Coronavirus (COVID-19) has adversely harmed the healthcare system and economy throughout the world. COVID-19 has similar symptoms as other chest disorders such as lung cancer (LC), pneumothorax, tuberculosis (TB), and pneumonia, which might mislead the clinical professionals in detecting a new variant of flu called coronavirus. This motivates us to design a model to classify multi-chest infections. A chest x-ray is the most ubiquitous disease diagnosis process in medical practice. As a result, chest x-ray examinations are the primary diagnostic tool for all of these chest infections. For the sake of saving human lives, paramedics and researchers are working tirelessly to establish a precise and reliable method for diagnosing the disease COVID-19 at an early stage. However, COVID-19’s medical diagnosis is exceedingly idiosyncratic and varied. A multi-classification method based on the deep learning (DL) model is developed and tested in this work to automatically classify the COVID-19, LC, pneumothorax, TB, and pneumonia from chest x-ray images. COVID-19 and other chest tract disorders are diagnosed using a convolutional neural network (CNN) model called CDC Net that incorporates residual network thoughts and dilated convolution. For this study, we used this model in conjunction with publically available benchmark data to identify these diseases. For the first time, a single deep learning model has been used to diagnose five different chest ailments. In terms of classification accuracy, recall, precision, and f1-score, we compared the proposed model to three CNN-based pre-trained models, such as Vgg-19, ResNet-50, and inception v3. An AUC of 0.9953 was attained by the CDC Net when it came to identifying various chest diseases (with an accuracy of 99.39%, a recall of 98.13%, and a precision of 99.42%). Moreover, CNN-based pre-trained models Vgg-19, ResNet-50, and inception v3 achieved accuracy in classifying multi-chest diseases are 95.61%, 96.15%, and 95.16%, respectively. Using chest x-rays, the proposed model was found to be highly accurate in diagnosing chest diseases. Based on our testing data set, the proposed model shows significant performance as compared to its competitor methods. Statistical analyses of the datasets using McNemar’s, and ANOVA tests also showed the robustness of the proposed model.

In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.

During recent years, the increase in the ageing population, the ubiquity of chronic diseases in the world, and the development in technologies have resulted in high demand for efficient healthcare systems. Physical anomalies mostly caused by injury, disease, and ageing lead to limit the regular ability of people to move and function. Primary health care providers often refer patients to conservative regular exercises as the first stage of the remedial process. The exercises operated under trained supervision are effective, but it is not feasible to supervise each patient under the growing number of such cases. Smart Physiotherapy exercise is one of the most beneficial and need of the time. The proper and systematic execution of recommended exercises is required for effective home-based physiotherapy. This study aims at exploring recent investigations performed by researchers in this discipline and subsequently, provide a ground for new researchers to improve or bring innovation in the approach. Electronic databases were searched between 2015 and 2020 in addition the reference lists of the articles that meet the criteria were also searched. The outcome of this study indicates that there is no prolific application that automatically monitors and guides the patients in performing the right and systematic exercises advised by the physiotherapist.

Today data transmission is very important through different channels. Need of network security comes to secure data transformation from one network to another network. As the complexity of the systems and the networks increases, weakness expands and the task of securing the networks is becomes more convoluted. Duty of securing is done by Cryptography techniques. A colossal amount of data is exchanged over public networks like the internet due to immense accommodation. This includes personal details and confidential information. It is important to prevent the data from falling into the wrong hands. So, due to this factor we use cryptography. Encryption and decryption are the basic terms that are used in cryptography. There are few algorithms which used including, AES (Advanced Encryption Standard), DES (Data Encryption Standard), 3DES (Triple Data Encryption Standard) and BLOWFISH. The main contribution of this paper is to provide an algorithm that is useful for data transformation in cognitive radio networks. In this research, we have drawn a new symmetric key technique that is for the usage of cryptography which is helpful to make the data saved from others.

Cloud services are offering a large number of utilities to the mobile users. Mobile users can share, store, develop, compute and many other services on the cloud. Due to extensive utilization of cloud services by the mobile users, security concerns are also evolving with the same pace. Among different security problems, secure access to the cloud services (cloud data utilization, data storage) is also a difficult and challenging task. This paper highlights the security concerns, particularly addresses the issue of secure access to cloud infrastructure, Such as access the cloud services securely by the mobile users. As Elastic Cloud Computing is valid only for Amazon, MLaden model is theoretical based model and not implemented practically, Wayne model enhances the end user security but not proposed tool for practical implementation. Intention of this paper is to propose mechanism to securely access the cloud services using GSM band. Only defined frequency band of GSM will provide the access to the cloud services. Users will be restricted to use the particular frequency to access the cloud services.