Se connecter

Bibliographies thématiques / Continuous Time Bayesian Network Classifier / Articles de revues

Pour voir les autres types de publications sur ce sujet consultez le lien suivant : Continuous Time Bayesian Network Classifier.

Articles de revues sur le sujet « Continuous Time Bayesian Network Classifier »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 19 juillet 2025

Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres

Choisissez une source :

Consultez les 50 meilleurs articles de revues pour votre recherche sur le sujet « Continuous Time Bayesian Network Classifier ».

À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.

Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.

Parcourez les articles de revues sur diverses disciplines et organisez correctement votre bibliographie.

1

Stella, F., and Y. Amer. "Continuous time Bayesian network classifiers." Journal of Biomedical Informatics 45, no. 6 (2012): 1108–19. http://dx.doi.org/10.1016/j.jbi.2012.07.002.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

2

Codecasa, Daniele, and Fabio Stella. "Learning continuous time Bayesian network classifiers." International Journal of Approximate Reasoning 55, no. 8 (2014): 1728–46. http://dx.doi.org/10.1016/j.ijar.2014.05.005.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

3

Villa, S., and F. Stella. "A continuous time Bayesian network classifier for intraday FX prediction." Quantitative Finance 14, no. 12 (2014): 2079–92. http://dx.doi.org/10.1080/14697688.2014.906811.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

4

Wei, Chenghao, Chen Li, Yingying Liu, et al. "Causal Discovery and Reasoning for Continuous Variables with an Improved Bayesian Network Constructed by Locality Sensitive Hashing and Kernel Density Estimation." Entropy 27, no. 2 (2025): 123. https://doi.org/10.3390/e27020123.

Texte intégral

Résumé :

The structure learning of a Bayesian network (BN) is a crucial process that aims to unravel the complex dependencies relationships among variables using a given dataset. This paper proposes a new BN structure learning method for data with continuous attribute values. As a non-parametric distribution-free method, kernel density estimation (KDE) is applied in the conditional independence (CI) test. The skeleton of the BN is constructed utilizing the test based on mutual information and conditional mutual information, delineating potential relational connections between parents and children witho

Styles APA, Harvard, Vancouver, ISO, etc.

5

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.

Texte intégral

Résumé :

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 th

Styles APA, Harvard, Vancouver, ISO, etc.

6

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 (2013): 20–37. http://dx.doi.org/10.4018/jiit.2013070102.

Texte intégral

Résumé :

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

Styles APA, Harvard, Vancouver, ISO, etc.

7

Procházka, Vít K., Štěpánka Matuštíková, Tomáš Fürst, 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 (2020): 20–21. http://dx.doi.org/10.1182/blood-2020-139830.

Texte intégral

Résumé :

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. Bay

Styles APA, Harvard, Vancouver, ISO, etc.

8

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 (2022): 17. http://dx.doi.org/10.3390/fi15010017.

Texte intégral

Résumé :

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 generati

Styles APA, Harvard, Vancouver, ISO, etc.

9

LANSNER, ANDERS, and ANDERS HOLST. "A HIGHER ORDER BAYESIAN NEURAL NETWORK WITH SPIKING UNITS." International Journal of Neural Systems 07, no. 02 (1996): 115–28. http://dx.doi.org/10.1142/s0129065796000816.

Texte intégral

Résumé :

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.

Styles APA, Harvard, Vancouver, ISO, etc.

10

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 (2012): 676–79. http://dx.doi.org/10.1166/asl.2012.2965.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

11

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

12

Xu, J., and C. R. Shelton. "Intrusion Detection using Continuous Time Bayesian Networks." Journal of Artificial Intelligence Research 39 (December 23, 2010): 745–74. http://dx.doi.org/10.1613/jair.3050.

Texte intégral

Résumé :

Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for both systems. We use anomaly detection, which identifies patterns not conforming to a historic norm. In both types of systems, the rates of change vary dramatically over time (due to burstiness) and over components (due to service difference). To efficiently model such systems, we use continuous time Bayesian networks (CTBNs) and avoid specifying a fixed upda

Styles APA, Harvard, Vancouver, ISO, etc.

13

Guo, Dai Fei, Jian Jun Hu, Ai Fen Sui, Guan Zhou Lin, and Tao Guo. "The Abnormal Mobile Malware Analysis Based on Behavior Categorization." Advanced Materials Research 765-767 (September 2013): 994–97. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.994.

Texte intégral

Résumé :

With the explosive growth of mobile malware in mobile internet, many polymorphic and metamorphic mobile malware appears and causes difficulty of detection. A mobile malware network behavior data mining method based on behavior categorization is proposed to detect the behavior of new or metamorphic mobile malware. The network behavior is divided into different categories after analyzing the behavior character of mobile malware and those different behavior data of known malware and normal action are used to train the Naïve Bayesian classifier respectively. Those Naïve Bayesian classifiers are

Styles APA, Harvard, Vancouver, ISO, etc.

14

Lyu, Na, Jiaxin Zhou, Xuan Feng, Kefan Chen, and Wu Chen. "A Timeliness-Enhanced Traffic Identification Method in Airborne Network." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 2 (2020): 341–50. http://dx.doi.org/10.1051/jnwpu/20203820341.

Texte intégral

Résumé :

High dynamic topology and limited bandwidth of the airborne network make it difficult to provide reliable information interaction services for diverse combat mission of aviation swarm operations. Therefore, it is necessary to identify the elephant flows in the network in real time to optimize the process of traffic control and improve the performance of airborne network. Aiming at this problem, a timeliness-enhanced traffic identification method based on machine learning Bayesian network model is proposed. Firstly, the data flow training subset is obtained by preprocessing the original traffic

Styles APA, Harvard, Vancouver, ISO, etc.

15

Bhattacharjya, Debarun, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush Varshney, and Dharmashankar Subramanian. "Event-Driven Continuous Time Bayesian Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3259–66. http://dx.doi.org/10.1609/aaai.v34i04.5725.

Texte intégral

Résumé :

We introduce a novel event-driven continuous time Bayesian network (ECTBN) representation to model situations where a system's state variables could be influenced by occurrences of events of various types. In this way, the model parameters and graphical structure capture not only potential “causal” dynamics of system evolution but also the influence of event occurrences that may be interventions. We propose a greedy search procedure for structure learning based on the BIC score for a special class of ECTBNs, showing that it is asymptotically consistent and also effective for limited data. We d

Styles APA, Harvard, Vancouver, ISO, etc.

16

Sturlaugson, Liessman, and John W. Sheppard. "Sensitivity Analysis of Continuous Time Bayesian Network Reliability Models." SIAM/ASA Journal on Uncertainty Quantification 3, no. 1 (2015): 346–69. http://dx.doi.org/10.1137/140953848.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

17

Codecasa, Daniele, and Fabio Stella. "Classification and clustering with continuous time Bayesian network models." Journal of Intelligent Information Systems 45, no. 2 (2014): 187–220. http://dx.doi.org/10.1007/s10844-014-0345-0.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

18

Ou, Guiliang, Yulin He, Philippe Fournier-Viger, and Joshua Zhexue Huang. "A Novel Mixed-Attribute Fusion-Based Naive Bayesian Classifier." Applied Sciences 12, no. 20 (2022): 10443. http://dx.doi.org/10.3390/app122010443.

Texte intégral

Résumé :

The Naive Bayesian classifier (NBC) is a well-known classification model that has a simple structure, low training complexity, excellent scalability, and good classification performances. However, the NBC has two key limitations: (1) it is built upon the strong assumption that condition attributes are independent, which often does not hold in real-life, and (2) the NBC does not handle continuous attributes well. To overcome these limitations, this paper presents a novel approach for NBC construction, called mixed-attribute fusion-based NBC (MAF-NBC). It alleviates the two aforementioned limita

Styles APA, Harvard, Vancouver, ISO, etc.

19

Li, Dawei, Xiaojian Hu, Cheng-jie Jin, and Jun Zhou. "Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers." Discrete Dynamics in Nature and Society 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/8523495.

Texte intégral

Résumé :

This study develops a tree augmented naive Bayesian (TAN) classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE) in Singapore is used to demonstrate the development of proposed algorithm, including wave

Styles APA, Harvard, Vancouver, ISO, etc.

20

Liu, Yang, Limin Wang, and Minghui Sun. "Efficient Heuristics for Structure Learning of k-Dependence Bayesian Classifier." Entropy 20, no. 12 (2018): 897. http://dx.doi.org/10.3390/e20120897.

Texte intégral

Résumé :

The rapid growth in data makes the quest for highly scalable learners a popular one. To achieve the trade-off between structure complexity and classification accuracy, the k-dependence Bayesian classifier (KDB) allows to represent different number of interdependencies for different data sizes. In this paper, we proposed two methods to improve the classification performance of KDB. Firstly, we use the minimal-redundancy-maximal-relevance analysis, which sorts the predictive features to identify redundant ones. Then, we propose an improved discriminative model selection to select an optimal sub-

Styles APA, Harvard, Vancouver, ISO, etc.

21

Boudali, H., and J. B. Dugan. "A Continuous-Time Bayesian Network Reliability Modeling, and Analysis Framework." IEEE Transactions on Reliability 55, no. 1 (2006): 86–97. http://dx.doi.org/10.1109/tr.2005.859228.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

22

Gatti, E., D. Luciani, and F. Stella. "A continuous time Bayesian network model for cardiogenic heart failure." Flexible Services and Manufacturing Journal 24, no. 4 (2011): 496–515. http://dx.doi.org/10.1007/s10696-011-9131-2.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

23

Shelton, C. R., and G. Ciardo. "Tutorial on Structured Continuous-Time Markov Processes." Journal of Artificial Intelligence Research 51 (December 23, 2014): 725–78. http://dx.doi.org/10.1613/jair.4415.

Texte intégral

Résumé :

A continuous-time Markov process (CTMP) is a collection of variables indexed by a continuous quantity, time. It obeys the Markov property that the distribution over a future variable is independent of past variables given the state at the present time. We introduce continuous-time Markov process representations and algorithms for filtering, smoothing, expected sufficient statistics calculations, and model estimation, assuming no prior knowledge of continuous-time processes but some basic knowledge of probability and statistics. We begin by describing "flat" or unstructured Markov processes and

Styles APA, Harvard, Vancouver, ISO, etc.

24

Ma, Rui, Long Han, and Hujun Geng. "Implementation and Error Analysis of MNIST Handwritten Dataset Classification Based on Bayesian Decision Classifier." Journal of Physics: Conference Series 2171, no. 1 (2022): 012049. http://dx.doi.org/10.1088/1742-6596/2171/1/012049.

Texte intégral

Résumé :

Abstract In recent years, with the continuous development of computer technology, pattern recognition technology has gradually entered people’s life and learning, and people’s demand for pattern recognition technology is also growing.In order to adapt to people’s life and study, the application of pattern recognition theory is more and more, such as speech recognition, character recognition, face recognition and so on.The main methods of pattern recognition are statistics, clustering,neural network and artificial intelligence.Statistical method is one of the most classic methods, and Bayesian

Styles APA, Harvard, Vancouver, ISO, etc.

25

Batenkov, Aleksandr, Kirill Batenkov, Andrey Bogachev, and Vladislav Mishin. "Mathematical Model of Object Classifier based on Bayesian Approach." Informatics and Automation 19, no. 6 (2020): 1166–97. http://dx.doi.org/10.15622/ia.2020.19.6.2.

Texte intégral

Résumé :

The paper claims that the primary importance in solving the classification problem is to find the conditions for dividing the General complexity into classes, determine the quality of such a bundle, and verify the classifier model. We consider a mathematical model of a non-randomized classifier of features obtained without a teacher, when the number of classes is not set a priori, but only its upper bound is set. The mathematical model is presented in the form of a statement of a minimax conditional extreme task, and it is a problem of searching for the matrix of belonging of objects to a clas

Styles APA, Harvard, Vancouver, ISO, etc.

26

Mohammed, Golam Sarwar Bhuyan, and Asma Yasmin Must. "Remote Patient Monitoring in FOG Computing Environment using Bayesian Belief Network Classifier Algorithm." International Journal of Innovative Science and Research Technology 7, no. 10 (2022): 50–54. https://doi.org/10.5281/zenodo.7215581.

Texte intégral

Résumé :

The comfort and ease of human lives are significantly improved by the Internet of Things (IoT) devices' integration of medical signal processing capabilities with cutting-edge sensors. Providing healthcare services to every patient, especially the elderly person those are living in the remote areas and suffering chronic diseases need to monitor in real-time. Remote patient monitoring systems are designed to obtain a number of physiological data from the patients. Most common data are Electrocardiogram (ECG), Electroencephalogram (EEG), heart beats and respiration rate, oxygen volume in blo

Styles APA, Harvard, Vancouver, ISO, etc.

27

Chakraborty, Chinmay, Bharat Gupta, and Soumya K. Ghosh. "Chronic Wound Characterization using Bayesian Classifier under Telemedicine Framework." International Journal of E-Health and Medical Communications 7, no. 1 (2016): 76–93. http://dx.doi.org/10.4018/ijehmc.2016010105.

Texte intégral

Résumé :

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

Styles APA, Harvard, Vancouver, ISO, etc.

28

Aversano, Lerina, Mario Luca Bernardi, Marta Cimitile, and Riccardo Pecori. "Continuous authentication using deep neural networks ensemble on keystroke dynamics." PeerJ Computer Science 7 (May 11, 2021): e525. http://dx.doi.org/10.7717/peerj-cs.525.

Texte intégral

Résumé :

During the last years, several studies have been proposed about user identification by means of keystroke analysis. Keystroke dynamics has a lower cost when compared to other biometric-based methods since such a system does not require any additional specific sensor, apart from a traditional keyboard, and it allows the continuous identification of the users in the background as well. The research proposed in this paper concerns (i) the creation of a large integrated dataset of users typing on a traditional keyboard obtained through the integration of three real-world datasets coming from exist

Styles APA, Harvard, Vancouver, ISO, etc.

29

Sun, Yanru, Zongxia Xie, Dongyue Chen, Emadeldeen Eldele, and Qinghua Hu. "Hierarchical Classification Auxiliary Network for Time Series Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20743–51. https://doi.org/10.1609/aaai.v39i19.34286.

Texte intégral

Résumé :

Deep learning has significantly advanced time series forecasting through its powerful capacity to capture sequence relationships. However, training these models with the Mean Square Error (MSE) loss often results in over-smooth predictions, making it challenging to handle the complexity and learn high-entropy features from time series data with high variability and unpredictability. In this work, we introduce a novel approach by tokenizing time series values to train forecasting models via cross-entropy loss, while considering the continuous nature of time series data. Specifically, we propose

Styles APA, Harvard, Vancouver, ISO, etc.

30

Wu, Si, and Shun-ichi Amari. "Computing with Continuous Attractors: Stability and Online Aspects." Neural Computation 17, no. 10 (2005): 2215–39. http://dx.doi.org/10.1162/0899766054615626.

Texte intégral

Résumé :

Two issues concerning the application of continuous attractors in neural systems are investigated: the computational robustness of continuous attractors with respect to input noises and the implementation of Bayesian online decoding. In a perfect mathematical model for continuous attractors, decoding results for stimuli are highly sensitive to input noises, and this sensitivity is the inevitable consequence of the system's neutral stability. To overcome this shortcoming, we modify the conventional network model by including extra dynamical interactions between neurons. These interactions vary

Styles APA, Harvard, Vancouver, ISO, etc.

31

Yao, Yongna. "Bayesian network model structure based on binary evolutionary algorithm." PeerJ Computer Science 9 (July 25, 2023): e1466. http://dx.doi.org/10.7717/peerj-cs.1466.

Texte intégral

Résumé :

With the continuous development of new technologies, the scale of training data is also expanding. Machine learning algorithms are gradually beginning to be studied and applied in places where the scale of data is relatively large. Because the current structure of learning algorithms only focus on the identification of dependencies and ignores the direction of dependencies, it causes multiple labeled samples not to identify categories. Multiple labels need to be classified using techniques such as machine learning and then applied to solve the problem. In the environment of more training data,

Styles APA, Harvard, Vancouver, ISO, etc.

32

Wei, Jiacheng, Tong Chen, Haolin Wen, and Haobang Liu. "Time-Varying Reliability Analysis of Integrated Power System Based on Dynamic Bayesian Network." Systems 13, no. 7 (2025): 541. https://doi.org/10.3390/systems13070541.

Texte intégral

Résumé :

In response to the limitations of traditional static reliability analysis methods in characterizing the reliability changes of the Integrated Power System, this paper proposes a time-varying reliability analysis framework based on a Dynamic Bayesian Network. By embedding a multi-physics coupled degradation model into the conditional probability nodes of the Dynamic Bayesian Network, a joint stochastic differential equation for the degradation process was constructed, and the dynamic correlation between continuous degradation and discrete fault events throughout the entire life cycle was achiev

Styles APA, Harvard, Vancouver, ISO, etc.

33

Daud, K., A. Farid Abidin, A. Paud Ismail, M. Daud A. Hasan, M. Affandi Shafie, and A. Ismail. "Evaluating windowing-based continuous S-transform with neural network classifier for detecting and classifying power quality disturbances." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 1136. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp1136-1142.

Texte intégral

Résumé :

The aim of this paper is to evaluate the implementation of windowing-based Continuous S-Transform (CST) techniques, namely, one-cycle and half-cycle windowing with Multi-layer Perception (MLP) Neural Network classifier. Both, the techniques and classifier are used to detect and classify the Power Quality Disturbances (PQDs) into one of possible classes, voltage sag, swell and interrupt disturbance signal. For realizing evaluation, we proposed the methodology that include the PQD generation, the signal detection using windowing-based CST, the features extraction from S-contour matrices, PQD cla

Styles APA, Harvard, Vancouver, ISO, etc.

34

K., Daud, Farid Abidin A., Puad Ismail A., Daud A. Hasan M., Affandi Shafie M., and Ismail A. "Evaluating windowing-based continuous S-transform with neural network classifier for detecting and classifying power quality disturbances." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 1136–42. https://doi.org/10.11591/ijeecs.v13.i3.pp1136-1142.

Texte intégral

Résumé :

The aim of this paper is to evaluate the implementation of windowing-based Continuous S-Transform (CST) techniques, namely, one-cycle and half-cycle windowing with Multi-layer Perception (MLP) Neural Network classifier. Both, the techniques and classifier are used to detect and classify the Power Quality Disturbances (PQDs) into one of possible classes, voltage sag, swell and interrupt disturbance signal. For realizing evaluation, we proposed the methodology that include the PQD generation, the signal detection using windowing-based CST, the features extraction from S-contour matrices, PQD cla

Styles APA, Harvard, Vancouver, ISO, etc.

35

Donnelly, Patrick J., and John W. Sheppard. "Classification of Musical Timbre Using Bayesian Networks." Computer Music Journal 37, no. 4 (2013): 70–86. http://dx.doi.org/10.1162/comj_a_00210.

Texte intégral

Résumé :

In this article, we explore the use of Bayesian networks for identifying the timbre of musical instruments. Peak spectral amplitude in ten frequency windows is extracted for each of 20 time windows to be used as features. Over a large data set of 24,000 audio examples covering the full musical range of 24 different common orchestral instruments, four different Bayesian network structures, including naive Bayes, are examined and compared with two support vector machines and a k-nearest neighbor classifier. Classification accuracy is examined by instrument, instrument family, and data set size.

Styles APA, Harvard, Vancouver, ISO, etc.

36

Zhang, Guoyin, Chengyan Lin, and Yangkang Chen. "Convolutional neural networks for microseismic waveform classification and arrival picking." GEOPHYSICS 85, no. 4 (2020): WA227—WA240. http://dx.doi.org/10.1190/geo2019-0267.1.

Texte intégral

Résumé :

Microseismic data have a low signal-to-noise ratio (S/N). Existing waveform classification and arrival-picking methods are not effective enough for noisy microseismic data with low S/N. We have adopted a novel antinoise classifier for waveform classification and arrival picking by combining the continuous wavelet transform (CWT) and the convolutional neural network (CNN). The proposed CWT-CNN classifier is applied to synthetic and field microseismic data sets. Results show that CWT-CNN classifier has much better performance than the basic deep feedforward neural network (DNN), especially for m

Styles APA, Harvard, Vancouver, ISO, etc.

37

Devni, Prima Sar, Rosadi Dedi, Ronnie Effendie Adhitya, and Danardono. "K-means and bayesian networks to determine building damage levels." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 2 (2019): 719–27. https://doi.org/10.12928/TELKOMNIKA.v17i2.11756.

Texte intégral

Résumé :

Many troubles in life require decision-making with convoluted processes because they are caused by uncertainty about the process of relationships that appear in the system. This problem leads to the creation of a model called the Bayesian Network. Bayesian Network is a Bayesian supported development supported by computing advancements. The Bayesian network has also been developed in various fields. At this time, information can implement Bayesian Networks in determining the extent of damage to buildings using individual building data. In practice, there is mixed data which is a combination of

Styles APA, Harvard, Vancouver, ISO, etc.

38

Badr, Ahmed, Ahmed Yosri, Sonia Hassini, and Wael El-Dakhakhni. "Coupled Continuous-Time Markov Chain–Bayesian Network Model for Dam Failure Risk Prediction." Journal of Infrastructure Systems 27, no. 4 (2021): 04021041. http://dx.doi.org/10.1061/(asce)is.1943-555x.0000649.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

39

Alonso-Tovar, José, Baidya Nath Saha, Jesús Romero-Hdz, and David Ortega. "Bayesian Network Classifier with Efficient Statistical Time-Series Features for the Classification of Robot Execution Failures." International Journal of Computer Science and Engineering 3, no. 11 (2016): 80–89. http://dx.doi.org/10.14445/23488387/ijcse-v3i11p114.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

40

Liu, Jianyu, Linxue Zhao, and Yanlong Mao. "Bayesian regularized NAR neural network based short-term prediction method of water consumption." E3S Web of Conferences 118 (2019): 03024. http://dx.doi.org/10.1051/e3sconf/201911803024.

Texte intégral

Résumé :

With the continuous construction of urban water supply infrastructure, it is extremely urgent to change the management mode of water supply from traditional manual experience to modern and efficient means. The water consumption forecast is the premise of water supply scheduling, and its accuracy also directly affects the effectiveness of water supply scheduling. This paper analyzes the regularity of water consumption time series, establishes a short-term water consumption prediction model based on Bayesian regularized NAR neural network, and compares and evaluates the prediction effect of the

Styles APA, Harvard, Vancouver, ISO, etc.

41

Beaudry, Eric, Froduald Kabanza, and Francois Michaud. "Planning for Concurrent Action Executions Under Action Duration Uncertainty Using Dynamically Generated Bayesian Networks." Proceedings of the International Conference on Automated Planning and Scheduling 20 (May 25, 2021): 10–17. http://dx.doi.org/10.1609/icaps.v20i1.13400.

Texte intégral

Résumé :

An interesting class of planning domains, including planning for daily activities of Mars rovers, involves achievement of goals with time constraints and concurrent actions with probabilistic durations. Current probabilistic approaches, which rely on a discrete time model, introduce a blow up in the search state-space when the two factors of action concurrency and action duration uncertainty are combined. Simulation-based and sampling probabilistic planning approaches would cope with this state explosion by avoiding storing all the explored states in memory, but they remain approximate solutio

Styles APA, Harvard, Vancouver, ISO, etc.

42

Conte, Claudia, Giorgio de Alteriis, Rosario Schiano Lo Moriello, Domenico Accardo, and Giancarlo Rufino. "Drone Trajectory Segmentation for Real-Time and Adaptive Time-Of-Flight Prediction." Drones 5, no. 3 (2021): 62. http://dx.doi.org/10.3390/drones5030062.

Texte intégral

Résumé :

This paper presents a method developed to predict the flight-time employed by a drone to complete a planned path adopting a machine-learning-based approach. A generic path is cut in properly designed corner-shaped standard sub-paths and the flight-time needed to travel along a standard sub-path is predicted employing a properly trained neural network. The final flight-time over the complete path is computed summing the partial results related to the standard sub-paths. Real drone flight-tests were performed in order to realize an adequate database needed to train the adopted neural network as

Styles APA, Harvard, Vancouver, ISO, etc.

43

Hosoda, Shion, Tsukasa Fukunaga, and Michiaki Hamada. "Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model." Bioinformatics 37, Supplement_1 (2021): i16—i24. http://dx.doi.org/10.1093/bioinformatics/btab287.

Texte intégral

Résumé :

Abstract Motivation Accumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka–Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions. Results In this study, we developed unsupervised learning-based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying micro

Styles APA, Harvard, Vancouver, ISO, etc.

44

Babu, Bollikonda Vinod, and Kiran K V D. "Lyrebird Green Anaconda Optimization based Bayesian Hierarchical Neural Attention Harmonic Network for Illicit Dark Web Classification." Journal of Trends in Computer Science and Smart Technology 7, no. 2 (2025): 240–65. https://doi.org/10.36548/jtcsst.2025.2.007.

Texte intégral

Résumé :

The quick development of internet technology has opened the door for a number of illegal activities targeted at users. The fact that these malevolent actions are usually carried out by anonymous people or organizations makes identification and tracking more difficult. In order to address these problems, a novel method for categorizing illegal dark content has been created called the Lyrebird Green Anaconda Optimization-based Bayesian Hierarchical Neural Attention Harmonic Network (LGAO_BHNAHN). To find and extract pertinent information, textural content extraction is done first. Following that

Styles APA, Harvard, Vancouver, ISO, etc.

45

Lee, Boon-Giin, and Wan-Young Chung. "MULTI-CLASSIFIER FOR HIGHLY RELIABLE DRIVER DROWSINESS DETECTION IN ANDROID PLATFORM." Biomedical Engineering: Applications, Basis and Communications 24, no. 02 (2012): 147–54. http://dx.doi.org/10.4015/s1016237212500159.

Texte intégral

Résumé :

For the past decade, it is well defined in the literature that fatigue is one of the most prospective factor in affecting the driver behavior. This paper presents a novel evaluation of driver fatigue condition based on multi-classifier technique and fusion of attributes approach. The process involved fusion of attributes including image of eye movement and photoplethysmography (PPG) signals that are given as inputs to multi-classifier. In order to develop the best inference classifiers, artificial neural network (ANN), dynamic bayesian network (DBN), support vector machine (SVM), independent c

Styles APA, Harvard, Vancouver, ISO, etc.

46

WU, CHUNG-HSIEN, JHING-FA WANG, CHAUG-CHING HUANG, and JAU-YIEN LEE. "SPEAKER-INDEPENDENT RECOGNITION OF ISOLATED WORDS USING CONCATENATED NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 05 (1991): 693–714. http://dx.doi.org/10.1142/s0218001491000417.

Texte intégral

Résumé :

A speaker-independent isolated word recognizer is proposed. It is obtained by concatenating a Bayesian neural network and a Hopfield time-alignment network. In this system, the Bayesian network outputs the a posteriori probability for each speech frame, and the Hopfield network is then concatenated for time warping. A proposed splitting Learning Vector Quantization (LVQ) algorithm derived from the LBG clustering algorithm and the Kohonen LVQ algorithm is first used to train the Bayesian network. The LVQ2 algorithm is subsequently adopted as a final refinement step. A continuous mixture of Gaus

Styles APA, Harvard, Vancouver, ISO, etc.

47

Wei, Xiaohan, Yulai Zhang, and Cheng Wang. "Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks." Entropy 24, no. 10 (2022): 1351. http://dx.doi.org/10.3390/e24101351.

Texte intégral

Résumé :

Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables a

Styles APA, Harvard, Vancouver, ISO, etc.

48

Meenachi, Loganathan, and Srinivasan Ramakrishnan. "Random Global and Local Optimal Search Algorithm Based Subset Generation for Diagnosis of Cancer." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 3 (2020): 249–61. http://dx.doi.org/10.2174/1573405614666180720152838.

Texte intégral

Résumé :

Background: Data mining algorithms are extensively used to classify the data, in which prediction of disease using minimal computation time plays a vital role. Objective: The aim of this paper is to develop the classification model from reduced features and instances. Methods: In this paper we proposed four search algorithms for feature selection the first algorithm is Random Global Optimal (RGO) search algorithm for searching the continuous, global optimal subset of features from the random population. The second is Global and Local Optimal (GLO) search algorithm for searching the global and

Styles APA, Harvard, Vancouver, ISO, etc.

49

Acerbi, Enzo, Marcela Hortova-Kohoutkova, Tsokyi Choera, et al. "Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism." Journal of Fungi 6, no. 3 (2020): 108. http://dx.doi.org/10.3390/jof6030108.

Texte intégral

Résumé :

Systems biology approaches are extensively used to model and reverse-engineer gene regulatory networks from experimental data. Indoleamine 2,3-dioxygenases (IDOs)—belonging in the heme dioxygenase family—degrade l-tryptophan to kynurenines. These enzymes are also responsible for the de novo synthesis of nicotinamide adenine dinucleotide (NAD+). As such, they are expressed by a variety of species, including fungi. Interestingly, Aspergillus may degrade l-tryptophan not only via IDO but also via alternative pathways. Deciphering the molecular interactions regulating tryptophan metabolism is part

Styles APA, Harvard, Vancouver, ISO, etc.

50

Kridalukmana, Rinta, Dania Eridani, and Risma Septiana. "A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent." Jurnal Ilmu Komputer dan Informasi 17, no. 1 (2024): 59–66. http://dx.doi.org/10.21609/jiki.v17i1.1199.

Texte intégral

Résumé :

Predicting immediate future actions taken by an intelligent agent is considered an essential problem inhuman-autonomy teaming (HAT) in many fields, such as industries and transportation, particularly toimprove human comprehension of the agent as their non-human counterpart. Moreover, the results of suchpredictions can shorten the human response time to gain control back from their non-human counterpartwhen it is required. An example case of HAT that can be benefitted from the action predictor is partiallyautomated driving with the autopilot agent as the intelligent agent. Hence, this research

Styles APA, Harvard, Vancouver, ISO, etc.

Nous offrons des réductions sur tous les plans premium pour les auteurs dont les œuvres sont incluses dans des sélections littéraires thématiques. Contactez-nous pour obtenir un code promo unique!