the proceedings contain 60 papers. the special focus in this conference is on intelligentdataengineering and automatedlearning. the topics include: A first approach to heuristic multilabel undersampling;development...
ISBN:
(纸本)9783319108391
the proceedings contain 60 papers. the special focus in this conference is on intelligentdataengineering and automatedlearning. the topics include: A first approach to heuristic multilabel undersampling;development of eye-blink controlled application for physically handicapped children;generation of reducts based on nearest neighbor relation;automatic content related feedback for MOOCs based on course domain ontology;user behavior modeling in a cellular network using latent dirichlet allocation;sample size issues in the choice between the best classifier and fusion by trainable combiners;on interlinking linked data sources by using ontology matching techniques and the map-reduce framework;managing borderline and noisy examples in imbalanced classification by combining SMOTE with ensemble filtering;a meta-topic identification model for twitter using semantic analysis;use of empirical mode decomposition for classification of MRCP based task parameters;diversified random forests using random subspaces;fast frequent pattern detection using prime numbers;multi-step forecast based on modified neural gas mixture autoregressive model;LBP and machine learning for diabetic retinopathy detection;application to the ATLLc water network;data analysis for detecting a temporary breath inability episode;CPSO applied in the optimization of a speech recognition system;object-neighbourhood clustering ensemble method;a novel recursive kernel-based algorithm for robust pattern classification;multi-objective genetic algorithms for sparse least square support vector machines;pixel classification and heuristics for facial feature localization and a new appearance signature for real time person re-identification.
the proceedings contain 59 papers. the topics discussed include: development of eye-blink controlled application for physically handicapped children;generation of reducts based on nearest neighbor relation;user behavi...
ISBN:
(纸本)9783319108391
the proceedings contain 59 papers. the topics discussed include: development of eye-blink controlled application for physically handicapped children;generation of reducts based on nearest neighbor relation;user behavior modeling in a cellular network using latent dirichlet allocation;sample size issues in the choice between the best classifier and fusion by trainable combiners;on interlinking linked data sources by using ontology matching techniques and the map-reduce framework;managing borderline and noisy examples in imbalanced classification by combining SMOTE with ensemble filtering;use of empirical mode decomposition for classification of MRCP based task parameters;diversified random forests using random subspaces;fast frequent pattern detection using prime numbers;multi-step forecast based on modified neural gas mixture autoregressive model;and data analysis for detecting a temporary breath inability episode.
Missing data is a prevalent problem in data science for many fields such as natural, social, and health sciences. Since most regression methods can not handle missing data directly, imputation methods are used in data...
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ISBN:
(纸本)9783031777301;9783031777318
Missing data is a prevalent problem in data science for many fields such as natural, social, and health sciences. Since most regression methods can not handle missing data directly, imputation methods are used in data pre-processing. Finding the best imputation method is non-trivial, however. Moreover, our results show that an independent choice for a best imputation method does not always result in the best predictive performance in the end;the combination matters. Furthermore, search-based approaches for finding a best-fitting imputer/regressor-pair can be computationally intensive. In this paper, we propose the MetaLIRS (Meta learning Imputation and Regression Selection) framework for developing resource-friendly ML-based recommendation models for method selection. With MetaLIRS, we constructed a proof-of-concept recommendation model based on 12 meta-features that achieves an accuracy of 63% for selecting the best-fitting imputer/regressor-pair. A data scientist can use this model for a quick resource-friendly recommendation on which imputation and regression method to use for their particular data set and task without the need for an expensive grid search among methods.
In the pursuit of sustainable manufacturing, ultra-short pulse laser micromachining stands out as a promising solution while also offering high-precision and qualitative laser processing. However, unlocking the full p...
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ISBN:
(纸本)9783031777301;9783031777318
In the pursuit of sustainable manufacturing, ultra-short pulse laser micromachining stands out as a promising solution while also offering high-precision and qualitative laser processing. However, unlocking the full potential of ultra-short pulse lasers requires an optimized monitoring system capable of early detection of defective workpieces, regardless of the preprocessing technique employed. While advances in machine learning can help predict process quality features, the complexity of monitoring data necessitates reducing both model size and data dimensionality to enable real-time analysis. To address these challenges, this paper introduces a machine learning framework designed to enhance surface quality assessment across diverse preprocessing techniques. To facilitate real-time laser processing monitoring, our solution aims to optimize the computational requirements of the machine learning model. Experimental results show that the proposed model not only outperforms the generalizability achieved by previous works across diverse preprocessing techniques but also significantly reduces the computational requirements for training. through these advancements, we aim to establish the baseline for a more sustainable manufacturing process.
this paper proposes the utilization of rough set theory for predicting student scholar performance. the rough set theory is a powerful approach that permits the searching for patterns in e-learningdatabase using the ...
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ISBN:
(纸本)9783319108407;9783319108391
this paper proposes the utilization of rough set theory for predicting student scholar performance. the rough set theory is a powerful approach that permits the searching for patterns in e-learningdatabase using the minimal length principles. Searching for models with small size is performed by means of many different kinds of reducts that generate the decision rules capable for identifying the final student grade.
In this paper, we created the multimedia web application with ITS (intelligent Transportation Systems) implemented in DMIS (Distributed Multimedia Information System)(http://***/cns_admi- ***),for traffic monitoring a...
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ISBN:
(纸本)9783319108407;9783319108391
In this paper, we created the multimedia web application with ITS (intelligent Transportation Systems) implemented in DMIS (Distributed Multimedia Information System)(http://***/cns_admi- ***),for traffic monitoring and managing in Doboj-Prnjavor regional road section, the Republic of Srpska (RS), Bosnia and Hercegovina (BH). the focus of the research goals is oriented on the CCMS (Central Control Multimedia System) modul for experimental monitoring of public bus transport and the creation of opportunities for informing the users proactively about the current location of the buses in the Doboj-Prnjavor road section and the time of arrival at the destination stop, and monitoring the number of contextual parameters of this system.
Extreme learning Machines (ELMs) are gaining fairly popularity in training neural networks, since they are quite simple and have good performance. However, an open problem is the number of neurons in the hidden layer....
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ISBN:
(纸本)9783319108407;9783319108391
Extreme learning Machines (ELMs) are gaining fairly popularity in training neural networks, since they are quite simple and have good performance. However, an open problem is the number of neurons in the hidden layer. this paper proposes a method for pruning the hidden layer neurons based on the linear combination of the hidden layer weights and the input data.
this paper proposes a multimodal extension of PBILC based on Gaussian mixture models for solving dynamic optimization problems. By tracking multiple optima, the algorithm is able to follow the changes in objective fun...
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ISBN:
(纸本)9783319108407;9783319108391
this paper proposes a multimodal extension of PBILC based on Gaussian mixture models for solving dynamic optimization problems. By tracking multiple optima, the algorithm is able to follow the changes in objective functions more efficiently than in the unimodal case. the approach was validated on a set of synthetic benchmarks including Moving Peaks, dynamization of the Rosenbrock function and compositions of functions from the IEEE CEC'2009 competition. the results obtained in the experiments proved the efficiency of the approach in solving dynamic problems with a number of competing peaks.
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