Video classification is a crucial aspect when we discuss human-machine interface as it helps to analyze various activities. Using transfer learning techniques can help us in making predictions accurately. The dataset used for research is a subdivision of the UCF101-Action Recognition Dataset, consisting of 10 classes in total, where each class contains more than 120 videos. Each video is converted into a series of frames at a frame rate of 5. Feature extraction is performed on these frames using InceptionV3. The fine-tuned model architecture is composed of 4 dense layers. These layers are built using “relu” activation function with 1024, 512, 256 and 128 neurons respectively and another dense layer is built using “softmax” activation function with 10 neurons so as to predict 10 classes. This technique finds a huge range of applications related to human-machine interface such as helping the visually challenged people in classifying various activities.
The electronic structure and ground state properties of SC16-ZnO, Zn7Mg1O8 and Zn7Al1O8 were studied using Wien2k code. The doping effect of magnesium and aluminium on band structure of Zinc Oxide reveals that the profiles are identical, however slightly shifted due to band broadening. Using super cell approach the electronic and optical properties are also studied for Zn7Al1O8 and Zn7Mg1O8. The calculated parameters like onset of critical point or threshold value, fundamental band gap and dielectric functions are reported for SC16-ZnO, Zn7Mg1O8 and Zn7Al1O8.
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