DOCSLIB.ORG

Department of Computer Science East Carolina University Self Study October 1, 2011

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Department of Computer Science East Carolina University Self Study October 1, 2011 Department of Computer Science East Carolina University Self Study October 1, 2011 1 Contents 1 Program Description 4 1.1 Exact title of the unit . .4 1.2 Department authorized to offer degree programs . .4 1.3 Exact titles of degrees granted . .4 1.4 College or school . .4 1.5 Brief history and mission . .4 1.6 Relationship of the program to UNC’s strategic goals, to the ECU mission and to ECU’s strategic directions . .9 1.7 Degree program objectives . 14 1.8 Program enrichment opportunities . 20 1.9 Responsiveness to local and national needs . 20 1.10 Program quality . 21 1.11 Administration . 21 2 Curriculum and Instruction 22 2.1 Foundation curriculum . 22 2.2 Instructional relationship to other programs . 23 2.3 Curriculum assessment and curricular changes . 24 2.4 Bachelor’s degrees . 27 2.5 Certificate programs . 31 2.6 Master’s degrees . 32 2.7 Doctoral degree . 35 3 Students 36 3.1 Enrollment . 36 3.2 Quality of incoming students . 37 3.3 Quality of current and outgoing students . 38 3.4 Degrees granted . 40 3.5 Diversity of student population . 41 3.6 Needs for graduates and student placement . 43 3.7 Funding . 44 3.8 Student involvement in the instructional process . 45 4 Faculty 46 4.1 Faculty list and curriculum vitae . 46 4.2 Faculty profile summary . 47 2 4.3 Visiting, part-time and other faculty . 48 4.4 Advising . 48 4.5 Faculty quality . 49 4.6 Faculty workload distribution . 50 5 Resources 52 5.1 Budget . 52 5.2 Space . 53 5.3 Technical and equipment support . 54 5.4 Library support . 57 6 Assessment of Outcomes and Faculty Expectations 61 6.1 Attributes that faculty expect graduates to attain . 61 6.2 How well is the program achieving expectations? . 65 6.3 Changes to be made and quality enhancement practices . 65 6.4 Assessment reports . 68 7 Research and Creative Activity 69 7.1 Current research and creative activity . 69 7.2 National comparison . 75 7.3 Interdisciplinary projects . 75 7.4 External research and creative activity support . 77 7.5 Research development . 78 7.6 Ethics training . 79 8 Service and Outreach 79 8.1 Consulting . 79 8.2 Community service and engagement . 79 8.3 Student involvement in community service and engagement 80 9 Accreditation 80 10 Summary Comments and Vision for the Future 81 10.1 Major strengths and weaknesses . 81 10.2 Vision and strategic plan . 82 Appendix A: Assessment Reports 87 Appendix B: Faculty Curriculum Vitae 3 1. Program Description 1.1. Exact title of the unit College of Technology and Computer Science, Department of Computer Science 1.2. Department authorized to offer degree pro- grams Department of Computer Science 1.3. Exact titles of degrees granted 1. Bachelor of Arts in Computer Science 2. Bachelor of Science in Computer Science 3. Master of Science in Computer Science 4. Master of Science in Software Engineering 1.4. College or school College of Technology and Computer Science 1.5. Brief history and mission Provide a brief history of the development of the unit undergraduate and graduate program(s). Briefly describe the vision and the mission of the pro- gram(s). Early History (Undergraduate Programs) Computer science classes were offered at ECU beginning in the mid 1960’s. The initial home department for computer science was the Department of Mathematics. This remained the case until Computer Science became its own department beginning fall 2000. In 1969 a minor in Information Science was offered that included courses in programming, computer organization, numerical analysis, 4 automata, and systems simulation. The first degree program in Com- puter Science was the BA degree which began in 1977. A BS profes- sional degree in Computer Science was offered beginning in 1986. It quickly became the more popular choice for students seeking a com- puter science major. One significant influence was the lack of a foreign language requirement for the BS degree. From the early 1990’s through the mid 2000’s, most curriculum revisions were made with the BS de- gree primarily in mind as discussed next. BS degree evolution As the 1990’s unfolded, the department engaged in several robust de- bates about the value of introducing computer science with and with- out programming in the first course. Initially, the introductory se- quence was a traditional two course programming sequence using the programming language Pascal as the tool, with the first course focused on programming and algorithmic fundamentals, and the second course focused on data structures and the requisite algorithms for manipula- tion of those structures (CSCI 2610 and 3510, each a three credit hour course) Early in the 1990’s, a decision was made to add a preliminary course in the degree program that did not involve programming (CSCI 2510). However, once a decision was made in the mid 1990’s to begin using C++ as the introductory programming language (a move made in order to make our students more marketable), it was quickly de- termined that CSCI 2610 and CSCI 3510 as currently structured were insufficient. A first attempt to remedy this deficiency was to increase the first programming course to a four credit hour course by including a two hour closed lab each week. However, even this was insufficient, and consequently CSCI 2510 began to include a few weeks of C++ programming anyway. The next major curriculum revision occurred in the 2002–2004 time frame when a two-part discussion ensued. One part was a dis- cussion about how to improve the retention of intellectually capable students who lacked sufficient background in problem solving. The second part was about the role of Java as a tool in teaching computer science. Starting from an initial proposal offered by Dr. Karl Abraham- son, the faculty reached a consensus that it would be useful if students were exposed to Java early in their studies. However, there was also a group of faculty who felt that it was important that students also gain exposure to concrete data structures and memory management issues that could best be studied if C++ and pointers were used. Eventually 5 the decision was reached to change the introductory sequence into four courses: 1. A computer science survey course (3 credit hours). 2. A traditional introduction to algorithmic problem solving and programming using Java (4 credit hours, included 2 hour closed lab). (CSCI 2310/11) 3. An introduction to algorithms and data structures that focused on concrete data structures and pointers using C++ (4 credit hours, extra hour for time to learn about new language) (CSCI 3300). 4. An advanced data structures and data abstraction course that could be taught using either C++ (and the STL), or Java (and its API) (CSCI 3310). However, in all cases since this course was first offered in spring 2006, the instructor has used Java. As part of the process, prerequisites for some advanced courses were revised to allow students to being taking some (but not all) ad- vanced courses after completing CSCI 3300 instead of waiting until CSCI 3310 was completed. However, this four sequence introduction did not last long as we began to focus on the process of securing ABET accreditation. In order to have the curriculum meet ABET standards, a second required course in Software Engineering was added. To keep the number of required hours at a reasonable level, the introductory computer science survey course was eliminated. The curriculum changes motivated by the goal of achieving ABET accreditation took effect during 2007. In terms of the other elements of the degree program with respect to computer science core offerings and electives, we have aligned our- selves fairly close to the ACM Computing Curriculum reports that have been issued over the years. Electives have been created based on fac- ulty research and teaching interests. A topics course (CSCI 4905) was introduced to enable “hot topics” to be offered more quickly. Required hours of mathematics and science have been increased to be compli- ant with ABET standards. Students are now required to complete at least two semesters of calculus, two semesters of statistics, and twelve hours of science. In addition, students continue to complete a course in discrete mathematics and a course in linear algebra. These last two courses are taught within the Computer Science department. 6 BA degree evolution As the various changes to the introductory curriculum took place form about 1990–2006, the BA degree was basically “along for the ride”, and very little attention was paid to it, partly because there were few BA students. Thanks to the initiative of our new chair in 2006, Dr. John Placer, the faculty revised the BA program during the academic year 2006–2007 to give it a more unique identity. As noted in the explanatory memo provided to the University Curriculum Committee when the proposed changes were submitted during fall semester 2006. The curriculum for the BA degree program in computer science is being modified to give the program an emphasis on practical skills in problem solving that can be applied to a variety of areas. The changes also highlight the impor- tance of professional ethics and the need for a student to develop good communication skills. Some of the more the- oretical courses in the core are being replaced by computer science courses that focus on important practical knowledge in computing such as database management, computer net- works, and the functionality of systems software. The calcu- lus course is being replaced by the more practical and com- monly needed statistics course. These changes along with the BA degrees requirements for a minor and the study of a foreign language will produce a well rounded graduate of computer science that is ready to assume employment in a variety of business and technical environments.
Load more

Recommended publications
  • Genetic Operators for Combinatorial Optimization in TSP and Microarray Gene Ordering
    Appl Intell (2007) 26:183–195 DOI 10.1007/s10489-006-0018-y Genetic operators for combinatorial optimization in TSP and microarray gene ordering Shubhra Sankar Ray · Sanghamitra Bandyopadhyay · Sankar K. Pal Published online: 9 November 2006 C Springer Science + Business Media, LLC 2007 Abstract This paper deals with some new operators of ge- 1 Introduction netic algorithms and[-27pc] demonstrates their effectiveness to the traveling salesman problem (TSP) and microarray The Traveling Salesman Problem (TSP) is one of the top ten gene ordering. The new operators developed are nearest problems, which has been addressed extensively by mathe- fragment operator based on the concept of nearest neigh- maticians and computer scientists. It has been used as one of bor heuristic, and a modified version of order crossover op- the most important test-beds for new combinatorial optimiza- erator. While these result in faster convergence of Genetic tion methods [1]. Its importance stems from the fact there is Algorithm (GAs) in finding the optimal order of genes in mi- a plethora of fields in which it finds applications e.g., shop croarray and cities in TSP, the nearest fragment operator can floor control (scheduling), distribution of goods and services augment the search space quickly and thus obtain much bet- (vehicle routing), product design (VLSI layout), microarray ter results compared to other heuristics. Appropriate number gene ordering and DNA fragment assembly. Since the TSP of fragments for the nearest fragment operator and appropri- has proved to belong to the class of NP-hard problems [2], ate substring length in terms of the number of cities/genes heuristics and metaheuristics occupy an important place in for the modified order crossover operator are determined sys- the methods so far developed to provide practical solutions tematically.
    [Show full text]
  • Neural Networks Technique for the Control of Artificial Mobile Agents
    NEURAL NETWORKS TECHNIQUE FOR THE CONTROL OF ARTIFICIAL MOBILE AGENTS A PROJECT THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Bachelor of Technology In Mechanical Engineering By Pratik Ranjan Bhanjdeo (111ME0284) Under Supervision of Prof. Dayal Ramakrushna Parhi Department of Mechanical Engineering National Institute of Technology, Rourkela 1 CERTIFICATE National Institute of Technology, Rourkela This is to certify that the work contained in this thesis, titled “NEURAL NETWORKS TECHNIQUE FOR THE CONTROL OF ARTIFICIAL MOBILE AGENTS” submitted by Pratik Ranjan Bhanjdeo (111ME0284) is an authentic work that has been carried out by him under my supervision and guidance in partial fulfillment for the requirement for the award of Bachelor of Technology Degree in Mechanical Engineering at National Institute of Technology, Rourkela. To the best of my knowledge, the matter embodied in the thesis has not been submitted to any other University/ Institute for the award of any Degree or Diploma. Place: Rourkela Date: 11TH MAY, 2015 Dr. Dayal Ramakrushna Parhi Professor Department of Mechanical Engineering National Institute of Technology Rourkela – 769008 2 Acknowledgment I am grateful to The Department of Mechanical Engineering for giving me the opportunity to carry out this project, which is an integral fragment of the curriculum in B. Tech at the National Institute of Technology, Rourkela. I would like to express my heartfelt gratitude and regards to my project guide, Prof. Dayal Ramakrushna Parhi, Department of Mechanical Engineering, for being the corner stone of the project. It was his incessant motivation and guidance during periods of doubts and uncertainties that has helped me to carry on with this project.
    [Show full text]
  • A Comprehensive Survey of Trends in Oracles for Software Testing
    1 A Comprehensive Survey of Trends in Oracles for Software Testing Mark Harman, Phil McMinn, Muzammil Shahbaz and Shin Yoo Abstract—Testing involves examining the behaviour of a system in order to discover potential faults. Determining the desired correct behaviour for a given input is called the “oracle problem”. Oracle automation is important to remove a current bottleneck which inhibits greater overall test automation; without oracle automation, the human has to determine whether observed behaviour is correct. The literature on oracles has introduced techniques for oracle automation, including modelling, specifications, contract-driven development and metamorphic testing. When none of these is completely adequate, the final source of oracle information remains the human, who may be aware of informal specifications, expectations, norms and domain specific information that provide informal oracle guidance. All forms of oracle, even the humble human, involve challenges of reducing cost and increasing benefit. This paper provides a comprehensive survey of current approaches to the oracle problem and an analysis of trends in this important area of software testing research and practice. Index Terms—Test oracle; Automatic testing; Testing formalism. F 1 INTRODUCTION undecidable problem. For example, attempting to cover all branches [6], [10], [16], [64], [77], Much work on testing seeks to automate as [159], [177] in a system under test can never be much of the test process as practical and de- complete because branch reachability is known sirable, in order to make testing faster, cheaper to be undecidable in general [192]. and more reliable. In order to automate testing, The problem of generating test inputs to we need an oracle, a procedure that determines achieve coverage of test adequacy criterion what the correct behaviour of a system should has been the subject of research interest for be for all input stimuli with which we wish nearly four decades [48], [106] and has been to subject the system under test.
    [Show full text]
  • Finalabc.Pdf
    A. Department goals and objectives are linked to the university and college mission and strategic priorities, and to their strategy for improving their position within the discipline. (1 page maximum) 1. What is the department’s mission and is it clearly aligned with the university and college mission and direction? The department strives to offer quality undergraduate and graduate courses / programs; places a high priority on quality research and effective teaching; seeks to attract highly- qualified students; and strives to attract and nurture high-caliber faculty. This mission is consistent with university goals of ensuring student success, enhancing academic excellence, expanding breakthrough research, and developing / recognizing our people. 2. How does the department’s mission relate to curriculum; enrollments; faculty teaching; research/professional/creative activity and outreach? Is it aligned with the college’s strategic priorities? The departmental mission aligns with university and college strategic priorities, although the department is now in financial jeopardy under KSU’s new RCM budget model due to developmental growth in faculty numbers under state direction to grow a doctoral program. 3. How does the department contribute to university-wide curricular needs through general education and service instruction? The department was limited to a single Liberal Education Course (CS 10051 Introduction to Computer Science), but may have an opportunity to offer more courses under the new Kent Core curriculum. However, unlike many universities, KSU does not have a university-wide computing requirement, and does not have a large number of engineering majors taking CS courses. 4. How does the department promote diversity? The department has an active NSF S-STEM grant for broadening participation, and a diverse set of faculty, two of whom are women, and outreach activities to women and other minority students in computing.
    [Show full text]
  • Information Processing Beyond Quantum Computation
    Information Processing beyond Quantum Computation∗ Apoorva Patel Centre for High Energy Physics and Supercomputer Education and Research Centre Indian Institute of Science, Bangalore-560012, India E-mail: [email protected] Abstract– Recent developments in quantum computation have Instructions made it clear that there is a lot more to computation than the con- ventional Boolean algebra. Is quantum computation the most ❄ general framework for processing information? Having gath- Physical Input ✲ ✲ Output ered the courage to go beyond the traditional definitions, we are Device now in a position to answer: Certainly not. The meaning of a mes- sage being “a collection of building blocks” can be explored in a ✻ variety of situations. A generalised computational framework is proposed based on group theory, and it is illustrated with well- Oracles/Look-up Tables known physical examples. A systematic information theoretical Fig. 1. Schematic representation of a computer. approach is yet to be developed in many of these situations. Some Every computation may not use oracles or look-up tables. directions for future development are pointed out. drawn parallel to a given line and passing through a point out- side the given line. Euclid considered one parallel line to be I. MOTIVATION the self-evident answer, but could not prove that. So he in- cluded this property as a postulate in his theory, even though The silicon transistor was invented about half a century ago. it was quite different from the first four which defined basic Since then the semiconductor technology has grown at a rapid components of geometry. This state of affairs no doubt trou- pace to pervade almost all aspects of our lives.
    [Show full text]
  • Revisited: Machine Intelligence in Heterogeneous Multi Agent Systems
    Revisited: Machine Intelligence in Heterogeneous Multi Agent Systems Kaustav Jyoti Borah1, Department of Aerospace Engineering, Ryerson University, Canada email: [email protected] Rajashree Talukdar2, B. Tech, Department of Computer Science and Engineering, SMIT, India. Abstract: Machine learning techniques have been widely applied for solving decision making problems. Machine learning algorithms perform better as compared to other algorithms while dealing with complex environments. The recent development in the area of neural network has enabled reinforcement learning techniques to provide the optimal policies for sophisticated and capable agents. In this paper we would like to explore some algorithms people have applied recently based on interaction of multiple agents and their components. We would like to provide a survey of reinforcement learning techniques to solve complex and real-world scenarios. 1. Introduction An entity that recognizes its ambience with the help of sensors and uses its effectors to act upon that environment is called an agent [1]. Multi agent systems is a subfield of machine learning is used in many intelligent autonomous systems to make it smarter. To coordinate with other independent agents’ behaviour in multiple agent systems environment, ML techniques aims to provide principles for construction of complex systems. When the agents are not dependent of one another in such a way that they can have approach to the environment independently. Hence, they need to embrace new circumstances. Therefore, learning and exploring about the environment demands the inclusion of a learning algorithm for each agent. Some amount of interaction becomes mandatory amongst the different agents in a multi agent system for them to act like a group.
    [Show full text]
  • Dr. Shobha G NATIONAL/INTERNATIONAL
    Dr. Shobha G NATIONAL/INTERNATIONAL CONFERENCES 1. Shobha.G, M. Krishna, S C Sharma, Neural Network Approach Towards Analysis and Prediction of Behavioral Patterns of Stock Market, Proceedings of International Conference on Systemics, Cybernetics and Informatics , Hyderabad, India, Vol 2 pg 400- 403 2004. 2. Shobha.G, M. Krishna, S C Sharma, e-Market Integrator , Proceedings of 7th International Conference on Signal Processing , Beijing China,Vol 3 pg 2612-2615 2004. 3. Shobha.G, M. Krishna, S C Sharma, Java code generator for a PL/SQLwarehouse builder , Proceedings of International Conference on Computers, Controls and Communications, Chennai, India,Vol 1 pg 96- 104 2004. 4. Shobha.G, M. Krishna, S C Sharma,Self organizing map neural network based data mining with cluster computing in inventory applications, Proceedings of Asia Pacific Conference on parallel and Distributed Computing Technologies ,Vol 1 pg 455-459 2004. 5. Shobha.G, M. Krishna, S C Sharma, Neural Network Approach For Cluster Computing Database Applications , Proceedings of First World Congress on Lateral Computing, WCLC 2004, Catalog No. 18122004, 2004. 6. Shobha.G, M. Krishna, S C Sharma, Signature Verification and Forgery Detection , Proceedings of of International Conference on Systemics, Cybernetics and Informatics , Hyderabad, India, Vol 1 pg 637 – 639, 2005. 7. Shobha.G, M. Krishna, S C Sharma, Mining Association Rules For Large DataSets , Proceedings of of International Conference on Data Mining , Las Vegas, Nevada, USA-2006. 8. Shobha.G, Predictor System for industrial data publications in the proceedings of the Second International conference on innovative computing, information and control,0-7695- 1-2882-1/07© 2007 IEEE, Japan, 2007.
    [Show full text]
  • Skruzdžių Kolonijų Technologijos Vaizdams Apdoroti
    VILNIAUS GEDIMINO TECHNIKOS UNIVERSITETAS Raimond LAPTIK SKRUZDŽIŲ KOLONIJŲ TECHNOLOGIJOS VAIZDAMS APDOROTI DAKTARO DISERTACIJA TECHNOLOGIJOS MOKSLAI, ELEKTROS IR ELEKTRONIKOS INŽINERIJA (01T) Vilnius 2009 Disertacija rengta 2005–2009 metais Vilniaus Gedimino technikos universitete. Mokslinis vadovas prof. dr. Dalius NAVAKAUSKAS (Vilniaus Gedimino technikos universitetas, technologijos mokslai, elektros ir elektronikos inžinerija – 01T). VGTU leidyklos TECHNIKA 1675-M mokslo literatūros knyga http://leidykla.vgtu.lt ISBN 978-9955-28-501-4 © VGTU leidykla TECHNIKA, 2009 © Laptik, R., 2009 [email protected] VILNIUS GEDIMINAS TECHNICAL UNIVERSITY Raimond LAPTIK ANT COLONY TECHNOLOGIES FOR IMAGE PROCESSING DOCTORAL DISSERTATION TECHNOLOGICAL SCIENCES, ELECTRICAL AND ELECTRONIC ENGINEERING (01T) Vilnius 2009 Doctoral dissertation was prepared at Vilnius Gediminas Technical University in 2005–2009. Scientific supervisor Prof Dr Dalius NAVAKAUSKAS (Vilnius Gediminas Technical University, Technological Sciences, Electrical and Electronic Engineering – 01T). Santrauka Disertacijoje nagrinėjamos vaizdų apdorojimo skruzdžių kolonijomis tech- nologijos ir susieti dalykai: optimizavimo skruzdžių kolonijomis algoritmai, vaizdų apdorojimo metodai ir jų įgyvendinimas lauku programuojamomis lo- ginėmis matricomis. Tikslas yra pasiūlyti ir ištirti optimizavimu skruzdžių ko- lonijomis grįstus vaizdų apdorojimo būdus ir priemones. Atliekama analitinė optimizavimo skruzdžių kolonijomis technologijų literatūros apžvalga, pagrin- džiant konkrečių vaizdų
    [Show full text]
  • Organization and Committees
    Committees General Chair Aleksander Spivakovsky, Verkhovna Rada of Ukraine, Kherson State University, Ukraine Steering Committee Vadim Ermolayev, Zaporizhzhya National University, Ukraine Heinrich C. Mayr, Alpen-Adria-Universät Klagenfurt, Austria Mykola Nikitchenko, Taras Shevchenko National University of Kyiv, Ukraine Aleksander Spivakovsky, Verkhovna Rada of Ukraine, Kherson State University, Ukraine Mikhail Zavileysky, DataArt, Russian Federation Grygoriy Zholtkevych, V.N.Karazin Kharkiv National University, Ukraine Program Chairs Nick Bassiliades, Aristotle University of Thessaloniki, Greece Vadim Ermolayev, Zaporizhzhya National University, Ukraine Proceedings Chairs Hans-Georg Fill, Universität Wien, Austria Vitaliy Yakovyna, Lviv Polytechnic National University, Ukraine Presentations Chair Heinrich C. Mayr, Alpen-Adria-Universät Klagenfurt, Austria Workshops Chair Vyacheslav Kharchenko, National Aerospace University “KhAI”, Ukraine Poster and Demo Chair Yaroslav Prytula, Ukrainian Catholic University, Ukraine PhD Symposium Chairs Grygoriy Zholtkevych, V.N. Karazin Kharkiv National University, Ukraine Frédéric Mallet, Université Cote d’Azur, Cnrs, Inria, I3S, France IT Talks Chairs Aleksander Spivakovsky, Verkhovna Rada of Ukraine, Kherson State University, Ukraine Mikhail Zavileysky, DataArt, Russian Federation Local Organization Chairs Anatoly Anisimov (chair), Taras Shevchenko National University of Kyiv, Ukraine Mykola Nikitchenko (chair), Taras Shevchenko National University of Kyiv, Ukraine Volodymyr Shevchenko (vice-chair),
    [Show full text]
  • Research and Creative Achievement Week 2011
    East Carolina2011 Universi ty : Research and Creative Achievement Week 2011 East Carolina University Creating a Better Tomorrow 5th Annual Research & Creative Achievement Week Mendenhall Student Center April 4 – 8 2011 1 East Carolina University : Research and Creative Achievement Week 2011 Research and Creative Achievement Week April 4 – 8, 2011 Mendenhall Student Center 2 East Carolina University : Research and Creative Achievement Week 2011 Research and Creative Achievement Week Table of Contents Letter from Vice Chancellor Deirdre M. Mageean…………………………... 4 Program Sponsors……………………………………………………………. 6 Research and Creative Achievement Week Planning Committee…………… 7 Research and Creative Achievement Week Subcommittees………………… 8 Schedule of Events…………………………………………………………… 9 Faculty Recognition: Research and Creative Activity……………………….. 12 Faculty Recognition: Inventor Discovery……………………………………. 17 Lectures and Symposia………………………………………………………. 18 Presentation of Awards………………………………………………………. 29 Index: Judges…………………………………………………………………. 32 Index: Mentors……………………………………………………………….. 34 Oral and Poster Presentation Sessions……………………………………… 38 Graduate Abstracts: Online, Oral, and Poster………………………………... 64 Undergraduate Abstracts: Online, Oral, and Poster………………………….. 174 3 East Carolina University : Research and Creative Achievement Week 2011 Letter from the Vice Chancellor March 2011 Dear ECU Community: The Division of Research and Graduate Studies invites you to participate in the ECU Research and Creative Achievement Week on the campus of East Carolina University. The week of April 4-8, 2011, has been set aside to highlight the extraordinary accomplishments of our students in research and creative activities. Because the number of events has increased, events will also be held the previous week. It is the hope of the organizing committee that you will attend, as much as your time allows, in order to see and hear what our students have achieved.
    [Show full text]
  • Neural Modeling of Flow Rendering Effectiveness
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Huddersfield Repository Examining Applying High Performance Genetic Data Feature Selection and Classification Algorithms for Colon Cancer Diagnosis MURAD AL-RAJAB, JOAN LU AND QIANG XU, University of Huddersfield, United Kingdom [email protected], [email protected], [email protected] Abstract Background and Objectives: This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. Methods: In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. Results: It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%).
    [Show full text]
  • Abstract Book RCAW 2015 03 20 Book.Pdf
    2015 We would like to give a special thanks to ECU School of Art and Design graphic design undergraduate student Kristen Bitar, for her cover design, poster, and program art. We would also like to recognize Master of Adult Education student Laura Taylor and Doctoral of Physics student Taylor Dement, for their development and managment of the program. 4 2015 | RCAW East Carolina University® Division of Research & March 2015 Graduate Studies Greenville Centre Ste 1500 2200 S. Charles Blvd Greetings! Mailstop 157 Greenville, NC 27858-4353 I am pleased to invite you to participate in the East Carolina University Research and Creative Achievement Week (RCAW). The week of March 23-27, 2015, has been set aside to highlight the extraordinary accomplishments of our students in research and creative activities. It is the hope of the organizing committee Michael R. Van Scott, PhD Institutional Official & Chief that you will attend, as much as your time allows, in order to see and hear what our students have achieved. Research Officer; In addition, we hope that you will strongly encourage your students to attend. The event is sponsored by a Associate Vice Chancellor, Interim 252-328-9471 partnership of these entities: Division of Academic Affairs, Division of Health Sciences, Brody Graduate Student 252-328-2769 fax Association, Graduate and Professional Student Senate, Office of Undergraduate Research, Office of Postdoctoral Affairs, Graduate School, and Division of Research & Graduate Studies. Research and Creative Achievement Week is a showcase of graduate and undergraduate student research and creative activities that are taking place here at ECU. There will be over 375 student presentations, an impressive number that reflects the current growth in research and creative activities at ECU in a variety of fields and disciplines.
    [Show full text]