Yazar "Albreem, Mahmoud A. M." seçeneğine göre listele

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    Application of machine intelligence technology in the detection of vaccines and medicines for SARS-CoV-2
    (VERDUCI PUBLISHER, VIA GREGORIO VII, ROME 186-00165, ITALY, 2020) Alsharif, Mohammed H.; Alsharif, Yahia H.; Albreem, Mahmoud A. M.; Jahid, Abu; Solyman, Ahmad Amin Ahmad; Yahya, Khalid O. Moh.; Alomari, Osama Ahmad; Hossain, Md. Sanwar
    Researchers have found many similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARSCoV-19 through existing data that reveal the SARS’s cause. Artificial intelligence (AI) learning models can be created to predict drug structures that can be used to treat COVID-19. Despite the effectively demonstrated repurposed drugs, more repurposed drugs should be recognized. Furthermore, technological advancements have been helpful in the battle against COVID-19. Machine intelligence technology can support this procedure by rapidly determining adequate and effective drugs against COVID-19 and by overcoming any barrier between a large number of repurposed drugs, laboratory/clinical testing, and final drug authorization. This paper reviews the proposed vaccines and medicines for SARSCoV-2 and the current application of AI in drug repurposing for COVID-19 treatment.
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    Artificial intelligence technology for diagnosing COVID-19 cases: a review of substantial issues
    (VERDUCI PUBLISHER, VIA GREGORIO VII, ROME 186-00165, ITALY, 2020) Alsharif, Mohammed H.; Alsharif, Yahia H.; Chaudhry, Shehzad Ashraf; Albreem, Mahmoud A. M.; Jahid, Abu; Hwang, Eenjun
    Today, the world suffers from the rapid spread of COVID-19, which has claimed thousands of lives. Unfortunately, its treatment is yet to be developed. Nevertheless, this phenomenon can be decelerated by diagnosing and quarantining patients with COVID-19 at early stages, thereby saving numerous lives. In this study, the early diagnosis of this disease through artificial intelligence (AI) technology is explored. AI is a revolutionizing technology that drives new research opportunities in various fields. Although this study does not provide a final solution, it highlights the most promising lines of research on AI technology for the diagnosis of COVID-19. The major contribution of this work is a discussion on the following substantial issues of AI technology for preventing the severe effects of COVID-19: (1) rapid diagnosis and detection, (2) outbreak and prediction of virus spread, and (3) potential treatments. This study profoundly investigates these controversial research topics to achieve a precise, concrete, and concise conclusion. Thus, this study provides significant recommendations on future research directions related to COVID-19.
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    Deep learning applications to combat the dissemination of COVID-19 disease: a review
    (VERDUCI PUBLISHER, VIA GREGORIO VII, ROME 186-00165, ITALY, 2020) Alsharif, Mohammed H.; Alsharif, Yahia H.; Yahya, Khalid O. Moh.; Alomari, Osama Ahmad; Albreem, Mahmoud A. M.; Jahid, Abu
    Recent Coronavirus (COVID-19) is one of the respiratory diseases, and it is known as fast infectious ability. This dissemination can be decelerated by diagnosing and quarantining patients with COVID-19 at early stages, thereby saving numerous lives. Reverse transcription-polymerase chain reaction (RTPCR) is known as one of the primary diagnostic tools. However, RT-PCR tests are costly and time-consuming; it also requires specific materials, equipment, and instruments. Moreover, most countries are suffering from a lack of testing kits because of limitations on budget and techniques. Thus, this standard method is not suitable to meet the requirements of fast detection and tracking during the COVID-19 pandemic, which motived to employ deep learning (DL)/ convolutional neural networks (CNNs) technology with X-ray and CT scans for efficient analysis and diagnostic. This study provides insight about the literature that discussed the deep learning technology and its various techniques that are recently developed to combat the dissemination of COVID-19 disease.
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    Sixth Generation (6G) Wireless Networks: Vision, Research Activities, Challenges and Potential Solutions
    (MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2020) Alsharif, Mohammed H.; Kelechi, Anabi Hilary; Albreem, Mahmoud A. M.; Chaudhry, Shehzad Ashraf; Zia, Muhammad Sultan; Kim, Sunghwan
    The standardization activities of the fifth generation communications are clearly over and deployment has commenced globally. To sustain the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the stratification of the communication needs of the 2030s. In support of this vision, this study highlights the most promising lines of research from the recent literature in common directions for the 6G project. Its core contribution involves exploring the critical issues and key potential features of 6G communications, including: (i) vision and key features; (ii) challenges and potential solutions; and (iii) research activities. These controversial research topics were profoundly examined in relation to the motivation of their various sub-domains to achieve a precise, concrete, and concise conclusion. Thus, this article will contribute significantly to opening new horizons for future research directions.
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    Toward 6G Communication Networks: Terahertz Frequency Challenges and Open Research Issues
    (TECH SCIENCE PRESS, 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052, 2021) Alsharif, Mohammed H.; Albreem, Mahmoud A. M.; Solyman, Ahmad Amin Ahmad; Kim, Sunghwan
    Future networks communication scenarios by the 2030s will include notable applications are three-dimensional (3D) calls, haptics communications, unmanned mobility, tele-operated driving, bio-internet of things, and the Nanointernet of things. Unlike the current scenario in which megahertz bandwidth are sufficient to drive the audio and video components of user applications, the future networks of the 2030s will require bandwidths in several gigahertzes (GHz) (from tens of gigahertz to 1 terahertz [THz]) to perform optimally. Based on the current radio frequency allocation chart, it is not possible to obtain such a wide contiguous radio spectrum below 90 GHz (0.09 THz). Interestingly, these contiguous blocks of radio spectrum are readily available in the higher electromagnetic spectrum, specifically in the Terahertz (THz) frequency band. The major contribution of this study is discussing the substantial issues and key features of THz waves, which include (i) key features and significance of THz frequency; (ii) recent regulatory; (iii) the most promising applications; and (iv) possible open research issues. These research topics were deeply investigated with the aim of providing a specific, synopsis, and encompassing conclusion. Thus, this article will be as a catalyst towards exploring new frontiers for future networks of the 2030s.
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    Toward Optimal Cost-Energy Management Green Framework for Sustainable Future Wireless Networks
    (TECH SCIENCE PRESS, 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052, 2021) Alsharif, Mohammed H.; Jahid, Abu; Albreem, Mahmoud A. M.; Uthansakul, Peerapong; Nebhen, Jamel; Yahya, Khalid O. Moh.
    The design of green cellular networking according to the traffic arrivals has the capability to reduce the overall energy consumption to a cluster in a cost-effective way. The cell zooming approach has appealed much attention that adaptively offloads the BS load demands adjusting the transmit power based on the traffic intensity and green energy availability. Besides, the researchers are focused on implementing renewable energy resources, which are considered the most attractive practices in designing energy-efficient wireless networks over the long term in a cost-efficient way in the existing infrastructure. The utilization of available solar can be adapted to acquire cost-effective and reliable power supply to the BSs, especially that sunlight is free, available everywhere, and a good alternative energy option for the remote areas. Nevertheless, planning a photovoltaic scheme necessitates viability assessment to avoid poor power supply, particularly for BSs. Therefore, cellular operators need to consider both technical and economic factors before the implementation of solar-powered BSs. This paper proposed the user-centric cell zooming policy of solar-powered cellular base stations taking into account the optimal technical criteria obtained by the HOMER software tool. The results have shown that the proposed system can provide operational expenditure (OPEX) savings of up to 47%. In addition, the efficient allocation of resource blocks (RBs) under the cell zooming technique attain remarkable energy-saving performance yielding up to 27%.