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1دورية أكاديمية
المؤلفون: Nandini Babbar, Ashish Kumar, Vivek Kumar Verma
المصدر: Brazilian Archives of Biology and Technology, Vol 67 (2024)
مصطلحات موضوعية: Wheat-yield prediction, Machine learning (ML), Deep learning, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Biotechnology, TP248.13-248.65
الوصف: Abstract Agriculture is the primary source of income for each country, serving as its mainstay. A promising study topic has been predicting wheat production based on environmental, soil, and water characteristics. Deep-learning-based algorithms are widely employed in crop prediction to extract significant crop traits. Wheat is linked to a variety of economic, societal, and health-related factors. Wheat yield forecasting and estimation on a regional scale, on the other hand, remains difficult. Two strategies for estimating wheat yield using deep learning (DL) models are presented in this study. To solve the limitations of regional forecasting, Convolutional Neural Networks (CNN) and Deep Learning Long Short-Term Memory (LSTM) technology are utilized to anticipate agricultural yields in a timely and reliable manner.
وصف الملف: electronic resource
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2دورية أكاديمية
المؤلفون: Suparna Chatterjee, Vivek Kumar Verma, Avijit Hazra, Jyotirmoy Pal
المصدر: Perspectives in Clinical Research, Vol 11, Iss 2, Pp 75-80 (2020)
مصطلحات موضوعية: corrosive, india, pesticide, poisoning, snakebite, Medicine, Medicine (General), R5-920
الوصف: Aim: Poisoning is a preventable cause of morbidity and mortality in India. We undertook a prospective observational study to estimate the incidence, nature, severity and treatment outcome trends of acute poisoning in a tertiary care hospital in eastern India. Methods: All patients, admitted during the study period with acute poisoning, drug overdose and envenomation, were enrolled. Food poisonings, animal bites, chronic drug or chemical poisonings were excluded. Medical records were scrutinized and caregiver interviews served as source documents. Demographics, nature and circumstances of the poisoning event, treatment offered, duration of hospitalization and outcome data were collected. Results: Over 18 months, 592 cases of acute poisoning, accounting for 0.63% of all hospital admissions, were enrolled. Males comprised 57.09%, median age was 22 years, and 52.20% hailed from rural area. Occupation-wise, excluding students and children, patients were mostly daily wage workers followed by housewives, service holders and farm workers. Snake bites comprised the largest category of cases at 264 (44.6%) followed by corrosives (13.68%), sedatives/hypnotics (13.18%), pesticides (12.16%), hydrocarbon oils (8.61%) and others. Majority (60.64%) of the cases was accidental and occurred at home (66.72%) and most (87.33%) were referred from primary health centers. Median time between event and arrival at primary care center was 1 hour while median time to arrival at the hospital was 11 hours. There were 89 deaths (mortality 15.03%) in the series. Male gender, rural residence, referred status and non-use of specific antidotes had negative impact on survival. Conclusion: This large prospective study from eastern India from a hospital perspective, has captured data not only on the incidence and nature of poisoning but also on treatment trends and mortality outcomes. Field studies conducted in the light of these results will clarify additional issues.
وصف الملف: electronic resource
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3دورية أكاديمية
المصدر: Journal of Clinical and Diagnostic Research, Vol 12, Iss 8, Pp OC11-OC12 (2018)
مصطلحات موضوعية: euglycaemic, endocrinologist, impaired glucose tolerance, Medicine
الوصف: Introduction: Diabetes is taking the form of a pandemic. All ages and all sections of society are affected by it and physicians are no exception to it. Aim: To know the glycaemic status and level of glycaemic control among the physicians who were attending a National Diabetes Conference. Materials and Methods: A cross-sectional study was conducted at a national conference “Diabetes India” held in Delhi in February 2017. Physicians and Endocrinologists were the delegates. All physicians willing for assessment of their Glycaemic status were included and HbA1C was done. Data was analysed and mean, percentage and p-value were calculated. Results: Out of 108 physicians, 3 (2.78%) had impaired glucose tolerance, 54 (50%) had diabetes mellitus and 51 (47.22%) were euglycaemic. Out of 51 physicians, only 3 physicians (5.88%) had HbA1C less than or equal to 5.6, 9 (17.65%) had HbA1C more than 6.5, 39 (76.47%) had impaired glucose tolerance. Conclusion: Very high incidence of diabetes mellitus and impaired glucose tolerance was found among physicians and endocrinologist in this study and the alarming result warrants further research with more participants.
وصف الملف: electronic resource
Relation: https://jcdr.net/articles/PDF/11888/34894_CE(RA1)_F(T)_PF1(AB_SS)_PN(SS).pdf; https://doaj.org/toc/2249-782X; https://doaj.org/toc/0973-709X
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المؤلفون: Vivek Kumar Verma, Amit Agrawal, Rashika Chaudhary, Amit Chauhan, Preeti Sharma, Aryan Yadav
المصدر: Materials Today: Proceedings. 79:260-266
مصطلحات موضوعية: General Medicine
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5دورية أكاديمية
المؤلفون: Vivek Kumar Verma, Bibhas Chandra, Sumit Kumar
المصدر: Inderscience Enterprises Ltd, International Journal of Management Practice. 16(6):677-707
الوصف: This study intends to extend the value-belief-norm model en route to predict the behavioural intention of green hotel consumers. The study uses a self-administered questionnaire to collect data from 541 consumers purposively chosen and analysed by using a two-step approach of covariance-based structural equation modelling (CB-SEM). Results disclose that values, attitude, moral reflectiveness, and conscientiousness were found to significantly affect green hotel visit intention. The role of attitude towards green hotels was superior to moral reflectiveness and conscientiousness in green hotel visit intention. The robustness and predictive power of the posited model have been significantly improved from 38.4% to 54.3%.
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6دورية أكاديمية
المؤلفون: Tarun Jain, Horesh Kumar, Payal Garg, Abhinav Pillai, Aditya Sinha, Vivek Kumar Verma
المصدر: IGI Global, International Journal of Cyber Behavior, Psychology and Learning (IJCBPL). 13(1):1-12
الوصف: Newspapers are a rich informational source. A headline of an article sparks an interest in the reader. So, news providing agencies tend to create catchy headlines to attract the reader's attention onto them, and this is how sarcasm manages to find its way into news headlines. Sarcasm employs the use of words that carry opposite meaning with respect to what needs to be conveyed. This leads to the need of developing methods by which we can correctly predict whether a piece of text, or news for that matter, truthfully means what it says or is simply being sarcastic about it. Here, the authors have used a dataset containing 55,329 tuples consisting of news headlines from The Onion and the Huffington Post, which was taken from Kaggle, on which they applied feature extraction techniques such as Count Vectorizer, TF-IDF, Hashing Vectorizer, and Global Vectorizer (GloVe). Then they applied seven classifiers on the obtained dataset. The experimental results showed that the highest accuracies among the ML models were 81.39% for LR model with Count Vectorizer, 79.2% for LR model with TF-IDF Vectorizer, and 78% for SVM model with Count Vectorizer. They also obtained the best accuracy of 90.7% using the Bi-LSTM Deep Learning Model. They have trained the seven models and compared them based on their respective accuracies and F1-Scores.
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المؤلفون: Varun Khemka, Sagar Bafna, Ayush Gupta, Somya Goyal, Vivek Kumar Verma
المصدر: A Roadmap for Enabling Industry 4.0 by Artificial Intelligence. :275-293
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::25316b0fbcc2e34829d5d1d7db195b97
https://doi.org/10.1002/9781119905141.ch15 -
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المؤلفون: Shivesh Sabbarwal, Prachi Srivastava, Vivek Kumar Verma, Manoj Kumar
المصدر: Crystal Research and Technology.
مصطلحات موضوعية: General Materials Science, General Chemistry, Condensed Matter Physics
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المؤلفون: Vivek Kumar Verma, Satya Sai Srikant
المصدر: IEIE Transactions on Smart Processing & Computing. 11:368-375
مصطلحات موضوعية: Signal Processing, Electrical and Electronic Engineering
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::5d5e5a109495a9b700b15d363878abcd
https://doi.org/10.5573/ieiespc.2022.11.5.368 -
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المؤلفون: Vivek Kumar Verma, Shivesh Sabbarwal, Prachi Srivastava, Manoj Kumar
المصدر: Physica Scripta.
مصطلحات موضوعية: Condensed Matter Physics, Mathematical Physics, Atomic and Molecular Physics, and Optics
الوصف: Determination of temperature-dependent nucleation rate is a crucial parameter to accessing the kinetic and thermodynamic barrier linked with developing subatomic-sized nuclei, which tend to restrain the nucleation process. In this study, we exclusively compute the nucleation rate, thermodynamic parameters, and interfacial energy of ultra-small gadolinium oxide nanoclusters at high temperatures. Here, the apparent value of activation energy (Ea.) and pre-exponential kinetic factor (Aa) was precisely computed by utilizing the most accurate Vyazovkin advanced and KAS iso-conversional method, which was further exploited to estimate the thermodynamic parameters, nucleation rate, and interfacial energy of ~1 nm-sized gadolinium nanoclusters, in the temperature ranging from 555 to 780 K by appraising thermogravimetric data. The obtained Z (α) master plot suggested the existence of random nucleation within the BSA matrix of Gd2O3 nanoclusters at high temperature over a specified conversion value. Additionally, four mathematical models were proposed using the above finding to interpret the nucleation rate and interfacial energy concerning high temperature and specified conversion points for the first time.
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