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| 1 | Extension of shelf‐life of whole‐wheat flour by gamma radiation显示文摘 | S. A.Marathe J. P.Machaiah B. Y. K.Rao M. D.Pednekar | 2002 | International Journal of Food Science & Technology2002，，2： | 1 |
| 2 | Congenital platelet disorders: overview of their mechanisms, diagnostic evaluation and treatment显示文摘 | C. P. M.HAYWARD A. K.RAO M.CATTANEO | 2006 | Haemophilia2006，，： | 1 |
| 3 | Cephalomedullary nails in the management of ipsilateral neck and shaft fractures of the femur-One or two femoral neck screws显示文摘 | S.Vidyadhara Sharath K.Rao | | 0，，： | 1 |
| 4 | Acetaldehyde‐Induced Increase in Paracellular Permeability in Caco‐2 Cell Monolayer显示文摘 | R. K.Rao | 2006 | Alcoholism: Clinical and Experimental Research2006，，8： | 1 |
| 5 | 音频解码中的DSP应用显示文摘 | Raghunath K.Rao | 2003 | 电子产品世界2003，10，04B： | 0 |
| 6 | DSP在音频解码中的应用显示文摘一.简介从上个世纪90年代以来,数字信号处理技术便逐步在消费类音频领域占据统治地位。起先数字信号处理器主要用于操作数字化的模拟音频信号。 | Raghunath K.Rao | 2003 | 中国集成电路2003，，48： | 0 |
| 7 | Hierarchical approach for ripeness grading of mangoes显示文摘Grading of fruits based on their ripeness has been a topic of research for the last two decades.Identifying the ripened mangoes has become more of an art than science and is a challenging task.This study aims at introducing a system to grademangoes with four classes based on their ripeness.The study was demonstrated through an extensive experimentation on a newly created dataset consisting of 981 images of Alphonsomango variety belonging to four classes viz.,under-ripen,perfectly ripen,over-ripen with internal defects and over-ripen without internal defects.In this study,a hierarchical approach was adopted to classify the mangoes into the four classes.At each stage of classification,L*a*b color space features were extracted.For the purpose of classification at each stage,a number of classifiers and their possible combinationswere tried out.The study revealed that,the Support VectorMachine(SVM)classifier works better for classifyingmangoes into under-ripen,perfectly ripen and overripen while the thresholding classifier has a superior classification performance on over-ripen with internal defects and over-ripen without internal defects.Further,to bring out the superiority of the hierarchical approach,a conventional single shot multi-class classification approach with SVMwas also studied.The results of the experimentation demonstrated that the hierarchical method with an accuracy of 88%outperforms the counterpart conventional single shot multi-class classification approach in addition to several existing contemporary models. | Anitha Raghavendra D.S.Guru Mahesh K.Rao R.Sumithra | 2020 | Artificial Intelligence in Agriculture2020，，1： | 0 |
| 8 | Mango internal defect detection based on optimal wavelength selection method using NIR spectroscopy显示文摘A non-destructive technique should be developed for performance analysis of mango fruits because the spongy tissue or internal defects could lower the quality of mango fruit and incur a lack of productivity.In this study,wavelength selection methods were proposed to identify the range of wavelengths for the classification of defected and healthy mango fruits.Feature selection methods were adopted here to achieve a significant selection of wavelengths.To measure the goodness of themodel,the datasetwas collected using the NIR(Near Infrared)spectroscopy with wavelength ranging from 673 nm–1900 nm.The classification was performed using Euclidean distance measure both in the original feature space and in FLD(Fisher's Linear Discriminant)transformed space.The experimental results showed that the lower range wavelength(673 nm–1100 nm)was the efficient wavelength for the detection of internal defects in mangoes.Further to express the effectiveness of the model,different feature selection techniques were investigated and found that the Fisher's criterion based technique appeared to be the best method for effective wavelength selection useful for classification of defected and healthy mango fruits.The optimal wavelengths were found in the range of 702.72 nm to 752.34 nm using Fisher's criterionwith a classification accuracy of 84.5%.This study showed that NIR systemis a useful technology for the automaticmango fruit assessmentwhich has the potential to be used for internal defects in online sorting,easily distinguishable by those who do not meet minimum quality requirements. | Anitha Raghavendra D.S.Guru Mahesh K.Rao | 2021 | Artificial Intelligence in Agriculture2021，，1： | 0 |
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