waashk/dblp huggingface dataset:
on cellular network channels data mining and decision making through ant colony optimization and multi agent systems strategies. finding suitable channels to allocate in order to serve increasing user demands in a cellular network, which is a dynamical system, constitute the most important issue in terms of network performance since they define the bandwidth management methodology. in modern cellular networks these strategies become challenging issues especially when advanced services are applied. the effectiveness of decision making for channel allocation in a cellular network is strongly connected to current traffic and wireless environment conditions. moreover, in large scale environments, network states change dynamically and the network performance prediction is a hard task. in the recent literature, the network adaptation to current real user needs seems it could be achieved through computational intelligence based channel allocation schemes mainly involving genetic algorithms. in this paper, a quite new approach for communication channels decision making, based on ant colony optimization, which is a special form of swarm intelligence, modelled through multi agent methodology is presented. the main novelty of this research lies on modelling this optimization scheme through multi agent systems. the simulation model architecture which includes network and ant agents are also presented as well as the performance results based on the above techniques. finally, the current study, also, shows that there is a great field of research concerning intelligent techniques modelled through multi-agent methodologies focused on channels decision making and bandwidth management in wireless communication systems.
responsible data releases. data releases to the public should ensure the privacy of individuals involved in the data. several privacy mechanisms have been proposed in the literature. one such technique is that of data anonymization. for example, synthetic data sets are generated and released. in this paper we analyze the privacy aspects of synthetic data sets. in particular, we introduce a natural notion of privacy and employ it for synthetic data sets.
online mass flow prediction in cfb boilers. fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (cfb) process. if control systems fail to compensate for the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. this phenomenon causes a reduction of efficiency and lifetime of process components. therefore, domain experts are interested in developing tools and techniques for getting better understanding of underlying processes and their mutual dependencies in cfb boilers. in this paper we consider an application of data mining technology to the analysis of time series data from a pilot cfb reactor. namely, we present a rather simple and intuitive approach for online mass flow prediction in cfb boilers. this approach is based on learning and switching regression models. additionally, noise canceling, and windowing mechanisms are used for improving the robustness of online prediction. we validate our approach with a set of simulation experiments with real data collected from the pilot cfb boiler.
evaluation of distraction in a driver-vehicle-environment framework: an application of different data-mining techniques. distraction during driving task is one of the most serious problems affecting traffic safety, being one of the main causes of accidents. therefore, a method to diagnose and evaluate distraction appears to be of paramount importance to study and implement efficient counter-measures. this research aims at illustrating our approach in diagnosis of distraction status, comparing some of the widely used data-mining techniques; in particular, fuzzy logic (with adaptive-network-based fuzzy inference system) and artificial neural networks. the results are compared to select which method gives the best performances.
the normalized compression distance as a distance measure in entity identification. the identification of identical entities accross heterogeneous data sources still involves a large amount of manual processing. this is mainly due to the fact that different sources use different data representations in varying semantic contexts. up to now entity identification requires either the --- often manual --- unification of different representations, or alternatively the effort of programming tools with specialized interfaces for each representation type. however, for large and sparse databases, which are common e.g. for medical data, the manual approach becomes infeasible.we have developed a widely applicable compression based approach that does not rely on structural or semantical unity. the results we have obtained are promising both in recognition precision and performance.
distances in classification. the notion of distance is the most important basis for classification. this is especially true for unsupervised learning, i.e. clustering, since there is no validation mechanism by means of objects of known groups. but also for supervised learning standard distances often do not lead to appropriate results. for every individual problem the adequate distance is to be decided upon. this is demonstrated by means of three practical examples from very different application areas, namely social science, music science, and production economics. in social science, clustering is applied to spatial regions with very irregular borders. then adequate spatial distances may have to be taken into account for clustering. in statistical musicology the main problem is often to find an adequate transformation of the input time series as an adequate basis for distance definition. also, local modelling is proposed in order to account for different subpopulations, e.g. instruments. in production economics often many quality criteria have to be taken into account with very different scaling. in order to find a compromise optimum classification, this leads to a pre-transformation onto the same scale, called desirability.
computer-aided diagnosis in brain computed tomography screening. currently, interpretation of medical images is almost exclusively made by specialized physicians. although, the next decades will most certainly be of change and computer-aided diagnosis systems will play an important role in the reading process. assisted interpretation of medical images has become one of the major research subjects in medical imaging and diagnostic radiology. from a methodological point of view, the main attraction for the resolution of this kind of problem arises from the combination of the image reading made by the radiologists, with the results obtained from using artificial intelligence based applications that will contribute to the reduction and eventually the elimination of perception errors. this article describes how machine learning algorithms can help distinguish normal readings in brain computed tomography from all its variations. the goal is to have a system that is able to detect normal appearing structures, thus identifying normal studies, making the reading by the radiologist unnecessary for a large proportion of the brain computed tomography scans.
data mining of agricultural yield data: a comparison of regression models. nowadays, precision agriculture refers to the application of state-of-the-art gps technology in connection with small-scale, sensor-based treatment of the crop. this introduces large amounts of data which are collected and stored for later usage. making appropriate use of these data often leads to considerable gains in efficiency and therefore economic advantages. however, the amount of data poses a data mining problem --- which should be solved using data mining techniques. one of the tasks that remains to be solved is yield prediction based on available data. from a data mining perspective, this can be formulated and treated as a multi-dimensional regression task. this paper deals with appropriate regression techniques and evaluates four different techniques on selected agriculture data. a recommendation for a certain technique is provided.
evaluation of fusion for similarity searching in online handwritten documents. with the spread of tabletpcs handwriting raises in its significance and importance in the digital domain. also there exist other devices with pen-based inputs like pdas, digitizer tablets and pads specially prepared with sensors. the advantage of handwritten input methods is their possibility of an ad hoc creation of technical sketches and drawings alongside with text and that keyboards may be in some cases and environments bothersome. therefore the amount of handwritten documents is likely to increase. but a great problem is a proper full text search on such documents. this paper discusses the effects of multi-sample and multi-algorithm fusion approaches, known from biometrics to increase the performance. the tests are done by using three different devices (logitech iopen, pegasus pc notesmaker, ace cad digimemo digital) and five different feature extraction methods (square grid, triangular grid, slope, curvature and slant of writing) and show that fusion can improve the retrieval performance in terms of precision and recall from 0.903 and 0.935 without fusion to 0.958 and 0.943 with fusion, respectively.
a sales forecast model for the german automobile market based on time series analysis and data mining methods. in this contribution, various sales forecast models for the german automobile market are developed and tested. our most important criteria for the assessment of these models are the quality of the prediction as well as an easy explicability. yearly, quarterly and monthly data for newly registered automobiles from 1992 to 2007 serve as the basis for the tests of these models. the time series model used consists of additive components: trend, seasonal, calendar and error component. the three latter components are estimated univariately while the trend component is estimated multivariately by multiple linear regression as well as by a support vector machine. possible influences which are considered include macro-economic and market-specific factors. these influences are analysed by a feature selection. we found the non-linear model to be superior. furthermore, the quarterly data provided the most accurate results.
knowledge representation in difficult medical diagnosis. this article is based on medical knowledge produced thought collaborative problem solving by a group of experts, in the field of medical diagnosis. in this work, we propose a representation format for a medical case base into a traditional rdbms representation, which is queryable using standard sql. we are concerned in difficult medical cases which imply a solution in several steps with several expert solvers (medical specialists). some queries on this case base are proposed. a case base was implemented and validated in real time with experts on the real scenarios.
forecasting product life cycle phase transition points with modular neural networks based system. management of the product life cycle and of the corresponding supply network largely depends on information in which specific phase of the life cycle one or another product currently is and when the phase will be changed. finding a phase of the product life cycle can be interpreted as forecasting transition points between phases of life cycle of these products. this paper provides a formulation of the above mentioned task of forecasting the transition points and presents the structured data mining system for solving that task. the developed system is based on the analysis of historical demand for products and on information about transitions between phases in life cycles of those products.the experimental results with real data display information about the potential of the created system.
clustering with domain value dissimilarity for categorical data. clustering is a representative grouping process to find out hidden information and understand the characteristics of dataset to get a view of the further analysis. the concept of similarity and dissimilarity of objects is a fundamental decisive factor for clustering and the measure of them dominates the quality of results. when attributes of data are categorical, it is not simple to quantify the dissimilarity of data objects that have unimportant attributes or synonymous values. we suggest a new idea to quantify dissimilarity of objects by using distribution information of data correlated to each categorical value. our method discovers intrinsic relationship of values and measures dissimilarity of objects effectively. our approach does not couple with a clustering algorithm tightly and so can be applied various algorithms flexibly. experiments on both synthetic and real datasets show propriety and effectiveness of this method. when our method is applied only to traditional clustering algorithms, the results are considerably improved than those of previous methods.
credit risk handling in telecommunication sector. this article presents an application of data mining methods in telecommunication sector. this sector becomes a new area of research for particular problem solving e.g. churn prediction, cross-up selling marketing campaigns, fraud detection, customer segmentation and profiling, data classification, association rules discovery, data clustering, parameter importance analysis etc. credit risk prediction became a new research domain in pattern recognition area aimed to find the most risky customers. this article is devoted to assessing credit risk from the moment of opening a customer account to the moment of closing an account due to non-payment. algorithms are used to identify and insolvency of a debtor. credit scoring is presented in a form of activation models, which are used to predict customers' debt as well as indicate clients with the highest, medium and smallest credit risk. practical part of the article is based on the real customer database in a telecommunication company.
combining multidimensional scaling and computational intelligence for industrial monitoring. large industrial complexes with hundreds of variables must be tightly monitored for safety, quality and resources optimization. multidimensional scaling and computational intelligence are proposed in this work as effective tools for building classifiers of the operating state of the industrial process into normal / abnormal working regions. the visred, visualization by data reduction computational framework, is extended with techniques from computational intelligence, such as neural networks (several architectures), support vector machines and neuro-fuzzy systems (in an evolving adaptive implementation) to build such classifiers. the visbreaker plant of an oil refinery is taken as case study and some scenarios show the potentiality of the combined approach.
integrating data mining and agent based modeling and simulation. in this paper, we introduce an integration study which combines data mining (dm) and agent based modeling and simulation (abms). this study, as a new paradigm for dm/abms, is concerned with two approaches: (i) applying dm techniques in abms investigation, and inversely (ii) utilizing abms results in dm research. detailed description of each approach is presented in this paper. a conclusion and the future work of this (integration) study are given at the end.
a data mining method for finding hidden relationship in blood and urine examination items for health check. our periodic health examination often describes whether each examination item in blood and urine takes in the reference range of each examination item and a simple summary report on checks in everyday life and the possibility of suspicious diseases. however, it uses n variable items such as ast(got), alt(gpt) which are less correlated, and often includes expensive tumor markers. therefore, this paper proposes a data mining method for finding hidden relationships between these items in order to reduce the examination fee and giving a report depending on individuals. since low correlation coefficients are shown in most pairs of items over all clients, a set of item's values in consecutive health examinations of each client is investigated for data mining. four groups are formed according to the frequency taking outside the reference range in an item for three consecutive examinations, and average values of the other items included in each group are calculated in all pairs of items. the experiment results for three consecutive health examinations show that a lot of item pairs have positive or negative correlations between different frequencies with an item and the averages with the other item despite the fact that their correlation coefficients are small. the result shows both possible reducting of reducing the examination fee as inexpensive as possible and the possibility of a health-care report reflecting individuals.
visualizing the competitive structure of online auctions. visualizations of product competition are common in marketing research. competitive product relationships can be modeled using data from a variety of sources, including questionnaires, surveys and brand switching data. product competition applications based on brand switching data are usually restricted to high volume, frequent purchase products such as coffee and frozen foods. analysis of competitive product structure requires data for multiple purchases from a single consumer, data that are not usually available for large value, rare purchase items such as cars and computers. we use bid information from online auctions as a source of competitive product structure information for these items. we develop a simple algorithm for creating a distance matrix representing market structure between brands and brand features from online auction data. we take data from ebay mobile phone auctions in the usa and based upon the auction data develop visualizations of product competition for brands and brand features.
ordinal evaluation: a new perspective on country images. we present a novel use of ordinal evaluation (ordeval) algorithm as a promising technique to study various marketing phenomena. ordeval algorithm has originated in data mining and is a general tool to analyze data with ordinal attributes, including surveys. its many favorable features, including context sensitivity, ability to exploit meaning of ordered features and ordered response, and robustness to noise and missing values in the data, offer marketing practitioners a perspective, not available with classical analytical toolbox.we present a case study applying ordeval algorithm on country-of-origin (coo) information. we demonstrate some interesting advantages it has to offer and show how to extract and interpret new insights allowing marketing practitioners to further optimize the management of products abroad.data for the empirical study was gathered by means of 1225 questionnaires. results indicate that, contrary to the classical view on coo-effects, the processing of country-related cognitions, affects and conations is a non-linear and asymmetric phenomenon. the practical implications of this finding for marketers are discussed more in detail.
sales intelligence using web mining. this paper presents a knowledge extraction system for providing sales intelligence based on information downloaded from the www. the information is first located and downloaded from relevant companies' websites and then machine learning is used to find these web pages that contain useful information where useful is defined as containing news about orders for specific products. several machine learning algorithms were tested from which k-nearest neighbour, support vector machines, multi-layer perceptron and c4.5 decision tree produced best results in one or both experiments however k-nearest neighbour and support vector machines proved to be most robust which is a highly desired characteristic in the particular application. k-nearest neighbour slightly outperformed the support vector machines in both experiments which contradicts the results reported previously in the literature.
a case of using formal concept analysis in combination with emergent self organizing maps for detecting domestic violence. in this paper, we propose a framework for iterative knowledge discovery from unstructured text using formal concept analysis and emergent self organizing maps. we apply the framework to a real life case study using data from the amsterdam-amstelland police. the case zooms in on the problem of distilling concepts for domestic violence from the unstructured text in police reports. our human-centered framework facilitates the exploration of the data and allows for an efficient incorporation of prior expert knowledge to steer the discovery process. this exploration resulted in the discovery of faulty case labellings, common classification errors made by police officers, confusing situations, missing values in police reports, etc. the framework was also used for iteratively expanding a domain-specific thesaurus. furthermore, we showed how the presented method was used to develop a highly accurate and comprehensible classification model that automatically assigns a domestic or non-domestic violence label to police reports.
on a new similarity analysis in frequency domain for mining faces within a complex background. a novel similarity analysis is presented in this paper for dealing with the problem of mining faces in a complex image background. the proposed approach integrates a robust feature extraction technique based on a specific method of eigenanalysis in the frequency domain of the unique classes identified in the problem at hand, with neural network based classifiers. such an eigenalysis aims at identifying principal characteristics in the frequency domain of the above mentioned uniquely identified classes. each unknown image, in the testing phase, is then, analyzed through a sliding window raster scanning procedure to sliding windows identified, through a first stage neural classifier, as belonging to one of the unique classes previously mentioned. after such a sliding window labeling procedure it is reasonable for a second stage neural classifier to be applied to the testing image viewed as a sequence of such labeled sliding windows for obtaining a final decision about whether a face exists within the given test image or not. although the proposed approach is a hierarchical procedure, its most critical stage is the similarity analysis performed through eigenanalysis in the frequency domain, since, if good identification/ labeling accuracy could be then obtained, it would facilitate final face mining.