Stress is the response or a change in our bodies to environmental factors like challenges or demands that are physical and emotional. The main cause of stress is illnesses and it is gaining more interest, a hot topic for many researchers. Stress can be brought about by a wide range of normal life occasions that are hard to avoid. Stress generally refers to two things: first, the psychological perception of pressure and the body’s response to it. On the other hand, it involves multiple systems, from metabolism to muscles to memory. Many methods and tools are being developed to reduce stress in humans. Stress can be a short-term issue or a long-term problem, depending on what changes in your life. The emphasis of this article is to reduce the effects of stress by developing a stress-releasing game and verifying its results through the Profile of Mood States (POMS) and POMS-2 survey. Games are associated with stress levels; hence, parameters like sounds, visuals, and colors associated with reducing stress are used to develop a game for the stress reduction in the players. The survey research aims to determine that the purpose-built game will affect the player's stress level using a reliable psychological survey paper. The survey collected a variety of information from its participants over six months. Different aspects of a person’s psychology and reactions are recorded in this scenario by calculating the mean, standard deviation, degree of freedom, zero-error, and probability-value%. The POMS and POMS-2 results are obtained from the custom-built game, and these are found to be effective in reducing stress.
Abstract— The Novel Coronavirus generally, knows as COVID-19 which first appeared in Wuhan city of China in December 2019, spread quickly around the world and became a pandemic. It has caused an overwhelming effect on daily lives, Public health, and the global economy. Many people have been affected and have died. It is critical to control and prevent the spread of COVID-19 disease by applying quick alternative diagnostic techniques. COVID-19 cases are rising day by day around the world, the on-time diagnosis of COVID-19 patients is an increasingly long and difficult process. COVID-19 patient test kits are costly and not available for every individual in poor countries. For this purpose, screening patients with the established techniques like Chest X-ray images seems to be an effective method. This study used a deep learning data augmentation on a publicly available data set and train advanced CNN models on it. The proposed model was tested using a state-of-the-art evaluation measures and obtained better results. Our model, the COVID-19 images is available at (https://github.com/ieee8023/covid-chestxray-dataset) and for Non-COVID-19 images is available at (https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia). The maximum accuracy achieved in the validation was 96.67%. Our model of COVID-19 detection achieved an average F measure of 98%, and an Area Under Curve (AUC) of 99%. The results demonstrate that deep learning proved to be an effective and easily deployable approach for COVID-19 detection.
With advancements in science and technology, cloud computing is the next big thing in the industry. Cloud cryptography is a technique that uses encryption algorithms to secure data. The significant advantage of cloud storage is no difficulty to get to, diminished equipment, low protection, and fixing cost so every association is working with the cloud. Encryption is the process of encoding information to prevent unauthorized access. Nowadays, we desire to secure the information that is to be stored in our computer or transmitted utilizing the internet against attacks.The cryptographic method depends on their response time, confidentiality, bandwidth, and integrity. Furthermore, security is a significant factor in cloud computing for ensuring client data is placed on the safe mode in the cloud. Our research paper compares the efficiency, usage, and utility of available cryptography algorithms.Evaluation results suggest which algorithm is better for which type of data and environment.
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