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Fog computing is a revolutionary technology that, by expanding the cloud computing paradigm to the network edge, brings a significant achievement in the resource-constrained IoT applications in intelligent environments. However, security matters still challenge the extensive deployment of fog computing infrastructure. Ciphertext policy attribute-based encryption prepares a solution for data sharing and security preservation issues in fog-enhanced intelligent environments. Nevertheless, the lack of an effective mechanism to moderate the execution time of CP-ABE schemes due to the diversity of attributes used in secret key and access structure, as well as ensuring data security, practically restricts the deployment of such schemes. In this regard, a collaborative semantic model, including an outsourced CP-ABE scheme with the attribute revocation ability, together with an impressive AES algorithm relying on an ensemble learning system, was proposed in this study. The ensemble learning model uses multiple classifiers, including the GMDH, SVM, and KNN, to specify attributes corresponding to CP-ABE. The Dragonfly algorithm with a semantic leveling method generates outstanding and practical feature subsets. The experimental results on five smart building datasets indicate that the recommended model performs more accurately than existing methods. Also, the encryption, decryption, and attribute revocation execution time are significantly modified with the average time of 1.95, 2.11, and 14.64 ms, respectively, compared to existing works and conducted the scheme's security. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach that uses a spectral clustering method to cluster patients with e-health IoT devices based on their similarity and distance and connect each cluster to an SDN edge node for efficient caching. The proposed MFO-Edge Caching algorithm is considered for selecting the near-optimal data options for caching based on considered criteria and improving QoS. Experimental results demonstrate that the proposed approach outperforms other methods in terms of performance, achieving decrease in average time between data retrieval delays and the cache hit rate of 76%. Emergency and on-demand requests are prioritized for caching response packets, while periodic requests have a lower cache hit ratio of 35%. The approach shows improvement in performance compared to other methods, highlighting the effectiveness of SDN-Edge caching and clustering for optimizing e-health network resources. [ABSTRACT FROM AUTHOR]

The infectious disease Covid-19 has been causing severe social, economic, and human suffering across the globe since 2019. The countries have utilized different strategies in the last few years to combat Covid-19 based on their capabilities, technological infrastructure, and investments. A massive epidemic like this cannot be controlled without an intelligent and automatic health care system. The first reaction to the disease outbreak was lockdown, and researchers focused more on developing methods to diagnose the disease and recognize its behavior. However, as the new lifestyle becomes more normalized, research has shifted to utilizing computer-aided methods to monitor, track, detect, and treat individuals and provide services to citizens. Thus, the Internet of things, based on fog-cloud computing, using artificial intelligence approaches such as machine learning, and deep learning are practical concepts. This article aims to survey computer-based approaches to combat Covid-19 based on prevention, detection, and service provision. Technically and statistically, this article analyzes current methods, categorizes them, presents a technical taxonomy, and explores future and open issues. [ABSTRACT FROM AUTHOR]

Due to the proliferation of mobile, IoT, and Interlayer fog computing devices, the number of requests to the cloud network has increased. Today, cloud networks, new virtualization technologies, and virtual machines are essential for proper management and recommending the appropriate virtual machine to execute requests on the network. In addition, it can affect the quality of network service, and this is done by using the appropriate mapping between virtual machines to recommend web services in the cloud network better. This paper proposes an optimal method for assigning recommendation systems to the user. Finding users' demands is essential, and the proposed method processes virtual machine input and the appropriate physical machine in cloud clusters in a parallel manner. As discussed in the results section, energy, response time, task execution in data centers, in different workflows, computational complexity, and spatial complexity are considered. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) technology improvement has led to concepts such as smart retail and supply chain management systems. The main problem with IoT‐based smart retail and supply chain management systems is the need for a high‐speed network for responding quickly to many transactions within the network. If the network is not able to respond to all transactions quickly, it will not be possible to handle all requests in time. Also, the network must be stable and reliable to ensure that all requests are received and answered. And finally, they must have a very low power consumption metric to reduce costs. This paper suggests a Multi‐Sink Routing Protocol for Low‐Power and Lossy Networks (RPL) topology for network node placement to achieve network performance improvement. The proposed topology is modeled in a simulated environment using an Instant Contiki operating system by Cooja simulator and is evaluated regarding the different performance metrics including packet delivery ratio (PDR), power consumption, node participation, and control traffic overhead. Also, the network behavior is analyzed by the Wireshark tool. The results show achieving better performance and improving the level of confidence. [ABSTRACT FROM AUTHOR]

In this paper we propose an auction-based offloading market in the HMC (Heterogeneous Mobile Cloud) environment where mobile devices, Cloudlets, and public cloud form a shared resource pool. In this market, users can register their service offers or submit their offloading requests to the auction manager. Then, the auction manger makes buyer_seller threads as the source–destination of task offloading process. In any made buyer_seller thread a trade-off between conflicting utility functions of buyer and seller has to be guaranteed. Furthermore, auction manager should impose various policies in order to maintain market stability, increase market competition, and reduce monopolies. A stable market is a market where the chance of uncommitted sellers to be considered in the made buyer_seller thread is kept low. Also, almost equal opportunities are made for the users in a competitive and non-monopolized market. To address these issues, first of all, a new task offloading approach based on Hill Climbing Algorithm is designed to assure a trade-off between buyers' and sellers' utilities. Then, a new factor in name reputation is defined to make competitive market while maintain market stability. Furthermore, a Cloudlet selection algorithm is developed to find the Cloudlet as a new offloading destination in a case of reneging on the offloading commitment, leading to market stability improvement. Finally, awarding bonus to new participants is another strategy imposed by auction manager to make competitive and non-monopolized market. Extensive amounts of simulation were carried out to evaluate the performance of our proposed offloading market against (Zhou et al. in J Syst Softw 152:151–164, 2019). The results show that our designed market outperforms (Zhou et al. 2019). [ABSTRACT FROM AUTHOR]

IoT networks can provide many benefits and opportunities, although their implementation poses challenges. Cloud-only storage of IoT data would be very costly and time-consuming. In this paper, a new scheme is proposed for caching IoT content on the edge with SDN-based processing capability. The proposed scheme considers a global SDN controller, which coordinates cache decisions across the entire IoT network. The Moth-Flame Optimization-Cluster Head Selection (MFO-CHS) algorithm is used to cluster devices where the selected cluster heads send the IoT data to the edge nodes for caching. In addition, by utilizing edge caching capabilities and using MFO to select and cache the appropriate contents on edge nodes, the proposed Moth-Flame Optimization-Edge Caching (MFO-EC) algorithm can provide data with lower latency on upcoming requests. Caching can also help ensure reliability and availability since intermittent connections and power limitations affect IoT devices. Caching decisions regarding IoT characteristics are not made intelligently in the default caching scheme for maximizing device longevity and managing the possibility that content producers may become unreachable. This scheme considers several metrics, and the proposed moth-flame optimization (MFO) algorithms, MFO-CHS, and MFO-EC algorithms, which are nature-inspired paradigms for the edge caching problem called "MFO-SDN-EC", Moth-Flame Optimization-Software-defined Networking -Edge Caching, are used to select the best options regarding considered criteria and improve the QoS in the SDN based IoT environment. Based on simulations, our proposed caching method can reduce energy consumption by 45%, decrease the average response time by 49%, and also increase cache-hit rates. Furthermore, our results demonstrate our algorithm's superiority over several current approaches in terms of assessment measures. [ABSTRACT FROM AUTHOR]

Introduction: Social Anxiety Disorder (SAD) as a negative emotion can be associated with specific patterns of brain behavioral system functions. The purpose of this research is to evaluate the effectiveness of Hofmann-based CBT (Cognitive Behavioral Therapy) in reducing anxiety symptoms and improving brain behavioral system functions in patients suffering from social anxiety disorder. Materials and Methods: This research is a semi-experimental study with a pretest/posttest plan. 20 SAD patients were randomly selected and divided equally into two groups (experimental and control). The experimental group underwent 12 sessions of Hofmann-based CBT, whereas the control group was placed in the waiting list. Anxiety disorder Interview Schedule for DSM-IV (ADIS-IV), Social phobia questionnaire, and Gary-Wilson's personality questionnaire were used for evaluating results at pretests and posttests. Data were analyzed with descriptive indices and multivariate analysis of covariance (MANCOVA) by SPSS software. Results: The experimental group showed significant improvement in total scores of social phobia, including fear (P<0.001), avoidance (P<0.001), and physiological discomfort (P<0.001) Also, brain behavioral systems (including behavioral activation, behavioral inhibition, and fight/flight) showed significant improvement (P<0.001) in behavioral inhibition in the experimental group. Conclusion: Cognitive behavioral therapy based on Hofmann model is effective in patients suffering from social anxiety disorder, especially in improving anxiety symptoms and behavioral inhibition functions. [ABSTRACT FROM AUTHOR]

In quantum computers, all the heavy computations are done through quantum processors. ALU is one of the most significant parts of every processor responsible for performing logical operations and primary binary operations. All of the arithmetic computations in ALU utilize adder/subtractor to produce the expected result. Fault-tolerant circuit designs have found increasing application in quantum computers regarding the noise sensitivity of qubits and the faulty environment in which they operate. Additionally, reversible quantum circuits can prevent heat circulation and information lost throughout the high-rate binary calculations. The primary subject is the development of digital reversible fault-tolerant quantum circuits with higher performance and more efficiency regarding the number of inputs and outputs employed by quantum gates, depth, and delay. Full adders and subtractors are highly significant in different parts of quantum digital circuits and are used widely in multipliers, dividers, ALUs, and floating-point operations. In this paper, applying the parity-preserving method and the properties of Boolean algebra two novel reversible fault-tolerant designs are proposed that can detect every single or the odd number of errors. Also, both proposed designs are capable to perform addition and subtraction operations by changing the control signal(ctr). Our offered designs can reduce energy loss in the form of emitted heat in circuits, reduce the cost of building quantum circuits, reduce computation time, and detect errors in case of happen. Furthermore, comprehensive simulations for each possible combination of inputs and corresponding outputs are done to prove the full functionality and accurate performance of the proposed designs. [ABSTRACT FROM AUTHOR]

Due to increasing volume of stored data in organizations, data owners upload their data to cloud servers. Sensitive data has to be encrypted prior to uploading to the cloud server because of security requirements. Therefore, capable of searching over the encrypted cloud data is a very important issue. One of the most important challenges that must be addressed is to search whole documents to find related documents to a query. In this paper, a scheme is suggested to eliminate the need of searching for whole documents. A secure multi-keyword ranked search method over the encrypted cloud data is introduced that provides dynamic operation on the document collection. Also, access control mechanism is considered in the proposed scheme. The vector space model is employed to B+tree index and query creating. Due to generation of a B+tree-based index structure, search time is getting sub-linear. First, the cloud server is assumed to be "Honest-but-Curious". Second, it is considered that the data are stored on an untrusted cloud server. The Merkle B-tree (MB-tree) is utilized to verify search results in the presence of access control rules when the cloud server is untrusted. Experiments are conducted using the real world dataset to confirm efficiency and precision of the offered scheme. [ABSTRACT FROM AUTHOR]

Software defined networks and the Internet of Things (IoT) are two major and emerging developments in networking that have much in common and their survival depends on each other. Software Defined Networking (SDN) is one of the 5G enabling innovations that can help design complex, manageable, cost-effective and adaptable networks. On the other hand, Edge Computing (EC) will do automatic analytical computing on data from sensors, network switches, or other devices instead of waiting for data to be returned to a centralized data store. The IoT also requires a decentralized Internet, as the demand for real-time data analysis is growing and centralized processing systems are being overrun. So, it is necessary to provide a solution to encompass the advantages of EC, IoT and SDN simultaneously, as an integrated platform. In this research, after presenting a brief explanation about the key points of the SDN, IoT, EC and related concepts, and recent articles in this regard are investigated. This systematic literature review (SLR) study focuses on different frameworks and platforms that meet the mentioned requirements by considering the advantages of integrating EC, SDN, and IoT technologies. This platform provides centralized management of heterogeneous devices and architectures and supports the problem of resource limitations in IoT. The short output of this literature contains the following: (1) presenting a summary of review studies and research articles in this area which have been published from 2013 till 2021; (2) providing some key technical questions; (3) presenting some different technical classifications to categorizing the characteristics and features of EC and SDN in IoT; (4) discussing the key challenges; (5) Presenting future directions for research and open issues. [ABSTRACT FROM AUTHOR]

Typing is a time-consuming task and predictive text is proposed as a solution. Recently, Generative Pre-trained Transformers (GPT) have employed autoregressive deep learning to tackle text prediction. However, they face costly retraining, especially for low-resource languages (such as Persian) or domains, and lack controllability. Text augmentation with prompting methods and fine-tuning GPT models on templated data using conditional elements (labels or keywords) aims to address these problems, enhancing controllable text generation in low-resource scenarios. These methods involve discovering and mapping conditional elements to training texts, which is unsuitable for typing assistants. Meanwhile, they do hold inappropriate pre-defined elements for wide use. This paper introduces Conditional GPT-2-Persian (CGPT-2-Persian), which utilizes the initial word of each sentence as the associated conditional element to address practical challenges, extracting labels, and handling in-domain unseen data posed by the mentioned methods. This method outperforms Persian text generation methods in terms of BLEU and ROUGE scores, achieving 87.39% and 14.29%, respectively, after fine-tuning for ten epochs. This study can be efficient for other low-resource languages or domains. [ABSTRACT FROM AUTHOR]

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively. [ABSTRACT FROM AUTHOR]

• A comprehensive systematic review on healthcare recommender systems is provided. • The main ideas, methods, tools, advantages, disadvantages, evaluation parameters, and evaluation methods are discussed and summarized. • A scientific taxonomy of healthcare recommender systems is presented. • A detailed list of challenges, open issues, and future research directions is outlined. The maintenance of health through prevention, diagnosis, and treatment of illnesses, injuries, and other mental and physical impairments in people is called healthcare that is performed by professionals in health fields. All activities performed in providing primary, secondary, and tertiary care, as well as in public health, are components of healthcare systems that are required to analyze massive amounts of patients' data to gain insights and aid in disease prediction. The healthcare recommender system (HRS) is a growing and robust platform for healthcare services. The purpose of this paper is to identify , taxonomically classify , and compare current HRS researches in a systematic way. This study presents a systematic literature review (SLR) method for HRSs regarding 41 papers published between 2010 and 2021; the selected articles fall into five classes: collaborative-based, content-based, knowledge-based, context-based, and hybrid. Seventeen essential factors are identified to examine the selected HRSs, and each of the papers were studied to find advantages, disadvantages, evaluation types, and tools. In addition, we address a discussion of challenges, future directions, and open issues. This paper demonstrates that HRSs are still in their early stages of maturity, but they are developing. It identifies the need for a scalable, confidential, and reliable framework to aid in the improvement of both patients' and health professionals' trust in HRSs. This research reveals a lack of standardization of medical codes across various healthcare systems. In addition, this review identifies the need for novel patterns to address cold start problems. [ABSTRACT FROM AUTHOR]