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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]

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]

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]

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]

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]

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]