A mobile ad hoc network (MANET) consists of a set of mobile hosts that can communicate with each other without the assistance of base stations. In MANETs, the dynamic nature of the network topology is a major reason for link failures. In this paper, we proposed the Ad hoc on-demand distance vector (AODV) routing (FLAODV) protocol for routing in mobile ad hoc networks to increase durability path. The performance of this routing protocol is studied using opnet10.5. The simulation results of the FLAODV show that the protocol is quite efficient and superior to AODV with the respects to the average Route Discovery Time, the average Packets overhead, average throughput and average end-to-end delay.

Ad hoc networks are dynamically configurable wireless networks that have no fixed infrastructures and do not require predefined configurations. \nIn large, distributed systems, like ad hoc networks, centralized learning of routing or movement policies may be impractical. We need to employ learning algorithms that can learn independently, without the need for extensive coordination. A search for alternative methods of routing packets has resulted in reinforcement learning (RL) as a good approach to adaptive routing. RL methods are able to learn and adapt to a unknown and changing environment. In This paper we propose a new intelligent routing protocol with RL approach for wireless ad hoc networks. The Intelligent Wireless Ad Hoc Routing protocol (IWARP) is an on-demand protocol and self configuring. It selects optimal routes based on local information and past experience.\nThe proposed scheme uses the distributed Q-Learning framework, to select a stable route in order to enhance system performance. Our study also compares the performance of the IWARP protocol with the well-known Ad hoc On-Demand Distance Vector (AODV) protocol, Results obtained by a simulation campaign show that IWARP increases the throughput and decreases the data dropped and number of hops per route.

In this article, a class of complex harmonic meromorphic functions with positive coefficients is introduced by making used of basic hypergeometric functions. We consider some properties such as coefficient inequality, growth theorems and extreme points.

Sentiment analysis or Opinion mining is the process of detecting the subjective information in given text. Text may include subjective information like opinions, attitudes and feelings. Sentiment analysis also has an important potential role as enabling technologies for other systems. This paper employed two semi supervised probabilistic approaches called JST model and Reverse JST model to detect sentimental topic. The system designed in this paper classifies positive and negative labels of an online review. In JST, the document level sentiment classification is based on topic detection and topic sentiment analysis. JST process, the sentiment labels are associated with documents, the topics are generated dependent on sentiment distribution and words are generated conditioned on the sentiment topic pair. In Reverse JST, the sentiment label is dependent on the topics. In this process, where the topics are associated with documents, the sentiment labels are associated with topics and words are associated with both topics and sentiment labels. In LDA, where topic are associated with documents and words are associated with topic distribution. JST and Reverse JST are evaluated on four different domains using the Gibbs Sampling Algorithm. The nature of JST makes it highly portable to other domains. It compares JST and Reverse JST with latent dirichlet allocation. In this paper observed topic and topic sentiment detected by JST are indeed coherent and informative.