Gender classification method based on Gait Energy Motion: GEM derived through wavelet analysis of human gait moving pictures is proposed. Through experiments with human gait moving pictures, it is found that the extracted features of wavelet coefficients using silhouettes images are useful for improvement of gender classification accuracy. Also, it is found that the proposed gender classification method shows the best classification performance, 97.63% of correct classification ratio.

Wireless Sensor Networks offer a powerful combination of distributed sensing, computing and communication. They lend themselves to countless applications and at the same time constrained by limited battery life, processing capability, memory and bandwidth which makes it soft target of malicious objects such as virus and worms. We study the potential threat for worm spread in wireless sensor network using epidemic theory. We propose a new model Susceptible-Exposed-Infectious-Quarantine-Recovered with Vaccination (SEIQRS-V), to characterize the dynamics of the worm spread in WSN. Threshold, equilibrium and their stability are discussed. Numerical methods are employed to solve the system of equations and MATLAB is used to simulate the system. The Quarantine is a method of isolating the most infected nodes from the network till they get recovered and the Vaccination is the mechanism to immunize the network temporarily to reduce the spread worms.

The field of Wireless Local Area Networks (LANs) is expanding rapidly as a result of advances in digital communications, portable computers, and semiconductor technology. The early adopters of this technology have primarily been vertical application that places a premium on the mobility offered by such systems.
Binary Exponential Backoff (BEB) refers to a collision resolution mechanism used in random access MAC protocols. This algorithm is used in Ethernet (IEEE 802.3) wired LANs. In Ethernet networks, this algorithm is commonly used to schedule retransmissions after collisions.
The paper’s goal is to minimize the time transmission cycle of the information between mobiles moving in a Wi-Fi by changing the BEB algorithm. The Protocol CSMA / CA manage access to the radio channel by performing an arbitration based on time. This causes many problems in relation to time transmission between mobiles moving in a cell 802.11. what we have done show that the protocol using CSMA / CA access time believed rapidly when the number of stations and / or the network load increases or other circumstances affects the network.

The robustness of urban bus-transport networks has important influence on the network performance. This paper proposes the model of layered public bus-transport network which is composed of the logical layer and the physical layer and expounds the relationship between these two layers. We map the bus-transport network into two spaces: space P and space L and take space P as logical layer while space L as physical layer. We define the load of edges in the physical layer according to the traffic flow in the logical layer and assume that a removed edge only leads to a redistribution of the load through it to its neighboring edges. We analysis the robustness of layered public bus-transport networks in the face of cascading failure under the case of removing the edge with the highest load and redistributing of the load. Through the simulation of the public bus-transport networks of three major cities in China, we find that in the layered public bus-transport network the traffic flow in the logical layer affects the distribution of load of edges in the physical layer. The removal of the edge with the highest load may lead to the cascading failures of the physical layer, and the avalanche size decreases with the increase of the tolerance parameter.

Recently, there has been a significant research in automatic text summarization using feature-based techniques in which most of them utilized any one of the soft computing techniques. But, making use of syntactic structure of the sentences for text summarization has not widely applied due to its difficulty of handling it in summarization process. On the other hand, feature-based technique available in the literature showed efficient results in most of the techniques. So, combining syntactic structure into the feature-based techniques is surely smooth the summarization process in a way that the efficiency can be achieved. With the intention of combining two different techniques, we have presented an approach of text summarization that combines feature and syntactic structure of the sentences. Here, two neural networks are trained based on the feature score and the syntactic structure of sentences. Finally, the two neural networks are combined with weighted average to find the sentence score of the sentences. The experimentation is carried out using DUC 2002 dataset for various compression ratios. The results showed that the proposed approach achieved F-measure of 80% for the compression ratio 50 % that proved the better results compared with the existing techniques.

Bayes estimators of the parameter of exponential distribution are obtained with non-informative quasi-prior distribution based on record values under three loss functions. These functions are weighted squared error loss, square log error loss and entropy loss functions. Finally the minimax estimators of the parameter are obtained by using Lehmann’s theorem. Comparisons in terms of risks with the estimators of parameter under three loss functions are also studied.

The paper presents two approaches to the sensitivity analysis in multi-objective linear programming (MOLP). The first one is the tolerance approach and the other one is the standard sensitivity analysis. We consider the perturbation of the objective function coefficients. In the tolerance method we simultaneously change all of the objective function coefficients. In the standard sensitivity analysis we change one objective function coefficient without changing the others. In the numerical example we compare the results obtained by using these two different approaches.

This paper proposes a natural language based feedback analysis system that extracts semantic relations from feedback data in order to map it with the domain ontology. After pre-processing a set of words or phrases are extracted from the input data. The data are analyzed semantically to interpret its meaning. This meaning is in an intermediate form which is then mapped to the terms defined in the ontology using similarity function. The opinion analysis of the semantic data is carried out for measuring the polarity of the feedback by the use of opinion analysis method. The system is evaluated on the input feedback data.

The process of data mining produces various patterns from a given data source. The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules. Numerous efficient algorithms have been proposed to do the above processes. Frequent pattern mining has been a focused topic in data mining research with a good number of references in literature and for that reason an important progress has been made, varying from performant algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining. Association Rule mining (ARM) is one of the utmost current data mining techniques designed to group objects together from large databases aiming to extract the interesting correlation and relation among huge amount of data. In this article, we provide a brief review and analysis of the current status of frequent pattern mining and discuss some promising research directions. Additionally, this paper includes a comparative study between the performance of the described approaches.

Refer to this research, a position modified parallel error-based fuzzy Proportional Derivative (PD) gravity controller is proposed for continuum robot manipulator. The main problem of the pure conventional nonlinear controller was equivalent dynamic formulation in uncertain systems. The main challenge of linear controllers is linearization techniques and the quality of performance. The nonlinear equivalent dynamic problem in uncertain system is solved by applied fuzzy logic theory to modified PD gravity. To estimate the continuum robot manipulator system’s dynamic, proportional plus modified derivative with 7 rules Mamdani inference system is design and applied to modified PD gravity methodology. The proportional coefficient of controller is tuned by new methodology in limitation uncertainties. The results demonstrate that the proposed controller is a partly model-free controllers which works well in certain and partly uncertain system.