CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

Shastri Sourabh,Singh Kuljeet,Deswal Monu,Kumar Sachin,Mansotra Vibhakar 대한공간정보학회 2022 Spatial Information Research Vol.30 No.1

The pandemic of novel coronavirus disease 2019 (Covid-19) has left the world to a standstill by creating a calamitous situation. To mitigate this devastating effect the inception of artificial intelligence into medical health care is mandatory. This study aims to present the educational perspective of Covid-19 and forecast the number of confirmed and death cases in the USA, India, and Brazil along with the discussion of endothelial dysfunction in epithelial cells and Angiotensin-Converting Enzyme 2 receptor (ACE2) with the Covid-19. Three different deep learning based experimental setups have been framed to forecast Covid-19. Models are (i) Bi-directional Long Short Term Memory (LSTM) (ii) Convolutional LSTM (iii) Proposed ensemble of Convolutional and Bi-directional LSTM network are known as CoBiD-Net ensemble. The educational perspective of Covid-19 has been given along with an architectural discussion of multi-organ failure due to intrusion of Covid-19 with the cell receptors of the human body. Different classification metrics have been calculated using all three models. Proposed CoBiD-Net ensemble model outperforms the other two models with respect to accuracy and mean absolute percentage error (MAPE). Using CoBiD-Net ensemble, accuracy for Covid-19 cases ranges from 98.10 to 99.13% with MAPE ranges from 0.87 to 1.90. This study will help the countries to know the severity of Covid-19 concerning education in the future along with forecasting of Covid-19 cases and human body interaction with the Covid-19 to make it the self-replicating phenomena.

Immunogenicity and protection of oral influenza vaccines formulated into microparticles

Shastri, Prathap Nagaraja,Kim, Min‐,Chul,Quan, Fu‐,Shi,D'Souza, Martin J.,Kang, Sang‐,Moo Wiley Subscription Services, Inc., A Wiley Company 2012 journal of pharmaceutical sciences Vol.101 No.10

<P><B>Abstract</B></P><P>Influenza is a deadly disease affecting humans and animals. It is recommended that every individual should be vaccinated annually against influenza. Considering the frequency of administration of this vaccine, we have explored the oral route of vaccination with a microparticulate formulation. Microparticles containing inactivated influenza A/PR/34/8 H1N1 virus with Eudragit S and trehalose as a matrix were prepared using the Buchi spray dryer. Particle size distribution of microparticles was measured and the bioactivity of vaccine in a microparticle form was analyzed using a hemagglutination activity test. Furthermore, the efficacy of microparticle vaccines was evaluated <I>in vivo</I> in Balb/c mice. Analysis of serum samples showed that microparticles resulted in enhanced antigen‐specific immunoglobulin G (IgG), IgG1, and IgG2a antibodies. Upon challenge with homologous and heterologous influenza viruses, microparticle vaccines showed significantly increased levels of protection. Use of microparticles to deliver vaccines could be a promising tool for the development of an oral influenza vaccine. © 2012 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 101:3623–3635, 2012</P>

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

Shastri Sourabh,Singh Kuljeet,Deswal Monu,Kumar Sachin,Mansotra Vibhakar 대한공간정보학회 2022 Spatial Information Research Vol.30 No.2

The pandemic of novel coronavirus disease 2019 (Covid-19) has left the world to a standstill by creating a calamitous situation. To mitigate this devastating effect the inception of artificial intelligence into medical health care is mandatory. This study aims to present the educational perspective of Covid-19 and forecast the number of confirmed and death cases in the USA, India, and Brazil along with the discussion of endothelial dysfunction in epithelial cells and Angiotensin-Converting Enzyme 2 receptor (ACE2) with the Covid-19. Three different deep learning based experimental setups have been framed to forecast Covid-19. Models are (i) Bi-directional Long Short Term Memory (LSTM) (ii) Convolutional LSTM (iii) Proposed ensemble of Convolutional and Bi-directional LSTM network are known as CoBiD-Net ensemble. The educational perspective of Covid-19 has been given along with an architectural discussion of multi-organ failure due to intrusion of Covid-19 with the cell receptors of the human body. Different classification metrics have been calculated using all three models. Proposed CoBiD-Net ensemble model outperforms the other two models with respect to accuracy and mean absolute percentage error (MAPE). Using CoBiD-Net ensemble, accuracy for Covid-19 cases ranges from 98.10 to 99.13% with MAPE ranges from 0.87 to 1.90. This study will help the countries to know the severity of Covid-19 concerning education in the future along with forecasting of Covid-19 cases and human body interaction with the Covid-19 to make it the self-replicating phenomena.

Variations and Characteristics of Volatile Organic Compounds (VOCs) during Winter, Summer, and Spring at Seosan

Avinash Shastri,Jayant Nirmalkar,Seunggi Kim,Mijung Song 한국대기환경학회 2022 한국대기환경학회 학술대회논문집 Vol.2022 No.10

Seasonal Variation and Potential Health Risk of Volatile Organic Compounds in a Rural Area of Seosan

Avinash Shastri,Jayant Nirmalkar,Seunggi Kim,Sangmin Oh,Kwangyul Lee,Mijung Song 한국대기환경학회 2023 한국대기환경학회 학술대회논문집 Vol.2023 No.10

An empirical investigation of the twin deficit hypothesis: Panel evidence from selected Asian economies

Shruti Shastri,A K Giri,Geetilaxmi Mohapatra 한양대학교 경제연구소 2017 JOURNAL OF ECONOMIC RESEARCH Vol.22 No.1

The paper examines the twin deficit hypothesis for a panel of eight South Asian and South East Asian economies having a history of persistent deficits on both fiscal and current accounts for the period 1985-2014. The results based on first and second generation panel cointegration tests indicate existence of a long-run relationship among budget balance, interest rate, exchange rate and current account balance. The estimates of long run coefficients obtained from common correlated effects mean group indicate a positive relationship between the two balances, the impact of the budget balance on the current account being stronger. Dumitrescu-Hurlin panel causality and block exogeniety tests suggest a feedback relationship between the two balances. The conventional hypothesis of causation running from budget balance to interest rates, to exchange rates and then to current account balance is however not borne out by the results.

모국어 말소리 대조 시 작업기억과 변별능력 간의 연관성

Usha Shastri,Keerthana Kulath Purath Raj,Mable Mathew,Mohan Kumar Kalaiah,Ajith Kumar Uppunda c 한국언어청각임상학회 2019 Communication Sciences and Disorders Vol.24 No.1

배경 및 목적: 모국어의 말소리(phone)를 변별할 때, 특히 도전적 상황에서 이를 수행할 때에는 매우 큰 개인차가 존재한다. 말소리 변별 시 유용한 단서를 활용할 수 있는 능력이 인지능력에 의해 촉진되는지, 이를 통해 개인차를 어느 정도 설명해 줄 수 있는지에 대해서는 잘 알려져 있지 않다. 본 연구는 말라얄람어(Malayalam)를 모국어로 사용하는 청자가 말라얄람어 말소리를 맥락 단서 없이 구분할때 작업기억능력과 변별능력 간에 어떤 연관성이 있는지 알아보고자 하였다. 방법: 말라얄람어를 모국어로 사용하는 18-25세 청자 40 명이 본 연구에 참여하였다. 참여자들로 하여금 무의미단어 사이에 삽입된 말라얄람어 8개 말소리를 변별하도록 하였다. 읽기폭 과제, 조작폭 과제, 숫자 바로외우기 과제, 숫자 거꾸로외우기 과제 등을 이용하여 작업기억능력을 측정하였고, 각 말소리의 변별점수, 전체 말소리 변별점수(8개 말소리로부터 얻은 평균변별점수)와 변별 시 반응시간을 함께 측정하였다. 결과: 참여자의 말소리 변별점수는 57.8%-99%의 범위를 보였다. 피어슨 적률상관분석 결과 모든 작업기억능력 측정치와 전체 말소리 변별점수 간에는 유의한 정적 상관이 나타나 작업기억능력이 말소리 변별에 중요한 역할을 하는 것으로 나타났다. 작업기억능력의 측정치는 말소리 변별점수 다양성의 24.7%를 설명할 수 있었다. 논의 및 결론: 맥락 단서가 없는 상황에서의 말소리 변별은 인지 부담을 높인다. 그러므로 높은 능력은 어려운 상황에서 모국어 말소리를 변별하는 데 도움을 준다. 이 연구는 모국어 말소리 지각에서 인지가 미치는 하향식 영향력을 제시하고 있다. Objectives: Large individual variability is documented for identification performance of native phones, especially in challenging situations. It is not known whether the ability to utilize cues available for phone identification is facilitated by cognitive abilities, thereby explaining a proportion of the individual variability. This study investigated the relationship between working memory capacity and identification of a few Malayalam phones in the absence of contextual cues among native listeners. Methods: Forty native listeners of Malayalam, aged between 18 and 25, participated in this study. Participants identified 8 Malayalam phones embedded in nonsense words. Working memory capacity was measured using tasks such as reading span, operation span, digit forward span, and digit backward span. Identification score for each phone, total phone identification score (average identification score from 8 phones), and reaction time during identification were obtained. Results: Phone identification score of participants ranged from 57.8% to 99%. Pearson product moment correlation analysis showed a significant positive correlation between all measures of working memory capacity and total phone identification score, indicating that working memory capacity play a role in the identification of phones. Reaction time showed a significant negative correlation with digit backward span and operation span. The measures of working memory capacity accounted for 24.7% of the variability in phone identification score. Conclusion: Identification of phones in the absence of contextual cues increases the cognitive load. Therefore, higher working memory capacity might aid in native phone identification in difficult situations. This study reveals the top down influence of cognition on native speech perception.

An empirical investigation of the twin deficit hypothesis: Panel evidence from selected Asian economies

( Shruti Shastri ),( A. K. Giri ),( Geetilaxmi Mohapatra ) 한양대학교 경제연구소 2017 JOURNAL OF ECONOMIC RESEARCH Vol.22 No.2

The paper examines the twin deficit hypothesis for a panel of eight South Asian and South East Asian economies having a history of persistent deficits on both fiscal and current accounts for the peri-od 1985-2014. The results based on first and second generation pan-el cointegration tests indicate existence of a long-run relationship among budget balance, interest rate, exchange rate and current ac-count balance. The estimates of long run coefficients obtained from common correlated effects mean group indicate a positive relation-ship between the two balances, the impact of the budget balance on the current account being stronger. Dumitrescu-Hurlin panel cau-sality and block exogeniety tests suggest a feedback relationship between the two balances. The conventional hypothesis of causation running from budget balance to interest rates, to exchange rates and then to current account balance is however not borne out by the results.

The Role of T Cells in Obesity-Associated Inflammation and Metabolic Disease

박찬수,Shastri Nilabh 대한면역학회 2022 Immune Network Vol.22 No.1

Chronic inflammation plays a critical role in the development of obesity-associated metabolic disorders such as insulin resistance. Obesity alters the microenvironment of adipose tissue and the intestines from anti-inflammatory to pro-inflammatory, which promotes low grade systemic inflammation and insulin resistance in obese mice. Various T cell subsets either help maintain metabolic homeostasis in healthy states or contribute to obesity-associated metabolic syndromes. In this review, we will discuss the T cell subsets that reside in adipose tissue and intestines and their role in the development of obesity-induced systemic inflammation.

Introducing 'Meta-Network': A New Concept in Network Technology

Gaur, Deepti,Shastri, Aditya,Biswas, Ranjit The Korea Institute of Information and Commucation 2008 Journal of information and communication convergen Vol.6 No.4

A well-designed computer network technology produces benefits on several fields within the organization, between the organizations(suborganizations) or among different organizations(suborganizations). Network technology streamlines business processes, decision process. Graphs are useful data structures capable of efficiently representing a variety of networks in the various fields. Metagraph is a like graph theoretic construct introduced recently by Basu and Blanning in which there is set to set mapping in place of node to node as in a conventional graph structure. Metagraph is thus a new type of data structure occupying its popularity among the computer scientists very fast. Every graph is special case of Metagraph. In this paper the authors introduce the notion of Meta-Networking as a new network technological representation, which is having all the capabilities of crisp network as well as few additional capabilities. It is expected that the notion of meta-networking will have huge applications in due course. This paper will play the role of introducing this new concept to the network technologists and scientists.