**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**2295

# Search results for: Maximum Independent subset

##### 2295 Mutually Independent Hamiltonian Cycles of Cn x Cn

**Authors:**
Kai-Siou Wu,
Justie Su-Tzu Juan

**Abstract:**

In a graph G, a cycle is Hamiltonian cycle if it contain all vertices of G. Two Hamiltonian cycles C_1 = ⟨u_0, u_1, u_2, ..., u_{n−1}, u_0⟩ and C_2 = ⟨v_0, v_1, v_2, ..., v_{n−1}, v_0⟩ in G are independent if u_0 = v_0, u_i = ̸ v_i for all 1 ≤ i ≤ n−1. In G, a set of Hamiltonian cycles C = {C_1, C_2, ..., C_k} is mutually independent if any two Hamiltonian cycles of C are independent. The mutually independent Hamiltonicity IHC(G), = k means there exist a maximum integer k such that there exists k-mutually independent Hamiltonian cycles start from any vertex of G. In this paper, we prove that IHC(C_n × C_n) = 4, for n ≥ 3.

**Keywords:**
Hamiltonian,
independent,
cycle,
Cartesian product,
mutually independent Hamiltonicity

##### 2294 On an Open Problem for Definable Subsets of Covering Approximation Spaces

**Authors:**
Mei He,
Ying Ge,
Jingyu Qian

**Abstract:**

**Keywords:**
Covering approximation space,
covering approximation operator,
definable subset,
inner definable subset,
outer definable subset.

##### 2293 Approximating Maximum Weighted Independent Set Using Vertex Support

**Authors:**
S. Balaji,
V. Swaminathan,
K. Kannan

**Abstract:**

**Keywords:**
weighted independent set,
vertex cover,
vertex support,
heuristic,
NP - hard problem.

##### 2292 Completion Number of a Graph

**Authors:**
Sudhakar G

**Abstract:**

In this paper a new concept of partial complement of a graph G is introduced and using the same a new graph parameter, called completion number of a graph G, denoted by c(G) is defined. Some basic properties of graph parameter, completion number, are studied and upperbounds for completion number of classes of graphs are obtained , the paper includes the characterization also.

**Keywords:**
Completion Number,
Maximum Independent subset,
Partial complements,
Partial self complementary

##### 2291 Image Segmentation Using Suprathreshold Stochastic Resonance

**Authors:**
Rajib Kumar Jha,
P.K.Biswas,
B.N.Chatterji

**Abstract:**

In this paper a new concept of partial complement of a graph G is introduced and using the same a new graph parameter, called completion number of a graph G, denoted by c(G) is defined. Some basic properties of graph parameter, completion number, are studied and upperbounds for completion number of classes of graphs are obtained , the paper includes the characterization also.

**Keywords:**
Completion Number,
Maximum Independent subset,
Partial complements,
Partial self complementary.

##### 2290 Some Properties of Superfuzzy Subset of a Fuzzy Subset

**Authors:**
Hassan Naraghi

**Abstract:**

In this paper, we define permutable and mutually permutable fuzzy subgroups of a group. Then we study their relation with permutable and mutually permutable subgroups of a group. Also we study some properties of fuzzy quasinormal subgroup. We define superfuzzy subset of a fuzzy subset and we study some properties of superfuzzy subset of a fuzzy subset.

**Keywords:**
Permutable fuzzy subgroup,
mutually permutable fuzzy subgroup,
fuzzy quasinormal subgroup,
superfuzzy subset.

##### 2289 Feature Subset Selection Using Ant Colony Optimization

**Authors:**
Ahmed Al-Ani

**Abstract:**

**Keywords:**
Ant Colony Optimization,
ant systems,
feature
selection,
pattern recognition.

##### 2288 A New Maximum Power Point Tracking for Photovoltaic Systems

**Authors:**
Mohamed Azab

**Abstract:**

**Keywords:**
Photovoltaic,
maximum power point tracking,
MPPT.

##### 2287 An Optimal Feature Subset Selection for Leaf Analysis

**Authors:**
N. Valliammal,
S.N. Geethalakshmi

**Abstract:**

**Keywords:**
Optimization,
Feature extraction,
Feature subset,
Classification,
GA,
KPCA,
SVM and Computation

##### 2286 Equivalence Class Subset Algorithm

**Authors:**
Jeffrey L. Duffany

**Abstract:**

**Keywords:**
np-complete,
complexity,
algorithm.

##### 2285 Definable Subsets in Covering Approximation Spaces

**Authors:**
Xun Ge,
Zhaowen Li

**Abstract:**

**Keywords:**
Covering approximation space,
covering approximation operator,
definable subset,
inner definable subset,
outer definable subset.

##### 2284 Application of Genetic Algorithms to Feature Subset Selection in a Farsi OCR

**Authors:**
M. Soryani,
N. Rafat

**Abstract:**

**Keywords:**
Feature Subset Selection,
Genetic Algorithms,
Optical Character Recognition.

##### 2283 On the Maximum Theorem: A Constructive Analysis

**Authors:**
Yasuhito Tanaka

**Abstract:**

**Keywords:**
Maximum theorem,
Constructive mathematics,
Sequentially
locally at most one maximum.

##### 2282 Routing Algorithm for a Clustered Network

**Authors:**
Hemanth KumarA.R,
Sudhakara G.,
Satyanarayana B.S.

**Abstract:**

The Cluster Dimension of a network is defined as, which is the minimum cardinality of a subset S of the set of nodes having the property that for any two distinct nodes x and y, there exist the node Si, s2 (need not be distinct) in S such that ld(x,s1) — d(y, s1)1 > 1 and d(x,s2) < d(x,$) for all s E S — {s2}. In this paper, strictly non overlap¬ping clusters are constructed. The concept of LandMarks for Unique Addressing and Clustering (LMUAC) routing scheme is developed. With the help of LMUAC routing scheme, It is shown that path length (upper bound)PLN,d < PLD, Maximum memory space requirement for the networkMSLmuAc(Az) < MSEmuAc < MSH3L < MSric and Maximum Link utilization factor MLLMUAC(i=3) < MLLMUAC(z03) < M Lc

**Keywords:**
Metric dimension,
Cluster dimension,
Cluster.

##### 2281 Incremental Learning of Independent Topic Analysis

**Authors:**
Takahiro Nishigaki,
Katsumi Nitta,
Takashi Onoda

**Abstract:**

**Keywords:**
Text mining,
topic extraction,
independent,
incremental,
independent component analysis.

##### 2280 A Hybrid Feature Subset Selection Approach based on SVM and Binary ACO. Application to Industrial Diagnosis

**Authors:**
O. Kadri,
M. D. Mouss,
L.H. Mouss,
F. Merah

**Abstract:**

This paper proposes a novel hybrid algorithm for feature selection based on a binary ant colony and SVM. The final subset selection is attained through the elimination of the features that produce noise or, are strictly correlated with other already selected features. Our algorithm can improve classification accuracy with a small and appropriate feature subset. Proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through a real Rotary Cement kiln dataset. The results show that our algorithm outperforms existing algorithms.

**Keywords:**
Binary Ant Colony algorithm,
Support VectorMachine,
feature selection,
classification.

##### 2279 A Sufficient Condition for Graphs to Have Hamiltonian [a, b]-Factors

**Authors:**
Sizhong Zhou

**Abstract:**

Let a and b be nonnegative integers with 2 ≤ a < b, and let G be a Hamiltonian graph of order n with n ≥ (a+b−4)(a+b−2) b−2 . An [a, b]-factor F of G is called a Hamiltonian [a, b]-factor if F contains a Hamiltonian cycle. In this paper, it is proved that G has a Hamiltonian [a, b]-factor if |NG(X)| > (a−1)n+|X|−1 a+b−3 for every nonempty independent subset X of V (G) and δ(G) > (a−1)n+a+b−4 a+b−3 .

**Keywords:**
graph,
minimum degree,
neighborhood,
[a,
b]-factor,
Hamiltonian [a,
b]-factor.

##### 2278 Combined Feature Based Hyperspectral Image Classification Technique Using Support Vector Machines

**Authors:**
Mrs.K.Kavitha,
S.Arivazhagan

**Abstract:**

A spatial classification technique incorporating a State of Art Feature Extraction algorithm is proposed in this paper for classifying a heterogeneous classes present in hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes in the hyper spectral images are assumed to have different textures, textural classification is entertained. Run Length feature extraction is entailed along with the Principal Components and Independent Components. A Hyperspectral Image of Indiana Site taken by AVIRIS is inducted for the experiment. Among the original 220 bands, a subset of 120 bands is selected. Gray Level Run Length Matrix (GLRLM) is calculated for the selected forty bands. From GLRLMs the Run Length features for individual pixels are calculated. The Principle Components are calculated for other forty bands. Independent Components are calculated for next forty bands. As Principal & Independent Components have the ability to represent the textural content of pixels, they are treated as features. The summation of Run Length features, Principal Components, and Independent Components forms the Combined Features which are used for classification. SVM with Binary Hierarchical Tree is used to classify the hyper spectral image. Results are validated with ground truth and accuracies are calculated.

**Keywords:**
Multi-class,
Run Length features,
PCA,
ICA,
classification and Support Vector Machines.

##### 2277 Slovenian Text-to-Speech Synthesis for Speech User Interfaces

**Authors:**
Jerneja Žganec Gros,
Aleš Mihelič,
Nikola Pavešić,
Mario Žganec,
Stanislav Gruden

**Abstract:**

**Keywords:**
text-to-speech synthesis,
prosody modeling,
speech
user interface.

##### 2276 Ensembling Adaptively Constructed Polynomial Regression Models

**Authors:**
Gints Jekabsons

**Abstract:**

**Keywords:**
Basis function construction,
heuristic search,
modelensembles,
polynomial regression.

##### 2275 Predicting Bankruptcy using Tabu Search in the Mauritian Context

**Authors:**
J. Cheeneebash,
K. B. Lallmamode,
A. Gopaul

**Abstract:**

Throughout this paper, a relatively new technique, the Tabu search variable selection model, is elaborated showing how it can be efficiently applied within the financial world whenever researchers come across the selection of a subset of variables from a whole set of descriptive variables under analysis. In the field of financial prediction, researchers often have to select a subset of variables from a larger set to solve different type of problems such as corporate bankruptcy prediction, personal bankruptcy prediction, mortgage, credit scoring and the Arbitrage Pricing Model (APM). Consequently, to demonstrate how the method operates and to illustrate its usefulness as well as its superiority compared to other commonly used methods, the Tabu search algorithm for variable selection is compared to two main alternative search procedures namely, the stepwise regression and the maximum R 2 improvement method. The Tabu search is then implemented in finance; where it attempts to predict corporate bankruptcy by selecting the most appropriate financial ratios and thus creating its own prediction score equation. In comparison to other methods, mostly the Altman Z-Score model, the Tabu search model produces a higher success rate in predicting correctly the failure of firms or the continuous running of existing entities.

**Keywords:**
Predicting Bankruptcy,
Tabu Search

##### 2274 Inference of Stress-Strength Model for a Lomax Distribution

**Abstract:**

**Keywords:**
Stress-Strength model; maximum likelihoodestimator; Bayes estimator; Lomax distribution

##### 2273 Ant Colony Optimization for Feature Subset Selection

**Authors:**
Ahmed Al-Ani

**Abstract:**

**Keywords:**
Ant Colony Optimization,
ant systems,
feature
selection,
pattern recognition.

##### 2272 Estimation of R= P [Y < X] for Two-parameter Burr Type XII Distribution

**Abstract:**

In this article, we consider the estimation of P[Y < X], when strength, X and stress, Y are two independent variables of Burr Type XII distribution. The MLE of the R based on one simple iterative procedure is obtained. Assuming that the common parameter is known, the maximum likelihood estimator, uniformly minimum variance unbiased estimator and Bayes estimator of P[Y < X] are discussed. The exact confidence interval of the R is also obtained. Monte Carlo simulations are performed to compare the different proposed methods.

**Keywords:**
Stress-Strength model,
Maximum likelihood estimator,
Bayes estimator,
Burr type XII distribution.

##### 2271 Integrated Subset Split for Balancing Network Utilization and Quality of Routing

**Authors:**
S. V. Kasmir Raja,
P. Herbert Raj

**Abstract:**

The overlay approach has been widely used by many service providers for Traffic Engineering (TE) in large Internet backbones. In the overlay approach, logical connections are set up between edge nodes to form a full mesh virtual network on top of the physical topology. IP routing is then run over the virtual network. Traffic engineering objectives are achieved through carefully routing logical connections over the physical links. Although the overlay approach has been implemented in many operational networks, it has a number of well-known scaling issues. This paper proposes a new approach to achieve traffic engineering without full-mesh overlaying with the help of integrated approach and equal subset split method. Traffic engineering needs to determine the optimal routing of traffic over the existing network infrastructure by efficiently allocating resource in order to optimize traffic performance on an IP network. Even though constraint-based routing [1] of Multi-Protocol Label Switching (MPLS) is developed to address this need, since it is not widely tested or debugged, Internet Service Providers (ISPs) resort to TE methods under Open Shortest Path First (OSPF), which is the most commonly used intra-domain routing protocol. Determining OSPF link weights for optimal network performance is an NP-hard problem. As it is not possible to solve this problem, we present a subset split method to improve the efficiency and performance by minimizing the maximum link utilization in the network via a small number of link weight modifications. The results of this method are compared against results of MPLS architecture [9] and other heuristic methods.

**Keywords:**
Constraint based routing,
Link Utilization,
Subsetsplit method and Traffic Engineering.

##### 2270 Genetic Algorithm for Feature Subset Selection with Exploitation of Feature Correlations from Continuous Wavelet Transform: a real-case Application

**Authors:**
G. Van Dijck,
M. M. Van Hulle,
M. Wevers

**Abstract:**

**Keywords:**
Classification,
genetic algorithm,
hierarchicalagglomerative clustering,
wavelet transform.

##### 2269 A Study on the Average Information Ratio of Perfect Secret-Sharing Schemes for Access Structures Based On Bipartite Graphs

**Authors:**
Hui-Chuan Lu

**Abstract:**

A perfect secret-sharing scheme is a method to distribute a secret among a set of participants in such a way that only qualified subsets of participants can recover the secret and the joint share of participants in any unqualified subset is statistically independent of the secret. The collection of all qualified subsets is called the access structure of the perfect secret-sharing scheme. In a graph-based access structure, each vertex of a graph G represents a participant and each edge of G represents a minimal qualified subset. The average information ratio of a perfect secret-sharing scheme realizing the access structure based on G is defined as AR = (Pv2V (G) H(v))/(|V (G)|H(s)), where s is the secret and v is the share of v, both are random variables from and H is the Shannon entropy. The infimum of the average information ratio of all possible perfect secret-sharing schemes realizing a given access structure is called the optimal average information ratio of that access structure. Most known results about the optimal average information ratio give upper bounds or lower bounds on it. In this present structures based on bipartite graphs and determine the exact values of the optimal average information ratio of some infinite classes of them.

**Keywords:**
secret-sharing scheme,
average information ratio,
star covering,
core sequence.

##### 2268 Reliability Analysis of Underground Pipelines Using Subset Simulation

**Authors:**
Kong Fah Tee,
Lutfor Rahman Khan,
Hongshuang Li

**Abstract:**

An advanced Monte Carlo simulation method, called Subset Simulation (SS) for the time-dependent reliability prediction for underground pipelines has been presented in this paper. The SS can provide better resolution for low failure probability level with efficient investigating of rare failure events which are commonly encountered in pipeline engineering applications. In SS method, random samples leading to progressive failure are generated efficiently and used for computing probabilistic performance by statistical variables. SS gains its efficiency as small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment. It is hoped that the development work can promote the use of SS tools for uncertainty propagation in the decision-making process of underground pipelines network reliability prediction.

**Keywords:**
Underground pipelines,
Probability of failure,
Reliability and Subset Simulation.

##### 2267 Integrated ACOR/IACOMV-R-SVM Algorithm

**Authors:**
Hiba Basim Alwan,
Ku Ruhana Ku-Mahamud

**Abstract:**

_{R}-SVM, will tune SVM parameters, while the second IACO

_{MV-R}-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. Three benchmark UCI datasets were used in the experiments to validate the performance of the proposed algorithms. The results show that the proposed algorithms have good performances as compared to other approaches.

**Keywords:**
Continuous ant colony optimization,
incremental continuous ant colony,
simultaneous optimization,
support vector machine.

##### 2266 A New Bound on the Average Information Ratio of Perfect Secret-Sharing Schemes for Access Structures Based On Bipartite Graphs of Larger Girth

**Authors:**
Hui-Chuan Lu

**Abstract:**

In a perfect secret-sharing scheme, a dealer distributes a secret among a set of participants in such a way that only qualified subsets of participants can recover the secret and the joint share of the participants in any unqualified subset is statistically independent of the secret. The access structure of the scheme refers to the collection of all qualified subsets. In a graph-based access structures, each vertex of a graph G represents a participant and each edge of G represents a minimal qualified subset. The average information ratio of a perfect secret-sharing scheme realizing a given access structure is the ratio of the average length of the shares given to the participants to the length of the secret. The infimum of the average information ratio of all possible perfect secret-sharing schemes realizing an access structure is called the optimal average information ratio of that access structure. We study the optimal average information ratio of the access structures based on bipartite graphs. Based on some previous results, we give a bound on the optimal average information ratio for all bipartite graphs of girth at least six. This bound is the best possible for some classes of bipartite graphs using our approach.

**Keywords:**
Secret-sharing scheme,
average information ratio,
star
covering,
deduction,
core cluster.