Search

Growing older is a phenomenon that is associated with increasingly complex health situations as a result of the coexistence of several chronic diseases. As a result, there is a downward tendency in both older people and their caretakers’ quality of life, which frequently results in frailty. There are numerous solutions available to treat the issue, which primarily affects older people. The basic and most popular imaging method for predicting cognitive impairment is magnetic resonance imaging. Furthermore, few of the earlier models had a definite level of accuracy when diagnosing the condition. Further, there is a critical need to put in place a stronger, more reliable approach to precise prediction. When compared to other procedures, using magnetic resonance images to predict cognitive decline is the safest and most straightforward. The advanced concept for a better optimized strategy to predict cognitive impairment at an early stage is presented in this research. The hybrid krill herd and grey wolf optimization method is offered as a solution to address the challenges in locating the impacted area. In a short amount of time, a significant number of MRI images are analyzed, and the results show a more precise or higher rate of recognition. [ABSTRACT FROM AUTHOR]

This paper improves the current situation of English language teaching from a technological perspective. Fuzzy that is a method of having local information to project the whole. Soft computing is a kind of optimization relative to traditional should computing, emphasizing the uncertainty and imprecision of things, and with the development of modern society, soft computing can solve many problems in production practice. Scenario teaching method is an important asset to enrich classroom content and mobilize students' learning enthusiasm. Situational teaching is the sum use of preparation before class to improvisation in the classroom. It has been proved that using scenario teaching in the process of English teaching can improve students' English ability. However, this traditional situational teaching method has problems such as incomplete evaluation index and poor teaching effect. To solve this problem, we constructed a new situational teaching method and a fuzzy evaluation system based on fuzzy sets and soft computing technology, and verified through experiments that both the new situational teaching method and the fuzzy evaluation system have good effects. At present, the popularity of situational English teaching is low, the overall level of English teaching is not high, fill-in-the-blank teaching is predominant, spoken English is weak, the cost of situational teaching is high, and teachers' willingness to implement it is low. To improve these problems, based on the research results above we propose the following four recommendations to improve the quality of contextualized teaching, solve the problems of contextualized teaching, and reduce teachers' pressure to prepare lessons. Finally, we make recommendations based on the current situation of the development of scenario-based teaching. (1) Build situational teaching in many ways. (2) Improve the quality of teachers' English. (3) Develop individualized improvement programs for students. (4) Promote the reform of teaching tools. [ABSTRACT FROM AUTHOR]

The continuous emergence of new social media has promoted the interaction of data in life. In this context, big data technology has gradually become a type of technology with special value in the field of education. It realizes business decision-making through the collection and analysis of specific types of data. The application of technology to the field of education can realize the analysis of students' learning needs and classroom teaching efficiency, while the application of big data technology to piano teaching can strengthen the combination of piano teaching and technology, and at the same time provide more data analysis and decision support for the whole process of piano teaching. Soft computing simplifies the calculation process by combining a variety of sophisticated algorithms. Soft computing is similar to human brain processing and perception. It can complete multi-variable complex calculations through low-cost and uncertain processes. With the continuous integration, the variables generated in the teaching process gradually increase, and the application of soft computing technology can effectively solve the complex calculation problems caused by many variables in the analysis of piano teaching. This paper first analyzes the basic theory of big data, soft computing and piano informatization teaching, and then obtains the problems existing in the current piano informatization teaching through questionnaire survey and analysis, and designs piano informatization, teaching innovation model based on big data and soft computing technology respectively. Through the post-investigation of the piano informatization teaching institutions combined with technical strategies, it is found that the degree of match between the piano informatization teaching content of the institution and the students' learning needs, the evaluation accuracy of piano teachers' teaching effects, and the students' piano learning performance have improved. [ABSTRACT FROM AUTHOR]

Mobile Adhoc Networks (MANET) in modern research have many optimal energy conservation mechanisms that can be deployed easily and in a faster manner. The routing approaches associated with energy consumption play a dominant role in routing the data packets between the mobile sensor nodes within the range of optimization. However, major challenges associated with energy consumption in MANETs include reduced lifetime of sensor nodes, poor coverage, and throughput. Most methods tend to reduce the interference of data while traversing between the sensor nodes and increase the capacity of the network. This results in delays while transmitting the packets across the network, and this may result in failure of packets being transmitted. To resolve this issue, in this paper, we propose an ant colony optimization combined with a flower pollination algorithm for minimal energy consumption and throughput maximisation in MANETs. This hybrid meta-heuristic model resolves the issues, including delays, poor coverage, and reduced network lifetime. This hybrid model uses the estimation of neighbourhood distance among the nodes for optimal placement of nodes for effective location. The estimation of location is found using a flower pollination algorithm with a levy flight mechanism. The estimation is carried out in a hyper sphere model that helps in finding the coverage area of the sensor nodes. Depending upon the estimation of neighbourhood distance among the sensor nodes, the consumption of energy among the sensor nodes in MANETs is reduced. The simulation was conducted between the proposed hybrid approach and conventional soft computing heuristics, where the results show that the proposed model achieves a higher rate of energy conservation and reduces delay than other methods. [ABSTRACT FROM AUTHOR]

As the population increases day by day, there has been a tremendous increment of ownership over two-wheeler and four-wheeler systems. Thereby usage of public transports system decreases than private vehicles system. In this scenario, this study focused on the significant factors in the mode choice behaviour of commuters from the extensive household survey conducted by Thiruvarur City to obtain detailed information on the modal split pattern analysis. The questionnaire of 4857 respondents was used in the Multinomial Logit Model using the Statistical Packages for Social Science (SPSS) tool to predict the mode choice behaviour of commuters. A total of seven explanatory variables were extracted from the responses; the Multinomial logit model was calibrated to fit the data, and the chi-square test was used to measure the goodness of fit. Apart from the MLM model, commuter behaviour prediction analysis is also made using soft computing technologies, which involves multi-linear regression and neuro-fuzzy model, keeping uncertainties involved in commuter behaviour to avoid collecting household data. It was found that two-wheeler commuters are more in numbers for their travel choice as cost minimizing than four-wheelers, public transport system commuters. Consequently, the developed model predicts the behaviour of commuters in a precise manner that closely matches the actual conduct. [ABSTRACT FROM AUTHOR]

Interval complex neutrosophic soft sets (I-CNSSs) refers to interval neutrosophic soft sets (I-NSSs) featuring three two-dimensional independent membership functions accordingly (falsity, indeterminacy, as well as uncertainty interval). A relation is a tool that helps in describing consistency and agreement between objects. Throughout this paper, we insert and discuss the interval complex neutrosophic soft relation (simply denoted by I-CNSR), a novel soft computing technique used to examine the interaction degree among corresponding models known as I-CNSSs. We present the definition of the Cartesian product of I-CNSSs followed by the definition of I-CNSR. Furthermore, the definitions and some theorems and properties related to the composition, inverse, and complement of I-CNSR are provided. The notions of symmetric, reflexive, transitive, and equivalent of I-CNSRs are proposed, and the algebraic properties of these concepts are verified. Furthermore, we demonstrate the relevance of our notion to real-world situations by offering a suggested method for solving a decision-making issue in the field of economics. Ultimately, an analysis is made between the current relationships and the proposed model to determine the model's significance. [ABSTRACT FROM AUTHOR]

COVID-19 is a contagious respiratory illness that can be passed from person to person. Because it affects the lungs, damages blood arteries, and causes cardiac problems, COVID-19 must be diagnosed quickly. The reverse transcriptase polymerase chain reaction (RT-PCR) is a method for detecting COVID-19, but it is time consuming and labor expensive, as well as putting the person collecting the sample in danger. As a result, clinicians prefer to use CT scan and Xray images. COVID-19 classification can be done manually, however AI makes the process go faster. AI approaches include image processing, machine learning, and deep learning. An AI model is required to diagnose COVID-19, and a dataset is necessary to train that model. A dataset consists of the information from which the model is trained. This paper consists of the review of different image processing, machine learning and deep learning papers proposed by different researchers. As well as models based on deep learning and pretrained model using gradient boosting algorithm The goal of this paper is to provide information for future researchers to work with. [ABSTRACT FROM AUTHOR]

Pythagorean fuzzy set is a reliable technique for soft computing because of its ability to curb indeterminate data when compare to intuitionistic fuzzy set. Among the several measuring tools in Pythagorean fuzzy environment, correlation coefficient is very vital since it has the capacity to measure interdependency and interrelationship between any two arbitrary Pythagorean fuzzy sets (PFSs). In Pythagorean fuzzy correlation coefficient, some techniques of calculating correlation coefficient of PFSs (CCPFSs) via statistical perspective have been proposed, however, with some limitations namely; (i) failure to incorporate all parameters of PFSs which lead to information loss, (ii) imprecise results, and (iii) less performance indexes. Sequel, this paper introduces some new statistical techniques of computing CCPFSs by using Pythagorean fuzzy variance and covariance which resolve the limitations with better performance indexes. The new techniques incorporate the three parameters of PFSs and defined within the range [-1, 1] to show the power of correlation between the PFSs and to indicate whether the PFSs under consideration are negatively or positively related. The validity of the new statistical techniques of computing CCPFSs is tested by considering some numerical examples, wherein the new techniques show superior performance indexes in contrast to the similar existing ones. To demonstrate the applicability of the new statistical techniques of computing CCPFSs, some multi-criteria decision-making problems (MCDM) involving medical diagnosis and pattern recognition problems are determined via the new techniques. [ABSTRACT FROM AUTHOR]

There are numerous large-scale global optimization problems encountered in real-world applications including engineering, manufacturing, economics, networking fields. Over the last two decades different varieties of swarm intelligence and nature inspired based evolutionary algorithms (EAs) were developed and still. Among them, particles swarm optimization, Firefly algorithm, Ant colony optimization, Bat algorithm are the most popular and recently developed leading swarm intelligence based approaches. They are mainly inspired by the social and cooperative behaviors of swarm likewise herds of animals, flocking of birds, schooling of fish, ant colonies, herds of bisons and packs of wolves working together for their common benefit. Due to easy implementation and high capability in achieving of absolute optimum, swarm intelligence based algorithms have attained a great deal attention in both academic and industrial applications. This paper proposes a hybrid swarm intelligence (HSI) algorithm that employs the Bat Algorithm (BA) and the Practical Swarm Optimization (PSO) as constituents to perform their search process for dealing with recently designed benchmark functions in the special session of the 2017 IEEE congress of evolutionary computation (CEC'17) [3]. The approximate solutions for most of the CEC'17 benchmark functions obtained by the suggested algorithm in its twenty five independent runs of trails are much promising as compared to its competitors. [ABSTRACT FROM AUTHOR]

Video compression is applied for reducing the requirement of hardware, bandwidth, hard drives and power consumption for storing and processing an excessive amount of data generated by videos. The computationally intensive and most time-consuming segment of video compression is known as motion estimation (ME). ME process can be regarded as an optimization problem where search is carried out in a predefined search area of the target frame to locate the identical macroblock (MB) corresponding to each MB in anchor frame by minimizing the objective function cum search criterion as minimum value of search criterion identifies the location of the best matching MB. Since the efficiency of ME decides the efficiency of Video compression, a rich number of fast block matching algorithms (BMAs) were reported to maintain the tradeoff between the computational complexity and visual experience of video during the ME process. Investigation reveals that most of the pattern-based BMAs are prone to the local optimum and stuck in sub-optimal results. Due to the emergence of various nature-inspired algorithms (NIA) like particle swarm optimization (PSO), genetic algorithm (GA), evolutionary algorithm, etc. and their application in optimizing all types of day to day problems has opened a new era in the field of ME. Our investigation focuses on the application of all types of NIA reported to date for optimizing the ME process in terms of speed, accuracy, and quality. This investigation will analyze all the NIAs and their methodologies through an extensive study of their accompanying publications and will enable us to do a detailed comparison to highlight the competitive advantage of soft computing techniques over existing pattern-based algorithms. [ABSTRACT FROM AUTHOR]

The problem of solving linear and nonlinear system of equations is a largely useful issue of significant importance such as that it is presented on real-time applications. Different numerical methods such as fixed-point, Newton Raphson, bisection and secant methods, and or others, namely; evolutionary and computational methods are used to solve nonlinear system under optimizing the space and time complexity. Genetic Algorithm (GA) proved to be an efficient soft computing approach to solve many linear/non-linear system of equations. In this article, a comparison between different GAs and numerical methods for solving a system of equations are introduced. From the results, a novel approach is introduced which is inspired by using a modified GA to get the optimistic solution for the system which has no numerical solution and verified with the highest performance measures in solving complicated problems. [ABSTRACT FROM AUTHOR]

Obtaining interval estimates of nonlinear model parameters is as important as point estimates of model parameters. Because the estimated value of the parameters cannot always be expressed as a single numerical quantity exactly. In this study, it is aimed to propose an interval estimation procedure for nonlinear model parameters with combining soft computing methods instead of using probabilistic assumptions. For this purpose, response and model parameters were presented as triangular fuzzy numbers (TFNs) in nonlinear regression model. The errors were defined as intervals through alpha-cut operations and minimized according to the least absolute deviation (LAD) metric. The novelty of the study is achieving the minimization in a multi-objective framework in which the objective functions are lower and upper bound of interval type error functions. The NSGA-II (Non-dominated Sorting Genetic Algorithm-II) and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods were used for multi-objective optimization (MOO) and multi-criteria decision making (MCDM) stages, respectively. Innovatively, in order to obtain reasonable interval estimates, predefined sized compromise solution set was composed and the fuzzy C-means (FCM) clustering algorithm was applied to the compromise set of interval estimates according to the predicted alpha-cut values. The proposed interval estimation approach is applied on a synthetic and a real data sets for application purpose. [ABSTRACT FROM AUTHOR]

The theory of soft sets is a powerful mathematical tool to handle the vagueness in data, while the m-polar fuzzy (mF, for short) sets have ability to deal with uncertainty as well as multi-polarity of the data in many situations. In this research article we present two novel hybrid models for soft computing, namely, m-polar fuzzy N-soft sets ((m, N)-soft sets, for short) and m-polar fuzzy N-soft rough sets ((m, N)-soft rough sets, for short). We define related concepts and investigate some of their fundamental properties. We also present applications of these hybrid models to multi-attribute decision-making. [ABSTRACT FROM AUTHOR]

This study presents a new soft computing approach based on failure mode and effects analysis (FMEA)'s concept for sustainable supplier selection problem (SSSP) in the light of multi-attributes decision analysis. The approach determines weights of experts by considering interval-valued fuzzy sets (IVFSs) and asymmetric uncertainty information simultaneously. The presented group decision approach assesses the sustainable suppliers as indicated by risks of economic, social and environmental measurements. Ideas of fuzzy possibilistic statistical modeling are brought into the soft computing approach. New definitions are enhanced by fuzzy possibilistic statistical positive and negative ideal solutions. In addition, new relations of fuzzy evaluating and prioritizing of the risks are presented. Then, an application example is tackled by the presented soft computing approach to exhibit its capacity by three measurements of the sustainability for the evaluation of sustainable suppliers. [ABSTRACT FROM AUTHOR]

Market Segmentation has been a key area of implementation of soft computing techniques in E-commerce applications. Various techniques have been used to achieve maximum results in the classification of the ecommerce market. From stochastic techniques to neural networks, there is a plethora of techniques that have been applied. In this paper, we use self organising Maps (SOMs) an unsupervised learning technique to study the various factors which can be used to segment the market. On the other hand supervised learning techniques such as Nearest Neighbour (NN) and Support vector machine (SVM) are used to quantitatively classify the purchase behaviour based on various factors. The better classification technique is identified through appropriate measures. Further, evolutionary algorithms are used to augment the performance of these classification techniques. Analysis of the results and various factors affecting it is also performed. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notion of fuzzy filters on equality algebras and study a fuzzy filter generated by a fuzzy set. We also solve the open problem which was presented by Kadji, Lele and Tonga in [Soft Computing, 21 (2017) 1913-1922]. In addition, we denote the set of all cosets of a fuzzy filter f by E/f, and prove that the structure (E/f, ∧ , ∼ , f1) is an equality algebra, and is isomorphic to the (E/ff(1), ∧ , ∼ , ff(1)), where ff(1) = {x ∈ E|f (x) = f (1)}. At the meantime, we define fuzzy congruences on equality algebras and show that there is one-to-one correspondence between fuzzy filters and fuzzy congruences. Finally, we investigate some topological properties of uniform topology induced by the special family of extreme fuzzy filters. [ABSTRACT FROM AUTHOR]

In this research paper, the notions of rough m-polar fuzzy (mF, for short) sets and mF soft sets, as new hybrid models for soft computing, are introduced and some of their fundamental properties are investigated. Rough degrees of mF sets are computed. In addition, potential applications of rough mF sets and mF soft sets to decision-making problems are explored. [ABSTRACT FROM AUTHOR]

There are two kinds of electronic components—passive and active. One of the most used electrical components is diode, because of its ability to convert AC (alternate current) power supply into DC (direct current) for use in the daily life. A diode can be used as both full-wave and half-wave rectifier. Bridge circuit theory is commonly applied to full-wave rectifiers in soft computing engineering. This feasibility based on circuit simulation study aptly uses bridge circuit theory in a half-wave rectifier by replacing two diodes from a block with two resistors and providing only one positive input. The experimental results verify that the bridge circuit theory works fairly well with half-wave rectifier, and the output is the same as that of a simple half-wave rectifier circuit. [ABSTRACT FROM AUTHOR]

Data mining and soft computing techniques have been widely used in Jiles-Atherton (J-A) hysteresis model parameters identification for ferromagnetic materials or ferromagnetic composites. However, the model cannot be applied to magnetostrictive composites (MSC). That is because not only the nonmagnetic matrix will change the magnetic field distribution in the composites, but also the magnetostriction will be affected by the fabrication procedure. In order to realize the pre-estimation of the magnetostrictive composites magnetic properties, we present a new prediction method. This method is based on modified J-A hysteresis model, utilizing data mining technique to identify model parameters from the raw measured data of magnetostrictive alloy. A methodology including-experimental determination, MJA model, parameters identification by differential evaluation algorithm, is discussed in detail. Then, the experimental data of magnetostrictive composites are compared to the simulations.The theoretical model agrees with the measurement results very well. Our study provides a reference for the performance evaluation of magnetostrictive composites before the preparation process. [ABSTRACT FROM AUTHOR]

EEG (Electroencephalography)-based motor imagery signal classification is very important in brain-computer interface (BCI) technology. In this paper, we improve a classification method by utilizing AdaBoost, an extension of artificial intelligence (AI), instead of linear discriminant analysis (LDA). In order to confirm the classification improvement, the classification accuracy of LDA and AdaBoost has been analyzed. The data set used in the simulation was Data set III of BCI Competition II, and the Matlab tool has been used for the simulation. The results of the proposed scheme with Adaboost show a dramatic improvement in classification compared to that than LDA. [ABSTRACT FROM AUTHOR]

Convergence and well-distribution are two basic issues in multi-objective optimization problems(MOPs). However, it is hard to optimize them simultaneously for the selection of leader particle is not always leading the population to the Pareto front. To make a better performance of multi-objective particle swarm optimization algorithm(MOPSO), decomposition and domination leadership particle selection mechanism have been introduced into MOPSO. Decomposition leader particle selection mechanism is used to keep the swarm with diversity, while domination leader particle selection mechanism make the particles move to the Pareto front. The performance of our proposed method is validated based inverted generation distance(IGD) and compared with five state-of-the-art algorithms on a number of unconstrained benchmark problems. Empirical analysis demonstrates the superiority of our proposed method on both proximity and diversity. [ABSTRACT FROM AUTHOR]

Rough set theory is a mathematical framework that can be visualized as a soft computing tool dealing with the vagueness and uncertainty of data and is applied to pattern recognition, data mining, and knowledge discovery. Document clustering is another area of research with values which are a bag of words that describe contents within clusters. This work analyzes how rough set theory is used for document clustering to fix issues that clustering methods manage. In this survey, an exhaustive literature reviewof the concept of rough sets, as well as howthe lower and upper approximation of a set can be used for document clustering, has been presented. Rough set clusters are shown to be useful for representing real-time applications such as biomedical inferences, network data handling, and citation analysis. The survey is done in phases, showing how machine learning algorithms have been incorporated for document clustering using rough set theory, as well as how rough set theory has been extended to adapt to document clustering with feature selection techniques and feature/dimensionality reduction and, finally, ending with a view of assorted clustering tasks where rough set theory is applied. The classification of rough set theory for document clustering is depicted and its applications presented in this paper. The rough set theory works with resolving ambiguity and uncertainty in data. To the best of our knowledge, a rough set clustering survey has not been done earlier in the literature reviewed and the survey ends with a critical analysis of rough set theory in each application of clustering. [ABSTRACT FROM AUTHOR]

An intelligent autonomous robot is supposed to collect information of the environment using camera(s) and/or sensor(s), analyze the collected data and plan its motion, execute the planned motion by avoiding collision with the obstacle (if any), on-line. Moreover, the robot should learn from its past experience. The principle of soft computing had been used in all the above areas of research by various researchers. A thorough literature review has been carried out in the present paper. Although a lot of studies had been reported in each of the above areas, there are scopes for further improvement. Some research issues have been identified for future study. [ABSTRACT FROM AUTHOR]

This paper describes research in progress on the application of Neuro-Fuzzy (NF) to support large-scale network traffic dumps analysis in the domain of Network Security. In particular we focus on patterns of benign and malicious activity that can be found in network traffic dumps and can facilitate to firewall rules generation. We propose several improvements to the NF algorithm that results in proper handling of large-scale datasets, significantly reduces number of rules and yields a decreased complexity of the classification model. This includes better automated extraction of rules parameters as well as bootstrap aggregation for generalization. Experimental results show that such optimization gives a smaller number of rules, while the accuracy increases in comparison to existing approaches. In particular, it showed an accuracy of 98% when using only 39 rules. In our research we contribute to forensics science and network security by bringing more generalized fuzzy rules. An increase in network communication over the last decade cause to appear such amount of data that can be related to Big Data due to its complexity. Application of Soft Computing methods, such that Neuro-Fuzzy may bring not only sufficient classification accuracy of normal and attack traffic, yet also facilitate in understanding traffic properties and developing a decision-support mechanism. In particular, we focus on network firewalls application. [ABSTRACT FROM AUTHOR]

Soft Computing (SC) techniques are oriented towards the analysis and design of intelligent systems covering an interdisciplinary methodological framework. Soft computing as it was defined by Professor Lotfi Zadeh is a collection of methodologies that cope with the main disadvantage of the conventional (hard) computing the poor performances when working in uncertain conditions. [Extracted from the article]

Neural Networks are used together with fuzzy inference systems in Neuro-Fuzzy, a prominent synergy of rules parameters unsupervised discovery and supervised tuning of classification model. The binary classification task in Network Forensics applications are the most widely used and applied for detection "benign" and "malicious" activities. However, in many areas it is not enough to distinguish between those two classes, yet also important to provide a more specific determination of what exactly "malicious" sub-class some action belongs to. Despite the inherited properties and limitations of Neural Networks, the Neuro-Fuzzy may be tuned to handle non-linear data in multinomial classification problems, which is not a simple addition to a binary classification model. This work targets the optimization of the Neuro-Fuzzy output layer construction and rules tuning in multinomial classification problems as well as solving accompanying challenges. Moreover, we performed extensive study of ML methods designed for binary- and multinomial classification problems. We believe that our approach will help to derive more accurate fuzzy rules multinomial model to be used for web attacks identification. [ABSTRACT FROM AUTHOR]

Crisis management is being dealt extensively with reliable data sources that are being collected continuously by national and local authorities. These open data creating a radical opportunity for the successful prediction of crisis. At the same time, it poses some challenges to the researchers for its heterogeneity, real time and massive data. Recently, soft computing based data mining plays a very vital role as a source of innovation in crisis information management, in obtaining more valuable information. In this paper, we propose to use neural network based forecasting for obtaining post crisis scenario. Further, we use hybrid soft computing methodologies by combining attribute selection with PSO (Particle Swarm Optimization), GA (Genetic Algorithm) and EA (Evolutionary Algorithm); hybrid classification by fuzzy-rough VQNN (Vaguely Quantified Nearest Neighbor) and MLP (Multilayer perceptron neural network). We use three open datasets such as: rival crisis, dyadic and global terrorism data collected from World Bank for our experimentation. Finally, we conclude with encouraging results obtained from EA based VQMLP that provides best prediction in terms of various performance measures considered, for all crisis scenarios which will help us in understanding to take future course of action in such a situation. [ABSTRACT FROM AUTHOR]

This paper shows a few novel calculations for wind speed estimation, which is focused around soft computing. The inputs of to the estimators are picked as the wind turbine power coefficient, rotational rate and blade pitch angle. Polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) technique to estimate the wind speed in this study. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The results are compared with the adaptive neuro-fuzzy (ANFIS) results. [ABSTRACT FROM AUTHOR]

One of the main objectives when designing welded products is to reduce strains and deformations. Strains can cause excessive angular distortion. This results in a welded product that does not meet acceptable tolerances. The geometry of the weld bead (height and width) depends on the input parameters (speed, voltage and current), and provides the welded joint with strength and quality. As welded products become increasingly complex, deformations become more difficult to predict as they depend greatly on the welding sequence. This paper shows how a combination of the Finite Element Method, Genetic Algorithms and Regression Trees may be used to design and optimize complex welded products. Initially, Artificial Neural Networks and Regression Trees that are based on heuristic methods and evolutionary algorithms were used in predicting the weld bead geometry according to the input parameters. Then, thermo-mechanical Finite Element models were created to obtain the temperature field and the angular distortion using the weld bead geometry that the best predictive models generated. Finally, optimization techniques that are based on Genetic Algorithms were used to validate these Finite Element models against experimental results, and to subsequently find the optimal welding sequence to use in the manufacture of complex welded products. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attack is a coordinated effort between several machines to attack one or multiple target systems. Indeed, the attack is through imposing additional burden on system resources or flood computation and communication resources, the server services for legitimate users, are unavailable. In general, if we have categorized security into three areas of confidentiality, integrity and availability, distributed denial of service attacks threaten a third option. Since the distributed denial of service attacks caused by improper operation of information system and its users from accessing the services excluded and also to maintain vital system resources, such attacks are detected immediately. Hence, in this paper, we investigate such attacks and some of their detection mechanisms. In this paper, initially introduced such attacks, then are presented the various categories of these attacks, afterwards, several methods have been proposed to detect distributed denial of service attacks. Finally, these methods are compared and the advantages and disadvantages of them are briefly mentioned. [ABSTRACT FROM AUTHOR]

In this paper, we give the definitions of a multi Q-fuzzy set and a multi Q-fuzzy parameterized soft (MkQFP-soft) set and their operations, namely complement, quality, subset, union, and intersection operations. We then propose a decision making method based on MkQFP-soft set theory. We finally give an example which shows that the method can be successfully applied to problems that contain uncertainties. [ABSTRACT FROM AUTHOR]

This paper reviews some basic properties of fuzzy, rough and soft sets, and compares soft sets to the related concepts of fuzzy and rough sets. The definitions of soft set intersection and union are revised. The two are based on the Cartesian product of the sets of parameters. It illustrates that one could obtain other specific union and intersection by using some cutting operations to the Cartesian product. The definition of soft set complement is also revised. One is taken by a collection of sets instead of the previous rigid complement. By means of soft mapping proposed by Molodtsov, both fuzzy set and rough set may be considered as a special soft set with specific parameters and set-valued mapping. And then the basic operations of fuzzy and rough sets are reconsidered in the framework of soft sets. With the new insight into soft sets, their connections and differences between soft sets and fuzzy sets, soft sets and rough sets, are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of fuzzy soft topogenous structure. Also, we investigate the natural connections between the fuzzy soft topogenous structure and the fuzzy soft uniformity structure. [ABSTRACT FROM AUTHOR]

Active Learning Method (ALM) is a powerful fuzzy soft computing tool, developed originally in order to promote an engineering realization of human brain. This algorithm, as a macro-level brain imitation, has been inspired by some behavioral specifications of human brain and active learning ability. ALM is an adaptive recursive fuzzy learning algorithm, in which a complex Multi Input, Multi Output system can be represented as a fuzzy combination of several Single-Input, Single-Output systems. SISO systems as associative layer of algorithm capture partial spatial knowledge of sample data space, and enable a granular knowledge resolution tuning mechanism through the learning process. The knowledge in each sub-system and its effectiveness in the whole system would be extracted by Ink Drop Spread in brief IDS operator and consolidated using a Fuzzy Rule Base (FRB), in order to acquire expert knowledge. In this paper we investigate ALM as a conspicuous classifier in different types of classification problems. Also, a new ALM architecture to actively analyze ill-balanced image patterns is proposed. Different types of data sets are used as a benchmark, including a remote sensing image classification problem, to evaluate the ALM Classifier (ALMC). With active pattern generation ability and knowledge resolution tuning, ALMC has been distinguished from many conventional classification tools especially for complex structures and image patterns analysis. This work demonstrates that ALMC is a good noise robust and active classifier, which is adaptively adjusted through structural evolution and pattern evaluation mechanism. These remarkable capabilities, along with its straightforward learning process, make ALMC as a convenient soft computing tool to use in different types of low dimensional pattern recognition problems. [ABSTRACT FROM AUTHOR]

Multiple Attributes Decision Making (MADM) is the process of finding the best candidate and involves the evaluation and selection among a finite number of potential candidates to solve real-life complex decision problems. In classical MADM methods, the relative importance of the conflicting criteria and performance ratings of candidates are determined precisely. However, in real-world systems related to human resource management, decision making problems are often uncertain or vague, and because of the lack of information, the future state of these systems cannot be known completely. Moreover, if decision makers cannot reach an agreement on the method of defining linguistic variables based on the traditional fuzzy sets, the Interval-Valued Fuzzy Sets (IVFSs) theory can provide a more accurate and practical modeling. This paper presents an Interval-Valued Fuzzy Preference Selection Index (IVF-PSI) method aiming at solving complex decision making problems, in which the performance ratings of candidates are described by using the concept of the IVFSs. Finally, the executive procedure of the proposed IVF-PSI method is illustrated by applying it to the expatriate selection process from the viewpoint of human resource managers. [ABSTRACT FROM AUTHOR]

The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous reordering of the rows and the columns of a square matrix such that the nonzero entries are collected within a band of small width close to the main diagonal. The MBMP is a NP-complete problem, with applications in many scientific domains, linear systems, artificial intelligence, and real-life situations in industry, logistics, information recovery. The complex problems are hard to solve, that is why any attempt to improve their solutions is beneficent. Genetic algorithms and ant-based systems are Soft Computing methods used in this paper in order to solve some MBMP instances. Our approach is based on a learning agent-based model involving a local search procedure. The algorithm is compared with the classical Cuthill-McKee algorithm, and with a hybrid genetic algorithm, using several instances from Matrix Market collection. Computational experiments confirm a good performance of the proposed algorithms for the considered set of MBMP instances. On Soft Computing basis, we also propose a new theoretical Reinforcement Learning model for solving the MBMP. [ABSTRACT FROM AUTHOR]

The Z-number is a new fuzzy-theoretic concept, proposed by Zadeh in 2011. It extends the basic philosophy of Computing With Words (CWW) to include the perception of uncertainty of the information conveyed by a natural language statement. The Z-number thus, serves as a model of linguistic summarization of natural language statements, a technique to merge human-affective perspectives with CWW, and consequently can be envisaged to play a radical role in the domain of CWW-based system design and Natural Language Processing (NLP). This article presents a comprehensive investigation of the Z-number approach to CWW. We present here: a) an outline of our understanding of the generic architecture, algorithm and challenges underlying CWW in general; b) a detailed study of the Z-number methodology - where we propose an algorithm for CWW using Z-numbers, define a Z-number based operator for the evaluation of the level of requirement satisfaction, and describe simulation experiments of CWW utilizing Z-numbers; and c) analyse the strengths and the challenges of the Z-numbers, and suggest possible solution strategies. We believe that this article would inspire research on the need for inclusion of human-behavioural aspects into CWW, as well as the integration of CWW and NLP. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for tracking a single moving object in a video sequence applying the concept of rough set theory. The novelty of this technique is that it does not consider any prior information about the video sequence unlike many existing techniques. The first target model is constructed using the median filtering based foreground detection technique and after that the target is reconstructed in every frame according to the rough set based feature reduction concept incorporating a measure of indiscernibility instead of indiscernibility matrix. The area of interest is initially defined roughly in every frame based on the object shift in the previous frames, and after reduction of redundant features the object is tracked. The measure of indiscernibility of a feature is defined based on its degree of belonging (DoB) to the target. Three quantitative indices based on rough sets, feature similarity and Bhattacharya distance are proposed to evaluate the performance of tracking and detect the mis-tracked frames in the process of tracking to make those corrected. Unlike many existing measures, the proposed ones do not require to know the ground truth or trajectory of the video sequence. Extensive experimental results are given to demonstrate the effectiveness of the method. Comparative performance is demonstrated both visually and quantitatively. [ABSTRACT FROM AUTHOR]

Time plays an important role in the vast majority of problems and, as such, it is a vital issue to be considered when developing computer systems for solving problems. In the literature, one of the most influential formalisms for representing time is known as Allen's Temporal Algebra based on a set of 13 relations (basic and reversed) that may hold between two time intervals. In spite of having a few drawbacks and limitations, Allen's formalism is still a convenient representation due to its simplicity and implementability and also, due to the fact that it has been the basis of several extensions. This paper explores the automatic learning of Allen's temporal relations by the inductive logic programming system FOIL, taking into account two possible representations for a time interval: (i) as a primitive concept and (ii) as a concept defined by the primitive concept of time point. The goals of the experiments described in the paper are (1) to explore the viability of both representations for use in automatic learning; (2) compare the facility and interpretability of the results; (3) evaluate the impact of the given examples for inducing a proper representation of the relations and (4) experiment with both representations under the assumption of a closed world (CWA), which would ease continuous learning using FOIL. Experimental results are presented and discussed as evidence that the CWA can be a convenient strategy when learning Allen's temporal relations. [ABSTRACT FROM AUTHOR]

Hierarchy plays a crucial role in the development of the granular computing. In this paper, three different hierarchies are considered for judging whether a granulation structure is finer or coarser than another one. The first hierarchy is based on the set containment of information granulations, the second hierarchy is based on the cardinal numbers of information granulations while the third hierarchy is based on the sum of cardinal numbers of information granulations. Through introducing set distance and knowledge distance, we investigate the algebraic lattices, in which the derived partial orders are corresponding to the three different hierarchies, respectively. From the viewpoint of distance, these results look forward to provide a more comprehensible perspective for the study of hierarchies on granulation structures. [ABSTRACT FROM AUTHOR]

With the gaining popularity of rough clustering, soft computing research community is studying relationships between rough and fuzzy clustering as well as their relative advantages. Both rough and fuzzy clustering are less restrictive than conventional clustering. Fuzzy clustering memberships are more descriptive than rough clustering. In some cases, descriptive fuzzy clustering may be advantageous, while in other cases it may lead to information overload. Many applications demand use of combined approach to exploit inherent strengths of each technique. Our objective is to examine correlation between these two techniques. This paper provides an experimental description of how rough clustering results can be correlated with fuzzy clustering results. We illustrate procedural steps to map fuzzy membership clustering to rough clustering. However, such a conversion is not always necessary, especially if one only needs lower and upper approximations. Experiments also show that descriptive fuzzy clustering may not always (particularly for high dimensional objects) produce results that are as accurate as direct application of rough clustering. We present analysis of the results from both the techniques. [ABSTRACT FROM AUTHOR]

A Manufacturing Execution System (MES) consists of high-cost, large-scale, multi-task software systems. Companies and factories apply these complex applications for the purposes of production management to monitor and track all aspects of factory-based manufacturing processes. Nevertheless, companies seek to control the production process with even greater rigour. Improvements associated with an MES involve the identification of new knowledge within the data set and its integration in the system, which implies a step forward to Business Process Management (BPM) systems, from which the users of an MES may gain relevant information, not only on execution procedures but to decide on the best scheduled arrangement. This work studies the data gathered from a real MES that is used in a plastic products factory. Several Artificial Intelligence and Soft Computing modelling methods based on fuzzy rules assist in the calculation of manufacturing costs and decisions over shift work rotas: two decisions that are of relevance for the improvement of the execution system. The results of the study, which identify the most suitable models to facilitate execution-related decision-making, are presented and discussed. [ABSTRACT FROM AUTHOR]

Many real life problems contain imprecise variables, constraints and objectives. Fuzzy set theory gives an opportunity to handle imprecise terms in such situations. Two-sided assembly line balancing (2sALB) problem which is a generalization of the well known simple assembly line balancing problem can also be modeled more realistically by employing fuzzy approaches. Such an approach is presented in this study to model and solve 2sALB problem by employing fuzzy mathematical programming and Bees Algorithm (BA). 2sALB problem is a combinatorial complex problem. For this reason BA is employed as a search mechanism for obtaining good solutions to it. BA is a relatively new member of swarm intelligence based meta-heuristics that tries to mimic natural behavior of real honey bees in food foraging in solving complex optimization problems. BA is generally applied to continuous optimization in the literature. Its application to combinatorial problems is rare. This study also presents one of the first application of BA to an assembly line balancing problem which is member of combinatorial optimization. [ABSTRACT FROM AUTHOR]