Kambiz Madani

A Comparative Study of Selected Classifiers with Classification Accuracy in User Profiling

In recent years the use of personalized service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. In literature a number of classification algorithms have been used to classify user related information to create accurate user profiles. Nevertheless, there is lack of comparison of these algorithms with classification accuracy of the user profile information. In our previous work [1], we compared four different classification algorithms which are; Naïve Bayesian (NB), Instance-Based Learner (IB1), Bayesian networks (BN) and Lazy Learning of Bayesian Rules (LBR) classifiers. According to our results NB and IB1 classifiers outperformed the BN and LBR classifiers with respect to classification accuracy. In this study we compare the performance of NB, IB1, Classification and Regression Tree (SimpleCART), Naïve Bayesian Tree (NBTree), Iterative Dichotomister Tree (Id3), J48 -a version of C4.5- and Sequential Minimal Optimization (SMO) algorithms with large user profile data. This study is aimed to find the best classification algorithm for user profiling process.Our simulation results show that, in general, the NBTree has the highest classification accuracy performance with the lowest error rate. On the other hand, we also found that the NBTree has one of the highest time requirements to build the classification model. Therefore, NBTree classification algorithm should be favoured over SMO, NB, IB1, J48, SimpleCART and Id3 classifiers in the personalization applications especially when the classification accuracy performance is important.

Implementation of Ontology for Intelligent Hospital Wards

We have developed and implemented an ontology for an intelligent hospital ward. Our aim is to address the pervasiveness of computing applications in healthcare environments, which require: sharing of data across the hospital, including data generated by sensors and embedded in such environments, and dealing with semantic heterogeneity that exists across the hospitals data repositories. Our conceptual ontological model that supports such an environment has been implemented using semantic Web tools and tested through the application developed with the J2EE technology.

Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules (LBR) and Instance-Based Learner (IB1) - comparative study

In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate. The obtained simulation results have been evaluated against the existing works of support vector machines (SVMs), decision trees (DTs) and neural networks (NNs).

A novel generic distributed intelligent re-configurable mobile network architecture

We present our vision of the software radio (SR) concept. To the authors knowledge this vision of an intelligent reconfigurable network encompasses and adds to the main SR concepts presented to date, namely SR/SDR, re-configurable radio systems and networks, and cognitive radio. The newly proposed architecture and supporting technology provides a flexible way to introduce re-configurability into any network node and protocol layer of a network fixed or mobile, while simultaneously providing a structured framework in which re-configurability can be introduced and hence networks can evolve. This concept has provided a solid foundation and plays a central role in providing a suitable context in which to examine reconfigurabilty issues in the mobile terminal and base station. We believe that this concept has the potential to help the telecommunications industry in facilitating; the introduction, development and harmonization of reconfigurability within existing and future fixed and mobile networks.

Investigation of Mobile IPv6 and SIP integrated architectures for IMS and VoIP applications

Mobile IPv6 and SIP are protocols designed to support different types of mobility. Mobile IPv6 has been used to support mobility in IP networks and SIP has been used for voice over IP applications. It is the signalling protocol of the IP multimedia subsystem (IMS). In this paper both protocols have been simulated and compared in order to observe their performance for voice over IP (VoIP) applications. In this paper the architectures proposed by researchers in order to combine mobile IPv6 and SIP have also been investigated and compared to analyse their advantages and disadvantages. A network scenario, running mobile IPv6 and SIP for IMS, has also been simulated in order to evaluate the performance offered by the two protocols and to compare them with the results from the simulation of the pure mobile IPv6 and SIP architectures. The comparison shows that the combined scenario offers better performance similar to the one obtained using only mobile IPv6 with route optimization. The scenario simulated was also compared with the integrated architectures for mobile IPv6 and SIP that were investigated.

A Comparative Study of Selected Classification Accuracy in User Profiling

In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian Networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate.

Intelligent reconfiguration of large mobile networks using complex organic distributed architecture

This paper presents a possible solution to the intelligent evolution of mobile systems using a complex organic distributed architecture (CODA), which supports intelligent reconfiguration of all system components. A key feature of this architecture is the deployment of multiple warehouses. The warehouses store data in a variety of ways depending on the type of intelligence, required. On line analytical processing (OLAP) software is used to monitor and control data in the system. An effective system of filters and wrappers ensures that data is secure. A system of feedback loops ensures that information travels through the system quickly and effectively.

Prioritisation queuing of user services for multi-layer wireless systems

A novel method for improving the spectrum efficiency of multi-layer cellular systems, by means of prioritisation based on the user mobility behaviour and service application has been proposed. The proposed system is shown to have a higher efficiency over a wider range of offered traffic, in comparison to the conventional FIFO queuing. Computational results suggest that using this new scheme, system capacity for handover voice calls at 2% dropping rate can be improved by as much as 27% compared with FIFO. This benefit came at the expense of some increase in the dropping rate of low priority calls. The improvement in capacity also reduces with service priority.

Investigation of handover in distributed control channel allocation (DCCA) for microcellular radio systems

After a brief review of previous studies of dynamic channel allocation (DCA), the concept and the operation principles of distributed control channel allocation (DCCA) is described. This system can adapt to local traffic density and has good potential to reduce the number of call handovers in a microcellular system. It is shown by computer simulations that significant reductions in the number of handovers and forced terminations of calls can be achieved, in comparison to autonomous DCA techniques.

Using artificial neural networks to improve performance in a wireless ATM network

This paper examines some of the limitations of existing handover schemes and the potential performance and efficiency gains that can be achieved through hybrid variants that utilise neural networks. The virtual connection tree (VCT) is chosen as a candidate handover scheme because it statistically demonstrates a fast handover methodology that uses resource pre-allocation to maintain the QoS requirements for wireless ATM traffic. A hybrid multicast re-routing; scheme is introduced which aims to utilise the available bandwidth more efficiently and consequently to handle a higher number of concurrent calls.