This document discusses the trend in use of financial metrics (fin-metrics) and electronic statistical (e-stat) tools for research data analysis among digital immigrant researchers. It analyzes 1720 empirical journals from various fields in Nigeria and beyond. The analysis found that most researchers still rely on manual rather than digital methods, which hinders international acceptance and originality. It was also found to be more common among lecturers without strong IT skills. The document recommends training to improve use of analytical tools and make research more credible, accurate, and internationally competitive.
June 2020: Top Read Articles in Advanced Computational Intelligenceaciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectiv...YogeshIJTSRD
The aim of information retrieval systems is to retrieve relevant information according to the query provided. The queries are often vague and uncertain. Thus, to improve the system, we propose an Automatic Query Expansion technique, to expand the query by adding new terms to the user s initial query so as to minimize query mismatch and thereby improving retrieval performance. Most of the existing techniques for expanding queries do not take into account the degree of semantic relationship among words. In this paper, the query is expanded by exploring terms which are semantically similar to the initial query terms as well as considering the degree of relationship, that is, “fuzzy membership- between them. The terms which seemed most relevant are used in expanded query and improve the information retrieval process. The experiments conducted on the queries set show that the proposed Automatic query expansion approach gave a higher precision, recall, and F measure then non fuzzy edge weights. Tarun Goyal | Ms. Shalini Bhadola | Ms. Kirti Bhatia "Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectivity Measures" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45074.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/45074/automatic-query-expansion-using-word-embedding-based-on-fuzzy-graph-connectivity-measures/tarun-goyal
Applying Soft Computing Techniques in Information RetrievalIJAEMSJORNAL
There is plethora of information available over the internet on daily basis and to retrieve meaningful effective information using usual IR methods is becoming a cumbersome task. Hence this paper summarizes the different soft computing techniques available that can be applied to information retrieval systems to improve its efficiency in acquiring knowledge related to a user’s query.
Semantics-based clustering approach for similar research area detectionTELKOMNIKA JOURNAL
The manual process of searching out individuals in an already existing
research field is cumbersome and time-consuming. Prominent and rookie
researchers alike are predisposed to seek existing research publications in
a research field of interest before coming up with a thesis. From
extant literature, automated similar research area detection systems have
been developed to solve this problem. However, most of them use
keyword-matching techniques, which do not sufficiently capture the implicit
semantics of keywords thereby leaving out some research articles. In this
study, we propose the use of ontology-based pre-processing, Latent Semantic
Indexing and K-Means Clustering to develop a prototype similar research area
detection system, that can be used to determine similar research domain
publications. Our proposed system solves the challenge of high dimensionality
and data sparsity faced by the traditional document clustering technique. Our
system is evaluated with randomly selected publications from faculties
in Nigerian universities and results show that the integration of ontologies
in preprocessing provides more accurate clustering results.
Comparative analysis of augmented datasets performances of age invariant face...journalBEEI
The popularity of face recognition systems has increased due to their non-invasive method of image acquisition, thus boasting the widespread applications. Face ageing is one major factor that influences the performance of face recognition algorithms. In this study, the authors present a comparative study of the two most accepted and experimented face ageing datasets (FG-Net and morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. Four types of noises were added to the two face ageing datasets at the preprocessing stage. The addition of noise at the preprocessing stage served as a data augmentation technique that increased the number of sample images available for deep convolutional neural network (DCNN) experimentation, improved the proposed AIFR model and the trait aging features extraction process. The proposed AIFR models are developed with the pre-trained Inception-ResNet-v2 deep convolutional neural network architecture. On testing and comparing the models, the results revealed that FG-Net is more efficient over Morph with an accuracy of 0.15%, loss function of 71%, mean square error (MSE) of 39% and mean absolute error (MAE) of -0.63%.
ESTIMATION OF REGRESSION COEFFICIENTS USING GEOMETRIC MEAN OF SQUARED ERROR F...ijaia
Regression models and their statistical analyses is one of the most important tool used by scientists and practitioners. The aim of a regression model is to fit parametric functions to data. It is known that the true regression is unknown and specific methods are created and used strictly pertaining to the roblem. For the pioneering work to develop procedures for fitting functions, we refer to the work on the methods of least
absolute deviations, least squares deviations and minimax absolute deviations. Today’s widely celebrated
procedure of the method of least squares for function fitting is credited to the published works of Legendre and Gauss. However, the least squares based models in practice may fail to provide optimal results in nonGaussian situations especially when the errors follow distributions with the fat tails. In this paper an unorthodox method of estimating linear regression coefficients by minimising GMSE(geometric mean of squared errors) is explored. Though GMSE(geometric mean of squared errors) is used to compare models it is rarely used to obtain the coefficients. Such a method is tedious to handle due to the large number of roots obtained by minimisation of the loss function. This paper offers a way to tackle that problem.
Application is illustrated with the ‘Advertising’ dataset from ISLR and the obtained results are compared
with the results of the method of least squares for single index linear regression model.
June 2020: Top Read Articles in Advanced Computational Intelligenceaciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectiv...YogeshIJTSRD
The aim of information retrieval systems is to retrieve relevant information according to the query provided. The queries are often vague and uncertain. Thus, to improve the system, we propose an Automatic Query Expansion technique, to expand the query by adding new terms to the user s initial query so as to minimize query mismatch and thereby improving retrieval performance. Most of the existing techniques for expanding queries do not take into account the degree of semantic relationship among words. In this paper, the query is expanded by exploring terms which are semantically similar to the initial query terms as well as considering the degree of relationship, that is, “fuzzy membership- between them. The terms which seemed most relevant are used in expanded query and improve the information retrieval process. The experiments conducted on the queries set show that the proposed Automatic query expansion approach gave a higher precision, recall, and F measure then non fuzzy edge weights. Tarun Goyal | Ms. Shalini Bhadola | Ms. Kirti Bhatia "Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectivity Measures" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45074.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/45074/automatic-query-expansion-using-word-embedding-based-on-fuzzy-graph-connectivity-measures/tarun-goyal
Applying Soft Computing Techniques in Information RetrievalIJAEMSJORNAL
There is plethora of information available over the internet on daily basis and to retrieve meaningful effective information using usual IR methods is becoming a cumbersome task. Hence this paper summarizes the different soft computing techniques available that can be applied to information retrieval systems to improve its efficiency in acquiring knowledge related to a user’s query.
Semantics-based clustering approach for similar research area detectionTELKOMNIKA JOURNAL
The manual process of searching out individuals in an already existing
research field is cumbersome and time-consuming. Prominent and rookie
researchers alike are predisposed to seek existing research publications in
a research field of interest before coming up with a thesis. From
extant literature, automated similar research area detection systems have
been developed to solve this problem. However, most of them use
keyword-matching techniques, which do not sufficiently capture the implicit
semantics of keywords thereby leaving out some research articles. In this
study, we propose the use of ontology-based pre-processing, Latent Semantic
Indexing and K-Means Clustering to develop a prototype similar research area
detection system, that can be used to determine similar research domain
publications. Our proposed system solves the challenge of high dimensionality
and data sparsity faced by the traditional document clustering technique. Our
system is evaluated with randomly selected publications from faculties
in Nigerian universities and results show that the integration of ontologies
in preprocessing provides more accurate clustering results.
Comparative analysis of augmented datasets performances of age invariant face...journalBEEI
The popularity of face recognition systems has increased due to their non-invasive method of image acquisition, thus boasting the widespread applications. Face ageing is one major factor that influences the performance of face recognition algorithms. In this study, the authors present a comparative study of the two most accepted and experimented face ageing datasets (FG-Net and morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. Four types of noises were added to the two face ageing datasets at the preprocessing stage. The addition of noise at the preprocessing stage served as a data augmentation technique that increased the number of sample images available for deep convolutional neural network (DCNN) experimentation, improved the proposed AIFR model and the trait aging features extraction process. The proposed AIFR models are developed with the pre-trained Inception-ResNet-v2 deep convolutional neural network architecture. On testing and comparing the models, the results revealed that FG-Net is more efficient over Morph with an accuracy of 0.15%, loss function of 71%, mean square error (MSE) of 39% and mean absolute error (MAE) of -0.63%.
ESTIMATION OF REGRESSION COEFFICIENTS USING GEOMETRIC MEAN OF SQUARED ERROR F...ijaia
Regression models and their statistical analyses is one of the most important tool used by scientists and practitioners. The aim of a regression model is to fit parametric functions to data. It is known that the true regression is unknown and specific methods are created and used strictly pertaining to the roblem. For the pioneering work to develop procedures for fitting functions, we refer to the work on the methods of least
absolute deviations, least squares deviations and minimax absolute deviations. Today’s widely celebrated
procedure of the method of least squares for function fitting is credited to the published works of Legendre and Gauss. However, the least squares based models in practice may fail to provide optimal results in nonGaussian situations especially when the errors follow distributions with the fat tails. In this paper an unorthodox method of estimating linear regression coefficients by minimising GMSE(geometric mean of squared errors) is explored. Though GMSE(geometric mean of squared errors) is used to compare models it is rarely used to obtain the coefficients. Such a method is tedious to handle due to the large number of roots obtained by minimisation of the loss function. This paper offers a way to tackle that problem.
Application is illustrated with the ‘Advertising’ dataset from ISLR and the obtained results are compared
with the results of the method of least squares for single index linear regression model.
Mining Social Media Data for Understanding Drugs UsageIRJET Journal
This document discusses mining social media data to understand drug usage. It proposes using big data techniques like Hadoop and MapReduce to extract and analyze data from social networks about drug abuse. The methodology involves collecting data from platforms using crawlers, storing it in Hadoop, filtering it, then applying complex analysis using cloud computing. Prior work on extracting health information from social media and multi-scale community detection in networks is reviewed. The challenges of privacy preservation and scalability when anonymizing big healthcare datasets are also discussed.
This document summarizes a research paper that empirically tests a model based on general deterrence theory regarding how user awareness of security countermeasures impacts perceptions of punishment certainty and severity for information systems misuse. The study developed hypotheses about the relationships between security policies, security education/training/awareness programs, computer monitoring, perceived certainty and severity of sanctions, and intentions for systems misuse. Data was collected through an online survey of professionals and analyzed using partial least squares and other statistical techniques. The results provided support for most of the hypotheses and showed that security countermeasures influence perceptions of punishment, which impact misuse intentions.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Data Mining Model for Predicting Student Enrolment in STEM Courses in Higher ...Editor IJCATR
Educational data mining is the process of applying data mining tools and techniques to analyze data at educational
institutions. In this paper, educational data mining was used to predict enrollment of students in Science, Technology, Engineering and
Mathematics (STEM) courses in higher educational institutions. The study examined the extent to which individual, sociodemographic
and school-level contextual factors help in pre-identifying successful and unsuccessful students in enrollment in STEM
disciplines in Higher Education Institutions in Kenya. The Cross Industry Standard Process for Data Mining framework was applied to
a dataset drawn from the first, second and third year undergraduate female students enrolled in STEM disciplines in one University in
Kenya to model student enrollment. Feature selection was used to rank the predictor variables by their importance for further analysis.
Various predictive algorithms were evaluated in predicting enrollment of students in STEM courses. Empirical results showed the
following: (i) the most important factors separating successful from unsuccessful students are: High School final grade, teacher
inspiration, career flexibility, pre-university awareness and mathematics grade. (ii) among classification algorithms for prediction,
decision tree (CART) was the most successful classifier with an overall percentage of correct classification of 85.2%. This paper
showcases the importance of Prediction and Classification based data mining algorithms in the field of education and also presents
some promising future lines.
This document is an annotated bibliography for an educational technology paper exploring the relationship between educational technology and inquiry-based learning. It summarizes 10 research articles that will be used to support three main arguments: 1) the role of educational technology in facilitating inquiry-based learning across contexts, 2) the role of inquiry-based learning and technology in science programs, and 3) inquiry-based learning using web-based technology. The annotations provide highlights of each article's relevance, supporting evidence and data, author credentials, date of publication, and reading level.
A forecasting of stock trading price using time series information based on b...IJECEIAES
Big data is a large set of structured or unstructured data that can collect, store, manage, and analyze data with existing database management tools. And it means the technique of extracting value from these data and interpreting the results. Big data has three characteristics: The size of existing data and other data (volume), the speed of data generation (velocity), and the variety of information forms (variety). The time series data are obtained by collecting and recording the data generated in accordance with the flow of time. If the analysis of these time series data, found the characteristics of the data implies that feature helps to understand and analyze time series data. The concept of distance is the simplest and the most obvious in dealing with the similarities between objects. The commonly used and widely known method for measuring distance is the Euclidean distance. This study is the result of analyzing the similarity of stock price flow using 793,800 closing prices of 1,323 companies in Korea. Visual studio and Excel presented calculate the Euclidean distance using an analysis tool. We selected “000100” as a target domestic company and prepared for big data analysis. As a result of the analysis, the shortest Euclidean distance is the code “143860” company, and the calculated value is “11.147”. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.
New Approaches in Cognitive Radios using Evolutionary Algorithms IJECEIAES
Cognitive radio has claimed a promising technology to exploit the spectrum in an ad hoc network. Due many techniques have become a topic of discussion on cognitive radios, the aim of this paper was developed a contemporary survey of evolutionary algorithms in Cognitive Radio. According to the art state, this work had been collected the essential contributions of cognitive radios with the particularity of base they research in evolutionary algorithms. The main idea was classified the evolutionary algorithms and showed their fundamental approaches. Moreover, this research will be exposed some of the current issues in cognitive radios and how the evolutionary algorithms will have been contributed. Therefore, current technologies have matters presented in optimization, learning, and classification over cognitive radios where evolutionary algorithms can be presented big approaches. With a more comprehensive and systematic understanding of evolutionary algorithms in cognitive radios, more research in this direction may be motivated and refined.
CITIZENS’ ACCEPTANCE OF E-GOVERNMENT SERVICESijcseit
The rate of computer and internet usage has been increasing rapidly around the world. In parallel with the
technologic developments in computer science, transformation from traditional services to online services
has gained speed. The aim of this study is to predict the factors that affect e-government service usage. A
research model is developed to achieve this aim. The proposed model bases on Technology Acceptance
Model and Theory of Planned Behaviour. A questionnaire is developed to evaluate the model. This
questionnaire composes of two parts: demographics part and item part. In the items part, 32 items
comprising the factors of the proposed model are asked to participants. 100 participants fill the
questionnaire. Reliability analysis of the questionnaire is evaluated with internal consistency reliability
method. Results show that all items satisfies the reliability conditions. The reliability of whole
questionnaire Cronbach Alpha is 0.885. The Cronbach’s alpha for the overall scale of each of the factors
ranges from 0.878 to 0.890. Regression analysis results showed that all hypotheses are supported. This
study provides some valuable references to understand citizens’ acceptance level of e-government services.
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.
REVIEWING PROCESS MINING APPLICATIONS AND TECHNIQUES IN EDUCATIONijaia
Process Mining (PM) emerged from business process management but has recently been applied to
educational data and has been found to facilitate the understanding of the educational process.
Educational Process Mining (EPM) bridges the gap between process analysis and data analysis, based on
the techniques of model discovery, conformance checking and extension of existing process models. We
present a systematic review of the recent and current status of research in the EPM domain, focusing on
application domains, techniques, tools and models, to highlight the use of EPM in comprehending and
improving educational processes.
IRJET- A Survey on Image Retrieval using Machine LearningIRJET Journal
This document summarizes a research paper on using machine learning for image retrieval. It discusses using optical character recognition (OCR) to extract text from images and then translate that text into machine-readable formats. The document reviews related work on combining visual and text features for image retrieval. It also provides an overview of the proposed system architecture, which combines color, texture, and edge features for image retrieval and evaluates performance using metrics like sensitivity, specificity, retrieval score, and accuracy.
An Analysis of Behavioral Intention toward Actual Usage of Open Source Softwa...IJAEMSJORNAL
This study focused on analyzing behavioral intention toward the actual usage of open source software in private universities in Tanzania. Questionnaires were used to collect quantitative data in two private universities namely Iringa University and Ruaha Catholic University. Stratified sampling technique was utilized to ensure sample representativeness among two universities where simple random sampling was used to draw a sample from each stratum during the survey. Finding Using Structural Equation Modeling indicated that performance expectancy (source code production and software localization) and social factor (Vendor, internet services provider and lecturer) have a significant influence toward behavioral intention while effort expectancy was found to be insignificant. In addition the behavioral intention was found to be significant toward student’s actual usage of open source software in Universities. This study recommended that for students to develop behavioral intention toward OSS actual usage, internet service provider have to increase the level of internet services that can assist the university communities to access and download open source software. In addition, to increase actual use, open source software vendors and lecturer or experts have to make sure that their software source code is free for distribution and localization, this will increase self-motivation and interest of the students toward actual usage of open source software.
The document discusses a study on user perceptions of machine learning. Researchers provided datasets to be analyzed by machine learning. Interviews found that machine learning produced results matching statistical analysis with less preparation time. Researchers saw potential for machine learning to help visualize data patterns but noted it may miss some relationships. There was interest in future machine learning use if researchers better understood the technique.
Identification of important features and data mining classification technique...IJECEIAES
Employees absenteeism at the work costs organizations billions a year. Prediction of employees’ absenteeism and the reasons behind their absence help organizations in reducing expenses and increasing productivity. Data mining turns the vast volume of human resources data into information that can help in decision-making and prediction. Although the selection of features is a critical step in data mining to enhance the efficiency of the final prediction, it is not yet known which method of feature selection is better. Therefore, this paper aims to compare the performance of three well-known feature selection methods in absenteeism prediction, which are relief-based feature selection, correlation-based feature selection and information-gain feature selection. In addition, this paper aims to find the best combination of feature selection method and data mining technique in enhancing the absenteeism prediction accuracy. Seven classification techniques were used as the prediction model. Additionally, cross-validation approach was utilized to assess the applied prediction models to have more realistic and reliable results. The used dataset was built at a courier company in Brazil with records of absenteeism at work. Regarding experimental results, correlationbased feature selection surpasses the other methods through the performance measurements. Furthermore, bagging classifier was the best-performing data mining technique when features were selected using correlation-based feature selection with an accuracy rate of (92%).
SOFTWARE TESTING: ISSUES AND CHALLENGES OF ARTIFICIAL INTELLIGENCE & MACHINE ...ijaia
The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has
been an increase in popularity for applications that implement AI and ML technology. As with traditional
development, software testing is a critical component of an efficient AI/ML application. However, the
approach to development methodology used in AI/ML varies significantly from traditional development.
Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and
to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For
future research, this study has key implications. Each of the challenges outlined in this paper is ideal for
further investigation and has great potential to shed light on the way to more productive software testing
strategies and methodologies that can be applied to AI/ML applications.
LAN Based HF Radio Simulator An Approach to Develop an Early Prototype for Lo...YogeshIJTSRD
This document summarizes the key aspects of developing a LAN-based HF radio simulator. It conducted surveys and interviews with users to understand requirements. The surveys found that a simulator could facilitate training by allowing practice without actual radios. A web-based platform was preferred. The document outlines the methodology, which includes specifying user and software requirements. It defines functional and non-functional requirements for the simulator. The simulator aims to allow trainees to practice operating radios in different scenarios while preserving performance records.
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.
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.
IRJET- Comparative Analysis of Various Tools for Data Mining and Big Data...IRJET Journal
This document compares and analyzes various tools for data mining and big data mining. It discusses traditional open source data mining tools like Orange, R, Weka, Shogun, Rapid Miner and KNIME. Each tool has different capabilities for data preprocessing, machine learning algorithms, visualization, platforms and programming languages. The document aims to help researchers select the most appropriate data mining tool for their needs and research.
Computer is an electronic device or combination of electronic devicesArti Arora
Computers play an essential role in the research process. They can be used to:
1. Access previously published research online and gather secondary data from websites.
2. Analyze results using software like SPSS and perform statistical analyses.
3. Disseminate research findings by publishing articles online in PDF format.
Computers make research faster and more accurate at every stage from collecting and storing data, to statistical analysis, and sharing results. However, computers are only a tool, and research still requires planning and expertise from qualified researchers and statisticians.
A Comprehensive Overview of Advance Techniques, Applications and Challenges i...IRJTAE
— The field of data science uses scientific methods, algorithms, processes, and systems to extract
insights and knowledge from structured and unstructured data. It combines principles from mathematics,
statistics, computer science, and domain expertise to analyse, interpret, and present data in meaningful ways. Its
primary aim is to uncover patterns, trends, and correlations across various domains to aid in making informed
decisions, predictions, and optimizations. Data science encompasses data collection, cleaning, analysis,
interpretation, and communication of findings. Techniques such as machine learning, statistical analysis, data
mining, and data visualization are commonly employed to derive valuable insights and solve complex problems.
Data scientists use programming languages and tools to manage large volumes of data, transforming raw
information into actionable intelligence, driving innovation, and enabling evidence-based decision-making in
businesses, research, and various other applications. This review seeks to provide a valuable resource for
researchers, practitioners, and enthusiasts who wish to gain in-depth knowledge and understanding of data
science and its implications for the ever-evolving data-driven world.
Continuous Improvement through Data Science From Products to Systems Beyond C...ijtsrd
The field of data science has become integral to the evolution of industries and technological advancements. This abstract explores the multifaceted role of data scientists in various domains, encompassing product and services development as well as specialized areas like Cyber Physical Systems. In product based companies, data scientists drive innovation by enhancing user experiences, optimizing costs, ensuring connectivity, and refining communication strategies. Leveraging machine learning models, they contribute to personalized interfaces, predictive maintenance, and efficient resource allocation, ultimately influencing the success of products in competitive markets. In services based companies, data scientists play a vital role in improving user interactions, optimizing operational costs, ensuring connectivity, and refining communication strategies. Through predictive analytics, they enable proactive service maintenance, improve resource allocation, and drive continuous improvement in service delivery. Within the context of Industry 4.0, data scientists contribute to the seamless integration of physical and digital systems. They monitor and analyze real time data from sensors, predict equipment failures, optimize system performance, and ensure the security of interconnected systems, fostering efficiency and reliability. Throughout these applications, data scientists operate at the nexus of technology, statistics, and domain expertise. Their responsibilities include data collection, preprocessing, model development, integration, and continuous improvement. Collaboration with cross functional teams ensures that data driven solutions align with organizational goals, fostering a holistic approach to problem solving. As the field of data science continues to evolve, data scientists remain pivotal in unlocking the potential of data to address complex challenges, drive innovation, and contribute to the ongoing transformation of industries and societies. Their role extends beyond analytical expertise, encompassing interdisciplinary collaboration skills that position them as essential contributors to the dynamic landscape of data driven decision making. Manish Verma "Continuous Improvement through Data Science: From Products to Systems: Beyond ChatGPT" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd61211.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/61211/continuous-improvement-through-data-science-from-products-to-systems-beyond-chatgpt/manish-verma
Mining Social Media Data for Understanding Drugs UsageIRJET Journal
This document discusses mining social media data to understand drug usage. It proposes using big data techniques like Hadoop and MapReduce to extract and analyze data from social networks about drug abuse. The methodology involves collecting data from platforms using crawlers, storing it in Hadoop, filtering it, then applying complex analysis using cloud computing. Prior work on extracting health information from social media and multi-scale community detection in networks is reviewed. The challenges of privacy preservation and scalability when anonymizing big healthcare datasets are also discussed.
This document summarizes a research paper that empirically tests a model based on general deterrence theory regarding how user awareness of security countermeasures impacts perceptions of punishment certainty and severity for information systems misuse. The study developed hypotheses about the relationships between security policies, security education/training/awareness programs, computer monitoring, perceived certainty and severity of sanctions, and intentions for systems misuse. Data was collected through an online survey of professionals and analyzed using partial least squares and other statistical techniques. The results provided support for most of the hypotheses and showed that security countermeasures influence perceptions of punishment, which impact misuse intentions.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Data Mining Model for Predicting Student Enrolment in STEM Courses in Higher ...Editor IJCATR
Educational data mining is the process of applying data mining tools and techniques to analyze data at educational
institutions. In this paper, educational data mining was used to predict enrollment of students in Science, Technology, Engineering and
Mathematics (STEM) courses in higher educational institutions. The study examined the extent to which individual, sociodemographic
and school-level contextual factors help in pre-identifying successful and unsuccessful students in enrollment in STEM
disciplines in Higher Education Institutions in Kenya. The Cross Industry Standard Process for Data Mining framework was applied to
a dataset drawn from the first, second and third year undergraduate female students enrolled in STEM disciplines in one University in
Kenya to model student enrollment. Feature selection was used to rank the predictor variables by their importance for further analysis.
Various predictive algorithms were evaluated in predicting enrollment of students in STEM courses. Empirical results showed the
following: (i) the most important factors separating successful from unsuccessful students are: High School final grade, teacher
inspiration, career flexibility, pre-university awareness and mathematics grade. (ii) among classification algorithms for prediction,
decision tree (CART) was the most successful classifier with an overall percentage of correct classification of 85.2%. This paper
showcases the importance of Prediction and Classification based data mining algorithms in the field of education and also presents
some promising future lines.
This document is an annotated bibliography for an educational technology paper exploring the relationship between educational technology and inquiry-based learning. It summarizes 10 research articles that will be used to support three main arguments: 1) the role of educational technology in facilitating inquiry-based learning across contexts, 2) the role of inquiry-based learning and technology in science programs, and 3) inquiry-based learning using web-based technology. The annotations provide highlights of each article's relevance, supporting evidence and data, author credentials, date of publication, and reading level.
A forecasting of stock trading price using time series information based on b...IJECEIAES
Big data is a large set of structured or unstructured data that can collect, store, manage, and analyze data with existing database management tools. And it means the technique of extracting value from these data and interpreting the results. Big data has three characteristics: The size of existing data and other data (volume), the speed of data generation (velocity), and the variety of information forms (variety). The time series data are obtained by collecting and recording the data generated in accordance with the flow of time. If the analysis of these time series data, found the characteristics of the data implies that feature helps to understand and analyze time series data. The concept of distance is the simplest and the most obvious in dealing with the similarities between objects. The commonly used and widely known method for measuring distance is the Euclidean distance. This study is the result of analyzing the similarity of stock price flow using 793,800 closing prices of 1,323 companies in Korea. Visual studio and Excel presented calculate the Euclidean distance using an analysis tool. We selected “000100” as a target domestic company and prepared for big data analysis. As a result of the analysis, the shortest Euclidean distance is the code “143860” company, and the calculated value is “11.147”. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.
New Approaches in Cognitive Radios using Evolutionary Algorithms IJECEIAES
Cognitive radio has claimed a promising technology to exploit the spectrum in an ad hoc network. Due many techniques have become a topic of discussion on cognitive radios, the aim of this paper was developed a contemporary survey of evolutionary algorithms in Cognitive Radio. According to the art state, this work had been collected the essential contributions of cognitive radios with the particularity of base they research in evolutionary algorithms. The main idea was classified the evolutionary algorithms and showed their fundamental approaches. Moreover, this research will be exposed some of the current issues in cognitive radios and how the evolutionary algorithms will have been contributed. Therefore, current technologies have matters presented in optimization, learning, and classification over cognitive radios where evolutionary algorithms can be presented big approaches. With a more comprehensive and systematic understanding of evolutionary algorithms in cognitive radios, more research in this direction may be motivated and refined.
CITIZENS’ ACCEPTANCE OF E-GOVERNMENT SERVICESijcseit
The rate of computer and internet usage has been increasing rapidly around the world. In parallel with the
technologic developments in computer science, transformation from traditional services to online services
has gained speed. The aim of this study is to predict the factors that affect e-government service usage. A
research model is developed to achieve this aim. The proposed model bases on Technology Acceptance
Model and Theory of Planned Behaviour. A questionnaire is developed to evaluate the model. This
questionnaire composes of two parts: demographics part and item part. In the items part, 32 items
comprising the factors of the proposed model are asked to participants. 100 participants fill the
questionnaire. Reliability analysis of the questionnaire is evaluated with internal consistency reliability
method. Results show that all items satisfies the reliability conditions. The reliability of whole
questionnaire Cronbach Alpha is 0.885. The Cronbach’s alpha for the overall scale of each of the factors
ranges from 0.878 to 0.890. Regression analysis results showed that all hypotheses are supported. This
study provides some valuable references to understand citizens’ acceptance level of e-government services.
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.
REVIEWING PROCESS MINING APPLICATIONS AND TECHNIQUES IN EDUCATIONijaia
Process Mining (PM) emerged from business process management but has recently been applied to
educational data and has been found to facilitate the understanding of the educational process.
Educational Process Mining (EPM) bridges the gap between process analysis and data analysis, based on
the techniques of model discovery, conformance checking and extension of existing process models. We
present a systematic review of the recent and current status of research in the EPM domain, focusing on
application domains, techniques, tools and models, to highlight the use of EPM in comprehending and
improving educational processes.
IRJET- A Survey on Image Retrieval using Machine LearningIRJET Journal
This document summarizes a research paper on using machine learning for image retrieval. It discusses using optical character recognition (OCR) to extract text from images and then translate that text into machine-readable formats. The document reviews related work on combining visual and text features for image retrieval. It also provides an overview of the proposed system architecture, which combines color, texture, and edge features for image retrieval and evaluates performance using metrics like sensitivity, specificity, retrieval score, and accuracy.
An Analysis of Behavioral Intention toward Actual Usage of Open Source Softwa...IJAEMSJORNAL
This study focused on analyzing behavioral intention toward the actual usage of open source software in private universities in Tanzania. Questionnaires were used to collect quantitative data in two private universities namely Iringa University and Ruaha Catholic University. Stratified sampling technique was utilized to ensure sample representativeness among two universities where simple random sampling was used to draw a sample from each stratum during the survey. Finding Using Structural Equation Modeling indicated that performance expectancy (source code production and software localization) and social factor (Vendor, internet services provider and lecturer) have a significant influence toward behavioral intention while effort expectancy was found to be insignificant. In addition the behavioral intention was found to be significant toward student’s actual usage of open source software in Universities. This study recommended that for students to develop behavioral intention toward OSS actual usage, internet service provider have to increase the level of internet services that can assist the university communities to access and download open source software. In addition, to increase actual use, open source software vendors and lecturer or experts have to make sure that their software source code is free for distribution and localization, this will increase self-motivation and interest of the students toward actual usage of open source software.
The document discusses a study on user perceptions of machine learning. Researchers provided datasets to be analyzed by machine learning. Interviews found that machine learning produced results matching statistical analysis with less preparation time. Researchers saw potential for machine learning to help visualize data patterns but noted it may miss some relationships. There was interest in future machine learning use if researchers better understood the technique.
Identification of important features and data mining classification technique...IJECEIAES
Employees absenteeism at the work costs organizations billions a year. Prediction of employees’ absenteeism and the reasons behind their absence help organizations in reducing expenses and increasing productivity. Data mining turns the vast volume of human resources data into information that can help in decision-making and prediction. Although the selection of features is a critical step in data mining to enhance the efficiency of the final prediction, it is not yet known which method of feature selection is better. Therefore, this paper aims to compare the performance of three well-known feature selection methods in absenteeism prediction, which are relief-based feature selection, correlation-based feature selection and information-gain feature selection. In addition, this paper aims to find the best combination of feature selection method and data mining technique in enhancing the absenteeism prediction accuracy. Seven classification techniques were used as the prediction model. Additionally, cross-validation approach was utilized to assess the applied prediction models to have more realistic and reliable results. The used dataset was built at a courier company in Brazil with records of absenteeism at work. Regarding experimental results, correlationbased feature selection surpasses the other methods through the performance measurements. Furthermore, bagging classifier was the best-performing data mining technique when features were selected using correlation-based feature selection with an accuracy rate of (92%).
SOFTWARE TESTING: ISSUES AND CHALLENGES OF ARTIFICIAL INTELLIGENCE & MACHINE ...ijaia
The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has
been an increase in popularity for applications that implement AI and ML technology. As with traditional
development, software testing is a critical component of an efficient AI/ML application. However, the
approach to development methodology used in AI/ML varies significantly from traditional development.
Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and
to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For
future research, this study has key implications. Each of the challenges outlined in this paper is ideal for
further investigation and has great potential to shed light on the way to more productive software testing
strategies and methodologies that can be applied to AI/ML applications.
LAN Based HF Radio Simulator An Approach to Develop an Early Prototype for Lo...YogeshIJTSRD
This document summarizes the key aspects of developing a LAN-based HF radio simulator. It conducted surveys and interviews with users to understand requirements. The surveys found that a simulator could facilitate training by allowing practice without actual radios. A web-based platform was preferred. The document outlines the methodology, which includes specifying user and software requirements. It defines functional and non-functional requirements for the simulator. The simulator aims to allow trainees to practice operating radios in different scenarios while preserving performance records.
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.
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.
IRJET- Comparative Analysis of Various Tools for Data Mining and Big Data...IRJET Journal
This document compares and analyzes various tools for data mining and big data mining. It discusses traditional open source data mining tools like Orange, R, Weka, Shogun, Rapid Miner and KNIME. Each tool has different capabilities for data preprocessing, machine learning algorithms, visualization, platforms and programming languages. The document aims to help researchers select the most appropriate data mining tool for their needs and research.
Computer is an electronic device or combination of electronic devicesArti Arora
Computers play an essential role in the research process. They can be used to:
1. Access previously published research online and gather secondary data from websites.
2. Analyze results using software like SPSS and perform statistical analyses.
3. Disseminate research findings by publishing articles online in PDF format.
Computers make research faster and more accurate at every stage from collecting and storing data, to statistical analysis, and sharing results. However, computers are only a tool, and research still requires planning and expertise from qualified researchers and statisticians.
A Comprehensive Overview of Advance Techniques, Applications and Challenges i...IRJTAE
— The field of data science uses scientific methods, algorithms, processes, and systems to extract
insights and knowledge from structured and unstructured data. It combines principles from mathematics,
statistics, computer science, and domain expertise to analyse, interpret, and present data in meaningful ways. Its
primary aim is to uncover patterns, trends, and correlations across various domains to aid in making informed
decisions, predictions, and optimizations. Data science encompasses data collection, cleaning, analysis,
interpretation, and communication of findings. Techniques such as machine learning, statistical analysis, data
mining, and data visualization are commonly employed to derive valuable insights and solve complex problems.
Data scientists use programming languages and tools to manage large volumes of data, transforming raw
information into actionable intelligence, driving innovation, and enabling evidence-based decision-making in
businesses, research, and various other applications. This review seeks to provide a valuable resource for
researchers, practitioners, and enthusiasts who wish to gain in-depth knowledge and understanding of data
science and its implications for the ever-evolving data-driven world.
Continuous Improvement through Data Science From Products to Systems Beyond C...ijtsrd
The field of data science has become integral to the evolution of industries and technological advancements. This abstract explores the multifaceted role of data scientists in various domains, encompassing product and services development as well as specialized areas like Cyber Physical Systems. In product based companies, data scientists drive innovation by enhancing user experiences, optimizing costs, ensuring connectivity, and refining communication strategies. Leveraging machine learning models, they contribute to personalized interfaces, predictive maintenance, and efficient resource allocation, ultimately influencing the success of products in competitive markets. In services based companies, data scientists play a vital role in improving user interactions, optimizing operational costs, ensuring connectivity, and refining communication strategies. Through predictive analytics, they enable proactive service maintenance, improve resource allocation, and drive continuous improvement in service delivery. Within the context of Industry 4.0, data scientists contribute to the seamless integration of physical and digital systems. They monitor and analyze real time data from sensors, predict equipment failures, optimize system performance, and ensure the security of interconnected systems, fostering efficiency and reliability. Throughout these applications, data scientists operate at the nexus of technology, statistics, and domain expertise. Their responsibilities include data collection, preprocessing, model development, integration, and continuous improvement. Collaboration with cross functional teams ensures that data driven solutions align with organizational goals, fostering a holistic approach to problem solving. As the field of data science continues to evolve, data scientists remain pivotal in unlocking the potential of data to address complex challenges, drive innovation, and contribute to the ongoing transformation of industries and societies. Their role extends beyond analytical expertise, encompassing interdisciplinary collaboration skills that position them as essential contributors to the dynamic landscape of data driven decision making. Manish Verma "Continuous Improvement through Data Science: From Products to Systems: Beyond ChatGPT" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd61211.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/61211/continuous-improvement-through-data-science-from-products-to-systems-beyond-chatgpt/manish-verma
STOCKSENTIX: A MACHINE LEARNING APPROACH TO STOCKMARKETIRJET Journal
This paper presents an approach for analyzing stock market news articles using web scraping, natural language processing, machine learning, and data visualization techniques. Key aspects of the approach include:
1) Web scraping is used to collect stock market news articles from various online sources.
2) Data preprocessing with Pandas cleans and structures the data. Sentiment analysis then categorizes the sentiment of each article as positive, negative, or neutral.
3) Matplotlib and other tools are used to visualize sentiment trends in an easily interpretable way to help identify patterns and aid decision making.
Introduction to Data Analytics and data analytics life cycleDr. Radhey Shyam
The document provides an overview of data analytics and big data concepts. It discusses the characteristics of big data, including the four V's of volume, velocity, variety and veracity. It also describes different types of data like structured, semi-structured and unstructured data. The document then introduces big data platforms and tools like Hadoop, Spark and Cassandra. Finally, it discusses the need for data analytics in business, including enabling better decision making and improving efficiency.
A sample study on applying data mining research techniques in educational sci...Wendy Hager
This document presents a sample study that applies data mining techniques to analyze data gathered from an educational study. The study used a computer self-efficacy scale applied to 531 university students. Descriptive statistics, decision trees, dependency networks, and clustering were used to analyze the data. Key findings from data mining that were not evident from descriptive statistics alone included relationships between computer competence, perceived talent, and experience. The study demonstrated how data mining provides flexibility in analyzing educational data without statistical software and can provide new insights not found through typical analyses.
An Overview of Python for Data AnalyticsIRJET Journal
This document provides an overview of using Python for data analytics. It discusses how Python is well-suited for data science tasks due to its many preconfigured libraries. The key Python libraries for data analysis that are mentioned include NumPy, Pandas, Seaborn, and Matplotlib. The document also describes the typical steps in a data analysis process, such as data collection, cleaning, exploratory analysis, modeling, and creating data products. A case study is presented that demonstrates analyzing a dataset on world happiness using Python functions, libraries, and plotting capabilities.
Applications Of Statistics In Software EngineeringKristen Carter
This document discusses applications of statistics in software engineering. It introduces a special issue that highlights papers applying statistical methods to solve software engineering problems and improve decision making. The issue includes papers on using statistical significance testing and Bayesian belief networks for risk management, using regression splines to understand factors affecting code inspection effectiveness, using Markov chains for reliability modeling, and applying clustering techniques for software partitioning and recovery. The document emphasizes that statistical analysis can help manage uncertainties in development, but challenges remain in collecting good data and integrating these methods into practice and education.
A Model for Encryption of a Text Phrase using Genetic Algorithmijtsrd
This document describes a model for encrypting text using genetic algorithms. The model involves:
1. Extracting blocks from the text and performing crossover and mutation operations on the blocks based on randomly generated points.
2. Generating a symmetric encryption key based on the crossover and mutation points.
3. Encrypting the text blocks using the key.
The model is implemented in a prototype to test its feasibility. The prototype encrypts text accurately based on the genetic algorithm operations.
IRJET- Fake News Detection using Logistic RegressionIRJET Journal
1) The document discusses a study that uses logistic regression to classify news articles as real or fake. It outlines the methodology which includes data preprocessing, feature extraction using bag-of-words and TF-IDF, and using a logistic regression classifier to predict fake news.
2) The model achieved an accuracy of approximately 72% at classifying news as real or fake when using TF-IDF features and logistic regression.
3) The study aims to address the growing issue of fake news proliferation online by developing a computational method for identifying unreliable news sources.
This is a presentation on data science in this presentation machine learning algorithems are explained with a brief description of artificial intellignece
Study of Software Defect Prediction using Forward Pass RNN with Hyperbolic Ta...ijtsrd
For the IT sector and software specialists, software failure prediction and proneness have long been seen as crucial issues. Conventional methods need prior knowledge of errors or malfunctioning modules in order to identify software flaws inside an application. By using machine learning approaches, automated software fault recovery models allow the program to substantially forecast and recover from software problems. This feature helps the program operate more efficiently and lowers errors, time, and expense. Using machine learning methods, a software fault prediction development model was presented, which might allow the program to continue working on its intended mission. Additionally, we assessed the models performance using a variety of optimization assessment benchmarks, including accuracy, f1 measure, precision, recall, and specificity. Convolutional neural networks and its hyperbolic tangent functions are the basis of the deep learning prediction model FPRNN HTF Forward Pass RNN with Hyperbolic Tangent Function technique. The assessment procedure demonstrated the high accuracy rate and effective application of CNN algorithms. Moreover, a comparative measure is used to evaluate the suggested prediction model against other methodologies. The gathered data demonstrated the superior performance of the FPRNN HTF technique. Swati Rai | Dr. Kirti Jain "Study of Software Defect Prediction using Forward Pass RNN with Hyperbolic Tangent Function" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd60159.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/60159/study-of-software-defect-prediction-using-forward-pass-rnn-with-hyperbolic-tangent-function/swati-rai
This document summarizes recent research areas in computer science. It discusses how computer science has impacted fields like science, medicine, business and mobile communication through research in areas such as algorithms, data management, distributed systems, e-commerce, education, hardware/architecture, human-computer interaction, machine intelligence, networking, security, software engineering and speech processing. It provides examples of current research topics including data mining, machine learning, artificial intelligence, bioinformatics, and education technology. The document concludes that computer science is a vast field with many problems left to solve across these research areas.
Impact of Information Technology on Information Seeking Behavior of the Users...IRJET Journal
The document discusses a study on the impact of information technology on the information seeking behaviors of users at selected universities in Pune, India. Some key findings of the study include:
- The majority of students, researchers, and faculty preferred both print and electronic copies of journal articles and reference materials.
- Most researchers attended training programs on searching scientific/technical information, compared to students and faculty.
- Over 90% of respondents found the training programs they attended to be useful.
- Respondents reported that electronic information sources made gathering information easier and that their information gathering habits had changed significantly in the past 5 years due to new technologies.
- Facilities like computers, the internet, and digit
Irjet v4 i73A Survey on Student’s Academic Experiences using Social Media Data53IRJET Journal
This document summarizes a research paper that analyzed social media posts by engineering students to understand their academic experiences. It developed a workflow that integrated qualitative analysis and data mining techniques. By applying classification algorithms to tweets with hashtags, it identified three main problems engineering students discussed: heavy study loads, lack of social engagement, and sleep deprivation. The analysis revealed students struggled with managing heavy course loads, which led to other issues affecting their health, motivation, and emotions. It also found posts discussing diversity issues and cultural stereotypes among engineering students.
The document discusses tools and techniques for big data analytics, including A/B testing, crowdsourcing, machine learning, and data mining. It provides an overview of the big data analysis pipeline, including data acquisition, information extraction, integration and representation, query processing and analysis, and interpretation. The document also discusses fields where big data is relevant like industry, healthcare, and research. It analyzes tools like A/B testing, machine learning, and data mining techniques in more detail.
A comparative analysis of data mining tools for performance mapping of wlan dataIAEME Publication
This document compares the performance of different data mining tools for anomaly detection in wireless network data. It analyzes four tools: Weka, SPSS, Tanagra, and Microsoft SQL Server's Business Intelligence Development Studio. The same wireless network log data with 1000 instances and 13 attributes is clustered into 3 groups (normal activities, suspicious activities, anomalous activities) using different unsupervised learning algorithms in each tool. The results from each tool are different due to using different distance measures and clustering algorithms. The paper aims to interpret the results from each tool and determine which provides the most accurate performance mapping for the wireless network data.
Enhancing COVID-19 forecasting through deep learning techniques and fine-tuningIJECEIAES
In this study, a comprehensive analysis of classical linear regression forecasting models and deep learning techniques for predicting coronavirus disease of 2019 (COVID-19) pandemic data was presented. Among the deep learning models, the long short-term memory (LSTM) neural network demonstrated superior performance, delivering accurate predictions with minimal errors. The neural network effectively addressed overfitting and underfitting issues through rigorous tuning. However, the diversity of countries and dataset attributes posed challenges in achieving universally optimal predictions. The current study explored the application of the LSTM in predicting healthcare resource demand and optimizing hospital management to provide potential solutions for overcrowding and cost reduction. The results showed the importance of leveraging advanced deep learning techniques for improved COVID-19 forecasting and extending the application of the models to address broader healthcare challenges beyond the pandemic. To further enhance the model performance, future work needed to incorporate additional attributes, such as vaccination rates and immune percentages.
Similar to The trend analysis of the level of fin metrics and e-stat tools for research data analysis in the digital immigrant age (20)
Abnormalities of hormones and inflammatory cytokines in women affected with p...Alexander Decker
Women with polycystic ovary syndrome (PCOS) have elevated levels of hormones like luteinizing hormone and testosterone, as well as higher levels of insulin and insulin resistance compared to healthy women. They also have increased levels of inflammatory markers like C-reactive protein, interleukin-6, and leptin. This study found these abnormalities in the hormones and inflammatory cytokines of women with PCOS ages 23-40, indicating that hormone imbalances associated with insulin resistance and elevated inflammatory markers may worsen infertility in women with PCOS.
A usability evaluation framework for b2 c e commerce websitesAlexander Decker
This document presents a framework for evaluating the usability of B2C e-commerce websites. It involves user testing methods like usability testing and interviews to identify usability problems in areas like navigation, design, purchasing processes, and customer service. The framework specifies goals for the evaluation, determines which website aspects to evaluate, and identifies target users. It then describes collecting data through user testing and analyzing the results to identify usability problems and suggest improvements.
A universal model for managing the marketing executives in nigerian banksAlexander Decker
This document discusses a study that aimed to synthesize motivation theories into a universal model for managing marketing executives in Nigerian banks. The study was guided by Maslow and McGregor's theories. A sample of 303 marketing executives was used. The results showed that managers will be most effective at motivating marketing executives if they consider individual needs and create challenging but attainable goals. The emerged model suggests managers should provide job satisfaction by tailoring assignments to abilities and monitoring performance with feedback. This addresses confusion faced by Nigerian bank managers in determining effective motivation strategies.
A unique common fixed point theorems in generalized dAlexander Decker
This document presents definitions and properties related to generalized D*-metric spaces and establishes some common fixed point theorems for contractive type mappings in these spaces. It begins by introducing D*-metric spaces and generalized D*-metric spaces, defines concepts like convergence and Cauchy sequences. It presents lemmas showing the uniqueness of limits in these spaces and the equivalence of different definitions of convergence. The goal of the paper is then stated as obtaining a unique common fixed point theorem for generalized D*-metric spaces.
A trends of salmonella and antibiotic resistanceAlexander Decker
This document provides a review of trends in Salmonella and antibiotic resistance. It begins with an introduction to Salmonella as a facultative anaerobe that causes nontyphoidal salmonellosis. The emergence of antimicrobial-resistant Salmonella is then discussed. The document proceeds to cover the historical perspective and classification of Salmonella, definitions of antimicrobials and antibiotic resistance, and mechanisms of antibiotic resistance in Salmonella including modification or destruction of antimicrobial agents, efflux pumps, modification of antibiotic targets, and decreased membrane permeability. Specific resistance mechanisms are discussed for several classes of antimicrobials.
A transformational generative approach towards understanding al-istifhamAlexander Decker
This document discusses a transformational-generative approach to understanding Al-Istifham, which refers to interrogative sentences in Arabic. It begins with an introduction to the origin and development of Arabic grammar. The paper then explains the theoretical framework of transformational-generative grammar that is used. Basic linguistic concepts and terms related to Arabic grammar are defined. The document analyzes how interrogative sentences in Arabic can be derived and transformed via tools from transformational-generative grammar, categorizing Al-Istifham into linguistic and literary questions.
A time series analysis of the determinants of savings in namibiaAlexander Decker
This document summarizes a study on the determinants of savings in Namibia from 1991 to 2012. It reviews previous literature on savings determinants in developing countries. The study uses time series analysis including unit root tests, cointegration, and error correction models to analyze the relationship between savings and variables like income, inflation, population growth, deposit rates, and financial deepening in Namibia. The results found inflation and income have a positive impact on savings, while population growth negatively impacts savings. Deposit rates and financial deepening were found to have no significant impact. The study reinforces previous work and emphasizes the importance of improving income levels to achieve higher savings rates in Namibia.
A therapy for physical and mental fitness of school childrenAlexander Decker
This document summarizes a study on the importance of exercise in maintaining physical and mental fitness for school children. It discusses how physical and mental fitness are developed through participation in regular physical exercises and cannot be achieved solely through classroom learning. The document outlines different types and components of fitness and argues that developing fitness should be a key objective of education systems. It recommends that schools ensure pupils engage in graded physical activities and exercises to support their overall development.
A theory of efficiency for managing the marketing executives in nigerian banksAlexander Decker
This document summarizes a study examining efficiency in managing marketing executives in Nigerian banks. The study was examined through the lenses of Kaizen theory (continuous improvement) and efficiency theory. A survey of 303 marketing executives from Nigerian banks found that management plays a key role in identifying and implementing efficiency improvements. The document recommends adopting a "3H grand strategy" to improve the heads, hearts, and hands of management and marketing executives by enhancing their knowledge, attitudes, and tools.
This document discusses evaluating the link budget for effective 900MHz GSM communication. It describes the basic parameters needed for a high-level link budget calculation, including transmitter power, antenna gains, path loss, and propagation models. Common propagation models for 900MHz that are described include Okumura model for urban areas and Hata model for urban, suburban, and open areas. Rain attenuation is also incorporated using the updated ITU model to improve communication during rainfall.
A synthetic review of contraceptive supplies in punjabAlexander Decker
This document discusses contraceptive use in Punjab, Pakistan. It begins by providing background on the benefits of family planning and contraceptive use for maternal and child health. It then analyzes contraceptive commodity data from Punjab, finding that use is still low despite efforts to improve access. The document concludes by emphasizing the need for strategies to bridge gaps and meet the unmet need for effective and affordable contraceptive methods and supplies in Punjab in order to improve health outcomes.
A synthesis of taylor’s and fayol’s management approaches for managing market...Alexander Decker
1) The document discusses synthesizing Taylor's scientific management approach and Fayol's process management approach to identify an effective way to manage marketing executives in Nigerian banks.
2) It reviews Taylor's emphasis on efficiency and breaking tasks into small parts, and Fayol's focus on developing general management principles.
3) The study administered a survey to 303 marketing executives in Nigerian banks to test if combining elements of Taylor and Fayol's approaches would help manage their performance through clear roles, accountability, and motivation. Statistical analysis supported combining the two approaches.
A survey paper on sequence pattern mining with incrementalAlexander Decker
This document summarizes four algorithms for sequential pattern mining: GSP, ISM, FreeSpan, and PrefixSpan. GSP is an Apriori-based algorithm that incorporates time constraints. ISM extends SPADE to incrementally update patterns after database changes. FreeSpan uses frequent items to recursively project databases and grow subsequences. PrefixSpan also uses projection but claims to not require candidate generation. It recursively projects databases based on short prefix patterns. The document concludes by stating the goal was to find an efficient scheme for extracting sequential patterns from transactional datasets.
A survey on live virtual machine migrations and its techniquesAlexander Decker
This document summarizes several techniques for live virtual machine migration in cloud computing. It discusses works that have proposed affinity-aware migration models to improve resource utilization, energy efficient migration approaches using storage migration and live VM migration, and a dynamic consolidation technique using migration control to avoid unnecessary migrations. The document also summarizes works that have designed methods to minimize migration downtime and network traffic, proposed a resource reservation framework for efficient migration of multiple VMs, and addressed real-time issues in live migration. Finally, it provides a table summarizing the techniques, tools used, and potential future work or gaps identified for each discussed work.
A survey on data mining and analysis in hadoop and mongo dbAlexander Decker
This document discusses data mining of big data using Hadoop and MongoDB. It provides an overview of Hadoop and MongoDB and their uses in big data analysis. Specifically, it proposes using Hadoop for distributed processing and MongoDB for data storage and input. The document reviews several related works that discuss big data analysis using these tools, as well as their capabilities for scalable data storage and mining. It aims to improve computational time and fault tolerance for big data analysis by mining data stored in Hadoop using MongoDB and MapReduce.
1. The document discusses several challenges for integrating media with cloud computing including media content convergence, scalability and expandability, finding appropriate applications, and reliability.
2. Media content convergence challenges include dealing with the heterogeneity of media types, services, networks, devices, and quality of service requirements as well as integrating technologies used by media providers and consumers.
3. Scalability and expandability challenges involve adapting to the increasing volume of media content and being able to support new media formats and outlets over time.
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The trend analysis of the level of fin metrics and e-stat tools for research data analysis in the digital immigrant age
1. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.1, 2012
The Trend Analysis of the Level of Fin-Metrics and E-Stat
Tools for Research Data Analysis in the Digital Immigrant Age
Momoh Saliu Musa
Department of Marketing, Federal Polytechnic, P.M.B 13, Auchi, Nigeria.
Tel: +2347032739670 e-mail: momohsm2011@yahoo.com
Shaib Ismail Omade (Corresponding author)
Department of Statistics,
School of Information and Communication Technology,
Federal Polytechnic, P.M.B 13, Auchi, Nigeria.
Phone: +2347032808765 e-mail: shaibismail@yahoo.com
Abstract
The current trend in the information technology age has taken over every spare of human discipline ranging
from communication, business, governance, defense to education world as the survival of these sectors
depend largely on the research outputs for innovation and development. This study evaluated the trend of
the usage of fin-metrics and e-stat tools application among the researchers in their research outputs- digital
data presentation and analysis in the various journal outlets. The data used for the study were sourced
primarily from the sample of 1720 out of 3823 empirical on and off line journals from various science and
social sciences fields. Statistical analysis was conducted to evaluate the consistency of use of the digital
tools in the methodology of the research outputs. Model for measuring the chance of acceptance and
originality of the research was established. The Cockhran test and Bartlet Statistic revealed that there were
significant relationship among the research from the Polytechnic, University and other institution in Nigeria
and beyond. It also showed that most researchers still appeal to manual rather than digital which hampered
the input internationally and found to be peculiar among lecturers in the system that have not yet appreciate
IT penetration in Learning. It therefore recommended that training and workshop should be conducted
within and outside the countries’ academic world to improve the analytical strength and applications of the
tools to enhance research credibility, accuracy, acceptability, usability and currency of research objectives
and purposes which are significant to the individual and society in general.
Keywords: e-Stat, Fin-matrics, trend, digital, Journals, IT
1. Introduction
The current trend of research in all areas of human disciplines have placed more emphasis on the empirical
reliability, validity, rightful use of statistical tools (manual or electronic) techniques employed, the use and
misuse of hypothesis testing procedure for justification of research outputs in various areas of studies as
observed in the literature of (Libutti & Kopala1995). Very importantly, the sampling procedure and
technique adopted is a significant factor in research. Reliability is the consistency of result of experiment
conducted over period of time. This could be longitudinal or cross session frame of research time. The
reliability is measured by test and retest, multiple form, parallel or correlation (Pesaran & Shin1999).
Validity is the truthfulness of research measured by context, content, criterion and face validity. Although,
researching findings might be reliable but scarcely valid therefore, all validity research is reliable. The
study attempts to evaluate the trend of fin-metrics and e-stat tools usage in data analysis among researchers.
It further compares the research outputs of polytechnics, monotechnics and universities for the level of
electronic data mining and analysis and softwares usability. The acceptance of the findings in most journals
based on empirical evaluation is of great importance in the contemporary research outputs as far as quality
research is concerned.
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2. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.1, 2012
2. The Statement of the Problem
Research outputs in most journal articles scarcely utilized e-stat and fin-matrix tools. This has presented
most research data analysis empirically deficient and lack the validity of finding and subsequently
analytical discussion. Therefore level of IT literacy has contributed greatly to this shortfall. However the
paper tends to evaluate the trend of e-stat and fin-matrix application among researchers in the area of
research outputs, empirical credibility across institutions and digital divide.
3. Objective
The followings are the objectives of this paper:
• To compare the applications of e-stat/finmetrix tools on research ouput based on institutions’
category
• To examine the currency of software usage in research data analysis
• To evaluate the significance of usage of softwares
• To measure the level of IT familiarity and applications among researchers
• To examine the various available and possible sotwares applications.
4. Research Hypothesis
The hypotheses for the research study are stated as:
• There is consistency in the usage of IT and software tools in research output drive
• There is significant relationship between the social sciences and sciences journal outputs based on
softwares applications.
5. Significance
The study is expected to establish the need of paradigm shift from the tradition method of analysis to the
digital appeal. To inform the researcher of the relevance of IT penetration and appreciation in the
robustness of research outputs for local and international journal outlets. To inform and educate researchers
on the viability and usability of softwares quality teachings and research for individual,
organizational/institutional needs and society in general.
6. Electronic tools/Softwares for Research analysis
It is very significant that the researchers today transformed the concept and principleof traditional methods
of analysis into digital. The analysis done electronically is mostly carried out via the applications of
softwares in the view of (Cooper 1988). The software is application form of user friendly computer
application packages majorly statistical and mathematical in nature which help aids researches in various
field of human endeavours. The softwares use are open source, properiatory, public and freeware. Open
source allows modifications and easy of use with cost, public attracts no cost and it is share wirelessly or
through the network environment. The properiatory is expensive and always on demand while freeware is
made available freely downloadable form and can be used for research purpose and transferable. The
followings are the list of softwares unfortunately not put to use in most of the Nigerian institutions because
of manpower, low level of IT literacy and usage, cost, availability and capability to instruct and use e-stat
tools.
Aabel – Graphic display and plotting of statistical data sets
ADAPA – batch and real-time scoring of statistical models
Angoss
ASReml – for restricted maximum likelihood analyses
BMDP – general statistics package
CalEst – general statistics and probability package with didactic tutorials
Data Applied – for building statistical models
DPS – comprehensive statistics package
EViews – for econometric analysis
FAME – a system for managing time series statistics and time series databases
GAUSS – programming language for statistics
GenStat – general statistics package
GLIM – early package for fitting generalized linear models
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3. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.1, 2012
GraphPad InStat – Very simple with lots of guidance and explanations
GraphPad Prism – Biostatistics and nonlinear regression with clear explanations
IMSL Numerical Libraries – software library with statistical algorithms
JMP – visual analysis and statistics package
LISREL – statistics package used in structural equation modeling
Maple – programming language with statistical features
Mathematica – programming language with statistical features
MATLAB – programming language with statistical features
MedCalc – for biomedical sciences
Mentor – for market research
Minitab – general statistics package
MLwiN – multilevel models (free to UK academics)
NCSS – general statistics package
NMath Stats – statistical package for .NET Framework
O-Matrix – programming language
OriginPro – statistics and graphing, programming access to NAG library
Partek – general statistics package with specific applications for genomic, HTS, and QSAR data
Primer-E Primer – environmental and ecological specific.
PV-WAVE – programming language comprehensive data analysis and visualization with IMSL statistical
package
Q research software – quantitative data analysis software for market research
Quantum – part of the SPSS MR product line, mostly for data validation and tabulation in Marketing and
Opinion Research
RATS – comprehensive econometric analysis package
SAS – comprehensive statistical package
SHAZAM – comprehensive econometrics and statistics package
SigmaStat – for group analysis
SOCR – online tools for teaching statistics and probability theory
Speakeasy – numerical computational environment and programming language with many statistical and
econometric analysis features
SPSS – comprehensive statistics package
Stata – comprehensive statistics package
Statgraphics – general statistics package
STATISTICA – comprehensive statistics package
StatXact – package for exact nonparametric and parametric statistics
Systat – general statistics package
S-PLUS – general statistics package
Unistat – general statistics package that can also work as Excel add-in
The Unscrambler (free-to-try commercial Multivariate analysis software for Windows)
WINKS – Statistical Data Analysis and Graphs from TexaSoft – a general statistics package designed for
scientific data analysis
XploRe
Analyse-it – add-on to Microsoft Excel for statistical analysis
Sigma Magic - add-on to Microsoft Excel for statistical analysis designed for Lean Six Sigma
SigmaXL – add-on to Microsoft Excel for statistical and graphical analysis
SPC XL – add-on to Microsoft Excel for general statistics
SUDAAN – add-on to SAS and SPSS for statistical surveys
XLfit add-on to Microsoft Excel for curve fitting and statistical analysis
XLSTAT add-on to Microsoft Excel for statistics and multivariate data analysis
Stats Helper – add-on to Microsoft Excel for descriptive statistics and Six Sigma Others are Gretl, SSP,
Excel, Simluk, ALGOL, MATCAD,
The lists of softwares above come in versions as a result of system upgrade and compatibility. This also
depends on the Pentium and system specifications as it may affect the installation process. (Bruce1990)
argued that statsoft sometimes require coding using excel to enhance and accept analysis to be performed
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4. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.1, 2012
effectively. The mastering of the softwares applications in research outputs dwell on the understanding of
coding techniques.
7. Use of Statistical Techniques
Most often than not researchers tends to use the test statistic interchangeably which has the ability of
influencing their result negatively. This is evident on the sample size and determination, test tool to apply
and the decision to be made at a given point in research. The sample size determination depends finite or
infinite which inform the right formulae to apply, the simple size may or may not follow normality as a
result the research make choice of student t-statistic or standard normal test (Z) opined by (Bruce 1997).
The nature of hypothesis requires special techniques such as ANOVA, Chisquares, Regression, Correlation
or non parametric procedures. These are inferential in nature while others could be descriptive depending
on the research questions and objectives. In addition, Anova investigates significant difference and comes
in various ways (one, two, three ways, nested multistage, co founding form). There are ordinary,
probabilistic and contingency chi-squares- measures relationship, discrepancies and dependency of
variables. Regression determines relationship which could be linear or nonlinear, simple or multiple.
Descriptive tends to measure centrality of degree of dispersion of variables under study. Whichever
techniques apply depend on the researchers’ frame of mind and objective in line with the hypothesis and
data mining available for the study see (Bourner 1996).
8. Methodology
On line and printed journals were randomly selected and studied based on institution, IT divide and the
application of software usage in the research data analysis (Leedy 1997). The journals were marked
according to the research study stratifications. The Cochran and Bartlet statistic was adopted to evaluate the
significance and consistency of the usage. The e-view was demonstrated as case in point research data
analysis and interpretation (Bruce 1993).
8.1. Analysis
The data were subjected to electronic analysis with the use of software such as STATA and SPSS to
determine consistency and relationship.
8.2.Findings
The social sciences have more research outputs than the science based on the research study and the
parameters usage. The online are more in the social science than the science and same applicable to the
printed journal. In terms of e-stat tool application, there were few or less usage of software for empirical
analysis which has contributed to low level of research and acceptability of findings. Hence, research from
the university is far significantly differing from those of the polytechnic. These might be justifiable by the
low level of IT penetration and application in the currency of research and teaching. This was also found to
have significantly affected the output of research from the polytechnic.
8.3 Discussion of Results
The OLS result in table1 showed the independent variables have positive relationship with Nigeria
economic growth. This study has contributed to the cointegrating and causal relationship between foreign
direct investment and economic growth in the case of three Sub-Saharan African countries. To this end, we
use two recent econometric procedures which are the (Pesaran 2001) approach to cointegration and the
procedure for non-causality test popularized by (Toda &Yamamoto1995). We build vector Auto-regression
models and compute bounds F-statistics to test for the absence of a long-run relationship between foreign
direct investment and growth. We also construct vector autoregressive models and compute modified Wald
statistics to test for the non-causality between FDI and economic growth. Granger test revealed that NGDP
causes SAFDI and both SAFDI and GFDI granger cause which implies that there is long run relationship
between FDI from South Africa and Ghana to the economic growth in Nigeria.
9. Conclusion
The findings revealed there were significant relationship among the research from the Polytechnic,
University and other institutions in Nigeria and beyond. It also showed that most researchers still appeal to
manual rather than digital which hampered the input internationally and found to be peculiar among
4
5. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.1, 2012
lecturers in the system that have not yet appreciate IT penetration in Learning. It therefore recommended
that training and workshop should be conducted within and outside the countries’ academic world to
improve the analytical strength and applications of the tools to enhance research credibility, accuracy,
acceptability, usability and currency of research objectives and purposes which are significant to the
individual and society in general.
References
Bourner, T. (1996) ‘The research process: four steps to success', in Greenfield, T. (ed), Research methods:
guidance for postgraduates, Arnold, London.
Bruce, C. S. (1990) 'Information skills coursework for postgraduate students: investigation and response at
the Queensland University of Technology' Australian Academic & Research Libraries, vol. 21, no. 4, pp.
224-232.
Bruce, C. (1993) 'When enough is enough’: or how should research students delimit the scope of their
literature review?', in Challenging the Conventional Wisdom in Higher Education: Selected Contributions
Presented at the Ninteeth Annual National Conference and Twenty-First Birthday Celebration of the Higher
Education Research and Development Society of Australasia Inc., HERDSA, University of New South
Wales, Sydney, Australia. pp. 435-439.
Bruce, C. S. (1997) 'From Neophyte to expert: counting on reflection to facilitate complex conceptions of
the literature review', in Zuber-Skerritt, O. (ed), Frameworks for postgraduate education, Southern Cross
University, Lismore, NSW.
Cooper, H. M. (1988) 'The structure of knowledge synthesis' Knowledge in Society, vol. 1, pp. 104-126
Leedy, P. D. (1997) Practical research: planning and design, 6th ed, Merrill, Upper Saddle River, N.J.
Libutti, P.& Kopala, M. (1995) 'The doctoral student, the dissertation, and the library: a review of the
literature' Reference Librarian, vol. 48, no. 5, pp. 5-25.
Mauch, J. E.& Birch, J. W. (2003) Guide to the successful thesis and dissertation: a handbook for students
and faculty, 5th ed, Marcel Dekker, New York.
Pesaran, M. H. & Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration
analysis. In S. Strom (Ed.), Econometrics and Economic Theory in the 20th Century (Chapter 11). The
Ragnar Frisch Centennial Symposium, Cambridge: Cambridge University Press. International Journal of
Economics and Finance Vol. 2, No. 2; May 2010 175
Pesaran, M. H., Shin, Y. & Smith, R. J. (2001). Bounds testing approaches to the analysis of level
relationships. Journal of Applied Econometrics, 16, 289-326.
Toda, H. Y. & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly
integrated processes. Journal of Econometrics, 66, 225-250.
9. Data Presentation/Analysis
Table1 Journal Nature
Category Social Science Science
Online 700 423
Printed 285 312
Total 985 735
Souce: Field Survey, 2011.
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ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.1, 2012
Table2 e-stat and fin-matrix usage level
Category Social E-stat/ Science E-stat/
Science Finmatrix Finmatrix
Usage usage
Online 700 120 423 102
Printed 285 25 312 29
Total 985 145 735 131
Souce: Field Survey, 2011.
Table3 Journal based on Institution
Category University Polytechnic/Others
Online 634 56
Printed 765 275
Total 1399 331
Souce: Field Survey, 2011
Table4 Institutions Based on IT level
Category University Polytechnic/others
High 954 220
Low 100 312
Poor 32 104
Total 1086 636
Souce: Field Survey, 2011.
10. Case of E-views analysis output
Empirical Analysis Result
Appendix
Dependent Variable: NGDP
Method: Least Squares
Table 1
Sample(adjusted): 1980 2009
Included observations: 30 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
SAFDI 37.32926 15.45081 2.416007 0.0230
GFDI 27.79444 12.89845 2.154867 0.0406
OTHERS 38.95335 40.22011 0.968504 0.3417
C -11009.12 11588.40 -0.950012 0.3509
R-squared 0.848101 Mean dependent var 87062.37
Adjusted R-squared 0.830574 S.D. dependent var 101650.4
S.E. of regression 41840.69 Akaike info criterion 24.24469
Sum squared resid 4.55E+10 Schwarz criterion 24.43152
Log likelihood -359.6704 F-statistic 48.38884
Durbin-Watson stat 0.961045 Prob(F-statistic) 0.000000
Source: E-Views version 3.1
Table2 Diagnostic Test
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ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.1, 2012
20
Series: Residuals
Sample 1980 2009
Observations 30
15
Mean -1.82E-11
Median -10652.92
Maximum 139096.1
10
Minimum -55766.85
Std. Dev. 39617.45
Skewness 1.924702
5 Kurtosis 6.782670
Jarque-Bera 36.40813
Probability 0.000000
0
-50000 0 50000 100000 150000
Source: E-Views version 3.1
Table3 Serial Correlation Test
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 4.783712 Probability 0.017845
Obs*R-squared 8.550633 Probability 0.013908
Source: E-Views version 3.1
Table 4 White Heteroskedasticity Test:
F-statistic 1.480964 Probability 0.228631
Obs*R-squared 8.360262 Probability 0.212880
Source: E-Views version 3.1
Table5 Ramsey RESET Test:
F-statistic 5.813862 Probability 0.002384
Log likelihood ratio 21.63842 Probability 0.000237
Source: E-Views version 3.1
Table6
Unit Root at 2 NGDP
ADF Test Statistic -5.911813 1% Critical Value* -3.7076
5% Critical Value -2.9798
10% Critical Value -2.6290
*MacKinnon critical values for rejection of hypothesis of a unit root.
Source: E-Views version 3.1
Unit Root 2 DFF GFDI
ADF Test Statistic -5.620173 1% Critical Value* -3.7076
5% Critical Value -2.9798
10% Critical Value -2.6290
*MacKinnon critical values for rejection of hypothesis of a unit root.
Source: E-Views version 3.1
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ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.1, 2012
Unit Root at 2 DFF
ADF Test Statistic -9.323026 1% Critical Value* -3.7076
5% Critical Value -2.9798
10% Critical Value -2.6290
*MacKinnon critical values for rejection of hypothesis of a unit root.
Source: E-Views version 3.1
Unit Root at 2 DFF
ADF Test Statistic -0.128973 1% Critical Value* -3.7076
5% Critical Value -2.9798
10% Critical Value -2.6290
*MacKinnon critical values for rejection of hypothesis of a unit root.
Source: E-Views version 3.1
Table 7 Johnsen Co-integration test
Sample: 1979 2009
Included observations: 28
Test
assumption:
Linear
deterministic
trend in the data
Series: DNGDP DGFDI DLFDI
Lags interval: No lags
Likelihood 5 Percent 1 Percent Hypothesized
Eigenvalue Ratio Critical Value Critical Value No. of CE(s)
0.637847 65.25730 29.68 35.65 None **
0.613204 36.81804 15.41 20.04 At most 1 **
0.305853 10.22199 3.76 6.65 At most 2 **
*(**) denotes
rejection of the
hypothesis at
5%(1%)
significance
level
L.R. test
indicates 3
cointegrating
equation(s) at
5% significance
level
Normalized
Cointegrating
Coefficients: 2
Cointegrating
Equation(s)
DNGDP DGFDI DLFDI C
1.000000 0.000000 -39.94833 -106.9733
(18.4275)
0.000000 1.000000 -0.066066 9.382873
(0.25829)
8
9. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.1, 2012
Log likelihood -752.1599
Source: E-Views version 3.1
Table 8
Sample(adjusted): 1983 2009
Included observations: 27 after
adjusting endpoints
Standard errors & t-statistics in
parentheses
DNGDP
DNGDP(-1) -0.033437
(0.22998)
(-0.14539)
DNGDP(-2) -0.144454
(0.21316)
(-0.67766)
C 13462.02
(8386.27)
(1.60525)
DGFDI -8.467041
(15.4113)
(-0.54941)
DSAFDI -9.591634
(20.3233)
(-0.47195)
R-squared 0.043083
Adj. R-squared -0.130901
Sum sq. resids 2.86E+10
S.E. equation 36064.12
F-statistic 0.247627
Log likelihood -318.8591
Akaike AIC 23.98956
Schwarz SC 24.22953
Mean dependent 9719.389
S.D. dependent 33912.74
Source: E-Views version 3.1
Table 9 Estimation Proc:
===============================
LS 1 2 DNGDP @ C DGFDI DSAFDI
VAR Model:
===============================
DNGDP = C(1,1)*DNGDP(-1) + C(1,2)*DNGDP(-2) + C(1,3) + C(1,4)*DGFDI + C(1,5)*DSAFDI
VAR Model - Substituted Coefficients:
===============================
DNGDP = - 0.03343657098*DNGDP(-1) - 0.144454019*DNGDP(-2) + 13462.01598 -
8.467040848*DGFDI - 9.59163364*DSAFDI
Table 10 Pairwise Granger Causality Tests
9
10. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.1, 2012
Sample: 1979 2009
Lags: 2
Null Hypothesis: Obs F-Statistic Probability
DGFDI does not Granger Cause DNGDP 27 0.30725 0.73857
DNGDP does not Granger Cause DGFDI 0.31069 0.73611
DSAFDI does not Granger Cause DNGDP 27 0.45098 0.64276
DNGDP does not Granger Cause DSAFDI 0.94809 0.40275
DSAFDI does not Granger Cause DGFDI 27 1.86962 0.17787
DGFDI does not Granger Cause DSAFDI 1.56559 0.23137
Source: E-Views version 3.1
10