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Advance Teaching pedagogy is one of the innovative approaches to enhance teaching learning process. Due to the development of new technologies and development of many globalization, teaching learning process plays a vital role among students where the education has been expanded to macro level. Hybrid teaching includes e learning in addition to the face to face teaching. In this paper, various innovative learning strategies for modern technology are discussed in details. Use of smart gadgets for different tasks like teaching, designing question papers, assessment of student, feedback and research methodology are discussed.

Image processing plays a vital role in the medical field. For the successful implementation of medical image processing accuracy of the segmented result is very important. The purpose of the proposed technique is to determine the best brain tumour segmentation method by comparing the segmented output of different methods. Early detection of brain tumour is very important because early treatment can help humans with brain tumour to live long. But the detection of brain tumour with the help of an expert is the time-consuming work. Computer-aided automatic detection methods are used to overcome the drawbacks faced by manual prediction. Because of varying nature of pixel distributions in MRI image, the segmentation process becomes very difficult. The selection of the best segmentation method is needed to extract the desired feature. In this paper, four magnetic resonance brain image segmentation techniques were used to extract the tumour, Otsu thresholding, level set method (LSM), fuzzy C-means level set method (FCMLSM) and discrete wavelet transform (DWT) with morphological processing. High-frequency noise is removed using anisodiffusion filter. Also these segmentation algorithms were compared by various quality metrics like accuracy, precision, sensitivity, specificity, etc. These quality parameters were evaluated with the help of true positive (TP), true negative (TN), false positive (FP) and false negative (FN) values.

Segmentation separates an image into different sections badsed on the desire of the user. Segmentation will be carried out in an image, until the region of interest (ROI) of an object is extracted. Segmentation reliability predicts the progress of the various segmentation techniques. In this paper, various segmentation methods are proposed and quality of segmentation is verified by using quality metrics like Mean Squared Error (MSE),Signal to Noise Ratio (SNR), Peak- Signal to Noise Ratio (PSNR), Edge Preservation Index (EPI) and Structural Similarity Index Metric (SSIM).

Objectives: To develop presents an efficient algorithm for binarization of text from videos. However, it is a challenging and difficult task in image processing, due to their complicated background and non uniform character size. Method/Analysis: This study focuses a novel approach for binarization of text region from videos based on Hill climbing Algorithm. Symmetric filtering and graph cut algorithms are used to refine the obtained clusters. Finally, an optimal clustering selection algorithm is applied to obtain the text region. Findings: The experimental results show that the proposed text binarization technique is robust in text detection with various character size and complicated background. Data sets are taken from the You Tube Video Text (YVT) harvested from YouTube. Application: This proposed binarization technique is applicable to automated licence plate recognition system, especially to identify Indian vehicles licence plates.