The perspective of this paper is to characterize a Casson type of Non-Newtonian fluid flow through heat as well as mass conduction towards a stretching surface with thermophoresis and radiation absorption impacts in association with periodic hydromagnetic effect. Here heat absorption is also integrated with the heat absorbing parameter. A time dependent fundamental set of equations, i.e. momentum, energy and concentration have been established to discuss the fluid flow system. Explicit finite difference technique is occupied here by executing a procedure in Compaq Visual Fortran 6.6a to elucidate the mathematical model of liquid flow. The stability and convergence inspection has been accomplished. It has observed that the present work converged at, Pr ≥ 0.447 indicates the value of Prandtl number and Le ≥ 0.163 indicates the value of Lewis number. Impact of useful physical parameters has been illustrated graphically on various flow fields. It has inspected that the periodic magnetic field has helped to increase the interaction of the nanoparticles in the velocity field significantly. The field has been depicted in a vibrating form which is also done newly in this work. Subsequently, the Lorentz force has also represented a great impact in the updated visualization (streamlines and isotherms) of the flow field. The respective fields appeared with more wave for the larger values of magnetic parameter. These results help to visualize a theoretical idea of the effect of modern electromagnetic induction use in industry instead of traditional energy sources. Moreover, it has a great application in lung and prostate cancer therapy.

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In this paper, we present an approach for identification of actions within depth action videos. First, we process the video to get motion history images (MHIs) and static history images (SHIs) corresponding to an action video based on the use of 3D Motion Trail Model (3DMTM). We then characterize the action video by extracting the Gradient Local Auto-Correlations (GLAC) features from the SHIs and the MHIs. The two sets of features i.e., GLAC features from MHIs and GLAC features from SHIs are concatenated to obtain a representation vector for action. Finally, we perform the classification on all the action samples by using the l2-regularized Collaborative Representation Classifier (l2-CRC) to recognize different human actions in an effective way. We perform evaluation of the proposed method on three action datasets, MSR-Action3D, DHA and UTD-MHAD. Through experimental results, we observe that the proposed method performs superior to other approaches.

This paper presents a simple, fast and efficacious system to promote the human action classification outcome using the depth action sequences. Firstly, the motion history mages (MHIs) and static history images (SHIs) are created from the front (XOY), side (YOZ) and top (XOZ) projected scenes of each depth sequence in a 3D Euclidean space through engaging the 3D Motion Trail Model (3DMTM). Then, the Local Binary Patterns (LBPs) algorithm is operated on the MHIs and SHIs to learn motion and static hierarchical features to represent the action sequence. The motion and static hierarchical feature vectors are then fed into a classifier ensemble to classify action classes, where the ensemble comprises of two classifiers. Thus, each ensemble includes a pair of Kernel-based Extreme Learning Machine (KELM) or ${\mathrm{l}}_{\mathrm{2}}$-regularized Collaborative Representation Classifier (${\mathrm{l}}_{\mathrm{2}}$-CRC) or Multi-class Support Vector Machine. To extensively assess the framework, we perform experiments on a couple of standard available datasets such as MSR-Action3D, UTD-MHAD and DHA. Experimental consequences demonstrate that the proposed approach gains a state-of-the-art recognition performance in comparison with other available approaches. Several statistical measurements on recognition results also indicate that the method achieves superiority when the hierarchical features are adopted with the KELM ensemble. In addition, to ensure real-time processing capability of the algorithm, the running time of major components is investigated. Based on machine dependency of the running time, the computational complexity of the system is also shown and compared with other methods. Experimental results and evaluation of the computational time and complexity reflect real-time compatibility and feasibility of the proposed system.

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