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As the amount of medical images transmitted over networks and kept on online servers continues to rise, the need to protect those images digitally is becoming increasingly important. However, due to the massive amounts of multimedia and medical pictures being exchanged, low computational complexity techniques have been developed. Most commonly used algorithms offer very little security and require a great deal of communication, all of which add to the high processing costs associated with using them. First, a deep learning classifier is used to classify records according to the degree of concealment they require. Medical images that aren't needed can be saved by using this method, which cuts down on security costs. Encryption is one of the most effective methods for protecting medical images after this step. Confusion and dispersion are two fundamental encryption processes. A new encryption algorithm for very sensitive data is developed in this study. Picture splitting with image blocks is now developed by using Zigzag patterns, rotation of the image blocks, and random permutation for scrambling the blocks. After that, this research suggests a Region of Interest (ROI) technique based on selective picture encryption. For the first step, we use an active contour picture segmentation to separate the ROI from the Region of Background (ROB).

The main aim of this study was to address the problem of congestion in TCP nonlinear systems in the presence of mismatched exogenous disturbances. To achieve this problem, two methods are proposed: the first is active queuing management, based on two proposed controllers, an NLPID and STC-SM, while the second is the application of active queuing management-based anti-disturbance techniques such as active disturbance rejection control (ADRC) and the nonlinear disturbance observer (NLDO). The proposed ADRC consists of a new NLPID and a new super-twisting sliding mode controller (STC-SM), which functions as a novel NLSEF, and a proposed NLESO estimates the applied disturbance and cancels it in a responsive manner. A new tracking differentiator with a novel function is also used to generate a smooth and accurate reference signal and derivative. The NLDO is proposed to estimate the disturbance and combine this with the control signal of the designed nonlinear controller as a way to compensate for the disturbance. The simulation results for the proposed scheme (ADRC) as applied to a nonlinear model of the TCP network are thus found to provide smoother and more accurate tracking of the desired value, with high robustness against applied disturbance, as compared to the other schemes introduced in this study. The proposed scheme also shows a noticeable improvement in terms of the utilized performance indices and the OPI.

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Since the outbreak of the COVID-19 epidemic, several control strategies have been proposed. The rapid spread of COVID-19 globally, allied with the fact that COVID-19 is a serious threat to people’s health and life, motivated many researchers around the world to investigate new methods and techniques to control its spread and offer treatment. Currently, the most effective approach to containing SARS-CoV-2 (COVID-19) and minimizing its impact on education and the economy remains a vaccination control strategy, however. In this paper, a modified version of the susceptible, exposed, infectious, and recovered (SEIR) model using vaccination control with a novel construct of active disturbance rejection control (ADRC) is thus used to generate a proper vaccination control scheme by rejecting those disturbances that might possibly affect the system. For the COVID-19 system, which has a unit relative degree, a new structure for the ADRC has been introduced by embedding the tracking differentiator (TD) in the control unit to obtain an error signal and its derivative. Two further novel nonlinear controllers, the nonlinear PID and a super twisting sliding mode (STC-SM) were also used with the TD to develop a new version of the nonlinear state error feedback (NLSEF), while a new nonlinear extended state observer (NLESO) was introduced to estimate the system state and total disturbance. The final simulation results show that the proposed methods achieve excellent performance compared to conventional active disturbance rejection controls.

From a future perspective and with the current advancements in technology, deep reinforcement learning (DRL) is set to play an important role in several areas like transportation, automation, finance, medical and in many more fields with less human interaction. With the popularity of its fast‐learning algorithms there is an exponential increase in the opportunities for handling dynamic environments without any explicit programming. Additionally, DRL sophisticatedly handles real‐world complex problems in different environments. It has grasped great attention in the areas of natural language processing (NLP), speech recognition, computer vision and image classification which has led to a drastic increase in solving complex problems like planning, decision‐making and perception. This survey provides a comprehensive analysis of DRL and different types of neural network, DRL architectures, and their real‐world applications. Recent and upcoming trends in the field of artificial intelligence (AI) and its categories have been emphasized and potential challenges have been discussed.

In the last decent, the number of Internet of Things (IoT) health-based paradigm reached to a huge number of users, services, and applications across different disciplines. Thus, hundreds of wireless devices seem to be distrusted over a limited or small area. To provide a more efficient network, the software-defined network (SDN) thought to be a good candidate to deal with these huge number of wireless users. In this work, after a novel SDN algorithm is proposed for the hospital environment, it is also designed and integrated into an Internet of Health Things (IoHT) paradigm. The novel algorithm called adaptive switching (AS) is proposed as a novel adaptive access strategy based on adaptively hoping among existing Go-Back-N and Selective Repeat techniques. Finally, the throughput performance of the proposed AS method is compared with the performances of traditional Go-Back-N and Selective Repeat ARQ methods using the developed MATLAB simulation. For this, an optimal P error rate that the network should prefer to switch either from Go-Back-N to Selective Repeat or from Selective Repeat to Go-Back-N method to maximize the network throughput performance is determined. The evaluated results are also confirmed by theoretical calculation results using well-known Mathis throughput formula. It is observed from the simulation results that the best throughput performance can be evaluated, when AS switches to Go-Back-N if the P error is less than 3.5% and it switches back to Selective Repeat when the P error is greater than 3.5%. By this way, it is also observed that the throughput always has its best possible results for all P error rates and up to 37.52% throughput improvement is provided by the use of novel proposed adaptive switching (AS) algorithm.