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Computer Science and Information Technologies
Published by Institute of Advanced Engineering and Science
ISSN : 2722323X     EISSN : 27223221     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Computer Science and Information Technologies ISSN 2722-323X, e-ISSN 2722-3221 is an open access, peer-reviewed international journal that publish original research article, review papers, short communications that will have an immediate impact on the ongoing research in all areas of Computer Science/Informatics, Electronics, Communication and Information Technologies. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. The journal is published four-monthly (March, July and November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 2 Documents
 1 
Search results for , issue "List of Accepted Papers (with minor revisions)" : 2 Documents clear
An Integrated Review On Machine Learning Approaches For Heart Disease Prediction: Direction Towards Future Research Gaps A, Fathima
Computer Science and Information Technologies List of Accepted Papers (with minor revisions)
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i1.p%p

Abstract

There has recently been a rapid increase in the count of statistical models obtainable for the prediction of heart disease. However, without a comprehensive overview, it remains unclear which, if any, should be applied in clinical care. Hence, this paper plans to make a clear literature review on state-of-the-art heart disease prediction models. It makes a plan to review 61 research papers and states a significant analysis. Initially, the analysis addresses the contributions of each literature works with its limitations and observes the simulation environment in which each contribution executes. Here, different types of machine learning algorithms deployed in each contribution are analyzed and state those limitations. In addition, the dataset utilized for existing heart disease prediction models are observed. Later the performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure etc are learned, and further, the best performance is also checked to confirm the effectiveness of entire contributions. Finally, comprehensive research challenges and the gap is portrayed based on the development of intelligent methods concerning the unresolved challenges in the case of heart disease prediction using data mining techniques.
Pre-processing Block Hardware Architecture in Image Processing using Reconfigurable Platform G N, Chiranjeevi
Computer Science and Information Technologies List of Accepted Papers (with minor revisions)
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i1.p%p

Abstract

Real time image processing is a challenging task in which fetching the sub image requires offset memory access apart from core processing needs. This paper aims at overcoming the offset needs for memory addressing in pre-processing blocks. Another feature of this present work is to appending the image data with customized algorithmic requiments viz duplicating, zero padding. For K x K kernel size, the proposed hardware architecture can be programmed to fetch K pixels in one cycle, reducing the data access time. Results have been compared with software based processing for K x K spatial filtering. performance indicates significant timing improvement using proposed pre-processing hardware block

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