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All Journal International Journal of Advances in Applied Sciences Computer Science and Information Technologies
Hanane Aznaoui
Cadi Ayyad University
Computer Science & IT Earth & Planetary Sciences Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Cloud computing and 5G challenges and open issues Arif Ullah; Hanane Aznaoui; Canan Batur Şahin; Mahnaz Sadie; Ozlem Batur Dinler; Laassar Imane
International Journal of Advances in Applied Sciences Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (529.6 KB) | DOI: 10.11591/ijaas.v11.i3.pp187-193

Abstract

The obtainable fourth-generation technology (4G) networks have been extensively used in the cloud application and are constantly evolving to match the needs of the future cloud applications. The fifth-generation (5G) networks are probable to immense expand today's cloud that can boost communication operations, cloud security, and network challenges and drive the cloud future to the edge and internet of things (IoT) applications. The existing cloud solutions are facing a number of challenges such as large number of connection of nodes, security, and new standards. This paper reviews the current research state-of-the-art of 5G cloud, key-enabling technologies, and current research trends and challenges in 5G along with cloud application.
Generalization of linear and non-linear support vector machine in multiple fields: a review Sundas Naqeeb Khan; Samra Urooj Khan; Hanane Aznaoui; Canan Batur Şahin; Özlem Batur Dinler
Computer Science and Information Technologies Vol 4, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i3.p226-239

Abstract

Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers. In other terms, SVM is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy. In this article, the discussion about linear and non-linear SVM classifiers with their functions and parameters is investigated. Due to the equality type of constraints in the formulation, the solution follows from solving a set of linear equations. Besides this, if the under-consideration problem is in the form of a non-linear case, then the problem must convert into linear separable form with the help of kernel trick and solve it according to the methods. Some important algorithms related to sentimental work are also presented in this paper. Generalization of the formulation of linear and non-linear SVMs is also open in this article. In the final section of this paper, the different modified sections of SVM are discussed which are modified by different research for different purposes.
Co-Authors Arif Ullah Canan Batur Şahin Canan Batur Şahin Laassar Imane Mahnaz Sadie Ozlem Batur Dinler Özlem Batur Dinler Samra Urooj Khan Sundas Naqeeb Khan