Pneumonia is a severe respiratory disease with potentially life-threatening consequences if not promptly diagnosed and treated. Chest X-rays are commonly employed for pneumonia detection, but interpreting the images can pose challenges. This study explores the efficacy of four popular transfer learning models, namely VGG16, ResNet, InceptionNet, and DenseNet, alongside a custom CNN model for this task. The model performance is evaluated using Mean Absolute Error (MAE) as the performance metric. The findings reveal that VGG16 outperforms the other transfer learning models, achieving the lowest MAE (66.19). To optimize the model training process, a distributed training strategy utilizing TensorFlow's TPU (Tensor Processing Unit) strategy is implemented. The custom CNN model is parallelized using TPU's multiple instances available over the cloud, enabling efficient computation parallelization and significantly reducing model training times. The experimental results demonstrate a remarkable decrease of 68.36% and 54.74% in model training times for the CNN model when trained using TPU compared to training on a CPU and GPU, respectively.

This article considers a system for the semiautomatic annotation of an audio-visual media of dance domain, DMAR (Dance Media Annotation, authoring and Retrieval system). We present the architecture of the system, a manual annotation tool, a semiautomatic authoring tool and a search engine for the choreographers, dancers and students to demonstrate how the dance media can be semantically annotated and how this information can be used for the retrieval of the dance media objects. This article then outlines the underlying XML Schema based content description structures of DMAR and discusses the merits and demerits of our approach of evolving semantic network as the basis for the audio-visual content description. Further, this article proposes a quality metric, fidelity to evaluate the expressive power of the dance annotations. Finally, evaluation results are presented to depict the performance of the dance video queries in terms of precision and recall.

Secure Data Communication using File Hierarchy Attribute Based Encryption in Wireless Body Area Networks