Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall.
With increasing advancements in the field of telecommunication, the attainment of a higher data transfer rate is essentially a greater need to meet high-performance communication. The exploitation of the fuzzy system in the wireless telecommunication systems, especially in Fifth Generation Mobile Networks (or) 5G networks is a vital paradigm in telecommunication markets. A comprehensive survey is dealt in the paper, where it initially reviews the basic understanding of fuzzy systems over 5G telecommunication. The literature studies are collected from various repositories that include reference materials, Internet, and other books. The collection of articles is based on empirical or evidence-based from various peer-reviewed journals, conference proceedings, dissertations, and theses. Most of the existing soft computing models are streamlined to certain applications of 5G networking. Firstly, it is hence essential to provide the readers to find research gaps and new innovative models on wide varied applications of 5G. Secondly, it deals with the scenarios in which the fuzzy systems are developed under the 5G platform. Thirdly, it discusses the applicability of fuzzy logic systems on various 5G telecommunication applications. Finally, the paper derives the conclusions associated with various studies on the fuzzy systems that have been utilized for the improvement of 5G telecommunication systems.
The focus of this study is to propose a generalised trust-model over routing protocols in mobile ad hoc networks (MANETs). It is observed that the presence of malicious nodes is a critical factor affecting the network performance in an ad hoc network. The novelty in the approach is that the notion of trust can be easily incorporated into any routing protocol in MANETs. The vector auto regression based trust model is introduced to identify malicious nodes that launch multiple attacks in the network. The proposed trust model is incorporated over ad hoc on-demand distance vector (AODV) routing protocol and optimised link state routing (OLSR) protocol in MANETs. The performance evaluations show that by carefully setting the trust parameters, substantial benefit in terms of throughput can be obtained with minimal overheads. The computed trust and confidence values are introduced into the path computation process of the ad hoc routing protocols. It was observed that the nodes in the network were able to learn the malicious activities of their neighbours and hence, alternate trustworthy paths are taken to avoid data loss in the network, with trade-offs in end-to-end packet delay and routing traffic.
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