This paper presents a new model of support vector machines (SVMs) that handle data with tolerance and uncertainty. The constraints of the SVM are converted to fuzzy inequality. Giving more relaxation to the constraints allows us to consider an importance degree for each training samples in the constraints of the SVM. The new method is called relaxed constraints support vector machines (RSVMs). Also, the fuzzy SVM model is improved with more relaxed constraints. The new model is called fuzzy RSVM. With this method, we are able to consider importance degree for training samples both in the cost function and constraints of the SVM, simultaneously. In addition, we extend our method to solve one-class classification problems. The effectiveness of the proposed method is demonstrated on artificial and real-life data sets.
This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body-part detectors, followed by grouping of part detections, to perform human identification. Three example-based body-part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self-occlusions through the use of ten sub-classifiers that detect body parts over a specific range of viewpoints. Each sub-classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint-invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state-of-the-art methods.
This paper presents a robust disturbance reduction scheme using an artificial neural network (ANN) for linear systems with small time delays. It is assumed that the nominal linear systems are stable, minimum phase and relative degree one systems. The proposed structure is an integration of a modified Smith predictor and an ANN-based disturbance reduction scheme. Unlike other disturbance rejection methods, the proposed approach does not require information about unknown load disturbance frequencies. An ANN is used to approximate the unknown load disturbances and to enhance the robustness of the proposed disturbance reduction scheme against modelling errors in the estimated time delay and the process model. Connective weights of the ANN are trained on-line using a back-propagation algorithm until uncertainties resulting from unknown load disturbances and modelling errors are minimized. The simulation results show the effectiveness of the presented disturbance reduction scheme for controlling linear delay systems subjected to step or periodic unknown load disturbances.
Spurred by rapid development of computers and Internet technology, online shopping is gradually overtaking in-store shopping, because of advantages such as convenience, more choice of products or services etc. Online stores must devote a great deal of time and resources to locating and attracting new customers. Growing a customer base requires first understanding customers and then providing the products or services they need, thus encouraging customers to purchase more. This paper develops a system to analyse customers' purchasing behaviour and track shifts in their interests. Customers' purchasing behaviour is measured using proposed standard product loyalty status and standard brand loyalty status. Using these metrics, together with the preference map established for each customer, a marketing specialist can easily locate potential customers to target when a company launches a new product. The new-product-launch strategy proposed in this paper can be used to create a list of potential customers for a product being launched under a variety of conditions. A prototype system has been built to test the feasibility of the proposed new-product-launch strategy. The result shows almost 40% of potential customers respond to the recommendation positively.
Abstract: This work will present a review of the concept of classifier combination based on the combined discriminant function. We will present a Bayesian approach, in which the discriminant function assumes the role of the posterior probability. We will propose a probabilistic interpretation of expert rules and conditions of knowledge consistency for expert rules and learning sets. We will suggest how to measure the quality of learning materials and we will use the measure mentioned above for an algorithm that eliminates contradictions in the rule set. In this work several recognition algorithms will be described, based on either: (i) pure rules, or; (ii) rules together with learning sets. Furthermore, the original concept of information unification, which enables the formation of rules on the basis of learning set or learning set on the basis of rules will be proposed. The obtained conclusions will serve as a spring-board for the formulation of new project guidelines for this type of decision-making system. At the end, experimental results of the proposed algorithms will be presented, both from computer generated data and for a real problem from the medical diagnostics field.
]]>Abstract: This paper is addressing problems related to the construction of classifiers based on the Similarity Discriminant Function (SDF), in which the traditional vector representation of a pattern is replaced with matrix data. We introduce potential modifications of the matrix data structure and propose new variants of the SDF. The algorithms that we present were tested on images of handwritten digits and on photographs of human faces, taken from the ORL and CMU-PIE databases. The results of experiments show that our modifications significantly improved the performance of the original SDF classifier.
]]>Abstract: A number knowledge-related challenges may complicate the work in global software development (GSD) projects. In practice, even a small amount of missing knowledge may cause an activity to fail to create and transfer information which is critical to later functions, causing these later functions to fail. Thus, knowledge engineering holds a central role in order to succeed with globally distributed product development. Furthermore, examining the challenges faced in GSD from a cognitive perspective will help to find solutions that take into account the knowledge needs of different stakeholders in GSD and thus help to establish conditions for successful GSD projects. In this paper, we will discuss these challenges and solutions based on an extensive literature study and practical experience gained in several international projects over the last decade. Altogether, over 50 case studies were analysed. We analysed the challenges identified in the cases from a cognitive perspective for bridging and avoiding the knowledge gaps and, based on this analysis, we will present example solutions to address the challenges during the GSD projects. We will conclude that through understanding both the nature of GSD and the KE challenges in depth, it will be possible for organizations to make their distributed operations successful.
]]>Abstract: Distributed problem-solving (DPS) systems use a framework of human organizational notions and principles of intelligent systems to solve complex problems. Human organizational notions are used to decompose a complex problem into sub-problems that can be solved using intelligent systems. The solutions of these sub-problems are combined to solve the original complex problem. In this paper, we propose a DPS system for probabilistic estimation of software development effort. Using a real-world software engineering dataset, we compare the performance of the DPS system with a neural network (NN) and show that the performance of the DPS system is equal to or better than that of the NN with the additional benefits of modularity, probabilistic estimates, greater interpretability, flexibility and capability to handle incomplete input data.
]]>Abstract: The objective of this study is to develop an algorithm to detect and classify six types of electrocardiogram (ECG) signal beats including normal beats (N), atrial pre-mature beats (A), right bundle branch block beats (R), left bundle branch block beats (L), paced beats (P), and pre-mature ventricular contraction beats (PVC or V) using a neural network classifier. In order to prepare an appropriate input vector for the neural classifier several pre-processing stages have been applied. Initially, a signal filtering method is used to remove the ECG signal baseline wandering. Continuous wavelet transform is then applied in order to extract features of the ECG signal. Next, principal component analysis is used to reduce the size of the data. A well-known neural network architecture called the multi-layered perceptron neural network is then utilized as the final classifier to classify each ECG beat as one of six groups of signals under study. Finally, the MIT-BIH database is used to evaluate the proposed algorithm, resulting in 99.5% sensitivity, 99.66% positive predictive accuracy and 99.17% total accuracy.
]]>Abstract: We propose a forward sequential feature selection scheme based on k-means clustering algorithm to derive the feature subset that classifies best the time series data base, according to the criterion of the corrected Rand index. Moreover, we investigate the effect of the standardization scheme on the feature selection and propose a standardization given by the transform to standard Gaussian distribution. Our interest in this work is in classification of oscillating dynamical systems on the basis of measures computed on time series from these systems. The features to be selected are measures of linear and non-linear analysis of time series, such as auto-correlation and Lyapunov exponents, as well as oscillation characteristics, such as the mean magnitude of peaks. Simulations on known oscillating deterministic and stochastic systems showed that, for repeated realizations of the same classification task, the proposed feature selection scheme selected very often the same best feature subset, giving high classification accuracy for any standardization. We found that, regardless of the standardization, the highest classification accuracy could be obtained with a small feature subset, containing most frequently an oscillating-related feature. The same setting was applied to records of epileptic electroencephalogram signals, giving varying results and dependent on the standardization.
]]>This paper describes a Speaker Verification System based on the use of multi resolution classifiers in order to cope with performance degradation due to natural variations of the excitation source and of the vocal tract. The different resolution representations of the speaker are obtained by considering multiple frame lengths in the feature extraction process and from these representations a single Pseudo-Multi Parallel Branch (P-MPB) Hidden Markov Model is obtained. In the verification process, different resolution representations of the speech signal are classified by multiple P-MPB systems: the final decision is obtained by means of different combination techniques. The system based on the Weighted Majority Vote technique considerably outperforms baseline systems: improvements are between 15% and 38%. The execution time of the verification process is also evaluated and it proves to be very acceptable, thus allowing the use of the approach for applications in real time systems.
Abstract: The employment of a decision support system for optimizing fund raising strategies is crucial to the management of non-profit organizations. Commonly considered methodologies utilize the organization's donor database in order to gather and analyse information. However, many organizations, especially small- and medium-sized ones, do not own or efficiently manage a database, and consequently, the usual methods are inapplicable. In this paper, a decision support system is developed that is able to identify the most promising fund raising strategies on the basis of the organization's profile. The profile factors of a non-profit organization are analysed and hierarchically organized in a decision tree in order to effectively employ the Choquet integral methodology, which is recommended in these kinds of multi-criteria decision problems. The results obtained in the real operational context show the effectiveness of the proposed system.
]]>Abstract: Under random environment, the balance survey is difficult in many fields, such as in the industrial structure, the economics, and the society. In this paper, we study the random dynamic input-output model with consumption, in which the time-delay is one; and analyze the existence of the balanced growth solution by the theory of Markov processes. We present a new criterion of the imbalanced survey, which is a positive right eigenvector of the expectation of a matrix associated with the input-output model. We also will prove that the collapse time is finite almost surely, which indicates that the random dynamic input-output model does not possess a balanced growth solution, and consequently, the economic system must be adjusted constantly.
]]>Absrtact: Discovering temporal patterns hidden in a sequence of events has applications in numerous areas like network failure analysis, customer behaviour analysis, web navigation pattern discovery, etc. In this article, we present an approach to the discovery of chronicles hidden in the interaction traces of a human activity with the intention of characterizing some interesting tasks. Chronicles are a special type of temporal patterns, where temporal orders of events are quantified with numerical bounds. The algorithm we present is the first existing chronicle discovery algorithm that is complete. It is a chronicle discovery framework that can be configured to behave exactly as non-complete algorithms existing in litterature with no reduction of performance, but it can also be extended to other useful chronicle discovery problems like hybrid episode discovery. We show that the complete chronicle discovery problem has a very high complexity but we argue and illustrate that this high complexity is acceptable when the knowledge discovery process in which our algorithm takes part is real time and interactive. The platform Scheme Emerger, also presented in this paper, has been developed in order to implement the algorithm and to support graphically the real time and interactive chronicle discovery process.
]]>Abstract: Averaging in the time domain may be used for noise attenuation in case of biomedical signals with a quasi-cyclical character. Traditional arithmetic averaging technique assumes the constancy of the noise power cycle-wise, however, most types of noise are not stationary and the variability of noise power is observed. It constitutes a motivation for using methods of weighted averaging, in particular Bayesian weighted averaging. This paper presents the computational study of Bayesian weighted averaging with traditional (sharp) and fuzzy partition of the input data in the presence of non-stationary noise. There is presented the known empirical Bayesian weighted averaging method (EBWA), with the parameter p describing the probabilistic model, and its modification NBWA which eliminates the parameter. Both methods can be extended by partitioning of the input data. The performance of presented methods is experimentally evaluated for an analytical signal as well as a real ECG signal and compared with traditional arithmetic averaging method. However, the methods can be applied to any signal with a quasi-cyclical character. The aim of the paper is to show the influence of the type of partition as well as the number of parts on the quality of the averaged signal.
]]>Abstract: There has been much work in establishing joint replenishment model and designing effective and robust algorithms. Little research has been done by direct grouping methods. In this paper, we present a differential evolution (DE) algorithm that uses direct grouping to solve joint replenishment problem (JRP). Extensive computational experiments are performed to compare the performances of the DE algorithm with results of evolutionary algorithm (GA). The experimental results indicate that the DE algorithm can find a replenishment policy that incurs a lower total cost than the GA. We also conducted a case study to test the proposed DE algorithm for the JRP. The findings suggest that the proposed model is successful in decreasing spare parts ordering costs and holding costs significantly in a power plant.
]]>Abstract: This interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro-manufacturing technique in the production of high-value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser-milling centre, which is a machine tool for manufacturing high-value micro-moulds, micro-dies and micro-tools. The new three-phase industrial system presented in this study is capable of identifying a model for the laser-milling process based on low-order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser-milling procedure based on low-order models, such as black-box, in order to approximate the optimal form of the laser-milling process. The three-step model has been tested with real data obtained for three different materials: aluminium, copper and hardened steel. These three materials are used in the manufacture of micro-moulds, micro-coolers and micro-dies, high-value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser-milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques.
]]>Abstract: We present a new method and tool for activity modelling through qualitative sequential data analysis. In particular, we address the question of constructing a symbolic abstract representation of an activity from an activity trace. We use knowledge engineering techniques to help the analyst build an ontology of the activity, that is, a set of symbols and hierarchical semantics that supports the construction of activity models. The ontology construction is pragmatic, evolutionist and driven by the analyst in accordance with their modelling goals and their research questions. Our tool helps the analyst define transformation rules to process the raw trace into abstract traces based on the ontology. The analyst visualizes the abstract traces and iteratively tests the ontology, the transformation rules and the visualization format to confirm the models of activity. With this tool and this method, we found innovative ways to represent a car-driving activity at different levels of abstraction from activity traces collected from an instrumented vehicle. As examples, we report two new strategies of lane changing on motorways that we have found and modelled with this approach.
]]>Abstract: The article presents a method of protein matrix-assisted laser desorption and ionization-time of flight (TOF) spectra analysis. The method performs peaks detection. Spectra are analysed with Gaussian mixture decomposition. The results obtained are used for peaks identification purposes. The concept of the method is that a single peak is represented by one Gaussian distribution. The expectation-maximization algorithm and maximum likelihood rule are used for spectra processing. The analysis can be done for a set of spectra with use of the mean spectrum, or it may be performed for a single spectrum at a time. The number of mixture-model components is estimated by the Bayesian information criterion. Before the main analysis, a few pre-processing steps need to be done. Spectra should be subjected to calibration, normalization, denoising, baseline correction, etc. The aim of the work is to identify peptides in the analysed sample on the basis of the parameters of the mixture model and to find differences between spectra in the analysed set.
]]>Abstract: This work presents a multi-agent system for knowledge-based high-level event composition, which interprets activities, behaviour and situations semantically in a scenario with multi-sensory monitoring. A perception agent (plurisensory agent and visual agent)-based structure is presented. The agents process the sensor information and identify (agent decision system) significant changes in the monitored signals, which they send as simple events to the composition agent that searches for and identifies pre-defined patterns as higher-level semantic composed events. The structure has a methodology and a set of tools that facilitate its development and application to different fields without having to start from scratch. This creates an environment to develop knowledge-based systems generally for event composition. The application task of our work is surveillance, and event composition/inference examples are shown which characterize an alarming situation in the scene and resolve identification and tracking problems of people in the scenario being monitored.
]]>Abstract: In this paper, a framework for recognizing network traffic in order to detect anomalies is proposed. We propose to combine and correlate parameters from different layers in order to detect 0-day attacks and reduce false positives. Moreover, we propose to combine statistical and signal-based features. The major contribution of this paper is novel framework for network security based on the correlation approach as well as new signal-based algorithm for intrusion detection on the basis of the Matching Pursuit (MP) algorithm. As to our best knowledge, we are the first to use MP for intrusion and anomaly detection in computer networks. In the presented experiments, we proved that our solution gives better results than intrusion detection based on discrete wavelet transform.
]]>Abstract: In today's highly competitive air transportation environment, the quality of airport service is an important issue. In this paper, we present a fuzzy multi-criteria decision-making (MCDM) method for the evaluation of the quality of airport service. The data are collected from the passengers who depart from two international airports in Taiwan. The fuzzy MCDM model is defuzzied using the inverse function arithmetic representation method. The results show that the average quality of the Taoyuan International Airport service is better than that of the Kaohsiung International Airport service. This paper also analyses the quality of airport service using the fuzzy expert system to improve the performance of airport service. The analysis results show that the Kaohsiung International Airport and the Taoyuan International Airports' improvement efforts should be concentrated on seven and five specific service items, respectively. The findings of this paper are to help the decision-makers and the managers of airports improve the quality of airport service.
]]>Abstract: Natural language processing (NLP) has been used to process text pertaining to patient records and narratives. However, most of the methods used were developed for specific systems, so new research is necessary to assess whether such methods can be easily retargeted for new applications and goals, with the same performance. In this paper, open-source tools are reused as building blocks on which a new system is built. The aim of our work is to evaluate the applicability of the current NLP technology to a new domain: automatic knowledge acquisition of diagnostic and therapeutic procedures from clinical practice guideline free-text documents. In order to do this, two publicly available syntactic parsers, several terminology resources and a tool oriented to identify semantic predications were tailored to increase the performance of each tool individually. We apply this new approach to 171 sentences selected by the experts from a clinical guideline, and compare the results with those of the tools applied with no tailoring. The results of this paper show that with some adaptation, open-source NLP tools can be retargeted for new tasks, providing an accuracy that is equivalent to the methods designed for specific tasks.
]]>Abstract: In this study, two variants of genetic programming, namely linear genetic programming (LGP) and multi-expression programming (MEP) are utilized to detect atrial fibrillation (AF) episodes. LGP- and MEP-based models are derived to classify samples of AF and Normal episodes based on the analysis of RR interval signals. A weighted least-squares (WLS) regression analysis is performed using the same features and data sets to benchmark the models. Another important contribution of this paper is identification of the effective time domain features of heart rate variability (HRV) signals upon an improved forward floating selection (IFFS) analysis. The models are developed using MIT-BIH arrhythmia database. The diagnostic performances of the LGP and MEP classifiers are evaluated through receiver operating characteristics (ROC) analysis. The results indicate that the LGP and MEP models are able to diagnose the AF arrhythmia with an acceptable high accuracy. The proposed models have significantly better diagnosis performances than the regression and several models found in the literature.
]]>Abstract: Market basket analysis is one of the typical applications in mining association rules. The valuable information discovered from data mining can be used to support decision making. Generally, support and confidence (objective) measures are used to evaluate the interestingness of association rules. However, in some cases, by using these two measures, the discovered rules may be not profitable and not actionable (not interesting) to enterprises. Therefore, how to discover the patterns by considering both objective measures (e.g. probability) and subjective measures (e.g. profit) is a challenge in data mining, particularly in marketing applications. This paper focuses on pattern evaluation in the process of knowledge discovery by using the concept of profit mining. Data Envelopment Analysis is utilized to calculate the efficiency of discovered association rules with multiple objective and subjective measures. After evaluating the efficiency of association rules, they are categorized into two classes, relatively efficient (interesting) and relatively inefficient (uninteresting). To classify these two classes, Decision Tree (DT)-based classifier is built by using the attributes of association rules. The DT classifier can be used to find out the characteristics of interesting association rules, and to classify the unknown (new) association rules.
]]>Abstract: Predicting the three-dimensional structure (fold) of a protein is a key problem in molecular biology. It is also interesting issue for statistical methods recognition. In this paper a multi-class support vector machine (SVM) classifier is used on a real world data set. The SVM is a binary classifier, but protein fold recognition is a multi-class problem. So several new approaches to deal with this issue are presented including a modification of the well-known one-versus-one strategy. However, in this strategy the number of different binary classifiers that must be trained is quickly increasing with the number of classes. The methods proposed in this paper show how this problem can be overcome.
]]>Abstract: This paper deals with the hybrid flowshop scheduling problems with sequence-dependent setup times. To minimize the makespan, we propose hybrid metaheuristic approach, which integrates several features from ant colony optimization, simulated annealing and variable neighbourhood search in a new configurable scheduling algorithm. Our proposed algorithms are tuned by means of design of experiments approach. We present computational experiments on standard test problems and compare the results with the several algorithms presented previously. The results illustrate that the hybrid metaheuristic outperforms the other algorithms.
]]>Abstract: This article presents three methods to forecast accurately the amount of traffic in TCP/IP based networks: a novel neural network ensemble approach and two important adapted time series methods (ARIMA and Holt-Winters). In order to assess their accuracy, several experiments were held using real-world data from two large Internet service providers. In addition, different time scales (5 min, 1 h and 1 day) and distinct forecasting lookaheads were analysed. The experiments with the neural ensemble achieved the best results for 5 min and hourly data, while the Holt-Winters is the best option for the daily forecasts. This research opens possibilities for the development of more efficient traffic engineering and anomaly detection tools, which will result in financial gains from better network resource management.
]]>Abstract: This paper proposes alternative approaches for the prediction of short-term traffic flow using three branches of computational intelligence techniques, namely linear genetic programming (LGP), multilayer perceptron (MLP) and fuzzy logic (FL). Different LGP, MLP and FL models are developed for estimating the 5- and 30-min traffic flow rates. New LGP- and MLP-based prediction equations are derived for the traffic flow rates in the 5- and 30-min time intervals. The models are established upon extensive databases of the traffic flow records obtained from Iran's Rasht-Qazvin highway. The results indicate that the proposed models are effectively capable of predicting the target values. The LGP-based models are found to be simple, straightforward and more practical for predictive purposes compared with the other derived models.
]]>Abstract: Modern Internet routers have to handle a large number of packet classification rules, which requires classification schemes to be scalable both in time and space. In this paper, we present a scalable packet classification algorithm that is developed by combining two new concepts to the well-known bit vector (BV) scheme. We propose a range search method based on a cache-aware tree (CATree) which makes full use of processor's cache line to reduce the number of dynamic random access memory (DRAM) accesses. Theoretically, the number of DRAM accesses of CATree is about log(m+1) times lower than that of the widely used binary search algorithm, where m is the number of keys in a single cache line. Based on our computational results on a set of 1024 keys, the CATree algorithm is 36% faster than binary search algorithm and the performance is better when applied to a larger set of keys. In addition, we develop a rule re-arrangement algorithm to reduce the bitmap space of BV. With this re-arrangement, the rules for the same action may be assigned an identical priority. This reduces the number of priorities as well as the memory space of the bitmap. Furthermore, this also reduces the number of memory accesses and hence, increases the CPU cache utilization. With CATree and rule re-arrangement, the cache-aware bit vector with rule re-arrangement algorithm achieves better performance in comparison with the regular BV scheme, both in space and time. In our experiments, the proposed algorithm reduces the bitmap memory space of a practical set of firewall rules by two orders of magnitude and is 91% faster than the regular BV.
]]>Abstract: Over the past few years, the credit risk evaluation of micro-, small- and medium-scale enterprises by banks and financial institutions has been an active area of research under the joint pressure of regulators and shareholders. The credit rating assessment forms an important part of credit risk assessment, involving risk parameters such as financial, business, industry and management areas. The mathematical models of evaluation are at the core of modern credit risk management systems. This paper focuses on the use of fuzzy logic and neural network techniques to design a methodology for evaluating the credit worthiness of the entrepreneur. The neuro-fuzzy logic approach takes into account the minute details of credit rating expert's thought process to arrive at the final decision. A flexible credit rating framework (CRF) has been designed to organize all the facts of the client in a hierarchical fashion. The neural networks provide self-learning capability to the CRF. The CRF can be customized to suit different business and industrial interests.
]]>Abstract: An external context like weather conditions, lighting, etc. influences classification results, but it is frequently omitted in a mathematical model of the problem at hand. Our aim is to propose a mathematical model, which extends the Bayesian problem of pattern recognition by incorporating external context variables. They are implanted as functions, which influence parameters of class distributions. We prove that context variables influence a shape or a position of the optimal class separating surface, without enlarging the dimensionality of a pattern space. Thus, one can treat the proposed extended Bayesian model as a fusion of patterns and external context variables, embedded into the same pattern space. Then, learning algorithms for neural network classifiers are proposed, which take context variables into account.
]]>Abstract: Providing accurate and dependable recommendations efficiently while preserving privacy is essential for e-commerce sites to recruit new customers and keep the existing ones. Such sites might be able to increase their sales and profits while customers can obtain precise and trustworthy predictions if they use appropriate collaborative filtering (CF) algorithms without deeply jeopardizing users' privacy. We propose a new recommendation algorithm, which is a hybrid-memory and model-based algorithm to generate truthful referrals efficiently. Moreover, we use randomization techniques to preserve users' privacy while still offering CF services with decent accuracy. We perform real data-based trials and analyse our proposed schemes in terms of privacy, accuracy, and performance.
]]>Abstract: The notion of information systems provides a convenient tool for knowledge representation of objects in terms of their attribute values, while partial ordering is usually used to research the rough monotonicity of an uncertainty measure in information systems. In this paper, we first reveal the limitations of existing partial orderings to describe information granulations in information systems with several illustrative examples. Then, a generalized partial ordering with a set-size nature is proposed to overcome their shortcoming and some of its important properties are derived. Finally, we prove that several existing information granulations all satisfy the granulation monotonicity induced by the proposed partial ordering. The presented partial ordering appears to be well suited to characterize the nature of information granulations in an information system. These results will be very helpful for studying granular computing and uncertainty in information systems.
]]>Abstract: Mobile data communications have evolved as the number of third generation (3G) subscribers has increased. The evolution has triggered an increase in the use of mobile devices, such as mobile phones, to conduct mobile commerce and mobile shopping on the mobile web. There are fewer products to browse on the mobile web; hence, one-to-one marketing with product recommendations is important. Typical collaborative filtering (CF) recommendation systems make recommendations to potential customers based on the purchase behaviour of customers with similar preferences. However, this method may suffer from the so-called sparsity problem, which means there may not be sufficient similar users because the user-item rating matrix is sparse. In mobile shopping environments, the features of users' mobile phones provide different functionalities for using mobile services; thus, the features may be used to identify users with similar purchase behaviour. In this paper, we propose a mobile phone feature (MPF)-based hybrid method to resolve the sparsity issue of the typical CF method in mobile environments. We use the features of mobile phones to identify users' characteristics and then cluster users into groups with similar interests. The hybrid method combines the MPF-based method and a preference-based method that uses association rule mining to extract recommendation rules from user groups and make recommendations. Our experiment results show that the proposed hybrid method performs better than other recommendation methods.
]]>Abstract: Data clustering is a key task for various processes including sequence analysis and pattern recognition. This paper studies a clustering algorithm that aimed to increase accuracy and sensitivity when working with biological data such as DNA sequences. The new algorithm is a modified version of fuzzy C-means (FCM) and is based on the well-known self-organizing map (SOM). In order to show the performance of the algorithm, seven different data sets are processed. The experimental results demonstrate that the proposed algorithm has the potential to outperform SOM and FCM in terms of clustering and classification accuracy abilities. Additionally, a brief comparison is made the proposed algorithm with some previously studied ‘FCM-SOM’ hybrid algorithms from the literature.
]]>The increasing volume of eGovernment-related services is demanding new approaches for service integration and interoperability in this domain. Semantic web (SW) technologies and applications can leverage the potential of eGovernment service integration and discovery, thus tackling the problems of semantic heterogeneity characterizing eGovernment information sources and the different levels of interoperability. eGovernment services will therefore be semantically described in the foreseeable future. In an environment with semantically annotated services, software agents are essential as the entities responsible for exploiting the semantic content in order to automate some tasks, and so enhance the user's experience. In this paper, we present a framework that provides a seamless integration of semantic web services and intelligent agents technologies by making use of ontologies to facilitate their interoperation. The proposed framework can assist in the development of powerful and flexible distributed systems in complex, dynamic, heterogeneous, unpredictable and open environments. Our approach is backed up by a proof-of-concept implementation, where the breakthrough of integrating disparate eGovernment services has been tested.
The use of semantic technology has recently witnessed a huge increase. One of the areas in which this technology is being used increasingly more often is that of context-aware agents. However, the use of ontologies in general and reasoning in particular can rapidly become resource intensive. Certainly if the data set, called the A-Box, used by these agents grows considerably over time. Moreover, in order to create context-aware applications, taking into account a wide range of different data sets and context parameters, agents have to be provided to expose that data. The collaboration between the agents in the system is necessary to correlate the information and augment the intelligence and added value of the context-aware agents. Therefore, there is a need to have a distributed approach by means of a service-platform, where the different agents in a context-aware environment can collaborate. The main focus of this article is on the research on the design of a service-platform for semantic ontology-based context-aware collaboration. The platform architecture to allow the collaboration and scheduling, together with the associated algorithms, will be presented. The engineering and implementation details will be highlighted. By means of detailed UML sequence diagrams, we will present the workflow and collaboration between the different modules in the platform. Additionally, supporting developments, such as the meta-ontology and our ontology generator, OTAGen, will be presented. Furthermore, we will detail how the platform can operate in an autonomous way, taking into account the changing context of the agents in the platform.
This paper applies cognitive models, inspired by cognitive science, with the aim to propose architectural and knowledge-based requirements to structure ontological models for the cognitive profiling of agents. The proposed architecture aims to address the lack of flexibility that most agent-based collaborations are affected by. The resulting agents, equipped with advanced cognitive profiling, have an increased cognitive awareness of themselves and are more capable of interacting with other agents in a multi-agents based environment. In this research, cognitive awareness identifies the ability of the web agents to diagnose their processing limitations and to establish interactions with the external environment. The outcome is the enhanced flexibility, reusability and predictability of the agent behaviour; thus contributing towards minimizing human cognitive demands. The concept of cognitive profiling presented in this paper considers the semantic web as an action mediating space, where ontological models provide affordances for improving cognitive awareness through shared knowledge-base. The conceptual model for the cognitive profile architecture is developed with Protégé Ontology editor to generate OWL Ontology and evaluated through a proof of concept. The results show that agents equipped with cognitive awareness can undertake complex tasks more dynamically.
This work presents a multi-agent system for knowledge-based high-level event composition, which interprets activities, behaviour and situations semantically in a scenario with multi-sensory monitoring. A perception agent (plurisensory agent and visual agent)-based structure is presented. The agents process the sensor information and identify (agent decision system) significant changes in the monitored signals, which they send as simple events to the composition agent that searches for and identifies pre-defined patterns as higher-level semantic composed events. The structure has a methodology and a set of tools that facilitate its development and application to different fields without having to start from scratch. This creates an environment to develop knowledge-based systems generally for event composition. The application task of our work is surveillance, and event composition/inference examples are shown which characterize an alarming situation in the scene and resolve identification and tracking problems of people in the scenario being monitored.