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Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human-level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL-powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL-based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. [ABSTRACT FROM AUTHOR]

The arithmetic and logic unit (ALU) is a key element of complex circuits and an intrinsic part of the most widely recognized complex circuits in digital signal processing. Also, recent attention has been brought to reversible logic and quantum-dot cellular automata (QCA) because of their intrinsic capacity to decrease energy dissipation, which is a crucial need for low-power digital circuits. QCA will be the preferred technology for developing the subsequent generation of digital systems. These technologies played a substantial role in the design of the ALU for operations such as multiplication, subtraction, and division. In developing reversible logic and QCA technologies, the ALU is frequently studied as a central unit. Implementing an efficient ALU with low quantum cost and a small number of cells based on an efficient reversible block can solve all previous issues. Therefore, this research constructs a Feynman gate, a Fredkin gate, and full adder circuits using reversible logic and QCA technology. Using all of the specified circuits, a 20-operation ALU is constructed. The power consumption of the proposed ALU under various energy ranges demonstrated significant improvements over earlier designs. [ABSTRACT FROM AUTHOR]

Digital signal processing (DSP) is used in computer processing to conduct different signal-processing tasks. The DSPs are used in the series numbers representing a continuous variable in a domain such as time, area, or frequency. The multiply-accumulate (MAC) unit is crucial in various DSP applications, including convolution, discrete cosine transform (DCT), Fourier Transform, etc. Thus, all DSPs contain a critical MAC unit in signal processing. The MAC unit conducts multiplication and accumulation operations for continuous and complicated DSP application processes. On the other hand, in the MAC structure, the stability of the circuit and the occupied area pose some significant challenges. However, high-performance quantum technology can easily overcome all the previous shortcomings. Hence, this paper suggests an efficient MAC for DSP applications using a Vedic multiplier, half adder, and accumulator based on quantum technology. All the proposed structures have used a single-layer layout without rotated cells. The suggested architecture is designed and validated based on the QCADesigner 2.0.3 tool. The findings revealed that all the developed circuits have a simple architecture with fewer quantum cells, optimal area, and low latency. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles (UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The resultssuggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. Also, Python is the most commonly used language in papers, accounting for 90% of the time. Also, in 2021, it has the most papers published. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata (QCA) is the best-suggested nanotechnology for designing digital electronic circuits. It has a higher switching frequency, low-power expenditures, low area, high speed and higher scale integration. Recently, many types of research have been on the design of reversible logic gates. Nevertheless, a high demand exists for designing high-speed, high-performance and low-area QCA circuits. Reversible circuits have notably improved with developments in complementary metal–oxide–semiconductor (CMOS) and QCA technologies. In QCA systems, it is important to communicate with other circuits and reversible gates reliably. So, we have used efficient approaches for designing a 3 × 3 reversible circuit based on XOR gates. Also, the suggested circuits can be widely used in reversible and high-performance systems. The suggested architecture for the 3 × 3 reversible circuit in QCA is composed of 28 cells, occupying only 0.04 μ m2. Compared to the state-of-the-art, shorter time, smaller areas, more operational frequency and better performance are the essential benefits of the suggested reversible gate design. Full simulations have been conducted with the utilization of QCADesigner software. Additionally, the proposed 3 × 3 gate has been schematized using two XOR gates. [ABSTRACT FROM AUTHOR]

Because of the high speed, low-power consumption, low latency and possible use at the atomic and molecular levels, Quantum-dot Cellular Automata (QCA) technology is one of the future nanoscale technologies that can replace the present transistor-based technology. For the purpose of creating QCA circuits, reversible logic can be regarded as an appropriate candidate. In this research, a new structure for multi-operative reversible designs is suggested. The Saeid Nima Gate (SNG), proposed in this research study, is a brand-new, incredibly effective, multi-operative, universal reversible gate implemented in QCA nanotechnology employing both majority and inverter gates. Reversible gates, also known as reversible logic gates, are gates that have n inputs and n outputs, which is an equal number of inputs and outputs. The amount of energy lost during computations will be reduced if the numbers of inputs and outputs are identical. The proposed gate is modified and reorganized to optimize further, employing exact QCA cell interaction. All fundamental logic gates are implemented using it to demonstrate the universality of the proposed SNG. Reversible logic has advanced, and as a result, our suggested solution has a lower quantum cost than previously reported systems. The suggested design is simulated using the QCADesigner-E tools. [ABSTRACT FROM AUTHOR]

Summary: Recently, cloud computing has been recognized as an effective paradigm for offering an on‐demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non‐deterministic Polynomial hard (NP‐hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta‐heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively. [ABSTRACT FROM AUTHOR]

The very-large-scale-integration industry has been highly trying to attain miniaturization. This function causes several challenges regarding size, switching speed, power, and fault-tolerant at the nanoscale. Quantum-dot cellular automata (QCA) can contain a remarkable reduction in scale, fast switching rate, ultra-low energy consumption, and high fault-tolerant. Thus, fault-tolerant logic has attained the attention of several investigators in the QCA science domain. On the other hand, a binary-to-gray converter converts the input data to the gray number to facilitate error correction in digital communication. So, in the present investigation, we have demonstrated a structure of fault-tolerant binary-to-gray converter in QCA employing the majority gate, inverter gate, and cell redundancy on the wire. With the utilization of the QCADesigner 2.0.3 simulator, we have simulated and tested proposed circuits. In this paper, four factors, namely single-cell missing, displacement, misalignment, and extra single-cell, have been inspected by simulation software. The proposed fault-tolerant two-bit, three-bit, and four-bit binary-to-gray converter could attain 100% fault tolerance while a single missing defect existed in the layout. [ABSTRACT FROM AUTHOR]

Summary: Internet of Mobile Things (IoMT) have become very popular recently. The routing protocol for low power and lossy networks (RPL) is standardized for static topologies. However, mobility is the nature of IoT. Mobility serves as a promising candidate to harness hand‐off time issues, delay in data transmission, overhead, and low packet delivery rate (PDR) effectively. This study presents a comprehensive account of the mobility‐aware RPL‐based routing protocols to validate and compare the experimental results. Remarkably, classification methods are used in many articles. The aim is to introduce significant research efforts to improve RPL objective functions (OF) performance in hand‐off time, PDR, delay, overhead, and so forth. In this regard, a complete analysis of the existing routing protocols in IoMT has been presented to compare the results. The main focus of this study is on approaches that proposed new OFs for supporting mobility in RPL. Two main categories are considered to study RPL‐based routing protocol mechanisms: The mobile and static sink. The related studies on the mobile sink are divided into three groups: Single metric‐based OF, composite metric OF, and hybrid routing protocols. Also, the related works based on the static sink are categorized into four groups: Fuzzy logic‐based OF, trickle timer‐based OF, composite metrics‐based OF, and modification control messages‐based OF approach. This paper presents a detailed comparison of mechanisms in each category. It also highlights the pros, cons, open issues, and evaluated metrics of each paper. Besides, challenges of mobility in the RPL‐based routing protocol mechanism in IoMT for future studies. [ABSTRACT FROM AUTHOR]

In the distributed database systems, the relations needed by a query can be kept in several locations. This process significantly increases potential corresponding Query Execution Plans (QEP's) for a user query. Henceforth, in addition to the expense of local computing, the charge of transferring data between different cloud sites should also be considered. It does not sound logical to investigate all potential query plans in a high setting like this. The best query plan (regarding cost) must be generated for processing a given query. A new hybrid multi-objective genetic and bat algorithm, a Multi-Objective Genetic Algorithm with BAT (MOGABAT), is used in the present article to produce the best query plans. The functionality comparison is made on different join graph structures, among MOGABAT, Multi-Objective BAT (MOBAT), and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The obtained results have shown that the quality of generated query plans is enhanced for the join graph structures. Nevertheless, more execution time is needed. [ABSTRACT FROM AUTHOR]

Cloud computing enables businesses to decrease the total costs by outsourcing their required services. Therefore, it provides a new challenge of data protection regarding reliability, integrity, and confidentiality because of outsourcing. As a result, cloud security is becoming a key differentiator and competitive edge between cloud providers. Nowadays, the use of blockchain in cloud computing is one of the most common innovations that can solve cloud computing security problems. Blockchain is a decentralized data management technology to provide security, anonymity, and data integrity without any third-party organization. This work presents a comprehensive study of cloud computing security concerns and blockchain solutions to address these concerns. Applying different filters and searching international databases, 21 articles from reputable journals are found and reviewed. We examine cloud security in three categories: data integration, trust, and privacy. The results show that blockchain provides a successful platform in this regard. However, security issues are one of the most important challenges that still need further study. The article also provides a roadmap framework for future research and action. [ABSTRACT FROM AUTHOR]

Internet of things has become an essential principle of human life with the widespread acceptance of intelligent environments, where everyday objects can communicate through the internet. Mobility is the most critical factor in today's internet of things devices to apply in real-world applications. Also, proper routing protocol plays a vital role in communication and reduces devices' energy consumption. The clustering approach is one of the efficient routing techniques to improve energy consumption and enhance network life. Due to the NP-Hard nature of clustering, a krill herd optimization algorithm is proposed in this paper to select the cluster head nodes and intermediate nodes required for routing. The simulation results using NS-3 confirmed that the proposed technique performs better than particle swarm optimization and cuckoo search in terms of network lifetime. The proposed technique increases the total network lifetime by at least 11.1% compared to the current clustering methods. [ABSTRACT FROM AUTHOR]

Quantum-dot Cellular Automata (QCA) is novel prominent nanotechnology. It promises a substitution to Complementary Metal–Oxide–Semiconductor (CMOS) technology with a higher scale integration, smaller size, faster speed, higher switching frequency, and lower power consumption. It also causes digital circuits to be schematized with incredible velocity and density. The full adder, compressor, and multiplier circuits are the basic units in the QCA technology. Compressors are an important class of arithmetic circuits, and researchers can use quantum compressors in the structure of complex systems. In this paper, first, a novel three-input multi-layer full-adder in QCA technology is designed, and based on it, a new multi-layer 4:2 compressor is presented. The proposed QCA-based full-adder and compressor uses an XOR gate. The proposed design offers good performance regarding the delay, area size, and cell number comparing to the existing ones. Also, in this gate, the output signal is not enclosed, and we can use it easily. The accuracy of the suggested circuits has been assessed with the utilization of QCADesigner 2.0.3. The results show that the proposed 4:2 compressor architecture utilizes 75 cell and 1.25 clock phases, which are efficient than other designs. [ABSTRACT FROM AUTHOR]

Providing dynamic resources is based on the virtualization features of the cloud environment. Cloud computing as an emerging technology uses a high availability of services at any time, in any place and independent of the hardware. However, fault tolerance is one of the main problems and challenges in cloud computing. This subject has an important effect on cloud computing, but, as far as we know, there is not a comprehensive and systematic study in this field. Accordingly, in this paper, the existing methods and mechanisms are discussed in different groups, such as proactive and reactive, types of fault detection, etc. Various fault tolerance techniques are provided and discussed. The advantages and disadvantages of these techniques are shown on the basis of the technology that they have used. Generally, the contributions of this research provide a summary of the available challenges associated with fault tolerance, a description of several important fault tolerance methods in the cloud computing and the key regions for the betterment of fault tolerance techniques in the future works. The advantages and disadvantages of the selected articles in each category are also highlighted and their significant challenges are discussed to provide the research lines for further studies. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has the idea of receiving data and sending them for each object within the communication network. One of the main issues in this type of networks is handling a growing volume of data with various data sources and data types to satisfy the performance requirements of applications. In this regard, data management in the IoT plays an important role in its efficient operations and has become a major research topic. However, the data management has a crucial role in the IoT, there is not any comprehensive and systematic work to analyze its approaches. Thus, main propose of this paper is to systematically review and study the existing data management approaches in the IoT. The data management approaches are classified into three main classes, including SQL database, NoSQL database, and graph database. In addition, the detailed comparison of the important mechanisms in each category brings a recommendation for further works. [ABSTRACT FROM AUTHOR]

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability. [ABSTRACT FROM AUTHOR]

• Presenting the fault-tolerant majority gate using simple cells. • Verifying recommended gate by physical proof. • Presenting results in a recommended design for significant improvement in power consumption, area, and delay. • Developing an efficient one-bit QCA-based adder and 1:2 decoder based on a suggested gate. Quantum-dot cellular automata (QCA) has recently attracted significant notice thanks to their inherent ability to decrease energy dissipation and decreasing area, which is the primary need of digital circuits. However, the lack of resistance of QCA circuits under defects in previous works is a vital challenge affecting the stability of the circuit and output production. In addition, with the high defect rate in QCA, suggesting resistance and stable structures is critical. Furthermore, the 3-input majority gate is a fundamental component of QCA circuits; therefore, improving this essential gate would enable the development of fault-tolerant circuits. This paper recommends a 3-input majority gate which is 100% fault-tolerant against single-cell omission defects. Moreover, the fundamental gates are introduced based on the proposed gate. In addition, an adder and a 1:2 decoder are also designed. Using QCADesigner 2.0.3 and QCAPro software, simulations of structures and analysis of power consumption are performed. [Display omitted] [ABSTRACT FROM AUTHOR]

Quantum-dot Cellular Automata (QCA) presents a new model at Nano-scale for possible substitution of conventional Complementary Metal–Oxide–Semiconductor (CMOS) technology. On the other hand, an Arithmetic Logic Unit (ALU) is a digital electronic circuit which performs arithmetic and bitwise logical operations on integer binary numbers. Therefore, QCA-based ALU is an important part of the processor in order to develop a full capability processor. Although the QCA has become very important, there is not any comprehensive and systematic work on studying and analyzing its important techniques in the field of ALU design. This paper provides the comprehensive, systematic and detailed study and survey of the state-of-the-art techniques and mechanisms in the field of QCA-based ALU designing. There are three categories in which QCA plays a role: ALU, logic unit (LU) and arithmetic unit (AU). Each category presents the important studies. In addition, this paper reviews the major developments in these three categories and it plans the new challenges. Furthermore, it provides the identification of open issues and guidelines for future research. Also, a Systematic Literature Review (SLR) on QCA-based ALU, LU and AU is discussed in this paper. We identified 1,960 papers, which are reduced to 26 primary studies through our paper selection process. According to the obtained results from 2001 to 2015, the number of published articles are very high in 2014 and low in 2005 and 2009. This survey paper also provides a discussion of considered mechanisms in terms of ALU, LU and AU attribute as well as directions for future research. [ABSTRACT FROM AUTHOR]

Quantum-dot Cellular Automata (QCA) as a novel technology in the nanometer scale has been considered as one of the substitutes to CMOS technology. The QCA helps to create faster computers with lower power consumption. On the other hand, a shift register as one of the most important logical circuit in the digital systems consists of a line of latches. Also, the QCA-based designs have more advantages compared to the conventional CMOS designs. However, some deposition defects are possible to occur in the QCA-based designs, which have necessitated the fault-tolerant structures. Therefore, this paper aims to design an optimized 2-bit universal shift register based on QCA technology through the optimized multiplexer and D flip-flop. This paper studies the functionality and the fault tolerance of the proposed universal shift register in the presence of the QCA deposition faults. The structure of the 2-bit universal register is extendable to 4-bit, 8-bit and higher. The proposed design has better performance regarding fault tolerant, complexity and area consumption compared to the current designs based on the achieved results via QCADesigner. [ABSTRACT FROM AUTHOR]

Summary: Many sorts of services in the cloud environments must be composited based on the user's requests to meet the requirements. Thus, the distributed services are joined to the cloud services through service composition. Also, it is known as NP‐hard problems and many researchers significantly are focused on this problem in recent years. Therefore, many different nature‐inspired meta‐heuristic techniques are proposed for solving this problem. The nature‐inspired meta‐heuristic techniques have an important role in solving the service composition problem in the cloud environments, but there is not a wide‐ranging and detailed paper about reviewing and studying the important mechanisms in this field. Therefore, this study presents a comprehensive analysis of the nature‐inspired meta‐heuristic techniques for the service composition issue in the cloud computing. The review also contains a classification of the important techniques. These classifications include Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Optimization Algorithm, Bat Algorithm, greedy algorithm, and hybrid algorithm. An important aim of this paper is to highlight the emphasis on the optimization algorithms, and the benefits to tackle the challenges are encountered in the cloud service composition. Also, this paper presents the advantages and disadvantages of the nature‐inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments. Moreover, this paper aims to provide more efficient service composition algorithms in the future. Finally, the obtained results have shown that the discussed algorithms have an important effect in solving the cloud service composition problem, and this effect has been increased in recent years. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a network of physical items implanted with software, sensors, etc., to link and exchange data with other devices. These devices vary in complexity from common household items to sophisticated industrial instruments. It would be challenging to choose an appropriate IoT service based on the requirements of the vast pool of accessible services with similar capabilities, given the growth of IoT-based service providers in the market. A suitable selection may be made using quality-of-service (QoS) parameters that characterize a service. IoT has several benefits over traditional communication systems. Also, it is a component of a safe and smart city system known as the Industrial Internet of Things (IIoT) which is particularly useful in the industrial field. However, it suffers from various issues such as high costs, energy consumption, and long delays. The production scheduling problem is one of the main issues in IIoT, and it is an NP-hard problem regarding cost and energy efficiency. Therefore, a meta-heuristic algorithm based on the elephant herd optimization algorithm is proposed to minimize resource costs, conversion costs, and the cost of continuous development delays. By combining the clan updating factor, separating operator, and the proposed algorithm, we created an effective and efficient method to solve the issue of production scheduling. Many experiments are performed to determine the performance of industrial environments. The outcomes demonstrate that the suggested technique can optimize planning and achieve cost reduction, efficient energy consumption, and latency decrease. [ABSTRACT FROM AUTHOR]

Quantum-dot Cellular Automata (QCA) has been potentially considered as a supersede to Complementary Metal-Oxide-Semiconductor (CMOS) because of its inherent advantages. Many QCA-based logic circuits with smaller feature size, improved operating frequency, and lower power consumption than CMOS have been offered. This technology works based on electron relations inside quantum-dots. Due to the importance of designing an optimized decoder in any digital circuit, in this paper, we design, implement and simulate a new 2-to-4 decoder based on QCA with low delay, area, and complexity. The logic functionality of the 2-to-4 decoder is verified using the QCADesigner tool. The results have shown that the proposed QCA-based decoder has high performance in terms of a number of cells, covered area, and time delay. Due to the lower clock pulse frequency, the proposed 2-to-4 decoder is helpful for building QCA-based sequential digital circuits with high performance. [ABSTRACT FROM AUTHOR]

Cloud computing as a new internet-based computing model provides different resources as a service dynamically. Today, cloud computing is actually one of the main improvements in the computing procedure. However, by raising user interactions, the complexity of cloud processes is increasing with the advancement of technology. To evaluate the cloud computing challenges, the simulation experiments just satisfy the non-functional properties with a limited majority in forms of QoS factors. In addition, using simulation approaches have not been sufficient for developed complex cloud services that omit some critical test cases in the state space of the model. On the other hand, formal verification is an essential section in the complex information systems development that satisfies both functional and non-functional properties. Therefore, it is essential that the cloud systems use formal verification approaches for increasing the correctness of the system quality in all of the state space of the model. Despite the importance of the formal verification approaches in the cloud environments, to the best of our knowledge, there is not any systematic, comprehensive and detailed survey and review in the field of formal verification approaches and standards in the cloud computing. This paper provides a Systematic Literature Review (SLR) method to examine the current technical studies (published between 2011 and July 2017) in formal verification of the cloud computing. Also, this paper categorizes the formal verification approaches in three classic fields: specification and process algebra, model checking, and theorem proving. The verification approaches are compared with each other according to some technical properties such as specification methods, modeling approaches, verification tools and verification methods. The advantages and disadvantages of each selected study as well as some hints are discussed for solving their problems. The brief contributions of this paper are as follows: (1) providing a comprehensive literature review of the formal verification approaches in the cloud computing, (2) designing a technical taxonomy for the verification approaches in various modeling and specification methods, (3) presenting a technical analysis and comparison for the main challenges of the formal verification in the cloud and (4) highlighting the future open issues in the recent topics. [ABSTRACT FROM AUTHOR]

Quantum-Dot Cellular Automata (QCA) technology uses quantum dots instead of transistors and diodes for performing the logical operation. Also, in logical circuits, many operations, such as multiplication, subtraction, and division are done using the adders. Therefore, this paper presents an efficient QCA-based adder design based on three layers. In contrast to the previous designs, the outputs in the proposed design come out from another side of the circuit which causes more efficient circuit design. In the proposed design, the input signals are not surrounded by the other cells and can easily be accessed. The simulation results using the QCA Designer approve that the offered circuit acts well and can be used as a high-performance design in the QCA. Also, they show that the design causes very low complexity, small area, short latency and fewer cell numbers. [ABSTRACT FROM AUTHOR]

Quantum dot Cellular Automata (QCA) as a new architecture at the nanoscale is developed as a feasible alternative for the existing Complementary Metal–Oxide–Semiconductor (CMOS) designs. The decoder in the QCA is a very important circuit to address the QCA-based random access memory arrays. It plays a key role in many circuit designs, such as Controlled Logic Block (CLB), Field Programmable Gate Array (FPGA), and memory circuits’ designs. In this paper, a five-input majority gate is used in a modular design methodology to design a 2-to-4 decoder in QCA. The proposed 2-to-4 QCA-based decoder can be utilized to synthesize n-to-2 n decoders as well. The functional correctness of the proposed circuit is evaluated using QCADesigner tool. Also, QCAPro simulator as a popular power estimator tool in QCA evaluates the power dissipation. The obtained results have shown the significant success in terms of clock speed (%57), wire-crossing (%33), cell number (%27), area (%26) and power dissipation than the existing designs. [ABSTRACT FROM AUTHOR]

Some new technologies such as Quantum-dot Cellular Automata (QCA) is suggested to solve the physical limits of the Complementary Metal-Oxide Semiconductor (CMOS) technology. The QCA as one of the novel technologies at nanoscale has potential applications in future computers. This technology has some advantages such as minimal size, high speed, low latency, and low power consumption. As a result, it is used for creating all varieties of memory. Counter circuits as one of the important circuits in the digital systems are composed of some latches, which are connected to each other in series and actually they count input pulses in the circuit. On the other hand, the reversible computations are very important because of their ability in reducing energy in nanometer circuits. Improving the energy efficiency, increasing the speed of nanometer circuits, increasing the portability of system, making smaller components of the circuit in a nuclear size and reducing the power consumption are considered as the usage of reversible logic. Therefore, this paper aims to design a two-bit reversible counter that is optimized on the basis of QCA using an improved reversible gate. The proposed reversible structure of 2-bit counter can be increased to 3-bit, 4-bit and more. The advantages of the proposed design have been shown using QCADesigner in terms of the delay in comparison with previous circuits. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata (QCA) has come out as one of the potential computational structures for the emerging nanocomputing systems. It has a large capacity in the development of circuits with high space density and dissipation of low heat and allows faster computers to develop with lower power consumption. The QCA is a new appliance to realize nanolevel digital devices and study and analyze their various parameters. It is also a potential technology for low force and high-density memory plans. Large memory designs in QCA show unique features because of their architectural structure. In QCA-based architectures, memory must be maintained in motion, i.e., the memory state has to be continuously moved through a set of QCA cells. These architectures have different features, such as the number of bits stored in a loop, access type (serial or parallel) and cell arrangement for the memory bank. However, the decisive features of the QCA memory cell design are the number of cells, to put off the use of energy. Although the review and study of the QCA-based memories are very important, there is no complete and systematic literature review about the systematical analyses of the state of the mechanisms in this field. Therefore, there are five main types to provide systematic reviews about the QCA-based memories; including read only memory (ROM), register, flip-flop, content addressable memory (CAM) and random access memory (RAM). Also, it has provided the advantages and disadvantages of the reviewed mechanisms and their important challenges so that some interesting lines for any coming research are provided. [ABSTRACT FROM AUTHOR]

Task scheduling is one of the major issues to achieve high performance in distributed systems such as Grid, Peer-to-Peer and cloud environment. Generally, there are two phases in heuristics-based task scheduling algorithms in heterogeneous distributed computing systems (HeDCSs). These phases are task prioritization and processor assigning respectively. Heuristic-based task scheduling algorithms may use different policies to assign priority to subtasks which produce different makespans in a heterogeneous computing system. Thus, a suitable scheduling algorithm is one that can efficiently assign a priority to tasks in order to minimize makespan. Recently, memetic algorithms (MAs) have been used as evolutionary or population-based global search approaches with local search heuristic to optimize NP-complete problems. Recent studies on MAs have discovered their success on a wide variety of real-world problems. Since the task scheduling problem is an NP-complete, in this paper, a new task scheduling algorithm on cloud environment using multiple priority queues and a memetic algorithm (MPQMA) is proposed. The proposed method uses a genetic algorithm (GA) along with hill climbing to assign a priority to each subtask while using a heuristic-based earliest finish time (EFT) approach to search for a solution for the task-to-processor mapping. The basic idea of our approach is using the advantage of MA to increase the convergence speed of the solutions. We implemented the algorithm on Azure Cloud Service by C# language where the experimental results for the set of randomly generated graphs revealed that the proposed MPQMA algorithm outperformed the existing three task scheduling algorithms in terms of makespan with fast convergence to the optimized solution. [ABSTRACT FROM AUTHOR]

In the information systems, customer relationship management (CRM) is the overall process of building and maintaining profitable customer relationships by delivering superior customer value and satisfaction with the goal of improving the business relationships with customers. Also, it is the strongest and the most efficient approach to maintaining and creating the relationships with customers. However, to the best of our knowledge and despite its importance, there is not any comprehensive and systematic study about reviewing and analyzing its important techniques. Therefore, in this paper, a comprehensive study and survey on the state of the art mechanisms in the scope of the CRM are done. It follows this goal by looking at five categories in which CRM plays a significant role: E-CRM, knowledge management, data mining, data quality and, social CRM. In each category, a couple of studies are presented and determinants of CRM are described and discussed. The major development in these five categories is reviewed and the new challenges are outlined. Also, a systematic literature review (SLR) in each of these five categories is provided. Furthermore, insights into the identification of open issues and guidelines for future research are provided. [ABSTRACT FROM AUTHOR]

Trust in the cloud environment is not written into an agreement and is something earned. In any trust evaluation mechanism, opinion leaders are the entities influencing the behaviors or attitudes of others, this makes them to be trustworthy, valid among other characteristics. On the other hand, trolls are the entities posting incorrect and unreal comments; therefore, their effect must be removed. This paper evaluates the trust by considering the influence of opinion leaders on other entities and removing the troll entities’ effect in the cloud environment. Trust value is evaluated using five parameters; availability, reliability, data integrity, identity and capability. Also, we propose a method for opinion leaders and troll entity identification using three topological metrics, including input-degree, output-degree and reputation measures. The method being evaluated in various situation where shows the results of accuracy by removing the effect of troll entities and the advice of opinion leaders. [ABSTRACT FROM AUTHOR]

Quantum-dot Cellular Automata (QCA) is a unique and appealing technique for developing and implementing high-performance and low-power digital circuits at the nanometer scale. In this technology, fault-tolerant circuits guarantee reliability circuits through cells redundancy. Therefore, fault-tolerant and optimized architecture are very important in designing a wide range of QCA circuits. However, although there are some QCA structures for multiplexer designs in the literature, QCA characteristics can be used to design a more optimized multiplexer rather than blindly modeling traditional logic in QCA. As a result, we developed an alternate version of the proposed approaches that leverage fault-tolerant design methodologies. In this article, a new structure for a fault-tolerant 2:1 multiplexer in QCA technology is suggested. Cell redundancy on the wire, NOT gates and majority gates are used. Simulating the suggested designs and demonstrating their correctness was done with QCADesigner 2.0.3. When the additional cell or single missing cell fault occurs in the QCA architecture, the suggested fault-tolerant multiplexer can achieve 90% fault tolerance. [ABSTRACT FROM AUTHOR]

With the galloping progress of the Internet of Things (IoT) and related technologies in multiple facets of science, distribution environments, namely cloud, edge, fog, Internet of Drones (IoD), and Internet of Vehicles (IoV), carry special attention due to their providing a resilient infrastructure in which users can be sure of a secure connection among smart devices in the network. By considering particular parameters which overshadow the resiliency in distributed environments, we found several gaps in the investigated review papers that did not comprehensively touch on significantly related topics as we did. So, based on the resilient and dependable management approaches, we put forward a beneficial evaluation in this regard. As a novel taxonomy of distributed environments, we presented a well-organized classification of distributed systems. At the terminal stage, we selected 37 papers in the research process. We classified our categories into seven divisions and separately investigated each one their main ideas, advantages, challenges, and strategies, checking whether they involved security issues or not, simulation environments, datasets, and their environments to draw a cohesive taxonomy of reliable methods in terms of qualitative in distributed computing environments. This well-performed comparison enables us to evaluate all papers comprehensively and analyze their advantages and drawbacks. The SLR review indicated that security, latency, and fault tolerance are the most frequent parameters utilized in studied papers that show they play pivotal roles in the resiliency management of distributed environments. Most of the articles reviewed were published in 2020 and 2021. Besides, we proposed several future works based on existing deficiencies that can be considered for further studies. [ABSTRACT FROM AUTHOR]

Summary: Peer‐to‐peer (P2P) network is a distributed network in which nodes with similar capabilities exchange information with each other. Due to the nature of the P2P network distribution, numerous network message transformation is required to exchange the data between nodes over the network, which may increase access latency. Because of the high amount of the stored data in the P2P networks, the replication of data is very important. A large amount of data is handled using data replication to increase data access, reduce access latency, and increase data availability. This issue has an important role in the P2P networks, but there is no complete and systematic research in this field. Therefore, this paper aims to provide a comprehensive study of data replication mechanisms in the P2P networks. The 1027 papers have been identified and have been reduced to 213 main studies using the paper selection process. Moreover, in this paper, the major advances are reviewed in four main groups (structured, unstructured, super‐peer, and hybrid networks), and the new challenges are also highlighted. Moreover, the open issues and guidelines for future studies are presented. [ABSTRACT FROM AUTHOR]

The article explores how the smart connectivity between intelligent applications and smart devices must actively deliver performance guarantees in the Quality of Service (QoS). Topics include the development of the internet of things (IoT), energy efficiency and security are attractive issues for high power-saving smart connectivity; and decision-making methods to improve the process of complex making decisions and predictions in IoT ecosystems.

Summary: The goal of a query optimizer is to provide an optimal Query Execution Plan (QEP) by comparing alternative query plans. In a distributed database system over cloud environment, the relations required by a query plan may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent plan alternatives to find an optimal QEP. Although it is not computationally reasonable to explore exhaustively all possible plans in such large search space. Although query optimization mechanisms are important in the cloud environments, to the best of our knowledge, there exists no complete and systematic review on investigating these issues. Therefore, in this paper, four categories to study these mechanisms are considered which are search‐based, machine learning‐based, schema‐based, and security‐based mechanisms. Also, this paper represents the advantages and disadvantages of the selected query optimization techniques and investigates the metrics of their techniques. Finally, the important challenges of these techniques are reviewed to develop more efficient query optimization techniques in the future. [ABSTRACT FROM AUTHOR]

Today, cloud providers represent their individual services with several functional and non-functional properties in various environments. Discovering and selecting an appropriate atomic service from a pool of activated services are a main challenge in the multi-cloud service composition. Minimizing the number of cloud providers is a critical matter in the service composition problem, which effects on energy consumption, response time and total cost. This paper presents a hybrid formal verification approach to assess the service composition in multi-cloud environments though the decreasing number of cloud providers to gain final service composition with a high level of Quality of Service (QoS). The presented approach provides behavioral modeling to examine the procedure of user' requests, service selection, and composition in a multi-cloud environment. Also, the proposed approach permits analysis of the service composition using a Multi-Labeled Transition Systems (MLTS)-based model checking and Pi-Calculus-based process algebra methods for monitoring the functional specifications and non-functional properties as the QoS standards. In addition, the proposed approach satisfies the functional properties for the multi-cloud service composition. The experimental results proved the feasibility of the proposed approach with performance evaluations and some confirmation setups. [ABSTRACT FROM AUTHOR]

Unfortunately, the Fig. 12 was incorrectly published in the original article. Hereby, the correct figure is given below: [ABSTRACT FROM AUTHOR]

Recently, Complementary Metal-Oxide-Semiconductor (CMOS) technology is limited by many barriers such as short channel effects and high power dissipation. Therefore, many alternatives are proposed to solve these shortages. One of the potential alternatives to CMOS is Quantum-dot Cellular Automaton (QCA) where binary information is encoded by using the electron location in a square cell. However, three types of fault (cell misalignment, cell missing, and cell dislocation) may cause the failure of the QCA-based designs. On the other hand, D-latch is one of the basic circuits in designing larger components such as counters and memories. Therefore, in this paper, a new fault-tolerance QCA-based D-latch is proposed and its performance is evaluated. The obtained results using QCADesigner have revealed that the proposed design produce correct output and thereby can tolerate 86% single-cell omission defects. Also, it has acceptable performance regarding the two and three cellular defects. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a new information technology sector in which each device may receive and distribute data across a network. Industrial IoT (IIoT) and related areas, such as Industrial Wireless Networks (IWNs), big data, and cloud computing, have made significant strides recently. Using IIoT requires a reliable and effective data collection system, such as a spanning tree. Many previous spanning tree algorithms ignore failure and mobility. In such cases, the spanning tree is broken, making data delivery to the base station difficult. This study proposes an algorithm to construct an optimal spanning tree by combining an artificial bee colony, genetic operators, and density correlation degree to make suitable trees. The trees’ fitness is measured using hop count distances of the devices from the base station, residual energy of the devices, and their mobility probabilities in this technique. The simulation outcomes highlight the enhanced data collection reliability achieved by the suggested algorithm when compared to established methods like the Reliable Spanning Tree (RST) construction algorithm in IIoT and the Hop Count Distance (HCD) based construction algorithm. This proposed algorithm shows improved reliability across diverse node numbers, considering key parameters including reliability, energy consumption, displacement probability, and distance. [ABSTRACT FROM AUTHOR]

• Providing a comprehensive analysis of the most current innovations in medical data processing. • Proposing a systematic review of the available platforms for medical data processing. • Providing an overview of the most basic ML/DL-based methodologies in medical data processing. • Presenting a summary of distributed computing methods for health-care data analysis with classifying them based on practical characteristics of the techniques. • Evaluating each method that is associated with numerous aspects such as advantages, challenges, databases, implementations, privacy, and security matters. • Outlining the vital aspects where the preceding strategies may be improved soon. Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL algorithm was the Convolutional Neural Network (CNN), with a percentage of 19.4%. Hence, despite how technology changes, delivering appropriate therapy for patients is the primary aim of healthcare-associated departments. Therefore, further studies are recommended to develop more functional architectures based on DL and distributed environments and better evaluate the present healthcare data analysis models. [ABSTRACT FROM AUTHOR]

In a cloud environment, scheduling problem as an NP-complete problem can be solved using various metaheuristic algorithms. The metaheuristic algorithms are very popular for scheduling tasks because of their effectiveness. A bacterial foraging is a swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. This paper proposes a task scheduling algorithm based on bacterial foraging optimization to reduce the idle time of virtual machines whereas the load balancing and reducing of runtime have occurred. The Cloudsim toolkit has assessed the performance of the proposed method in comparison with some scheduling algorithms. According to the obtained results, the makespan and energy consumption were reduced by using the proposed algorithm. [ABSTRACT FROM AUTHOR]

With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error. • Proposing a framework to integrate edge and blockchain into lung cancer detection to ensure data protection and accuracy • Using the TL technique to help get the highest accuracy while also reducing your training time • Building a local lung cancer dataset composed of local patients • Proposing a new CapsNets method for lung cancer classification that employs the exceptional performance in local mode; • Helping doctors manage the growing number of lung cancer patients and carrying out speedier, automated medical diagnostics [ABSTRACT FROM AUTHOR]