and Zebin, Tahmina (2019) An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks.

This study investigates whether feedforward neural networks with two hidden layers generalise better than those with one. In contrast to the existing literature, a method is proposed which allows these networks to be compared empirically on a hidden-node-by-hidden-node basis. This is applied to ten public domain function approximation datasets. Networks with two hidden layers were found to be better generalisers in nine of the ten cases, although the actual degree of improvement is case dependent. The proposed method can be used to rapidly determine whether it is worth considering two hidden layers for a given problem

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This paper describes a project aimed at making Computational Fluid Dynamics (CFD)- based fire simulation accessible to members of the fire safety engineering community. Over the past few years, the practice of CFD-based fire simulation has begun the transition from the confines of the research laboratory to the desk of the fire safety engineer. To a certain extent, this move has been driven by the demands of performance based building codes. However, while CFD modeling has many benefits over other forms of fire simulation, it requires a great deal of expertise on the user's part to obtain reasonable simulation results. The project described in this paper, SMARTFIRE, aims to relieve some of this dependence on expertise so that users are less concerned with the details of CFD analysis and can concentrate on results. This aim is achieved by the use of an expert system component as part of the software suite which takes some of the expertise burden away from the user. SMARTFIRE also makes use of the latest developments in CFD technology in order to make the CFD analysis more efficient. This paper describes design considera tions of the SMARTFIRE software, emphasizing its open architecture, CFD engine and knowledge-based systems.

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Abstract. This paper proposes an improved CBR approach for the identification of money transfer fraud in Mobile Money Transfer (MMT) environments. Standard CBR capability is augmented by machine learning techniques to assign parameter weights in the sample dataset and automate k-value random selection in k-NN classification to improve CBR performance. The CBR system observes users' transaction behaviour within the MMT service and tries to detect abnormal patterns in the transaction flows. To capture user behaviour effectively, the CBR system classifies the log information into five contexts and then combines them into a single dimension, instead of using the conventional approach where the transaction amount, time dimensions or features dimension are used individually. The applicability of the proposed augmented CBR system is evaluated using simulation data. From the results, both dimensions show good performance with the context of information weighted CBR system outperforming the individual features approach.