Defuzzification converts the final fuzzy output set of fuzzy controller and fuzzy inference systems to a significant crisp value. However, there are various mathematical methods for defuzzification, but there is not any certain systematic method for choosing the best strategy. In this paper, first we explain the structure of a fuzzy inference system and then after a short review of defuzzification criteria and properties, the main classification groups of most widely used defuzzification methods are presented. In the following after discussing some existing techniques, two new defuzzification methods are proposed by presenting their general performance and computational formulas. However, the principle of these two methods is using weights associated with output fuzzy set like WFM or QM, but unlike the existing approaches, they consider the final aggregated consequent and implicated functions simultaneously to calculate the weights. To show how the proposed methods act, two numerical examples are solved using the presented methods and the results are compared with some of common defuzzification techniques.

In the past decade, Improve the quality in face recognition system is a challenge. It is a challenging problem and widely studied in the different type of imag-es to provide the best quality of faces in real life. These problems come due to illumination and pose effect due to light in gradient features. The improvement and optimization of human face recognition and detection is an important problem in the real life that can be handles to optimize the error rate, accuracy, peak signal to noise ratio, mean square error, and structural similarity Index. Now-a-days, there several methods are proposed to recognition face in different problem to optimize above parameters. There occur many invariant changes in hu-man faces due to the illumination and pose variations. In this paper we proposed a novel method in face recogni-tion to improve the quality parameters using speed up robust feature and linear discriminant analysis for opti-mize result. SURF is used for feature matching. In this paper, we use linear discriminant analysis for the edge dimensions reduction to live faces from our data-sets. The proposed method shows the better result as compare to the previous result on the basis of comparative analysis because our method show the better quality and better results in live images of face.

Social network analysis is a widely used technique to analyze relationships among wiki-users in Wikipedia. In this paper the method to identify hidden social networks participating in information conflicts in wiki-environment is proposed. In particular, we describe how text clustering techniques can be used for extraction of hidden social networks of wiki-users caused information conflict. By clustering unstructured text articles caused information conflict we create social network of wiki-users. For clustering of the conflict articles a hybrid weighted fuzzy-c-means method is proposed.

This paper presents a comparison study of different control strategies for stabilizing highly non-linear Gantry inverted pendulum (GIP) system. The control objective was achieved using three different soft-computing techniques i.e. Fuzzy logic (FL), Adaptive neuro fuzzy inference system (ANFIS) and Neural networks (NN's). The results obtained from fuzzy controller were further optimized using ANFIS and NN's controllers. The performance parameters considered for analysis were Settling time (seconds), Maximum Overshoot (degree) and Steady state error. The simulation results that both fuzzy and ANFIS controllers were able to stabilize the non-linear GIP system within specified time. It was also observed that ANFIS controller shows better learning ability as compared to NN's controller. The study also elaborates the relationship between Membership functions (MF's) and training error tolerance for ANFIS controller and relation between hidden neurons and Mean squared error (MSE) and Regression (R) value for NN's controller.

Extracting information from database is typically done by using a structured language such as SQL (Structured Query Language). But non expert users can’t use this later. Wherefore using Natural Language (NL) for communicating with database can be a powerful tool. But without any help, computers can’t understand this language; that is why it is essential to develop an interface able to translate user’s query given in NL into an equivalent one in Database Query Language (DBQL).
This paper presents a model of a generic natural language query interface for querying database. This model is based on machine learning approach which allows interface to automatically improve its knowledge base through experience. The advantage of this interface is that it functions independently of the database language, content and model. Experimentations are realized to study the performance of this interface and make necessary corrections for its amelioration.

Telephone systems commonly transmit narrowband (NB) speech with an audio bandwidth limited to the traditional telephone band of 300-3400 Hz. To improve the quality and intelligibility of speech degraded by narrow bandwidth, researchers have tried to standardize the telephonic networks by introducing wideband (50-7000 Hz) speech codecs. Wideband (WB) speech transmission requires the transmission network and terminal devices at both ends to be upgraded to the wideband that turns out to be time-consuming. In this situation, novel Bandwidth extension (BWE) techniques have been developed to overcome the limitations of NB speech. This paper discusses the basic principles, realization, and applications of BWE. Challenges and limitations of BWE are also addressed.

Plant identification has been a challenging task for many researchers. Several researches proposed various techniques for plant identification based on leaves shape. However, image segmentation is an essential and critical part of analyzing the leaves images. This paper, proposed an efficient plant species identification model using the digital images of leaves. The proposed identification model adopts the particle swarm optimization for leaves images segmentation. Then, feature selection process using information gain and discritization process are applied to the segmented image’s features. The proposed model was evaluated on the Flavia dataset. Experimental results on different kind of classifiers show an improvement in the identification accuracy up to 98.7%.

String variant alias names are surnames which are string variant form of the primary name. Extracting string variant aliases are important in tasks such as information retrieval, information extraction, and name resolution etc. String variant alias extraction involves candidate alias name extraction and string variant alias validation. In this paper, string variant aliases are first extracted from the web and then using seven different string similarity metrics as features, candidate aliases are validated using ensemble classifier random forest. Experiments were conducted using string variant name-alias dataset containing name-alias data for 15 persons containing 30 name-alias pairs. Experimental results show that the proposed method outperforms other similar methods in terms of accuracy.