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您的检索式：作者名="Salman Hashmi"
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| 1 | Inflammatory breast cancer clusters: A hypothesis显示文摘Cancer clusters have long been a focus of interest because of the possibility of identifying etiologic agents. Only on rare occasions, however, have such cluster investigations been successful. One major difficulty in cluster investigations, particularly in the area of breast cancer, is the long latent period. There have been a number of publications providing a discouraging picture regarding cancer cluster investigations. The possibility of learning from a cluster investigation, however, is greatly increased if the cancer involved is relatively rare and if it has a short latent period. Inflammatory breast cancer(IBC) fits these criteria and is worth pursuing because of the strong evidence that environmental factors play a major role. In this report we describe our experience with several clusters and the lessonslearned which are now being utilized to improve investigation of future IBC clusters. The first IBC cluster that we evaluated was in 2000, when we were asked to investigate an apparent cluster of IBC in Castro Valley, California where three women in an office setting of 24 people were diagnosed with IBC in a ten month period from May 1999 to March 2000. Our investigation of this striking cluster did not yield a specific trigger for this cluster but it did indicate that the women involved all had at least two IBC risk factors that may well have made them susceptible to getting IBC. We are now investigating another apparent cluster in Texas and are aware of several others requiring careful consideration. We see a need for a consistent protocol for the evaluation of IBC clusters focusing on the laboratory investigation of environmental triggers, primarily infectious agents and chemical carcinogens. | Paul H Levine Salman Hashmi Ashley A Minaei Carmela Veneroso | 2014 | World Journal of Clinical Oncology2014，5，3： | 1 |
| 2 | YOLOv2PD:An Efficient Pedestrian Detection Algorithm Using Improved YOLOv2 Model显示文摘Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results. | Chintakindi Balaram Murthy Mohammad Farukh Hashmi Ghulam Muhammad Salman A.AlQahtani | 2021 | Computers, Materials & Continua2021，，12： | 0 |
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