This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good perfor...

The Covid-19 pandemic has resulted in all activities having to be carried out by implementing physical distancing or social accordance with health protocols for mutual safety. government encourages people do more at home, including shopping. Consumer perception of purchasing goods online is a process evaluating various alternatives and choosing one alternative purchase using internet media. app...

Abstract Accurate network traffic classification is an essential and challenging issue for wireless management survivability. Existing algorithms, on the other hand, cannot meet required specifications of real networks' in terms user privacy control overhead, latency, above all, speed. For classification, machine learning‐based hybrid optimization techniques have been deployed. This paper takes...

Observation in absolute darkness and daytime under every atmospheric situation is one of the advantages of thermal imaging systems. In spite of increasing trend of using these systems, there are still lots of difficulties in analysing thermal images due to the variable features of pedestrians and atmospheric situations. In this paper an efficient method is proposed for detecting pedestrians in ...

In this paper an adaptive neural-fuzzy walking control of an autonomous biped robot is proposed. This control system uses a feed forward neural network based on nonlinear regression. The general regression neural network is used to construct the base of an adaptive neuro-fuzzy system. The membership functions used in the antecedent part of the fuzzy system are asymmetric and with varying shapes...

The present paper, deal with energy modeling and optimization of developed mathematical of high temperature fuel cell, e.g. Molten Carbonate Fuel Cell (MCFC). In the present w o r k d e v e l o p e d mathematical model of molten carbonate fuel [1]. Codes for the MCFC single cell stack model have been generated using the MATLAB software. Using the developed Mathematical model, various parameters...

Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is devel...

At present data transmission widely uses wireless network framework for transmitting large volume of data. It generates numerous security problems and privacy issues which laid a way developing IDS. IDS act as preventive technique in securing computer networks. Previously there are metaheuristic deep learning algorithms used detecting threats. Some affected by dynamic growth feature spaces othe...

The class of adaptive systems known as Artificial Neural Networks (ANN) was motivated by the amazing parallel processing capabilities of biological brains (especially the human brain). The main driving force was to re-create these abilities by constructing artificial models of the biological neuron. The power of biological neural structures stems from the enormous number of highly interconnecte...

This research intends to develop a method based on the Artificial Neural Network (ANN) to predict permanent earthquake-induced deformation of the earth dams and embankments. For this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. In order to predict earthquake-induced deformation o...