With the evolution of fog computing, processing takes place locally in a virtual platform rather than in a centralized cloud server. Fog computing combined with cloud computing is more efficient as fog computing alone does not serve the purpose. Inefficient resource management and load balancing leads to degradation in quality of service as well as energy losses. Traffic overhead is increased because all the requests are sent to the main server causing delays which cannot be tolerated in healthcare scenarios. To overcome this problem, the authors are consolidating fog computing resources so that requests are handled by foglets and only critical requests are sent to the cloud for processing. Servers are placed locally in each city to handle the nearby requests in order to utilize the resources efficiently along with load balancing among all the servers, which leads to reduced latency and traffic overhead with the improved quality of service.
Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty. INDEX TERMS Tuberculosis identification, computer-aided diagnostics, medical image analysis, Bayesian convolutional neural networks, model uncertainty.
Wireless sensor networks (WSNs) have captivated substantial attention from both industrial and academic research in the last few years. The major factor behind the research efforts in that field is their vast range of applications which include surveillance systems, military operations, health care, environment event monitoring, and human safety. However, sensor nodes are low potential and energy constrained devices; therefore, energy-efficient routing protocol is the foremost concern. In this paper, an energy-efficient routing protocol for wireless sensor networks is proposed. Our protocol consists of a routing algorithm for the transmission of data, cluster head selection algorithm, and a scheme for the formation of clusters. On the basis of energy analysis of the existing routing protocols, a multistage data transmission mechanism is proposed. An efficient cluster head selection algorithm is adopted and unnecessary frequency of reclustering is exterminated. Static clustering is used for efficient selection of cluster heads. The performance and energy efficiency of our proposed routing protocol are assessed by the comparison of the existing routing protocols on a simulation platform. On the basis of simulation results, it is observed that our proposed routing protocol (EE-MRP) has performed well in terms of overall network lifetime, throughput, and energy efficiency.
Recognition of facial images is one of the most challenging research issues in surveillance systems due to different problems including varying pose, expression, illumination, and resolution. The robustness of recognition method strongly relies on the strength of extracted features and the ability to deal with low-quality face images. The proficiency to learn robust features from raw face images makes deep convolutional neural networks (DCNNs) attractive for face recognition. The DCNNs use softmax for quantifying model confidence of a class for an input face image to make a prediction. However, the softmax probabilities are not a true representation of model confidence and often misleading in feature space that may not be represented with available training examples. The primary goal of this paper is to improve the efficacy of face recognition systems by dealing with false positives through employing model uncertainty. Results of experimentations on open-source datasets show that 3-4% of accuracy is improved with model uncertainty over the DCNNs and conventional machine learning techniques.
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