Query: An Overview of Data Mining Techniques Applied for Heart Disease Diagnosis and Prediction

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Extraction of Significant Patterns from Heart Disease Warehouses for Heart Attack Prediction <=SEP=> The diagnosis of diseases is a significant and tedious task in medicine. The detection of heart disease from various factors or symptoms is a multi-layered issue which is not free from false presumptions often accompanied by unpredictable effects. Thus the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. The healthcare industry gathers enormous amounts of heart disease data that regrettably, are not “mined” to determine concealed information for effective decision making by healthcare practitioners. In this paper, we have proposed an efficient approach for the extraction of significant patterns from the heart disease warehouses for heart attack prediction. Initially, the data warehouse is preprocessed to make it appropriate for the mining process. After preprocessing, the heart disease warehouse is clustered using the K-means clustering algorithm, which will extract the data relevant to heart attack from the warehouse. Subsequently the frequent patterns are mined from the extracted data, relevant to heart disease, using the MAFIA algorithm. Then the significant weightage of the frequent patterns are calculated. Further, the patterns significant to heart attack prediction are chosen based on the calculated significant weightage. These significant patterns can be used in the development of heart attack prediction system.

A survey of decision tree classifier methodology <=SEP=> Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition, to name only a few. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. This paper presents a survey of current methods for DTC designs and the various existing issues. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed_ Finally, several remarks are made concerning possible future research directions.

Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary Heart Disease <=SEP=> The prediction of survival of Coronary heart disease (CHD) has been a challenging research problem for medical society. The goal of this paper is to develop data mining algorithms for predicting survival of CHD patients based on 1000 cases .We carry out a clinical observation and a 6-month follow up to include 1000 CHD cases. The survival information of each case is obtained via follow up. Based on the data, we employed three popular data mining algorithms to develop the prediction models using the 502 cases. We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results indicated that the SVM is the best predictor with 92.1 % accuracy on the holdout sample artificial neural networks came out to be the second with91.0% accuracy and the decision trees models came out to be the worst of the three with 89.6% accuracy. The comparative study of multiple prediction models for survival of CHD patients along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data.

Selected techniques for data mining in medicine <=SEP=> Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis to be coupled with methods for efficient computer-assisted analysis. This paper presents selected data mining techniques that can be applied in medicine, and in particular some machine learning techniques including the mechanisms that make them better suited for the analysis of medical databases (derivation of symbolic rules, use of background knowledge, sensitivity and specificity of induced descriptions). The importance of the interpretability of results of data analysis is discussed and illustrated on selected medical applications.

Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks <=SEP=> -We give conditions ensuring that multilayer jeedJorward networks with as Jew as a single hidden layer and an appropriately smooth hidden layer activation fimction are capable o f arbitrarily accurate approximation to an arbitrao' function and its derivatives. In fact, these networks can approximate functions that are not dtifferentiable in the classical sense, but possess only a generalized derivative, as is the case jor certain piecewise dtlf[erentiable Junctions. The conditions imposed on the hidden layer activation function are relatively mild: the conditions imposed on the domain o f the fimction to be approximated have practical intplications. Our approximation results provide a previously missing theoretical justification jor the use of multilayer /~'ed/brward networks in applications requiring simultaneous approximation o f a function and its derivatives. Keywords--Approximation, Derivatives, Sobolev space, Feedforward networks. 1. I N T R O D U C T I O N The capability of sufficiently complex multilayer feedforward networks to approximate an unknown mapping./~. R' ----~ R arbitrarily well has been recently investigated by Cybenko (1989), Funahashi (1989), Hecht-Nielsen (1989), Hornik, Stinchcombe, and White (1989) (HSW) (all for sigmoid hidden layer activation functions) and Stinchcombe and White (1989) (SW) (non-sigmoid hidden layer activation functions). In applications, it may be desirable to approximate not only the unknown mapping, but also its unknown derivatives. This is the case in Jordan's (1989) recent investigation of robot learning of smooth movement . Jordan states: The Jacobian matrix 8z/Ox . . . is the matrix that relates small changes in the controller output to small changes in the task space results and cannot be assumed to be available a priori, or provided by the environment. However, all of the derivatives in the matrix are forward derivatives. They are easily obtained by differentiation if a forward model is available. The forward model itself must be learned, but this can be achieved directly by system idenAcknowledgements: We arc indebted to Angelo Melino for pressing us on the issue addressed here and to the referees for numerous helpful suggestions. White's participation was supported by NSF Grant SES-8806990. Requests for reprints should be sent to Halbert White, Department of Economics, D-008, University of California, San Diego. La Jolla, CA 92093. tification. Once the model is accurate over a particular domain, its derivatives provide a learning operator that allows the system to convert errors in task space into errors in articulatory space and thereby change the controller. Thus, learning an adequate approximation to the Jacobian matrix of an unknown mapping is a key component of Jordan 's approach to robot learning of smooth movement . Despite the success of Jordan 's experiments, there is no existing theoretical guarantee that multilayer feedforward networks generally have the capability to approximate an unknown mapping and its derivatives simultaneously. For example, a network with hard limiting hidden layer activations approximates unknown mappings with a piecewise-constant function, the first derivatives of which exist and are zero almost everywhere. Obviously, the derivatives of such a network output function cannot approximate the derivatives of an arbitrary function. Intuition suggests that networks having smooth hidden layer activation functions ought to have output function derivatives that will approximate the derivatives of an unknown mapping. However , the justification for this intuition is not obvious. Consider the class of single hidden layer feedforward networks having network output functions belonging to the set 2. "(G) -~ {g : x"-~ R[ g(x) : /:,G(_i';,,):

Domainless Adaptation by Constrained Decoding on a Schema Lattice <=SEP=> In many applications such as personal digital assistants, there is a constant need for new domains to increase the system’s coverage of user queries. A conventional approach is to learn a separate model every time a new domain is introduced. This approach is slow, inefficient, and a bottleneck for scaling to a large number of domains. In this paper, we introduce a framework that allows us to have a single model that can handle all domains: including unknown domains that may be created in the future as long as they are covered in the master schema. The key idea is to remove the need for distinguishing domains by explicitly predicting the schema of queries. Given permitted schema of a query, we perform constrained decoding on a lattice of slot sequences allowed under the schema. The proposed model achieves competitive and often superior performance over the conventional model trained separately per domain.

Some Basic Cryptographic Requirements for Chaos-Based Cryptosystems <=SEP=> In recent years, a large amount of work on chaos-based cryptosystems have been published. However many of the proposed schemes fail to explain or do not possess a number of features that are fundamentally important to all kind of cryptosystems. As a result, many proposed systems are difficult to implement in practice with a reasonable degree of security. Likewise, they are seldom accompanied by a thorough security analysis. Consequently, it is difficult for other researchers and end users to evaluate their security and performance. This work is intended to provide a common framework of basic guidelines that, if followed, every new cryptosystem would benefit from. The suggested guidelines address three main issues: implementation, key management, and security analysis, aiming at assisting designers of new cryptosystems to present their work in a more systematic and rigorous way to fulfill some basic cryptographic requirements. Meanwhile, several recommendations are made regarding some practical aspects of analog chaos-based secure communications, such as channel noise, limited bandwith, and attenuation.

A Framework for Cloud Forensic Readiness in Organizations <=SEP=> Many have argued that cloud computing is one of the fastest growing and most transformative technologies in the history of computing. It has radically changed the way in which information technologies can manage, access, deliver and create services. It has also brought numerous benefits to end-users and organizations. However, this rapid growth in cloud computing adoption has also seen it become a new arena for cybercrime. This has, in turn, led to new technical, legal and organizational challenges. In addition to the large number of attacks which affect cloud computing and the decentralized nature of data processing in the cloud, many concerns have been raised. One of these concerns is how to conduct a proper digital investigation in cloud environments and be ready to collect data proactively before an incident occurs in order to save time, money and effort. This paper proposes the technical, legal and organizational factors that influence digital forensic readiness for Infrastructure as a Service consumers.

Scalable Formal Concept Analysis algorithm for large datasets using Spark <=SEP=> In the process of knowledge discovery and representation in large datasets using formal concept analysis, complexity plays a major role in identifying all the formal concepts and constructing the concept lattice(digraph of the concepts). For identifying the formal concepts and constructing the digraph from the identified concepts in very large datasets, various distributed algorithms are available in the literature. However, the existing distributed algorithms are not very well suitable for concept generation because it is an iterative process. The existing algorithms are implemented using distributed frameworks like MapReduce and Open MP, these frameworks are not appropriate for iterative applications. Hence, in this paper we proposed efficient distributed algorithms for both formal concept generation and concept lattice digraph construction in large formal contexts using Apache Spark. Various performance metrics are considered for the evaluation of the proposed work, the results of the evaluation proves that the proposed algorithms are efficient for concept generation and lattice graph construction in comparison with the existing algorithms.

Integrating Conflicting Data: The Role of Source Dependence <=SEP=> Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sourc es can often provide conflicting values. To present quality data to users, it is critical that data integration systems can resolve conflicts and discover true values. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the majority of the sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this paper, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a novel approach that considers pendencebetween data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources ( e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide dependence between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information. We also extend our model by considering accuracyof data sources and similarity between values. Our experiments on synthetic data as well as real-world data show that our algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources.

Mass-media coverage , its influence on public awareness of climate-change issues , and implications for Japan ’ s national campaign to reduce greenhouse gas emissions <=SEP=> We analyse Japanese newspaper coverage of global warming from January 1998 to July 2007 and how public opinion during parts of that period were influenced by newspaper coverage. We show that a dramatic increase in newspaper coverage of global warming from January 2007 correlated with an increase in public concern for the issue. Before January 2007, we find that coverage of global warming had an immediate but short-term influence on public concern. With such transitory high levels of media coverage we suggest that for more effective communication of climate change, strategies aimed at maintaining mass-media coverage of global warming are required. 2008 Elsevier Ltd. All rights reserved.

mmWave communications for 5G: implementation challenges and advances <=SEP=> The requirement of the fifth generation (5G) wireless communication for high throughput motivates the wireless industry to use the mmWave (millimeter wave) communications for its wide bandwidth advantage. To compensate the heavy path loss and increase the communications capacity, phased array beamforming and massive multiple-input multiple-output (MIMO) techniques are employed at both the user equipment (UE) and base stations (BS). Considering the commercial requirements, 5G mmWave large array systems should be implemented in an energy- and cost-efficient way with a small form factor. To address above issues and realize a reliable communications link, taking into account the particular characteristics of 5G mmWave systems, this paper firstly examines the design challenges and trade-offs in system implementations, then some of the design strategies are summarized. At last, recent advance in RF front-end circuits and receiver sub-systems is then highlighted.

A 6 Vpp, 52 dB, 30-dB Dynamic Range, 43 Gb/s InP DHBT Differential Limiting Amplifier <=SEP=> We report the design, fabrication and measurement of a 43 Gb/s differential limiting amplifier integrating a transimpedance amplifier (TIA) as input stage and a distributed amplifier (Driver) as output stage, realised in a 1.5-µm indium phosphide (InP) double heterojunction bipolar transistor (DHBT) technology. With a differential gain higher than 52 dB, the TIA-Driver provides a constant 6 Vpp differential output amplitude for an input amplitude ranging from 12.7 mVpp to 405 mVpp, achieving more than 30 dB of electrical dynamic range, for a power consumption of 2.7 W. Designed to be used between a photodiode and an electro-optical modulator, this circuit is well suited for optical-to-electrical-to-optical (O-E-O) wavelength converters at up to 43 Gb/s.

A CMOS Transceiver for a Multistandard 13.56-MHz RFID Reader SoC <=SEP=> A CMOS transceiver for a multistandard 13.56-MHz radio-frequency identification reader system-on-a-chip (SoC) is designed and fabricated. The SoC consists of an RF/analog part for modulation/demodulation and a digital part for controlling the transceiver functionality. Prior to designing the integrated circuit, pre-experiments using discrete components and commercial tags are performed. With the results, overall functions and specifications are determined. For supporting multistandard, several blocks are designed with digital controls according to the standards. In the transmitter, a digitally controlled amplitude modulator for various modulation indexes and a power control circuit are adopted. In the receiver, a variable gain amplifier and a level-controllable comparator, which are also controlled digitally according to the standard, are introduced. The full transceiver SoC is implemented in the Chartered 0.18-¿m CMOS technology. The measurement results of the implemented chip indicate that the designed transceiver operates in a multistandard mode.

Eclectic extraction of propositional rules from Neural Networks <=SEP=> Artificial Neural Network is among the most popular algorithm for supervised learning. However, Neural Networks have a well-known drawback of being a “Black Box” learner that is not comprehensible to the Users. This lack of transparency makes it unsuitable for many high risk tasks such as medical diagnosis that requires a rational justification for making a decision. Rule Extraction methods attempt to curb this limitation by extracting comprehensible rules from a trained Network. Many such extraction algorithms have been developed over the years with their respective strengths and weaknesses. They have been broadly categorized into three types based on their approach to use internal model of the Network. Eclectic Methods are hybrid algorithms that combine the other approaches to attain more performance. In this paper, we present an Eclectic method called HERETIC. Our algorithm uses Inductive Decision Tree learning combined with information of the neural network structure for extracting logical rules. Experiments and theoretical analysis show HERETIC to be better in terms of speed and performance.

Stress Detection and Management: A Survey of Wearable Smart Health Devices <=SEP=> In today's world, many people feel stressed out from school, work, or other life events. Therefore, it is important to detect stress and manage it to reduce the risk of damage to an individual's well being. With the emergence of the Internet of Things (IoT), devices can be made to detect stress and manage it effectively by using cloudbased services and smartphone apps to aggregate and compute large data sets that track stress behavior over long periods of time. Additionally, there is added convenience via the connectivity and portability of these IoT devices. They allow individuals to seek intervention prior to elevated health risks and achieve a less stressful life.

Personal Archive Service System using Blockchain Technology: Case Study, Promising and Challenging <=SEP=> This paper is to explore applications of the blockchain technology to the concept of “proof of X” such as proof of identity, proof of property ownership, proof of specific transaction, proof of college degree, proof of medical records, proof of academic achievements, etc. It describes a novel approach of building a decentralized transparent immutable secure personal archive management and service system. Personal archive is defined as a collection of various artifacts that reflect personal portfolio as well as personal unique identifications. Personal portfolio is beyond of a statement of personal achievement. It is an evidentiary document designed to provide qualitative and quantitative chronically documents and examples. Subjects can tag their information with proof, that is, certified by trusted entities or organizations like universities. Such proofs are associated with confidentiality levels exposed in the public domains. Personal identifications include biometrics as well as other multi-factors such as something the subject “has”, the subject “knows” or the subject “acts”. Stack holders in a consortium oriented blockchain network serve as verifiers and /or miners that provide their trusted services the delegated proof of stake. Such personal archive based system can be exploited to various applications including professional network like Linkedin, instant credit approval like alipay or live human from social bots like internet social media. A prototype simulation shows that such personal portfolio management and service system is feasible and immune to many ID attacks.

Reactive power capability of the wind turbine with Doubly Fed Induction Generator <=SEP=> With the increasing integration into power grids, wind power plants play an important role in the power system. Many requirements for the wind power plants have been proposed in the grid codes. According to these grid codes, wind power plants should have the ability to perform voltage control and reactive power compensation at the point of common coupling (PCC). Besides the shunt flexible alternating current transmission system (FACTS) devices such as the static var compensator (SVC) and the static synchronous compensator (STATCOM), the wind turbine itself can also provide a certain amount of reactive power compensation, depending on the wind speed and the active power control strategy. This paper analyzes the reactive power capability of Doubly Fed Induction Generator (DFIG) based wind turbine, considering the rated stator current limit, the rated rotor current limit, the rated rotor voltage limit, and the reactive power capability of the grid side convertor (GSC). The boundaries of reactive power capability of DFIG based wind turbine are derived. The result was obtained using the software MATLAB.

Toward Enabling Automated Cognition and Decision-Making in Complex Cyber-Physical Systems <=SEP=> This article presents a framework for empowering automated cognition and decision making in complex cyber-physical systems (CPS). The key idea is for each cyber-physical component in the system to be able to construct from observations and actions a reduced model of its behavior and respond to external stimuli in the form of a (timed) probabilistic automaton such as a semi-Markov decision process. The reduced order behavioral models of various cyber-physical components can then be integrated into an optimization and decision-making module to determine the best actions for the components under different operating scenarios and cost / payoff functions by solving a bounded-rationality global decision-making problem.1

CSDSM: Cognitive switch-based DDoS sensing and mitigation in SDN-driven CDNi word <=SEP=> Content Delivery Networks (CDNs) are increasingly deployed for their efficient content delivery and are often integrated with Software Defined Networks (SDNs) to achieve centrality and programmability of the network. However, these networks are also an attractive target for network attackers whose main goal is to exhaust network resources. One attack approach is to over-flood the OpenFlow switch tables containing routing information. Due to the increasing number of different flooding attacks such as DDoS, it becomes difficult to distinguish these attacks from normal traffic when evaluated with traditional attack detection methods. This paper proposes an architectural method that classifies and defends all possible forms of DDoS attack and legitimate Flash Crowd traffic using a segregated dimension functioning cognitive process based in a controller module. Our results illustrate that the proposed model yields significantly enhanced performance with minimal false positives and false negatives when classified with optimal Support Vector Machine and Logistic Regression algorithms. The traffic classifications initiate deployment of security rules to the OpenFlow switches, preventing new forms of flooding attacks. To the best of our knowledge, this is the first work conducted on SDN-driven CDNi used to detect and defend against all possible DDoS attacks through traffic segregated dimension functioning coupled with cognitive classification.

Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing <=SEP=> Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief Networks (DBNs) represent the state-of-the-art for many machine learning and computer vision classification problems. To overcome the large computational cost of deep networks, spiking deep networks have recently been proposed, given the specialized hardware now available for spiking neural networks (SNNs). However, this has come at the cost of performance losses due to the conversion from analog neural networks (ANNs) without a notion of time, to sparsely firing, event-driven SNNs. Here we analyze the effects of converting deep ANNs into SNNs with respect to the choice of parameters for spiking neurons such as firing rates and thresholds. We present a set of optimization techniques to minimize performance loss in the conversion process for ConvNets and fully connected deep networks. These techniques yield networks that outperform all previous SNNs on the MNIST database to date, and many networks here are close to maximum performance after only 20 ms of simulated time. The techniques include using rectified linear units (ReLUs) with zero bias during training, and using a new weight normalization method to help regulate firing rates. Our method for converting an ANN into an SNN enables low-latency classification with high accuracies already after the first output spike, and compared with previous SNN approaches it yields improved performance without increased training time. The presented analysis and optimization techniques boost the value of spiking deep networks as an attractive framework for neuromorphic computing platforms aimed at fast and efficient pattern recognition.

Privacy protection based access control scheme in cloud-based services <=SEP=> With the rapid development of the computer technology, cloud-based services have become a hot topic. Cloud-based services not only provide users with convenience, but also bring many security issues. Therefore, the study of access control scheme to protect users' privacy in cloud environment is of great significance. In this paper, we present an access control system with privilege separation based on privacy protection (PS-ACS). In the PS-ACS scheme, we divide the users into personal domain (PSD) and public domain (PUD) logically. In the PSD, we set read and write access permissions for users respectively. The Key-Aggregate Encryption (KAE) is exploited to implement the read access permission which improves the access efficiency. A high degree of patient privacy is guaranteed simultaneously by exploiting an Improved Attribute-based Signature (IABS) which can determine the users' write access. For the users of PUD, a hierarchical attribute-based encryption (HABE) is applied to avoid the issues of single point of failure and complicated key distribution. Function and performance testing result shows that the PS-ACS scheme can achieve privacy protection in cloud-based services.

Input feature selection for classification problems <=SEP=> Feature selection plays an important role in classifying systems such as neural networks (NNs). We use a set of attributes which are relevant, irrelevant or redundant and from the viewpoint of managing a dataset which can be huge, reducing the number of attributes by selecting only the relevant ones is desirable. In doing so, higher performances with lower computational effort is expected. In this paper, we propose two feature selection algorithms. The limitation of mutual information feature selector (MIFS) is analyzed and a method to overcome this limitation is studied. One of the proposed algorithms makes more considered use of mutual information between input attributes and output classes than the MIFS. What is demonstrated is that the proposed method can provide the performance of the ideal greedy selection algorithm when information is distributed uniformly. The computational load for this algorithm is nearly the same as that of MIFS. In addition, another feature selection algorithm using the Taguchi method is proposed. This is advanced as a solution to the question as to how to identify good features with as few experiments as possible. The proposed algorithms are applied to several classification problems and compared with MIFS. These two algorithms can be combined to complement each other's limitations. The combined algorithm performed well in several experiments and should prove to be a useful method in selecting features for classification problems.

RF MEMS Actuated Reconfigurable Reflectarray Patch-Slot Element <=SEP=> This paper describes the design of a reconfigurable reflectarray element using commercially available radio frequency micro-electromechanical system (RF MEMS) switches. The element consists of a microstrip patch on the top surface and a slot with an actuated variable length in the ground plane. RF MEMS switches are mounted on the slot to electronically vary the slot length by actuating the switches and thus obtaining the desired phase response. Waveguide measurements and high frequency structure simulator (HFSS) simulations are used to characterize the reflectarray element. The four MEMS switches element gives 10 independent states with a phase swing of 150 deg and a loss variation from 0.4 dB to 1.5 dB at 2 GHz (more switches can provide larger phase shift). The loss is mainly attributed to the dielectric loss and the conductor loss, which occur due to the relatively strong electric fields in the substrate region below the patch and the large currents on the top surface of the patch, respectively, close to the patch resonance. Detailed analysis is performed to characterize the effect of the switches by taking into consideration the switch model and wire bonding effects.

Generalizing and Improving Weight Initialization <=SEP=> We propose a new weight initialization suited for arbitrary nonlinearities by generalizing previous weight initializations. The initialization corrects for the influence of dropout rates and an arbitrary nonlinearity’s influence on variance through simple corrective scalars. Consequently, this initialization does not require computing mini-batch statistics nor weight pre-initialization. This simple method enables improved accuracy over previous initializations, and it allows for training highly regularized neural networks where previous initializations lead to poor convergence.

Probabilistic Inductive Logic Programming <=SEP=> Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far. In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be adapted to cover state-of-the-art statistical relational learning approaches.

Intuitive visualization of Pareto Frontier for multi-objective optimization in n-dimensional performance space <=SEP=> A visualization methodology is presented in which a Pareto Frontier can be visualized in an intuitive and straightforward manner for an n-dimensional performance space. Based on this visualization, it is possible to quickly identify ‘good’ regions of the performance and optimal design spaces for a multi-objective optimization application, regardless of space complexity. Visualizing Pareto solutions for more than three objectives has long been a significant challenge to the multi-objective optimization community. The Hyper-space Diagonal Counting (HSDC) method described here enables the lossless visualization to be implemented. The proposed method requires no dimension fixing. In this paper, we demonstrate the usefulness of visualizing n-f space (i.e. for more than three objective functions in a multiobjective optimization problem). The visualization is shown to aid in the final decision of what potential optimal design point should be chosen amongst all possible Pareto solutions.

MATCHINGS AND PAIRED DOMINATION IN FUZZY GRAPHS USING STRONG ARCS <=SEP=> The concepts of covering and matching in fuzzy graphs using strong arcs are introduced and obtained the relationship between them analogous to Gallai’s results in graphs. The notion of paired domination in fuzzy graphs using strong arcs is also studied. The strong paired domination number γspr of complete fuzzy graph and complete bipartite fuzzy graph is determined and obtained bounds for the strong paired domination number of fuzzy graphs. An upper bound for the strong paired domination number of fuzzy graphs in terms of strong independence number is also obtained.

Dioxin characterisation , formation and minimisation during municipal solid waste ( MSW ) incineration : review <=SEP=> The present review discusses the current views on methods to minimise dioxins, namely polychlorinated dibenzodioxins (PCDDs) and dibenzofurans (PCDFs), formation in MSW incineration systems. The structure of this group of compounds is discussed initially and then the toxic equivalence scale is presented for the most common isomers and congeners in the dioxin family. The current status on dioxin limits imposed in various countries and by various organisations is presented. A detailed analysis of the theories leading to dioxin formation in MSW incineration is given, since, this has been one of the most controversial areas of dioxin chemistry for the past 20 years. Three dioxin formation theories were considered possible for a long time; (i) from PCDD/PCDFs originally present in the furnace feedstock; (ii) from precursor compounds (foundation formatting molecules which could react rapidly with other groups in the system to form dioxins) in the MSW feed; (iii) from de novo synthesis of smaller, relatively innocuous chemical molecules combining together to form the dioxins. Methods (ii) and (iii) are based on heterogeneously catalysed reactions. Some researchers are considering possible homogeneous thermal reaction formation of dioxin. This review demonstrates that with the advanced modern MSW combustion systems, option (i) is a most unlikely route and also methods (ii) and (iii) are quite feasible. Based on thermodynamic and kinetic data in the literature, the rate and extent of the formation of dioxins and their precursors by certain mechanisms can definitely be contributing to routes (ii) and (iii). Since even the most advanced MSW combustion systems do not produce complete combustion, predominantly because of inadequate feed preparation and turbulence, some de novo synthesis of precursors can also take place. These ‘de novo precursors’ could be carried through the combustion unit adsorbed or absorbed on particulate material such as soot and dust, but also these precursors could be formed during the cooling process by heterogeneous catalytic reactions and go on to form dioxins. The maximum rate of formation of PCDD/PCDFs from both sources lies in the temperature range 300–400 ◦C. This knowledge of formation rates and mechanisms provides the basis of designing combustion systems. A two stage approach is adopted; firstly, system design to achieve complete combustion and minimise formation; secondly, end-of-pipe treatment systems to remove dioxins. In the first case, combustion temperature should be above 1000 ◦C, combustion residence time should be greater than 1 s, combustion chamber turbulence should be represented by a Reynolds number greater than 50,000, good MSW feed preparation and controlled feed rate are also critical. In the second category, very rapid gas cooling from 400 to 250 ◦C should be achieved, semi-dry lime scrubbing and Abbreviations:AC, activated carbon; ACSS, activated carbon scrubbing solution; APCD, air pollution control device; APME, Association of Plastics Manufacturers in Europe; ASME, American Society of Mechanical Engineers; BF, bag filter; BPM, best practical means; COT, UK Committee on Toxicology of Chemicals in Food, Consumer Products and Environment; CTZ, critical temperature zone; DOE, Department of the Environment, UK; DIAC, direct injec tion of activated carbon; DS, dry scrubber; DSI, dry sorbent injection; EADON, toxic equivalence factors proposed by Eadon method; EEC, European Economi c Community; EGB, electro granular bed; ESP, electrostatic precipitator; FF, fabric filter; FRG, Federal Republic of Germany; GCP, good combustion pr actice; HMIP, Her Majesty’s Inspectorate on Pollution, UK; HOC, hearth-oven coke; HRGC, high resolution gas chromatography; HRMS, high resolution mass spectrometry; IARC, International Agency for Research on Cancer; Kow, octanol-water partition coefficient; MSW, municipal solid waste; MSWI, municipal solid waste incinerator; NATO, Northern Atlantic Treaty Organisation; NOAEL, no observed adverse effect level; OCDD, octachlorinated dibenzodio xin; OCDF, octachlorinated dibenzofuran; PCB, polychlorinated biphenyls; PCCS, programmable computer control system; PCDD, polychlorinated dibenzodiox in; PCDF, polychlorinated dibenzofuran; PCP, pentachlorophenol; RDF, refuse derived fuel; SCR, selective catalytic reduction/reactor; SDA, spray dry abs orber; TALuft, Technische Airbildung Luft; TCDD, tetrachlorinated dibenzodioxin; TCDF, tetrachlorinated dibenzofuran; TDI, tolerable daily intake; TEF, toxi c equivalence factor; TEQ, toxic equivalence; USEPA, United States Environmental Protection Agency; WHO, World Health Organisation; WQ, water quench; WS, wet scrubber E-mail address:kemckayg@ust.hk (G. McKay). 1385-8947/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S1385-8947(01)00228-5 344 G. McKay / Chemical Engineering Journal 86 (2002) 343–368 bag filtration coupled with activated carbon injection adsorption as end-of-pipe treatments can all play a role in prevention or minimisation of dioxins in the final flue gas emission to the atmosphere. © 2002 Elsevier Science B.V. All rights reserved.

Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM) <=SEP=> Purpose – The integration of relevant antecedents into TAM would lead to better understanding of the decision factors which act as enablers for the adoption of internet banking. The purpose of the paper is to determine the influence of the antecedents subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk, trialability and government support on the existing constructs of the technology acceptance model (TAM) and to test measurement invariance and the moderating effect of the demographic variables on the relationship between the latent constructs used in this augmented TAM. Design/methodology/approach – A survey questionnaire was administered on internet banking users and a total of 300 responses were collected. A two-step approach suggested by Hair et al. (2006) and Schumacker and Lomax (2004) was used in this study. The proposed model was assessed using the confirmatory factor analysis approach. The structural model was then tested in order to establish nomological validity. The data based on four demographic dimensions gender, age, income, education were divided into two groups for each of these demographic dimensions. The invariance test was first performed on the measurement model and then on the structural model. The measurement model and structural model were subjected to tests of equivalence of parameters across groups. Findings – To a large extent the results of the study supports the proposed model and thereby contributes to understand the influence of subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk and government support on internet banking adoption. The predictor variables in the augmented TAM were able to explain 29.9 per cent of the variance in the actual usage of internet banking as compared to the TAM which was able to explain only 26.5 per cent variance in the actual usage of internet banking. A significant difference in the relationship between the different constructs of the model was observed when the model was subjected to multi-group invariance testing. Research limitations/implications – The study suffers from the same limitations as most other studies involving TAM. In this study self-reported measures about the usage were taken as the actual usage. The findings of the study can be of use to marketers for target-specific marketing by customizing the marketing campaign focussing on the factors that were found to be strong influencers leading to the usage of internet banking for each target audience. Originality/value – The main challenge in this study was to develop the conceptual model for the internet banking adoption by extending the TAM and to get a robust theoretical support from the extant literature for the relevant factors along with their relationship to uncover new insights about factors responsible for the internet banking adoption. The augmented model had an improved predictive capability and explanatory utility.