Various and unpredictable electronic warfare situations drive the development of an integrated electronic warfare (EW) simulator that can perform electronic warfare modeling and simulation on radar threats. This paper introduces the basic components of simulation system that enables our agents to be operational in EW settings. In various simulation of EW environments, our agents can preset their path in the existence of enemy radars' surveillance and autonomously be aware of radar threats while they proceed in their own route. As reversely extrapolating radar threats given radio-active parameters received, our agents perform an appropriate jamming technique in order to deceive the enemy radar keeping track of our agents. Based upon the response of the radar threat attacked by the jamming techniques, our agents figure out the types of the radar threat and verify its identification. For the actual and helpful information, real radars with the probability of similarity could be prioritized from radar database. The integrated EW simulator that we have designed and developed in this paper enables our agents to perform such capabilities as reverse extrapolation of RF threats, its verification using jamming, and recommendation of similar radars, and to evaluate their autonomous behaviors in a tapestry of realistic scenarios.

In the present era with the development of the innovation and the globalization, attrition of customer is considered as the vital metric which decides the incomes and gainfulness of the association. It is relevant for all the business spaces regardless of the measure of the business notwithstanding including the new companies. As per the business organization, about 65% of income comes from the customer's client. The objective of the customer attrition analysis is to anticipate the client who is probably going to exit from the present business association. The attrition analysis also termed as churn analysis. The point of this paper is to assemble a precise prescient model using the Enhanced Deep Feed Forward Neural Network Model to predict the customer whittling down in the Banking Domain. The result obtained through the proposed model is compared with various classes of machine learning algorithms Logistic regression, Decision tree, Gaussian Naïve Bayes Algorithm, and Artificial Neural Network. The outcome demonstrates that the proposed Enhanced Deep Feed Forward Neural Network Model performs best in accuracy compared with the existing machine learning model in predicting the customer attrition rate with the Banking Sector.

This paper presents the football match prediction using a tree-based model algorithm (C5.0, Random Forest, and Extreme Gradient Boosting). Backward wrapper model was applied as a feature selection methodology to help select the best feature that will improve the accuracy of the model. This study used 10 seasons of football data match history (2007/2008 – 2016/2017) in the English Premier League with 15 initial features to predict the match results. With the tuning process, each model showed improvement in accuracy. Random Forest algorithm generated the best accuracy with 68,55% while the C5.0 algorithm had the lowest accuracy at 64,87% and Extreme Gradient Boosting algorithm produced accuracy of 67,89%. With the output produced in this study, the Decision Tree based algorithm is concluded as not good enough in predicting a football match history.

Classification is found to be an important field of research for many applications such as medical diagnosis, credit risk and fraud analysis, customer segregation, and business modeling. The main intention of classification is to predict the class labels for the unlabeled test samples using a labelled training set accurately. Several classification algorithms exist to classify the test samples based on the trained samples. However, they are not suitable for many real world applications since even a small performance degradation of classification algorithms may lead to substantial loss and crucial implications. In this paper, a simple classification method using the average weighted pattern score with attribute rank based feature selection has been proposed. Feature selection is carried out by computing the attribute score based ranking and the classification is performed using average weighted pattern computation. Experiments have been performed with 40 standard datasets and the results are compared with other classifiers. The outcome of the analysis shows the good performance of the proposed method with higher classification accuracy.

This paper describes the process of power system stabilizer (PSS) optimization by using bacterial foraging (BG) to improve the power system stability and damping out the oscillation during large and small disturbances in a multi-machine power system. The proposed PSS type is P. Kundur (Lead-Lag) with speed deviation as the input signal. BG used to optimize the PSS gains. The proposed BG based delta w lead-lag PSS (P. Kundur structure) (BG-PSS) evaluated in the well-known benchmark simulation problem P. Kundur 4-machines 11-buses 2-areas power system. The BG-PSS compared with MB-PSS with simplified settings: IEEE® type PSS4B according to IEEE Std. 421.5, Conventional Delta w PSS (as the proposed PSS without optimization) from P. Kundur, and Conventional Acceleration Power (Delta Pa) PSS to demonstrate its robustness and superiority versus the three PSSs types to damp out the inter-area oscillations in a multi-machine power system. The damping ratio and the real part of the eigenvalues used as the fitness function in the optimization process. The nonlinear simulation results obtained in the MATLAB / SIMULINK environment prove that the proposed PSS is highly effective, robust, & superior to the other used controllers in restrictive the inter-area oscillation in a large power system & to maintain the wide-area stability of the system. Also, the performance indices eigenvalue analysis, peak overshoot, settling time, and steady-state error used to validate the superior oscillation damping and fast recovered transient dynamic behavior over the three considered controllers.

This article addresses an optimal allocation of multi Distributed Generation (DG) units in an Indian practical radial distribution network (RDN) for minimization of network loss and voltage deviation. For this work, combined sensitivity index (CSI) is utilized to identify the appropriate positions/locations of DG units. However, the appropriate size of DG is determined through a nature-inspired; population-based Bird Swarm Algorithm (BSA). Secondly, the influence of DG penetration level on network loss and voltage profile is investigated and presented. In this regard, two types of DG technologies (solar and biomass) are considered for loss reduction and voltage deviation reduction. The performance of CSI and BSA methodology is successfully evaluated on an Indian practical 52-bus RDN.