Search

Femtocells are the feasible solutions to extend the network coverage of indoor users and to enhance the network capacity in long-term evolution advanced (LTE-A)-based 5G networks. However, the femtocell base station shares the same frequency spectrum of microcell base station in unplanned manner. Hence, interference mitigation is a crucial problem in densely deployed femtocell environment and it is more severe with the deployment of femtocells in LTE-A network. In this paper, a modified dirty paper coding is proposed for interference mitigation along with the optimization of feedback bits using natural inspired meta-heuristic firefly algorithm. The proposed meta-heuristic algorithm reduces the interference by periodically unicasting the channel state information. Since the bandwidth of feedback system is limited, it is optimized in such a way that it does not affect the performance of the system. As compared to the conventional zero-forcing pre-coding, the proposed modified dirty paper coding along with firefly algorithm scheme offers improved sum rate of 70% and 64% with increase in the number of feedback bits and number of users, respectively.

Cloud services with the property of elasticity are always bearing different loads over time. The efficiency of cloud management can be improved through the accurate prediction for cloud loads, which can be served as the foundation and recommendation for the future developing of the network scheme. As the basis and premise for management and decision making, a good prediction depends on suitable models and research methods. However, the interaction of users and the cloud network is high variability in the time and spatial dimensions, so traditional linear forecasting models cannot always process cloud computing loads well because of nonlinear changes. In this paper, a novel cloud load prediction model is proposed by improving whale optimization algorithm based on the hybrid strategy (HWOA) and combined with extreme learning machine (ELM) for strong nonlinear mapping ability. The proposed cloud load forecasting model is to employ HWOA optimizer to optimize the ELM model random parameters. Main research work includes that (1) the HWOA optimizer is to solve the whale optimizer local extremum problem; (2) the proposed HWOA optimizer reduces the ELM random parameters on cloud load forecasting; (3) the convergence performance is verified by benchmark testing functions; and (4) three groups of simulation experiments are conducted to evaluate the cloud load forecasting results. The results prove that the HWOA optimizer has a good convergence and outperforms the several conventional popular swarm intelligence optimizers. Also, the prediction results verify that our proposed model is more competitive for providing a solid foundation for efficient resource management and maximized economic benefits in cloud environment. For cloud load research groups and practitioners, this paper provides a new idea of cloud load forecasting.

In order to improve the intelligence and analysis effect of architectural decoration esthetics, based on VR technology and machine vision algorithm, this paper constructs an architectural decoration esthetic analysis system based on VR technology and machine vision. According to the demand of architectural decoration esthetic analysis, this paper proposes a three-dimensional space detection algorithm. Moreover, with the support of machine vision, this paper conducts feature recognition of architectural decoration based on the interactive characteristics of virtual reality, combines with machine vision to perform architectural decoration feature recognition, and combines with VR calculation to enhance the immersion of architectural decoration analysis. In addition, this paper creates a virtual environment close to the real environment, and can adjust the structure of architectural decoration in real time to achieve the optimization of architectural decoration design. Finally, this paper designs experiments to verify the performance of the system and algorithm proposed in this paper. The research results show that the system model constructed in this paper is effective and can meet the needs of analysis of architectural decoration esthetics.

Deep learning (DL) is gaining significant prevalence in every field of study due to its domination in training large data sets. However, several applications are utilizing machine learning (ML) methods from the past several years and reported good performance. However, their limitations in terms of data complexity give rise to DL methods. Intrusion detection is one of the prominent areas in which researchers are extending DL methods. Even though several excellent surveys cover the growing body of research on this subject, the literature lacks a detailed comparison of ML methods such as ANN, SVM, fuzzy approach, swarm intelligence and evolutionary computation methods in intrusion detection, particularly on recent research. In this context, the present paper deals with the systematic review of ML methods and DL methods in intrusion detection. In addition to reviewing ML and DL methods, this paper also focuses on benchmark datasets, performance evaluation measures and various applications of DL methods for intrusion detection. The present paper summarizes the recent work, compares their experimental results for detecting network intrusions. Furthermore, current research challenges are identified for helping fellow researchers in the era of DL-based intrusion detection.

In this paper, ant colony optimization (ACO) and grey wolf optimization (GWO) algorithms are used for solving the joint user and receive antenna scheduling problem of multi-user multiple input multiple-output systems in the uplink channel. A subset of users among all the available users should be allowed to send their data to the appropriate receive antenna of the base station (BS). The search space for assigning appropriate user with the appropriate receive antenna of the BS is very large. Searching through this large search space is quite time-consuming process which cannot be accomplished within the real-time frame. To overcome this, ACO and GWO soft computing techniques have been implemented for this problem. In this paper, it is shown that both ACO and GWO accomplish this huge task with very low computation complexity. Simulation results presented in this paper are verifying the effectiveness of ACO and GWO for solving such complex problems. Moreover, it has also been showcased that GWO performs better than ACO and binary particle swarm optimization (BPSO) techniques. Both GWO and ACO attains the system sum-rate very close to that of exhaustive search algorithm. Furthermore, different statistical parameters for these soft computing techniques like BPSO, ACO, and GWO have been presented and compared to assess the efficacy of these meta-heuristic methods.

Label-specific features learning can effectively exploit the unique features of each label, which alleviates the high dimensionality and improves the classification performance of multi-label. However, most existing label-specific features learning algorithms assume that label space is complete, ignoring the effect of missing labels on the classification accuracy. Some methods try to recover the missing labels first and then learn the mapping between the completed label matrix and the feature matrix. However, early intervention in the recovery of missing labels may affect the distribution of original labels to a certain extent. In this paper, feature-label dual-mapping for missing label-specific features learning is proposed. According to the information that the label depends on the feature, the dual-mapping weight of the complete feature space and the missing label space is jointly learned. Therefore, the proposed algorithm is to conduct latent missing labels recovery by feature-label dual-mapping to directly obtain target weight in this paper, avoiding the negative influence of early label recovery intervention. Compared with several state-of-the-art methods in 10 benchmark multi-label data sets, the results show that the proposed algorithm is reasonable and effective.

Automatic target detection plays a significant role during war operations. The concept behind automatic target detection in war is military object recognition from the captured images. For object recognition in the given image, convolutional neural network (CNN) architectures are used efficiently. However, CNN suffers from a problem of location invariant and their performance depends mainly on the size of the training set. Generally, the available training data will be in less proportion for military objects due to its operational and security issues. Due to these two issues of CNN, the performance of CNN may degrade abruptly. To address military object recognition, a relatively new neural network architecture based on capsule network (CapsNet) is introduced in this paper. A variant of CapsNet called the multi-level CapsNet framework is projecting in this paper for efficient military object recognition under the case of a small training dataset. The validation of the introduced framework is done on a dataset of military objects collected from the Internet. The dataset contains particularly five different military objects and similar civil ones. Experiments demonstrate that the proposed framework can accomplish a high recognition precision, superior to many other algorithms such as conventional support vector machines and transfer learning-based CNNs.

This paper proposes a Bayesian hybrid approach based on neural networks and fuzzy systems to construct fuzzy rules to assist experts in detecting features and relations regarding the presence of autism in human beings. The model proposed in this paper works with a database generated through mobile devices that deals with diagnoses of autistic characteristics in human beings who answer a series of questions in a mobile application. The Bayesian model works with the construction of Gaussian fuzzy neurons in the first and logical neurons in the second layer of the model to form a fuzzy inference system connected to an artificial neural network that activates a robust output neuron. The new fuzzy neural network model was compared with traditional state-of-the-art machine learning models based on high-dimensional based on real-world data sets comprising the autism occurrence in children, adults, and adolescents. The results (97.73- Children/94.32-Adolescent/97.28-Adult) demonstrate the efficiency of our new method in determining children, adolescents, and adults with autistic traits (being among the top performers among all ML models tested), can generate knowledge about the dataset through fuzzy rules.

A photovoltaic system is used to produce energy that is dependent on environmental conditions such as irradiance, temperature, and the load attached to it. Due to the nonlinear characteristics, parameter uncertainties and load disturbance of photovoltaic (PV) systems and the problem of low efficiency due to the variation of environmental conditions, the maximum power point tracking method is required to extract the maximum power from the PV system. So, in this paper a novel indirect adaptive fuzzy fractional-order sliding mode is proposed to tackle this challenge. This method is based on a two-loop method. The first loop is designed to seek the maximum power point, and the second loop is designed to track the maximum power point. At first, a novel fractional-order sliding surface is proposed. Then, chattering in control signal is removed using a continuous function. In this paper, an indirect adaptive fuzzy system is used to estimate the unknown function of the system and adaptation laws are obtained based on the Lyapunov theory. The performance of the photovoltaic system is investigated by applying the proposed method, conventional sliding mode controller, integral sliding mode controller and backstepping sliding mode controller. Simulation results indicate the effectiveness of the proposed method.

The new energy vehicle system is in the initial stage of application, so the probability of fault is greater. Therefore, its reliability urgently needs to be improved. In order to improve the fault diagnosis effect of new energy vehicles, this paper proposes a fault diagnosis system of new energy vehicle electric drive system based on improved machine learning and also proposes several typical fault detection and diagnosis methods. Through the study of the operating characteristics and structural characteristics of the electric drive system and the analysis of the fault mechanism, this paper classifies the main faults of the electric drive system according to the self-test requirements of the electric drive system and the online diagnosis requirements and presents the self-test fault tree and online diagnosis fault tree of the electric drive system. In addition, this paper designs experiments to verify the performance of the system proposed by this paper, simulates the current common system fault conditions, and uses the system constructed in this paper to perform system fault diagnosis. The research results show that the performance of the fault diagnosis system for drive energy vehicles constructed in this paper is reliable.

In this research paper, we will solve problems of Bi-level linear fractional programming (BL-LFP) by proposing an interactive approach. Based on the imposition of the relationship DM. to obtained adequate solution, DMs will updating the minimal adequate level at upper level permanently, and that is through. Firstly, the decision makers uncertainty is described by introducing the membership function and non-membership function. Secondly, the opting of minimum adequate degree, leads to obtain the adequate solution, with the overall adequate balance considerations among two levels. For more, this paper gives an algorithm of the proposed approach. At the end, we give a numerical example to explain the feasibility of that approach.

There have been many recent fears of severe house-price decreases in some provinces in China causing a nationwide collapse of the housing market. Therefore, this paper aims to clarify the linkage structure of China's housing market and its risk contagion routes. Given monthly data of provincial housing and stock-market capital returns from 2001M01 to 2019M12, on the basis of graph theory, this paper first explores the linkage structure of provincial housing markets. Relying on the linkage structure, this paper then simulates the effect of unexpected negative shocks from the stock market on the probabilities of a housing-market collapse based on the epidemic model. The results show that (i) consistently with practical evidence, the probability of housing-market collapse is relatively high in the southwest of China and (ii) reducing housing-market linkage, such as through a blocking mechanism, to prevent collapse is helpful.

$$Q$$ -neutrosophic soft sets are essentially neutrosophic soft sets characterized by three independent two-dimensional membership functions which stand for uncertainty, indeterminacy and falsity. Thus, it can be applied to two-dimensional imprecise, indeterminate and inconsistent data which appear in most real-life problems. Graph theory has now become a major branch of applied mathematics, and it is generally regarded as a branch of combinatorics. In this research paper, we introduce the concept of $$Q$$ -neutrosophic soft graphs (Q- $${\mathcal{N}\mathcal{S}}Gs$$ ) that are made by combining $$Q$$ -neutrosophic soft sets with graphs and describe different methods of their construction. A $$Q$$ -neutrosophic soft graphs model is very much efficient for such real word problems which can construct more precise, flexible, and comparable system as compared to the classical, fuzzy and neutrosophic graph models. In this paper, we first define the concept of $$Q$$ -neutrosophic soft graph (Q- $${\mathcal{N}\mathcal{S}}G$$ ). In this paper we introduce the concept of $$Q$$ - $${\mathcal{N}\mathcal{S}}G$$ , strong Q- $${\mathcal{N}\mathcal{S}}G$$ , union and intersection of Q- $${\mathcal{N}\mathcal{S}}G$$ . The new concept is called Q- $${\mathcal{N}\mathcal{S}}G$$ multicriteria decision-making method (Q- $${\mathcal{N}\mathcal{S}}GMCDM$$ for short). Finally, we describe the utility of the $$Q$$ -neutrosophic soft graph and its applications in communication network and decision-making problem.

With the extensive growth of data that has been joined with the thriving development of the Internet in this century, finding or getting valuable information and knowledge from these huge noisy data became harder. The Concept of Knowledge Graph (KG) is one of the concepts that has come into the public view as a result of this development. In addition, with that thriving development especially in the last two decades, the need to process and extract valuable information in a more efficient way is increased. KG presents a common framework for knowledge representation, based on the analysis and extraction of entities and relationships. Techniques for KG construction can extract information from either structured, unstructured or even semi-structured data sources, and finally organize the information into knowledge, represented in a graph. This paper presents a characterization of different types of KGs along with their construction approaches. It reviews the existing academia, industry and expert KG systems and discusses in detail about the features of it. A systematic review methodology has been followed to conduct the review. Several databases (Scopus, GS, WoS) and journals (SWJ, Applied Ontology, JWS) are analysed to collect the relevant study and filtered by using inclusion and exclusion criteria. This review includes the state-of-the-art, literature review, characterization of KGs, and the knowledge extraction techniques of KGs. In addition, this paper overviews the current KG applications, problems, and challenges as well as discuss the perspective of future research. The main aim of this paper is to analyse all existing KGs with their features, techniques, applications, problems, and challenges. To the best of our knowledge, such a characterization table among these most commonly used KGs has not been presented earlier.

Big data (BD) approach has significantly impacted on the development and expansion of supply chain network management and design. The available problems in the supply chain network (SCN) include production, distribution, transportation, ordering, and inventory holding problems. These problems under the BD environment are challenging and considerably affect the efficiency of the SCN. The drastic environmental and regulatory changes around the world and the rising concerns about carbon emissions have increased the awareness of customers regarding the carbon footprint of the products they are consuming. This has enforced supply chain managers to change strategies to reframe carbon emissions.. The decisions such as an optimization of the suitable network of the proper lot sizes can play a crucial role in minimizing the whole carbon emissions in the SCN. In this paper, a new integrated production–transportation–ordering–inventory holding problem for SCN is developed. In this regard, a mixed-integer nonlinear programming (MINLP) model in the multi-product, multi-level, and multi-period SCN is formulated based on the minimization of the total costs and the related cost of carbon emissions. The research also uses a chance-constrained programming approach. The proposed model needs a range of real-time parameters from capacities, carbon caps, and costs. These parameters along with the various sizes of BD, namely velocity, variety, and volume, have been illustrated. A lot-sizing policy along with carbon emissions is also provided in the proposed model. One of the important contributions of this paper is the three various carbon regulation policies that include carbon capacity-and-trade, the strict capacity on emission, and the carbon tax on emissions in order to assess the carbon emissions. As there is no benchmark available in the literature, this study contributes toward this aspect by proposing two hybrid novel meta-heuristics (H-1) and (H-2) to optimize the large-scale problems with the complex structure containing BD. Hence, a generated random dataset possessing the necessary parameters of BD, namely velocity, variety, and volume, is provided to validate and solve the suggested model. The parameters of the proposed algorithms are calibrated and controlled using the Taguchi approach. In order to evaluate hybrid algorithms and find optimal solutions, the study uses 15 randomly generated data examples having necessary features of BD and T test significance. Finally, the effectiveness and performance of the presented model are analyzed by a set of sensitivity analyses. The outcome of our study shows that H-2 is of higher efficiency.

Multilevel thresholding-based image segmentation plays a vital role in image processing. It significantly impacts many applications, such as remote sensing, pattern recognition, and medical image diagnosis. Premature convergence due to stuck into the local optima is the main challenge of any evolutionary algorithm-based multilevel image thresholding. Most of the evolutionary algorithms use their stochastic property to comprehensively utilize the search space, which strongly influences premature convergence. This paper presents a novel chaotic symbiotic organisms search (CSOS) optimization for multilevel image segmentation that maintains a strategic distance from premature convergence and improves the performance of conventional symbiotic organisms search (SOS) optimization in multilevel image segmentation. We have analyzed the performance of the proposed CSOS using state-of-the-art entropies such as Kapur’s, Tsallis’, Renyi’s, and Masi’s entropy as objective functions. The experiments on standard used color images are presented to establish the practicality of the proposed algorithm. The results show that the CSOS algorithm with Masi’s entropy is more effective and has wide adaptability to the high-dimensional optimization problems than the other recently proposed algorithms considered in this paper.

Image super resolution technology plays an important role in the field of computer vision. With the application of deep learning in the field of image super-resolution, the generative adversarial network is applied to image super-resolution and obtains images with great quality. In this paper, we propose a novel generative adversarial network structure called Cascading Residual–Residual Attention Generative Adversarial Network (CRRAGAN). First, this paper proposes a novel and efficient feature extraction module: Cascading Residual–Residual Block, which can extract multi-scale information and low-level cascade information to high-level information. CRRAGAN directly uses the channel attention module to capture low-resolution image key information and fuse it into the next stage feature. Second, a new loss combination function is proposed, a weighted sum of image loss, adversarial loss, perceptual loss, and charbonnier loss, to make the network training more stable. In the end, we compare our proposed method with 15 previous state-of-the-art methods and discuss the performance of different training datasets. Experimental results demonstrate that our model exhibits improved performance.

In this paper, traffic and vehicle density optimization issues are discussed, and a suitable solution for autonomous driving environments is proposed. The problem of scalable traffic flow management is hindered by the unpredictable vehicle density and its navigation. This problem increases the flow congestion at the intersection during the change of lane in particular. This paper introduces density-scaling traffic management for autonomous vehicle driving scenarios. The proposed technique relies on navigation output and the autonomous vehicles' trajectory decisions for predicting their availability and time duration. The traffic flow is streamlines based on the predictions; the prediction error is mitigated by rescheduling the successive traffic flow. In this process, predictive iterated learning is employed for improving the scalable traffic flows in an intersection. The predictive learning handles navigation assistance output change of trajectory independently and correlates the decisions' match. The process of prediction matching is performed for improving the flow management with the time factor. The traffic management technique is useful in providing latency-less travel and errorless decision and flow complexity and collision probability.

In recent years, the amount of Internet information data has exploded, and the problem of “information overload” has become a huge challenge for Internet development. Through the prior knowledge of the known data distribution, combined with sample training data to estimate the mathematical model of the overall data. Intelligent recommendation methods based on knowledge graphs and Bayesian networks are a hot spot in the current Internet research, and they are of great significance to search engines and e-commerce. This paper studies the fusion of knowledge graph and Bayesian belief network and its application in personalized recommendation and designs a Bayesian network that describes the purchase behavior of users based on the purchase records of customers across the network. User recommendation is the process of fusing knowledge graphs and Bayesian network reasoning. For the design of the recommendation network, a random reasoning algorithm with approximate reasoning is selected in consideration of calculation scale and accuracy. This paper uses the content filtering technology of KNN algorithm to supplement the sparse matrix, which can improve the quality of collaborative filtering recommendation, thereby improving the quality of final recommendation. In this paper, based on the knowledge graph, the EPCA algorithm and the robust recommendation algorithm are merged to form a robust collaborative recommendation model based on Bayesian probability matrix decomposition. The robust recommendation algorithm further improves the robustness of the recommendation algorithm by introducing the suspected users and target items detected by the EPCA algorithm. Research shows that the PS value of the VarSelect SVD algorithm used in this paper is approximately between 0.36 and 1.04. Compared with the first two algorithms, the PS value of VarSelect SVD has been significantly reduced, and the algorithm robustness has been significantly improved.

Spherical fuzzy sets have recently become more popular in various fields. It was proposed as a generalization of picture fuzzy sets and Pythagorean fuzzy sets in order to deal with uncertainty and fuzziness information. This paper presents a multi-attribute group decision making method based on novel sine aggregation operators to help decision makers choose the optimal alternative. Moreover, the well-known sine trigonometry function preserves the periodic and symmetric nature about the origin, and hence, it satisfies the decision makers preferences over the multi-time phase parameters. Keeping these features and the importance of the spherical fuzzy (SF) sets, the objective of this paper is to present some robust sine trigonometric (ST) operation laws for SF sets. Associated with these laws, we define some series of new aggregation operators (AOs) named as ST-weighted averaging and geometric operators to aggregate the spherical fuzzy information. Afterward, we present group decision making techniques to solve the multi-attribute group decision making problems based on proposed AOs and illustrate with a numerical example of an internet finance soft power evaluation problem to validate it. Also, we conduct some comparison analysis to study the reasonability and practicality of the proposed method.

This paper focuses on the generalized Calogero–Bogoyavlenskii–Schiff equation to extract new complex solutions by using two analytical methods, namely, Bernoulli sub-equation function method, Modified exponential function method. For better understanding of physical meanings of solutions, simulations are reported by using a computational package program. Moreover, strain conditions for validity of complex solutions are also archived. Finally, a conclusion part completes the paper by mentioning the novelties of paper.

The paper initially proves that locally finite covering (LFC-, for short) rough set structures are interior and closure operators. To be precise, given an LFC-space $$(U ,\mathbf{C})$$ , we prove that the lower H-rough set operator $$H_{*}$$ is an interior operator and the upper H-rough set operator $$H^{*}$$ is a closure operator. Besides, we prove a duality of the concept approximations $$(H_{*}, H^{*})$$ and investigate many theoretical and mathematical properties of the H-rough set operators. After pointing out that Khalimsky (K-, for brevity) topological rough set operators have their own features, we prove that the K-topological lower (resp. upper) approximation operator is not an interior (resp. closure) operator from the viewpoint of K-topology. Besides, we intensively investigate theoretical and mathematical properties of the K-topological rough set operators. This research area can be considered as a part of geometric-based rough set theory. These obtained results can promote the studies of rough set theory associated with information geometry, object classification, artificial or computational intelligence, and so on. In the present paper, each of the sets U, $$\mathbf{C}$$ and $$X(\subseteq U)$$ need not be finite.

In this paper, new concepts to use clique covering of a fuzzy graph are introduced for optimization of parameters involved in business strategy. For this purpose, four algorithms are designed for finding necessary parameters and sets of a fuzzy graph which is helpful for constructing a cordon of linear programming problems. The linear programming problems are constructed with suitable optimization functions and constraints. The strengths of the cliques present in fuzzy graph get a new look in this paper. Facility location problems are characterized and solved with a new strategy optimization problems by using concept of clique covering of fuzzy graphs for a smooth business strategy to have a maximized total gain. This optimization process will help for developing a part of sustainable economic growth all over the world. Some new definitions are given with relevant examples of fuzzy graphs. An illustration is given to elaborate all mathematical terminologies. Also, a real-life application to optimize different parameters in a business network by solving the programming problems with the help of the mathematical software “LINGO” keeping the fuzziness of the parameters involved in the considered fuzzy graph.

Nowadays, industries have to deal with an increasingly competitive environment. In this sense, the operational process optimization to increase efficiency is crucial. The industrial systems are presently leaning toward every potential means of automation for improved accuracy and superior time management. There are significant challenges in industrial logistics management: streamline operations, customer service, transportation cost, planning, and risk management. Hence in this paper, the IoT-assisted Intelligent Logistics Transportation Management Framework has been proposed in the industrial environment to design an optimized logistics plan, improve customer service, and reduce transportation cost. This paper concentrates on identifying the optimal routes for the directed autonomous vehicle, considering different vehicles’ necessities, renewable generations, logistic requests, and the essential transportation system. The experimental results have been performed, and the proposed method enhances the overall performance ratio of 97.8% and customer satisfaction ratio of 98.9% and reduces the cost function ratio of 15.2%, energy consumption ratio of 11.4%, and computation time of 0.19 s compared to other existing methods.

The production function has always been an active field in the research and application of economics. Researchers generally use the production function of constant elasticity of substitution, i.e., the C–D (Cobb–Douglas) production function and the CES (Constant Elasticity of Substitution) production function, to explore economic growth, but rarely use the VES (Variable Elasticity of Substitution) production function. The paper gives an extended VES production function model in the case the elasticity of substitution is a time linear function. With regard to the parameter estimation, the paper uses a modern intelligent algorithm, i.e., the PSO (Particle Swarm Optimization) algorithm. The conventional PSO algorithm has a low rate and the poor accuracy of convergence, so the paper proposes an improved PSO algorithm. In the final part, the paper calculates the contribution rates of influencing factors to Chinese economic growth, and the results are consistent with the actual situation.

This paper is about a novel representation based on lossless encoding for medical images. It relies on directional codes for efficient storage and transmission. Image compression is a recent research, where removal or elimination of redundant information is carried out in an image. In this paper, for medical image compression, an edge- and texture-based entropy coding is presented. The proposed work is partitioned into two stages. In the first stage, a novel lossless hex-directional chain code or chain code 16 (C16) boundary representation approach is applied for edge encoding. In this C16 method, a set of pixels are connected along the edge of an image based on the direction code. Eliminating redundant information and retaining the topological information is the basic idea of chain code. It concerns the shape and the structure of the object which helps in preserving the quality of an image. In the second stage, predictor-based entropy coding is applied for adopting texture feature as a basic primitive. Finally, the outcome of the two stages is provided to the entropy coder for attaining compression. The proposed method is tested with standard benchmark chain code dataset and medical image datasets for various modalities of different sizes. Performance metrics such as compression ratio (%), encoding time and mean square error are used to evaluate the proposed technique. From the results attained, it is evident that the proposed technique results in better compression performance when compared to the existing methods.

The feature representation of images is the key to bridge the semantic gap and make computer understand images in the tasks of image retrieval. In image recognition, semantic regions and salient targets are irreplaceable parts of image cognition. The purpose of segmenting semantic regions is to analyze the connections between image targets, while some distinctive targets are able to highlight the important semantics. However, the semantic regions and salient targets were often ignored in previous retrieval methods. Considering the two aspects, this paper proposes an unsupervised image retrieval method based on multi-semantic region weighting and multi-scale flatness weighting. Firstly, we divide the semantic regions by using the Fully Convolutional Network and calculate the multi-Semantic weight map (S-mask) to obtain the global features. Secondly, we introduce a flatness-weighted strategy to weight feature maps and aggregate the multi-scale features to obtain the local features. Finally, we cascade the global features and the local features to construct the final image representation. There are two main contributions in this paper. One is that the S-mask assigns different weights to the semantic regions. It distinguishes the importance of the semantic regions and balances the weight within the semantic region. The other is that the flatness-weighted strategy suppresses the background and highlights the target region. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on Paris and Oxford databases.

This paper contributes to cubic aggregation operator and applications in decision-making problem. In this paper, we use Dombi operational laws and Heronian mean operators, to develop a new concept of cubic fuzzy Heronian mean Dombi aggregation operators, i.e., cubic fuzzy Heronian mean Dombi aggregation (CFHMDA), cubic fuzzy weighted Heronian mean Dombi aggregation (CFWHMDA), cubic fuzzy geometric Heronian mean Dombi aggregation (CFGHMDA), and cubic fuzzy weighted geometric Heronian mean Dombi aggregation (CFWGHMDA) operators. The proposed operators are not deal single aspect, but also deal with the relation between multi-aspects making them more effectively solving decision-making (MADM) problems. We proposed a new algorithm to solve a multi-attribute decision-making problem based on the developed operators. Finally, we solved a MADM problems with the cubic fuzzy Heronian mean Dombi aggregation operators.

This paper presents an evolutionary approach-based solution of multi-objective transportation-p-facility location problem (MOT-p-FLP) that minimizes overall transportation time, cost of transportation, and carbon emission (CE) from available sites to facility sites by seeking transported product quantities and the facility locations in the Euclidean plane. Genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), and modified Self-Adaptive Multi-Population Elitism Jaya Algorithm (SAMPE JA) are utilized to solve the problem. We compared obtained compromise solutions of the problem by evolutionary algorithms with population size, the maximum number of generations, crossover probability, mutation probability, and computational time. Sensitivity analysis for supply, demand, and carbon cap parameters is incorporated in the model’s solution, which helps the decision maker make the appropriate decision. These evolutionary algorithms (NSGA-II and NSGA-III) give Pareto-optimal solutions, and it helps management decide on the selection of p-facility locations. They could transport their product to the facility locations easily, with minimum transport, CE costs, and transportation time. As a result, management can give equal attention to their profit and environment, which helps to build world market credibility. At last, the paper concludes the results.

In this paper, we build the divergence measure of intuitionistic fuzzy sets from the Archimedean t-conorm operators and give their properties. Next, we built the entropy and knowledge measures of intuitionistic fuzzy sets based on their divergence measures. In this paper, relationships between entropy and knowledge measures are also investigated, which gives us many interesting results. We then apply the proposed measures into building the multi-criteria decision-making model. Finally, a numerical example to illustrate the model is also given in this paper.

The paper points out the methodological aspects of soft topological spaces which are defined over an initial universe set U with a fixed set of parameters E. The basic change of view is due to the fact that soft topology is actually a topology on the product of two sets, and in many cases, standard methods of general topology can be applied. Furthermore, in many papers some notions are introduced by different ways and it would be good to give a unified approach for a transfer of topological notions to a soft set theory and to create a bridge between general topology and soft set theory. On the other hand, not all counterparts of soft concepts are studied on classical topology and some types of separation axioms support this fact.

Cloud computing attracted great attention in both industry and research communities for the sake of its ubiquitous, elasticity and economic services. The first class concern of cloud providers is power management for both reducing their total cost of ownership and green computing objectives. To reach the goal, a system framework is presented which has different modules. The main concentration of the paper is on virtual machine (VM) consolidation module which launches users requested VMs on the minimum number of active servers to reduce datacenter total power consumption (TPC). In this paper, the VMs consolidation is abstracted to two-dimensional bin-packing problem and also is formulated to an integer linear programming. Since the papers in the literature scarcely are aware of skewness in resources of requested VMs and for discrete nature of search space, this paper presents the resource skewness-aware VMs consolidation algorithm based on improved thermodynamic simulated annealing approach because resource skewness potentially compels the algorithm to activate additional servers. The proposed SA-based algorithm is validated in extensive scenarios with different resource skewness in comparison with two heuristics and two meta-heuristics. The average results reported from different scenarios proves superiority of proposed algorithm in comparison with other approaches in terms of the number of used servers, TPC, and total resource wastage of datacenter.

In many practical applications, we have several different criteria for making a decision. In such situations, we need to take all these criteria into account. One of the most widely used approaches for solving such multi-criteria decision making problems was proposed in 1979 by S. Opricovic; it is known as VIKOR. This approach has been widely and successfully used—to the extent that the corresponding 2004 paper became, in 2009, the most cited paper in the field of economics. However, this approach is largely empirical; as a result, it is not clear why this approach has been so successful. In this paper, we provide theoretical justifications for the VIKOR approach and thus explain its empirical success.

This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramer–Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms.

In this paper, we define fuzzy matrices (FM), intuitionistic fuzzy matrices (IFM) and a new operation $$ * $$ defined on the set $$ I_{n} = \left\{ {1,2, \ldots ,n} \right\}. $$ The system $$ (I_{n} , * ) $$ is an abelian group. This group is pivotal in our paper. Circulant intuitionistic fuzzy matrices is defined also in this paper via the group $$ (I_{n} , * ) $$ as a new way for defining circulant matrices. However, we study and investigate several properties of circulant intuitionistic fuzzy matrices. The first row (column) of a circulant matrix plays an important role in our studying. From any intuitionistic fuzzy matrix R (not necessarily circulant) we can construct two circulant intuitionistic fuzzy matrices $$ R_{L} $$ and $$ R_{U} $$ such that $$ R_{L} \le R \le R_{U} $$ . We also show that some compositions of circulant intuitionistic fuzzy matrices are also circulant. Moreover, we proved that $$ RS = SR $$ for two circulant intuitionistic fuzzy matrices $$ R $$ and $$ S $$ . However, we give some examples to clarify our results.

The primary conventional method for simplifying legged robots’ complex walking dynamics involves using low-dimensional models such as the linear inverted pendulum model (LIPM). This paper emphasizes utilizing the LIPM plus flywheel model (LIPPFM) for analysis of the complete dynamic motion of the humanoid robot. Inclining toward a more realistic case, the model is improvised to remove the COM’s height constraint (center of mass) and consider the effect of the upper body part using the mass of the pendulum. Furthermore, the double support phase is being discussed in the locomotion phase of the humanoid robot. MPC (model predictive control) approach has been used in this paper, which is tuned with the ACO (ant colony optimization) technique. The desired trajectory, joint angles, has been imparted to the MPC, which provides the robot’s joint motion. This joint motion has been further transferred to ACO, optimizing the step adjustment and providing an expected trajectory to walk over an uneven surface. The simulation has been carried out in an uneven environment based on ACO tuned MPC controller, and further, it has been validated using real-time experiments on humanoid robot NAO. The controller shows a reasonable degree of efficiency in both the real NAO and simulated NAO with a deviation under 5%. The comparative study among various controller shows that the proposed controller lowers the peak overshoot and the settling time. In comparison with the previously developed controller, the deviation in roll angle and pitch angle justifies the selection of the proposed controller.

Sentiment analysis has been used in many studies to predict stock market trends. Current sentimental analysis approaches focus only on the upward and downward movement of the price, which is not sufficient for more precise prediction of stock sentiments. Previous studies have focused on the trend (valence) regarding stocks because it represents the upward and downward trend of a stock. However, investors should be attention on trading (arousal) because the stock price increases or decreases even quickly or slowly; they may not to do trade. Therefore, this paper applies the concept of dimensional valence–arousal to define sentiments which intensities of trading and trends of the stock market for data annotation. The paper proposes a deep learning prediction model to predict the intensities of stock dimensional valence–arousal (SDVA), called the hierarchical title–keyword-based attentional hybrid network (HAHTKN). The paper modifies the hierarchical attention network (HAN) to our proposed HAHTKN model, comprising (1) a title encoder, (2) a keyword encoder, (3) a title–keyword encoder, (4) word-level encoder, (5) a sentence-level encoder and (6) stock DVA prediction layer. The experimental results show that the proposed HAHTKN prediction model for the SDVA task outperformed other baseline machine learning models and HAN-based models.

The neural architecture search (NAS) method provides a new approach to the design of generative adversarial network (GAN)’s architecture. The existing state-of-the-art algorithm, AutoGAN, is an example in discovering GAN’s architecture using reinforcement learning (RL)-based NAS. However, performance differences between the candidate architectures are not taken into consideration. In this paper, one new ImprovedAutoGAN algorithm based on AutoGAN is proposed. We found that which candidate architectures to use can affect the performance of the final network. So in the process of architecture search, compared with AutoGAN, in which candidate architectures are selected randomly, our method uses gradient bandit algorithm to increase the probability of selecting networks with better performance. This paper also introduces a temperature coefficient in the algorithm to prevent the search results from getting trapped in the local optimum. The GANs are searched using the same search space as AutoGAN, and the discovered GAN has a Frechet inception distance (FID) score of 11.60 on CIFAR-10, reaching the best level in the current RL-based NAS methods. Experiments also show that the transferability of this GAN is satisfying.

The proliferation of social networks resulted in a remarkable increase in their popularity empowering users to create, share and exchange content for interaction and communication among them. However, these have also opened new avenues for malicious and unauthorized use. This paper presents an intrinsic profiling-based technique for the assessment of authorship verification and its application toward detection of compromised accounts. For the same, efficiency of different textual features has been examined. Four categories of features, namely content free, content specific, stylometric and folksonomy, are extracted and evaluated. Experiments are performed with 3057 twitter users taking 4000 latest tweets for each user. Various feature selection techniques are used to rank and select optimal features for each user which are further combined using a popular rank aggregation technique called BORDA. The problem of authorship verification in this paper is studied as a unary classification problem. Performance of various one-class classifiers, namely Local Outlier Factor, Isolation Forest and One-Class SVM, is analyzed on the basis of different evaluation metrics. Experimental results depict that OCC-SVM with rbf kernel outperformed other one class classifiers attaining an average F-score of 87.29% and Matthews Correlation Coefficient of 74.42% under varied parameter settings.

The extended belief rule-based (EBRB) system is an enhancement of the traditional belief rule-based (BRB) system, which extends the belief distribution of the antecedent attributes. Compared with BRB system, EBRB system has stronger knowledge expression ability. EBRB system uses relational data to generate rules based on traditional belief rule base, which is simple and effective. However, EBRB system needs to traverse all the rules in the rule base and has the problems of inefficiency and inconsistency. Although the existing search optimization methods can solve this problem to some extent, they generally have the shortcomings of insufficient generalization ability. In view of this, this paper proposes a new rule search optimization algorithm based on K-means clustering tree (KMT-EBRB). By combining with the bagging algorithm, an EBRB system based on random clustering forest (RKMF-EBRB) is implemented. To evaluate the performance of the developed model, this paper chooses 12 common UCI datasets for two groups of experiments. Firstly, the properties of EBRB system based on K-means tree and K-means forest are studied, and then the new method is compared with existing methods. The experimental results show that K-means tree can improve the search efficiency of EBRB system, while random K-means clustering forest can further improve the accuracy and stability of EBRB reasoning.

An efficient feature detection algorithm and image classification is a very crucial task in computer vision system. There are various state-of-the-art feature detectors and descriptors available for an object recognition task. In this paper, the authors have compared the performance of Shi-Tomasi corner detector with SIFT and SURF feature descriptors and evaluate the performance of Shi-Tomasi in combination with SIFT and SURF feature descriptors. To make the computations faster, authors have reduced the size of features computed in all cases by applying locality preserving projection methodology. Features extracted using these algorithms are further classified with various classifiers like K-NN, decision tree and random forest. For experimental work, a public dataset, namely Caltech-101 image dataset, is considered in this paper. This dataset comprises of 101 object classes. These classes have further contained many images. Using a combination of Shi-Tomasi, SIFT and SURF features, the authors have achieved a recognition accuracy of 85.9%, 80.8% and 74.8% with random forest, decision tree and K-NN classifier, respectively. In this paper, the authors have also computed true positive rate, false positive rate and area under curve in all cases. Finally, the authors have applied the adaptive boosting methodology to improve the recognition accuracy. Authors have reported improved recognition accuracy of 86.4% using adaptive boosting with random forest classifier and a combination of Shi-Tomasi, SIFT and SURF features.

In this paper, we concentrate the application of parametric reduction of soft set in decision making problem. The main purpose of the paper is to select the key parameters for selection or purchase of objects. Using key parameters person or selection committee will select the optimal choice object. We put forward an algorithm in decision making problem and provide some examples. We focus on parameter reduction of soft set and how it helpful in effective and efficient decision making of the people in a short time.

Statistical process control is an effective quality control technique to monitor a production process with balanced data under certain conditions. However, there are some situations where dealing with uncertainty and unbalanced data is considered. In such situations, the traditional statistical control charts are not effective to obtain control limits. The aim of this paper is fourfold. First of all, the collected unbalanced data are converted to triangular fuzzy numbers for each sample. Second, this paper develops a fuzzy $$ \bar{X} - S $$ control chart while dealing with unbalanced fuzzy data. Third, a proposed approach is presented on how to deal with unbalanced fuzzy data for calculations of control limits. Besides, fuzzy process capability analyses are conducted to measure process performance. Finally, an illustrative example is conducted to show the effectiveness of the proposed fuzzy $$ \bar{X} - S $$ control chart with unbalanced data for uncertainty.

The novelty of this paper is to construct several explicit formulas for the number of distinct fuzzy matrices of a finite order which leads us to invent new integer sequences and helps to develop fuzzy subgroups of some finite groups of matrices. In order to achieve the sequences, we analyze the behavioral study of a natural equivalence relation on the set of all fuzzy matrices of a given order. In addition, this paper derives some important relevant results by enumerating non-equivalent class of fuzzy matrices. We achieve these results by incorporating the notion of $$k$$ -level fuzzy matrices, $$\alpha$$ -cuts and chains.

With the increasing hazard to the environment and progressions in renewable energy technologies, hybrid renewable energy systems can meet the energy demand. Total dependability can be attained by adding battery banks to hybrid systems. This paper aims to optimally design a multi-source grid isolated hybrid generation system for future smart cities in the state of Tamil Nadu, India. This off-grid power generation consists of wind turbine generators, photovoltaic panels, inverters, diesel generators, and batteries. The design intents at minimizing Net Present Cost, Unmet Load, and CO2 emissions which are conventionally contradictory to each other. This paper analyzes the optimal off-grid combination of components and control strategies, utilizing improved Hybrid Optimization using the Genetic Algorithm. The results obtained portrays that a mix of hybrid renewable energy generators at off-grid locations without diesel generators can be a cost-effective choice of new smart cities, and it is sustainable, environmentally viable, and techno-economically feasible. Subvention is imperative to take on the large-scale system due to the high capital cost.

Spatial feature matching is the key to detecting incremental changes in spatial data and extracting the updated information. The accuracy of spatial feature matching can depend on the structural organization of the data being compared; inconsistent data structures make comparison more difficult. OpenStreetMap (OSM) road network data, for example, is updated frequently to the point of being unstable, making the matching process used in information extraction susceptible to interference. To use OSM for comparison with other road data sources, this problem must be addressed. This paper proposes a new multi-scale dynamic matching algorithm based on a hierarchical stroke mesh (HSM) to detect matches between OSM data and professional surveying and mapping data and to update the change information. By improving the integrity and continuity of the stroke generation method and its algorithm for evaluating the importance of information, the algorithm proposed in this paper identifies the spatial hierarchy contained in the road network and abstracts the road network. The result is the HSM. The algorithm is based on multi-scale matching constraint rules designed from coarse to fine in terms of both resolution and granularity. It is used to detect one-to-one or one-to-many mapping relationships among different mesh levels (mesh, mesh boundary segment, and mesh inner segment). This allows progressive iterative matching between the older survey data and the newer OSM data. The results show that the HSM algorithm proposed in this paper can detect incremental changes between the two vector data sources quickly and accurately. Compared with others, this algorithm can effectively improve matching accuracy while sacrificing little performance.

Most production scheduling problems, including standard flexible job shop scheduling problems, assume that machines are continuously available. However, in most cases, due to preventive maintenance activities, machines may not be available for a certain time. Meanwhile, in the entire workshop production process, the transportation process of workpieces cannot be ignored. Therefore, the impact of transportation on the production planning should be considered in the scheduling process. To consider both preventive maintenance and transportation processes in the flexible job shop scheduling problem, this paper proposes a flexible job shop scheduling problem considering preventive maintenance activities and transportation processes and establishes a multi-objective flexible job shop scheduling model optimizing the total energy consumption and total makespan. Furthermore, a multi-region division sampling strategy-based multi-objective optimization algorithm integrated with a genetic algorithm and a differential evolution algorithm (MDSS-MOGA-DE) is proposed to solve the model. In the proposed algorithm, a multi-region division sampling strategy and two evaluation functions are utilized to improve the diversity of solutions. In addition, this paper combines a genetic operation and a differential operation to further enhance the search ability of the algorithm. The validity of the algorithm is verified by a real case. The computational results reveal that the proposed model and algorithm obtain appropriate results and have the potential to be applied to other similar problems.

The decision-making under several uncertainties is a major concern to choose the best alternative among the several separate alternatives. This paper deals with the uncertainty of using a complete ranking classification of generalized trapezoidal fuzzy numbers (GTrFNs). In the view of that several measures such as mode, spread, midpoint the score, radius, left and right fuzziness score and linguistic expression are considered to compute the ranking order of GTrFN. Using the proposed complete ranking of GTrFN, this paper presents a method for solving fuzzy multi-criteria decision-making problems. The comparative analysis of existing methods with our proposed method is also described.

The aim of this paper is to identify the co-expressed potential genes that may serve for the development of the portions of normal or tumor. This paper differentiates the co-expressed genes into normal samples and tumor samples from gene expression dataset GSE25066. Since the dataset has vague boundaries and having common characteristics between the clusters, identifying the subgroups contain similar gene expression is really a tricky task one. Therefore, this paper introduces an effective fuzzy iterative clustering algorithm by incorporating kernel function, possibilistic c-means, fuzzy memberships, neighborhood information, median of neighboring objects and penalty term. The performances of the proposed clustering techniques have been shown through the succession experimental works on GSE25066. The effects of clustering results have been proved through comparing the resulted classes with ground truth.

This paper deals with the robust pinning synchronization issue of uncertain fractional-order neural networks with discontinuous activations (FNNDAs) by means of the linear matrix inequalities (LMIs). In this paper, a class of FNNDAs model is presented. Moreover, an appropriate pinning controller is designed to ensure the error dynamical system gets robust Mittag–Leffler stability via Lyapunov function approach, non-smooth analysis theory and inequality analysis technique. In addition, the robust pinning synchronization conditions of FNNDAs drive system and FNNDAs response system are obtained in terms of the LMIs. Finally, a typical numerical simulation is provided to show the effectiveness of the obtained results.