Search

Due to the COVID-19 outbreak, industries have gained a thrust on contactless processing for computing technologies and industrial automation. Cloud of Things (CoT) is one of the emerging computing technologies for such applications. CoT combines the most emerging cloud computing and the Internet of Things. The development in industrial automation made them highly interdependent because the cloud computing works like a backbone in IoT technology. This supports the data storage, analytics, processing, commercial application development, deployment, and security compliances. Now amalgamation of cloud technologies with IoT is making utilities more useful, smart, service-oriented, and secure application for sustainable development of industrial processes. As the pandemic has increased access to computing utilities remotely, cyber-attacks have been increased exponentially. This paper reviews the CoT's contribution to industrial automation and the various security features provided by different tools and applications used for the circular economy. The in-depth analysis of security threats, availability of different features corresponding the security issues in traditional and non-traditional CoT platforms used in industrial automation have been analysed. The security issues and challenges faced by IIoT and AIoT in industrial automation have also been addressed. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of cloud computing resource utilization and avoid the problem of computing resource allocation and scheduling lagging behind load changes, the author proposes a cloud computing resource on-demand allocation and elastic scheduling method based on network load prediction. Firstly, the author takes the network load data of Wikimedia as the research object and proposes an adaptive two-stage multi network model load prediction method based on LSTM (i.e. ATSMNN-LSTM load prediction method). This method can classify the network load data into climbing and descending types based on the trend and characteristics of the input network load data, And adaptively schedule the input network load data to the LSTM load prediction model that matches its type for prediction based on the classification results. The author proposes a maximum cloud service revenue computing resource quantity search algorithm based on network load prediction (i.e. MaxCSPRNWP algorithm), which aims to improve cloud service revenue as the optimization objective. Under the premise of ensuring task service quality and system stability, the algorithm allocates cloud computing resources on demand and flexibly schedules them in advance based on the predicted network load results. The experimental results show that the ATSMNNLSTM load prediction method proposed by the author can obtain more accurate network load prediction results compared to other load prediction methods, and the MaxCSPR-NWP algorithm, which is based on network load prediction and is capable of effectively converting the network load prediction results into the required number of cloud servers, is the maximum cloud service revenue computing resource quantity search algorithm proposed by the author, not only does it achieve the early allocation and scheduling of cloud computing resources, thereby avoiding the impact of lagging behind in computing resource allocation and scheduling due to load changes on the quality of cloud computing task services and resource utilization efficiency, at the same time, it has also achieved on-demand allocation and flexible scheduling of cloud computing resources with the goal of improving cloud service revenue. [ABSTRACT FROM AUTHOR]

Considering the expansion of the Internet of Things (IoT) and the volume of data and user requests, Mobile Edge Computing (MEC) is considered a novel and efficient solution that puts decentralized servers at the network's edge. This has the effect of lowering bandwidth demand and transmission latency. Optimal edge server placement and allocation, as the first stage of MEC, can improve end-user service quality, edge computing system utility, and cost and energy consumption. The majority of previous edge server placement studies have employed only one objective or developed a fitness function by the weighted sum method for optimization. Usually, using a single optimization objective without considering other objectives cannot yield the desired results for a problem with a multi-objective design. On the other hand, assigning weights to objectives can lead to losing optimal points in non-convex problems and selecting improper weights. Therefore, in this paper, we propose a multi-objective solution for the positioning and allocation of edge servers for MEC services based on the NSGA-II algorithm. In this regard, we identify two workload variance and latency reduction objectives with extensive evaluations. The experimental evaluation of the results using real-world data reveals that solutions based on the NSGA-II yield superior convergence and diversity of Pareto front points compared to Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Biogeography Based Optimization (MOBBO), and Adaptive Weighted Sum Method (AWSM). Additionally, it effectively mitigates workload variance on servers and exhibits an average latency reduction of 8.79% in comparison to the adaptive weighted-sum approach, 9.19% in comparison to MOPSO, and 0.28% in comparison to MOBBO. [ABSTRACT FROM AUTHOR]

Cloud computing is an on-demand virtual-based technology to develop, configure, and modify applications online through the internet. It enables the users to handle various operations such as storage, back-up, and recovery of data, data analysis, delivery of software applications, implementation of new services and applications, hosting websites and blogs, and streaming of audio and video files. Thereby, it provides us many benefits although it is backlashed due to problems related to cloud security like data leakage, data loss, cyber attacks, etc. To address the security concerns, researchers have developed a variety of authentication mechanisms. This means that the authentication procedure used in the suggested method is multi-levelled. As a result, a better QKD method is offered to strengthen cloud security against different types of security risks. Key generation for enhanced QKD is based on the ABE public key cryptography approach. Here, an approach named CPABE is used in improved QKD. The Improved QKD scored the reduced KCA attack ratings of 0.3193, this is superior to CMMLA (0.7915), CPABE (0.8916), AES (0.5277), Blowfish (0.6144), and ECC (0.4287), accordingly. Finally, this multi-level authentication using an improved QKD approach is analysed under various measures and validates the enhancement over the state-of-the-art models [ABSTRACT FROM AUTHOR]

The integration of mobile learning applications has emerged as indispensable in higher education, yet the widespread adoption of mobile cloud computing technology within university settings remains limited in developing countries. This research seeks to bridge this gap by elucidating the primary factors influencing the decision to adopt mobile cloud computing in universities. To attain this objective, a comprehensive model is proposed, encompassing seven established technological factors along with novel considerations such as quality of service and social influence. The study involves data collection from 210 students and academic staff An-Najah National University in Palestine. The investigation underscores the paramount influence of quality of service, perceived usefulness, perceived ease of use, facilitating conditions, social influence, management support, and privacy on mobile cloud adoption. Furthermore, the research brings to light the persistent concerns surrounding security and privacy, serving as significant impediments to mobile cloud adoption in Palestinian Universities. The implications of these findings extend to higher educational institutions, mobile cloud service providers, and decision-makers. The identified factors serve as critical considerations for successful implementation of mobile cloud computing technology. Moreover, the research sheds light on the societal and scientific significance by addressing the motivation behind the title--exploring the utility of Mobile Computing Technology in enhancing educational processes. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a technology designed for the on-demand provisioning of computing and storage services, strategically positioned close to users. In the MEC environment, frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery, ultimately enhancing the quality of the user experience. However, due to the typical placement of edge devices and nodes at the network's periphery, these components may face various potential fault tolerance challenges, including network instability, device failures, and resource constraints. Considering the dynamic nature of MEC,making high-quality content caching decisions for real-time mobile applications, especially those sensitive to latency, by effectively utilizing mobility information, continues to be a significant challenge. In response to this challenge, this paper introduces FT-MAACC, a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms. This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content. Furthermore, it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system's reliability, availability, and persistence. Empirical results unequivocally demonstrate that FT-MAACC outperforms its peer methods in cache hit rates and transmission latency. [ABSTRACT FROM AUTHOR]

AbstractEmotion recognition using EEG is a difficult study because the signals’ unstable behavior, which is brought on by the brain’s complex neuronal activity, makes it difficult to extract the underlying patterns inside it. Therefore, to analyse the signal more efficiently, in this article, a hybrid model based on IEMD-KW-Ens (Improved Empirical Mode Decomposition-Kruskal Wallis-Ensemble classifiers) technique is used. Here IEMD based technique is proposed to interpret EEG signals by adding an improved sifting stopping criterion with median filter to get the optimal decomposed EEG signals for further processing. A mixture of time, frequency and non-linear distinct features are extracted for constructing the feature vector. Afterward, we conducted feature selection using KW test to remove the insignificant ones from the feature set. Later the classification of emotions in three-dimensional model is performed in two categories i.e. machine learning based RUSBoosted trees and deep learning based convolutional neural network (CNN) for DEAP and DREAMER datasets and the outcomes are evaluated for valence, arousal, and dominance classes. The findings demonstrate that the hybrid model can successfully classify emotions in multichannel EEG signals. The decomposition approach is also instructive for improving the model’s utility in emotional computing. [ABSTRACT FROM AUTHOR]

Cloud Computing offers on-demand infrastructure, platform and software services over the Internet on a pay-as-you-use model. Many e-commerce businesses and social networking sites are moving towards the cloud. Security breaches have also started to grow along with their popularity. The malicious processes can target virtualization-based cloud infrastructure and harm virtual resources, thereby becoming a threat to the cloud. Several Intrusion Detection Systems (IDSs) have been developed to detect attacks based on predefined behavior patterns. However, malicious processes can hide their behavior in the presence of security mechanisms on a Virtual Machine (VM). With the increasing frequency of such attacks, IDSs must be improved using Machine Learning (ML) and Deep Learning (DL) techniques. This study proposes a novel intrusion detection framework that can detect known and unknown attacks by system call sequence analysis. The framework analyzes the system call sequences of VMs with a hybrid model of Long Short Term Memory (LSTM) and system call frequency-based anomaly detection techniques. The proposed framework is evaluated using the Australian Defence Force Academy-Linux Dataset (ADFA-LD) dataset. Compared to the existing frameworks, the highest accuracy of 97.2% and a false positive rate of 2.4% are achieved for our proposed framework. [ABSTRACT FROM AUTHOR]

Cloud is a model of computing providing on-demand availability of computer system resources, mainly data storage and computing power, without the user's active management. This provides sharing and reduces user computing and storage costs. With the development of cloud scale and intensification, cloud security is becoming a vital issue in the cloud computing field. Here, a newly optimised hybridisation algorithm, namely, Shuffled Shepherd Fire Hawk Optimization (SSFHO) algorithm is proposed for key generation to initiate privacy protection in the cloud. The different entities involved in this approach are Data Owner (DO), Data User (DU), blockchain, and Cloud Service Provider (CSP). Here, the access control process includes seven categories, such as initialisation, registration, generation of key, data access control, authorisation, authorisation revocation and data protection. Also, registration is done in three phases, like registration of cloud, registration of user and resource publishing. Moreover, SSFHO is the integration among Shuffled Shepherd Optimization Algorithm (SSOA) and Fire Hawk Optimization (FHO) algorithm. The performance of the model is done with various performance metrics like, authorisation time, privacy rate, and memory. These revealed high values of privacy rate of 0.93, low values of authorisation time and memory of 0.559 and 0.843 GHz. [ABSTRACT FROM AUTHOR]

Traumatic subarachnoid hemorrhage (tSAH) is a common consequence of head trauma. Treatment of patients with tSAH commonly involves serial computed tomography (CT) scans to assess for expansile hemorrhage. However, growing evidence suggests that these patients rarely deteriorate or require neurosurgical intervention. We assessed the utility of repeat CT scans in adult patients with isolated tSAH and an intact initial neurological examination. Patients presenting to Mass General Brigham hospitals with tSAH between 2000 and 2021 were eligible for inclusion in this retrospective cohort study. Patients were excluded if subarachnoid hemorrhage was nontraumatic, they experienced another form of intracerebral hemorrhage, or they had a documented Glasgow Coma Scale score of ≤12 and/or poor presenting neurological examination. Univariate and multivariate regression models were used for statistical analysis. Overall, 405 patients were included (191 male). The most common mechanism of trauma was fall from standing (58%). The mean number of total CT scans for all patients was 2.3, with 329 patients (80%) receiving ≥2 scans. In 309 patients, no significant neurological symptoms were present. No patients developed acute neurological deterioration or required neurosurgical intervention related to their bleed, although 5 patients had mild hemorrhagic expansion on follow-up imaging. In this study, repeat imaging rarely demonstrated meaningful hemorrhagic expansion in this cohort of neurologically intact patients with isolated tSAH. In these patients with mild traumatic brain injury, excessive CT scans are perhaps unlikely to affect patient management and may present unnecessary burden to patients and hospital systems. [ABSTRACT FROM AUTHOR]

Cloud computing (CC) refers to a current computing method that provides the virtualization of computing services as a utility to Cloud service users. Problems based on ineffective task mapping to cloud resource frequently happen in a cloud atmosphere. Task scheduling (TS), thus, means effective scheduling of rational allocation and computational actions of computing resource in certain limitations in the IaaS cloud network. Job scheduling was to allocate tasks to the most appropriate sources to reach more than one goal. Thus, choosing a suitable work scheduling technique for rising CC resource efficiency, whereas maintaining high quality of service (QoS) assurances, becomes a significant problem that remains to attract interest of researchers. Metaheuristic techniques shown remarkable efficacy in supplying near-optimal scheduling solutions for a complicated large-sized issues. Recently, a rising number of independent scholar has examined the QoS rendered by TS approaches. Therefore, this study develops an Energy Efficient Task Scheduling Strategy using Modified Coot Optimization Algorithm (EETSS-MCOA) for CC environment. The EETSS-MCOA method carries out the derivation of features and MCOA is applied to schedule tasks. In addition, the MCOA algorithm is derived by the combination of adaptive β hill climbing concept with the COA for enhanced task scheduling. The conventional COA is stimulated by the swarming characteristics of birds known as coots. The COA followed two distinct stages of bird movements on water surface. The experimental results of the EETSS-MCOA model are validated on CloudSim tool. The solutions attained by the EETSS-MCOA model are found to be better than the existing algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing alludes to the on-demand availability of personal computer framework resources, primarily information storage and processing power, without the customer's direct personal involvement. Cloud computing has developed dramatically among many organizations due to its benefits such as cost savings, resource pooling, broad network access, and ease of management; nonetheless, security has been a major concern. Researchers have proposed several cryptographic methods to offer cloud data security; however, their execution times are linear and longer. A Security Key 4 Optimization Algorithm (SK4OA) with a non-linear run time is proposed in this paper. The secret key of SK4OA determines the run time rather than the size of the data as such is able to transmit large volumes of data with minimal bandwidth and able to resist security attacks like brute force since its execution timings are unpredictable. A data set from Kaggle was used to determine the algorithm's mean and standard deviation after thirty (30) times of execution. Data sizes of 3KB, 5KB, 8KB, 12KB, and 16 KB were used in this study. There was an empirical analysis done against RC4, Salsa20, and Chacha20 based on encryption time, decryption time, throughput and memory utilization. The analysis showed that SK4OA generated lowest mean non-linear run time of 5.545±2.785 when 16KB of data was executed. Additionally, SK4OA's standard deviation was greater, indicating that the observed data varied far from the mean. However, RC4, Salsa20, and Chacha20 showed smaller standard deviations making them more clustered around the mean resulting in predictable run times. [ABSTRACT FROM AUTHOR]

Advancing medical technology revolutionizes our ability to diagnose various disease processes. Conventional Single-Energy Computed Tomography (SECT) has multiple inherent limitations for providing definite diagnoses in certain clinical contexts. Dual-Energy Computed Tomography (DECT) has been in use since 2006 and has constantly evolved providing various applications to assist radiologists in reaching certain diagnoses SECT is rather unable to identify. DECT may also complement the role of SECT by supporting radiologists to confidently make diagnoses in certain clinically challenging scenarios. In this review article, we briefly describe the principles of X-ray attenuation. We detail principles for DECT and describe multiple systems associated with this technology. We describe various DECT techniques and algorithms including virtual monoenergetic imaging (VMI), virtual non-contrast (VNC) imaging, Iodine quantification techniques including Iodine overlay map (IOM), and two- and three-material decomposition algorithms that can be utilized to demonstrate a multitude of pathologies. Lastly, we provide our readers commentary on examples pertaining to the practical implementation of DECT's diverse techniques in the Gastrointestinal, Genitourinary, Biliary, Musculoskeletal, and Neuroradiology systems. [ABSTRACT FROM AUTHOR]

Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy -hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption. [ABSTRACT FROM AUTHOR]

The new and rising paradigm of cloud computing offers customers various possibilities of task computation based on their desires and choices. Customers receive services from cloud computing systems as a utility. Customers are enthusiastic about low-cost service availability and task completion times that are kept to be minimum. To achieve client fulfilment, the service provider must schedule the jobs to the right resources if the cloud server gets many user requests. The rapid growth in data volume necessitates petabytes processing of data each day. Unstructured, semi-structured, and structured data are all described in terms of their rapid growth and availability. In order to make correct and timely decisions, it must be processed appropriately. In this research, we present BWUJS (Black Widow Updated Jellyfish Search), a multi-objective hybrid optimization-based task scheduling algorithm. This work considers task generation from the Bigdata perspective. The clustering of tasks is performed via the Map Reduce framework with an Improved K-means clustering model. After task clustering, the task priority estimation is performed. Finally, the scheduling is performed via BWJSU based on certain constraints like priority, makespan, completion time, resource utilization, and degree of imbalance. [ABSTRACT FROM AUTHOR]

Cloud computing is an on-demand model of computing that utilizes virtualization expertise to offer cloud resources such as CPU, memory, storage, and network in virtual machines. As a result, most big data analytics in many modern enterprise applications are run from the cloud. Since resources in these private clouds are limited, getting the most out of resource applications and providing guaranteed user service by efficiently scheduling tasks and resources is the ultimate goal. However, existing big data processing system schedulers need to consider application performance and resource utilization when performing allocations. Therefore, it is difficult to design workflows for low turnaround time and high resource consumption in extensive data systems. In this paper, we propose a resource management system for efficient job scheduling, called RMS, which dynamically schedules big data jobs in Kubernetes cluster nodes for Spark applications and autonomously adjusts scheduling policies in heterogeneous node clusters to enhance application execution and resource consumption. The RMS mechanism will ensure adequate guidance and resources available in its planning objectives and satisfactory resource utilization. The experimental analysis of different RMS and performance preferences using different methods depends on the predicted completion time and the benchmark statistical result of different significant data performance indicators traces. The results show that RMS decreases the cost and scheduling overhead and improves job execution performance. [ABSTRACT FROM AUTHOR]

In the modern landscape, exploiting multiple cloud services stands out as a potent strategy for enhancing both data privacy and accessibility. Our research delves into the cost-effective shift towards multicloud storage computing, with a key emphasis on seamlessly integrating on-demand resource provisioning and robust access control policies. By fragmenting datasets across diverse cloud platforms, we bolster privacy while adeptly managing complexities through cutting-edge Multi-Cloud technologies. Our approach dynamically allocates resources and enforces stringent access controls, thus optimizing security and resource utilization. This framework adeptly tackles the challenges associated with managing diverse cloud environments, offering organizations a comprehensive solution for secure and efficient data management in the multi-cloud era. [ABSTRACT FROM AUTHOR]

Fog computing is today considered a promising candidate to improve the user experience in dynamic on-demand computing services. However, its ubiquitous application would require support for this service in wireless multi-hop mesh systems, where the use of conventional IP-based solutions is challenging. As a complementary solution, in this paper, we consider a Named-Data Networking (NDN) approach to enable fog computing services in autonomous dynamic mesh formations. In particular, we jointly implement two critical mechanisms required to extend the NDN-based fog computing architecture to wireless mesh systems. These are (i) dynamic face management systems and (ii) a learning-based route discovery strategy. The former makes it possible to solve NDN issues related to an inability to operate over a broadcast medium. Also, it improves the data-link layer reliability by enabling unicast communications between mesh nodes. The learning-based forwarding strategy, on the other hand, efficiently reduces the amount of overhead needed to find routes in the dynamically changing mesh networks. Our numerical results show that, for static wireless meshes, our proposal makes it possible to fully benefit from the computing resources sporadically available up to several hops away from the consumer. Additionally, we investigate the impacts of various traffic types and NDN caching capabilities, revealing that the latter result in much better system performance while the popularity of the compute service contributes to additional performance gains. [ABSTRACT FROM AUTHOR]

Summary: The expeditious deployment of Cloud applications and services on wide‐ranging Cloud Data Centres (CDC) gives rise to the utilization of many resources. Moreover, by the increase in resource utilization, virtualization also greatly impacts achieving desired performance. The major challenges in virtualization are detecting over‐utilized or under‐utilized hosts at the right time and the proper scaling of Virtual Machines (VM) on the accurate host. Auto‐scaling in Cloud Computing allows the service providers to scale up or down the resources automatically and provides on‐demand computing power and storage capacities. Effective utilization and autonomous scaling of resources eventually reduce the load, energy consumption, and operating costs. In this paper, an efficient auto‐scaling approach for predicting host load through VM migration has been proposed. The ensemble method using different time‐series forecasting models has been proposed to forecast the approaching workload on the host. Based on this predicted load, different algorithms have been devised to detect over‐utilized and under‐utilized hosts and VMs can be migrated. The designed approach has been validated by experimentation on a real‐time Google cluster dataset. The proposed technique significantly improves average CPU utilization and reduces over‐utilization and under‐utilization. It also minimizes response time, service level agreement violations, and the slighter number of migrations and scaling overhead. [ABSTRACT FROM AUTHOR]

The echo state property (ESP) is a key concept for understanding the working principle of the most widely used reservoir computing model, the echo state network (ESN). The ESP is achieved most of the operation time under general conditions, yet the property is lost when a combination of driving input signals and intrinsic reservoir dynamics causes unfavorable conditions for forgetting the initial transient state. A widely used treatment, setting the spectral radius of the weight matrix below the unity, is not sufficient as it may not properly account for the nature of driving inputs. Here, we characterize how noisy driving inputs affect the dynamical properties of an ESN and the empirical evaluation of the ESP. The standard ESN with a hyperbolic tangent activation function is tested using the MNIST handwritten digit datasets at different additive white Gaussian noise levels. The correlations among the neurons, input mapping, and memory capacity of the reservoir nonlinearly decrease with the noise level. These trends agree with the deterioration of the MNIST classification accuracy against noise. In addition, the ESP index for noisy driving input is developed as a tool to help easily assess ESPs in practical applications. Bifurcation analysis explicates how the noise destroys an asymptotical convergence in an ESN and confirms that the proposed index successfully captures the ESP against noise. These results pave the way for developing noise-robust reservoir computing systems, which may promote the validity and utility of reservoir computing for real-world machine learning applications. [ABSTRACT FROM AUTHOR]

Cloud computing is a dynamic paradigm which aims to provide on-demand computing resources to a user over the Internet. Most of the existing cloud providers use fixed pricing strategy. However, a few cloud service providers have recently priced their services using auction-style pricing. While cloud consumers take care of Quality of Service (QoS), their preferences for QoS differ from those of cloud providers. Consumers that use the cloud, for example, typically expect high availability, therefore, they need to bring availability, reliability, and scalability together to achieve true high availability. In contrast, high availability entails more expenditures in hardware and/or software resources, resulting in higher expenses for cloud providers. In this paper, we propose a novel auction strategy to include QoS parameters bundle into the price, allocating available cloud services between users, and establishing an auction strategy to enable revenue approximation. In order to accomplish this, we formulate the problem of allocating a cloud service coupled with bundle of QoS parameters to the winners as a multi-attribute auction problem. To address this problem, we present a novel multi-attribute auction pricing model with numerous consumers and a single cloud provider. We also introduce the new QoS Parameter Bundle Pricing Scheme that establishes the relation between a cloud service's bundle of QoS parameters and the estimated price of cloud resources, which can produce more revenue than a fixed pricing strategy. Last, we put forward three algorithms MAAPM (i.e., to solve a multi-attribute auction problem), FMAA (i.e., represents existing state-of-the-art auction mechanisms in the literature) and Fixed-Payment (i.e., represents the currently used fixed payment strategy). Extensive simulations have revealed that MAAPM outperforms FMAA and Fixed Payment in all the metrics except the execution time, also MAAPM results at growing revenues for cloud providers and decreasing expenditure for users. [ABSTRACT FROM AUTHOR]

A recent quantum simulation of observables of the kicked Ising model on 127 qubits implemented circuits that exceed the capabilities of exact classical simulation. We show that several approximate classical methods, based on sparse Pauli dynamics and tensor network algorithms, can simulate these observables orders of magnitude faster than the quantum experiment and can also be systematically converged beyond the experimental accuracy. Our most accurate technique combines a mixed Schrödinger and Heisenberg tensor network representation with the Bethe free entropy relation of belief propagation to compute expectation values with an effective wave function-operator sandwich bond dimension >16,000,000, achieving an absolute accuracy, without extrapolation, in the observables of <0.01, which is converged for many practical purposes. We thereby identify inaccuracies in the experimental extrapolations and suggest how future experiments can be implemented to increase the classical hardness. [ABSTRACT FROM AUTHOR]

This article discusses the utility of preoperative computed tomography (CT) scans in predicting long-term outcomes after minimally invasive surgical aortic valve replacement (mSAVR). The study analyzed 203 patients who underwent mSAVR and found that certain anatomical variables assessed by preoperative CT were correlated with survival and major cardiac events. The authors conclude that preoperative CT should be included in the preparatory pathway for patients with severe aortic valve disease. [Extracted from the article]

Cloud computing has emerged as a promising Internet technology enabling cloud users to access computing resources on-demand via the Internet in a "pay-as-you-use" fashion. Many cloud service providers (CSPs) have arisen over the last few years with similar features at varying prices and performance levels. With the rising number of CSPs, cloud customers face the challenge of choosing the right CSP that satisfies their Quality of Service requirements. However, it poses a major challenge: "How to evaluate a suitable CSP with high accuracy and consistency." To address this challenge, this paper proposes a hybrid Multi-Criteria Decision Making methodology to aid the decision-maker to evaluate different cloud services. Specifically, a novel approach is introduced based on a comprehensive assessment that combines subjective and objective aspects. The comprehensive assessment results are utilized to rank the eligible CSPs based on their prioritized list. The simulation results are validated through a real-life case study which further justifies that the proposed approach provides better satisfaction degree from the user's perspective and is efficient in terms of accuracy and reliability. Finally, we perform a sensitivity analysis to show the robustness and stability of our approach. [ABSTRACT FROM AUTHOR]

Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage, processing power, and other computer system resources. It is also referred to as a system that will let the consumers utilize computational resources like databases, servers, storage, and intelligence over the Internet. In a cloud network, load balancing is the process of dividing network traffic among a cluster of available servers to increase efficiency. It is also known as a server pool or server farm. When a single node is overwhelmed, balancing the workload is needed to manage unpredictable workflows. The load balancer sends the load to another free node in this case. We focus on the Balancing of workflows with the proposed approach, and we present a novel method to balance the load that manages the dynamic scheduling process. One of the preexisting load balancing techniques is considered, however it is somewhat modified to fit the scenario at hand. Depending on the experimentation's findings, it is concluded that this suggested approach improves load balancing consistency, response time, and throughput by 6%. [ABSTRACT FROM AUTHOR]

On-demand provisioning of computing resources using the network over the Cloud consumes a tremendous amount of energy. The energy consumed does not scale out efficiently with the computational use of the resources. A large portion accounts for the power consumption to drive the hardware and cooling facilities for the hardware resources. Nowadays, energy and power consumption have become the most critical concerns in data centers. This work highlights a novel technique to achieve energy efficiency through server consolidation to address the abovementioned situations by effectively scheduling Virtual Machines (VMs) onto the servers and rescheduling active VMs upon deletion of existing VMs through cold migration. An advanced technology, i.e. Wake on LAN (WoL), is employed through power wake for dynamic and wake up of the servers on private Cloud. Compared to existing models, the proposed approach's maximum estimated energy and power saved are experimentally verified to be 54% and 50%, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing epitomizes an important invention in the field of Information Technology, which presents users with a way of providing on-demand access to a pool of shared computing resources. A major challenge faced by the cloud system is to assign the exact quantity of resources to the users based on the demand, while meeting the Service Level Agreement (SLA). Elasticity is a major aspect that provides the cloud with the capability of adding and removing resources "on the fly" for handling load variations. However, elastic scaling requires suspension of the application tasks forcibly, while performing resource distribution; thereby Quality of Service (QoS) gets affected. In this research, an elastic scaling approach based on optimization is developed which aims at attaining an improved user experience. Here, load prediction is performed based on various factors, like bandwidth, CPU, and memory. Later, horizontal as well as vertical scaling is performed based on the predicted load using the devised leader Harris honey badger algorithm. The devised optimization enabled elastic scaling is evaluated for its effectiveness based on metrics, such as predicted load error, cost, and resource utilization, and is found to have attained values of 0.0193, 153.581, and 0.3217. [ABSTRACT FROM AUTHOR]

Introduction: Cone-beam computed tomography (CBCT) is widely used in the preoperative qualitative and quantitative assessment of dental implant sites, offering dimensional accuracy, spatial resolution, gray density, and contrast comparable to those of classical CT scan, yet with disputable ability to determine bone mass density. Materials and methods: A systematic review of the literature was performed using the PubMed and SCOPUS databases, with terms referring to low bone mass and cone-beam computed tomography (CBCT). Results: Sixteen studies were included in the review. The results show different perspectives, but the evidence favors the use of CBCT, combined with dual-energy X-ray absorptiometry bone density scan (DXA) evaluation, for the assessment of the osteoporosis status of the aging population and, more specifically, in postmenopausal women. Radiographic density (RD) values of the dens and the left part of the first cervical vertebra show the strongest correlation coefficients and the highest sensitivity, specificity, and accuracy for predicting osteoporosis (OP) in the lumbar vertebrae and the femoral neck. Conclusions: Our review suggests the potential of CBCT as a screening tool for patients with low bone mass using different radiomorphometric indices. Linear measurements of the inferior mandibular cortex were lower in osteoporotic individuals, indicating the perspective of CBCT also as a diagnostic tool for this disease. [ABSTRACT FROM AUTHOR]

The "Channel Computing Resources from the East to the West" project is a major strategic project to build a balance of computing resources and on-demand scheduling in China's territorial space. Since the full launch of the Chinese "Channel Computing Resources from the East to the West" project construction, a series of problems have been exposed on many different fields, such as the supply side, demand side, energy side, technology side, and mechanism side. It is urgent to re-analyze and define the internal logic of the "Channel Computing Resources from the East to the West" project from a theoretical level. The internal logic of "Channel Computing Resources from the East to the West", that is, the infrastructure of Chinese computing, was analyzed from different perspectives such as economic form, technological trend, technological competition, and cost-benefit. It also proposed to build a new type of infrastructure for the Chinese national computing network, called computing NET, and build a national computing NET construction path from the aspects of policy layout, network direct connection, technical support, and mechanism innovation. [ABSTRACT FROM AUTHOR]

Cloud computing provides the on-demand service of the user with the use of distributed physical machines, in which security has become a challenging factor while performing various tasks. Several methods were developed for the cloud computing workflow scheduling based on optimal resource allocation; still, the security consideration and efficient allocation of the workflow are challenging. Hence, this research introduces a hybrid optimization algorithm based on multi-objective workflow scheduling in the cloud computing environment. The Regressive Whale Water Tasmanian Devil Optimization (RWWTDO) is proposed for the optimal workflow scheduling based on the multi-objective fitness function with nine various factors, like Predicted energy, Quality of service (QoS), Resource utilization, Actual task running time, Bandwidth utilization, Memory capacity, Make span equivalent of the total cost, Task priority, and Trust. Besides, secure data transmission is employed using the triple data encryption standard (3DES) to acquire enhanced security for workflow scheduling. The method's performance is evaluated using the resource utilization, predicted energy, task scheduling cost, and task scheduling time and acquired the values of 1.00000, 0.16587, 0.00041, and 0.00314, respectively. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services have become a promising technology. However, the limited energy supply of UAVs, which constrains their service duration, has emerged as an obstacle in UAV-enabled networks. In this context, a novel task offloading framework is proposed in UAV-enabled mobile edge computing (MEC) networks. Specifically, heterogeneous UAVs with different communication and computing capabilities are considered and the energy consumption of UAVs is minimized via jointly optimizing user association and UAV deployment. The optimal transport theory is introduced to analyze the user association sub-problem, and the UAV deployment for each sub-region is determined by a dragonfly algorithm (DA). Simulation results show that the energy consumption performance is significantly improved by the proposed algorithm. [ABSTRACT FROM AUTHOR]

Attribute-based encryption (ABE) is a technique used to encrypt data, it has the flexibility of access control, high security, and resistance to collusion attacks, and especially it is used in cloud security protection. However, a large number of bilinear mappings are used in ABE, and the calculation of bilinear pairing is time-consuming. So there is the problem of low efficiency. On the other hand, the decryption key is not uniquely associated with personal identification information, if the decryption key is maliciously sold, ABE is unable to achieve accountability for the user. In practical applications, shared message requires hierarchical sharing in most cases, in this paper, we present a message security hierarchy ABE scheme for this scenario. Firstly, attributes were grouped and weighted according to the importance of attributes, and then an access structure based on a threshold tree was constructed according to attribute weight. This method saved the computing time for decryption while ensuring security and on-demand access to information for users. In addition, with the help of computing power in the cloud, two-step decryption was used to complete the access, which relieved the computing and storage burden on the client side. Finally, we simulated and tested the scheme based on CP-ABE, and selected different security levels to test its performance. The security proof and the experimental simulation result show that the proposed scheme has high efficiency and good performance, and the solution implements hierarchical access to the shared message. [ABSTRACT FROM AUTHOR]

Cloud computing benchmarks the dream of rendering computing as a utility, providing high agility and reachability from an existing set of technologies. It facilitates a wider dimension to architect and manage remote resources. Cloud technology with exponential growth is drilling towards issues that tend to lower its explored possibilities. As cloud systems by virtue deal with various virtualized resources, scheduling is opted as an important metric for measuring and leveraging performance. But scheduling efficiency is deteriorated by various parameters that pave scope for our research and projects immense need for improvising the overall makespan of the system. The proposed work aims at projecting a greater drift in the first phase by witnessing a sequence of phases like pre-processing the user tasks for improved accuracy, classifying the tasks with respect to resource demand and execution time using the improved density based clustering method (IDCM). The second phase deals with enhanced coot optimization algorithm for task scheduling (ECOA-TS) that proceeds and proves its novelty by adopting Cauchy mutation overcoming the convergence backdrop for generating an optimal mapping between clustered user tasks and VMs. The overall performance of the proposed work overrides by reduced makespan against existing state-of-the-art optimization algorithms like particle swam optimization (PSO), grey wolf optimization (GWO) and whale optimization algorithm (WOA) by 27.41%, 19.8%, and 15.33% respectively. [ABSTRACT FROM AUTHOR]

The on-demand provision of computing resources as services over the internet is known as cloud computing. The distributed denial of service (DDoS) attack is a major security risk that affects cloud services. Because of the computational complexity that must be handled, detecting DDoS attacks is a very difficult operation for cloud computing. The back propagation neural network (BPNN) method is frequently employed for DDoS attack detection due to its great flexibility and straightforward construction. But it has drawbacks such as slow convergence, inconsistencies, and instability during training. In this work, the proposed optimized BPNN uses bacterial colony optimization (BCO) for optimizing the connection weights and biases to enhance the performance of BPNN. The optimized BPNN is developed to identify DDoS attacks in the cloud environment. The performance of the BCO-BPNN detection scheme is assessed using four DDoS attack datasets such as NSL-KDD, ISCXIDS2012, CIC-IDS2017, and UNSW-NB15. Its respective detection accuracy with the NSL-KDD, ISCXIDS2012, CIC-IDS2017, and UNSW-NB15 datasets is 0.9892%, 0.9883%, 0.9341%, and 0.9987%. The results of the studies demonstrate that the suggested BCO-BPNN performs better than existing BPNN variants, conventional BPNN, and support vector machine (SVM) methods. [ABSTRACT FROM AUTHOR]

With the convergence of the Internet of Things, 5G, and artificial intelligence, limited network bandwidth and bursts of incoming service requests seem to be the most important factors affecting user experience. Therefore, caching technology was introduced. In this paper, a caching placement optimization strategy based on comprehensive utility (CPOSCU) in edge computing is proposed. Firstly, the strategy involves quantifying the placement factors of data blocks, which include the popularity of data blocks, the remaining validity ratio of data blocks, and the substitution rate of servers. By analyzing the characteristics of cache objects and servers, these placement factors are modeled to determine the cache value of data blocks. Then, the optimization problem for cache placement is quantified comprehensively based on the cache value of data blocks, data block retrieval costs, data block placement costs, and replacement costs. Finally, to break out of the partial optimal solution for cache placement, a penalty strategy is introduced, and an improved tabu search algorithm is used to find the best edge server placement for cached objects. Experimental results demonstrate that the proposed caching strategy enhances the cache service rate, reduces user request latency and system overhead, and enhances the user experience. [ABSTRACT FROM AUTHOR]

This study compared the predictive utility of Marshall, Rotterdam, Stockholm, Helsinki, and NeuroImaging Radiological Interpretation System (NIRIS) scorings based on early non-contrast brain computed tomography (CT) scans in patients with traumatic brain injury (TBI). The area under a receiver operating characteristic curve (AUROC) was used to determine the predictive utility of scoring systems. Subgroup analyses were performed among patients with head AIS scores > 1. A total of 996 patients were included, of whom 786 (78.9%) were males. In-hospital mortality, ICU admission, neurosurgical intervention, and prolonged total hospital length of stay (THLOS) were recorded for 27 (2.7%), 207 (20.8%), 82 (8.2%), and 205 (20.6%) patients, respectively. For predicting in-hospital mortality, all scoring systems had AUROC point estimates above 0.9 and 0.75 among all included patients and patients with head AIS > 1, respectively, without any significant differences. The Marshall and NIRIS scoring systems had higher AUROCs for predicting ICU admission and neurosurgery than the other scoring systems. For predicting THLOS ≥ seven days, although the NIRIS and Marshall scoring systems seemed to have higher AUROC point estimates when all patients were analyzed, five scoring systems performed roughly the same in the head AIS > 1 subgroup. [ABSTRACT FROM AUTHOR]

Privacy and security EI-enabled CPS revolutionizes the development of new computing paradigms to facilitate the flow of data and the delivery of content. EI-enabled CPS collects physiological data from patients through technologies such as wireless sensor networks, radio frequency identification and wearable devices. Compared with the traditional supply chain financial model, EI-enabled CPS brings changes to the supply chain in four aspects: credit evaluation, risk protection, credit cycle and punishment mechanisms. Cloud computing has become an indispensable auxiliary technology for EI-enabled CPS due to its powerful computing ability and high concurrency. [Extracted from the article]

Cloud computing is the on-demand availability of computing resources, especially storage and computing power. The Cloud Data Center (CDC) consumes a huge amount of electrical energy due to inefficient resource utilization and scheduling algorithms. This leads to issues like high energy consumption along with high maintenance costs and carbon emissions. Virtual machine consolidation (VMC) is one of the energy-saving approaches used in data centers. The VMC problem in CDCs with energy constraints is a strict NP-hard problem. In this paper, a dynamic VMC algorithm is proposed to address the issues discussed above. The proposed algorithm categorizes the servers into three categories such as underloaded, overloaded, and normally loaded based on the CPU load. It migrates a few virtual machines (VMs) from overloaded machines to the normally loaded machine(s) for load balancing and migrates all the VMs of underloaded machines to normally loaded servers to switch-off idle servers. Further, modified water wave optimization (MWWO) approach is used to find a suitable migration plan which will reduce the load on overloaded servers and increase the overall resource utilization. It has been known and proven that an overloaded host consumes more energy over some time than the normally utilized host. The experimental evaluation is done on CloudSim Plus which is a cloud simulation tool. We have used two real-time workloads for experiments to reveal the efficiency of the proposed MWWO-VMC algorithm. The simulation experimental results are compared with various heuristic and metaheuristic dynamic VM consolidation algorithms. In comparison to previous similar works, the MWWO-VMC algorithm is successful in reducing energy consumption and VM migrations, increasing resource utilization and maximizing the dormant servers. [ABSTRACT FROM AUTHOR]

Cloud computing is the on‐demand provision of computing resources over the Internet, such as cloud storage, computing power, network, and so on. Cloud computing has several advantages, including high speed, cost reduction, data security, and scalability. The main challenge in cloud environment is to balance the workloads and network traffic among the available resources to achieve maximum performance. Several methods have been proposed in the literature for effective load balancing, including heuristic, meta‐heuristic, and hybrid algorithms. The performance of these techniques has been improved by combining machine learning based Artificial Intelligence (AI) techniques and meta‐heuristic algorithms. Most of the existing load balancing techniques are not aware of the content type of user tasks. However, from the literature, the content type of the tasks can be very effective to design a balanced workload distribution system in the cloud. In this work, a novel AI‐assisted hybrid approach called Content‐aware Machine Learning based Load Balancing Scheduler (CA‐MLBS) is proposed. The scheduling system CA‐MLBS combines machine learning and meta‐heuristic algorithms to perform classification based on file type. To achieve this, a Support Vector Machine (SVM) based classifier is used to classify user tasks into different content types such as video, audio, image, and text. A metaheuristic algorithm based on Particle Swarm Optimization (PSO) is used to map users' tasks in the cloud. The proposed approach was implemented and evaluated using a renowned Cloudsim simulation kit and compared with Ant Colony Optimization File Type Format (ACOFTF) and Data Files Type Formatting (DFTF) heuristics. The results of the proposed study show that the proposed CA‐MLBS technique achieved improvements of up to 29%, 29%, and 44% in terms of makespan, response time, and throughput, respectively. [ABSTRACT FROM AUTHOR]

In the context of the 6th generation (6G) network, UAV-aided networking is emerging as a promising solution to burst traffic offloading and on-demand service coverage. Given the dynamic networking environment, a UAV migration scheme needs to be as flexible, efficient, and adaptive as possible. To address this issue, a UAV migration-based decision-making scheme is put forward to make a tradeoff between migration costs and the satisfaction of service requirements. In the scheme, a UAV adaptive migration strategy (UAMS) is adopted to improve the migration efficiency. Meanwhile, the corresponding signaling interaction process is designed according to different UAV migration scenarios. The experimental results show that the proposed approach makes a significant improvement in the performance of the whole system and takes obvious advantage over traditional solutions. [ABSTRACT FROM AUTHOR]