International Journal of Computer (IJC - Global Society of Scientific Research and Researchers, GSSRR)
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Data Auditing and Security in Cloud Computing: Issues, Challenges and Future Directions
Get PDFCloud computing is one of the significant development that utilizes progressive computational power and upgrades data distribution and data storing facilities. With cloud information services, it is essential for information to be saved in the cloud and also distributed across numerous customers. Cloud information repository is involved with issues of information integrity, data security and information access by unapproved users. Hence, an autonomous reviewing and auditing facility is necessary to guarantee that the information is effectively accommodated and used in the cloud. In this paper, a comprehensive survey on the state-of-art techniques in data auditing and security are discussed. Challenging problems in information repository auditing and security are presented. Finally, directions for future research in data auditing and security have been discussed
SBKMMA: Sorting Based K Means and Median Based Clustering Algorithm Using Multi Machine Technique for Big Data
Get PDFBig data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. KMeans is a traditional partition algorithm which is simple and popularly used. This algorithm has disadvantages such as to identify K clusters, initial allocation etc. In this paper we mainly focus on the initial centroids and improving the efficiency by reducing the number of iterations. Sorting based KMeans algorithm and Sorting based KMedian algorithm are enhanced form of KMeans algorithm where the data are sorted and uses KMeans algorithm. The proposed algorithm focuses on the initial centroid selection with the help of sorting. Here the centroids are default assigned to the objects in the beginning after sorting.
An Application to Support Stuttering People by Implementing Linguistic Variable Based on Fuzzy Logic
Get PDFFuzzy logic is the idea behind the artificial intelligence and automated machines are the brain child of it. Linguistic variable are the special type of variables used in the fuzzy logic to represent the characteristic of an object. The scope of the fuzzy concept is wide and implemented successfully in the field of science. Stuttering or stammering is the speech syndrome causes improper speech or broken accent and make people to afraid to speak in public areas. The aim of the paper is to design an application to support stuttering people to reduce their problem and improve their life style. The research utilises the concept of linguistic variable in fuzzy logic and develop the application
Finding a Correct Measure of Information Systems: The Integration of UTAUT and Lin Model into IS Success Model
Get PDFFinding effective and comprehensive model to measure information systems’ acceptance, use, individual and organizational impacts is tricky. This has been contributed by limited factors of existing models and frameworks. This conceptual paper aimed at proposing a comprehensive and effective information success model called Lashayo10, which will have a capability to define, explain, and measure important factors for successful acceptance, user satisfaction and use of Information Systems (IS). Random literature review will be used to assess the literature critically, and to propose an enhancement model for the IS success. The proposed model (Lashayo10) will adapt DeLone and McLean IS model integrated with UTAUT and Lin (2008) model. The Lashayo10 will be subjected to empirical validation by researchers in information systems’ projects. The novelty of this study lies on the number of effective and comprehensive measurement factors which are proposed in a single holistic model
Comparing and Contrasting E-learning Systems’ Adoption in Tanzania: The Experience from Students-Instructors of Eight Universities
Get PDFStudents and instructors contrasting interests were major adoption block in e-learning systems in the world’s universities and Tanzania in particular. This paper aimed to examine aspects in which students-instructors are similar and different in e-learning systems’ adoption in Tanzania’s universities. This paper uses results from two empirical models which were developed from two sample of 1,005 students and 86 instructors from eight universities in Tanzania. Specifically, it intends to achieve the following objectives: (1) to determine common and contrasting factors affecting students-instructors in e-learning systems adoption (2) to examine common hypotheses and their strengths (3) to deduce a unified model (view). Results showed that there were considerable common interests between these two key stakeholders (instructors and students) in e-learning systems however there were also contrasting interests too, this implied that specific and common interests shall always be considered in adopting and measuring these systems. These findings will help policy makers in their plan and strategy for e-learning systems’ adoption and measuring in universities in Tanzania especially in environment where both instructors and students need optimal e-learning systems. The novelty of this research lies in identified common core factors between students and instructors with their corresponding common hypotheses strengths
Reframing in Clustering: An Introductory Survey
Get PDFReframing is an essential task for improving the performance of machine learning and data mining algorithms in the areas where there are context changes between the source and target domains. A major assumption in many reframing algorithms is that the target domain has some labelled data. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a clustering task in one domain of interest, but we only have sufficient source data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. Moreover, both source and target data may be unlabelled. In such cases, reframing in clustering, if done successfully, would greatly improve the performance of clustering by avoiding much expensive data labeling efforts. In recent years, reframing in clustering has emerged as a new clustering framework to address this problem. In this paper, we present a review on the state-of-the-art reframing in clustering approaches, and to the best of our knowledge it has never been done in the literature. We give a definition of reframing in clustering. We also explore some potential future issues in this area of research
Exploiting Class Label Frequencies for Text Classification
Get PDFDocument classification is an example of Machine Learning (ML) in the form of Natural Language Processing (NLP). By classifying text, we are aiming to assign one or more classes or categories to a document, making it easier to manage and sort. In the vast majority of document classification techniques a document is represented as a bag of words consisting of all the individual terms making up the document together with the number of times each term appears in the document. The number of term occurrences is known as local term frequencies and it is very common to make use of the local term frequencies at the price of some added information in the classification model. In this work, we extend our previous work on medical article classification [1,2] by simplifying the weighting scheme in the ranking process using class label frequencies to device a simple weighting formula inspired from traditional information retrieval task. We also evaluate the proposed approach using more research experimental data. The method we propose here, called CLF KNN first, it uses a lexical approach to identify frequency terms in the document texts and then, it uses this information coupled with class label information in corpus in a sophisticated way to devise a weighting ranking scheme in classification decision process. The evaluation experiments on two collections: The Ohsumed collection of medical documents and the 20 Newsgroup messages collection, show that the proposed method significantly outperforms traditional KNN classification
Adaption of Moodle as E-Learning in Saudi Arabian University: Empirical Examination and its Outcomes Using TAM
Get PDFModern universities increasingly rely on e-Learning management systems to ensure their students have a richer and more-efficient learning experience. The choice and adaption of such systems need careful evaluation to achieve their intended goals. This paper describes the experience of using Moodle as an e-Learning System in Shaqra University, Saudi Arabia. The paper describes the benefits of the system and highlights the problems that were encountered while adapting it. It also explains an analysis study which uses an acceptance model to assure the adaptability of the system in the university by the stakeholders. The study confirms the importance of the project for the university and its impact on the working ethics for the employees as well as the students
Using Computing Containers and Continuous Integration to Improve Numerical Research Reproducibility
Get PDFCloud computing has opened new options of collaboration between research teams in the field of high performance computing and numerical research. Running computational workloads in virtual machines became common in recent years. However, the use of computing containers provides many additional advantages besides just proving new possible runtime choice. One of the most important (and often underappreciated) is an option to improve the reproducibility of research results based on complex mathematical modeling. This paper provides an overview of architecture based on computing containers and continuous integration tools we used to achieve reproducible numerical result
Development of Human Resource Assessment and Selection Model for Computer System Design
Get PDFThe paper offers the model of assessment and selection of human resources, which is based on a multi-criteria expert method – a TOPSIS method. The proposed model allows us for carrying out assessment and selection of human resources based on the results of expert estimates and testing. The use of this model will allow us for developing a computer-based system supporting the decision-making on assessment and selection of human resources