As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. so nowadays the content based image retrieval are becoming a source of exact and fast retrieval. In this paper the techniques of content based image retrieval are discussed, analysed and compared. It also introduced the feature like neuro fuzzy technique, color histogram, texture and edge density for accurate and effective Content Based Image Retrieval System. General TermsContent Based Image Retrieval Technology, neuro-fuzzy system.

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In recent times, the Internet of Medical Things (IoMT) is a new loomed technology, which has been deliberated as a promising technology designed for various and broadly connected networks. In an intelligent healthcare system, the framework of IoMT observes the health circumstances of the patients dynamically and responds to backings their needs, which helps detect the symptoms of critical rare body conditions based on the data collected. Metaheuristic algorithms have proven effective, robust, and efficient in deciphering real-world optimization, clustering, forecasting, classification, and other engineering problems. The emergence of extraordinary, very large-scale data being generated from various sources such as the web, sensors, and social media has led the world to the era of big data. Big data poses a new contest to metaheuristic algorithms. So, this research work presents the metaheuristic optimization algorithm for big data analysis in the IoMT using gravitational search optimization algorithm (GSOA) and reflective belief network with convolutional neural networks (DBN-CNNs). Here the data optimization has been carried out using GSOA for the collected input data. The input data were collected for the diabetes prediction with cardiac risk prediction based on the damage in blood vessels and cardiac nerves. Collected data have been classified to predict abnormal and normal diabetes range, and based on this range, the risk for a cardiac attack has been predicted using SVM. The performance analysis is made to reveal that GSOA-DBN_CNN performs well in predicting diseases. The simulation results illustrate that the GSOA-DBN_CNN model used for prediction improves accuracy, precision, recall, F1-score, and PSNR.

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Recently, networks have shifted from traditional in-house servers to third-party-managed cloud platforms due to its cost-effectiveness and increased accessibility toward its management. However, the network remains reactive, with less accountability and oversight of its overall security. Several emerging technologies have restructured our approach to the security of cloud networks; one such approach is the zero-trust network architecture (ZTNA), where no entity is implicitly trusted in the network, regardless of its origin or scope of access. The network rewards trusted behaviour and proactively predicts threats based on its users’ behaviour. The zero-trust network architecture is still at a nascent stage, and there are many frameworks and models to follow. The primary focus of this survey is to compare the novel requirement-specific features used by state-of-the-art research models for zero-trust cloud networks. In this manner, the features are categorized across nine parameters into three main types: zero-trust-based cloud network models, frameworks and proofs-of-concept. ZTNA, when wholly realized, enables network administrators to tackle critical issues such as how to inhibit internal and external cyber threats, enhance the visibility of the network, automate the calculation of trust for network entities and orchestrate security for users. The paper further focuses on domain-specific issues plaguing modern cloud computing networks, which leverage choosing and implementing features necessary for future networks and incorporate intelligent security orchestration, automation and response. The paper also discusses challenges associated with cloud platforms and requirements for migrating to zero-trust architecture. Finally, possible future research directions are discussed, wherein new technologies can be incorporated into the ZTA to build robust trust-based enterprise networks deployed in the cloud.