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With the evolution of new technologies, the production of digital data is constantly growing. It is thus necessary to develop data management strategies in order to handle the large-scale datasets. The data gathered through different sources, such as sensor networks, social media, business transactions, etc. is inherently uncertain due to noise, missing values, inconsistencies and other problems that impact the quality of big data analytics. One of the key challenges in this context is to detect and repair dirty data, i.e. data cleansing, and various techniques have been presented to solve this issue. However, to the best of our knowledge, there has not been any comprehensive review of data cleansing techniques for big data analytics. As such, a comprehensive and systematic study on the state-of-the-art mechanisms within the scope of the big data cleansing is done in this survey. Therefore, five categories to review these mechanisms are considered, which are machine learning-based, sample-based, expert-based, rule-based, and framework-based mechanisms. A number of articles are reviewed in each category. Furthermore, this paper denotes the advantages and disadvantages of the chosen data cleansing techniques and discusses the related parameters, comparing them in terms of scalability, efficiency, accuracy, and usability. Finally, some suggestions for further work are provided to improve the big data cleansing mechanisms in the future.

Medical information systems such as Internet of Medical Things (IoMT) are gained special attention over recent years. X-ray and MRI images are important sources of information to be examined for a particular type of anomalies. Reports based on the images and laboratory examination results could be mined with machine learning techniques as well. Thyroid disease diagnosis is an important capability of medical information systems. The main objective of this study is to improve the diagnosis accuracy of thyroid diseases from semantic reports and examination results using artificial neural network (ANN) in IoMT systems. In order to improve generalization and avoid over-fitting of ANN during the training process, a set of multiple multilayer perceptron (MMLP) neural network with the back-propagation error ability is proposed in this paper. Moreover, an adaptive learning rate algorithm is used to deal with the slow convergence and the local minima problem of the back-propagation error algorithm. The proposed MMLP significantly increased the overall accuracy of thyroid disease classification. With MMLP with a set of 6 networks, an improvement of 0.7% accuracy is achieved compared to a single network. In addition, comparing to the standard back-propagation, by using an adaptive learning rate algorithm in the proposed MMLP, an improvement of 4.6% accuracy and the final accuracy of 99% have been obtained in IoMT systems. The proposed MMLP is compared to recent researches reported for thyroid disease diagnosis, and its superiority is shown.

The growing number of services that can meet the users’ functional requirements, inspired many researchers to provide some approaches to rank and select the best possible services regarding their quality of service (QoS) and users’ preferences. Considering various criteria which should be considered in the service selection process, multi-criteria decision making (MCDM) techniques have been vastly applied to help a decision-maker in determining the weight of each QoS factor and ranking the services provided by different service providers. This paper provides an extensive investigation of the state of the art MCDM-based service selection schemes proposed in the literature. It provides the required background knowledge and puts forward a taxonomy of the investigated service selection schemes regarding their applied MCDM methods. Also, it describes how the MCDM methods are adapted by the studied schemes, which datasets and QoS criteria are employed by each system, and which factors and environments are utilized to evaluate the service selection schemes. Finally, the concluding remarks are provided, and directions for future studies are highlighted.

In this paper, we analyse the security of two mutual authentication protocols that have been recently proposed by Gao et al. (IEEE Access, 7:8376–8384, 2019), a hash-based protocol and a Rabin public key based protocol. Our security analysis clearly shows important security pitfalls in these schemes. More precisely, in each protocol, we introduce efficient approaches to desynchronize the tag and the reader/server. The proposed attacks are almost deterministic and the complexity of each attack is a session for the hash-based and three sessions for Rabin public key based protocol. In addition, in the case of the hash-based protocol, we extend the proposed desynchronization attack to a traceability attack in which the adversary can trace any given tag based on the proposed attack with probability of almost one. In the case of Rabin public key based protocol, we extend the proposed desynchronization attack to a tag impersonation attack with the success probability of one. Besides, we propose an enhanced version of the Rabin public key based protocol to provide a secure authentication between the tag and the reader. We evaluate the security of the proposed protocol formally using the Scyther tool and also in Real-or-Random model.

Security attacks are a nightmare to many computing environments such as fog computing, and these attacks should be addressed. Fog computing environments are vulnerable to various kinds of DDoS attacks, which can keep fog resources busy. Typically in such attacks, fog environments often have less available resources, which can negatively impact the scheduling of Internet of Things (IoT) submitted workflows. However, most of the existing scheduling schemes do not consider DDoS attacks’ effect in the scheduling process, increasing the deadline missed workflows and offloaded tasks on the cloud. For dealing with these issues, a hybrid optimization algorithm is proposed, comprising both Particle Swarm Optimization (PSO) and Salp Swarm algorithm (SSA), to solve the workflow scheduling problem in multiple fog computing environments. Two discrete-time Markov chain models are proposed for each fog computing environment to address DDoS attacks’ effects on them. Our first Markov model computes the average available network bandwidth for each fog. The second Markov model finds the average number of available virtual machines (VMs) for each fog; the models address different levels of DDoS attacks. Extensive simulations show that by predicting the effects of DDoS attacks on fog environments, the proposed approach can effectively mitigate the number of offloaded tasks on cloud data centers and can reduce the number of the deadline missed workflows.

This paper presents a systematic review of the literature and the classification of fuzzy logic application in an infectious disease. Although the emergence of infectious diseases and their subsequent spread have a significant impact on global health and economics, a comprehensive literature evaluation of this topic has yet to be carried out. Thus, the current study encompasses the first systematic, identifiable and comprehensive academic literature evaluation and classification of the fuzzy logic methods in infectious diseases. 40 papers on this topic, which have been published from 2005 to 2019 and related to the human infectious diseases were evaluated and analyzed. The findings of this evaluation clearly show that the fuzzy logic methods are vastly used for diagnosis of diseases such as dengue fever, hepatitis and tuberculosis. The key fuzzy logic methods used for the infectious disease are the fuzzy inference system; the rule-based fuzzy logic, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy cognitive map. Furthermore, the accuracy, sensitivity, specificity and the Receiver Operating Characteristic (ROC) curve were universally applied for a performance evaluation of the fuzzy logic techniques. This thesis will also address the various needs between the different industries, practitioners and researchers to encourage more research regarding the more overlooked areas, and it will conclude with several suggestions for the future infectious disease researches.

Recent advances in sensor networks and the Internet of Things (IoT) technologies have led to the gathering of an enormous scale of data. The exploration of such huge quantities of data needs more efficient methods with high analysis accuracy. Artificial Intelligence (AI) techniques such as machine learning and evolutionary algorithms able to provide more precise, faster, and scalable outcomes in big data analytics. Despite this interest, as far as we are aware there is not any complete survey of various artificial intelligence techniques for big data analytics. The present survey aims to study the research done on big data analytics using artificial intelligence techniques. The authors select related research papers using the Systematic Literature Review (SLR) method. Four groups are considered to investigate these mechanisms which are machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory. A number of articles are investigated within each category. Furthermore, this survey denotes the strengths and weaknesses of the selected AI-driven big data analytics techniques and discusses the related parameters, comparing them in terms of scalability, efficiency, precision, and privacy. Furthermore, a number of important areas are provided to enhance the big data analytics mechanisms in the future.

Fog computing is an interesting technology aimed at providing various processing and storage resources at the IoT networks’ edge. Energy consumption is one of the essential factors that can directly impact the maintenance cost and CO2 emissions of fog environments. Energy consumption can be mitigated by effective scheduling approaches, in which tasks are going to be mapped on the best possible resources regarding some conflicting objectives. To deal with these issues, we introduce an opposition-based hybrid discrete optimization algorithm, called DMFO-DE. For this purpose, first, a discrete and Opposition-Based Learning (OBL) version of the Moth–Flame Optimization (MFO) algorithm is provided, and it then is combined with the Differential Evolution (DE) algorithm to improve the convergence speed and prevent local optima problem. The DMFO-DE is then employed for scientific workflow scheduling in fog computing environments using the Dynamic Voltage and Frequency Scaling (DVFS) method. The Heterogeneous Earliest Finish Time (HEFT) algorithm is used to find the tasks execution order in the scientific workflows. Our workflow scheduling approach mainly tries to decrease the scheduling process’s energy consumption by minimizing the applied Virtual Machines (VMs), makespan, and communication between dependent tasks. For evaluating the performance of the proposed scheduling scheme, extensive simulations are conducted on the scientific workflows with four different sizes. The experimental results indicate that scheduling using the DMFO-DE algorithm can outperform other metrics such as the number of applied VMs, and energy consumption.

Play Fairal gorithm has been enhanced in various ways. One of the methods is to increase the confusion rates by transforming the algorithm into 3D-playfair which has four tables of 4×4 and accepts trigraph rather than digraph. This paper further enhances the 3D-playfair algorithm by extending its tables and hereby increasing the security level as well as supporting the characters of all the world's living languages. This is achieved by using four matrices of 128×128 to store all the possible characters which are 65536. As a result, the proposed algorithm has more resistanceto brute force andfrequency analysis attacks.