scholarly journals The use of artificial intelligence to forecast events related to COVID-19 in Brazil: A Systematic Literature Review / O uso da inteligência artificial na predição de eventos relacionados à COVID-19 no Brasil: Uma Revisão Sistemática da Literatura

2021 ◽  
Vol 7 (12) ◽  
pp. 120974-120995
Author(s):  
Augusto Vinícius da Silva ◽  
Gabriel Caldas Barros e Sá ◽  
Samara Martins Nascimento ◽  
Náthalee Cavalcanti de Almeida Lima
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Literature Review ◽  
Systematic Literature Review ◽  
World Health ◽  
Future Studies

The COVID-19 pandemic has been a challenge to world health, and Brazil has been ranked among the first countries with most deaths and cumulative confirmed cases. Among several tools, Artificial Intelligence (AI) has been used to identify solutions that may predict events related to COVID-19. Thus, this work is a Systematic Literature Review that aims to present an overview of researches that use AI to forecast events and data related to the COVID-19 in Brazil. The methodology of this study was based on the protocol presented by Kitchenham and Charters and the results show that the main techniques used to forecast events related to the coronavirus by the time this work was conducted are ARIMA, SVR and Neural Networks, besides, this study also details information such as the most used attributes and the most sought objectives in the prediction. Based on the results obtained, it can be said that few significant studies seek to forecast the progress of coronavirus in Brazil and, therefore, the present study contributes to the grouped analysis of the techniques and solutions discovered, in addition to providing possible directions for future studies.

scholarly journals Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 198
Author(s):  
Mujaheed Abdullahi ◽  
Yahia Baashar ◽  
Hitham Alhussian ◽  
Ayed Alwadain ◽  
Norshakirah Aziz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Internet Of Things ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Rapid Development ◽  
Security And Privacy ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

scholarly journals The Clinical Spectrum of Primary Cutaneous CD4+ Small/Medium-Sized Pleomorphic T-Cell Lymphoproliferative Disorder: An Updated Systematic Literature Review and Case Series

Dermatology ◽  
2020 ◽  
Vol 237 (4) ◽  
pp. 618-628
Author(s):  
Philip Surmanowicz ◽  
Sean Doherty ◽  
Arunima Sivanand ◽  
Nikoo Parvinnejad ◽  
Jean Deschenes ◽  
...  
Keyword(s):  
T Cell ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Lymphoproliferative Disorder ◽  
Chart Review ◽  
Retrospective Chart Review ◽  
Cutaneous Lymphoma ◽  
World Health ◽  
Treatment Modalities ◽  
Systemic Treatments

<b><i>Background:</i></b> Primary cutaneous CD4+ small/medium pleomorphic T-cell lymphoproliferative disorder (SMPLPD) is a provisional entity within the 2016 World Health Organization classification of primary cutaneous lymphomas. The condition is currently classified as a lymphoproliferative disorder to emphasize its benign course and discourage aggressive, systemic treatment modalities. <b><i>Objective:</i></b> To provide a relevant synthesis for the dermatological practitioner on the prevalence, presentation, and treatment of SMPLPD. <b><i>Methods:</i></b> We conducted an updated systematic literature review and a retrospective chart review of diagnosed cases of SMPLPD from 2 Canadian academic cutaneous lymphoma centers. <b><i>Results:</i></b> A total of 23 studies with 136 cases were extracted from the systematic review and 24 patients from our retrospective chart review. SMPLPD proved relatively common accounting for 12.5% of all cutaneous T-cell lymphomas encountered in our cutaneous lymphoma clinics, second in frequency only to mycosis fungoides. The typical clinical presentation was that of an older individual (median age 59 years) with an asymptomatic solitary lesion on their upper extremity. The most common clinical differentials were cutaneous lymphoid hyperplasia, basal cell carcinoma, and lymphoma unspecified. T follicular helper markers were reliably detected. The main treatment modalities were surgical excision, local radiation therapy, and topical or intralesional steroids. Cure was achieved in the vast majority of cases. <b><i>Conclusions:</i></b> SMPLPD is an underdiagnosed T-cell lymphoma with an overtly benign clinical course. The condition has an excellent prognosis and responds well to skin-directed therapies. Practitioners should be aware of this condition to avoid aggressive systemic treatments.

scholarly journals On Deploying Blockchain Technologies in Supply Chain Strategies and the COVID-19 Pandemic: A Systematic Literature Review and Research Outlook

2021 ◽  
Vol 13 (19) ◽  
pp. 10566
Author(s):  
Mohammad Nabipour ◽  
M. Ali Ülkü
Keyword(s):  
Supply Chain ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Web Of Science ◽  
Environmental Issues ◽  
Future Studies ◽  
Blockchain Technology ◽  
Industrial Sectors ◽  
Holistic Review ◽  
Review Study

The emergence of a new pandemic, known as COVID-19, has touched various sections of the supply chain (SC). Since then, numerous studies have been conducted on the issue, but the need for a holistic review study that highlights the gaps and limits of previous research, as well as opportunities and agendas for future studies, is palpable. Through a systematic literature review on blockchain technology (BCT) deployment in supply-chain management (SCM) concerning the COVID-19 pandemic, this research seeks to add to the content of previous studies and to enlighten the path for future studies. Relevant papers were found using a variety of resources (Scopus, Google Scholar, Web of Science, and ProQuest). Seventy-two articles were systematically selected, considering the PRISMA procedure, and were thoroughly analyzed based on BCT, methodologies, industrial sectors, geographical, and sustainability context. According to our findings, there is a significant lack of empirical and quantitative methodologies in the literature. The majority of studies did not take specific industries into account. Furthermore, the articles focusing on the sustainability context are few, particularly regarding social and environmental issues. In addition, most of the reviewed papers did not consider the geographical context. The results indicate that the deployment of BCT in several sectors is not uniform, and this utilization is reliant on their services during the COVID-19 pandemic. Furthermore, the concentration of research on the impacts of the BCT on SCM differs according to the conditions of various countries in terms of the consequences of the COVID-19 pandemic. The findings also show that there is a direct relationship between the deployment of BCT and sustainability factors, such as economic and waste issues, under the circumstances surrounding COVID-19. Finally, this study offers research opportunities and agendas to help academics and other stakeholders to gain a better knowledge of the present literature, recognize aspects that necessitate more exploration, and drive prospective studies.

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