In this paper, we examine the combined effects of thermal radiation, buoyancy force and magnetic field on oscillatory flow of a conducting optically thin dusty fluid through a vertical channel filled with a saturated porous medium. The governing partial differential equations are obtained and solved analytically by variable separable method. Numerical results depicting the effects of various embedded parameters like radiation number, Hartmann number and Grashof number on dusty fluid velocity profiles, temperature profiles, Nusselt number and skin friction coefficient are presented graphically and discussed qualitatively.

In this paper, a quantum based binary neural network algorithm is proposed, named as novel quantum binary neural network algorithm (NQ-BNN). It forms a neural network structure by deciding weights and separability parameter in quantum based manner. Quantum computing concept represents solution probabilistically and gives large search space to find optimal value of required parameters using Gaussian random number generator. The neural network structure forms constructively having three number of layers input layer: hidden layer and output layer. A constructive way of deciding the network eliminates the unnecessary training of neural network. A new parameter that is a quantum separability parameter (QSP) is introduced here, which finds an optimal separability plane to classify input samples. During learning, it searches for an optimal separability plane. This parameter is taken as the threshold of neuron for learning of neural network. This algorithm is tested with three benchmark datasets and produces improved results than existing quantum inspired and other classification approaches.

Satellite data in conjunction with geoinformatics are used to study the land cover change dynamics, extraction of crop information and the monitoring of crop growth. The information derived from the satellite may contain contaminated values due to the atmospheric effects, geometric errors, snow and clouds. These contaminated values can be identified and eliminated using time series analysis to further streamline for agricultural monitoring and prognostic applications. The inherent advantages and disadvantages of existing streamlining methods limit their usability in particular situation. The method proposed in this study synergizesthe use of interpolation, running median and moving average. This has clearly shown the proposal’s capability in preserving the trend in the series in addition to streamlining the temporal profile of satellite data in the Himalayan foothills. This will make the road map for satisfactory crop growth monitoring and crop yieldestimation. Analysis based on the root mean square error and F-Test has been presented to deduce the results and interpretations.