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您的检索式：作者名="Manish Kumar Gaur"
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| 1 | Preoperative therapy in locally advanced esophageal cancer显示文摘Esophageal cancer is an aggressive malignancy associated with dismal treatment outcomes. Presence of two distinct histopathological types distinguishes it from other gastrointestinal tract malignancies. Surgery is the cornerstone of treatment in locally advanced esophageal cancer(T2 or greater or node positive); however, a high rate of disease recurrence(systemic and loco-regional) and poor survival justifies a continued search for optimal therapy. Various combinations of multimodality treatment(preoperative/perioperative, or postoperative; radiotherapy, chemotherapy, or chemoradiotherapy) are being explored to lower disease recurrence and improve survival. Preoperative therapy followed by surgery is presently considered the standard of care in resectable locally advanced esophageal cancer as postoperative treatment may not be feasible for all the patients due to the morbidity of esophagectomy and prolonged recovery time limiting the tolerance of patient. There are wide variations in the preoperative therapy practiced across the centres depending upon the institutional practices, availability of facilities and personal experiences. There is paucity of literature to standardize the preoperative therapy. Broadly, chemoradiotherapy is the preferred neo-adjuvant modality in western countries whereas chemotherapy alone is considered optimal in the far East. The present review highlights the significant studies to assist in opting for the best evidence based preoperative therapy(radiotherapy, chemotherapy or chemoradiotherapy) for locally advanced esophageal cancer. | Pankaj Kumar Garg Jyoti Sharma Ashish Jakhetiya Aakanksha Goel Manish Kumar Gaur | 2016 | World Journal of Gastroenterology2016，22，39： | 13 |
| 2 | Multiple Forgery Detection in Video Using Convolution Neural Network显示文摘With the growth of digital media data manipulation in today’s era due to the availability of readily handy tampering software,the authenticity of records is at high risk,especially in video.There is a dire need to detect such problem and do the necessary actions.In this work,we propose an approach to detect the interframe video forgery utilizing the deep features obtained from the parallel deep neural network model and thorough analytical computations.The proposed approach only uses the deep features extracted from the CNN model and then applies the conventional mathematical approach to these features to find the forgery in the video.This work calculates the correlation coefficient from the deep features of the adjacent frames rather than calculating directly from the frames.We divide the procedure of forgery detection into two phases–video forgery detection and video forgery classification.In video forgery detection,this approach detect input video is original or tampered.If the video is not original,then the video is checked in the next phase,which is video forgery classification.In the video forgery classification,method review the forged video for insertion forgery,deletion forgery,and also again check for originality.The proposed work is generalized and it is tested on two different datasets.The experimental results of our proposed model show that our approach can detect the forgery with the accuracy of 91%on VIFFD dataset,90%in TDTV dataset and classify the type of forgery–insertion and deletion with the accuracy of 82%on VIFFD dataset,86%on TDTV dataset.This work can helps in the analysis of original and tempered video in various domain. | Vinay Kumar Vineet Kansal Manish Gaur | 2022 | Computers, Materials & Continua2022，，10： | 0 |
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