A. K. Soni

Robust kernelized approach to clustering by incorporating new distance measure

A new data clustering algorithm Density oriented Kernelized version of Fuzzy c-means with new distance metric (DKFCM-new) is proposed. It creates noiseless clusters by identifying and assigning noise points into separate cluster. In an earlier work, Density Based Fuzzy C-Means (DOFCM) algorithm with Euclidean distance metric was proposed which only considered the distance between cluster centroid and data points. In this paper, we tried to improve the performance of DOFCM by incorporating a new distance measure that has also considered the distance variation within a cluster to regularize the distance between a data point and the cluster centroid. This paper presents the kernel version of the method. Experiments are done using two-dimensional synthetic data-sets, standard data-sets referred from previous papers like DUNN data-set, Bensaid data-set and real life high dimensional data-sets like Wisconsin Breast cancer data, Iris data. Proposed method is compared with other kernel methods, various noise resistant methods like PCM, PFCM, CFCM, NC and credal partition based clustering methods like ECM, RECM, CECM. Results shown that proposed algorithm significantly outperforms its earlier version and other competitive algorithms.

An adaptive learning approach to software cost estimation

Software cost estimation predicts the amount of effort and development time required to build a software system. It is one of the most critical tasks and it helps the software industries to effectively manage their software development process. There are a number of cost estimation models. The most widely used model is Constructive Cost Model (COCOMO). In this paper, the use of back propagation neural networks for software cost estimation is proposed. The model is designed in such a manner that accommodates the COCOMO model and improves its performance. It also enhances the predictability of the software cost estimates. The model is tested using two datasets COCOMO dataset and COCOMO NASA 2 dataset. The test results from the trained neural network are compared with that of the COCOMO model. From the experimental results, it was concluded that the integration of the conventional COCOMO model and the neural network approach improves the cost estimation accuracy and the estimated cost can be very close to the actual cost.

A Comparative Study on Fuzzy Approaches for COCOMO‟s Effort Estimation

The effort invested in a software project is one of the most challenging task and most analyzed variables in recent years in the process of project management. Many effort estimation models are developed. Each of these models has their own pros and cons in estimating the development cost and effort. This is because in initial stages project data is often incomplete and unclear. The most widely used model for effort estimation is Constructive Cost Model (COCOMO) but there is a great deal of imprecision present in its input which leads to imprecision in its output thereby resulting in erroneous effort estimation. Fuzzy logic based cost estimation models address the vagueness and imprecision present in these models to make reliable and accurate estimates of effort. The aim of this paper is to analyze the use of fuzzy logic in the COCOMO model and to provide indepth review and comparison of software effort estimation models.

A hybrid approach for software cost estimation using polynomial neural networks and intuitionistic fuzzy sets

Software cost estimation SCE is an important and critical activity of any software development organisation. It helps the project managers to effectively manage their projects and prevent them from over budgeting. In this study we introduce a new design methodology for software cost estimation using polynomial neural networks PNNs and intuitionistic fuzzy sets which resulted in improved SCEs. The performance of the proposed model is tested through a series of experiments on three publicly available software development data, i.e., COCOMO81, NASA93, and Maxwell datasets. The proposed technique of using IFCM intuitionistic fuzzy C Means along with PNNs has drastically improved the cost estimations in comparison with the use of fuzzy C means FCM with PNN as reported in the literature.

Image segmentation of noisy digital images using extended fuzzy C-means clustering algorithm

Fuzzy C-Means algorithm fails to segment the noisy image properly. In this paper, we present an algorithm called Extended Fuzzy C means EFCM, which pre-processes the image to reduce the noise effect and then apply FCM algorithm for image segmentation. Pre-processing of image is influenced by the direct eight neighbourhood pixels of every pixel of an image under consideration. Proposed algorithm has least execution time and it yields regions more homogeneous than those of other techniques. It removes noisy spots and is less sensitive to noise. The proposed technique is a powerful method for noisy image segmentation compared to other image segmentation techniques.

Robust Intuitionistic Fuzzy C-means clustering for linearly and nonlinearly separable data

Intuitionistic Fuzzy C-means (IFCM) is a robust clustering method which is based upon intuitionistic fuzzy set theory. It uses Euclidean distance as a distance metric, hence can only cluster hyper spherically distributed data-sets in data space or in feature space. FCM and KFCM with a new distance measure (FCM-σ and KFCM-σ) can detect non-hyperspherical clusters in data space and feature space but they are sensitive to noise and produce inefficient results in the presence of noise. This paper present a robust Intuitionistic Fuzzy c-means(IFCM-σ) and a robust kernel Intutitionistic Fuzzy C-Means(KIFCM-σ) with a new distance metric that incorporates the distance variation in a cluster to regularize the distance between data point and the cluster centroid. Propose algorithms are the hybridization of IFCM, kernel function, and new distance metric in the data space and in the feature space which avoid various problems of IFCM and FCM-σ. Experiments are done using two-dimensional synthetic data-sets and noisy digital images, and results are compared with IFCM, KIFCM, FCM-σ and KFCM-σ. The results show that our proposed algorithms, especially KIFCM-σ are more effective.

Reactivity of etoricoxib based on computational study of molecular orbitals, molecular electrostatic potential surface and Mulliken charge analysis

Etoricoxib is one of the selective cyclooxygenase inhibitor drug which plays a significant role in the pharmacological management of arthritis and pain. The theoretical investigation of its reactivity is done using Density Functional Theory calculations. Molecular Electrostatic Potential Surface of etoricoxib and its Mulliken atomic charge distribution are used for the prediction of its electrophilic and nucleophilic sites. The detailed analysis of its frontier molecular orbitals is also done.Etoricoxib is one of the selective cyclooxygenase inhibitor drug which plays a significant role in the pharmacological management of arthritis and pain. The theoretical investigation of its reactivity is done using Density Functional Theory calculations. Molecular Electrostatic Potential Surface of etoricoxib and its Mulliken atomic charge distribution are used for the prediction of its electrophilic and nucleophilic sites. The detailed analysis of its frontier molecular orbitals is also done.

Effect of aqueous medium on molecular structure, vibrations, electronic transitions and natural bond orbitals of carbamazepine: A DFT study

ABSTRACT Effect of aqueous medium on the optimised parameters, vibrations, electronic transitions and bond orbitals of carbamazepine molecule has been reported using Density Functional Theory calculations. We initially optimized the structure of the molecule in vapour phase and in water and than by placing water molecule at different sites in the vicinity of carbamazepine molecule. Subsequently vibrational frequencies have been calculated for optimized molecule. Vibrational spectra of carbamazepine have also been reported. We further report the simulated electronic transitions and detailed natural bond orbital population analysis in both the phases.