A mixed integer optimisation model for data classification

In this work, a mixed integer linear programming (MILP) model is proposed for the multi-class data classification problem using a hyper-box representation. The latter representation is particularly suitable for capturing disjoint data regions. The objective function used is the minimisation of the total number of misclassified data samples. In order to improve the training and testing accuracy of our approach, an iterative solution procedure is developed to assign potential multiple boxes to each single class. Finally, the applicability of the proposed approach is demonstrated through a number of illustrative examples. According to the computational results obtained, the proposed optimisation-based approach is competitive in terms of prediction accuracy when compared with various standard classifiers.     

HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens

The analysis of air quality and the continuous monitoring of air pollution levels are important subjects of the environmental science and research. This problem actually has real impact in the human health and quality of life. The determination of the conditions which favor high concentration of pollutants and most of all the timely forecast of such cases is really crucial, as it facilitates the imposition of specific protection and prevention actions by civil protection. This research paper discusses an innovative threefold intelligent hybrid system of combined machine learning algorithms HISYCOL (henceforth). First, it deals with the correlation of the conditions under which high pollutants concentrations emerge. On the other hand, it proposes and presents an ensemble system using combination of machine learning algorithms capable of forecasting the values of air pollutants. What is really important and gives this modeling effort a hybrid nature is the fact that it uses clustered datasets. Moreover, this approach improves the accuracy of existing forecasting models by using unsupervised machine learning to cluster the data vectors and trace hidden knowledge. Finally, it employs a Mamdani fuzzy inference system for each air pollutant in order to forecast even more effectively its concentrations.     

A Scheduling Technique Wireless Personal for Bandwidth Communication Allocation in Networks

Future generation wireless personalcommunication networks (PCN) are expected to providemultimedia capable wireless extensions of fixedATM/B-ISDN. This paper presents a scheduling techniquefor PCN based on TDMA and the leaky bucket regulator, thewell known bandwidth enforcement mechanism for fixedATM. The main objective of the proposed technique is toensure fair and efficient treatment of various types oftraffic on the air interface, includingconstant-bit-rate (CBR) voice and variable-bit-rate(VBR) video. Two alternative priority mechanisms areintroduced and their performance is evaluated. Theperformance comparison of the alternatives reveals aninteresting tradeoff between fairness and quality ofservice (QoS).     

An Efficient Location Management Protocol for Wireless ATM Customer Premises Networks

In this paper we present a new location management protocol for wireless ATM networks, called LMCP (Location Management and Control Protocol). This protocol is based on the PNNI (Private Network-to-Network Interface) routing functionality to advertise the movement of mobile terminals within predefined areas. Moreover, LMCP uses specialized entities to store and retrieve the current location area of the mobile terminals. These entities are located in mobility enhanced switches that control the execution of mobility procedures (e.g., handovers).The main benefit from the application of LMCP is the establishment of connections that do not contain any misrouted segments. Furthermore, it requires minor modifications to the PNNI and enables its inter-working with other location management mechanisms. The protocol is compared with other similar mechanisms, and its efficiency is demonstrated by the results of an analytical model.     

Multi-parameter performance analysis for decentralized cognitive radio networks

In this paper, we investigate the impact of primary user activity, secondary user activity, interface switching, channel fading and finite-length queuing on the performance of decentralized cognitive radio networks. The individual processes of these service-disruptive effects are modeled as Markov chains based on cross-layer information locally available at the network nodes. A queuing analysis is conducted and various performance measures are derived regarding the packet loss, throughput, spectral efficiency, and packet delay distribution. Numerical results demonstrate the impact of various system parameters on the system performance, providing insights for cross-layer design and autonomous decision making in decentralized cognitive radio networks.     

Supporting the WWW in Wireless Communications Through Mobile Agents

Mobile computing is considered of major importance to the computing industry for the forthcoming years due to the progress in wireless mobile communications. We present a proxy-based architecture that manages to accelerate Web browsing in wireless Customer Premises Networks (CPN). We discuss how such an architecture is implemented using the IBM Aglets framework for mobile agents. The suggested architecture relies heavily on proxy caches – maintained in base stations – which are relocated to follow a roaming user in other control areas (clusters of cells). The cache management scheme involves relocation of full caches to the most-likely-to-be visited control areas, but also fractions of the cache to less-likely-to-be-visited neighbours. A movement prediction algorithm, based on a learning automaton, is used to determine the future location of the terminal. The discussed architecture components have been implemented as Aglets to allow for the efficient introduction of the service in a CPN infrastructure.     

Optimization methodology of dynamic data structures based on genetic algorithms for multimedia embedded systems

Modern multimedia application exhibit high resource utilization. In order to efficiently run this kind of applications in embedded systems, the dynamic memory subsystem needs to be optimized. A key role in this optimization is played by the dynamic data structures that reside in every real-life application. This paper presents a novel and automated way to optimize dynamic data structures. The search space is pruned using genetic algorithms that converge to the best multilayered data structure implementation for the targeted applications.     

Supply chain optimisation for the process industries: Advances and opportunities

Supply chain management and optimisation is a critical aspect of modern enterprises and a flourishing research area. This paper presents a critical review of methodologies for enhancing the decision-making for process industry supply chains towards the development of optimal infrastructures (assets and network) and planning. The presence of uncertainty within supply chains is discussed as an important issue for efficient capacity utilisation and robust infrastructure decisions. The incorporation of business/financial and sustainability aspects is also considered and future challenges are identified.