Biophysics of Computation

Single multiplicative neuron model is a novel neural network model introduced recently, which has been used for time series prediction and function approximation. The model is based on a polynomial architecture that is the product of linear functions in different dimensions of the space. Particle swarm optimization (PSO), a global optimization method, is proposed to train the single neuron model in this paper. An improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO. The proposed CRPSO, PSO, back-propagation algorithm and genetic algorithm are employed to train the model for three well-known time series prediction problems. The experimental results demonstrate the superiority of CRPSO-based neuron model in efficiency and robustness over the other three algorithms.

PSO-based single multiplicative neuron model for time series prediction

Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.

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An optical neural chip for implementing complex-valued neural network.

Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review

Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L

Summary of predicted and experimental results. The crack growth under multiple cracks is highly influenced by neighbouring cracks.No mathematical relation is available to predict the crack initiation direction.ANN has been used to predict crack growth direction in multiple crack geometry.The effect of degree of input variables and hybrid combination of activation functions is studied.Nonlinear and linear activation functions are used through the one and two-hidden layer ANN.The experimental dataset as first or second degree were used.Higher order input variables are found to be more suitable. The objective of this study is to design an efficient artificial neural network (ANN) architecture in order to predict the crack growth direction in multiple crack geometry. Nonlinear logistic (sigmoid and tangent hyperbolic) and linear activation functions have been used through the one- and two-hidden layer ANN. 85 tests were conducted on aluminium alloys under different crack positions, defined by crack tip distance, crack offset distance, crack size, and crack inclination with loading axis. The experimental data set as first degree or second degree were used to train 22 proposed ANN models to predict the output for new data sets (not included in the training sets). The model results were then compared with the experimental data. It was observed that ANN model with combinations of activation functions and two hidden layers predict the crack initiation direction with good accuracy when higher order input variables are presented to the network.

Application of artificial neural network for predicting crack growth direction in multiple cracks geometry

In this paper, a learning algorithm for a single integrate-and-fire neuron (IFN) is proposed and tested for various applications in which a multilayer perceptron neural network is conventionally used. It is found that a single IFN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Several benchmark and real-life problems of classification and time-series prediction have been illustrated. It is observed that the inclusion of some more biological phenomenon in an artificial neural network can make it more powerful.

Time-series prediction with single integrate-and-fire neuron

In this paper, Levenberg-Marquardt (LM) learning algorithm for a single Integrate-and-Fire Neuron (IFN) is proposed and tested for various applications in which a neural network based on multilayer perceptron is conventionally used. It is found that a single IFN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Several benchmark and real-life problems of classification and function-approximation have been illustrated. It is observed that the inclusion of robust algorithm and more biological phenomenon in an artificial neural network can make it more powerful. Keywords—Levenberg-Marquardt learning algorithm, Integrate-and-Fire neuron model, Multilayer Perceptron, Classification, Function approximation.

Levenberg-Marquardt Learning Algorithm for Integrate-and-Fire Neuron Model

Dental caries and periodontal diseases have historically been considered the most important global oral health burdens. Many chemicals and synthetic drugs have marked the side effects. Hence, there has been a paradigm shift from the use of modern drugs to the age-old herbs. Achyranthes aspera is one such important plant with various established pharmaceutical properties. The aim of this study was to assess the antibacterial activity of the A. aspera extract against Streptococcus mutans. Aqueous extract of A. aspera was prepared. Different concentrations of the root and stem extracts of A. aspera were transferred to the agar plates, which had been streaked with the bacterium S. mutans. The plates were incubated aerobically at 37°C for 24 h, and the zones of inhibition were measured using cup plate method. A. aspera extract showed statistically significant zones of inhibition. A. aspera showed marked antibacterial activity against S. mutans.

Evaluation of antibacterial activity of Achyranthes aspera extract against Streptococcus mutans: An in vitro study.

We report control of synchronization between pair of coupled neurons under external electrical stimulation A nonlinear controlling mechanism is proposed for keeping the coupled system in synchronized state We studied transitions between synchrony and asynchrony because of variation in coupling strength We discuss the dynamical analysis for the Modified FitzHugh-Nagumo neuron model in detail This work focuses the application of control system theory for understanding possible synchronization phenomena in a pair of biological neuron models

Abhishek Yadav

Papers

Application of artificial neural network for predicting crack growth direction in multiple cracks geometry

Time-series prediction with single integrate-and-fire neuron

Levenberg-Marquardt Learning Algorithm for Integrate-and-Fire Neuron Model

Evaluation of antibacterial activity of Achyranthes aspera extract against Streptococcus mutans: An in vitro study.

Controlling Synchronization of Modified FitzHugh-Nagumo Neurons Under External Electrical Stimulation