10 results match your criteria Applied Soft Computing[Journal]

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A bi-objective home healthcare routing and scheduling problem considering patients' satisfaction in a fuzzy environment.

Appl Soft Comput 2020 Aug 8;93:106385. Epub 2020 May 8.

Geneva School of Business Administration, University of Applied Sciences Western Switzerland (HES-SO), 1227 Carouge, Switzerland.

Home care services are an alternative answer to hospitalization, and play an important role in reducing the healthcare costs for governments and healthcare practitioners. To find a valid plan for these services, an optimization problem called the home healthcare routing and scheduling problem is motivated to perform the logistics of the home care services. Although most studies mainly focus on minimizing the total cost of logistics activities, no study, as far as we know, has treated the patients' satisfaction as an objective function under uncertainty. Read More

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http://dx.doi.org/10.1016/j.asoc.2020.106385DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7205736PMC

Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction.

Appl Soft Comput 2020 Apr 9:106282. Epub 2020 Apr 9.

University of Granada, Spain.

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Read More

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http://dx.doi.org/10.1016/j.asoc.2020.106282DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195106PMC

Temporal event searches based on event maps and relationships.

Appl Soft Comput 2019 Dec 25;85:105750. Epub 2019 Sep 25.

School of Science and Technology, The Open University of Hong Kong, Hong Kong.

To satisfy a user's need to find and understand the whole picture of an event effectively and efficiently, in this paper we formalize the problem of temporal event searches and propose a framework of event relationship analysis for search events based on user queries. We define three kinds of event relationships: temporal, content dependence, and event reference, that can be used to identify to what extent a component event is dependent on another in the evolution of a target event (i.e. Read More

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http://dx.doi.org/10.1016/j.asoc.2019.105750DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105191PMC
December 2019
2.810 Impact Factor

QML-AiNet: An immune network approach to learning qualitative differential equation models.

Appl Soft Comput 2015 Feb;27:148-157

School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, UK.

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. Read More

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http://dx.doi.org/10.1016/j.asoc.2014.11.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4308000PMC
February 2015
3 Reads

Outbreak detection model based on danger theory.

Appl Soft Comput 2014 Nov 22;24:612-622. Epub 2014 Aug 22.

Data Mining and Optimization Research Group, Centre for Artificial Intelligence Technology, Faculty of Science & Information Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia.

In outbreak detection, one of the key issues is the need to deal with the weakness of early outbreak signals because this causes the detection model to have has less capability in terms of robustness when unseen outbreak patterns vary from those in the trained model. As a result, an imbalance between high detection rate and low false alarm rate occurs. To solve this problem, this study proposes a novel outbreak detection model based on danger theory; a bio-inspired method that replicates how the human body fights pathogens. Read More

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http://dx.doi.org/10.1016/j.asoc.2014.08.030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185443PMC
November 2014

ANFIS-based approach for predicting sediment transport in clean sewer.

Appl Soft Comput 2012 Mar;12(3):1227-1230

River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300 Nibong Tebal, Penang, Malaysia.

The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). Read More

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http://dx.doi.org/10.1016/j.asoc.2011.12.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3273703PMC
March 2012
1 Read

Applications of machine learning techniques to a sensor-network-based prosthesis training system.

Appl Soft Comput 2011 Apr 23;11(3):3229-3237. Epub 2010 Dec 23.

Institute of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.

In the past, the utilization of the limb prosthesis has improved the daily life of amputees or patients with movement disorders. However, a leg-amputee has to take a series of training after wearing a limb prosthesis, and the training results determine whether a patient can use the limb prosthesis correctly in her/his daily life. Limb prosthesis vendors thus desire to offer the leg-amputee a complete and well-organized training process, but they often fail to do so owing to the factors such as the limited support of human resource and financial condition of the amputee. Read More

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http://dx.doi.org/10.1016/j.asoc.2010.12.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185859PMC

Evolving a Bayesian Classifier for ECG-based Age Classification in Medical Applications.

Appl Soft Comput 2008 Jan;8(1):599-608

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

OBJECTIVE: To classify patients by age based upon information extracted from their electro-cardiograms (ECGs). To develop and compare the performance of Bayesian classifiers. METHODS AND MATERIAL: We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Read More

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http://dx.doi.org/10.1016/j.asoc.2007.03.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3193938PMC
January 2008
1 Read

Genetic Programming Neural Networks: A Powerful Bioinformatics Tool for Human Genetics.

Appl Soft Comput 2007 Jan;7(1):471-479

Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University, 519 Light Hall, Nashville, TN 37232.

The identification of genes that influence the risk of common, complex disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of genetic and gene-environment combinations associated with disease risk. Read More

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http://dx.doi.org/10.1016/j.asoc.2006.01.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2952963PMC
January 2007
2 Reads

Routine Discovery of Complex Genetic Models using Genetic Algorithms.

Appl Soft Comput 2004 Feb;4(1):79-86

Program in Human Genetics, Department of Molecular Physiology and Biophysics, 519 Light Hall, Vanderbilt University Medical School, Nashville, TN 37232-0700, USA.

Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. Read More

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http://dx.doi.org/10.1016/j.asoc.2003.08.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2952957PMC
February 2004
2 Reads
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