3篇
您的检索式：作者名="Ruby Singh"
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| 1 | Multicomponent One-pot Diastereoselective Synthesis of Biologically Important Scaffold under Microwaves显示文摘生物学上重要的 spiro 支架 4 的简单、快的三部件的 diastereoselective 合成在合理纯净从容易地可得到的 1H-indole-2,3-dione 开始被执行 1,乙醇 cyanoacetate 2 并且 3 在在高度的微波下面产出的 4-hydroxycoumarin (88%92%) 并且突然时间。 | DANDIA, Anshu SINGH, Ruby SARAWGI, Pritima KHATURIA, Sarita | 2006 | Chinese Journal of Chemistry2006，24，7： | 9 |
| 2 | A novel convolutional neural network with gated recurrent unit for automated speech emotion recognition and classification显示文摘Automated Speech Emotion Recognition (SER) becomes more popular and has increased applicability.SER concentrates on the automatic identification of the emotional state of a humanbeing using speech signals. It mainly depends upon the in-depth analysis of the speech signal,extracts features containing emotional details from the speech signal, and utilises patternrecognition techniques for emotional state identification. The major problem in automatic SERis to extract discriminate, powerful, and emotional salient features from the acoustical content ofspeech signals. The proposed model aims to detect and classify three emotional states of speechsuch as happy, neutral, and sad. The presented model makes use of Convolution neural network– Gated Recurrent unit (CNN-GRU) based feature extraction technique which derives a set offeature vectors. A comprehensive simulation takes place using the Berlin German Database andSJTU Chinese Database which comprises numerous audio files under a collection of differentemotion labels. | P.Ravi Prakash D.Anuradha Javid Iqbal Mohammad Gouse Galety Ruby Singh S.Neelakandan | 2023 | Journal of Control and Decision2023，10，1： | 1 |
| 3 | DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm显示文摘In contemporary medicine,cardiovascular disease is a major public health concern.Cardiovascular diseases are one of the leading causes of death worldwide.They are classified as vascular,ischemic,or hypertensive.Clinical information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart illness.Heart failure rates have risen drama-tically in recent years as a result of changes in modern lifestyles.Heart diseases are becoming more prevalent in today’s medical setting.Each year,a substantial number of people die as a result of cardiac pain.The primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of diseases.To improve the efficiency of the classification algo-rithms,we construct a data pre-processing stage using feature selection.Experi-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modified Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classification performance on the UCI Machine Learning Heart Disease dataset.The DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease dataset.We demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease dataset.Our proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data. | S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
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