Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.

Delphi Survey on China's Advanced Energy Technology towards 2035

Facing the multiple challenges of low-carbon transformation,China urgently needs to adopt a new energy development path.This study used the Delphi method combined with vision analysis to analyze the advanced energy fields and gain insights into the development trends of energy technology towards 2035.The Delphi survey convened 762 domestic experts to predict the development demands and trends of advanced energy technology,and to identify important technology topics.A key list,including 91 technology topics in 9 sub-fields,was analyzed with respect to promoting economic growth,improving quality of life,and safeguarding national security.Furthermore,we conducted a research on these technology topics in context of technology research and development(R&D)level,leading countries,technology realization possibility,and constraints.The results from the Delphi survey show that the R&D level of China’s advanced energy technology is still at an elementary stage,and China,significantly,lags behind the US and the EU in the fields of advanced energy.These results reveal that insufficient R&D investment is the primary factor restricting the technology development in China’s advanced energy,though human resources,infrastructure,regulations,policies and standards also play significant roles in restricting technology development.The results from the Delphi survey may serve as a reference and help in exploring the development path of low-carbon transformation and achieving goals for addressing climate change in China.