The problem of respiratory sound classification has received good attention from the clinical scientists and medical researcher’s community in the last year to the diagnosis of COVID-19 disease. The Artificial Intelligence (AI) based models deployed into the real-world to identify the COVID-19 disease from human-generated sounds such as voice/speech, dry cough, and breath. The CNN (Convolutional Neural Network) is used to solve many real-world problems with Artificial Intelligence (AI) based machines. We have proposed and implemented a multi-channeled Deep Convolutional Network (DCNN) for automatic diagnosis of COVID-19 disease from human respiratory sounds like a voice, dry cough, and breath, and it will give better accuracy and performance than previous models. We have applied multi-feature channels such as the data De-noising Auto Encoder (DAE) technique, GFCC (Gamma-tone Frequency Cepstral Coefficients), and IMFCC (Improved Multi-frequency Cepstral Coefficients) methods on augmented data to extract the deep features for the input of the CNN. The proposed approach improves system performance to the diagnosis of COVID-19 disease and provides better results on the COVID-19 respiratory sound dataset.

The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose respiratory diseases of COVID-19 from human respiratory sounds such as a voice, cough, and breath. An augmentation-based mechanism is applied to improve the preprocessing performance of the COVID-19 sounds dataset and to automate COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound features such as the input function to the 1D CNN instead of adopting the standard input of MFCC (Mel-frequency cepstral coefficient), and it is performed better accuracy and performance than previous models. Results As a result, around 4% accuracy is achieved than traditional MFCC. We have classified COVID-19 sounds, asthma sounds, and regular healthy sounds using a 1D CNN classifier and shown around 90% accuracy to detect the COVID-19 disease from respiratory sounds. Conclusion A Data De-noising Auto Encoder (DDAE) was adopted to extract the acoustic sound signals in-depth features instead of traditional MFCC. The proposed model improves efficiently to classify COVID-19 sounds for detecting COVID-19 positive symptoms.

The World Health Organization (WHO) has announced a COVID-19 was a global pandemic in March 2020. It was initially started in china in the year 2019 December and affected an expanding number of nations in various countries in the last few months. In this particular situation, many techniques, methods, and AI-based classification algorithms are put in the spotlight in reacting to fight against it and reduce the rate of such a global health crisis. COVID-19's main signs are heavy temperature, different cough, cold, breathing shortness, and a combination of loss of sense of smell and chest tightness. The digital world is growing day by day; in this context digital stethoscope can read all of these symptoms and diagnose respiratory disease. In this study, we majorly focus on literature reviews of how SARS-CoV-2 is spreading and in-depth analysis of the diagnosis of COVID-19 disease from human respiratory sounds like cough, voice, and breath by analyzing the respiratory sound parameters. We hope this review will provide an initiative for the clinical scientists and researcher's community to initiate open access, scalable, and accessible work in the collective battle against COVID-19.

Ciphertext-policy attribute-based encryption (CP-ABE) schemes are an appropriate cryptographic technique to enable privacy along with access control in the cloud, but the existing CP-ABE schemes do not directly apply for big data because they have the issue of long ciphertext and long secret key size (LC-LS). To address LC-LS, the constant size ciphertext and secret key (CSC-S) schemes proposed. However, the existing CSC-S schemes suffer from the key escrow security issue and efficiency issue. To address both simultaneously, the authors propose an efficient escrow-free CP-ABE with constant size ciphertext and secret key (EEF-CPABE) for big data storage in the Cloud. The EEF-CPABE scheme reduces the encryption and decryption computation overhead by designing CSC-S. Further, the data owner generates the decryption global key to decrypt the data along with user secret key which solves the key escrow issue. Security and performance analysis demonstrate that the EEF-CPABE scheme resists against authority, and chosen plain-text attacks and more efficient than CSC-S schemes.

Efficient energy management is a key issue in battery equipped wireless sensor networks (WSNs). The cluster based routing in WSNs is a prominent approach for energy conservation of the network which provides a hierarchical data collection mechanism. In order to maximize the energy conservation of sensor nodes, this paper proposes an Energy-efficient Layered Clustering Algorithm (ELCA) for routing in wireless sensor networks. ELCA constructs two layers of clusters to reduce the transmission rate and to balance the energy consumption of sensors. As early energy depletion of clusterheads (CHs) is a major limitation in clustering, this algorithm provides local remedy for energy suffering CHs through efficient CH substitution scheme. The performance of the proposed algorithm is analysed through extensive simulation experiments and verified by compared results with existing clustering algorithms.