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Several infectious diseases have affected the lives of many people and have caused great dilemmas all over the world. COVID-19 was declared a pandemic caused by a newly discovered virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by the World Health Organisation in 2019. RT-PCR is considered the golden standard for COVID-19 detection. Due to the limited RT-PCR resources, early diagnosis of the disease has become a challenge. Radiographic images such as Ultrasound, CT scans, X-rays can be used for the detection of the deathly disease. Developing deep learning models using radiographic images for detecting COVID-19 can assist in countering the outbreak of the virus. This paper presents a computer-aided detection model utilizing chest X-ray images for combating the pandemic. Several pre-trained networks and their combinations have been used for developing the model. The method uses features extracted from pre-trained networks along with Sparse autoencoder for dimensionality reduction and a Feed Forward Neural Network (FFNN) for the detection of COVID-19. Two publicly available chest X-ray image datasets, consisting of 504 COVID-19 images and 542 non-COVID-19 images, have been combined to train the model. The method was able to achieve an accuracy of 0.957 8 and an AUC of 0.982 1, using the combination of InceptionResnetV2 and Xception. Experiments have proved that the accuracy of the model improves with the usage of sparse autoencoder as the dimensionality reduction technique.

Wireless Sensor Networks(WSNs) is spatially distributed in sensor nodes without relay. A Mobile Data Investor , M-Investor is used to gather the data from sensor node and upload the data into data sink. When one M-Investor is moving, gathers the data from each and every nodes of entire network and upload the data into data sink. Also it raises distance/time constraints. The proposed system uses multiple M-Investors that are formed by portioning a network into a number of small sub networks. Each of them gathers the data by dynamically moving through a number of smallest sub tours in the entire network, upload the data into a data sink and it reduces distance/time constraints. The proposed system introduces supportive caching policies for minimizing electronic Data provisioning cost in Social Wireless Sensor Networks (SWSNET). SWSNETs are formed by mobile data investor , such as data enabled phones, electronic book readers etc., sharing common interests in electronic content, and physically gathering together in public places. Electronic object caching in such SWSNETs are shown to be able to reduce the Data provisioning cost which depends heavily on the service and pricing dependences among various stakeholders including Data providers (DP), network service providers, and End Customers (EC).