Assigning a label pertaining to an image belonging to its category is defined as object taxonomy. In this paper, we propose a transform based descriptor which effectively extracts intensity gradients defining edge directions from segmented regions. Feature vectors comprising color, shape and texture information are obtained in compressed and de-correlated space. Firstly, Fuzzy c-means clustering is applied to an image in complex hybrid color space to obtain clusters based on color homogeneity of pixels. Further, HOG is employed on these clusters to extract discriminative features detecting local object appearance which is characterized with fine scale gradients at different orientation bins. To increase numerical stability, the obtained features are mapped onto local dimension feature space using PCA. For subsequent classification, diverse similarity measures and Neural networks are used to obtain an average correctness rate resulting in highly discriminative image classification. We demonstrated our proposed work on Caltech-101 and Caltech-256 datasets and obtained leading classification rates in comparison with several benchmarking techniques explored in literature.
EEG is used to study the electrical changes in the brain and can derive a conclusion as epileptic or not, using an automated method for accurate detection of seizures. Deep learning, a technique ahead of machine learning tools, can selfdiscover related data for the detection and classification of EEG analysis. Our work focuses on deep neural network architecture to visualize the temporal dependencies in EEG signals. Algorithms and models based on Deep Learning techniques like Conv1D, Conv1D + LSTM, and Conv1D + Bi-LSTM for binary and multiclass classification. Convolution Neural Networks can spontaneously extract and learn features independently in the multichannel time-series EEG signals. Long Short-Term Memory (LSTM) network, with its selective memory retaining capability, Fully Connected (FC) layer, and softmax activation, discover hidden sparse features from EEG signals and predicts labels as output. Two independent LSTM networks combine to form Bi-LSTM in opposite directions and appreciate added visibility to upcoming information to provide efficient work contrary to previous methods. Long-term EEG recordings on the Bonn EEG database, Hauz Khas epileptic database, and Epileptic EEG signals from Spandana Hospital, Bangalore, assess performance. Metrics like precision, recall, f1-score, and support exhibit an improvement over traditional ML algorithms evaluated in the literature.
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