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Selecting and using the same health monitoring devices for a particular problem is a tedious task. This paper aims to provide a comprehensive review of 40 research papers giving the Smart health monitoring system using Internet of things (IoT) and Deep learning.

Health Monitoring Systems play a significant role in the healthcare sector. The development and testing of health monitoring devices using IoT and deep learning dominate the healthcare sector.

In addition, the detailed conversation and investigation are finished by techniques and development framework. Authors have identified the research gap and presented future research directions in IoT, edge computing and deep learning.

The gathered research articles are examined, and the gaps and issues that the current research papers confront are discussed. In addition, based on various research gaps, this assessment proposes the primary future scope for deep learning and IoT health monitoring model.

The service denial threats are regularly regarded as tools for effortlessly triggering online-based services offline. Moreover, the present occurrences reveal that these threats are being constantly employed for masking other vulnerable threats like disseminating malware, information losses, wire scams and mining bitcoins (Sujithra et al., 2018; Boujnouni and Jedra, 2018). In some cases, service denials have been employed to cyberheist financial firms which sums around $100,000. Documentation from Neustar accounts that is about 70 percent of the financial sector are aware of the threat, and therefore, incidents result in few losses, more than 35 percent of service denial attempts are identified as malware soon after the threat is sent out (Divyavani and Dileep Kumar Reddy, 2018). Intensive packet analysis (IPA) explores the packet headers from Layers 2 to 4 along with the application information layer from Layers 5 to 7 for locating and evading vulnerable network-related threats. The networked systems could be simply contained by low potent service denial operations in case the supplies of the systems are minimized by the safety modules. The paper aims to discuss these issues.

The initial feature will be resolved using the IPDME by locating the standard precise header delimiters such as carriage return line feed equally locating the header names. For the designed IPDME, the time difficulties in locating the initial position of the header field within a packet with static time expenses of four cycles. For buffering packets, the framework functions at the speed of cables. Soon after locating the header position, the value of the field is mined linearly from the position. Mining all the field values consequentially resolves the forthcoming restrictions which could be increased by estimating various information bytes per cycle and omitting non-required information packets. In this way, the exploration space is minimized from the packet length to the length of the header. Because of the minimized mining time, the buffered packets could be operated at an increasing time.

Based on the assessments of IPDME against broadly employed SIP application layer function tools it discloses hardware offloading of IPDME it could minimize the loads on the essential system supplies of about 25 percent. The IPDME reveals that the acceleration of 22X– 75X as evaluated against PJSIP parser and SNORT SIP pre-processor. One IPDME portrays an acceleration of 4X–6X during 12 occurrences of SNORT parsers executing on 12 processors. The IPDME accomplishes 3X superior to 200 parallel occurrences of GPU speeded up processors. Additionally, the IPDME has very minimal latencies with 12X–1,010X minimal than GPUs. IPDME accomplishes minimal energy trails of nearly 0.75 W using two engines and for 15 engines it is 3.6 W, which is 22.5X–100X less as evaluated to the graphic-based GPU speeding up.

IPDME assures that the system pools are not fatigued on Layer 7 mining by transmitting straightforwardly based on network intrusions without branching into the operating systems. IPDME averts the latencies because of the memory accesses by sidestepping the operating system which essentially permits the scheme to function at wired speed. Based on the safety perception, IPDME ultimately enhances the performance of the safety systems employing them. The increased bandwidth of the IPDME assures that the IPA’s could function at their utmost bandwidth. The service time for the threat independent traffic is enhanced because of minimization over the comprehensive latencies over the path among the network intrusions and the related applications.

An intelligent personal assistant for personal computers (PCs) is a vital application for the current generation. The current computer personal assistant services checking frameworks are not proficient at removing significant data from PCs and long-range informal communication information.

The proposed verbalizers use long short-term memory to classify the user task and give proper guidelines to the users. The outcomes show that the proposed method determinedly handles heterogeneous information and improves precision. The main advantage of long short-term memory is that handle the long-term dependencies in the input data.

The proposed model gives the 22% mean absolute error. The proposed method reduces mean square error than support vector machine (SVM), convolutional neural network (CNN), multilayer perceptron (MLP) and K-nearest neighbors (KNN).

This paper fulfills the necessity of intelligent personal assistant for PCs using verbalizer.

The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method.

This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques.

The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively.

The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.

Denial-of-service (DoS) attacks develop unauthorized entry to various network services and user information by building traffic that creates multiple requests simultaneously making the system unavailable to users. Protection of internet services requires effective DoS attack detection to keep an eye on traffic passing across protected networks, freeing the protected internet servers from surveillance threats and ensuring they can focus on offering high-quality services with the fewest response times possible.

This paper aims to develop a hybrid optimization-based deep learning model to precisely detect DoS attacks.

The designed Aquila deer hunting optimization-enabled deep belief network technique achieved improved performance with an accuracy of 92.8%, a true positive rate of 92.8% and a true negative rate of 93.6.

The introduced detection approach effectively detects DoS attacks available on the internet.

Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.

VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.

The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.

User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

The purpose of this paper is to demonstrate that the national security exception will not allow governments to respond to cyberspace threats within the confines of the world trade organization (WTO) rules.

This paper is a desktop study of international trade laws with a specific focus on the convergence of cybersecurity measures and the national security exception provisions of the WTO.

The trends towards cybersecurity measures may mean there will inevitably be an evolution of trade norms. The question is, will the collective of the WTO be a part of the evolution, or merely an observer? In the authors’ view, it is crucial that it is the former.

This study makes three contributions. It provides a literature review and discussion on cybersecurity and the impact on trade. It demonstrates that the national security exception provision will not excuse these measures, and it aims to underscore the importance of the WTO as a community of nations where negotiation on important global issues is possible.

The amount of features in handwritten digit data is often very large due to the different aspects in personal handwriting, leading to high-dimensional data. Therefore, the employment of a feature selection algorithm becomes crucial for successful classification modeling, because the inclusion of irrelevant or redundant features can mislead the modeling algorithms, resulting in overfitting and decrease in efficiency.

The minimum redundancy and maximum relevance (mRMR) and the recursive feature elimination (RFE) are two frequently used feature selection algorithms. While mRMR is capable of identifying a subset of features that are highly relevant to the targeted classification variable, mRMR still carries the weakness of capturing redundant features along with the algorithm. On the other hand, RFE is flawed by the fact that those features selected by RFE are not ranked by importance, albeit RFE can effectively eliminate the less important features and exclude redundant features.

The hybrid method was exemplified in a binary classification between digits “4” and “9” and between digits “6” and “8” from a multiple features dataset. The result showed that the hybrid mRMR +  support vector machine recursive feature elimination (SVMRFE) is better than both the sole support vector machine (SVM) and mRMR.

In view of the respective strength and deficiency mRMR and RFE, this study combined both these methods and used an SVM as the underlying classifier anticipating the mRMR to make an excellent complement to the SVMRFE.