Oceans cover more than 75% of the planet’s land surface, making it the most water-rich place on the Earth. We know very little about oceans because of the extraordinary activities that take place in the depths. Underwater wireless sensors are devices that are able to monitor and record the physical and environmental parameters of their surroundings, as well as transmit these data in a continuous manner to one of the source sensors. The network that is formed by the collection of these underwater wireless sensors is referred to as an underwater wireless sensor network (UWSN). The analysis of performance parameters is thought to be most effectively done with this particular technology. In this paper, we will investigate various performance parameters in a random waypoint mobility model by shifting the maximum speed of a node and altering the number of nodes in the model. These parameters include average transmission delay, average jitter, average pathloss, percentage of utilization, and energy consumed in transmit, receive, and idle modes. The QualNet 7.1 simulator is utilized in order to conduct analyses and performance studies.

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Health care system, lifestyle, Industrial growth, economy and livelihood of human-beings worldwide effected due to triggered global pandemic by COVID-19 virus originated and first reported from Wuhan city, Republic Country of China. COVID cases are difficult to predict and detect on its early stages due to that its spread and mortality is uncontrollable. RT-PCR (Reverse Transcription Polymerase Chain Reaction) is still first and foremost diagnostic methodology accepted worldwide, hence it creates a scope of new diagnostic tools and techniques of detection approach which can produce effective and faster results compared to its predecessor. Innovational through current studies that complements to the existence of COVID-19 to findings in Chest X-ray snap shots, the proposed research’s method makes use of present deep getting to know models (U-Net and ResNet) to method those snap shots and classify them as the positive patient or the negative patient of COVID-19. The proposed technique entails the pre-treatment phase through dissection of lung, getting rid of the environment which does now no longer provide applicable facts and can provide influenced consequences; then after this, preliminary degree comes up with the category version educated below the switch mastering system; and in conclusion, consequences are evaluated and interpreted through warmth maps visualization. The proposed research method completed a detection accuracy of COVID-19 round 99%.

This research work presents an object identification method based on the machine learning technique based on human vision system. The objective is to prevent processing a complete image in sort to locate objects. Presently, the-state-of-the-art techniques divide an image into sub-regions and search for an object in all the subparts. This is ineffective for applications like embedded systems where the computation power is restricted or the resolution of the images are high. To address this issue, an object identification task was formulated as a decision-making problem. Followed the concept of DRL proposed, accepted RL algorithm DQL was applied to learn a policy from input data, i.e. images, to identify objects in a scene. In this manner, with the policy learned, a set of actions that transforms a box was apply in order to make tighter a bounding box around the target object.

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