This paper discusses the several research methodologies that can
be used in Computer Science (CS) and Information Systems
(IS). The research methods vary according to the science
domain and project field. However a little of research
methodologies can be reasonable for Computer Science and
Information System.
This paper discusses the several research methodologies that can
be used in Computer Science (CS) and Information Systems
(IS). The research methods vary according to the science
domain and project field. However a little of research
methodologies can be reasonable for Computer Science and
Information System.
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This paper discusses the several research methodologies that can
be used in Computer Science (CS) and Information Systems
(IS). The research methods vary according to the science
domain and project field. However a little of research
methodologies can be reasonable for Computer Science and
Information System.
call for papers, research paper publishing, where to publish research paper, journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJEI, call for papers 2012,journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, research and review articles, engineering journal, International Journal of Engineering Inventions, hard copy of journal, hard copy of certificates, journal of engineering, online Submission, where to publish research paper, journal publishing, international journal, publishing a paper, hard copy journal, engineering journal
Cloud computing in eHealthis an emerging area for only few years. There needs to identify the state of the
art and pinpoint challenges and possible directions for researchers and applications developers. Based on
this need, we have conducted a systematic review of cloud computing in eHealth. We searched ACM
Digital Library, IEEE Xplore, Inspec, ISI Web of Science and Springer as well as relevant open-access
journals for relevant articles. A total of 237 studies were first searched, of which 44 papers met the Include
Criteria. The studies identified three types of studied areas about cloud computing in eHealth, namely (1)
cloud-based eHealth framework design (n=13);(2) applications of cloud computing (n=17); and (3)
security or privacy control mechanisms of healthcare data in the cloud (n=14). Most of the studies in the
review were about designs and concept-proof. Only very few studies have evaluated their research in the
real world, which may indicate that the application of cloud computing in eHealth is still very immature.
However, our presented review could pinpoint that a hybrid cloud platform with mixed access control and
security protection mechanisms will be a main research area for developing citizen centred home-based
healthcare applications.
The main objective of this paper is to develop a basic prototype model which can determine and extract
unknown knowledge (patterns, concepts and relations) related with multiple factors from past database records of
specific students. Data mining is science and engineering study of extracting previously undiscovered patterns
from a huge set of data. Data mining techniques are helpful for decision making as well as for discovering patterns
of data. In this paper students eligibility prediction system using Rule based classification is proposed to predict
the eligibility of students based on their details with high prediction accuracy. In Educational Institutes, a
tremendous amount of data is generated. This paper outlines the idea of predicting a particular student’s placement
eligibility by performing operations on the data stored. In this paper an efficient algorithm with the technique
Fuzzy for prediction is proposed.
Predictive models are quasi experimental structures used to determine the future
patterns in data. These meaningful data patterns form the building block of any
decision support system. Researchers all over the world have built many prediction
models for major industries. Research works in the educational sector has increased
steeply. This steep increase may be due to the high availability of data in the
educational domain. This survey tries to comprehend a few literary works on
academic performance prediction of engineering students with the focus on grade
predictions. Meaningful interpretations have been made and inferences are presented
at the end of this paper
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
Software Effort Estimation using Neuro Fuzzy Inference System: Past and Presentrahulmonikasharma
Most important reason for project failure is poor effort estimation. Software development effort estimation is needed for assigning appropriate team members for development, allocating resources for software development, binding etc. Inaccurate software estimation may lead to delay in project, over-budget or cancellation of the project. But the effort estimation models are not very efficient. In this paper, we are analyzing the new approach for estimation i.e. Neuro Fuzzy Inference System (NFIS). It is a mixture model that consolidates the components of artificial neural network with fuzzy logic for giving a better estimation.
Data Mining Techniques for School Failure and Dropout SystemKumar Goud
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
An Overview of Data Mining Concepts Applied in Mathematical Techniquesijcoa
Data Mining and Knowledge Discovery in Databases (KDD) are rapidly evolving areas of research that contribute to several disciplines including Mathematics, Statistics, Pattern Recognition, Visualization, and Parallel Computing. This paper is projected to serve as an overview to the concepts of these rapidly evolving research and application areas. The basic concepts of these areas are outlined in this paper with some key ideas and motivate the importance of data mining concepts applied in Mathematical Techniques
A Model of Decision Support System for Research Topic Selection and Plagiaris...theijes
The paper proposes a model of the decision support system for deciding a research topic in academia. The biggest challenge for a student in the field of research is to identify area and topic of research. The paper explains the model which helps student to identify the most suitable area and/or topic for academic research. The model is also design to assist supervisors to explore latest areas of research as well as to get rid of non intentional plagiarism. The model facilitates the user to select either keyword bases topic search or questionnaire based topic search. The model uses local database and service of a Meta search engine in decision making activity
Significant Role of Statistics in Computational SciencesEditor IJCATR
This paper is focused on the issues related to optimizing statistical approaches in the emerging fields of Computer Science
and Information Technology. More emphasis has been given on the role of statistical techniques in modern data mining. Statistics is
the science of learning from data and of measuring, controlling, and communicating uncertainty. Statistical approaches can play a vital
role for providing significance contribution in the field of software engineering, neural network, data mining, bioinformatics and other
allied fields. Statistical techniques not only helps make scientific models but it quantifies the reliability, reproducibility and general
uncertainty associated with these models. In the current scenario, large amount of data is automatically recorded with computers and
managed with the data base management systems (DBMS) for storage and fast retrieval purpose. The practice of examining large preexisting
databases in order to generate new information is known as data mining. Presently, data mining has attracted substantial
attention in the research and commercial arena which involves applications of a variety of statistical techniques. Twenty years ago
mostly data was collected manually and the data set was in simple form but in present time, there have been considerable changes in
the nature of data. Statistical techniques and computer applications can be utilized to obtain maximum information with the fewest
possible measurements to reduce the cost of data collection.
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
Cloud computing in eHealthis an emerging area for only few years. There needs to identify the state of the
art and pinpoint challenges and possible directions for researchers and applications developers. Based on
this need, we have conducted a systematic review of cloud computing in eHealth. We searched ACM
Digital Library, IEEE Xplore, Inspec, ISI Web of Science and Springer as well as relevant open-access
journals for relevant articles. A total of 237 studies were first searched, of which 44 papers met the Include
Criteria. The studies identified three types of studied areas about cloud computing in eHealth, namely (1)
cloud-based eHealth framework design (n=13);(2) applications of cloud computing (n=17); and (3)
security or privacy control mechanisms of healthcare data in the cloud (n=14). Most of the studies in the
review were about designs and concept-proof. Only very few studies have evaluated their research in the
real world, which may indicate that the application of cloud computing in eHealth is still very immature.
However, our presented review could pinpoint that a hybrid cloud platform with mixed access control and
security protection mechanisms will be a main research area for developing citizen centred home-based
healthcare applications.
The main objective of this paper is to develop a basic prototype model which can determine and extract
unknown knowledge (patterns, concepts and relations) related with multiple factors from past database records of
specific students. Data mining is science and engineering study of extracting previously undiscovered patterns
from a huge set of data. Data mining techniques are helpful for decision making as well as for discovering patterns
of data. In this paper students eligibility prediction system using Rule based classification is proposed to predict
the eligibility of students based on their details with high prediction accuracy. In Educational Institutes, a
tremendous amount of data is generated. This paper outlines the idea of predicting a particular student’s placement
eligibility by performing operations on the data stored. In this paper an efficient algorithm with the technique
Fuzzy for prediction is proposed.
Predictive models are quasi experimental structures used to determine the future
patterns in data. These meaningful data patterns form the building block of any
decision support system. Researchers all over the world have built many prediction
models for major industries. Research works in the educational sector has increased
steeply. This steep increase may be due to the high availability of data in the
educational domain. This survey tries to comprehend a few literary works on
academic performance prediction of engineering students with the focus on grade
predictions. Meaningful interpretations have been made and inferences are presented
at the end of this paper
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
Software Effort Estimation using Neuro Fuzzy Inference System: Past and Presentrahulmonikasharma
Most important reason for project failure is poor effort estimation. Software development effort estimation is needed for assigning appropriate team members for development, allocating resources for software development, binding etc. Inaccurate software estimation may lead to delay in project, over-budget or cancellation of the project. But the effort estimation models are not very efficient. In this paper, we are analyzing the new approach for estimation i.e. Neuro Fuzzy Inference System (NFIS). It is a mixture model that consolidates the components of artificial neural network with fuzzy logic for giving a better estimation.
Data Mining Techniques for School Failure and Dropout SystemKumar Goud
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
An Overview of Data Mining Concepts Applied in Mathematical Techniquesijcoa
Data Mining and Knowledge Discovery in Databases (KDD) are rapidly evolving areas of research that contribute to several disciplines including Mathematics, Statistics, Pattern Recognition, Visualization, and Parallel Computing. This paper is projected to serve as an overview to the concepts of these rapidly evolving research and application areas. The basic concepts of these areas are outlined in this paper with some key ideas and motivate the importance of data mining concepts applied in Mathematical Techniques
A Model of Decision Support System for Research Topic Selection and Plagiaris...theijes
The paper proposes a model of the decision support system for deciding a research topic in academia. The biggest challenge for a student in the field of research is to identify area and topic of research. The paper explains the model which helps student to identify the most suitable area and/or topic for academic research. The model is also design to assist supervisors to explore latest areas of research as well as to get rid of non intentional plagiarism. The model facilitates the user to select either keyword bases topic search or questionnaire based topic search. The model uses local database and service of a Meta search engine in decision making activity
Significant Role of Statistics in Computational SciencesEditor IJCATR
This paper is focused on the issues related to optimizing statistical approaches in the emerging fields of Computer Science
and Information Technology. More emphasis has been given on the role of statistical techniques in modern data mining. Statistics is
the science of learning from data and of measuring, controlling, and communicating uncertainty. Statistical approaches can play a vital
role for providing significance contribution in the field of software engineering, neural network, data mining, bioinformatics and other
allied fields. Statistical techniques not only helps make scientific models but it quantifies the reliability, reproducibility and general
uncertainty associated with these models. In the current scenario, large amount of data is automatically recorded with computers and
managed with the data base management systems (DBMS) for storage and fast retrieval purpose. The practice of examining large preexisting
databases in order to generate new information is known as data mining. Presently, data mining has attracted substantial
attention in the research and commercial arena which involves applications of a variety of statistical techniques. Twenty years ago
mostly data was collected manually and the data set was in simple form but in present time, there have been considerable changes in
the nature of data. Statistical techniques and computer applications can be utilized to obtain maximum information with the fewest
possible measurements to reduce the cost of data collection.
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
Knowledge Management Cultures: A Comparison of Engineering and Cultural Scien...Ralf Klamma
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Lugović, S., Čolić, M., & Dunđer, I. (2014, January), Znanstveni pristup dizajnu informacijskih sustava, Design Science and Information Systems, Overview of Design Science models over the years presented @ International Scientific Conference On Printing & Design 2014
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The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
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Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
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Accelerate your Kubernetes clusters with Varnish Caching
A Review of Intelligent Agent Systems in Animal Health Care
1. A Review of Intelligent Agent Systems in Animal
Health Care
Omankwu, Obinnaya Chinecherem, Nwagu, Chikezie Kenneth, and Inyiama, Hycient
1
Computer Science Department, Michael Okpara University of Agriculture, Umudike
Umuahia, Abia State, Nigeria
saintbeloved@yahoo.com
2
Computer Science Department, Nnamdi Azikiwe University, Awka Anambra State, Nigeria,
Nwaguchikeziekenneth@hotmail.com
3
Electronics & Computer Engineering Department, Nnamdi Azikiwe University, Awka
Anambra State, Nigeria.
ABSTRACT
This paper discusses the several research methodologies that can
be used in Computer Science (CS) and Information Systems
(IS). The research methods vary according to the science
domain and project field. However a little of research
methodologies can be reasonable for Computer Science and
Information System.
Keywords- Computer Science (CS), Information Systems (IS),
Research Methodologies.
1. INTRODUCTION
Science is an organized or systematic body of knowledge (Jain,
1997). Science embraces many different domains however those
domains are related. Logic and mathematics sciences are the
core of all sciences. From there and descending it emerge the
natural sciences such as physic, chemistry and biology. In the
next come the social sciences.
As (Denning, 2005) reported Computer Science (CS) should not
be called as a science. Although CS is definitely a recent
discipline, few will still argued that it is not provided with
attributes to be qualified as a science. Computer Science has its
specificity and has its bases in logic and mathematics.
However CS is transversal to very different domains in science.
To understand which research methodologies that can be used in
CS and IS we have to understand the differences between CS
and IS.
Computer science (CS) characterized as an empirical discipline,
in which each new program can be seen as an experiment, the
structure and behavior of which can be studied (Allen ,2003). In
particular, the field of CS is concerned with a number of
different issues seen from a technological Perspective, e.g.
theoretical aspects, such as numerical analysis, data Structures
and algorithms; how to store and manipulate, the relationship
between different pieces of software and techniques and tools
for developing software .The field of Information Systems (IS)
is concerned with the interaction between social and
technological issues (Allen ,2003).
In other words, it is a field which focuses on the actual
“link” between the human and social aspects (within an
organization or other broader social setting), and the
hardware, Software and data aspects of information
technology (IT). In the next sections we will discuss the
different methods of research methodology and its
reasonability for the CS and IS domains.
Research Methods
Before we start in discuss the different types of research
methodologies we have to define the research. In an
academic context, research is used to refer to the activity of
a diligent and systematic inquiry or investigation in an area,
with the objective of discovering or revising facts, theories,
applications etc. The goal is to discover and disseminate
new knowledge. There are several methods that can be used
in CS and IS in next subsection we will show these
methodologies.
Experimental Method
Experimental shows the experiments that will occur in
order extract results from real world implementations.
Experiments can test the veracity of theories. This method
within CS is used in several different fields like artificial
neural networks, automating theorem proving, natural
languages, analyzing performances and behaviors, etc. It is
important to restate that all the experiments and results
should be reproducible. Concerning, for example, network
environments with several connection resources and users,
the experiments are an important methodology Also in CS
fields and especially IS fields that take in consideration the
Human- Computer Interaction. It is mandatory the usage of
experimental approaches. If we use the experimental
method in IS field we may need to use some methods or
tools in conjunction with the experimental method. These
methods or tools used to support and prove the legibility of
the developed project. For example if a student wants to
implement new social network with new concepts or
develop an existing social network, who he can measure the
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 4, April 2018
115 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
2. legibility of his implementation? The answer of this question
consists of two parts according to the nature of the project. The
technical issue is the first part of the project that can be tested by
benchmarks like (availability, reliability, scalability, stability,
etc.). The usability of the project is the second part of testing
that needs a feedback from the users of the system; the results
for the second part can be obtained by the statistical analysis of
a questionnaire which is a tool the used in conjunction with the
experimental method.
Simulation Method
Simulation method used especially in CS because it offers the
possibility to investigate systems or regimes that are outside of
the experimental domain or the systems that is under invention
or construction. Normally complex phenomena that cannot be
implemented in laboratories evolution of the universe. Some
domains that adopt computer simulation methodologies are
sciences such as astronomy, physics or economics; other areas
more specialized such as the study of non-linear systems, virtual
reality or artificial life also exploit these methodologies. A lot of
projects can use the simulation methods, like the study of a new
developed network protocol. To test this protocol you have to
build a huge network with a lot of expensive network tools, but
this network can't be easily achieved. For this reason we can use
the simulation method.
Theoretical Method
The theoretical approaches to CS are based on the classical
methodology since they are related to logic and mathematics.
Some ideas are the existence of conceptual and formal models
(data models and algorithms). Since theoretical CS inherits its
bases from logic and mathematics, some of the main techniques
when dealing with problems are iteration, recursion and
induction. Theory is important to build methodologies, to
develop logic and semantic models and to reason about the
programs in order to prove their correctness. Theoretical CS is
dedicated to the design and algorithm analysis in order to find
solutions or better solutions (performance issues, for example).
Encompassing all fields in CS, the theoretical methodologies
also tries to define the limits of computation and the
computational paradigm. In other words we can say that we can
use the theoretical method to model a new system. However the
theoretical method can help in finding new mathematical models
or theories, but this method still needs other methods to prove
the efficiency of the new models or theories. For example when
a student need to develop a new classifier in AI by using the
mathematical representation and theoretical method, he need to
prove the efficiency of this model by using one of the previous
methods.
Conclusion
In this paper we try to differentiate between the domains of
science and CS and IS to understand the best methods that can
be used in CS and IS. Each project in CS or IS have its free
nature so the paper give examples of different kinds of projects
in CS and IS and the prober research methodologies that can
used in these projects.
References
1. R. K. Jain and H. C. Triandis: Management of Research
and Development Organizations: Managing the
Unmanageable. John Wiley & Sons, (1997).
2. Gordana Dodig-Crnkovic: Scientific Methods in
Computer Science. Conference for the Promotion of
Research in IT at New Universities and at University
Colleges in Sweden (2002).
3. Denning Peter J.: Is Computer Science Science?.
COMMUNICATIONS OF THE ACM, Vol. 48, No. 4
(2005).
4. Allen Newell, Herbert A. Simon: Computer Science as
Empirical Inquiry: Symbols and Search. Communication.
ACM 19(3): 113-126(1976).
5. Suprateek Sarker, Allen S. Lee: Using A Positivist Case
Research Methodology To Test Three Competing Theories-
In-Use Of Business Process Redesign. J. AIS (2001).
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 4, April 2018
116 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
