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Publisher: Horizon Research Publishing   (Total: 54 journals)   [Sort by number of followers]

Showing 1 - 54 of 54 Journals sorted alphabetically
Advances in Diabetes and Metabolism     Open Access   (Followers: 28)
Advances in Economics and Business     Open Access   (Followers: 18)
Advances in Energy and Power     Open Access   (Followers: 18)
Advances in Pharmacology and Pharmacy     Open Access   (Followers: 10)
Advances in Signal Processing     Open Access   (Followers: 13)
Advances in Zoology and Botany     Open Access  
Bioengineering and Bioscience     Open Access   (Followers: 1)
Cancer and Oncology Research     Open Access   (Followers: 10)
Chemical and Materials Engineering     Open Access   (Followers: 25)
Civil Engineering and Architecture     Open Access   (Followers: 24)
Computational Research     Open Access   (Followers: 1)
Computer Science and Information Technology     Open Access   (Followers: 14)
Energy and Environmental Engineering     Open Access   (Followers: 7)
Environment and Ecology Research     Open Access   (Followers: 8)
Food Science and Technology     Open Access   (Followers: 3)
Immunology and Infectious Diseases     Open Access   (Followers: 9)
Intl. J. of Biochemistry and Biophysics     Open Access   (Followers: 1)
Intl. J. of Cardiovascular and Cerebrovascular Disease     Open Access   (Followers: 2)
Intl. J. of Neuroscience and Behavioral Science     Open Access   (Followers: 1)
Linguistics and Literature Studies     Open Access   (Followers: 5)
Manufacturing Science and Technology     Open Access   (Followers: 3)
Mathematics and Statistics     Open Access   (Followers: 5)
Nanoscience and Nanoengineering     Open Access   (Followers: 1)
Natural Resources and Conservation     Open Access   (Followers: 6)
Nursing and Health     Open Access   (Followers: 4)
Open J. of Dentistry and Oral Medicine     Open Access   (Followers: 1)
Sociology and Anthropology     Open Access   (Followers: 5)
Sport and Art     Open Access   (Followers: 1)
Universal J. of Accounting and Finance     Open Access   (Followers: 4)
Universal J. of Agricultural Research     Open Access   (Followers: 1)
Universal J. of Applied Mathematics     Open Access   (Followers: 5)
Universal J. of Applied Science     Open Access   (Followers: 2)
Universal J. of Biomedical Engineering     Open Access  
Universal J. of Chemistry     Open Access   (Followers: 1)
Universal J. of Clinical Medicine     Open Access  
Universal J. of Communications and Network     Open Access   (Followers: 1)
Universal J. of Computational Mathematics     Open Access   (Followers: 5)
Universal J. of Control and Automation     Open Access   (Followers: 4)
Universal J. of Educational Research     Open Access   (Followers: 1)
Universal J. of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Universal J. of Engineering Science     Open Access   (Followers: 2)
Universal J. of Food and Nutrition Science     Open Access   (Followers: 6)
Universal J. of Geoscience     Open Access   (Followers: 4)
Universal J. of Industrial and Business Management     Open Access   (Followers: 1)
Universal J. of Management     Open Access   (Followers: 2)
Universal J. of Materials Science     Open Access   (Followers: 3)
Universal J. of Mechanical Engineering     Open Access   (Followers: 17)
Universal J. of Medical Science     Open Access  
Universal J. of Microbiology Research     Open Access  
Universal J. of Physics and Application     Open Access   (Followers: 2)
Universal J. of Plant Science     Open Access  
Universal J. of Psychology     Open Access   (Followers: 3)
Universal J. of Public Health     Open Access   (Followers: 3)
World J. of Computer Application and Technology     Open Access   (Followers: 4)
Similar Journals
Journal Cover
Computer Science and Information Technology
Number of Followers: 14  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2331-6063 - ISSN (Online) 2331-6071
Published by Horizon Research Publishing Homepage  [54 journals]
  • Feature Selection in Sparse Matrices

    • Abstract: Publication date:  May 2019
      Source:Computer Science and Information Technology  Volume  7  Number  3  Rahul Kumar   Vatsal Srivastava   and Manish Pathak   Feature selection, as a pre-processing step to machine learning, is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. There are two main approaches for feature selection: wrapper methods, in which the features are selected using the supervised learning algorithm, and filter methods, in which the selection of features is independent of any learning algorithm. However, most of these techniques use feature scoring algorithms that make some basic assumptions about the distribution of the data like normality, balanced distribution of classes, non-sparsity or dense data-set, etc. The data generated in the real world rarely follow such strict criteria. In some cases such as digital advertising, the generated data matrix is actually very sparse and follows no distinct distribution. For this reason, we have come up with a new approach towards feature selection for cases where the data-sets do not follow the above-mentioned assumptions. Our methodology also presents an approach to solve the problem of skewness of data. The efficiency and effectiveness of our methods is then demonstrated by comparison with other well-known techniques of statistics like ANOVA, mutual information, KL divergence, Fisher score, Bayes' error, Chi-square, etc. The data-set used for validation is a real-world user-browsing history data-set used for ad-campaign targeting. It has very high dimensions and is highly sparse as well. Our approach reduces the number of features to a significant degree without compromising on the accuracy of the final predictions.
      PubDate: May 2019
  • Research Project Model Canvas

    • Abstract: Publication date:  May 2019
      Source:Computer Science and Information Technology  Volume  7  Number  3  Hiago Silva   and Alexandre Cardoso   This work presents a proposal of a visual tool to assist the creation of academic research projects, dissertations and theses. Its metrics are based on business and management success cases. In the creation and management of projects in teams are used visual strategies to present and record the parameters involved in the scope of the project through a screen, which can be composed of a frame with predefined fields or connection lines forming a flowchart. There are tools that can provide researchers with the conditions to view isolated parts of the project as bibliographic references only or correlation nodes between keywords, then it becomes necessary to create a strategy that enables the creator of the project and the team involved to visualize the essence of the project in the eminence of being created and to predict needs, failures, objectives as well as to restructure the project to adapt the research conditions. This strategy has the form of a framework, called Research Project Model Canvas with fields defined according to the needs of creating a research project, and its tables are organized in a logical order of reading, presentation and connection between each one.
      PubDate: May 2019
  • Performance of Datamining Techniques in the Prediction of Chronic Kidney

    • Abstract: Publication date:  Mar 2019
      Source:Computer Science and Information Technology  Volume  7  Number  2  Kehinde A. Otunaiya   and Garba Muhammad   Data mining being an experimental science is very important especially in the health sector where we have large volumes of data. Since data mining is an experimental science, getting accurate predictions could be tasking. Getting maximum accuracy of each classifier is necessary. It is therefore important that the appropriate feature selection method should be selected. Feature selection is highly relevant in predictive analysis and should not be overlooked. It helps reduce the execution time and provide a more accurate and reliable result. Therefore, more researches on predictive analysis and how reliable these predictions are needs to be delved into. Application of data mining techniques in the health sector ensures that the right treatment is given to patients. This study was implemented using WEKA. This study is aimed at using 3 classifiers: multilayer perceptron, naive bayes and J48 decision tree in the prediction of chronic kidney disease dataset. The aim of this research is to evaluate the performance of the classifiers used based on the following metrics-accuracy, specificity, sensitivity, error rate and precision. Based on the performance metrics mentioned above, results shows that J48 decision tree gave the best result but naive bayes had the lowest execution time therefore making it the fastest classifier.
      PubDate: Mar 2019
  • A Simple and Fast Line-Clipping Method as a Scratch Extension for Computer
           Graphics Education

    • Abstract: Publication date:  Mar 2019
      Source:Computer Science and Information Technology  Volume  7  Number  2  Dimitrios Matthes   and Vasileios Drakopoulos   Line clipping is a fundamental topic in an introductory computer graphics course. An understanding of a line-clipping algorithm is reinforced by having students write actual code and see the results by choosing a user-friendly integrated development environment such as Scratch, a visual programming language especially useful for children. In this article a new computation method for 2D line clipping against a rectangular window is introduced as a Scratch extension in order to assist computer graphics education. The proposed method has been compared with Cohen-Sutherland, Liang-Barsky, Cyrus-Beck, Nicholl-Lee-Nicholl and Kodituwakku-Wijeweera-Chamikara methods, with respect to the number of operations performed and the computation time. The performance of the proposed method has been found to be better than all of the above-mentioned methods and it is found to be very fast, simple and can be implemented easily in any programming language or integrated development environment. The simplicity and elegance of the proposed method makes it suitable for implementation by the student or pupil in a lab exercise.
      PubDate: Mar 2019
  • GDG in UNIX' No Way!

    • Abstract: Publication date:  Mar 2019
      Source:Computer Science and Information Technology  Volume  7  Number  2  Kannan Deivasigamani   IBM mainframes in the z/OS environment provide a generational structure often referred to as Generation Data Group (GDG) for file storage to maintain data snapshots of related data.[1] These data resulting from business operations within a servicing organization are not uncommon. This structure can hold TEXT data sets without a problem. However, in the case of a UNIX or Linux platform, a comparable structure is unavailable for use by SAS for storing data as TEXT files. This paper contains a solution to this problem and shows a comparison of what the mainframe GDG offers and the solution offered. A developer or a programmer may find that the solution, TextGDS (SAS macro) is even better than the mainframe GDG structure in certain respects. Although there are both limitations and delimitations when using TextGDS, the tool helps to fill the void with UNIX-SAS.
      PubDate: Mar 2019
  • Modification of the Norwegian Traffic Light States as the Method to Reduce
           the Travel Delay

    • Abstract: Publication date:  Jan 2019
      Source:Computer Science and Information Technology  Volume  7  Number  1  Setiyo Daru Cahyono   Sutomo   Seno Aji   Sudarno   Pradityo Utomo   and Tomi Tristono   Traffic lights have a vital role as regulatory systems to control the vehicles flowed in urban networks. This research is based on the real case. The traffic lights are installed at a massive intersection of an urban network consisting of four sections. The systems control implements the modification of the Norwegian traffic lights states. The behavior of traffic lights states were modeled using Petri net method. For the model verification and validation, the invariants and simulation were applied. The purpose of the implementation of this control system was to reduce travel delays. The intersection performance level was good while the average travel delay on all sections was low. The method used for the testing was the comparison of the simulation results due to the settings that apply the standard system to the imitation of the reality of the system using modifications of the Norwegian traffic lights states. The control system was able to reduce the travel delays slightly. The average of Level of Service (LoS) index of the roads for all sections was at level D. It improved the performance of the intersection, but not yet significant. In addition to setting traffic lights, the presence of flyovers is urgent to improve travel delays.
      PubDate: Jan 2019
  • Business Intelligence Improved by Data Mining Algorithms and Big Data
           Systems: An Overview of Different Tools Applied in Industrial Research

    • Abstract: Publication date:  Jan 2019
      Source:Computer Science and Information Technology  Volume  7  Number  1  Alessandro Massaro   Valeria Vitti   Angelo Galiano   and Alessandro Morelli   The proposed paper shows different tools adopted in an industry project oriented on business intelligence (BI) improvement. The research outputs concern mainly data mining algorithms able to predict sales, logistic algorithms useful for the management of the products dislocation in the whole marketing network constituted by different stores, and web mining algorithms suitable for social trend analyses. For the predictive data mining and web mining algorithms have been applied Weka, Rapid Miner and KNIME tools, besides for the logistic ones have been adopted mainly Dijkstra's and Floyd-Warshall's algorithms. The proposed algorithms are suitable for an upgrade of the information infrastructure of an industry oriented on strategic marketing. All the facilities are enabled to transfer data into a Cassandra big data system behaving as a collector of massive data useful for BI. The goals of the BI outputs are the real time planning of the warehouse assortment and the formulation of strategic marketing actions. Finally is presented an innovative model oriented on E-commerce sales neural network forecasting based on multi-attribute processing. This model can process data of the other data mining outputs supporting logistic actions. This model proves how it is possible to embed many data mining algorithms into a unique prototypal information system connected to a big data, and how it can work on real business intelligence. The goal of the proposed paper is to show how different data mining tools can be adopted into a unique industry information system.
      PubDate: Jan 2019
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Fax: +00 44 (0)131 4513327
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