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The performance of Adaboost is highly sensitive to noisy and outlier samples. This is therefore the weights of these samples are exponentially increased in successive rounds. In this paper, three novel schemes are proposed to hunt the corrupted samples and eliminate them through the training process. The methods are: I) a hybrid method based on K-means clustering and K-nearest neighbor, II) a two-layer Adaboost, and III) soft margin support vector machines. All of these solutions are compared to the standard Adaboost on thirteen Gunnar Raetsch's datasets under three levels of class-label noise. To test the proposed method on a real application, electroencephalography (EEG) signals of 20 schizophrenic patients and 20 age-matched control subjects, are recorded via 20 channels in the idle state. Several features including autoregressive coefficients, band power and fractal dimension are extracted from EEG signals of all participants. Sequential feature subset selection technique is adopted to select the discriminative EEG features. Experimental results imply that exploiting the proposed hunting techniques enhance the Adaboost performance as well as alleviating its robustness against unconfident and noisy samples over Raetsch benchmark and EEG features of the two groups. [ABSTRACT FROM AUTHOR]

BACKGROUND: The initiation biomarker-driven trials have revolutionized oncology drug development by challenging the traditional phased approach and introducing basket studies. Notable successes in non-small cell lung cancer (NSCLC) with ALK, ALK/ROS1, and EGFR inhibitors have prompted the need to expand this approach to other cancer sites. OBJECTIVES: This study explores the use of dose response modeling and time-to-event algorithms on the biomarker molecular targeted agent (MTA). By simulating subgroup identification in MTA-related time-to-event data, the study aims to develop statistical methodology supporting biomarker-driven trials in oncology. METHODS: A total of n patients are selected assigned for different doses. A dataset is prepared to mimic the situation on Subgroup Identification of MTA for time to event data analysis. The response is measured through MTA. The MTA value is also measured through ROC. The Markov Chain Monte Carlo (MCMC) techniques are prepared to perform the proposed algorithm. The analysis is carried out with a simulation study. The subset selection is performed through the Threshold Limit Value (TLV) by the Bayesian approach. RESULTS: The MTA is observed with range 12–16. It is expected that there is a marginal level shift of the MTA from pre to post-treatment. The Cox time-varying model can be adopted further as causal-effect relation to establishing the MTA on prolonging the survival duration. The proposed work in the statistical methodology to support the biomarker-driven trial for oncology research. CONCLUSION: This study extends the application of biomarker-driven trials beyond NSCLC, opening possibilities for implementation in other cancer sites. By demonstrating the feasibility and efficacy of utilizing MTA as a biomarker, the research lays the foundation for refining and validating biomarker use in clinical trials. These advancements aim to enhance the precision and effectiveness of cancer treatments, ultimately benefiting patients. [ABSTRACT FROM AUTHOR]

Multi-Target Regression (MTR) is used to study the relationship between the same set of input variables and multiple continuous target variables simultaneously. A dataset with many input and output variables is the prime issue to address in the MTR, which is computationally complex to build a prediction model. Also, dimensionality reduction from multiple target variables is a challenging and essential task that aims to reduce the size of the dataset to optimize the time complexity of analysis and remove the redundant and irrelevant variables. This paper proposes an efficient feature selection strategy, Multi-Target Feature Subset Selection (MTFSS), for MTR that constructs a unique subset of features by considering multiple targets. On the other hand, two feature evaluators, correlation and ReliefF, support the MTR dataset without discretization. Furthermore, two new score functions, weighted mean aggregation strategy and threshold function, are introduced to identify the significant features. To evaluate the effectiveness of the proposed MTFSS, experiments were carried out on a benchmark dataset. The experimental results demonstrate that the proposed MTFSS can select fewer features and perform better than the original dataset results. Also, the correlation-based feature evaluator performs better than ReliefF with better performance. [ABSTRACT FROM AUTHOR]

Background: DNA methylation is expected to become a kind of new diagnosis and treatment method of Alzheimer's disease (AD). Neuroinflammation- and immune-related pathways represent one of the major genetic risk factors for AD.Objective: We aimed to investigate DNA methylation levels of 7 key immunologic-related genes in peripheral blood and appraise their applicability in the diagnosis of AD.Methods: Methylation levels were obtained from 222 participants (101 AD, 72 MCI, 49 non-cognitively impaired controls). Logistic regression models for diagnosing AD were established after least absolute shrinkage and selection operator (LASSO) and best subset selection (BSS), evaluated by respondent working curve and decision curve analysis for sensitivity.Results: Six differentially methylated positions (DMPs) in the MCI group and 64 in the AD group were found, respectively. Among them, there were 2 DMPs in the MCI group and 30 DMPs in the AD group independent of age, gender, and APOE4 carriers (p <  0.05). AD diagnostic prediction models differentiated AD from normal controls both in a training dataset (LASSO: 8 markers, including methylation levels at ABCA7 1040077, CNR1 88166293, CX3CR1 39322324, LRRK2 40618505, LRRK2 40618493, NGFR 49496745, TARDBP 11070956, TARDBP 11070840 area under the curve [AUC] = 0.81; BSS: 2 markers, including methylation levels at ABCA7 1040077 and CX3CR1 39322324, AUC = 0.80) and a testing dataset (AUC = 0.84, AUC = 0.82, respectively).Conclusion: Our work indicated that methylation levels of 7 key immunologic-related genes (ABCA7, CNR1, CX3CR1, CSF1R, LRRK2, NGFR, and TARDBP) in peripheral blood was altered in AD and the models including methylation of immunologic-related genes biomarkers improved prediction of AD. [ABSTRACT FROM AUTHOR]

In recent years, driver behavior analysis plays a vital role to enhance passenger coverage and management resources in the smart transportation system. The real-world environment possesses the driver principles contains a lot of information like driving activities, acceleration, speed, and fuel consumption. In big data analysis, the driver pattern analyses are complex because mining information is not utilized to feature evaluations and classification. In this paper, a new efficient Fuzzy Logical-based driver behavioral pattern analysis has been proposed to offer effective recommendations to the drivers. Primarily, the feature selection can be carried out with the assist of fuzzy logical subset selection. The selected features are then evaluated using frequent pattern information and these measures will be optimized with a multilayer perception model to create behavioral weight. Afterward, the information weights are trained with a test through an optimized spectral neural network. Finally, the neurons are activated by a recurrent neural network to classify the behavioral approach for the superior recommendation. The proposed method will learn the characteristics of driving behaviors and model temporal features automatically without the need for specialized expertise in feature modelling or machine learning techniques. The simulation results manifest that the proposed framework attains better performance with 98.4% of prediction accuracy and 86.8% of precision rate as compared with existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Jeffries-Matusita (JM) distance, a transformation of the Bhattacharyya distance, is a widely used measure of the spectral separability distance between the two class density functions and is generally used as a class separability measure. It can be considered to have good potential to be used for evaluation of the effectiveness of a feature in discriminating two classes. The capability of JM distance as a ranking based feature selection technique for binary classification problems has been verified in some research works as well as in our earlier work. It was found by our simulation experiments with benchmark data sets that JM distance works equally well compared to other popular feature ranking methods based on mutual information, information gain or Relief. Extension of JM distance measure for feature ranking in multiclass problems has also been reported in the literature. But all of them are basically rank based approaches which deliver the ranking of the features and do not automatically produce the final optimal feature subset. In this work, a novel heuristic approach for finding out the optimum feature subset from JM distance based ranked feature lists for multiclass problems have been developed without explicitly using any specific search technique. The proposed approach integrates the extension of JM measure for multiclass problems and the selection of the final optimal feature subset in a unified process. The performance of the proposed algorithm has been evaluated by simulation experiments with benchmark data sets in comparison with two other previously developed multiclass JM distance measures (weighted average JM distance and another multiclass extension equivalent to Bhattacharyya bound) and some other popular filter based feature ranking algorithms. It is found that the proposed algorithm performs better in terms of classification accuracy, F-measure, AUC with a reduced set of features and computational cost. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a vital data preprocessing task which aims at selecting a small subset of features while maintaining a high level of classification accuracy. FS is a challenging optimization problem due to the large search space and the existence of local optimal solutions. Particle swarm optimization (PSO) is a promising technique in selecting optimal feature subset due to its rapid convergence speed and global search ability. But PSO suffers from stagnation or premature convergence in complex FS problems. In this paper, a novel three layer PSO (TLPSO) is proposed for solving FS problem. In the TLPSO, the particles in the swarm are divided into three layers according to their evolution status and particles in different layers are treated differently to fully investigate their potential. Instead of learning from those historical best positions, the TLPSO uses a random learning exemplar selection strategy to enrich the searching behavior of the swarm and enhance the population diversity. Further, a local search operator based on the Gaussian distribution is performed on the elite particles to improve the exploitation ability. Therefore, TLPSO is able to keep a balance between population diversity and convergence speed. Extensive comparisons with seven state-of-the-art meta-heuristic based FS methods are conducted on 18 datasets. The experimental results demonstrate the competitive and reliable performance of TLPSO in terms of improving the classification accuracy and reducing the number of features. [ABSTRACT FROM AUTHOR]

Formal Concept Analysis (FCA) has been proven to be an effective method of restructuring complete lattices and various algebraic domains. In this paper, the notion of contractive mappings over formal contexts is proposed, which can be viewed as a generalization of interior operators on sets into the framework of FCA. Then, by considering subset-selections consistent with contractive mappings, the notions of attribute continuous formal contexts and continuous concepts are introduced. It is shown that the set of continuous concepts of an attribute continuous formal context forms a continuous domain, and every continuous domain can be restructured in this way. Moreover, the notion of F-morphisms is identified to produce a category equivalent to that of continuous domains with Scott continuous functions. The paper also investigates the representations of various subclasses of continuous domains including algebraic domains and stably continuous semilattices. [ABSTRACT FROM AUTHOR]

Formal Concept Analysis (FCA) has been proven to be an effective method of restructuring complete lattices and various algebraic domains. In this paper, the notion of contractive mappings over formal contexts is proposed, which can be viewed as a generalization of interior operators on sets into the framework of FCA. Then, by considering subset-selections consistent with contractive mappings, the notions of attribute continuous formal contexts and continuous concepts are introduced. It is shown that the set of continuous concepts of an attribute continuous formal context forms a continuous domain, and every continuous domain can be restructured in this way. Moreover, the notion of F-morphisms is identified to produce a category equivalent to that of continuous domains with Scott continuous functions. The paper also investigates the representations of various subclasses of continuous domains including algebraic domains and stably continuous semilattices. [ABSTRACT FROM AUTHOR]

Attribute reduction is considered as an important processing step for pattern recognition, machine learning and data mining. In this paper, we combine soft set and rough set to use them in applications. We generalize rough set model and introduce a soft metric rough set model to deal with the problem of heterogeneous numerical feature subset selection. We construct a soft metric on the family of knowledge structures based on the soft distance between attributes. The proposed model will degrade to the classical one if we specify a zero soft real number. We also provide a systematic study of attribute reduction of rough sets based on soft metric. Based on the constructed metric, we define co-information systems and consistent co-decision systems, and we provide a new method of attribute reductions of each system. Furthermore, we present a judgement theorem and discernibility matrix associated with attribute of each type of system. As an application, we present a case study from Zoo data set to verify our theoretical results. [ABSTRACT FROM AUTHOR]

Background: Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD). Objective: To determine which digital outcome measures of mobility discriminate mobility in people with PD from healthy control (HC) subjects over a week of continuous monitoring. Methods: We recruited 29 people with PD, and 27 age-matched HC subjects. Subjects were asked to wear three inertial sensors (Opal by APDM) attached to both feet and to the lumbar region, and a subset of subjects also wore two wrist sensors, for a week of continuous monitoring. We derived 43 digital outcome measures of mobility grouped into five domains. An Area Under Curve (AUC) was calculated for each digital outcome measures of mobility, and logistic regression employing a 'best subsets selection strategy' was used to find combinations of measures that discriminated mobility in PD from HC. Results: Duration of recordings was 66±14 hours in the PD and 59±16 hours in the HC. Out of a total of 43 digital outcome measures of mobility, we found six digital outcome measures of mobility with AUC > 0.80. Turn angle (AUC = 0.89, 95% CI: 0.79–0.97) and swing time variability (AUC = 0.87, 95% CI: 0.75–0.96) were the most discriminative individual measures. Turning measures were most consistently selected via the best subsets strategy to discriminate people with PD from HC, followed by gait variability measures. Conclusion: Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures. [ABSTRACT FROM AUTHOR]

In the context of optimal subset selection, hybrid feature selection approaches play a significant role that has been the topic of a substantial number of studies because of the growing need for data mining applications. In feature subset selection (FSS) problem; there are two significant shortcomings that need to be addressed: At first, finding a suitable filter method that can be reasonably fast and energetically computed for large volume of data, and second, an efficient wrapper strategy that can discriminate the samples over the entire search space in a considerable amount of time. After a study of the shortcomings of individual feature selection methods (filter or wrapper), this paper investigated a new hybrid feature selection approach with conjunction of filter and wrapper method that can take benefit of both ways for a classification problem. The proposed hybrid uses the filter method as conditional mutual information maximization and wrapper method as genetic algorithm to enhance the overall classification performance and speed up the search process to identify the essential features. The proposed method is known as FWFSS. To get rid of meaningless features and determine the biomarkers, wrapper method as genetic algorithm uses the naïve Bayes (NB) classifier as a fitness function. The proposed method is verified on the University of California, Irvine (UCI) repository, and microarray datasets. From experimental study, it is observed that our approach outperforms convenient methods regarding classification accuracy, the number of optimal features reported in the literature. [ABSTRACT FROM AUTHOR]

Spiking Neural P system is a computing model inspired on how the neurons in a living being are interconnected and exchange information. As a model in embrane computing, it is a non-deterministic and massively-parallel system. The latter makes GPU a good candidate for accelerating the simulation of these models. A matrix representation for systems with and without delay have been previously designed, and algorithms for simulating them with deterministic systems was also developed. So far, non-determinism has been problematic for the design of parallel simulators. In this work, an algorithm for simulating non-deterministic spiking neural P system with delays is presented. In order to study how the simulations get accelerated on a GPU, this algorithm was implemented in CUDA and used to simulate non-uniform and uniform solutions to the Subset Sum problem as a case study. The analysis is completed with a comparison of time and space resources in the GPU of such simulations. [ABSTRACT FROM AUTHOR]

The aim of this research was to investigate statistical learning techniques to predict gender based upon psychological constructs (measuring motivations to participate in masters sports). Motivations of marathoners scales (MOMS) sports psychological data for 3928 masters athletes (2,010 males) from the World Masters Games (the largest sporting event in the world by participant numbers) was investigated using the regularized linear modelling methodologies ridge regression, the lasso and the elastic net. Comparison was made between previously published research utilizing logistic regression, discriminate function analysis, radial basis functions, multilayer perceptrons and a selection of boosted decision tree based models. It was hypothesized that the regularized linear models would perform better than other models except the boosted decision trees, however ensembles of the regularized linear models and gradient boosted machines would result in improved accuracy over any prior models in the literature. Implementing modern regression methods with regularization provided improvements in classification accuracy based on gender compared to non-regularized linear models, however not boosted trees such as a gradient boosted machine (GBM). Models that were solely or partially based on L2 regularization (including a penalty term to reduce the sum of squares of the parameters) performed better than those than relied solely or primarily on L1 regularization (including a penalty term to reduce the sum of the absolute values of the parameters) or subset selection. This finding had implications for analysis of MOMS data in general with respect to using the 56 questions in MOMS as opposed to the underlying nine constructs for analysis in order to compensate for multicollinearity. Ensemble methods stacking ridge regression and GBMs with out-of-sample prediction further improved accuracy, giving higher accuracy scores (0.7236) than obtained in any preceding literature. This demonstrates the potential benefits from such an ensemble approach in terms of developing models with improved accuracy, as well as increasing the likelihood of developing practical applications from predictions using MOMS psychometric data. [ABSTRACT FROM AUTHOR]

The multi-target regression problem comprises the prediction of multiple continuous variables at the same time using a common set of input variables, and in the last few years, this problem has gained an increasing attention due to the broad range of real-world applications that can be analyzed under this framework. The complexity of the multi-target regression problem is higher than the single-target regression one since target variables often have statistical dependencies, and these dependencies should be correctly exploited in order to effectively solve this problem. Consequently, additional difficulties appear when the aim is to perform a selection of instances on this type of data. In this work, an ensemble-based method to perform the instance selection task in multi-target regression problems is proposed. First, a well-known instance selection method is adapted to directly work with multi-target data. Second, the proposed ensemble-based approach uses a set of these adapted methods to select the final subset of instances. The members of the ensemble select partial data subsets, where each member is performed on a different input space that is expanded with target variables, exploiting therefore the underlying inter-target dependencies. Finally, the ensemble-based method aggregates all the selected partial data subsets into a final subset of relevant instances by means of solving an optimization problem with a simple greedy heuristic. The experimental study carried out on 18 datasets shows the effectiveness of our proposal for selecting instances in the multi-target regression problem. Results demonstrate that the size of datasets is considerably reduced, whilst the predictive performance of the multi-target regressors is maintained or even improved. Also, it is observed that the proposed method is robust to the presence of noise in data. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to introduce and examine the concepts of deferred statistical convergence of order β and strong r-deferred Cesáro summability of order β for sequences of fuzzy numbers and give some relations between deferred statistical convergence sequences of order β and strongly r-deferred Cesáro summable sequences of order β, where β ∈ (0, 1] and p ∈ ℝ +. Mathematics Subject Classification: 40A05, 40C05, 46A45, 03E72. [ABSTRACT FROM AUTHOR]

We study extremal and algorithmic questions of subset and careful synchronization in monotonic automata. We show that several synchronization problems that are hard in general automata can be solved in polynomial time in monotonic automata, even without knowing a linear order of the states preserved by the transitions. We provide asymptotically tight bounds on the maximum length of a shortest word synchronizing a subset of states in a monotonic automaton and a shortest word carefully synchronizing a partial monotonic automaton. We provide a complexity framework for dealing with problems for monotonic weakly acyclic automata over a three-letter alphabet, and use it to prove NP-completeness and inapproximability of problems such as FINITE AUTOMATA INTERSECTION and the problem of computing the rank of a subset of states in this class. We also show that checking whether a monotonic partial automaton over a four-letter alphabet is carefully synchronizing is NP-hard. Finally, we give a simple necessary and sufficient condition when a strongly connected digraph with a selected subset of vertices can be transformed into a deterministic automaton where the corresponding subset of states is synchronizing. [ABSTRACT FROM AUTHOR]

Recently, available data has increased explosively in both number of samples and dimensionality. The huge number of high dimensional data generates the presence of noisy, redundant and irrelevant dimensions. Such dimensions can increase the time and computational cost in the learning process and even degenerate the performance of learning tasks. One of the ways to reduce dimensionality is by Feature Selection (FS). The aim of this paper is study the feature selection based on expert knowledge and traditional methods (filter, wrapper and embedded) and analyze their performance in classification tasks. Three datasets related to cancer domain in humans were used for feature selection: Breast Cancer (BC), Primary Tumor (PT) and Central Nervous System (CNS). C4.5, K-Nearest Neighbors, Support Vector Machine and Multi Layer Perceptron were trained with the best subset of features for each cancer dataset. The subset of features selected by the wrapper method presents the best average accuracy in the datasets BC and PT, while the subset of features selected by the embedded method reaches the highest average accuracy in the CNS dataset. [ABSTRACT FROM AUTHOR]

Feature selection is one of the key problems in machine learning and data mining. It involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. It can reduce the dimensionality of original data, speed up the learning process and build comprehensible learning models with good generalization performance. Nowadays, ensemble idea has been used to improve the performance of feature selection by integrating multiple base feature selection models into an ensemble one. In this paper, in order to improve the efficiency of feature selection in dealing with large scale, high dimension and imbalanced problems, a Min-Max Ensemble Feature Selection (M2-EFS) is proposed, which is based on balanced data partition and min-max ensemble strategy. The experimental results demonstrate that the M2-EFS can obtain higher performance than other classical ensemble methods in most cases, especially for large scale, high dimension and imbalanced data. [ABSTRACT FROM AUTHOR]

In this paper, the degree to which an L-subset of a vector space is an L-convex set is introduced and their characterizations are given. Besides, we obtain an L-fuzzy convexity induced by L-convex degrees, and analyze their L-fuzzy convexity preserving mappings and L-fuzzy convex-to-convex mappings. [ABSTRACT FROM AUTHOR]

The concept of A-subset is introduced in lattice implication algebras. Firstly, the properties of the A-subset are discussed when A is a general set of lattice implication algebras. Next, the properties of A-subset are investigated when the A is an LI-ideal of lattice implication algebras. We obtain some properties of A-subset and prove that B(A)(the A-subset of B) is an LI-ideal in lattice implication algebras. Finally, the properties of A-subset are investigated in lattice implication product algebras. We prove that the A-subset of ⨅ni=1 Bi is the product of A-subset of Bi(i = 1, 2, √, n) respectively ((⨅ni=1 Bi) (⨅ni=1 Ai)(⨅ni=1 Bi (Ai)). [ABSTRACT FROM AUTHOR]

The set of elementary regions of a transition system, ordered by set inclusion, forms an orthomodular poset, also referred to as quantum logic, which is regular and rich. Starting from an abstract regular and rich quantum logic, one can construct an elementary transition system such that the orginal logic embeds into its set of regions, and which is saturated of transitions. We study the problem of selecting subsets of transitions on the same set of states, which generate the same set of regions. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of interior Γ-hyperideals of ordered Γ-semihypergroups, which is a generalization of the concept of interior Γ-hyperideals of Γ-semihypergroups to ordered Γ-semihypergroup theory, and investigate its related properties. Furthermore, we define and discuss the fuzzy interior Γ-hyperideals of ordered Γ-semihypergroups. In particular, we give the characterization of fuzzy interior Γ-hyperideal generated by a fuzzy subset in an ordered Γ-semihypergroup. In addition, we consider the concepts of (λ,μ)-fuzzy Γ-hyperideals and (λ,μ)-fuzzy interior Γ-hyperideals in ordered Γ-semihypergroups. Finally, we introduce the notion of (λ,μ)-fuzzy simple ordered Γ-semihypergroups and prove that an ordered Γ-semihypergroup is simple if and only if it is (λ,μ)-fuzzy simple. Especially, characterization of simple ordered -semihypergroups in terms of (μ)-fuzzy interior-hyperideals is given. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notions of pairwise soft sub kernel, and pairwise ⋁-soft sets in a soft bitopological space (X, η1, η2,E). Also, we study the fundamental properties of pairwise ⋁-soft sets and we investigate the associated soft topology ηp⋁. Moreover, we introduce the notions of generalized pairwise ⋀(⋁)-soft sets and we investigate their basic properties. Also, we define a soft closure operator on the family of all generalized pairwise ⋀-soft sets and generate, in usual manner, an Alexandroff soft topology ηgp⋀ on X which is finer than ηp⋁. Furthermore, we ⋁prove that (X, ηgp⋀,E) is always a soft T1/2 space. In addition, we introduce characterizations of pairwise soft T*1/2 by using generalized pairwise ⋀-soft sets and we show that the concepts of generalized pairwise ⋀-soft set and generalized pairwise closed soft set are independent. [ABSTRACT FROM AUTHOR]

Ranked set sampling (RSS) is a cost-effective sampling technique that induces stratification on the population through rank orders of samples. This, in turn, provides a more structured sample than a simple random sample does with the same sample size. This yields more efficient estimators of some parameters of interest. In this method a fairly large number of randomly identified sampling units are portioned into small subsets of the same size. The units of each subset are ranked separately with respect to the characteristic of interest without using their actual measurements. The measurements of the units with some specified ranks constitute a ranked set sample. In this paper we wish to discuss theory, methods and some recently reported applications of RSS to highlight its advantages. This method could be of some particular interest to those who look for a cost-effective and more efficient data collection technique for sampling and monitoring situations. [ABSTRACT FROM AUTHOR]

A fuzzy soft set is a mapping from parameter set to the fuzzy subsets of universe. Fuzzy soft set theory provides a parameterized point of view for uncertainty modeling and soft computing model. In this research article, we apply the concept of fuzzy soft sets to graphs. We present the concept of fuzzy soft graphs, various methods of their construction, and investigate some of their related properties. We discuss certain types of irregular fuzzy soft graphs. We also describe applications of fuzzy soft graphs in social network and road network. [ABSTRACT FROM AUTHOR]

Notion of singular fuzzy ideals of commutative rings is introduced in this paper. These singular fuzzy ideals are defined via essential fuzzy ideals. Various characteristic features of such ideals to establish relationship between fuzzy non singularity and fuzzy semiprime character of commutative rings are presented. [ABSTRACT FROM AUTHOR]

In this paper by considering of congruence relations induced by fuzzy ideals, we study rough sets in BCK-algebras. To this purpose we clarify a lower and upper approximations for any subset of Χ in this algebras. Finally, lower and upper rough ideals with respect to fuzzy ideal μ of Χ are discussed. [ABSTRACT FROM AUTHOR]

In this paper, first we investigate some properties of L-ordered groups such as L-subgroup generated by an arbitrary subset and product ofL-ordered groups. Then we introduce the notions of totallyL-ordered group and strong totallyL-ordered group, where L is a frame and some related results are obtained. We show that the set of all isomorphisms on a totally Lsubgroup, forms an L-ordered group. Moreover, if it is a strong totally L-ordered group, then the set of all isomorphisms on it is an L-lattice ordered group. [ABSTRACT FROM AUTHOR]

We prove that if a closed subset X of the Baire space is searchable in the sense of Escardó [Logical Methods in Computer Science 4(3) (2008), 3], and by a functional definable in Gödel's T, then X is countable, and the Cantor–Bendixson rank of X is bounded by the ordinal ε0. To this end we introduce evaluation trees, well founded decorated trees that induce operators from the full set-theoretical class ℕℕ → ℕ to ℕℕ. We prove that when a search operator for a set X is induced from an evaluation tree T , then the Cantor-Bendixson rank of X is bounded by the Kleene-Brouwer order type of T . Further we use a theorem due to Howard [Journal of Symbolic Logic 45(3) (1980), 493-504], estimating the ordinal complexity of the reduction tree of a term in system T, to show that all functionals of type (ℕℕ → ℕ) → ℕℕ definable in T can be computed using an evaluation tree of ordinal rank below ε0. [ABSTRACT FROM AUTHOR]

Let p>1 and denote, respectively, by up and h(Ωa,b), the p-torsion function and the Cheeger constant of the annulus $\Omega_{a,b}=\{x\in\mathbb{R}^{N}\dvt a<\vert x\vert , N>1. Thus, up is the solution of the p-torsional creep problem \[\cases{-\operatorname{div}\bigl(\vert \nabla u\vert ^{p-2}\nablau\bigr)=1&in $\Omega_{a,b}$,\cru=0&on $\partial\Omega_{a,b}$}\] and \[h(\Omega_{a,b}):=\min \biggl\{\frac{\vert \partial E\vert }{\vert E\vert }\dvt E\subset\overline{\Omega_{a,b}}\biggr\},\] where |∂E| and |E| denote, respectively, the (N-1)-dimensional Lebesgue perimeter of ∂E in $\mathbb{R}^{N}$ and the N-dimensional Lebesgue volume of the smooth subset $E\subset\overline{\Omega_{a,b}}$.We prove that \[\lim_{p\rightarrow1^{+}}\Vert u_{p}\Vert _{\infty}^{1-p}=\lim_{p\rightarrow1^{+}}\Vert \nabla u_{p}\Vert _{\infty}^{1-p}=N\frac{b^{N-1}+a^{N-1}}{b^{N}-a^{N}}=\frac{\vert \partial\Omega_{a,b}\vert }{\vert \Omega_{a,b}\vert }\] and combine this fact with a characterization of the Cheeger constant that we proved in a previous paper, to give a new proof of the calibrability of Ωa,b, that is, $h(\Omega_{a,b})=\frac{\vert \partial\Omega_{a,b}\vert }{\vert \Omega_{a,b}\vert }$.Moreover, we prove that up is concave and satisfies lim p→1+(up(x)/|up|∞)=1, uniformly in the set a+ε≤|x|≤b-ε, for all ε>0 sufficiently small.Our results rely on estimates for mp, the radius of the sphere on which up assumes its maximum value. We derive these estimates by combining Pohozaev's identity for the p-torsional creep problem with a kind of l'Hôpital rule for monotonicity. [ABSTRACT FROM AUTHOR]

Processing a huge amount of information takes extensive execution time and computational sources most of the time with low classification accuracy. As a result, it is needed to employ a phase of pre-analysis processing, which can influence the performance of execution time and computational sources and classification accuracy. One of the most important phases of pre-processing is Feature selection, which can improve the classification accuracy of steganalysis. The experiments are accomplished by using a large and important data set of 686 features vectores named SPAM. One of the promising application domains for such a feature selection process is steganalysis. In this paper, we propose a new metaheuristic approach for image steganalysis method for detecting stego images from the cover images in JPEG images using a feature selection technique based on an improved artificial bee colony. Within the ABC structure the k-Nearest Neighbor (kNN) method is employed for fitness evaluation. ABC and kNN have been adjusted together to make an operative dimension reduction method Experimental results demonstrate the effectiveness and accuracy of the proposed technique compared to recent ABC-based feature selection methods and other existing techniques. [ABSTRACT FROM AUTHOR]

In this paper, we introduce two different stabilizers of X with respect to Y for subsets X and Y of an MV-algebra A. We show that stabilizers are ideals and we investigate the relation between stabilizers and some other ideals in MV-algebras. We also show that if I and J are ideals in an MV-algebra A, then the stabilizer of I with respect to J is the relative pseudo-complement of I with respect to J in the lattice of ideals of A. [ABSTRACT FROM AUTHOR]

The aim of this paper is introduce the concepts of FP-fuzzy soft h-ideals and FP-equivalent fuzzy soft h-ideals of hemirings. Some characterizations and properties of them are given. In particular, some good examples are explored. Finally, we investigate aggregate fuzzy h-ideals of hemirings. [ABSTRACT FROM AUTHOR]

Sevilla carpets have already been used to compare different solutions of the Subset Sum problem: either designed in the framework of P systems with active membranes (both in the case of membrane division and membrane creation), and in the framework of tissue-like P systems with cell division. Recently, the degree of parallelism and other descriptive complexity details have been found to be relevant when designing parallel simulators running on GPUs. We present here a new way to use the information provided by Sevilla carpets in this context, and a script that allows to generate them automatically from P-Lingua files. [ABSTRACT FROM AUTHOR]

In this paper, a novel feature selection algorithm FEAST is proposed based on association rule mining. The proposed algorithm first mines association rules from a data set; then, it identifies the relevant and interactive feature values with the constraint association rules whose consequent is the target concept, detects and eliminates the redundant feature values with the constraint association rules whose consequent and antecedent are both of single feature value. Finally, it obtains the feature subset by mapping the feature values to the corresponding features. As the support and confidence thresholds are two important parameters in association rule mining and play a vital role in FEAST, a partial least square regression (PLSR) based threshold prediction method is presented as well. The effectiveness of FEAST is tested on both synthetic and real world data sets, and the classification results of five different types of classifiers with seven representative feature selection algorithms are compared. The results on the synthetic data sets show that FEAST can effectively identify irrelevant and redundant features while reserving interactive ones. The results on the real world data sets show that FEAST outperforms other feature selection algorithms in terms of classification accuracies. In addition, the PLSR based threshold prediction method is performed on the real world data sets, and the results show it works well in recommending proper support and confidence thresholds for FEAST. [ABSTRACT FROM AUTHOR]

In this paper we propose a nonparametric subset selection procedure for selecting a subset containing the best population assuming that the underlying populations differ only in their location parameters. The population which corresponds to the largest location parameter is labeled as the best population. This type of formulation is usually encountered in agriculture, engineering, business etc. In agriculture, when variation in yield of all the varieties of a crop is same, the aim is to select those varieties which provide largest average yield for recommendation to the farming community. Similarly a business concern using different advertising methods to enhance the sales prefers those advertising methods with largest average sales among the methods having the same variability in sales. The proposed procedure is shown to satisfy the P*-condition and have all the properties desired by any selection procedure. The proposed procedure includes some of the existing procedures as its members which do exist in the literature under similar settings. Using multivariate techniques, the proposed procedure is compared with the existing procedures in terms of asymptotic relative efficiency (Pitman), with interesting results. The implementation of the proposed procedure is illustrated with the help of existing tables and the sample size sufficient for their implementation is determined using extensive simulation study. [ABSTRACT FROM AUTHOR]