Review Article
An Overview of Computer Memory Systems and Emerging Trends
Victor Worlanyo Gbedawo,
Gideon Agyeman Owusu,
Carl Komla Ankah,
Mohammed Ibrahim Daabo
Issue:
Volume 7, Issue 2, December 2023
Pages:
19-26
Received:
30 September 2023
Accepted:
20 October 2023
Published:
31 October 2023
Abstract: Central processing units (CPUs) in modern computing devices rely on computer memory systems to store and retrieve the data they require to perform their duties. This research covers the types, functions, and historical evolution of computer memory systems. It also looks at new developments in memory technology that are influencing the direction of computing. Using the search criteria "computer memory system" AND (PUBYEAR > 2019-2023), a thorough review of all publications published between 2019 and 2023 was conducted in the Web of Science database and IEEE Xplore database. The results were reported in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standards. In the instance of Web of Science, the database searches yielded a total of 28, 423 results, and 98,142 results in the case of IEEE Xplore. After reading the papers' abstracts, 126,263 search results were eliminated since they didn't fit the criteria. The remaining 302 articles were considered. A total of 32 studies were chosen for inclusion in the review after applying inclusion and exclusion criteria. The thorough analysis outlines the current state of computer memory systems as well as any new trends. Additionally, the report outlines prospective research goals and avenues for computer memory systems research.
Abstract: Central processing units (CPUs) in modern computing devices rely on computer memory systems to store and retrieve the data they require to perform their duties. This research covers the types, functions, and historical evolution of computer memory systems. It also looks at new developments in memory technology that are influencing the direction of ...
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Research Article
Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques
Seth Gyamerah,
Godfred Tour Soori,
Dennis Redeemer Korda*,
John Kwame Tawiah,
Eric Ayintareba Akolgo,
Emmanuel Oteng Dapaah
Issue:
Volume 7, Issue 2, December 2023
Pages:
27-39
Received:
29 October 2023
Accepted:
17 November 2023
Published:
11 December 2023
Abstract: Dimensionality reduction is critical for analyzing and interpreting high-dimensional data across domains like genomics, imaging, and finance. This paper presents a comparative analysis of dimensionality reduction techniques, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Recursive Feature Elimination (RFE), and Lasso regression. These methods are applied to datasets from genomics, medical imaging, and finance to evaluate their ability to reduce dimensions while preserving relevant information. The results demonstrate that PCA and LDA are highly effective for genomics data, reducing gene expression profiles from over 60,000 dimensions to 10-50 components while maintaining precision of over 80%. For medical images, PCA and LDA reduce pixel dimensions by over 90% without compromising precision. However, no single technique optimizes dimensionality reduction and precision for complex finance data. Overall, the analysis provides domain-specific insights, highlighting PCA and LDA as leading techniques for genomics and imaging. The choice of method should be guided by data characteristics. Testing on more diverse, real-world datasets is needed to establish validity further. This research aims to inform the selection of appropriate data reduction techniques across critical applications involving high-dimensional data.
Abstract: Dimensionality reduction is critical for analyzing and interpreting high-dimensional data across domains like genomics, imaging, and finance. This paper presents a comparative analysis of dimensionality reduction techniques, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Recursive Feature Elimination (RFE), and La...
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