Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children's Medical Center dataset.
Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world.
Technology and the rapid growth in the area of brain imaging technologies have forever made for a pivotal role in analyzing and focusing the new views of brain anatomy and functions. The mechanism of image processing has widespread usage in the area of medical science for improving the early detection and treatment phases. Deep neural networks (DNN), till date, have demonstrated wonderful performance in classification and segmentation task. Carrying this idea into consideration, in this paper, a technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with the image decomposition property of wavelet transform is proposed. The combination of both has a tremendous effect on sinking the size of the feature set for enduring further classification task by using DNN. A brain image dataset was taken and the proposed DWA-DNN image classifier was considered. The performance criterion for the DWA-DNN classifier was compared with other existing classifiers such as autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods. INDEX TERMSNeural network (NN), deep neural network (DNN), autoencoder (AE), image classification.
In image-based medical decision-making, different modalities of medical images of a given organ of a patient are captured. Each of these images will represent a modality that will render the examined organ differently, leading to different observations of a given phenomenon (such as stroke). The accurate analysis of each of these modalities promotes the detection of more appropriate medical decisions. Multimodal medical imaging is a research field that consists in the development of robust algorithms that can enable the fusion of image information acquired by different sets of modalities. In this paper, a novel multimodal medical image fusion algorithm is proposed for a wide range of medical diagnostic problems. It is based on the application of a boundary measured pulse-coupled neural network fusion strategy and an energy attribute fusion strategy in a non-subsampled shearlet transform domain. Our algorithm was validated in dataset with modalities of several diseases, namely glioma, Alzheimer's, and metastatic bronchogenic carcinoma, which contain more than 100 image pairs. Qualitative and quantitative evaluation verifies that the proposed algorithm outperforms most of the current algorithms, providing important ideas for medical diagnosis.
In every aspect of human life, sound plays an important role. From personal security to critical surveillance, sound is a key element to develop the automated systems for these fields. Few systems are already in the market, but their efficiency is a point of concern for their implementation in real-life scenarios. The learning capabilities of the deep learning architectures can be used to develop the sound classification systems to overcome efficiency issues of the traditional systems. Our aim, in this paper, is to use the deep learning networks for classifying the environmental sounds based on the generated spectrograms of these sounds. We used the spectrogram images of environmental sounds to train the convolutional neural network (CNN) and the tensor deep stacking network (TDSN). We used two datasets for our experiment: ESC-10 and ESC-50. Both systems were trained on these datasets, and the achieved accuracy was 77% and 49% in CNN and 56% in TDSN trained on the ESC-10. From this experiment, it is concluded that the proposed approach for sound classification using the spectrogram images of sounds can be efficiently used to develop the sound classification and recognition systems. INDEX TERMS Deep learning, convolutional neural network, tensor deep stacking networks, spectrograms.
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India is the second most dense and populated country in the world and one of the fastest growing economies. It is experiencing extreme congestion problems on road specifically on undivided two lane highways with mixed traffic. Facilitating infrastructure, imposing proper taxes to restrict personal vehicle growth and enhancing public transport facilities are long term solutions to this problem. These permanent solutions need government's involvement. The Indian government has spent a huge amount in the urban infrastructure sector. Many public transports like Bus Rapid Transit, Metro are being built in several new places to encourage the use of public transport. However, still there is a rapid growth of private vehicles . The country's growing population is also one of the reasons for increased transportation needs. Meeting such needs with infrastructure growth is seemingly less viable because of space and cost constraints. In general, short term traffic forecasting deals with analyzing previous traffic data and predict the traffic flow for next 5-30 min. The duration of 5-30 min is very important because traffic can grow drastically sometimes in next 5 min and can be very congested
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