Academic literature on the topic 'Continuous Time Bayesian Network Classifier'
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Journal articles on the topic "Continuous Time Bayesian Network Classifier"
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Stella, F., and Y. Amer. "Continuous time Bayesian network classifiers." Journal of Biomedical Informatics 45, no. 6 (December 2012): 1108–19. http://dx.doi.org/10.1016/j.jbi.2012.07.002.
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Codecasa, Daniele, and Fabio Stella. "Learning continuous time Bayesian network classifiers." International Journal of Approximate Reasoning 55, no. 8 (November 2014): 1728–46. http://dx.doi.org/10.1016/j.ijar.2014.05.005.
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Villa, S., and F. Stella. "A continuous time Bayesian network classifier for intraday FX prediction." Quantitative Finance 14, no. 12 (April 22, 2014): 2079–92. http://dx.doi.org/10.1080/14697688.2014.906811.
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Naddaf-Sh, M.-Mahdi, SeyedSaeid Hosseini, Jing Zhang, Nicholas A. Brake, and Hassan Zargarzadeh. "Real-Time Road Crack Mapping Using an Optimized Convolutional Neural Network." Complexity 2019 (September 29, 2019): 1–17. http://dx.doi.org/10.1155/2019/2470735.
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Pavement surveying and distress mapping is completed by roadway authorities to quantify the topical and structural damage levels for strategic preventative or rehabilitative action. The failure to time the preventative or rehabilitative action and control distress propagation can lead to severe structural and financial loss of the asset requiring complete reconstruction. Continuous and computer-aided surveying measures not only can eliminate human error when analyzing, identifying, defining, and mapping pavement surface distresses, but also can provide a database of road damage patterns and their locations. The database can be used for timely road repairs to gain the maximum durability of the asphalt and the minimum cost of maintenance. This paper introduces an autonomous surveying scheme to collect, analyze, and map the image-based distress data in real time. A descriptive approach is considered for identifying cracks from collected images using a convolutional neural network (CNN) that classifies several types of cracks. Typically, CNN-based schemes require a relatively large processing power to detect desired objects in images in real time. However, the portability objective of this work requires to utilize low-weight processing units. To that end, the CNN training was optimized by the Bayesian optimization algorithm (BOA) to achieve the maximum accuracy and minimum processing time with minimum neural network layers. First, a database consisting of a diverse population of crack distress types such as longitudinal, transverse, and alligator cracks, photographed at multiple angles, was prepared. Then, the database was used to train a CNN whose hyperparameters were optimized using BOA. Finally, a heuristic algorithm is introduced to process the CNN’s output and produce the crack map. The performance of the classifier and mapping algorithm is examined against still images and videos captured by a drone from cracked pavement. In both instances, the proposed CNN was able to classify the cracks with 97% accuracy. The mapping algorithm is able to map a diverse population of surface cracks patterns in real time at the speed of 11.1 km per hour.
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Hemalatha, C. Sweetlin, and V. Vaidehi. "Associative Classification based Human Activity Recognition and Fall Detection using Accelerometer." International Journal of Intelligent Information Technologies 9, no. 3 (July 2013): 20–37. http://dx.doi.org/10.4018/jiit.2013070102.
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Human fall poses serious health risks especially among aged people. The rate of growth of elderly population to the total population is increasing every year. Besides causing injuries, fall may even lead to death if not attended immediately. This demands continuous monitoring of human movements and classifying normal low-level activities from abnormal event like fall. Most of the existing fall detection methods employ traditional classifiers such as decision trees, Bayesian Networks, Support Vector Machine etc. These classifiers may miss to cover certain hidden and interesting patterns in the data and thus suffer high false positives rates. Hence, there is a need for a classifier that considers the association between patterns while classifying the input instance. This paper presents a pattern mining based classification algorithm called Frequent Bit Pattern based Associative Classification (FBPAC) that distinguishes low-level human activities from fall. The proposed system utilizes single tri-axial accelerometer for capturing motion data. Empirical studies are conducted by collecting real data from tri-axial accelerometer. Experimental results show that within a time-sensitive sliding window of 10 seconds, the proposed algorithm achieves 99% accuracy for independent activity and 92% overall accuracy for activity sequence. The algorithm gives reasonable accuracy when tested in real time.
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Procházka, Vít K., Štěpánka Matuštíková, Tomáš Fürst, David Belada, Andrea Janíková, Kateřina Benešová, Heidi Mociková, et al. "Bayesian Network Modelling As a New Tool in Predicting of the Early Progression of Disease in Follicular Lymphoma Patients." Blood 136, Supplement 1 (November 5, 2020): 20–21. http://dx.doi.org/10.1182/blood-2020-139830.
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Background: Twenty percent of patients (pts) with high-tumor burden follicular lymphoma (FL) develop progression/relapse of disease within 24 months of frontline immune-chemotherapy (POD24). Those ultra-high-risk cases are at 50% risk of dying within 5-years since the POD event. Unmet need is to identify such pts at the time of initial treatment. The traditional approach used for building predictive scores (such as FLIPI, PRIMA-PI) is multivariable logistic regression (LR). LR is the tool of choice in case of many predictors (continuous or categorical) and a single binary (yes/no) outcome. Bayesian network (BN) offer an alternative strategy which may overcome several drawbacks of LR (risk of overfitting, missing data handling, problems of odds ratio interpretation), brings more insight into the complex relations among the variables, and offer an individualized prediction. Aim: The goal was to build a model to predict the risk of POD24 from the parameters known at diagnosis and compare LR to BN approach. Methods: The study (ClinicalTrials.gov No NCT03199066) comprised 1394 FL (grade I-IIIA) patients from the Czech Lymphoma Study Group registry treated with frontline rituximab-containing regimen and diagnosed between 10. 4. 2000 and 28. 12. 2016. The following parameters were analyzed: gender, age at diagnosis, clinical stage, lymphoma grade, no. of LNs regions, bone marrow involvement, no. of extranodal localizations, longest tumor diameter, systemic symptoms, performance status, LDH, beta-2-microglobulin, hemoglobin, and leucocyte, lymphocyte, and thrombocyte counts, induction regime, radiotherapy, ASCT, maintenance application, response to treatment, and OS, PFS and POD24 as outcome parameters. POD24 was defined as relapse, progression, change of therapy for 24 months since the induction started. Only parameters known at diagnosis were used for the prediction of POD24. Results: The median age was 59 yrs (range 26-89 yrs) with female predominance (59.2%), advanced disease stage (III/IV) was seen in 85.9% of the cases and FLIPI risk groups distribution was as follows: low (18.8%), intermediate (30.9%) and high (50.3%). The most frequent regime used was R-CHOP (76.8%), followed by R-CVP (12.4%), R-bendamustine (4.7%), intensive protocols (3.3), and fludarabine-based (2.8%). Consolidative IF-radiotherapy was applied in 5.1% and up-front ASCT in 2.9% of the pts. Maintenance immunotherapy was given in 67.1% of the pts. Response to therapy was known in all but 28 pts (98%) with CR/CRu 67.9%, PR 26.6%, SD 1.8%, and PD in 3.2% of the cases. After a median follow-up of 7.64 yrs, 484 (34.7%) of the pts progressed or relapsed and 316 (22.6%) have died. POD24 was recorded in 266 (19.0%) of the pts. The 5-year OS reached 86.4% and 5-year PFS 64.2%. LR model (PFS) building strategy included testing for significance as this model performed better than the model with all parameters. Overfitting was prevented by splitting the data into training (75%) and testing (25%) set. The performance of the model was assessed using the AUC criterion computed on the ROC curve. The LR model reached AUC of 0.69, and at 80% specificity, it reached about 51% sensitivity. Next, the BN (Augmented Naïve Bayes Classifier) was trained. Links of all predictors to POD24 were forced and all links to age and gender were forbidden, otherwise the network structure was inferred from the data. The performance of the BN was similar to the LR - AUC of 0.67 and about 50% sensitivity at the specificity of 80%. Both these models were compared to the standard PRIMA-PI risk classifier and were found to better stratify the population into risk groups (Table 1). An example of a patient is presented who was low-risk according to PRIMA-PI but actually experienced the POD24 event. The BN estimated the probability of the event to 91% (Figure 1). Conclusion: Lymphoma-related death following POD24 remains the most frequent cause of mortality in FL patients. BN modelling is a non-inferior prognostic tool compared to LR in term of POD24 prediction. Unlike LR, it also allows visualisation of complex relations among the predictors and individualized prediction of the patient's POD24 risk, even if some of the predictors are unknown. Both "ad hoc" trained LR and BN were found to better stratify the population into risk groups with respect to POD24 event than the traditional PRIMA-PI score. Acknowledgement: MZ Czech Republic DRO grant (FNOL, 00098892). Disclosures Procházka: F. Hoffmann-La Roche AG: Consultancy, Honoraria; Takeda Pharmaceuticals, Inc: Consultancy, Research Funding. Belada:Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses, Research Funding; Celgene: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses, Research Funding. Trněný:Janssen: Consultancy, Honoraria, Other: Travel Expenses; Gilead: Consultancy, Honoraria, Other: Travel Expenses; Takeda: Consultancy, Honoraria, Other: Travel Expenses; Bristol-Myers Squibb Company: Consultancy, Honoraria, Other: Travel Expenses; Amgen: Honoraria; Abbvie: Consultancy, Honoraria, Other: Travel Expenses; Roche: Consultancy, Honoraria, Other: Travel Expenses; MorphoSys: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Celgene: Consultancy.
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Liu, Yunchuan, Amir Ghasemkhani, and Lei Yang. "Drifting Streaming Peaks-over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast." Future Internet 15, no. 1 (December 28, 2022): 17. http://dx.doi.org/10.3390/fi15010017.
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This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least 33.6% for the whole year data and at least 37% for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least 35.8% for the whole year data and at least 35.2% for the ramp events.
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LANSNER, ANDERS, and ANDERS HOLST. "A HIGHER ORDER BAYESIAN NEURAL NETWORK WITH SPIKING UNITS." International Journal of Neural Systems 07, no. 02 (May 1996): 115–28. http://dx.doi.org/10.1142/s0129065796000816.
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We treat a Bayesian confidence propagation neural network, primarily in a classifier context. The onelayer version of the network implements a naive Bayesian classifier, which requires the input attributes to be independent. This limitation is overcome by a higher order network. The higher order Bayesian neural network is evaluated on a real world task of diagnosing a telephone exchange computer. By introducing stochastic spiking units, and soft interval coding, it is also possible to handle uncertain as well as continuous valued inputs.
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Du, Rei-Jie, Shuang-Cheng Wang, Han-Xing Wang, and Cui-Ping Leng. "Optimization of Dynamic Naive Bayesian Network Classifier with Continuous Attributes." Advanced Science Letters 11, no. 1 (May 30, 2012): 676–79. http://dx.doi.org/10.1166/asl.2012.2965.
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Wang, Shuangcheng, Siwen Zhang, Tao Wu, Yongrui Duan, Liang Zhou, and Hao Lei. "FMDBN: A first-order Markov dynamic Bayesian network classifier with continuous attributes." Knowledge-Based Systems 195 (May 2020): 105638. http://dx.doi.org/10.1016/j.knosys.2020.105638.
Full textDissertations / Theses on the topic "Continuous Time Bayesian Network Classifier"
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CODECASA, DANIELE. "Continuous time bayesian network classifiers." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/80691.
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Streaming data are relevant to finance, computer science, and engineering, while they are becoming increasingly important to medicine and biology. Continuous time Bayesian networks are designed for analyzing efficiently multivariate streaming data, exploiting the conditional independencies in continuous time homogeneous Markov processes. Continuous time Bayesian network classifiers are a specialization of continuous time Bayesian networks designed for multivariate streaming data classification when time duration of events matters and the class occurs in the future. Continuous time Bayesian network classifiers are presented and analyzed. Structural learning is introduced for this class of models when complete data are available. A conditional log-likelihood scoring is derived to improve the marginal log- likelihood structural learning on continuous time Bayesian net- work classifiers. The expectation maximization algorithm is developed to address the unsupervised learning of continuous time Bayesian network classifiers when the class is unknown. Performances of continuous time Bayesian network classifiers in the case of classification and clustering are analyzed with the help of a rich set of numerical experiments on synthetic and real data sets. Continuous time Bayesian network classifiers learned by maximizing marginal log-likelihood and conditional log-likelihood are compared with continuous time naive Bayes and dynamic Bayesian networks. Results show that the conditional log-likelihood scoring combined with Bayesian parameter estimation outperforms marginal log-likelihood scoring and dynamic Bayesian networks in the case of supervised classification. Conditional log-likelihood scoring becomes even more effective when the amount of available data is limited. Continuous time Bayesian network classifiers outperform dynamic Bayesian networks even on data sets generated from dis- crete time models. Clustering results show that in the case of unsupervised learning the marginal log-likelihood score is the most effective way to learn continuous time Bayesian network classifiers. Continuous time models again outperform dynamic Bayesian networks even when applied on discrete time data sets. A Java software toolkit implementing the main theoretical achievements of the thesis has been designed and developed under the name of the CTBNCToolkit. It provides a free stand- alone toolkit for multivariate trajectory classification and an open source library, which can be extend in accordance with the GPL v.2.0 license. The CTBNCToolkit allows classification and clustering of multivariate trajectories using continuous time Bayesian network classifiers. Structural learning, maximizing marginal log-likelihood and conditional log-likelihood scores, is provided.
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VILLA, SIMONE. "Continuous Time Bayesian Networks for Reasoning and Decision Making in Finance." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/69953.
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L'analisi dell'enorme quantità di dati finanziari, messi a disposizione dai mercati elettronici, richiede lo sviluppo di nuovi modelli e tecniche per estrarre efficacemente la conoscenza da utilizzare in un processo decisionale informato. Lo scopo della tesi concerne l'introduzione di modelli grafici probabilistici utilizzati per il ragionamento e l'attività decisionale in tale contesto.
Nella prima parte della tesi viene presentato un framework che utilizza le reti Bayesiane per effettuare l'analisi e l'ottimizzazione di portafoglio in maniera olistica. In particolare, esso sfrutta, da un lato, la capacità delle reti Bayesiane di rappresentare distribuzioni di probabilità in modo compatto ed efficiente per modellare il portafoglio e, dall'altro, la loro capacità di fare inferenza per ottimizzare il portafoglio secondo diversi scenari economici.
In molti casi, si ha la necessità di ragionare in merito a scenari di mercato nel tempo, ossia si vuole rispondere a domande che coinvolgono distribuzioni di probabilità che evolvono nel tempo. Le reti Bayesiane a tempo continuo possono essere utilizzate in questo contesto. Nella seconda parte della tesi viene mostrato il loro utilizzo per affrontare problemi finanziari reali e vengono descritte due importanti estensioni. La prima estensione riguarda il problema di classificazione, in particolare vengono introdotti un algoritmo per apprendere tali classificatori da Big Data e il loro utilizzo nel contesto di previsione dei cambi valutari ad alta frequenza. La seconda estensione concerne l'apprendimento delle reti Bayesiane a tempo continuo in domini non stazionari, in cui vengono modellate esplicitamente le dipendenze statistiche presenti nelle serie temporali multivariate consentendo loro di cambiare nel corso del tempo.
Nella terza parte della tesi viene descritto l'uso delle reti Bayesiane a tempo continuo nell'ambito dei processi decisionali di Markov, i quali consentono di modellare processi decisionali sequenziali in condizioni di incertezza. In particolare, viene introdotto un metodo per il controllo di sistemi dinamici a tempo continuo che sfrutta le proprietà additive e contestuali per scalare efficacemente su grandi spazi degli stati. Infine, vengono mostrate le prestazioni di tale metodo in un contesto significativo di trading.The analysis of the huge amount of financial data, made available by electronic markets, calls for new models and techniques to effectively extract knowledge to be exploited in an informed decision-making process. The aim of this thesis is to introduce probabilistic graphical models that can be used to reason and to perform actions in such a context. In the first part of this thesis, we present a framework which exploits Bayesian networks to perform portfolio analysis and optimization in a holistic way. It leverages on the compact and efficient representation of high dimensional probability distributions offered by Bayesian networks and their ability to perform evidential reasoning in order to optimize the portfolio according to different economic scenarios. In many cases, we would like to reason about the market change, i.e. we would like to express queries as probability distributions over time. Continuous time Bayesian networks can be used to address this issue. In the second part of the thesis, we show how it is possible to use this model to tackle real financial problems and we describe two notable extensions. The first one concerns classification, where we introduce an algorithm for learning these classifiers from Big Data, and we describe their straightforward application to the foreign exchange prediction problem in the high frequency domain. The second one is related to non-stationary domains, where we explicitly model the presence of statistical dependencies in multivariate time-series while allowing them to change over time. In the third part of the thesis, we describe the use of continuous time Bayesian networks within the Markov decision process framework, which provides a model for sequential decision-making under uncertainty. We introduce a method to control continuous time dynamic systems, based on this framework, that relies on additive and context-specific features to scale up to large state spaces. Finally, we show the performances of our method in a simplified, but meaningful trading domain.
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Fan, Yu. "Continuous time Bayesian Network approximate inference and social network applications." Diss., [Riverside, Calif.] : University of California, Riverside, 2009. http://proquest.umi.com/pqdweb?index=0&did=1957308751&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268330625&clientId=48051.
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Thesis (Ph. D.)--University of California, Riverside, 2009.Includes abstract. Title from first page of PDF file (viewed March 8, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 130-133). Also issued in print.
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ACERBI, ENZO. "Continuos time Bayesian networks for gene networks reconstruction." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/52709.
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Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and improve the models' expressiveness.
Continuous time Bayesian networks is proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data methods's comparison was carried out for networks of increasing dimension, for measurements taken at different time granularity densities and for measurements evenly vs. unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network dimensions. Furthermore, their performance degraded smoothly as the dimension of the network increased. Continuous time Bayesian network were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms as well as new plausible biological insights.
Continuous time Bayesian networks resulted to be effective on networks of both small and big dimensions and particularly feasible when the measurements are not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops suggesting that Th17 cells may be auto regulating their own differentiation process.
Book chapters on the topic "Continuous Time Bayesian Network Classifier"
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Codecasa, Daniele, and Fabio Stella. "A Classification Based Scoring Function for Continuous Time Bayesian Network Classifiers." In New Frontiers in Mining Complex Patterns, 35–50. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08407-7_3.
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Shi, Dongyu, and Jinyuan You. "Update Rules for Parameter Estimation in Continuous Time Bayesian Network." In Lecture Notes in Computer Science, 140–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_17.
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Acerbi, Enzo, and Fabio Stella. "Continuous Time Bayesian Networks for Gene Network Reconstruction: A Comparative Study on Time Course Data." In Bioinformatics Research and Applications, 176–87. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08171-7_16.
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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Probabilistic Graphical Model for Continuous Variables." In Intelligent Control and Learning Systems, 251–65. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_14.
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AbstractMost of the sampled data in complex industrial processes are sequential in time. Therefore, the traditional BN learning mechanisms have limitations on the value of probability and cannot be applied to the time series. The model established in Chap. 10.1007/978-981-16-8044-1_13 is a graphical model similar to a Bayesian network, but its parameter learning method can only handle the discrete variables. This chapter aims at the probabilistic graphical model directly for the continuous process variables, which avoids the assumption of discrete or Gaussian distributions.
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Flores, M. Julia, José A. Gámez, and Ana M. Martínez. "Supervised Classification with Bayesian Networks." In Intelligent Data Analysis for Real-Life Applications, 72–102. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1806-0.ch005.
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Bayesian Network classifiers (BNCs) are Bayesian Network (BN) models specifically tailored for classification tasks. There is a wide range of existing models that vary in complexity and efficiency. All of them have in common the ability to deal with uncertainty in a very natural way, at the same time providing a descriptive environment. In this chapter, the authors focus on the family of semi-naïve Bayesian classifiers (naïve Bayes, AODE, TAN, kDB, etc.), motivated by the good trade-off between efficiency and performance they provide. The domain of the BNs is generally of discrete nature, but since the presence of continuous variables is very common, the chapter discusses more classical and novel approaches to handling numeric data. In this chapter the authors also discuss more recent techniques such as multi-dimensional and dynamic models. Last but not least, they focus on applications and recent developments, including some of the BNCs approaches to the multi-class problem together with other traditionally successful and cutting edge cases regarding real-world applications.
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Veetil, Sanjai, and Qigang Gao. "Real-time Network Intrusion Detection Using Hadoop-Based Bayesian Classifier." In Emerging Trends in ICT Security, 281–99. Elsevier, 2014. http://dx.doi.org/10.1016/b978-0-12-411474-6.00018-9.
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Chakraborty, Chinmay, Bharat Gupta, and Soumya K. Ghosh. "Chronic Wound Characterization Using Bayesian Classifier under Telemedicine Framework." In Medical Imaging, 741–60. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0571-6.ch030.
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Chronic wound (CW) treatment by large is a burden for the government and society due to its high cost and time consuming treatment. It becomes more serious for the old age patient with the lack of moving flexibility. Proper wound recovery management is needed to resolve this problem. Careful and accurate documentation is required for identifying the patient's improvement and or deterioration timely for early diagnostic purposes. This paper discusses the comprehensive wound diagnostic method using three important modules, viz. Wounds Data Acquisition (WDA) module, Tele-Wound Technology Network (TWTN) module and Wound Screening and Diagnostic (WSD) module. Here the wound image characterization and diagnosis tool has been proposed under telemedicine to classify the percentage wise wound tissue based on the color variation over regular intervals for providing a prognostic treatment with better degree of accuracy. The Bayesian classifier based wound characterization (BWC) technique is proposed that able to identify wounded tissue and correctly predict the wound status with a good degree of accuracy. Results show that BWC technique provides very good accuracy, i.e. 87.40%, whereas the individual tissue wise accuracy for granulation tissue is 89.44%, slough tissue is 81.87% and for necrotic tissue is 90.91%.
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Chakraborty, Chinmay, Bharat Gupta, and Soumya K. Ghosh. "Identification of Chronic Wound Status under Tele-Wound Network through Smartphone." In E-Health and Telemedicine, 735–50. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8756-1.ch037.
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This paper presents a tele-wound framework for monitoring chronic wound status based on color variation over a period of time. This will facilitate patients at remote locations to connect to medical experts through mobile devices. Further this will help medical professionals to monitor and manage the wounds in more timely, accurate and precise manner using the proposed framework. Tele-medical agent (TMA) collects the chronic wound data using smart phone and send it to the Tele-medical hub (TMH). In TMH, the wound image has been segmented using Fuzzy C-Means which gives highest segmented accuracy i.e. 92.60%, then the wound tissue is classified using proposed Bayesian classifier. The smart phone supported prototype system has been demonstrated with snapshots using very compatible and easy to integrate Hypertext preprocessor (PHP) and MySqL. The proposed system may facilitate better wound management and treatment by providing percentage of wound tissues.
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Wong, Andrew K. C., Yang Wang, and Gary C. L. Li. "Pattern Discovery as Event Association." In Machine Learning, 1924–32. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch804.
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A basic task of machine learning and data mining is to automatically uncover <b>patterns</b> that reflect regularities in a data set. When dealing with a large database, especially when domain knowledge is not available or very weak, this can be a challenging task. The purpose of <b>pattern discovery</b> is to find non-random relations among events from data sets. For example, the “exclusive OR” (XOR) problem concerns 3 binary variables, A, B and C=A<img src="http://resources.igi-global.com/Marketing/Preface_Figures/x_symbol.png">B, i.e. C is true when either A or B, but not both, is true. Suppose not knowing that it is the XOR problem, we would like to check whether or not the occurrence of the compound event [A=T, B=T, C=F] is just a random happening. If we could estimate its frequency of occurrences under the random assumption, then we know that it is not random if the observed frequency deviates significantly from that assumption. We refer to such a compound event as an event association pattern, or simply a <b>pattern</b>, if its frequency of occurrences significantly deviates from the default random assumption in the statistical sense. For instance, suppose that an XOR database contains 1000 samples and each primary event (e.g. [A=T]) occurs 500 times. The expected frequency of occurrences of the compound event [A=T, B=T, C=F] under the independence assumption is 0.5×0.5×0.5×1000 = 125. Suppose that its observed frequency is 250, we would like to see whether or not the difference between the observed and expected frequencies (i.e. 250 – 125) is significant enough to indicate that the compound event is not a random happening.<div><br></div><div>In statistics, to test the correlation between random variables, <b>contingency table</b> with chi-squared statistic (Mills, 1955) is widely used. Instead of investigating variable correlations, pattern discovery shifts the traditional correlation analysis in statistics at the variable level to association analysis at the event level, offering an effective method to detect statistical association among events.</div><div><br></div><div>In the early 90’s, this approach was established for second order event associations (Chan & Wong, 1990). A higher order <b>pattern discovery</b> algorithm was devised in the mid 90’s for discrete-valued data sets (Wong & Yang, 1997). In our methods, patterns inherent in data are defined as statistically significant associations of two or more primary events of different attributes if they pass a statistical test for deviation significance based on <b>residual analysis</b>. The discovered high order patterns can then be used for classification (Wang & Wong, 2003). With continuous data, events are defined as Borel sets and the pattern discovery process is formulated as an optimization problem which recursively partitions the sample space for the best set of significant events (patterns) in the form of high dimension intervals from which probability density can be estimated by Gaussian kernel fit (Chau & Wong, 1999). Classification can then be achieved using Bayesian classifiers. For data with a mixture of discrete and continuous data (Wong & Yang, 2003), the latter is categorized based on a global optimization discretization algorithm (Liu, Wong & Yang, 2004). As demonstrated in numerous real-world and commercial applications (Yang, 2002), pattern discovery is an ideal tool to uncover subtle and useful patterns in a database. </div><div><br></div><div>In pattern discovery, three open problems are addressed. The first concerns learning where noise and uncertainty are present. In our method, noise is taken as inconsistent samples against statistically significant patterns. Missing attribute values are also considered as noise. Using a standard statistical <b>hypothesis testing</b> to confirm statistical patterns from the candidates, this method is a less ad hoc approach to discover patterns than most of its contemporaries. The second problem concerns the detection of polythetic patterns without relying on exhaustive search. Efficient systems for detecting monothetic patterns between two attributes exist (e.g. Chan & Wong, 1990). However, for detecting polythetic patterns, an exhaustive search is required (Han, 2001). In many problem domains, polythetic assessments of feature combinations (or higher order relationship detection) are imperative for robust learning. Our method resolves this problem by directly constructing polythetic concepts while screening out non-informative pattern candidates, using statisticsbased heuristics in the discovery process. The third problem concerns the representation of the detected patterns. Traditionally, if-then rules and graphs, including networks and trees, are the most popular ones. However, they have shortcomings when dealing with multilevel and multiple order patterns due to the non-exhaustive and unpredictable hierarchical nature of the inherent patterns. We adopt <b>attributed hypergraph</b> (AHG) (Wang & Wong, 1996) as the representation of the detected patterns. It is a data structure general enough to encode information at many levels of abstraction, yet simple enough to quantify the information content of its organized structure. It is able to encode both the qualitative and the quantitative characteristics and relations inherent in the data set.<br></div>
Conference papers on the topic "Continuous Time Bayesian Network Classifier"
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Villa, Simone, and Fabio Stella. "Learning Continuous Time Bayesian Networks in Non-stationary Domains." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/804.
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Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node in a continuous time Bayesian network to change over time. Structural learning of nonstationary continuous time Bayesian networks is developed under different knowledge settings. A macroeconomic dataset is used to assess the effectiveness of learning non-stationary continuous time Bayesian networks from real-world data.
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Matthews, Jordan, Timothy Klatt, Carolyn C. Seepersad, Michael Haberman, and David Shahan. "Hierarchical Design of Composite Materials With Negative Stiffness Inclusions Using a Bayesian Network Classifier." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13128.
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Recent research in the field of composite materials has shown that it is theoretically possible to produce composite materials with macroscopic mechanical stiffness and loss properties that surpass those of conventional composites. This research explores the possibility of designing and fabricating these composite materials by embedding small volume fractions of negative stiffness inclusions in a continuous host material. Achieving high stiffness and loss from these materials by design, however, is a nontrivial task. This paper presents a hierarchical multiscale material model for these materials, coupled with a set-based, multilevel design approach based on Bayesian network classifiers. Bayesian network classifiers are used to map promising regions of the design space at each hierarchical modeling level, and then the maps are intersected to identify sets of multilevel or multiscale solutions that are likely to provide desirable system performance. Length scales range from the behavior of the structured microscale negative stiffness inclusions to the effective properties of mesoscale composite materials to the performance of an illustrative macroscale component — a vibrating beam coated with the high stiffness, high loss composite material.
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Wiest, Tyler, Carolyn Conner Seepersad, and Michael Haberman. "Design Space Exploration in Sparse, Mixed Continuous/Discrete Spaces via Synthetically Enhanced Classification." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85274.
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Exploration of a design space is the first step in identifying sets of high-performing solutions to complex engineering problems. For this purpose, Bayesian network classifiers (BNCs) have been shown to be effective for mapping regions of interest in the design space, even when those regions of interest exhibit complex topologies. However, identifying sets of desirable solutions can be difficult with a BNC when attempting to map a space where high-performance designs are spread sparsely among a disproportionately large number of low-performance designs, resulting in an imbalanced classifier. In this paper, a method is presented that utilizes probabilities of class membership for known training points, combined with interpolation between those points, to generate synthetic high-performance points in a design space. By adding synthetic design points into the BNC training set, a designer can rebalance an imbalanced classifier and improve classification accuracy throughout the space. For demonstration, this approach is applied to an acoustics metamaterial design problem with a sparse design space characterized by a combination of discrete and continuous design variables.
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Choi, YooJung, Adnan Darwiche, and Guy Van den Broeck. "Optimal Feature Selection for Decision Robustness in Bayesian Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/215.
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In many applications, one can define a large set of features to support the classification task at hand. At test time, however, these become prohibitively expensive to evaluate, and only a small subset of features is used, often selected for their information-theoretic value. For threshold-based, Naive Bayes classifiers, recent work has suggested selecting features that maximize the expected robustness of the classifier, that is, the expected probability it maintains its decision after seeing more features. We propose the first algorithm to compute this expected same-decision probability for general Bayesian network classifiers, based on compiling the network into a tractable circuit representation. Moreover, we develop a search algorithm for optimal feature selection that utilizes efficient incremental circuit modifications. Experiments on Naive Bayes, as well as more general networks, show the efficacy and distinct behavior of this decision-making approach.
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Santoso, Ryan, Xupeng He, Marwa Alsinan, Hyung Kwak, and Hussein Hoteit. "Bayesian Long-Short Term Memory for History Matching in Reservoir Simulations." In SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203976-ms.
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Abstract History matching is critical in subsurface flow modeling. It is to align the reservoir model with the measured data. However, it remains challenging since the solution is not unique and the implementation is expensive. The traditional approach relies on trial and error, which are exhaustive and labor-intensive. In this study, we propose a new workflow utilizing Bayesian Markov Chain Monte Carlo (MCMC) to automatically and accurately perform history matching. We deliver four novelties within the workflow: 1) the use of multi-resolution low-fidelity models to guarantee high-quality matching, 2) updating the ranges of priors to assure convergence, 3) the use of Long-Short Term Memory (LSTM) network as a low-fidelity model to produce continuous time-response, and 4) the use of Bayesian optimization to obtain the optimum low-fidelity model for Bayesian MCMC runs. We utilize the first SPE comparative model as the physical and high-fidelity model. It is a gas injection into an oil reservoir case, which is the gravity-dominated process. The coarse low-fidelity model manages to provide updated priors that increase the precision of Bayesian MCMC. The Bayesian-optimized LSTM has successfully captured the physics in the high-fidelity model. The Bayesian-LSTM MCMC produces an accurate prediction with narrow uncertainties. The posterior prediction through the high-fidelity model ensures the robustness and precision of the workflow. This approach provides an efficient and high-quality history matching for subsurface flow modeling.
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Nannapaneni, Saideep, Sankaran Mahadevan, and Abhishek Dubey. "Real-Time Control of Cyber-Physical Manufacturing Process Under Uncertainty." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6460.
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Modern manufacturing processes are increasing becoming cyber-physical in nature, where a computational system monitors the system performance, provides real-time process control by analyzing sensor data collected regarding process and product characteristics, in order to increase the quality of the manufactured product. Such real-time process monitoring and control techniques are useful in precision and ultra-precision machining processes. However, the output product quality is affected by several uncertainty sources in various stages of the manufacturing process such as the sensor uncertainty, computational system uncertainty, control input uncertainty, and the variability in the manufacturing process. The computational system may be a single computing node or a distributed computing network; the latter scenario introduces additional uncertainty due to the communication between several computing nodes. Due to the continuous monitoring process, these uncertainty sources aggregate and compound over time, resulting in variations of product quality. Therefore, characterization of the various uncertainty sources and their impact on the product quality are necessary to increase the efficiency and productivity of the overall manufacturing process. To this end, this paper develops a two-level dynamic Bayesian network methodology, where the higher level captures the uncertainty in the sensors, control inputs, and the manufacturing process while the lower level captures the uncertainty in the communication between several computing nodes. In addition, we illustrate the use of a variance-based global sensitivity analysis approach for dimension reduction in a high-dimensional manufacturing process, in order to enable real-time analysis for process control. The proposed methodologies of process control under uncertainty and dimension reduction are illustrated for a cyber-physical turning process.
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Heidari, Hojat, and Abdolreza Ohadi. "Fault Detection in Gearbox With Non-Stationary Rotational Speed Using CWT Feature Extraction, PCA Reduction and ANN Classifier Methods." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71271.
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Gearbox fault diagnosis is one of the core research areas in the field of condition monitoring of rotating machines. The aim of this paper is to present an intelligent method for fault diagnosis of a kind of automotive gearbox in run-up condition based on vibration signals. The vibration signals are obtained from an acceleration sensor and sampled at constant time increment by AdvantechTM PCI-1712 card. Automotive gearbox test setup has been designed and constructed in Acoustics Research Laboratory in Amirkabir University of Technology. To process the non-stationary vibration signals, the re-sampling technique at constant angle increment is combined with the continuous wavelet transform (CWT) and the wavelet coefficients of the signals are obtained. The statistical parameters of the wavelet coefficients are extracted, and then the principle component analysis (PCA) is introduced to enhance the pattern recognition and reduce the dimensionality of the original feature space. Gearbox is considered in healthy, chipped tooth and worn teeth gears conditions. Finally, a feedforward multilayer perceptron (MLP) neural network is used for classification. The experimental results show that the adoption of PCA diagnosis method leads to higher accuracy and less training time for fault detection of the gear chip and wear.
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Zonzini, Federica, Francesca Romano, Antonio Carbone, Matteo Zauli, and Luca De Marchi. "Enhancing Vibration-Based Structural Health Monitoring via Edge Computing: A Tiny Machine Learning Perspective." In 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/qnde2021-75153.
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Abstract Despite the outstanding improvements achieved by artificial intelligence in the Structural Health Monitoring (SHM) field, some challenges need to be coped with. Among them, the necessity to reduce the complexity of the models and the data-to-user latency time which are still affecting state-of-the-art solutions. This is due to the continuous forwarding of a huge amount of data to centralized servers, where the inference process is usually executed in a bulky manner. Conversely, the emerging field of Tiny Machine Learning (TinyML), promoted by the recent advancements by the electronic and information engineering community, made sensor-near data inference a tangible, low-cost and computationally efficient alternative. In line with this observation, this work explored the embodiment of the One Class Classifier Neural Network, i.e., a neural network architecture solving binary classification problems for vibration-based SHM scenarios, into a resource-constrained device. To this end, OCCNN has been ported on the Arduino Nano 33 BLE Sense platform and validated with experimental data from the Z24 bridge use case, reaching an average accuracy and precision of 95% and 94%, respectively.
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Aileni, Raluca maria. "HEALTHCARE PREDICTIVE MODELS BASED ON BIG DATA FUSION FROM BIOMEDICAL SENSORS." In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-046.
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The paper presents a method for analyzing data from sensors and developing the predictive models based on learning methods. There are some methods, described on scientific literature, such as statistical methods (linear regression, logistic regression, and Bayesian models), advanced methods based on machine learning and data mining (decision trees and artificial neural networks) and survival models. All of these methods are intended to discover the correlation and covariance between biomedical parameters. This paper presents the decision tree method for predictive health modeling based on machine learning and data mining. Based on this method used can be developed a decision support system for healthcare. Machine learning is used in healthcare predictive modeling for learning to recognize complex patterns within big data received from biomedical sensors. The sensors data fusion refers to the usage of the sensors wireless network and data fusion on the same level (for similar sensors - e. g. temperature sensors) and on different levels (different sensors category - pulse, breath, temperature, moisture sensors) for developing the decision systems. Big data concept is familiar for medical sciences (genomics, biomedical research) and also for physical sciences (meteorology, physics and chemistry), financial institutions (banking and capital markets) and government (defense). For predictive models in clinical analysis is important to establish the time steps discretization of occurrence of a particular event (critical state required continuous monitoring) for observe the impact of the correlated values for biomedical parameters. These aspects presented are useful for healthcare learning about correlation between diseases and biomedical parameters.