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| 1 | Real-time implementation of Chebyshev neural network observer for twinrotor control system显示文摘 | Ferdose Ahammad Shaik Shubhi Purwar Bhanu Pratap | 2011 | Expert Systems with Applications2011，38，13： | 1 |
| 2 | Study of polycrystalline sintered compacts of diamond显示文摘 | Bhanu Pratap Singh | 1987 | Journal of Materials Science1987，22，7： | 1 |
| 3 | Real-time implementation of Chebyshev neural network observer for twin rotor control system显示文摘 | Ferdose Ahammad Shaik Shubhi Purwar Bhanu Pratap | 2011 | Expert Systems with Applications2011，38，13： | 1 |
| 4 | Real-time implementation of Chebyshev neural network observer for twin rotor control system显示文摘 | Ferdose Ahammad Shaik Shubhi Purwar Bhanu Pratap | 2011 | Expert Systems With Applications2011，，10： | 1 |
| 5 | Pitfalls in internal jugular vein cannulation显示文摘Central venous catheter insertion in the internal jugular vein(IJV)is frequently performed in acute care settings,facilitated by its easy availability and increased use of ultrasound in healthcare settings.Despite the increased safety profile and insertion convenience,it has complications.Herein,we aim to inform readers about the existing literature on the plethora of complications with potentially disastrous consequences for patients undergoing IJV cannulation. | Deb Sanjay Nag Amlan Swain Seelora Sahu Bhanu Pratap Swain Merina Sam | 2024 | World Journal of Clinical Cases2024，12，10： | 0 |
| 6 | Relevance of sleep for wellness:New trends in using artificial intelligence and machine learning显示文摘Sleep and well-being have been intricately linked,and sleep hygiene is paramount for developing mental well-being and resilience.Although widespread,sleep disorders require elaborate polysomnography laboratory and patient-stay with sleep in unfamiliar environments.Current technologies have allowed various devices to diagnose sleep disorders at home.However,these devices are in various validation stages,with many already receiving approvals from competent authorities.This has captured vast patient-related physiologic data for advanced analytics using artificial intelligence through machine and deep learning applications.This is expected to be integrated with patients’Electronic Health Records and provide individualized prescriptive therapy for sleep disorders in the future. | Deb Sanjay Nag Amlan Swain Seelora Sahu Abhishek Chatterjee Bhanu Pratap Swain | 2024 | World Journal of Clinical Cases2024，12，7： | 0 |
| 7 | Effect of elevated [CO_2] and nutrient management on wet and dry season rice production in subtropical India显示文摘The present experiment was conducted to evaluate the effect of elevated [CO_2] with varying nutrient management on rice–rice production system. The experiment was conducted in the open field and inside open-top chambers(OTCs) of ambient [CO_2](≈ 390 μmol L-1) and elevated [CO_2] environment(25% above ambient) during wet and dry seasons in 2011–2013at Kharagpur, India. The nutrient management included recommended doses of N, P, and K as chemical fertilizer(CF), integration of chemical and organic sources, and application of increased(25% higher) doses of CF. The higher [CO_2] level in the OTC increased aboveground biomass but marginally decreased filled grains per panicle and grain yield of rice, compared to the ambient environment. However, crop root biomass was increased significantly under elevated [CO_2]. With respect to nutrient management, increasing the dose of CF increased grain yield significantly in both seasons. At the recommended dose of nutrients, integrated nutrient management was comparable to CF in the wet season, but significantly inferior in the dry season, in its effect on growth and yield of rice. The [CO_2] elevation in OTC led to a marginal increase in organic C and available P content of soil, but a decrease in available N content. It was concluded that increased doses of nutrients via integration of chemical and organic sources in the wet season and chemical sources alone in the dry season will minimize the adverse effect of future climate on rice production in subtropical India. | Sushree Sagarika Satapathy Dillip Kumar Swain Surendranath Pasupalak Pratap Bhanu Singh Bhadoria | 2015 | The Crop Journal2015，3，6： | 0 |
| 8 | Spray Prediction Model for Aonla Rust Disease Using Machine Learning显示文摘Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are present in adequate amounts as required by the plant.On the other hand,if the factors are inadequate,they may also support the growth of a disease in the plants.The current study focuses on the Rust disease in Aonla fruits and leaves by utilizing a real time dataset of weather parameters.Fifteen different models are tested for spray prediction on conducive days.Two resampling techniques,random over sampling(ROS)and synthetic minority oversampling technique(SMOTE)have been used to balance the dataset and five different classifiers:support vector machine(SVM),logistic regression(LR),k-nearest neighbor(kNN),decision tree(DT)and random forest(RF)have been used to classify a particular day based on weather conditions as conducive or non-conducive.The classifiers are then evaluated based on four performance metrics:accuracy,precision,recall and F1-score.The results indicate that for imbalanced dataset,kNN is appropriate with high precision and recall values.Considering both balanced and imbalanced dataset models,the proposed model SMOTE-RF performs best among all models with 94.6%accuracy and can be used in a real time application for spray prediction.Hence,timely fungicide spray prediction without over spraying will help in better productivity and will prevent the yield loss due to rust disease in Aonla crop. | Hemant Kumar Singh Bhanu Pratap S.K.Maheshwari Ayushi Gupta Anuradha Chug Amit Prakash Singh Dinesh Singh | 2023 | Journal of Agricultural Science and Technology(B)2023，13，1： | 0 |
| 9 | Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies显示文摘Background:The suitability and performance of the bankruptcy prediction models is an empirical question.The aim of this paper is to develop a bankruptcy prediction model for Indian manufacturing companies on a sample of 208 companies consisting of an equal number of defaulted and non-defaulted firms.Out of 208 companies,130 are used for estimation sample,and 78 are holdout for model validation.The study reestimates the accounting based models such as Altman EI(Journal of Finance 23:19189-209,1968)Z-Score,Ohlson JA(Journal of Accounting Research 18:109-131,1980)Y-Score and Zmijewski ME(Journal of Accounting Research 22:59-82,1984)X-Score model.The paper compares original and re-estimated models to explore the sensitivity of these models towards the change in time periods and financial conditions.Methods:Multiple Discriminant Analysis(MDA)and Probit techniques are employed in the estimation of Z-Score and X-Score models,whereas Logit technique is employed in the estimation of Y-Score and the newly proposed models.The performance of all the original,re-estimated and new proposed models are assessed by predictive accuracy,significance of parameters,long-range accuracy,secondary sample and Receiver Operating Characteristic(ROC)tests.Results:The major findings of the study reveal that the overall predictive accuracy of all the three models improves on estimation and holdout sample when the coefficients are re-estimated.Amongst the contesting models,the new bankruptcy prediction model outperforms other models.Conclusions:The industry specific model should be developed with the new combinations of financial ratios to predict bankruptcy of the firms in a particular country.The study further suggests the coefficients of the models are sensitive to time periods and financial condition.Hence,researchers should be cautioned while choosing the models for bankruptcy prediction to recalculate the models by looking at the recent data in order to get higher predictive accuracy. | Bhanu Pratap Singh Alok Kumar Mishra | 2016 | Financial Innovation2016，2，1： | 0 |
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