Mobile Ad-hoc Networks (MANETs) are independent systems that can work without the requirement for unified controls, pre-setup to the paths/routes or advance communication structures. The nodes/hubs of a MANET are independently controlled, which permit them to behave unreservedly in a randomized way inside the MANET. The hubs can leave their MANET and join different MANETs whenever the need arises. These attributes, in any case, may contrarily influence the performance of the routing conventions (or protocols) and the general topology of the systems. Along these lines, MANETs include uniquely planned routing conventions that responsively as well as proactively carry out the routing. This paper assesses and looks at the effectiveness (or performance) of five directing conventions which are AOMDV, DSDV, AODV, DSR and OLSR in a MANET domain. The research incorporates executing a simulating environment to look at the operation of the routing conventions dependent on the variable number of hubs. Three evaluation indices are utilized: Throughput (TH), Packet Delivery Ratio (PDR), and End-to-End delay (E2E). The assessment outcomes indicate that the AODV beats other conventions in the majority of the simulated scenarios.

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This paper evaluates the performance of localized weather forecasting model using Artificial Neural Network (ANN) with different ANN algorithms in a tropical climate. Three ANN algorithms namely, Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient are used in the short-term weather forecasting model. The study focuses on the data from NorthWest Malaysia (Chuping). Meteorological data such as atmospheric pressure, temperature, dew point, humidity and wind speed are used as input parameters. One hour ahead forecasted results for atmospheric pressure, temperature and humidity were compared and analyzed and they show that ANN with Levenberg-Marquardt algorithm performs best.

<span>Developments in computer networking have raised concerns of the associated Botnets threat to the Internet security. Botnet is an inter-connected computers or nodes that infected with malicious software and being controlled as a group without any permission of the computer’s owner. <br /> This paper explores how network traffic characterising can be used for identification of botnet at local networks. To analyse the characteristic, behaviour or pattern of the botnet in the network traffic, a proper network analysing tools is needed. Several network analysis tools available today are used for the analysis process of the network traffic. In the analysis phase, <br /> the botnet detection strategy based on the signature and DNS anomaly approach are selected to identify the behaviour and the characteristic of the botnet. In anomaly approach most of the behavioural and characteristic identification of the botnet is done by comparing between the normal and anomalous traffic. The main focus of the network analysis is studied on UDP protocol network traffic. Based on the analysis of the network traffic, <br /> the following anomalies are identified, anomalous DNS packet request, <br /> the NetBIOS attack, anomalous DNS MX query, DNS amplification attack and UDP flood attack. This study, identify significant Botnet characteristic in local network traffic for UDP network as additional approach for Botnet detection mechanism.</span>

This paper evaluates the performance of a rainfall forecasting model. In this paper Artificial Neural Network (ANN) and Fuzzy C-Means (FCM) clustering algorithm are combined and used to forecast short-term localized rainfall in tropical climate. State forecast (raining or not raining) and value forecast (rain intensity) are tested using a number of trained networks. Different types of ANN structured were trained with a combination of multilayer perceptron with back propagation network. Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient training algorithm are used in the network training. Each neurons uses linear, logistic sigmoid and hyperbolic tangent sigmoid as transfer function. Input parameter preliminary analysis, data cleaning and FCM clustering were used to prepare input data for the ANN forecast model. Meteorological data such as atmospheric pressure, temperature, dew point, humidity and wind speed have been used as input parameters. The predicted rainfall forecast for 1 to 6 hour ahead are compared and analyzed. 1 hour ahead for state and value forecast yield high accuracy. Result shows that, the combined of FCM-ANN forecast model produces better accuracy compared to a basic ANN forecast model.