The paper first considers a new complex representation of amino acids of which the real parts and imaginary parts are taken respectively from hydrophilic properties and residue volumes of amino acids. Then it applies complex Fourier transform on the represented sequence of complex numbers to obtain the spectrum in the frequency domain. By using the method of ‘Inter coefficient distances’ on the spectrum obtained, it constructs phylogenetic trees of different Protein sequences. Finally on the basis of such phylogenetic trees pair wise comparison is made for such Protein sequences. The paper also obtains pair wise comparison of the same protein sequences following the same method but based on a known complex representation of amino acids, where the real and imaginary parts refer to hydrophobicity properties and residue volumes of the amino acids respectively. The results of the two methods are now compared with those of the same sequences obtained earlier by other methods. It is found that both the methods are workable, further the new complex representation is better compared to the earlier one. This shows that the hydrophilic property (polarity) is a better choice than hydrophobic property of amino acids especially in protein sequence comparison.

No abstract

A new method for security-constrained corrective rescheduling of real power using the Hopfield neural network is presented. The proposed method is based on solution of a set of differential equations obtained from transformation of an energy function. Results from this work are compared with the results from a method based on dual linear programming formulation of the optimal corrective rescheduling. The minimum deviations in real power generations and loads at buses are combined to form the objective function for optimization. Inclusion of inequality constraints on active line flow limits and equality constraint on real power generation load balance assures a solution representing a secure system, Transmission losses are also taken into account in the constraint function.Keywords: corrective strategy, security enhancement.Feedback ANN, Real power optimal dispatch, .O INTRODUCTIONA great deal of research has gone into finding fast and reliable solution techniques for security-constrained real power corrective rescheduling. Many solution techniques, each with its specific mathematical model and computational procedure, have been reported in the pertinent literature in the last twenty five years. All of these techniques can be broadly classified into two groups of mathematical models (i) Linear Programming (LP) based models [l-61 and (ii) Non Linear Programming (NLP) based models [7-91. In this work, a method is proposed to solve Hopfield Network-based constrained linear programming problems. The real power security-constrained optimal dispatch (following P-Q decomposition philosophy) problem is based on the minimum deviations of the control variables approach. Our control variables are chosen as the real power generation and load at each bus. The real part of the transmission loss is considered as a function of the net real power injections.Non-linear analog neurons connected in highly interconnected networks are proven to be very effective in computation [lo]. These networks provide a collectively computed solution to a problem based on the analog input information.96 WM 184-2 PWRS A paper recommended and approved by the IEEE Power System Engineering Committee of the IEEE Power Engineering "kink and Hopfield have shown in their ear1ie:r work [lo-131 that the interconnected networks of analog processors can be used for the solution of constrained optimization problem. The main idea behind solving the optimization problem is to formulate an appropriate computational energy function 'E(X)' so that the lowest energy state would correspond to the required solution of 'X' . Following this same philosophy, Hopfield Networks have been used to solve power system problems such as maintenance scheduling of thermal units [14], economic load dispatch [15], unit commitment [16] andl reactive power optimal distribution [17,18]. The basic methodology followed in the optimization problems is to express a problem in the form cif Hopfield network energy function and then solve for 'X' to seek the minimum of its energy function....

Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell nuclei is a challenging task due to the cellular heterogeneity. This paper proposes an efficient Convolutional Neural Network (CNN) based architecture for classification of histological routine colon cancer nuclei named as RCCNet. The main objective of this network is to keep the CNN model as simple as possible. The proposed RCCNet model consists of 1, 512, 868 learnable parameters which are significantly less compared to the popular CNN models such as AlexNet, CIFAR-VGG, GoogLeNet, and WRN. The experiments are conducted over publicly available routine colon cancer histological dataset "CRCHistoPhenotypes". The results of the proposed RCCNet model are compared with five state-ofthe-art CNN models in terms of the accuracy, weighted average F1 score and training time. The proposed method has achieved a classification accuracy of 80.61% and 0.7887 weighted average F1 score. The proposed RCCNet is more efficient and generalized in terms of the training time and data over-fitting, respectively.

No abstract