Internet of Things (IoT) is a novel paradigm, which not only facilitates a large number of devices to be ubiquitously connected over the Internet but also provides a mechanism to remotely control these devices. The IoT is pervasive and is almost an integral part of our daily life. These connected devices often obtain user's personal data and store it online. The security of collected data is a big concern in recent time. As devices are becoming increasingly connected, privacy and security issues become more and more critical and these need to be addressed on an urgent basis.IoT implementations and devices are eminently prone to threats that could compromise the security and privacy of the consumers, which, in turn, could influence its practical deployment. In recent past, some research has been carried out to secure IoT devices with an intention to alleviate the security concerns of users. There have been research on blockchain technologies to tackle the privacy and security issues of the collected data in IoT. The purpose of this paper is to highlight the security and privacy issues in IoT systems. To this effect, the paper examines the security issues at each layer in the IoT protocol stack, identifies the under-lying challenges and key security requirements and provides a brief overview of existing security solutions to safeguard the IoT from the layered context.

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In recently years, in the era of multimedia technologies need for information/data retrieval systems getting more attention. The data might be image, video, audio and/or text files. Digital libraries, surveillance application, web applications and many other applications that handle huge volume of data essentially have data retrieval components. These data always include large-scale of independent information with both textual and visual contents. The large numbers of images has posed increasing challenges to computer systems to store and manage data effectively and efficiently. In this paper, we proposed a method of Geo-location-based image retrieval (GLBIR. The proposed method identifies a geo location in an image using visual attention-based mechanism and represents them using its color layout descriptors and curve let descriptors. These features are extracted from geo location of query image from Flickr. The likeness between the query geo coordinates and image is ranked according to a similarity measure computed from the feature vectors. Our proposed model does not full semantic understanding of image content, uses visual metrics for example proximity ,color contrast, size and nearness to image's boundaries to locate viewer's attention. We evaluate our approach on the image dataset from Flickr. We analyzed results analyzed and compared with state of art CBIR Systems and GLBIR Technique.

Content Based Image Retrieval is very hottest research area in computer vision and image processing. To perceive arbitrary natural scene from complex environment is a challenging issue in visual imaging and processing research area. Neural Network is a grid of "neuron like" nodes, in this paper we follow towards Neural Network (NN), is committed to contributing a new technical concept for the scene understanding and recognition by consolidating new intellectual visual features into the scene expression, which can be very crucial and provide cognitive intelligence to cloud robot. Inspired by Artificial Neural Network intelligence due to its dynamic nature, we make use of the attributes of the Gabor filter and Laplacian of Gaussian filter which is to be akin to robot visual perception, and apply the wavelet transform to inspect a new approach in complex environment natural scene perception and understanding for virtual phenomena. Through the study of Neural Network, the perception ability of the natural scene image from complex environment for cloud robot is enhanced with the integration of cognitive visual features and the scene expression.

Process recommendation is an important technique in business process management (BPM), which can be introduced to support modelling biomedical processes. However, most existing process recommendation algorithms in terms of behaviour (what tasks need to be executed in what order) suffer from the problem of state-space explosion when unfolding a process with many parallel patterns. To address this issue, the authors propose an independent path-based process recommendation algorithm to speed up the biomedical process recommendation while guaranteeing the accuracy. The experimental results of the proposed algorithm showed higher accuracy and better efficiency than the state-of-the-art algorithms.