The rapid growth of smart grid systems demands efficient management of smart grid services. Smart grids are expected to enable the delivery and management of electricity in a more reliable, efficient, economical, and secured manner. Thus, the development of effective power management solutions for smart grids to meet these challenges is an important area of research in recent times. In this article, we propose using learning automata (LA), a computational learning utility, for efficient power management in smart grids (LAPM). The proposed system, LAPM, helps in identifying the electricity required for various distribution substations and controls the usage of power by various devices (i.e., preventing unauthorized use of power). The use of LA enables performing a dynamic analysis of power usage and providing decision making for its effective usage. The system is evaluated on a real-life-resembling environment, with respect to parameters such as power utilization and customer satisfaction.

Internet of Things (IoT) refers to the networked interconnection of everyday objects. IoT is an upcoming research field and is being regarded as the revolution in the world of communication because of its extensible applications in numerous fields. Due to open and self-assimilation nature of these networks they are highly prone to attacks. Because of this reason security is of primary concern here. The security attack can be of various types, the idea here is to prevent IoT networks from Distributed Denial of Service (DDoS) attack. The objective of Denial of Service (DoS) is to make the server resources unavailable to the intended user, and when several such DoS attacks are present in a network then the attack is known as a DDoS attack. Our strategy is to prevent DDoS attack in IoT networks by using Learning Automata (LA) concepts. In this paper, we present a Service Oriented Architecture (SOA) which is used as a system model for IoT here. SOA provides a platform to the developers using which they can develop various applications for IoT without any concern regarding the nature of the objects, thereby acting as a middleware. The DDoS prevention strategy has been targeted for the SOA based architecture for IoT. The simulation results show that the proposed scheme is effective in preventing DDoS attacks in IoT.

Sentiment analysis of short texts like single sentences and reviews available on different social networking sites is challenging because of the limited contextual information. Based on the sentiments and opinions available, developing a recommendation system is an interesting concept, which includes strategies that combine the small text content with prior knowledge. In this paper, we explore a new application of Recursive Neural Networks (RNN) with deep learning system for sentiment analysis of reviews. The proposed RNN-based Deep-learning Sentiment Analysis (RDSA) recommends the places that are near to the user's current location by analyzing the different reviews and consequently computing the score grounded on it. Deep Learning is used to optimize the recommendations depending on the sentiment analysis performed on the different reviews, which are taken from different social networking sites. The Experiments performed indicate that the RNN based Deep-learning Sentiment Analysis (RDSA) improvises the behavior by increasing the accuracy of the sentiment analysis, which in turn yields better recommendations to the user and thus helps to identify a particular position as per the requirement of the user need.