In today’s digital world, and in light of the growing pandemic, many yoga instructors opt to teach online. However, even after learning or being trained by the best sources available, such as videos, blogs, journals, or essays, there is no live tracking available to the user to see if he or she is holding poses appropriately, which can lead to body posture issues and health issues later in life. Existing technology can assist in this regard; however, beginner-level yoga practitioners have no means of knowing whether their position is good or poor without the instructor’s help. As a result, the automatic assessment of yoga postures is proposed for yoga posture recognition, which can alert practitioners by using the Y_PN-MSSD model, in which Pose-Net and Mobile-Net SSD (together named as TFlite Movenet) play a major role. The Pose-Net layer takes care of the feature point detection, while the mobile-net SSD layer performs human detection in each frame. The model is categorized into three stages. Initially, there is the data collection/preparation stage, where the yoga postures are captured from four users as well as an open-source dataset with seven yoga poses. Then, by using these collected data, the model undergoes training where the feature extraction takes place by connecting key points of the human body. Finally, the yoga posture is recognized and the model assists the user through yoga poses by live-tracking them, as well as correcting them on the fly with 99.88% accuracy. Comparatively, this model outperforms the performance of the Pose-Net CNN model. As a result, the model can be used as a starting point for creating a system that will help humans practice yoga with the help of a clever, inexpensive, and impressive virtual yoga trainer.

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

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Machine learning and deep learning techniques are always compressed, convergence models. The most appropriate optimization would be the gradient descent, which is capable of finding the best solution with minimal cost. This algorithm can also be used with convolution neural network for better performance. Here a research work proposes a novel automatic image segmentation technique to discriminate between normal and acute lymphoblastic leukemia on bone marrow samples and has been done with the proposed GD_CNN. This method consists of background extraction and RBC separation. In the next stage, the median filtering and morphology technique is used to remove the noise and outliers. An improved GD_CNN model is used to classify normal or abnormal bone marrow samples. This proposed work is implemented using MATLAB 10.0 tool and tested on the bone marrow samples taken from ALL-IDB (Acute Lymphoblastic Leukemia Image Database for image processing) database. The proposed model provides high-quality classification accuracy to identify acute lymphoblastic leukemia.

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