Abstract: The character and symbol recognition system comprises a detachable body having a photographic camera to capture real time image of one of sheet or poster comprising of printed and handwritten characters and symbols; an input unit to acquire the real time captured image; a pre-processing unit to detect a character and symbol region; a classification unit equipped with at least two channel neural network based on CNN and LSTM to separate the character and symbol region; a central processing unit to calculate weights for transitions to the candidates thereby generate one of a first character or first symbol string transition data based on a set of the candidates and the weights; and a control unit to detect one or both of the printed and handwritten characters and symbols thereby display the detected information on a display unit and play the detected information on a speaker to alert a rider.

Abstract: The medical diagnostic system comprises a deep neural network model trained with catheter hardness data, lesion hardness data, and operation time to complete catheter treatment; an irradiation energy emitting device to calculate irradiation energy of a patient; a control unit to identify a plurality of lesions from irradiation energy data; and a catheter to insert into an artery in the patient's arm, wherein the catheter tip is positioned to at least the patient's aortailiac bifurcation, wherein a therapeutic catheter is introduced into a catheter lumen and the therapeutic catheter tip is projected from the catheter tip thereby the harder lesion is initially treated, and the therapeutic catheter tip of the therapeutic catheter is projected from the catheter tip to treat the softer lesion.

Abstract: The present disclosure generally relates to a method for COVID-19 detection for spread prevention and medical assistance using machine learning comprises pre-processing a CT image set for removing noise using HU values; performing a region-based segmentation using a three-dimensional convolutional neural network model; classifying each segmented region by employing a classification model; and comparing classified segmented region with a dataset for detecting COVID-19 using a machine learning approach thereby generating an alert about the COVID-19 positive person.

Abstract: An analytics and diagnostic node according to the present disclosure monitors oversubscription and determines flow metrics by receiving mirror command frames from one or more switching nodes. The mirror command frames could correspond to a multiple flows traversing over a connection within a network. The analytics and diagnostic node collects at least one latency metric for each of the flows using timestamps found within the mirror command frames. Based on the latency metrics and timestamps, the analytic diagnostic node determines an average data rate for each of the flows. The analytics and diagnostic node also computes the cumulative data rates corresponding to different bucket intervals based on the average data rates. To detect oversubscription, the analytics and diagnostic node compares the cumulative data rates with one or more oversubscription rules.

Abstract: An analytics and diagnostic node according to the present disclosure monitors oversubscription and determines flow metrics by receiving mirror command frames from one or more switching nodes. The mirror command frames could correspond to a multiple flows traversing over a connection within a network. The analytics and diagnostic node collects at least one latency metric for each of the flows using timestamps found within the mirror command frames. Based on the latency metrics and timestamps, the analytic diagnostic node determines an average data rate for each of the flows. The analytics and diagnostic node also computes the cumulative data rates corresponding to different bucket intervals based on the average data rates. To detect oversubscription, the analytics and diagnostic node compares the cumulative data rates with one or more oversubscription rules.