In the present scenario, the Cloud Computing is very famous and flexible technology. It facilitates customers with effortlessness, quickness, competence etc in their work area through the services. Cloud provides huge data centre to handle the large amount of information. The Cloud Computing benefits the organizations to handle their large volume of information. The main issue in cloud computing is data security, because number of customers are sharing same cloud. This study aimed at designing a new security method by using a hybrid cryptosystem, for data security in the cloud. The need for the current investigation is to protect data from unauthorized access or hackers in cloud at the time of data transmission by encrypting the user data. Cloud computing constitutes several security issues including data access control, identity management, auditing, integrity control and risk management therefore, this hybrid cryptosystem is designed and comprises of both symmetric and asymmetric cryptography algorithm in which Blowfish symmetric algorithm deals with data confidentiality whereas, RSA asymmetric algorithm deals with an authentication. This method also includes the Secure Hash Algorithm -2 for data integrity. The present study concluded that the proposed method provides high security on data transmission over the internet and proper network access on demand to a shared tank of constructive computing resources, mainly net, server, and storage application.

The Two-stream convolution neural network (CNN) has proven a great success in action recognition in videos. The main idea is to train the two CNNs in order to learn spatial and temporal features separately, and two scores are combined to obtain final scores. In the literature, we observed that most of the methods use similar CNNs for two streams. In this paper, we design a two-stream CNN architecture with different CNNs for the two streams to learn spatial and temporal features. Temporal Segment Networks (TSN) is applied in order to retrieve long-range temporal features, and to differentiate the similar type of sub-action in videos. Data augmentation techniques are employed to prevent over-fitting. Advanced cross-modal pre-training is discussed and introduced to the proposed architecture in order to enhance the accuracy of action recognition. The proposed two-stream model is evaluated on two challenging action recognition datasets: HMDB-51 and UCF-101. The findings of the proposed architecture shows the significant performance increase and it outperforms the existing methods.

Intelligent transportation system (ITS) is an advance leading edge technology that aims to deliver innovative services to different modes of transport and traffic management. Traffic flow prediction (TFP) is one of the key macroscopic parameters of traffic that supports traffic management in ITS. Growth of the real-time data in transportation from various modern equipments, technology, and other resources has led to generate big data, posing a huge concern to deal with. Recently, deep learning (DL) techniques have demonstrated the capability to extract comprehensive features efficiently, using multiple hidden layers, from such huge raw, unstructured, and nonlinear data. Nonlinearity in traffic data is the major cause of inaccuracy in TFP. In this article, we propose a flow strength indicator-based Chronological Dolphin Echolocation-Fuzzy, a bioinspired optimization method with fuzzy logic for incremental learning of deep belief network. Technical indicators provide flow strength features as an input to the model. Hidden layers of DL architecture consequently learn more features and propagate it as an input to next layer for supervised learning. The degree of membership to the features is identified by the membership functions, followed by weight optimization using Dolphin Echolocation algorithm to fit the model for the nonlinear data. Experiments performed on two different data sets, namely Traffic-major roads and performance measurement system-San Francisco (PEMS-SF), show good results for the proposed deep architecture. The analysis of the proposed method using log mean square error and log root mean square deviation acquires a minimum value of 2.4141 and 0.61 for the Traffic-major roads database taken for the time step duration of 1 year and a minimum value of 1.6691 and 0.5208 for PEMS-SF data set for the time step interval of 5 minutes, respectively. These positive results demonstrate key importance of our traffic flow model for the transportation system.

Onlinecustomer feedbacks are taken as an integral part for making a decision before purchasing any product. In this paper the customer's textual feedback has been taken into account for rating the restaurants available in Kolkata. To get the accuracy of a recommender system we have to first understand the users' preferences and then mine those data to valuable information so that it can benefit our society. The users' sentimental approach is measured first and then we have to calculate each user's sentiment on the food item. With that we can also consider an interpersonal sentimental influence. Then we have to consider the reputation of the restaurants from the sentimental distributions of a group of user's set. The performance evaluation of our experimental results is based on the concept of collaborative filtering and it is made on the real life dataset collected from the “Zomato” rating website. This analysis can well denote the user's preferences which can upgrade our recommendation system.

Information on the connected colour temperature, which affects the image due to the surrounding illumination, is critical, particularly for natural lighting and capturing images. Several methods are introduced to detect colour temperature precisely; however, the majority of them are difficult to use or may generate internal noise. To address these issues, this research developed a hybrid deep model that properly measures temperature from RGB images while reducing noise. The proposed study includes image collection, pre-processing, feature extraction and CCT evaluation. The input RGB pictures are initially generated in the CIE 1931 colour space. After that, the raw input samples are pre-processed to improve picture quality by performing image cropping and scaling, denoising by hybrid median-wiener filtering and contrast enhancement via Rectified Gamma-based Quadrant Dynamic Clipped Histogram Equalisation (RG_QuaDy_CHE). The colour and texture features are eliminated during pre-processing to obtain the relevant CCT-based information. The Local Intensity Grouping Order Pattern (LIGOP) operator extracts the texture properties. In contrast, the colour properties are extracted using the RGB colour space’s mean, standard deviation, skewness, energy, smoothness and variance. Finally, using the collected features, the CCT values from the submitted images are estimated using a unique Deep Convolutional Attention-based Bidirectional Recurrent Neural Network (DCA_BRNNet) model. The Coati Optimisation Algorithm (COA) is used to improve the performance of a recommended classifier by modifying its parameters. In the Result section, the suggested model is compared to various current techniques, obtaining an MAE value of 529K and an RMSE value of 587K, respectively.