| 1. |
- Bhatt, Dulari, et al.
(författare)
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CNN Variants for Computer Vision : History, Architecture, Application, Challenges and Future Scope
- 2021
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 10:20
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Forskningsöversikt (refereegranskat)abstract
- Computer vision is becoming an increasingly trendy word in the area of image processing. With the emergence of computer vision applications, there is a significant demand to recognize objects automatically. Deep CNN (convolution neural network) has benefited the computer vision community by producing excellent results in video processing, object recognition, picture classification and segmentation, natural language processing, speech recognition, and many other fields. Furthermore, the introduction of large amounts of data and readily available hardware has opened new avenues for CNN study. Several inspirational concepts for the progress of CNN have been investigated, including alternative activation functions, regularization, parameter optimization, and architectural advances. Furthermore, achieving innovations in architecture results in a tremendous enhancement in the capacity of the deep CNN. Significant emphasis has been given to leveraging channel and spatial information, with a depth of architecture and information processing via multi-path. This survey paper focuses mainly on the primary taxonomy and newly released deep CNN architectures, and it divides numerous recent developments in CNN architectures into eight groups. Spatial exploitation, multi-path, depth, breadth, dimension, channel boosting, feature-map exploitation, and attention-based CNN are the eight categories. The main contribution of this manuscript is in comparing various architectural evolutions in CNN by its architectural change, strengths, and weaknesses. Besides, it also includes an explanation of the CNN's components, the strengths and weaknesses of various CNN variants, research gap or open challenges, CNN applications, and the future research direction.
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| 2. |
- Mishra, Nivedita, et al.
(författare)
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Memcached : An Experimental Study of DDoS Attacks for the Wellbeing of IoT Applications
- 2021
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:23
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Tidskriftsartikel (refereegranskat)abstract
- Distributed denial‐of‐service (DDoS) attacks are significant threats to the cyber world because of their potential to quickly bring down victims. Memcached vulnerabilities have been targeted by attackers using DDoS amplification attacks. GitHub and Arbor Networks were the victims of Memcached DDoS attacks with 1.3 Tbps and 1.8 Tbps attack strengths, respectively. The bandwidth amplification factor of nearly 50,000 makes Memcached the deadliest DDoS attack vector to date. In recent times, fellow researchers have made specific efforts to analyze and evaluate Memcached vulnerabilities; however, the solutions provided for security are based on best practices by users and service providers. This study is the first attempt at modifying the architecture of Memcached servers in the context of improving security against DDoS attacks. This study discusses the Memcached protocol, the vulnerabilities associated with it, the future challenges for different IoT applications associated with caches, and the solutions for detecting Memcached DDoS attacks. The proposed solution is a novel identification‐pattern mechanism using a threshold scheme for detecting volume‐based DDoS attacks. In the undertaken study, the solution acts as a pre‐emptive measure for detecting DDoS attacks while maintaining low latency and high throughput.
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| 3. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(författare)
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A Study of the Recent Trends of Immunology : Key Challenges, Domains, Applications, Datasets, and Future Directions
- 2021
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:23
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Tidskriftsartikel (refereegranskat)abstract
- The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in‐depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.
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| 4. |
- Poveda, Alaitz, et al.
(författare)
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Environment-wide association study (EWAS) on cardiometabolic traits: A systematic assessment of the association of lifestyle variables on a longitudinal setting
- 2021
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The present study aims to assess the over-time association of ∼300 lifestyle exposures with nine cardiometabolic traits with the ultimate aim of identifying exposures/exposure groups that could inform lifestyle interventions aiming at controlling cardiometabolic diseases. The analyses were undertaken in a longitudinal sample comprising >31000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test association with 10-year change of the cardiometabolic traits. ‘Physical activity’ and ‘General Health’ were the exposure categories containing the highest number of ‘tentative signals’ in analyses assessing the average association of lifestyle variables, while ‘Tobacco use’ was the top-category for the 10-year change association analyses. Thirteen modifiable variables showed a consistent average association among the majority of cardiometabolic traits. These variables belonged to four main groups: i) Smoking, ii) Diet (secoisolariciresinol intake and brewed coffee), iii) Leisure time physical activity and iv) a group of variables more specific to the Swedish lifestyle (snuff status, hunting/fishing during leisure time and boiled coffee). Interestingly, sweet drinks, fish intake and salt content, all lifestyle exposures frequently mentioned in public health recommendations were not broadly associated with the analysed cardiometabolic traits.
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