Abstract: The system comprises a feature extraction processor for extracting a set of features from user data; a sensitivity score calculation processor for calculating the sensitivity score using a machine learning technique; an SVM model for classifying the dataset; a tracking unit for monitoring the outbound data transfers made by a DLP agent and identifying the outgoing data transfers of different data types; a central processing unit for determining a first reputation score and destination entity for a first outbound data transfer to a specified recipient entity, determining a subsequent standing score, and determining a first violation of a DLP policy, thereby determining a second violation of the DLP policy; and a controlling unit for carrying out at least one of the reporting and/or remedial actions in response to the first, second, or both violations and generating alert for approving or denying a particular first or second outbound data transfer.

Abstract: The system comprises a FL-based generative adversarial network (GAN) for generating adversarial examples, wherein the GAN includes a generator for generating the adversarial examples and a discriminator for distinguishing the adversarial examples from the original data, wherein the FL network includes multiple clients, each having a local dataset and a local DNN model, and a central server for coordinating the training process; a DNN for classifying data, where the DNN is trained using the generated adversarial examples, wherein the training process includes exchanging the model updates between the client’s server and the central server; an evaluation module for measuring the adversarial accuracy and adversarial robustness of the DNN using appropriate metrics, including the adversarial accuracy, the adversarial loss, and the robustness to perturbations; and an adjustment module for adjusting the architecture or parameters of the DNN based on the evaluation results to improve its adversarial robustness.

Abstract: The system comprises an image acquisition device for collecting medical images; an image pre-processing device for enhancing the visual quality; an image segmentation device for extracting the region of interest from the image’s background by identifying each image’s pixel characteristics, and dividing the image into segments; a feature extraction and selection device for extracting a set of features and selecting the optimized features; a model training device for training a fuzzy logic-based prediction model and a plurality of diagnosis-specific treatment response models to predict treatment response; and a central processing device coupled to a user input device for receiving a subject patient dataset including features obtained for a reduced feature dataset and comparing the subject patient dataset to a feature data scheme for predicting a response for the subject patient thereby predicting the diseases in its early stage.

Abstract: The system comprises a plurality of sensing nodes for detecting real time health parameters of patients; a user interface having a plurality of entry fields allotted to a plurality of medical practitioners and pharmacy shops for updating an information of treatment, medication, disease, stage of disease, treatment duration, and the like; a 6G transceiver for wirelessly communicating with the plurality of sensing nodes and user interfaces; a blockchain-based memory for storing the real time health parameters and the information of treatment, medication, disease, stage of disease, treatment duration, and the like; and a central processing unit for generating a health record of each patients thereby storing into the blockchain-based memory such that a medical practitioner is allowed to access the stored data to study the health record of a patient before treatment or before suggesting any medication/vaccine to avoid the complications or any side effect.