Editors-in-Chief
Dr. Sarhan M. Musa
Prairie View A&M University, US
Published
Table of Contents
Open AccessArticles
by Naser Mohammadi
2024,2(1);
215 Views
Abstract
The surprising results of recent developments in various fields of artificial intelligence application have caused people to have a feeling of amazement combined with fear of the category of artificial intelligence. In many of since filed the data play a main role for development the sciences. Meanwhile data mining is one of the main subjects in artificial intelligence. In this editorial, brief and useful explanations about artificial intelligence, artificial neural networks, machine learning and deep learning are given, which help the reader to get a correct and clear understanding of the category of artificial intelligence.
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Open AccessArticles
by T.V.S. Ramamohan Rao
2024,2(1);
214 Views
Abstract
Studies dealing with the market power of a firm depend on the elasticity of demand even when market imperfection is acknowledged. This study suggests that a firm derives its market power due to its interface with consumers on the market as well as its interaction with rival firms on the market. As a result, its market share and market power over a unit of sales in the industry require attention in the context of imperfect markets. Similarly, non-price strategies of firms offer some market power that should be incorporated in the definition. Thus the modified market power indices are a significant contribution to the theoretical results.
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Open AccessArticles
by Cheryl Ann Alexander, Lidong Wang
2024,2(1);
293 Views
Abstract
This paper introduces the requirements of Big Data analytics for cybersecurity, and the challenges of Big Data analytics in cybersecurity are highlighted. Methods and technologies used for the cybersecurity of big data are presented. The cybersecurity of big data in healthcare is introduced. Deep learning (DL) has been used in big data and cybersecurity. The applications of DL in cybersecurity and healthcare are summarized, including examples of tasks and specific DL method(s) for each task. The cybersecurity of big data in a medical center is presented as a case study. In today’s highly technological healthcare environment, big data cybersecurity in healthcare is primarily through data encryption for both data at rest and data in motion. The healthcare industry utilizes encryption to protect the sensitive information included in the clinical chart so that only authorized users and recipients are able to view and read the data. Cybercriminals are typically unable to read the data because they lack the decryption key.
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Open AccessArticles
by Riadh Ayachi, Mouna Afif, Yahia said, Abdessalem Ben Abdelali
2024,2(1);
137 Views
Abstract
The COVID-19 pandemic continues to pose significant health challenges worldwide. While traditional methods like Reverse Transcription Polymerase Chain Reaction (RT-PCR) are widely used for diagnosis, they often suffer from limitations in speed and accessibility. This chapter proposes an automatic COVID-19 diagnosis technique using CT scan image segmentation, offering a promising alternative for early detection. The proposed method leverages Convolutional Neural Networks (CNNs), enhanced with a multi-scale approach, to capture more detailed semantic features and achieve high-quality segmentation despite challenges such as low image resolution, varying infection characteristics, and limited training data. To address the data scarcity issue, various data augmentation techniques, including scale variation, translation, and rotation, were applied to enhance the model's performance. The proposed CNN was evaluated using the COVID-19 CT dataset, achieving an overall accuracy of 96.85%, with COVID-19 sensitivity at 93.31%, Common Pneumonia sensitivity at 92.93%, and a true negative rate of 97.82%. These results demonstrate the effectiveness and efficiency of the proposed method in detecting COVID-19 at an early stage, reducing the need for human intervention, and accelerating the diagnostic process. Compared to existing methods, this approach offers significant improvements in accuracy and reliability, making it a valuable tool in the ongoing fight against the pandemic.
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Open AccessArticles
by Riadh Ayachi, Mouna Afif, Yahia said, Abdessalem Ben Abdelali
2024,2(1);
144 Views
Abstract
Since 2019, the world has known a global pandemic caused by the COVID-19 virus. The epidemic was spreading very fast and many precautions should be respected to fight the disease. In order to limit the CVID-19 spread, different ways can be adopted. Social distancing is one of the most important cautions that should be respected. It means keeping a safe social distance between persons in order to avoid COVID-19 contagion. The social distance is about one meter at least. Building new tools used to perform social distance system present a very challenging task.
We propose in this paper to build a social distancing system based on one-stage neural networks. The proposed system is developed based on an improved version FCOS model. In order to ensure an embedded implementation of the proposed work, we used EfficientNet v1 as a network backbone and we applied compression techniques to reduce the model complexity and computation resources. The inference stage of the model has been performed on a ZCU 102 board. Training and testing experiments have demonstrated the efficiency of the proposed work in terms of accuracy as well as processing time.
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