ZiZoNet: A Zoom-In and Zoom-Out Mechanism for Crowd Counting in Static Images

As people gather during different social, political or musical events, automated crowd analysis can lead to effective and better management of such events to prevent any unwanted scene as well as avoid political manipulation of crowd numbers. Crowd counting remains an integral part of crowd analysis and also an active research area in the field of computer vision. Existing methods fail to perform where crowd density is either too high or too low in an image, thus resulting in either overestimation or underestimation. These methods also mix crowd-like cluttered background regions (e.g. tree leaves or small and continuous patterns) in images with actual crowd, resulting in further crowd overestimation. In this work, we present a novel deep convolutional neural network (CNN) based framework ZiZoNet for automated crowd counting in static images in very low to very high crowd density scenarios to address above issues. ZiZoNet consists of three modules namely Crowd Density Classifier (CDC), Decision Module (DM) and Count Regressor Module (CRM). The test image, divided into 224x224 patches, passes through the CDC module that classifies each patch to a class label (no-crowd, low-crowd, medium-crowd, high-crowd). Based on the CDC information and using either heuristic Rule-set Engine (RSE) or machine learning based Random Forest based Decision Block (RFDB), DM decides which mode (zoom-in, normal or zoom-out) this image should use for crowd counting. CRM then performs patch-wise crowd estimate for this image accordingly as decided or instructed by the DM module. Extensive experiments on three diverse and challenging crowd counting benchmarks (UCF-QNRF, ShanghaiTech, AHU-Crowd) show that our method outperforms current state-of-the-art models under most of the evaluation criteria

Effective Uni-Modal to Multi-Modal Crowd Estimation based on Deep Neural Networks

Crowd estimation is a vital component of crowd analysis. It finds many applications in real-worldscenarios, e.g. huge gatherings management like Hajj, sporting and musical events, or political rallies. Automated crowd counting facilitates better and effective management of such events and consequently prevents any undesired situation. This is a very challenging problem in practice since there exists a significant difference in the crowd number in and across different images, varying image resolution, large perspective, severe occlusions, and dense crowd-like cluttered background regions. Current approaches do not handle huge crowd diversity well and thus perform poorly in cases ranging from extreme low to high crowd-density, thus, yielding huge crowd underestimation or overestimation. Also, manual crowd counting proves to be infeasible due to very slow and inaccurate results. To address these major crowd counting issues and challenges, we investigate two different types of input data: uni-modal (image) and multi-modal (image and audio). In the uni-modal setting, we propose and analyze four novel end-to-end crowd counting networks, ranging from multi-scale fusion-based models to uni-scale one-pass and two-pass multitask networks. The multi-scale networks employ the attention mechanism to enhance the model efficacy. On the other hand, the uni-scale models are well-equipped with novel and simple-yet effective patch re-scaling module (PRM) that functions identical but is more lightweight than multi-scale approaches. Experimental evaluation demonstrates that the proposed networks outperform the state-of-the-art in majority cases on four different benchmark datasets with up to 12.6% improvement for the RMSE evaluation metric. The better cross-dataset performance also validates the better generalization ability of our schemes. For the multi-modal input, effective feature-extraction (FE) and strong information fusion between two modalities remain a big challenge. Thus, the multi-modal novel network design focuses on investigating different features fusion techniques amid improving the FE. Based on the comprehensive experimental evaluation, the proposed multi-modal network increases the performance under all standard evaluation criteria with up to 33.8% improvement in comparison to the state-of-the-art. The application of multi-scale uni-modal attention networks also proves more effective in other deep learning domains, as demonstrated successfully on seven different scene-text recognition task datasets with better performance

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset

Fludarabine induced immune thrombocytopenia in a patient with CD5 positive B cell chronic lymphocytic leukemia

Fludarabine is a purine nucleoside analogue, which inhibits DNA synthesis by inhibiting DNA polymerase and ribonucleoside reductase.1 It affects both dividing and non- dividing cells.2 Fludarabine possesses proven efficacy in the treatment of a variety of indolent B cell lymphoproliferative disorders including chronic lymphocytic leukemia3, low-grade non-Hodgkin\u27s lymphoma4 and Waldenstrom macroglobulinemia.5 It is also a part of conditioning regimes in non-myeloablative bone marrow transplantation.6 The common side effects include myelosuppression, immunosuppression, and neurologic toxicity.7 The rare side effects are immune mediated hemolytic anemia8 and thrombocytopenia.9 Here we describe a case of a middle-aged lady who was diagnosed as B cell chronic lymphocytic leukemia and developed immune mediated thrombocytopenia following oral Fludarabine

Heat Transfer Applications of TiO2 Nanofluids

To achieve acme heat transfer is our main disquiet in many heat transfer applications such as radiators, heat sinks and heat exchangers. Due to furtherance in technology, requirement for efficient systems have increased. Usually cooling medium used in these applications is liquid which carries away heat from system. Liquids have poor thermal conductivity as compared to solids. In order to improve the efficiency of system, cooling medium with high thermal conductivity should be used. Quest to improve thermal conductivity leads to usage of different methods, and one of them is addition of nanoparticles to base liquid. Application of nanofluids (a mixture of nanoparticles and base fluid) showed enhancement in heat transfer rate, which is not possible to achieve by using simple liquids. Different researchers used TiO2 nanoparticles in different heat transfer applications to observe the effects. Addition of titanium oxide nanoparticles into base fluid showed improvement in the thermal conductivity of fluid. This chapter will give an overview of usage of titanium oxide nanoparticles in numerous heat transfer applications