ABSTRACT
Researchers around the world are struggling to discover ground-breaking equip-ment aimed at building a great healthcare structure to fight the novel corona virus for the duration of this global epidemic. How deep learning (DL) encountered the COVID-19 epidemic and what are the current guidelines for exploring the potential in COVID-19 are the subject to walk around. Over time, genetic material of novel corona viruses mutates itself and changed its characteristics to create different vari¬ants of viruses. These distinctive variants can trigger different waves of destructive infection in different parts of world. The substantiation of DL pertinences on the precedent pandemic motivates the professionals by giving an innovative trend to organize this outburst to make it least effective. The main target of this article is to study the utility of deep learning-based approaches on COVID-19 and also their credibility in terms of containment of the pandemic based on recent works around the globe. The study has listed down recent works within DL approaches regarding marking out of virus-affected people, investigation of its protein formation, vaccine & medicine finding, virus relentlessness, and contamination to direct the enduring eruption. DL is endowed with a suitable contrivance intended for rapid selection COVID-19 along with pronouncement possible high-risk patients, which possibly will be cooperative for medical resource optimization and early prevention prior to patients suffering rigorous indication. In this study, the wide-ranging consequence of DL on several magnitudes to be in command of novel coronavirus (COVID-19) is discussed, and attempts are made to investigate it. Despite rich studies being con¬ducted through DL algorithms, there are still many limitations and contradictions in the area of COVID research. The continuous evolution of DL on coronavirus handles contamination and is costly to create the right resolution task. Apart from this, in this work, a DL-based pandemic analysis has been done using the received dataset from about 55 hospitals in West Bengal, India. According to some research scientists, we may enter the third and fourth waves too, thus this work will be help¬ful for further research activity in the years to come. Finally, it is expected this work will help many researchers throughout the world get some opportunity to find out the final remedy to get rid of this deadly virus. © 2023 Scrivener Publishing LLC. All rights reserved.
ABSTRACT
Deep learning (DL), a subdivision of machine learning (ML), i.e., an integral part of artificial intelligence used in various applications in today's life. At present, machine learning approach is almost completely dependent on DL techniques, which produce accurate results with the help of human centric nature of learning. It has gone off in the community awareness, mostly as extrapolative and analyt-ical products that saturate our planet in most useful, organized, and time- and cost-competent method of ML approach. There are some algorithms, like genera¬tive adversarial networks, multilayer perceptions, convolution neural networks, or self-organizing maps, that have entirely changed the thinking toward information processing means. Currently, DL is using in numerous domains like knowledge, commerce, science, administration sectors;it can be employed on novel corona virus prediction, detection, and analysis of clinical and method logical character¬istics too is also a matter of discussion here. Our work is absolutely displays on the notion of crucial sophisticated design, method, inspirational characteristics and constraint of DL. This writing section describes a detailed analysis of chronolog¬ical and modern trailblazing approaches to the distribution of conjecture, myth, and text;social network analysis;and innovative advances in natural language pro¬cessing, extensive research around spin, and in-depth learning activities. The main target of this work is to describe the newly developed DL techniques for Internet of Things (IoT) architecture and its security. IoT security threats associated with the underlying or newly introduced threat are talked about and diverse possible IoT system attacks and probable threats connected to all facets are thrashed out. The possibilities, advantages, and limitations of both systems are illustrated systematically by analyzing the DL strategy aimed at IoT security. We provide perspectives and related issues regarding IoT security from ML/DL. Discussed approaches and problems of potential expectations can serve as research guide-lines for the future endeavor. © 2023 Scrivener Publishing LLC. All rights reserved.
ABSTRACT
With the growth of technology, the concept of online learning has grown in popularity. The worldwide epidemic situation (Covid 19) has increased the use of online learning not just in Higher Education Institutions (HEIs), but also at all levels of education (ISED levels). In these hard times, technical advancements have played a greater role in creating awareness of the existence of online learning which has evolved as an alternate avenue for gaining and disseminating knowledge in a systematic way. In this paper we investigate to detect the sentiment dynamics (SD) of tweets related to online education available on twitter platform and deduce conclusions about its impact on student’s emotions. Over one lakh subjective tweets about the world's emerging online education system have been gathered via Twitter. Sentiment analysis was performed on the gathered dataset using the combination of dictionary-based and statistical-based approaches. Based on the findings of this analysis, we can infer the impact of online education and how people's attitudes have changed as a result of changes in the educational system. As a result, we would like to present a better comprehension of the sentiment dynamics of online education adoption. © 2022. Specialusis Ugdymas. All Rights Reserved.