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Recently, various types of single valued neutrosophic (SVN) rough set models were presented based on the same inclusion relation. However, there is another SVN inclusion relation in SVN sets. In this paper, we propose a new type of SVN covering rough set model based on the new inclusion relation. Furthermore, the graph and matrix representations of the new SVN covering approximation operators are presented. Firstly, the notion of SVN β 2 -covering approximation space is proposed, which is decided by the new inclusion relation. Then, a type of SVN covering rough set model under the SVN β 2 -covering approximation space is presented. Moreover, there is a corresponding SVN relation rough set model based on a SVN relation induced by the SVN β 2 -covering, and two conditions under which the SVN β 2 -covering can induce a symmetric SVN relation are presented. Thirdly, the graph and matrix representations of the new SVN covering rough set model are investigated. Finally, we propose a novel method for decision making (DM) problems in paper defect diagnosis under the new SVN covering rough set model.

The plastic modifications in synaptic connectivity is primarily from changes triggered by neuromodulated dopamine signals. These activities are controlled by neuromodulation, which is itself under the control of the brain. The subjective brain’s self-modifying abilities play an essential role in learning and adaptation. The artificial neural networks with neuromodulated plasticity are used to implement transfer learning in the image classification domain. In particular, this has application in image detection, image segmentation, and transfer of learning parameters with significant results. This paper proposes a novel approach to enhance transfer learning accuracy in a heterogeneous source and target, using the neuromodulation of the Hebbian learning principle, called NDHTL (Neuromodulated Dopamine Hebbian Transfer Learning). Neuromodulation of plasticity offers a powerful new technique with applications in training neural networks implementing asymmetric backpropagation using Hebbian principles in transfer learning motivated CNNs (Convolutional neural networks). Biologically motivated concomitant learning, where connected brain cells activate positively, enhances the synaptic connection strength between the network neurons. Using the NDHTL algorithm, the percentage of change of the plasticity between the neurons of the CNN layer is directly managed by the dopamine signal’s value. The discriminative nature of transfer learning fits well with the technique. The learned model’s connection weights must adapt to unseen target datasets with the least cost and effort in transfer learning. Using distinctive learning principles such as dopamine Hebbian learning in transfer learning for asymmetric gradient weights update is a novel approach. The paper emphasizes the NDHTL algorithmic technique as synaptic plasticity controlled by dopamine signals in transfer learning to classify images using source-target datasets. The standard transfer learning using gradient backpropagation is a symmetric framework. Experimental results using CIFAR-10 and CIFAR-100 datasets show that the proposed NDHTL algorithm can enhance transfer learning efficiency compared to existing methods.

Canonical correlation analysis (CCA) is the default method for investigating the linear dependence structure between two random vectors, but it might not detect nonlinear dependencies. This paper models the nonlinear dependencies between two random vectors by the perturbed independence distribution, a multivariate semiparametric model where CCA provides an insight into their nonlinear dependence structure. The paper also investigates some of its probabilistic and inferential properties, including marginal and conditional distributions, nonlinear transformations, maximum likelihood estimation and independence testing. Perturbed independence distributions are closely related to skew-symmetric ones.

This Special Issue covers symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multi-criteria decision-making problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the Special Issue.

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

Cloud based healthcare computing have changed the face of healthcare in many ways. The main advantages of cloud computing in healthcare are scalability of the required service and the provision to upscale or downsize the data storge, collaborating Artificial Intelligence (AI) and machine learning. The current paper examined various research studies to explore the utilization of intelligent techniques in health systems and mainly focused into the security and privacy issues in the current technologies. Despite the various benefits related to cloud-computing applications for healthcare, there are different types of management, technology handling, security measures, and legal issues to be considered and addressed. The key focus of this paper is to address the increased demand for cloud computing and its definition, technologies widely used in healthcare, their problems and possibilities, and the way protection mechanisms are organized and prepared when the company chooses to implement the latest evolving service model. In this paper, we focused on a thorough review of current and existing literature on different approaches and mechanisms used in e-Health to deal with security and privacy issues. Some of these approaches have strengths and weaknesses. After selecting original articles, the literature review was carried out, and we identified several models adopted in their solutions. We arrived at the reviewed articles after comparing the models used.

Both the theory of differential subordination and its dual, the theory of differential superordination, introduced by Professors Miller and Mocanu are based on reinterpreting certain inequalities for real-valued functions for the case of complex-valued functions. Studying subordination and superordination properties using different types of operators is a technique that is still widely used, some studies resulting in sandwich-type theorems as is the case in the present paper. The fractional integral of confluent hypergeometric function is introduced in the paper and certain subordination and superordination results are stated in theorems and corollaries, the study being completed by the statement of a sandwich-type theorem connecting the results obtained by using the two theories.

The idea of inequality has been extended from the real plane to the complex plane through the notion of subordination introduced by Professors Miller and Mocanu in two papers published in 1978 and 1981. With this notion came a whole new theory called the theory of differential subordination or admissible functions theory. Later, in 2003, a particular form of inequality in the complex plane was also defined by them as dual notion for subordination, the notion of differential superordination and with it, the theory of differential superordination appeared. In this paper, the theory of differential superordination is applied to confluent hypergeometric function. Hypergeometric functions are intensely studied nowadays, the interest on the applications of those functions in complex analysis being renewed by their use in the proof of Bieberbach’s conjecture given by de Branges in 1985. Using the theory of differential superodination, best subordinants of certain differential superordinations involving confluent (Kummer) hypergeometric function are stated in the theorems and relation with previously obtained results are highlighted in corollaries using particular functions and in a sandwich-type theorem. An example is also enclosed in order to show how the theoretical findings can be applied.

The new general theory dedicated to the stability for LTI MIMO, in particular nonsquare, fractional-order systems described by the Gr&uuml, nwald&ndash, Letnikov discrete-time state&ndash, space domain is presented in this paper. Such systems under inverse model control, principally MV/perfect control, represent a real research challenge due to an infinite number of solutions to the underlying inverse problem for nonsquare matrices. Therefore, the paper presents a new algorithm for fractional-order perfect control with corresponding stability formula involving recently given H- and &sigma, inverse of nonsquare matrices, up to now applied solely to the integer-order plants. On such foundation a new set of stability-related tools is introduced, among them the key role played by so-called control zeros. Control zeros constitute an extension of transmission zeros for nonsquare fractional-order LTI MIMO systems under inverse model control. Based on the sets of stable control zeros a minimum-phase behavior is specified because of the stability of newly defined perfect control law described in the non-integer-order framework. The whole theory is complemented by pole-free fractional-order perfect control paradigm, a special case of fractional-order perfect control strategy. A significant number of simulation examples confirm the correctness and research potential proposed in the paper methodology.

Recently, two classes of univalent functions S e * and K e were introduced and studied. A function f is in S e * if it is analytic in the unit disk, f ( 0 ) = f &prime, ( 0 ) - 1 = 0 and z f &prime, ( z ) f ( z ) ≺ e z . On the other hand, g &isin, K e if and only if z g &prime, &isin, S e * . Both classes are symmetric, or invariant, under rotations. In this paper, we solve a few problems connected with the coefficients of functions in these classes. We find, among other things, the estimates of Hankel determinants: H 2 , 1 , H 2 , 2 , H 3 , 1 . All these estimates improve the known results. Moreover, almost all new bounds are sharp. The main idea used in the paper is based on expressing the discussed functionals depending on the fixed second coefficient of a function in a given class.

The lurch index has recently been introduced in applied kinematics as an integral descriptor of the fluency of the motion of a rigid body in space. It may be defined in different versions, according to the component of motion under investigation. In the present paper, we analyze a rotational lurch index, which describes the fluency of the spin component of motion and whose value depends, through involved relations, on the dynamics of three canonical descriptors of the orientation of a rigid body in space. The aim of the present paper is to offer a closed-form expression of the instantaneous component of the rotational lurch, which leads to the namesake index upon integration and normalization. The closed form of the index is, then, used to evaluate its practical calculation, based on numerical approximations on a number of data sets.

In this paper the model of infection diseases by Marchuk is considered. Mathematical questions which are important in its study are discussed. Among them there are stability of stationary points, construction of the Cauchy matrices of linearized models, estimates of solutions. The novelty we propose is in a distributed feedback control which affects the antibody concentration. We use this control in the form of an integral term and come to the analysis of nonlinear integro-differential systems. New methods for the study of stability of linearized integro&ndash, differential systems describing the model of infection diseases are proposed. Explicit conditions of the exponential stability of the stationary points characterizing the state of the healthy body are obtained. The method of the paper is based on the symmetry properties of the Cauchy matrices which allow us their construction.

This paper focuses on multi-attribute group decision-making (MAGDM) course in which attributes are evaluated in terms of interval-valued intuitionistic fuzzy (IVIF) information. More explicitly, this paper introduces new aggregation operators for IVIF information and further proposes a new IVIF MAGDM method. The power average (PA) operator and the Muirhead mean (MM) are two powerful and effective information aggregation technologies. The most attractive advantage of the PA operator is its power to combat the adverse effects of ultra-evaluation values on the information aggregation results. The prominent characteristic of the MM operator is that it is flexible to capture the interrelationship among any numbers of arguments, making it more powerful than Bonferroni mean (BM), Heronian mean (HM), and Maclaurin symmetric mean (MSM). To absorb the virtues of both PA and MM, it is necessary to combine them to aggregate IVIF information and propose IVIF power Muirhead mean (IVIFPMM) operator and the IVIF weighted power Muirhead mean (IVIFWPMM) operator. We investigate their properties to show the strongness and flexibility. Furthermore, a novel approach to MAGDM problems with IVIF decision-making information is introduced. Finally, a numerical example is provided to show the performance of the proposed method.

In this paper, Normalized Weighted Bonferroni Mean (NWBM) and Normalized Weighted Bonferroni Harmonic Mean (NWBHM) aggregation operators are proposed. Besides, we check the properties thereof, which include idempotency, monotonicity, commutativity, and boundedness. As the intuitionistic fuzzy numbers are used as a basis for the decision making to effectively handle the real-life uncertainty, we extend the NWBM and NWBHM operators into the intuitionistic fuzzy environment. By further modifying the NWBHM, we propose additional aggregation operators, namely the Intuitionistic Fuzzy Normalized Weighted Bonferroni Harmonic Mean (IFNWBHM) and the Intuitionistic Fuzzy Ordered Normalized Weighted Bonferroni Harmonic Mean (IFNONWBHM). The paper winds up with an empirical example of multi-attribute group decision making (MAGDM) based on triangular intuitionistic fuzzy numbers. To serve this end, we apply the IFNWBHM aggregation operator.

In this paper, an intra prediction method is proposed for coding of depth pictures using plane modelling. Each pixel in the depth picture is related to the distance from a camera to an object surface, and pixels corresponding to a flat surface of an object form a relationship with the 2D plane surface. The plane surface can be represented by a simple equation in the 3D camera coordinate system in such a way that the coordinate system of depth pixels can be transformed to the camera coordinate system. This paper finds the parameters which define the plane surface closest to given depth pixels. The plane model is then used to predict the depth pixels on the plane surface. A depth prediction method is also devised for efficient intra prediction of depth pictures, using variable-size blocks. For prediction with variable-size blocks, the plane surface that occupies a large part of the picture can be predicted using a large block size. The simulation results of the proposed method show that the mean squared error is reduced by up to 96.6% for a block size of 4 × 4 pixels and reduced by up to 98% for a block size of 16 × 16, compared with the intra prediction modes of H.264/AVC and H.265/HEVC.

In the algebra of single-valued structures, cyclicity is one of the fundamental properties of groups. Therefore, it is natural to study it also in the algebra of multivalued structures (algebraic hyperstructure theory). However, when one considers the nature of generalizing this property, at least two (or rather three) approaches seem natural. Historically, all of these had been introduced and studied by 1990. However, since most of the results had originally been published in journals without proper international impact and later—without the possibility to include proper background and context-synthetized in books, the current way of treating the concept of cyclicity in the algebraic hyperstructure theory is often rather confusing. Therefore, we start our paper with a rather long introduction giving an overview and motivation of existing approaches to the cyclicity in algebraic hyperstructures. In the second part of our paper, we relate these to EL-hyperstructures, a broad class of algebraic hyperstructures constructed from (pre)ordered (semi)groups, which were defined and started to be studied much later than sources discussed in the introduction were published. V algebře jednoznačných struktur je cyklicita jednou z význačných vlastností grup. Proto je zajímavé studovat ji také v algebře mnohoznačných struktur, tj. algebraické teorii hyperstruktur. Nicméně, pokud uvážíme možnosti zobecnění cyklicity, je zřejmé, že přirozené jsou dva (nebo spíše tři) přístupy. Historicky byly všechny studovány již před rokem 1990. Většina těchto výsledků však byla publikována v časopisech bez patřičného ohlasu a jejich shrnutí poté bylo bez patřičného kontextu zařazeno do některých monografií, odkud je nyní přebíráno často ve zkreslené podobě. Proto svůj článek začínáme poněkud delším úvodem, který si klade za cíl zasadit studovanou problematiku do patřičného kontextu a uvést motivaci jednotlivých přístupů. Ve druhé části článku se poté věnujeme cyklicitě v EL-hypergrupách, tj. široké třídě hyperstruktur, která byla definována mnoho let poté, co byly publikovány články zmiňované v úvodu.

The concept of quantum B-algebra was introduced by Rump and Yang, that is, unified algebraic semantics for various noncommutative fuzzy logics, quantum logics, and implication logics. In this paper, a new notion of q-filter in quantum B-algebra is proposed, and quotient structures are constructed by q-filters (in contrast, although the notion of filter in quantum B-algebra has been defined before this paper, but corresponding quotient structures cannot be constructed according to the usual methods). Moreover, a new, more general, implication algebra is proposed, which is called basic implication algebra and can be regarded as a unified frame of general fuzzy logics, including nonassociative fuzzy logics (in contrast, quantum B-algebra is not applied to nonassociative fuzzy logics). The filter theory of basic implication algebras is also established.

The purpose of the paper is to study new algebraic operations and fundamental properties of totally dependent-neutrosophic sets and totally dependent-neutrosophic soft sets. First, the in-coordination relationships among the original inclusion relations of totally dependent-neutrosophic sets (called type-1 and typ-2 inclusion relations in this paper) and union (intersection) operations are analyzed, and then type-3 inclusion relation of totally dependent-neutrosophic sets and corresponding type-3 union, type-3 intersection, and complement operations are introduced. Second, the following theorem is proved: all totally dependent-neutrosophic sets (based on a certain universe) determined a generalized De Morgan algebra with respect to type-3 union, type-3 intersection, and complement operations. Third, the relationships among the type-3 order relation, score function, and accuracy function of totally dependent-neutrosophic sets are discussed. Finally, some new operations and properties of totally dependent-neutrosophic soft sets are investigated, and another generalized De Morgan algebra induced by totally dependent-neutrosophic soft sets is obtained.

Zadeh’s fuzzy set theory for imprecise or vague data has been followed by other successful models, inclusive of Molodtsov’s soft set theory and hybrid models like fuzzy soft sets. Their success has been backed up by applications to many branches like engineering, medicine, or finance. In continuation of this effort, the purpose of this paper is to put forward a versatile methodology for the valuation of goods, particularly the assessment of real state properties. In order to reach this target, we develop the concept of (partial) valuation fuzzy soft set and introduce the novel problem of data filling in partial valuation fuzzy soft sets. The use of fuzzy soft sets allows us to quantify the qualitative attributes involved in an assessment context. As a result, we illustrate the effectiveness and validity of our valuation methodology with a real case study that uses data from the Spanish real estate market. The main contribution of this paper is the implementation of a novel methodology, which allows us to assess a large variety of assets where data are heterogeneous. Our technique permits to avoid the appraiser’s subjectivity (exhibited by practitioners in housing valuation) and the well-known disadvantages of some alternative methods (such as linear multiple regression).

Integral inequalities play critical roles in measure theory and probability theory. Given recent profound discoveries in the field of fuzzy set theory, fuzzy inequality has become a hot research topic in recent years. For classical Sandor type inequality, this paper intends to extend the Sugeno integral. Based on the (s,m)-convex function in the second sense, a new Sandor type inequality is proposed for the Sugeno integral. Examples are given to verify the conclusion of this paper.

Uncertain differential equations are important mathematical models in uncertain environments. This paper investigates uncertain multi-dimensional and multiple-factor differential equations. First, the solvability of the equations is analyzed. The α-path distributions and expected values of solutions are given. Then, structure preserving uncertain differential equations, uncertain Hamiltonian systems driven by Liu processes, which possess a kind of uncertain symplectic structures, are presented. A symplectic scheme with six-order accuracy and a Yao-Chen algorithm are applied to design an algorithm to solve uncertain Hamiltonian systems. At last, numerical experiments are given to investigate four uncertain Hamiltonian systems, which highlight the efficiency of our algorithm.

As a natural extension of the fuzzy variable, a bifuzzy variable is defined as a mapping from a credibility space to the collection of fuzzy variables, which is an appropriate tool to model the two-fold fuzzy phenomena. In order to enrich its theoretical foundation, this paper explores some important measures for regular bifuzzy variables, the most commonly used type of bifuzzy variables. Firstly, we introduce the regular bifuzzy variables’ mean chance measure and some properties, including self-duality and its calculation formulas. Furthermore, we also investigate the mean chance distribution for strictly monotone functions of regular bifuzzy variables based on the proposed operational law. Finally, we present the expected value operator as well as equivalent analytical formulas of the expected value of regular bifuzzy variables and their strictly monotone functions.

Graph connectivity theory is important in network implementations, transportation, network routing and network tolerance, among other things. Separation edges and vertices refer to single points of failure in a network, and so they are often sought-after. Chandramouleeswaran et al. introduced the principle of semiring valued graphs, also known as S-valued symmetry graphs, in 2015. Since then, works on S-valued symmetry graphs such as vertex dominating set, edge dominating set, regularity, etc. have been done. However, the connectivity of S-valued graphs has not been studied. Motivated by this, in this paper, the concept of connectivity in S-valued graphs has been studied. We have introduced the term vertex S-connectivity and edge S-connectivity and arrived some results for connectivity of a complete S-valued symmetry graph, S-path and S-star. Unlike the graph theory, we have observed that the inequality for connectivity κ(G)≤κ′(G)≤δ(G) holds in the case of S-valued graphs only when there is a symmetry of the graph as seen in Examples 3–5.

In the last decade, the devices and appliances utilizing the Internet of Things (IoT) have expanded tremendously, which has led to revolutionary developments in the network industry. Smart homes and cities, wearable devices, traffic monitoring, health systems, and energy savings are typical IoT applications. The diversity in IoT standards, protocols, and computational resources makes them vulnerable to security attackers. Botnets are challenging security threats in IoT devices that cause severe Distributed Denial of Service (DDoS) attacks. Intrusion detection systems (IDS) are necessary for safeguarding Internet-connected frameworks and enhancing insufficient traditional security countermeasures, including authentication and encryption techniques. This paper proposes a wrapper feature selection model (SSA–ALO) by hybridizing the salp swarm algorithm (SSA) and ant lion optimization (ALO). The new model can be integrated with IDS components to handle the high-dimensional space problem and detect IoT attacks with superior efficiency. The experiments were performed using the N-BaIoT benchmark dataset, which was downloaded from the UCI repository. This dataset consists of nine datasets that represent real IoT traffic. The experimental results reveal the outperformance of SSA–ALO compared to existing related approaches using the following evaluation measures: TPR (true positive rate), FPR (false positive rate), G-mean, processing time, and convergence curves. Therefore, the proposed SSA–ALO model can serve IoT applications by detecting intrusions with high true positive rates that reach 99.9% and with a minimal delay even in imbalanced intrusion families.

In a regular (d,k)-CNF formula, each clause has length k and each variable appears d times. A regular structure such as this is symmetric, and the satisfiability problem of this symmetric structure is called the (d,k)-SAT problem for short. The regular exact 2-(d,k)-SAT problem is that for a (d,k)-CNF formula F, if there is a truth assignment T, then exactly two literals of each clause in F are true. If the formula F contains only positive or negative literals, then there is a satisfiable assignment T with a size of 2n/k such that F is 2-exactly satisfiable. This paper introduces the (d,k)-SAT instance generation model, constructs the solution space, and employs the method of the first and second moments to present the phase transition point d* of the 2-(d,k)-SAT instance with only positive literals. When d&lt, d*, the 2-(d,k)-SAT instance can be satisfied with high probability. When d&gt, d*, the 2-(d,k)-SAT instance can not be satisfied with high probability. Finally, the verification results demonstrate that the theoretical results are consistent with the experimental results.

With the increasing complexity of the human social environment, it is impossible to describe each object in detail with accurate numbers when solving multiple attribute decision-making (MADM) problems. Compared with the fuzzy set (FS), the intuitionistic fuzzy set (IFS) not only has obvious advantages in allocating ambiguous values to the object to be considered, but also takes into account the degree of membership and non-membership, so it is more suitable for decision makers (DMs) to deal with complex realistic problems. Therefore, it is of great significance to propose a MADM method under an intuitionistic fuzzy environment. Moreover, compared with the traditional 2WD, by putting forward the option of delay, the decision-making risk can be effectively reduced using three-way decision (3WD). In addition, the binary relations between objects in the decision-making process have been continuously generalized, such as equivalence relation which have symmetrical relationship, dominance relation and outranking relation, which are worthy of study. In this paper, we propose 3WD-MADM method based on IF environment and the objective IFS is calculated by using the information table. Then, the hybrid information table is used to solve the supplier selection problem to demonstrate the effectiveness of the proposed method.

The quantity of network attacks and the harm from them is constantly increasing, so the detection of these attacks is an urgent task in the information security field. In this paper, we investigate an approach to building intrusion detection systems using a classifier based on fuzzy rules. The process of creating a fuzzy classifier based on a given set of input and output data can be presented as a solution to the problems of clustering, informative features selection, and the parameters of the rule antecedents optimization. To solve these problems, the whale optimization algorithm is used. The performance of algorithms for constructing a fuzzy classifier based on this metaheuristic is estimated using the KDD Cup 1999 intrusion detection dataset. On average, the resulting classifiers have a type I error of 0.92% and a type II error of 1.07%. The obtained results are also compared with the results of other classifiers. The comparison shows the competitiveness of the proposed method.

In this paper, we aim to reveal the influence of fault fracture zones on the stability of submarine tunnels and the surrounding rock under different water and drainage measures. Firstly, four typical working conditions of submarine tunnels intersecting with fault fracture zones were selected. On the basis of the typical cross section of the intersections of submarine tunnels and faults, they were divided into four working conditions. Then, the displacement and plastic zones of the surrounding rock of the tunnel were studied, and the stability of the rock surrounding the submarine tunnel was discussed. This research structure indicates that the bending moment and axial force of the lining structure of the submarine tunnel increase with increasing sealing degree, but the safety factor exhibits a downward trend. When the fault fracture zone goes through the section above the tunnel axis, the bending moment and axial force at the lining vault are greater than the other working conditions, and the displacement of the surrounding rock at the vault and spandrel is prominent. When the fault fracture zone completely passes through the tunnel, the safety factor of the lining structure is at its lowest, and the displacement of the surrounding rock at the arch waist develops laterally. When the fault fracture zone passes through the part below the tunnel axis, the arch foot displacement converges significantly, and the surrounding rock displacement exhibits a downward inclination. In addition, the plastic zone is mainly developed in the arch and the shoulder. These research results provide a reliable reference for tunnel design and excavation support.

With the rapid increase in the number of Android malware, the image-based analysis method has become an effective way to defend against symmetric encryption and confusing malware. At present, the existing Android malware bytecode image detection method, based on a convolution neural network (CNN), relies on a single DEX file feature and requires a large amount of computation. To solve these problems, we combine the visual features of the XML file with the data section of the DEX file for the first time, and propose a new Android malware detection model, based on a temporal convolution network (TCN). First, four gray-scale image datasets with four different combinations of texture features are created by combining XML files and DEX files. Then the image size is unified and input to the designed neural network with three different convolution methods for experimental validation. The experimental results show that adding XML files is beneficial for Android malware detection. The detection accuracy of the TCN model is 95.44%, precision is 95.45%, recall rate is 95.45%, and F1-Score is 95.44%. Compared with other methods based on the traditional CNN model or lightweight MobileNetV2 model, the method proposed in this paper, based on the TCN model, can effectively utilize bytecode image sequence features, improve the accuracy of detecting Android malware and reduce its computation.

The rapid growth of performance in the field of neural networks has also increased their sizes. Pruning methods are getting more and more attention in order to overcome the problem of non-impactful parameters and overgrowth of neurons. In this article, the application of Global Sensitivity Analysis (GSA) methods demonstrates the impact of input variables on the model’s output variables. GSA gives the ability to mark out the least meaningful arguments and build reduction algorithms on these. Using several popular datasets, the study shows how different levels of pruning correlate to network accuracy and how levels of reduction negligibly impact accuracy. In doing so, pre- and post-reduction sizes of neural networks are compared. This paper shows how Sobol and FAST methods with common norms can largely decrease the size of a network, while keeping accuracy relatively high. On the basis of the obtained results, it is possible to create a thesis about the asymmetry between the elements removed from the network topology and the quality of the neural network.

Internet of Things (IoT) is composed of various kinds of devices such as cars, electrical appliances, machines and sensors. With IoT technologies, devices can exchange information through the network, people are allowed to get information collected by devices without interacting with them, and automatic operations for devices are realized. Because of the variety of IoT devices, some of them possess limited computational capability. On the other hand, data transmission in IoT networks is usually through a public channel. To ensure efficiency and security for IoT environments, Lee et al. proposed a three-factor authentication scheme with hash function and XOR operation. They claimed their scheme possessed superior properties and could resist common attacks. After analyzing their scheme, we find that their scheme is vulnerable to five flaws. In this paper, how these found flaws threaten Lee et al.’s scheme is shown in detail. Then, we propose an improvement to overcome the found flaws and preserve the advantages by employing ECC.

Trajectory optimization is the series of actions that are taken into consideration in order to produce the best path such that it improves the overall performances of physical properties or reduces the consumption of the resources where the restriction system remains maintained. In this paper, first, a compact mathematical model for a symmetrical annular-shaped omnidirectional wheeled mobile robot (SAOWMR) is derived and verified. This general mathematical model provides an opportunity to conduct research, experiments, and comparisons on these omnidirectional mobile robots that have two, three, four, six, or even more omnidirectional wheels without the need to switch models or derive a new model. Then, a new computationally efficient method is proposed to achieve improvements in the trajectory planning optimization (TPO) for a SAOWMR. Moreover, the proposed method has been tested in collision-free navigation by incorporation of the path constraints. Numerical tests and simulations are presented aiming to ensure the efficiency and effectiveness of the proposed method.

Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm algorithm (SSA), as a swarm-based algorithm on account of the predation behavior of the salp, can solve complex daily life optimization problems in nature. SSA also has the problems of local stagnation and slow convergence rate. This paper introduces an improved salp swarm algorithm, which improve the SSA by using the chaotic sequence initialization strategy and symmetric adaptive population division. Moreover, a simulated annealing mechanism based on symmetric perturbation is introduced to enhance the local jumping ability of the algorithm. The improved algorithm is referred to SASSA. The CEC standard benchmark functions are used to evaluate the efficiency of the SASSA and the results demonstrate that the SASSA has better global search capability. SASSA is also applied to solve engineering optimization problems. The experimental results demonstrate that the exploratory and exploitative proclivities of the proposed algorithm and its convergence patterns are vividly improved.

In this paper, we consider the inverse scattering problem and, in particular, the problem of reconstructing the spectral density associated with the Yukawian potentials from the sequence of the partial-waves fℓ of the Fourier–Legendre expansion of the scattering amplitude. We prove that if the partial-waves fℓ satisfy a suitable Hausdorff-type condition, then they can be uniquely interpolated by a function f˜(λ)∈C, analytic in a half-plane. Assuming also the Martin condition to hold, we can prove that the Fourier–Legendre expansion of the scattering amplitude converges uniformly to a function f(θ)∈C (θ being the complexified scattering angle), which is analytic in a strip contained in the θ-plane. This result is obtained mainly through geometrical methods by replacing the analysis on the complex cosθ-plane with the analysis on a suitable complex hyperboloid. The double analytic symmetry of the scattering amplitude is therefore made manifest by its analyticity properties in the λ- and θ-planes. The function f(θ) is shown to have a holomorphic extension to a cut-domain, and from the discontinuity across the cuts we can iteratively reconstruct the spectral density σ(μ) associated with the class of Yukawian potentials. A reconstruction algorithm which makes use of Pollaczeck and Laguerre polynomials is finally given.

An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-based model for an IDS in the IoT.

In recent decades, the internet has grown and changed the world tremendously, and this, in turn, has brought about many cyberattacks. Cybersecurity represents one of the most serious threats to society, and it costs millions of dollars each year. The most significant question remains: Where do these attacks come from? The answer is that botnets provide platforms for cyberattacks. For many organizations, a botnet-assisted attack is a terrifying threat that can cause financial losses and leave global victims in its wake. It is therefore imperative to defend organizations against botnet-assisted attacks. Software defined networking (SDN) has emerged as one of the most promising paradigms for this because it allows exponential increases in the complexity of network management and configuration. SDN has a substantial advantage over traditional approaches with regard to network management because it separates the control plane from network equipment. However, security challenges continue to arise, which raises the need for different types of implementation strategies to spread attack vectors, despite the significant benefits. The main objective of this survey is to assess botnet detection techniques by using systematic reviews and meta-analyses (PRISMA) guidelines. We evaluated various articles published since 2006 in the field of botnet detection, based on machine learning, and from 2015 in the field of SDN. Specifically, we used top-rated journals that featured the highest impact factors. In this paper, we aim to elaborate on several research areas regarding botnet attacks, detection techniques, machine learning, and SDN. We also address current research challenges and propose directions for future research.

Fuzzy-rough cognitive networks (FRCNs) are interpretable recurrent neural networks, primarily designed for solving classification problems. Their structure is simple and transparent, while the performance is comparable to the well-known black-box classifiers. Although there are many applications on fuzzy cognitive maps and recently for FRCNS, only a very limited number of studies discuss the theoretical issues of these models. In this paper, we examine the behaviour of FRCNs viewing them as discrete dynamical systems. It will be shown that their mathematical properties highly depend on the size of the network, i.e., there are structural differences between the long-term behaviour of FRCN models of different size, which may influence the performance of these modelling tools.

In this paper, new forms of Maxwell’s equations in vector and scalar variants are presented. The new forms are based on the use of Gauss’s theorem for magnetic induction and electrical induction. The equations are formulated in both differential and integral forms. In particular, the new forms of the equations relate to the non-stationary expressions and their integral identities. The indicated methodology enables a thorough analysis of non-stationary boundary conditions on the behavior of electromagnetic fields in multiple continuous regions. It can be used both for qualitative analysis and in numerical methods (control volume method) and optimization. The last Section introduces an application to equations of magnetic fluid in both differential and integral forms.

The neural-network-based human activity recognition (HAR) technique is being increasingly used for activity recognition in virtual reality (VR) users. The major issue of a such technique is the collection large-scale training datasets which are key for deriving a robust recognition model. However, collecting large-scale data is a costly and time-consuming process. Furthermore, increasing the number of activities to be classified will require a much larger number of training datasets. Since training the model with a sparse dataset can only provide limited features to recognition models, it can cause problems such as overfitting and suboptimal results. In this paper, we present a data augmentation technique named gravity control-based augmentation (GCDA) to alleviate the sparse data problem by generating new training data based on the existing data. The benefits of the symmetrical structure of the data are that it increased the number of data while preserving the properties of the data. The core concept of GCDA is two-fold: (1) decomposing the acceleration data obtained from the inertial measurement unit (IMU) into zero-gravity acceleration and gravitational acceleration, and augmenting them separately, and (2) exploiting gravity as a directional feature and controlling it to augment training datasets. Through the comparative evaluations, we validated that the application of GCDA to training datasets showed a larger improvement in classification accuracy (96.39%) compared to the typical data augmentation methods (92.29%) applied and those that did not apply the augmentation method (85.21%).

With the rapid development of cloud storage, an increasing number of users store their images in the cloud. These images contain many business secrets or personal information, such as engineering design drawings and commercial contracts. Thus, users encrypt images before they are uploaded. However, cloud servers have to hide secret data in encrypted images to enable the retrieval and verification of massive encrypted images. To ensure that both the secret data and the original images can be extracted and recovered losslessly, researchers have proposed a method that is known as reversible data hiding in encrypted images (RDHEI). In this paper, a new RDHEI method using median edge detector (MED) and two’s complement is proposed. The MED prediction method is used to generate the predicted values of the original pixels and calculate the prediction errors. The adaptive-length two’s complement is used to encode the most prediction errors. To reserve room, the two’s complement is labeled in the pixels. To record the unlabeled pixels, a label map is generated and embedded into the image. After the image has been encrypted, it can be embedded with the data. The experimental results indicate that the proposed method can reach an average embedding rate of 2.58 bpp, 3.04 bpp, and 2.94 bpp on the three datasets, i.e., UCID, BOSSbase, BOWS-2, which outperforms the previous work.

Many real-world networks can be modeled as attributed networks, where nodes are affiliated with attributes. When we implement attributed network embedding, we need to face two types of heterogeneous information, namely, structural information and attribute information. The structural information of undirected networks is usually expressed as a symmetric adjacency matrix. Network embedding learning is to utilize the above information to learn the vector representations of nodes in the network. How to integrate these two types of heterogeneous information to improve the performance of network embedding is a challenge. Most of the current approaches embed the networks in Euclidean spaces, but the networks themselves are non-Euclidean. As a consequence, the geometric differences between the embedded space and the underlying space of the network will affect the performance of the network embedding. According to the non-Euclidean geometry of networks, this paper proposes an attributed network embedding framework based on hyperbolic geometry and the Ricci curvature, namely, RHAE. Our method consists of two modules: (1) the first module is an autoencoder module in which each layer is provided with a network information aggregation layer based on the Ricci curvature and an embedding layer based on hyperbolic geometry, (2) the second module is a skip-gram module in which the random walk is based on the Ricci curvature. These two modules are based on non-Euclidean geometry, but they fuse the topology information and attribute information in the network from different angles. Experimental results on some benchmark datasets show that our approach outperforms the baselines.

The symmetry of the omnidirectional robot motion abilities around its central vertical axis is an important advantage regarding its driveability for the flexible interoperation with fixed conveyor systems. The paper illustrates a Hardware in the Loop architectural approach for integrated development of an Ominidirectional Mobile Robot that is designed to serve in a dynamic logistic environment. Such logistic environments require complex algorithms for autonomous navigation between different warehouse locations, that can be efficiently developed using Robot Operating System nodes. Implementing path planning nodes benefits from using Matlab-Simulink, which provides a large selection of algorithms that are easily integrated and customized. The proposed solution is deployed for validation on a NVIDIA Jetson Nano, the embedded computer hosted locally on the robot, that runs the autonomous navigation software. The proposed solution permits the live connection to the omnidirectional prototype platform, allowing to deploy algorithms and acquire data for debugging the location, path planning and the mapping information during real time autonomous navigation experiments, very useful in validating different strategies.

Binary relations are most important in various fields of pure and applied sciences. The concept of linear Diophantine fuzzy sets (LDFSs) proposed by Riaz and Hashmi is a novel mathematical approach to model vagueness and uncertainty in decision-making problems. In LDFS theory, the use of reference or control parameters corresponding to membership and non-membership grades makes it most accommodating towards modeling uncertainties in real-life problems. The main purpose of this paper is to establish a robust fusion of binary relations and LDFSs, and to introduce the concept of linear Diophantine fuzzy relation (LDF-relation) by making the use of reference parameters corresponding to the membership and non-membership fuzzy relations. The novel concept of LDF-relation is more flexible to discuss the symmetry between two or more objects that is superior to the prevailing notion of intuitionistic fuzzy relation (IF-relation). Certain basic operations are defined to investigate some significant results which are very useful in solving real-life problems. Based on these operations and their related results, it is analyzed that the collection of all LDF-relations gives rise to some algebraic structures such as semi-group, semi-ring and hemi-ring. Furthermore, the notion of score function of LDF-relations is introduced to analyze the symmetry of the optimal decision and ranking of feasible alternatives. Additionally, a new algorithm for modeling uncertainty in decision-making problems is proposed based on LDFSs and LDF-relations. A practical application of proposed decision-making approach is illustrated by a numerical example. Proposed LDF-relations, their operations, and related results may serve as a foundation for computational intelligence and modeling uncertainties in decision-making problems.

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.

Owing to the increased use of urban rail transit, the flow of passengers on metro platforms tends to increase sharply during peak periods. Monitoring passenger flow in such areas is important for security-related reasons. In this paper, in order to solve the problem of metro platform passenger flow detection, we propose a CNN (convolutional neural network)-based network called the MP (metro platform)-CNN to accurately count people on metro platforms. The proposed method is composed of three major components: a group of convolutional neural networks is used on the front end to extract image features, a multiscale feature extraction module is used to enhance multiscale features, and transposed convolution is used for upsampling to generate a high-quality density map. Currently, existing crowd-counting datasets do not adequately cover all of the challenging situations considered in this study. Therefore, we collected images from surveillance videos of a metro platform to form a dataset containing 627 images, with 9243 annotated heads. The results of the extensive experiments showed that our method performed well on the self-built dataset and the estimation error was minimum. Moreover, the proposed method could compete with other methods on four standard crowd-counting datasets.

The software-defined networking (SDN) paradigm has recently emerged as a trend to build various protocols, develop more reliable networks, enhance the data flow controlling, and provide security in a much simpler and flexible way. SDN helps to ease management and handle asymmetric connectivity across various nodes. It solves the problems of network and cloud security and hence provides the best solution for the safety of data on the network. Therefore, we feel the urge to research more and provide the basics of SDN forensics, mention its advantages in network especially in the cloud, and present its elaborate prospects in context with Network Forensic (NF) and Cloud Forensic (CF). In this research article, we explained in detail the NF and CF with emphasis on Network security (NS) and Cloud Security (CS). The paper also provided the various security approaches and categories. Then, an overview of the software-defined networking (SDN) is mentioned. We also discussed the use of SDN in Network Forensic and Cloud Forensic. Furthermore, to aid the SDN forensic, we presented the advantages, challenges, and issues along with future research directions of SDN in network forensic and cloud forensic, and at last, we thus express and explore the need for security in forensic based on the SDN paradigm in the form of a set of suggested recommendations.

It is a familiar fact that inequalities have become a very popular method using fractional integrals, and that this method has been the driving force behind many studies in recent years. Many forms of inequality have been studied, resulting in the introduction of new trend in inequality theory. The aim of this paper is to use a fuzzy order relation to introduce various types of inequalities. On the fuzzy interval space, this fuzzy order relation is defined level by level. With the help of this relation, firstly, we derive some discrete Jensen and Schur inequalities for convex fuzzy interval-valued functions (convex fuzzy-IVF), and then, we present Hermite–Hadamard inequalities (HH-inequalities) for convex fuzzy-IVF via fuzzy interval Riemann–Liouville fractional integrals. These outcomes are a generalization of a number of previously known results, and many new outcomes can be deduced as a result of appropriate parameter “γ” and real valued function “Ω” selections. We hope that our fuzzy order relations results can be used to evaluate a number of mathematical problems related to real-world applications.

The aim of this paper is not only to propose a new extreme distribution, but also to show that the new extreme model can be used as an alternative to well-known distributions in the literature to model various kinds of datasets in different fields. Several of its statistical properties are explored. It is found that the new extreme model can be utilized for modeling both asymmetric and symmetric datasets, which suffer from over- and under-dispersed phenomena. Moreover, the hazard rate function can be constant, increasing, increasing–constant, or unimodal shaped. The maximum likelihood method is used to estimate the model parameters based on complete and censored samples. Finally, a significant amount of simulations was conducted along with real data applications to illustrate the use of the new extreme distribution.

Now that untact services are widespread and worldwide, the number of users visiting online shopping malls has increased. For example, the recommendation systems in Netflix, Amazon, etc., have gained a lot of attention by attracting many users and have made large profit by recommending suitable products to their users. In the paper, we conduct a study to enhance recommendation accuracy using Word2Vec, widely used in natural language processing. We collect user shopping history with personal click preference information of product items as data, representing a document for natural language processing. The sequence of product item clicks is fed into the Word2Vec technology algorithm to obtain the vectors symmetrically representing all of the product items clicked by users. Training and test data have a series of vectors representing a sequence of the clicked product items as inputs and a purchased product as a target. Machine learning models recommend a product as a symmetric vector for each input and calculate the similarity among the recommended vectors and all other registered products they sell in the system to recommend multiple products as final recommendation results. We use XGBoost regressor and classifier models to recommend some products that users would like and evaluate the recommendation accuracy. A finally recommended product by the models is a vector, and the system recommends some more products by calculating the similarity as mentioned above. We evaluated the classifier model’s recommendation accuracy without Word2Vec encoding first and then with the Word2Vec technique. Meanwhile, we can represent the products with single or multiple dimensional vectors. We noted that the recommendation accuracy increases when we use multiple dimensions of Word2Vec vectors from the experiments. We also evaluated the performances when the system recommends one or multiple products. For the recommendation of multiple products (five here), a regression model has higher accuracy than a classification model in all dimensions of vectors.

Online environments have evolved from the early-stage technical systems to social platforms with social communication mechanisms resembling the interactions which can be found in the real world. Online marketers are using the close relations between the users of social networks to more easily propagate the marketing contents in their advertising campaigns. Such viral marketing campaigns have proven to provide better results than traditional online marketing, hence the increasing research interest in the topic. While the majority of the up-to-date research focuses on maximizing the global coverage and influence in the complete network, some studies have been conducted in the area of budget-constrained conditions as well as in the area of targeting particular groups of nodes. In this paper, a novel approach to targeting multi-attribute nodes in complex networks is presented, in which an MCDA method with various preference weights for all criteria is used to select the initial seeds to best reach the targeted nodes in the network. The proposed approach shows some symmetric characteristics—while the global coverage in the network is decreased, the coverage amongst the targeted nodes grows.