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The human machine interface research in the light of modern fast computers and advanced sensors is taking new heights. The classification and processing of neural activity in the brain accessed by Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), EEG Electroencephalogram (EEG) etc., are peeling off new paradigms for pattern recognition in human brain-machine interaction applications. In the present paper, an effective novel scheme based upon a synergetic approach employing the Genetic Algorithm (GA), Support Vector Machine and Wavelet packet transform for motor imagery classification and optimal Channel selection is proposed. GA with SVM acting as the objective function is employed for simultaneous selection of features and channels optimally. The binary population of GA is uniquely represented in three-dimensional structure and a new cross-over operator for GA are introduced. The new modified cross-over operator is proposed for the modified three-dimensional population. The ‘data set I’ of ‘BCI Competition IV’ is taken for evaluation of the efficacy of the proposed scheme. For subject ‘a’ accuracy is 88.9 6.9 with 10 channels, for subject ‘b’ accuracy is 79.20±5.36with 11 channels, for subject ‘f’ accuracy is 90.50±3.56 with 13 channels, and for subject ‘g’ accuracy is 92.23±3.21with 12 channels. The proposed scheme outperforms in terms of classification accuracy for subjects ‘a, b, f, g’ and in terms of number of channels for subject ‘a’ and that for subject ‘b’ is same as reported earlier in literature. Therefore, proposed scheme contributes a significant development in terms of new three-dimensional representation of binary population for GA as well as significant new modification to the GA operators. The efficacy of the scheme is evident from the results presented in the paper for dataset under consideration.

Pandemic has brought all together a new environment of working and compelled all the off line educational institutions to become online educational platforms and strengthen their online resources. We need to understand online platforms as universities, institutes, schools, colleges or any educational institute which are working online and providing degrees, certificates, diplomas for several courses and programs. In different researches related to online education and Covid -19, investigations addressed student’s perspective or teachers perspective. Literature review has showed the gap in exploring the turnaround strategies inspired by the parent’s perspective for online education especially with respect to young children (Age group 8 to 12 years). Apart from literature review and analysis of secondary data from websites and search engines, qualitative research was undertaken to know about parent’s views in general about the online platforms and particularly about WHJ (White Hat Junior). The focused group discussion and the indepth interviews revealed very useful information with regard to Online educational platforms and especially WHJ in relation to Covid -19 times. Findings relate to awareness, acceptability, perception change, costs, safety issues, etc. It has brought out elaborately in this case based research, how parents expectation may impact the turnaround strategies of their wards’ online educational platforms. In different researches related to online education and Covid -19, investigations addressed student’s perspective or teacher’s perspective.