessary to convert the string into an exact match.
    [Show full text]
  • Automating Error Frequency Analysis Via the Phonemic Edit Distance Ratio
    JSLHR Research Note Automating Error Frequency Analysis via the Phonemic Edit Distance Ratio Michael Smith,a Kevin T. Cunningham,a and Katarina L. Haleya Purpose: Many communication disorders result in speech corresponds to percent segments with these phonemic sound errors that listeners perceive as phonemic errors. errors. Unfortunately, manual methods for calculating phonemic Results: Convergent validity between the manually calculated error frequency are prohibitively time consuming to use in error frequency and the automated edit distance ratio was large-scale research and busy clinical settings. The purpose excellent, as demonstrated by nearly perfect correlations of this study was to validate an automated analysis based and negligible mean differences. The results were replicated on a string metric—the unweighted Levenshtein edit across 2 speech samples and 2 computation applications. distance—to express phonemic error frequency after left Conclusions: The phonemic edit distance ratio is well hemisphere stroke. suited to estimate phonemic error rate and proves itself Method: Audio-recorded speech samples from 65 speakers for immediate application to research and clinical practice. who repeated single words after a clinician were transcribed It can be calculated from any paired strings of transcription phonetically. By comparing these transcriptions to the symbols and requires no additional steps, algorithms, or target, we calculated the percent segments with a combination judgment concerning alignment between target and production. of phonemic substitutions, additions, and omissions and We recommend it as a valid, simple, and efficient substitute derived the phonemic edit distance ratio, which theoretically for manual calculation of error frequency. n the context of communication disorders that affect difference between a series of targets and a series of pro- speech production, sound error frequency is often ductions.
    [Show full text]
  • APPROXIMATE STRING MATCHING METHODS for DUPLICATE DETECTION and CLUSTERING TASKS by Oleksandr Rudniy
    Copyright Warning & Restrictions The copyright law of the United States (Title 17, United States Code) governs the making of photocopies or other reproductions of copyrighted material. Under certain conditions specified in the law, libraries and archives are authorized to furnish a photocopy or other reproduction. One of these specified conditions is that the photocopy or reproduction is not to be “used for any purpose other than private study, scholarship, or research.” If a, user makes a request for, or later uses, a photocopy or reproduction for purposes in excess of “fair use” that user may be liable for copyright infringement, This institution reserves the right to refuse to accept a copying order if, in its judgment, fulfillment of the order would involve violation of copyright law. Please Note: The author retains the copyright while the New Jersey Institute of Technology reserves the right to distribute this thesis or dissertation Printing note: If you do not wish to print this page, then select “Pages from: first page # to: last page #” on the print dialog screen The Van Houten library has removed some of the personal information and all signatures from the approval page and biographical sketches of theses and dissertations in order to protect the identity of NJIT graduates and faculty. ABSTRACT APPROXIMATE STRING MATCHING METHODS FOR DUPLICATE DETECTION AND CLUSTERING TASKS by Oleksandr Rudniy Approximate string matching methods are utilized by a vast number of duplicate detection and clustering applications in various knowledge domains. The application area is expected to grow due to the recent significant increase in the amount of digital data and knowledge sources.
    [Show full text]
  • Finite State Recognizer and String Similarity Based Spelling
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by BRAC University Institutional Repository FINITE STATE RECOGNIZER AND STRING SIMILARITY BASED SPELLING CHECKER FOR BANGLA Submitted to A Thesis The Department of Computer Science and Engineering of BRAC University by Munshi Asadullah In Partial Fulfillment of the Requirements for the Degree of Bachelor of Science in Computer Science and Engineering Fall 2007 BRAC University, Dhaka, Bangladesh 1 DECLARATION I hereby declare that this thesis is based on the results found by me. Materials of work found by other researcher are mentioned by reference. This thesis, neither in whole nor in part, has been previously submitted for any degree. Signature of Supervisor Signature of Author 2 ACKNOWLEDGEMENTS Special thanks to my supervisor Mumit Khan without whom this work would have been very difficult. Thanks to Zahurul Islam for providing all the support that was required for this work. Also special thanks to the members of CRBLP at BRAC University, who has managed to take out some time from their busy schedule to support, help and give feedback on the implementation of this work. 3 Abstract A crucial figure of merit for a spelling checker is not just whether it can detect misspelled words, but also in how it ranks the suggestions for the word. Spelling checker algorithms using edit distance methods tend to produce a large number of possibilities for misspelled words. We propose an alternative approach to checking the spelling of Bangla text that uses a finite state automaton (FSA) to probabilistically create the suggestion list for a misspelled word.
    [Show full text]
  • Adaptive Name Matching in Information Integration
    Information Integration on the Web Adaptive Name Matching in Information Integration Mikhail Bilenko and Raymond Mooney, University of Texas at Austin William Cohen, Pradeep Ravikumar, and Stephen Fienberg, Carnegie Mellon University hen you combine information from heterogeneous information sources, you W must identify data records that refer to equivalent entities. However, records that describe the same object might differ syntactically—for example, the same person can be referred to as “William Jefferson Clinton” and “bill clinton.” Figure 1 presents Identifying more complex examples of duplicate records that are tant in duplicate identification. We examine a few effec- not identical. tive and widely used metrics for measuring similarity. approximately Variations in representation across information sources can arise from differences in formats that Edit distance duplicate database store data, typographical and optical character recog- An important class of such metrics are edit dis- nition (OCR) errors, and abbreviations. Variations tances. Here, the distance between strings s and t is records that refer to are particularly pronounced in data that’s automati- the cost of the best sequence of edit operations that cally extracted from Web pages and unstructured or converts s to t. For example, consider mapping the the same entity is semistructured documents, making the matching task string s = “Willlaim” to t = “William” using these essential for information integration on the Web. edit operations: essential for Researchers have investigated the problem of identifying duplicate objects under several monikers, • Copy the next letter in s to the next position in t. information including record linkage, merge-purge, duplicate • Insert a new letter in t that does not appear in s.
    [Show full text]
  • A Parallel String Matching Tool
    Clgrep: A Parallel String Matching Tool Peng Wu a thesis submitted for the degree of Master of Science at the University of Otago, Dunedin, New Zealand. March 18, 2013 Abstract In this study, we widely investigate the problem of string matching in the context of Heterogeneous Parallel Computing. A overview of string match- ing is made, in which the different forms of string matching problem are distinguished, and the classifications of string matching algorithm are dis- cussed. As an alternative to grep for computational intensive string matching and in addition to support the research of the study, a parallel exact string matching utility `Clgrep' is developed. By experimental studies, we investigate the use of heuristics-based algo- rithms, specifically QS and Horspool algorithms, in the context of Het- erogeneous Parallel Computing. The results suggest that the performance of Heterogeneous Parallel Computing matching, either on multi-core CPU or GPU, is highly related to the computational intensity of certain cases. When computational power is intensively required, the SIMD Parallel Com- puting model of Clgrep can be several times more efficient than correspond- ing sequential matching program. ii Acknowledgements This study has been a difficult task as the problem is novel and involves many programming challenges. Everyday, I am fighting with the errors in the program and the issues raised in the experiment. I do not remember how many hours were spent in the complex experiment. However, I can not forget the people helped me during the study. I would like to thank my parents, without them my study would merely be a dream.
    [Show full text]
  • Improvising of Web Recommendation System by Using K-Means and Bitap Algorithm
    November 2017, Volume 4, Issue 11 JETIR (ISSN-2349-5162) IMPROVISING OF WEB RECOMMENDATION SYSTEM BY USING K-MEANS AND BITAP ALGORITHM 1 Dipali Wankhede,2 S.G.Tuppad 1ME Student,2Professor 1Computer Science, 1MSSCET, Jalna,India Abstract— The increase in the amount of information over the Internet in recent years has led to the risk of data flooding, which has resulted in problems accessing user-related information. In addition, the increase in the number of websites and web pages has made it difficult for webmasters to determine the content of their content. The online user's information needs can be obtained by evaluating the user's web usage. Web Based Data Mining (WUM) is used to extract knowledge from web users. Access logs are accessed by using data mining techniques. One of the WUM applications is a guideline, a privacy filtering technique used to determine whether a user approves a specified item or identifies a list of things that may be important to the user. This article is an architecture that combines product data with user access log data and generates a set of instructions for that user. The operation records interesting results in terms of precision, recall, and metrics. Index Terms— Web Usage mining, Online Web Recommendation System, Clustering, Pattern Matching, Boyer Moore, K- means, Recommendation I. INTRODUCTION In recent years, eCommerce, web services and web-based information systems have been used rapidly. The explosion of the web and the emergence of ecommerce have encouraged designers to develop. The ecommerce product introduction system has transformed the global business landscape.
    [Show full text]
  • NEWS for R Version 4.1.1 (2021-08-10)
    NEWS for R version 4.1.1 (2021-08-10) NEWS R News LATER NEWS • News for R 3.0.0 and later can be found in file `NEWS.Rd' in the R sources and files `NEWS' and `doc/html/NEWS.html' in an R build. CHANGES IN R VERSION 2.15.3 NEW FEATURES: • lgamma(x) for very small x (in the denormalized range) is no longer Inf with a warning. • image() now sorts an unsorted breaks vector, with a warning. • The internal methods for tar() and untar() do a slightly more general job for `ustar'- style handling of paths of more than 100 bytes. • Packages compiler and parallel have been added to the reference index (`refman.pdf'). • untar(tar = "internal") has some support for pax headers as produced by e.g. gnu- tar --posix (which seems prevalent on OpenSUSE 12.2) or bsdtar --format pax, including long path and link names. • sQuote() and dQuote() now handle 0-length inputs. (Suggestion of Ben Bolker.) • summaryRprof() returns zero-row data frames rather than throw an error if no events are recorded, for consistency. • The included version of PCRE has been updated to 8.32. • The tcltk namespace can now be re-loaded after unloading. The Tcl/Tk event loop is inhibited in a forked child from package parallel (as in e.g. mclapply()). • parallel::makeCluster() recognizes the value `random' for the environment vari- able R_PARALLEL_PORT: this chooses a random value for the port and reduces the chance of conflicts when multiple users start a cluster at the same time. UTILITIES: • The default for TAR on Windows for R CMD build has been changed to be `internal' if no tar command is on the path.
    [Show full text]