The feelings of WEB users have a great influence on rest of the users, product sellers and market analysis. It is necessary to well structure the unstructured data from various social platforms for proper and meaningful analyses. For the classification of multilingual data, the analysis of feelings has recognized significant attention. This is called textual organization that may be used to classify state of mind or feelings expressed in different ways like: negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. in the field of Automatic Language Processing (NLP). To solve this kind of problem, sentiment analysis and deep learning techniques are two merging techniques. Because of machine learning ability, deep learning models are effectively used for this purpose. Recurrent Neural Networks (RNN) and Naive Bayes algorithm are two popular deep learning architectures to analyze feelings in sentences. These architectures may be used in natural language processing. In this research article, we propose solutions to multilingual sentiment analysis problem by implementing algorithms and in order to contract the result, we compare precision factor to find the best solution for multilingual sentimental analysis.

Handover usually deals with the mobility of the end users in a mobile network to assure about the ongoing session of a user. It is observed that frequent handover results in call dropping due to latency. In order to overcome this issue, a fuzzy based handover decision scheme for mobile devices using a predictive model is proposed. First, an MFNN (Multi-layer Feed Forward Network) is used to determine the next cell of the user along with best hand off time. To obtain the best access network, multiple-attribute Access Network Selection Function (ANSF) is used. The fuzzy rule is applied by considering the parameter data rate, reliability, signal strength, battery power and mobility as input and the output obtained is the optimal network. The proposed scheme selects the best access network and enhances the quality of services.

A blockchain-based Aadhar system: distributed authentication system

In metro cities, the effective and efficient management of traffic is one of the most demanding and time taking tasks. Vehicular ad hoc networks (VANET) provide unfailing, low-cost solutions for transportation systems with intelligence. VANET, a subclass of mobile ad hoc networks (MANET), allows exchange of information among vehicles and/or roadside devices. VANET can be implemented in numerous application areas such as effective traffic management, safety, and user comfort for drivers as well as passengers. It provides phenomenal growth to both in industries and research communities. Secure mobility and handoff management are the most promising and challenging research issues in VANET. In this paper, we have introduced the security in intra domain mobility handoff in PMIPv6 for VANET. Existing intra handover schemes does not include the authentication cost while evaluating the total packet delivery cost for intra domain handoff. Our proposed scheme includes the authentication cost of an intra-domain handover for evaluating the total packet delivery cost of handover for next-generation mobility management protocols, which is PMIPv6 for the vehicular network. We have considered the vital parameters such as the number of MAGs, setup cost, binding update cost, unit transmission cost for analyzing the total packet delivery cost. Furthermore, a comparative study with authentication and without authentication cost for the considered parameters shows that our proposed scheme secures the handover process with slight variation in cost.