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| 1 | Comparison of bron-choscopic and non-bronchoscopictechniques for diagnosis of ventilatorassociated pneumonia显示文摘 | Khilnani GC Luqman Arafath TK Hadda V | 2011 | Indian JCrit Care Med2011，15，1： | 1 |
| 2 | Comparison of bron-choscopic and non-bronchoscopic techniques for diagnosis of ventilator associated pneumonia显示文摘 | Khilnani GC Arafath TK Hadda V | 2011 | Indian J CritCareMed2011，15，1： | 1 |
| 3 | Comparison of bron- choscopic and nombronchoscopic techniques for diagnosis of ventilator associated pneumonia显示文摘 | Khilnani G Arafath TK Vijay H | 2011 | Indian J Crit Care Med2011，15，1： | 1 |
| 4 | Closed-form solution for process-induced stresses and deformation of a composite part cured on a solid tool: Part I -- Flat geometries显示文摘 | Rahim A Arafath A Vaziri R | 2009 | Composites Part A2009，39，： | 1 |
| 5 | Closed-form solution for process-induced stresses and deformation of a composite part cured on a solid tool: Part II --Curved geometries显示文摘 | Rahim A Arafath A Vaziri R | 2009 | Composites Part A2009，40，： | 1 |
| 6 | Comparison of bron - choscopic and non - bronchoscopic techniques for diagnosis of ventilator associated pneumonia 显示文摘 | Khilnani GC Luqman Arafath TK Hadda V | 2011 | Indian J Crit Care Med2011，15，1： | 1 |
| 7 | Comparison of broncho- scopic and non-bronchoscopic techniques for diagnosis of ventilator associated pneumonia 显示文摘 | Khilnani G C Arafath T K Hadda V | 2011 | Indian J Crit Care Med2011，15，1： | 1 |
| 8 | Closed-form solution for process-induced stresses and deformation of a composite part cured on a solid tool:Part I-Flat geometries显示文摘 | Arafath A R A Vaziri R Poursartip A | 2008 | Composites Part A:Applied Science and Manufacturing2008，39，7： | 1 |
| 9 | Closed-form solution for process-induced stresses and deformation of a composite part cured on a solid tool:Part II-Curved geometries显示文摘 | Arafath A R A Vaziri R Poursartip A | 2009 | Composites Part A:Applied Science and Manufacturing2009，40，10： | 1 |
| 10 | Comparison of bronchoscopic and non-bronchoscopic techniques for diagnosis of ventilator associated pneumonia显示文摘 | Khilnani GC Arafath TK Hadda V | 2011 | Indian J Crit Care Med2011，15，1： | 1 |
| 11 | Comparison of bron- choscopic and non-bronchoscopic techniques for diagnosis of ventilator associated pneumonia 显示文摘 | Khilnani GC Luqman Arafath TK Hadda V | 2011 | Indian J Crit Care Med2011，15，1： | 1 |
| 12 | Analyticaltechniquesfor boron quantification supporting desalinationprocesses:A review显示文摘 | FarhatA AhmadF ArafatH | 2013 | Desalination2013，310，： | 1 |
| 13 | Closed form solution for process-induced stresses and deformation of a composite part cured on a solid tool: Part I-Flat geometries 显示文摘 | ARAFATH A R A VAZIRI R POURSARTIP A | 2008 | Composites Part A: Applied Science and Manufactur- ing2008，39，7： | 1 |
| 14 | Quantum Computing Based Neural Networks for Anomaly Classification in Real-Time Surveillance Videos显示文摘For intelligent surveillance videos,anomaly detection is extremely important.Deep learning algorithms have been popular for evaluating realtime surveillance recordings,like traffic accidents,and criminal or unlawful incidents such as suicide attempts.Nevertheless,Deep learning methods for classification,like convolutional neural networks,necessitate a lot of computing power.Quantum computing is a branch of technology that solves abnormal and complex problems using quantum mechanics.As a result,the focus of this research is on developing a hybrid quantum computing model which is based on deep learning.This research develops a Quantum Computing-based Convolutional Neural Network(QC-CNN)to extract features and classify anomalies from surveillance footage.A Quantum-based Circuit,such as the real amplitude circuit,is utilized to improve the performance of the model.As far as my research,this is the first work to employ quantum deep learning techniques to classify anomalous events in video surveillance applications.There are 13 anomalies classified from the UCF-crime dataset.Based on experimental results,the proposed model is capable of efficiently classifying data concerning confusion matrix,Receiver Operating Characteristic(ROC),accuracy,Area Under Curve(AUC),precision,recall as well as F1-score.The proposed QC-CNN has attained the best accuracy of 95.65 percent which is 5.37%greater when compared to other existing models.To measure the efficiency of the proposed work,QC-CNN is also evaluated with classical and quantum models. | MD.Yasar Arafath A.Niranjil Kumar | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
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