Secure semantic interoperability solutions are required in the age of the digital era as heterogeneous IoT medical devices increase, and these devices produce vast amounts of data daily. Since the data is enormous, the hospital could not keep the complete data collection on its hospital information server. The cloud often handles storage, and state-of-the-art privacy protection relies on reliable cloud servers. Even if the documents are encrypted, the server may still determine what is contained there. This study ensures healthcare data privacy using AES and ElGamal encryption techniques while maintaining semantic Interoperability. The suggested technique provides a mechanism for document decryption on the customer’s end. By the amalgamation of medical ontology UMLS and Intuitionistic Fuzzy logic, most linguistic issues, such as word sense disambiguation, identification of lexical variances, hypernymy-hyponymy issues, and meronymy-holonymy issues, are addressed in this work. Results show that the proposed method outperforms existing solutions.
In this work, we present an innovative technique for
manually written character recognition that is disconnected,
using deep neural networks. Since of the accessibility of enormous
knowledge calculation and numerous algorithmic advances that
are emerging, it has become easier in this day and age to train
deep neural systems. And seeks to classify the numerical digits so
that digits can be translated into pixels. Today, the computing
force measure required to prepare a neural system has increased
owing to the proliferation of GPUs and other cloud-based
administrations like Google and Amazon offer tools to prepare a
cloud-based neural system. We also developed a system for the
recognition of character dependent on manually written image
division. This project uses libraries such as NumPy, pandas,
sklearn, seaborn to accomplish this either by linear and
non-linear algorithm, to know its precision on confusion matrix
and accuracy. This idea spins with RBF(radial basis function)
which consists of two parameters as C and Gamma and
classifying the pixel digits. To train those models, research work
includes Convolutional Neural Network (CNN), Dynamic Neural
Network(DNN), Recurrent Neural Network(RNN), and
TensorFlow algorithms using Keras , which can be accurately
used for the classification of the digits.
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