SEARCH FOR HARMONIZED KEYWORDS USING THE VOTED LAB FEATURE AND ALLOW RE CRYPTOSYSTEM FOR ELECTRONIC HEALTH CLOUDS

The searchable file encryption (SE) plan is really a technology to include security protection and favorable operability functions together, which could play a huge role within the e-health record system. A digital health record product is a singular application which will bring great convenience in healthcare. Within this paper, we introduce a singular cryptographic primitive named as conjunctive keyword search with designated tester and timing enabled proxy re-file encryption function, which is a type of a period-dependent SE plan. We design a singular searchable file encryption plan supporting secure conjunctive keyword search and approved delegation function. In contrast to existing schemes, the work is capable of timing enabled proxy re-file encryption with effective delegation revocation. The security and privacy from the sensitive private information would be the major concerns from the users that could hinder further development and broadly adoption from the systems. We formulate a method model along with a security model for that suggested Re-deck plan to exhibit that it's a competent plan demonstrated secure within the standard model. The comparison and extensive simulations show it features a low computation and storage overhead. It might enable patients to delegate partial access legal rights to other people to function search functions over their records inside a short time period. The size of the timeframe for that delegate to look and decrypt the delegator’s encrypted documents could be controlled

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop

Identification of Normal and Abnormal Mammographic Images Using Deep Neural Network

This article presents an image retrieval strategy that considers the similarity of a selected image (CBIR-Content-Based Image Retrieval). The analogy is defined by the wavelet technique, combined with Hu moments, for removing features. The classification of mammography’s is conducted through the Artificial Neural Networks Auto-organizing System (SME). A Data Base (QUALIM), University of the Federal Republic of São Paulo (UNIFESP), and Medical Image Classification Laboratory are used to test the method. The system suggested is tested. We have employed two widely used pattern recognition approaches—facial recognition digital mammograms for examination. The techniques are based on the new classification schemes Ada Boost and Support Vector Machines (SVM).Several experiments were carried out to evaluate the accuracy of these two algorithms in different circumstances. The results for the Ada Boost classification system are positive, especially for mass lesions classification. In all cases, the algorithm was 76% exact, and only 90% accurate for mass. The SVM-based algorithm was not available. To improve the precision of the process, we need to choose enhanced image functionality for digital mammograms than those commonly used. Detection of breast cancer is the most challenging aspect in the field of health monitoring. This document has been used to evaluate breast cancer detection through a dataset of the mammographic image analysis company (MIAS). The suggested method has four key steps: preprocessing, segmentation, retrieval, and classification of images. Initially in mammograms, laplacian filtration was used to describe the edges' area and was thus particularly susceptible to noise. The modified adjustable Fuzzy-C-MEANS (ARKFCM) was used in the following segmentation to find the object within the complicated module. The conventional ARKFCM masses of undefined mass were painful to divide into mammograms. The Euclidean distance in ARKFCM was replaced with the correlation function to solve this problem to increase its segmental efficiency. In the segmented cancer region, the removal of representative subsets was performed by extracting hybrid properties (histogram of guided grade (HOG), uniformity, and energy). Each feature value was defined for the Deep Neural classifier—network (DNN) for detection in normal and pathological areas of mammograms. The findings of the study show that the technique shows an improvement of up to 3-9% in precision compared with other methods currently in use in breast cancer classifications. Describes a new way to identify borders between different brightness areas. The goal is to create distinctions between regions that are unclearly distinct and defined by tonal shifts. The limits are set by the coordinates of the start and endpoints. The proposed method can be used as a stage in advanced techniques for hierarchical image analysis that increases the semantically awareness of picture content with each step. This study applies and evaluates mammograms. Interpretation of mammograms is a complex research area discussed by many authors. The treatment of breast cancer is achieved using these approaches