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Now a day’s there is a rapid growth in technology. In the modern health care environment, the usage of IOT technologies brings convenience of physicians and patients since they are applied to various medical areas. The development of this new technology in healthcare applications without considering security makes patient privacy vulnerable. In this paper at first we highlight the major security requirements while storing the patient information in the cloud. Through IOT Doctor’s Treatment suggestions, the patient updated treatment information will be directly stored in the cloud. In order to achieve effective for storing the information in cloud we provide secure communication protocols. We also propose a secure IOT based on sensors used in modern healthcare.

In the present environment people are over burden themselves by indulging in various things, which are harmful to our brains. When brain is constantly working or exposed to the toxic environment, it will shut down the pineal gland. So there is a need to measure brain waves and sustain the energy at a high level. In this paper we are presenting about EEG, which is capable of capturing brain waves by attaching electrodes to the scalp which in the form of waves, so BCI is used to translate these waves into commands for these purpose it uses classification algorithm.

The inaccessible nature of the Deep Web motivated us to propose a learning-based application framework for exploring Deep Web, based on the query provided by the user, through TOR interface. The proposed framework, DWIRS: Deep Web Indexing and Ranking Search, employs two algorithms: BSVM (Boosted Support Vector Machine) for indexing, and, BRF (Boosted Random Forest) for ranking Deep Web pages. To evaluate the performance of DWIRS framework, DWIRS Document Clustering Workbench, a standalone GUI application has been developed and validated. A mobile application for exploring Deep Web has also been developed. The purposed DWIRS framework is efficient.

An anomaly is a deviation from the normal. In a large dataset or continuously streaming data an anomaly occurs when such data has occurred which either do not follow certain set of rules or pattern that every other data point follows. A key goal of information analytics is to identify patterns of anomalous behaviour. Such identification of anomalies is required in a variety of applications such as systems management, sensor networks, and security. However, most of the current state of the art on anomaly detection relies on using a predefined knowledge base. A key challenge is defining and creating the predefined knowledge base and the need to have prior information about the domain. There are several approaches for change or anomaly detection. In this paper we focus on the broad categories: the rule-based, pattern-matching and statistical approaches for anomaly detection.

Multi-document text summarization focuses on extracting the key information from the collection of related documents and presents it as a brief summary.In this paper, we are presenting multi document text summarization using Deep learning algorithm with fuzzy logic which is an important research area in NLP, data mining(DM) and Machine Learning(ML). To improve the accuracy here , we are using Restricted Boltzman machine to generate a shortend version of original document without losing its valuable information. The current method consists of two steps: 1.Training phase 2. Testing phase. The prominent role of the training phase is to extract effective summary generation. Afterwards the testing phase is implemented to validate the efficiency and accuracy of the proposed method.

In recent years cloud computing emerged as a critical tool to enhance competitiveness. Current network infrastructures in cloud data centers limit the ability of cloud provider to offer guaranteed cloud network resources to users. In order to ensure security and performance as defined in the service level agreement (SLA) between cloud user and provider, cloud providers need the ability to provision network resources dynamically and on the fly. In this paper we propose to extend a federated cloud networking security architecture, Network Function Virtualization (NFV) and network orchestrator so that it can secure IoT devices and networks.