Temporal-based medical diagnoses using a Fuzzy Temporal Reasoning System

We apply the Fuzzy Temporal Constraint System we have developed to the case of SARS (Severe Acute Respiratory Syndrome). The idea is to characterize the temporal evolution of the symptoms of this ill-known disease by modelling patients’ data in a Fuzzy Temporal Constraint Network. We discuss how the system is able to manage both fuzzy qualitative and metric constraints allowing to represent in a flexible manner the symptoms of different patients. In this way it is possible to deduce characteristic periods of an ill-known disease such as SARS was. A new user interface is included into the architecture of the System.     

Complex Attributed Network Embedding for medical complication prediction

Knowledge and Information Systems - To assure the development of effective treatment plans, it is crucial for understanding the complication relationships among diseases. In practice, traditional...     

Towards early purchase intention prediction in online session based retailing systems

Electronic Markets - Purchase prediction has an important role for decision-makers in e-commerce to improve consumer experience, provide personalised recommendations and increase revenue. Many...     

煤矿外因火灾早期探测方法研究

针对煤矿井下环境特点,提出了基于数字图像处理和支持向量机的煤矿外因火灾早期探测方法。该方法根据火灾初期的变化特征,用图像处理方法提取温度变化率、面积增长率、火焰闪烁频率和整体稳定性等特征值,并将其作为输入信号,利用支持向量机进行数据融合后实现火灾探测。实验结果表明,该方法能够对煤矿井下高危火源和干扰信号进行分类识别,具有探测率高、误判率低、实时性好、鲁棒性强的特点。     

Applying evolutionary methods for early prediction of sleep onset

Driving safety can be achieved by predicting imminent falling asleep at the wheel. Several methods of early detection have been investigated by continuous monitoring of physiological and behavioral parameters. Requirements for noninvasive, unattended, personal adaptation need to be met, along with the effectiveness of the detection method, in order to perform reliably when applied. Because wakefulness and sleep are reflected in several human physiological conditions, such as cardiac activity, breathing, movement, and galvanic skin conductance, captured bioelectric signal features were extracted. A fuzzy decision-fusion logic was tuned to make inferences about oncoming driver fatigue and drowsiness. The evolving fuzzy neural network paradigm was applied to the previous developed framework to improve reliability while keeping target system complexity low.     

Deep convolutional learning for general early design stage prediction models

Designers rely on performance predictions to direct the design toward appropriate requirements. Machine learning (ML) models exhibit the potential for rapid and accurate predictions. Developing conventional ML models that can be generalized well in unseen design cases requires an effective feature engineering and selection. Identifying generalizable features calls for good domain knowledge by the ML model developer. Therefore, developing ML models for all design performance parameters with conventional ML will be a time-consuming and expensive process. Automation in terms of feature engineering and selection will accelerate the use of ML models in design.Deep learning models extract features from data, which aid in model generalization. In this study, we (1) evaluate the deep learning model’s capability to predict the heating and cooling demand on unseen design cases and (2) obtain an understanding of extracted features. Results indicate that deep learning model generalization is similar to or better than that of a simple neural network with appropriate features. The reason for the satisfactory generalization using the deep learning model is its ability to identify similar design options within the data distribution. The results also indicate that deep learning models can filter out irrelevant features, reducing the need for feature selection.     

On the permissible ratios of image compression in early medical diagnostics

The compression of scan patterns in diagnostic imaging is considered. An integral approach is proposed to the elaboration of objective quantitative criteria for estimating the admissible distortions of a compressed image.     

医疗诊断与预测中的增量式Apriori方法研究

医疗诊断与预测因数据量太大而需要流式存储,使得频繁项集挖掘出现耗时大,效率低下等问题.以解决这些问题为目的,研究了一种改进的基于大规模数据流的频繁项集挖掘方法,即增量式频繁项集挖掘方法.文章的重要结果是,该方法可结合历史数据与当前数据簇,快速求出近似全局支持度,并找出全局频繁项集集合.将该方法应用于Apriori算法上,通过实验得出了该增量式Apriori方法具有高效率的结论.     

Case study: factors for early prediction of software development success

Project managers can make more effective and efficient project adjustments if they detect project high-risk elements early. We analyzed 42 software development projects in order to investigate some early risk factors and their effect on software project success. Developers in our organization found the most important factors for project success to be: (1) the presence of a committed sponsor and (2) the level of confidence that the customers and users have in the project manager and development team. However, several other software project factors, which are generally recognized as important, were not considered important by our respondents.     

MRA-based revised CBR model for cost prediction in the early stage of construction projects

Accurate prediction of construction cost in the initial phase of a construction project is critical to the success of the project. Accordingly, many researchers have proposed various methodologies for predicting the cost in the initial phase with the use of limited information. This study was aimed at improving the prediction performance of a cost prediction model based on the Case-Based Reasoning (CBR) technique, which has recently become widely used. Toward this end, an improved CBR model that uses the Multiple Regression Analysis (MRA) technique in the revision phase of the CBR technique was developed. To verify the prediction performance of the proposed model, a case study was performed on 41 business facilities and 99 multi-family housing projects. The results showed that the prediction performance of the revised CBR model for business facilities and multi-family housings improved by 17.23% and 4.39%, respectively, compared to that of the existing CBR model. The proposed MRA-based revised CBR model is expected to be useful in estimating the construction cost in the initial phase of a project.     

On the effectiveness of early life cycle defect prediction with Bayesian Nets

Standard practice in building models in software engineering normally involves three steps: collecting domain knowledge (previous results, expert knowledge); building a skeleton of the model based on step 1 including as yet unknown parameters; estimating the model parameters using historical data. Our experience shows that it is extremely difficult to obtain reliable data of the required granularity, or of the required volume with which we could later generalize our conclusions. Therefore, in searching for a method for building a model we cannot consider methods requiring large volumes of data. This paper discusses an experiment to develop a causal model (Bayesian net) for predicting the number of residual defects that are likely to be found during independent testing or operational usage. The approach supports (1) and (2), does not require (3), yet still makes accurate defect predictions (an R ² of 0.93 between predicted and actual defects). Since our method does not require detailed domain knowledge it can be applied very early in the process life cycle. The model incorporates a set of quantitative and qualitative factors describing a project and its development process, which are inputs to the model. The model variables, as well as the relationships between them, were identified as part of a major collaborative project. A dataset, elicited from 31 completed software projects in the consumer electronics industry, was gathered using a questionnaire distributed to managers of recent projects. We used this dataset to validate the model by analyzing several popular evaluation measures (R ², measures based on the relative error and Pred). The validation results also confirm the need for using the qualitative factors in the model. The dataset may be of interest to other researchers evaluating models with similar aims. Based on some typical scenarios we demonstrate how the model can be used for better decision support in operational environments. We also performed sensitivity analysis in which we identified the most influential variables on the number of residual defects. This showed that the project size, scale of distributed communication and the project complexity cause the most of variation in number of defects in our model. We make both the dataset and causal model available for research use.     

Quick energy prediction and comparison of options at the early design stage

The energy-efficient building design requires building performance simulation (BPS) to compare multiple design options for their energy performance. However, at the early stage, BPS is often ignored, due to uncertainty, lack of details, and computational time. This article studies probabilistic and deterministic approaches to treat uncertainty; detailed and simplified zoning for creating zones; and dynamic simulation and machine learning for making energy predictions. A state-of-the-art approach, such as dynamic simulation, provide a reliable estimate of energy demand, but computationally expensive. Reducing computational time requires the use of an alternative approach, such as a machine learning (ML) model. However, an alternative approach will cause a prediction gap, and its effect on comparing options needs to be investigated. A plugin for Building information modelling (BIM) modelling tool has been developed to perform BPS using various approaches. These approaches have been tested for an office building with five design options. A method using the probabilistic approach to treat uncertainty, detailed zoning to create zones, and EnergyPlus to predict energy is treated as the reference method. The deterministic or ML approach has a small prediction gap, and the comparison results are similar to the reference method. The simplified model approach has a large prediction gap and only makes only 40% comparison results are similar to the reference method. These findings are useful to develop a BIM integrated tool to compare options at the early design stage and ascertain which approach should be adopted in a time-constraint situation.     

Very early prediction of wine yield based on satellite data from VEGETATION

A forecast model for estimating the annual variation in regional wine yield based on remote sensing was developed for the main wine regions of Portugal. Normalized Difference Vegetation Index (NDVI) time-series obtained by the VEGETATION sensor, on board the most recent Satellite Pour l'Observation de la Terre (SPOT) satellite, over the period 1998–2008 were used for four test sites located in the main wine regions of Portugal: Douro (two sites), Vinhos Verdes and Alentejo. The CORINE (Coordination of Information on the Environment) Land Cover maps from 2000 were initially used to select the suitable regional test sites. The NDVI values of the second decade of April of the previous season to harvest were significantly correlated to the wine yield for all studied regions. The relation between the NDVI and grapevine induction and differentiation of the inflorescence primordial or bud fruitfulness during the previous season is discussed. This NDVI measurement can be made about 17 months before harvest and allows us to obtain very early forecasts of potential regional wine yield. Appropriate statistical tests indicated that the wine yield forecast model explains 77–88% of the inter-annual variability in wine yield. The comparison of official wine yield and the adjusted prediction models, based on 36 annual data records for all regions, shows an average spread deviation between 2.9% and 7.1% for the different regions. The dataset provided by the VEGETATION sensor proved to be a valuable tool for vineyard monitoring, mainly for inter-annual comparisons on a regional scale due to their high data acquisition rates and wide availability. The accuracy, very early indication and low-cost of the developed forecast model justify its use by the winery and viticulture industry.     

基于医疗文本数据聚类的帕金森病早期诊断预测

针对多发于老龄人群的帕金森病（PD）的早期智能化诊断的问题,提出基于医疗检测文本信息数据的聚类技术来对PD进行分析预测。首先,对原始数据集进行预处理以获取有效特征信息,并通过主成分分析（PCA）方法将原始特征分别降维到8个不同维度的维度空间;然后,应用5个传统的经典聚类模型和3种不同的聚类集成方法分别对8个维度空间的数据进行聚类;最后,采用4个聚类性能指标来预测数据集中的多巴胺异常PD患者、健康体和无多巴胺缺失（SWEDD） PD患者。仿真结果显示,PCA特征维度值取30时,高斯混合模型（GMM）的聚类准确度达到89.12%;PCA特征维度值取70时,谱聚类（SC）的聚类准确度达到61.41%;PCA特征维度值取80时,元聚类算法（MCLA）的聚类准确度达到59.62%。对比实验结果表明,5种经典聚类方法中,PCA的特征维度值小于40时,高斯混合模型聚类效果最佳;3种聚类集成方法中,对于不同的特征维度,MCLA的聚类性能均表现优异,进而为PD的早期智能化辅助诊断提供了技术和理论支撑。     

基于医疗文本数据聚类的帕金森病早期诊断预测

针对多发于老龄人群的帕金森病（PD）的早期智能化诊断的问题，提出基于医疗检测文本信息数据的聚类技术来对PD进行分析预测。首先，对原始数据集进行预处理以获取有效特征信息，并通过主成分分析（PCA）方法将原始特征分别降维到8个不同维度的维度空间；然后，应用5个传统的经典聚类模型和3种不同的聚类集成方法分别对8个维度空间的数据进行聚类；最后，采用4个聚类性能指标来预测数据集中的多巴胺异常PD患者、健康体和无多巴胺缺失（SWEDD） PD患者。仿真结果显示，PCA特征维度值取30时，高斯混合模型（GMM）的聚类准确度达到89.12%；PCA特征维度值取70时，谱聚类（SC）的聚类准确度达到61.41%；PCA特征维度值取80时，元聚类算法（MCLA）的聚类准确度达到59.62%。对比实验结果表明，5种经典聚类方法中，PCA的特征维度值小于40时，高斯混合模型聚类效果最佳；3种聚类集成方法中，对于不同的特征维度，MCLA的聚类性能均表现优异，进而为PD的早期智能化辅助诊断提供了技术和理论支撑。     

基于Android的医疗管理与干预系统的设计与实现

现代移动医疗越来越受到人们的重视,市场上也出现很多的医疗终端软件,主要有电子档案管理、免费咨询以及挂号预约等功能,医生则根据用户所说的相关症状分析其健康问题,但是在一些情况下也不可避免会发生误诊事件。针对这种现象,为减少误诊事件的发生,客户端通过蓝牙连接设备,获取设备采集的各项健康数据发送至服务器,并在服务器端和移动端绘制折线图将其可视化。这样医生可以在分析用户病例记录的同时结合其各项健康数据折线图,准确地判断病症原因,从而提出更加实用的健康指导。