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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.
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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.
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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 update interval common to discrete-time models. We build generative models from the normal training data, and abnormal behaviors are flagged based on their likelihood under this norm. For NIDS, we construct a hierarchical CTBN model for the network packet traces and use Rao-Blackwellized particle filtering to learn the parameters. We illustrate the power of our method through experiments on detecting real worms and identifying hosts on two publicly available network traces, the MAWI dataset and the LBNL dataset. For HIDS, we develop a novel learning method to deal with the finite resolution of system log file time stamps, without losing the benefits of our continuous time model. We demonstrate the method by detecting intrusions in the DARPA 1998 BSM dataset.
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Sturlaugson, Liessman, and John W. Sheppard. "Sensitivity Analysis of Continuous Time Bayesian Network Reliability Models." SIAM/ASA Journal on Uncertainty Quantification 3, no. 1 (January 2015): 346–69. http://dx.doi.org/10.1137/140953848.
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Codecasa, Daniele, and Fabio Stella. "Classification and clustering with continuous time Bayesian network models." Journal of Intelligent Information Systems 45, no. 2 (November 22, 2014): 187–220. http://dx.doi.org/10.1007/s10844-014-0345-0.
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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 (April 3, 2020): 3259–66. http://dx.doi.org/10.1609/aaai.v34i04.5725.
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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 demonstrate the power of the representation by applying it to model paths out of poverty for clients of CityLink Center, an integrated social service provider in Cincinnati, USA. Here the ECTBN formulation captures the effect of classes/counseling sessions on an individual's life outcome areas such as education, transportation, employment and financial education.
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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 (October 17, 2022): 10443. http://dx.doi.org/10.3390/app122010443.
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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 limitations by relying on a mixed-attribute fusion mechanism with an improved autoencoder neural network for NBC construction. MAF-NBC transforms the original mixed attributes of a data set into a series of encoded attributes with maximum independence as a pre-processing step. To guarantee the generation of useful encoded attributes, an efficient objective function is designed to optimize the weights of the autoencoder neural network by considering both the encoding error and the attribute’s dependence. A series of persuasive experiments was conducted to validate the feasibility, rationality, and effectiveness of the designed MAF-NBC approach. Results demonstrate that MAF-NBC has superior classification performance than eight state-of-the-art Bayesian algorithms, namely the discretization-based NBC (Dis-NBC), flexible naive Bayes (FNB), tree-augmented naive (TAN) Bayes, averaged one-dependent estimator (AODE), hidden naive Bayes (HNB), deep feature weighting for NBC (DFW-NBC), correlation-based feature weighting filter for NBC (CFW-NBC), and independent component analysis-based NBC (ICA-NBC).
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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.
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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 used to detect the mobile malware network behavior. The experiment result shows that Behavior Categorization based Naïve Bayesian Classifier (BCNBC) can improve the detection accuracy and it can meet the requirement of real time process in mobile internet.
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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 (April 2020): 341–50. http://dx.doi.org/10.1051/jnwpu/20203820341.
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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 dataset, and the sub-classifier is constructed based on Bayesian network model. Then, the multi-window dynamic Bayesian network classifier model is designed to enable the early identification of elephant flow. The simulation results show that compared with the existing elephant flow identification method, the proposed method can effectively improve the timeliness of identification under the condition of ensuring the accuracy of identification.
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Boudali, H., and J. B. Dugan. "A Continuous-Time Bayesian Network Reliability Modeling, and Analysis Framework." IEEE Transactions on Reliability 55, no. 1 (March 2006): 86–97. http://dx.doi.org/10.1109/tr.2005.859228.
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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 (December 8, 2011): 496–515. http://dx.doi.org/10.1007/s10696-011-9131-2.
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Song, Rongjia, Lei Huang, Weiping Cui, María Óskarsdóttir, and Jan Vanthienen. "Fraud Detection of Bulk Cargo Theft in Port Using Bayesian Network Models." Applied Sciences 10, no. 3 (February 5, 2020): 1056. http://dx.doi.org/10.3390/app10031056.
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The fraud detection of cargo theft has been a serious issue in ports for a long time. Traditional research in detecting theft risk is expert- and survey-based, which is not optimal for proactive prediction. As we move into a pervasive and ubiquitous paradigm, the implications of external environment and system behavior are continuously captured as multi-source data. Therefore, we propose a novel data-driven approach for formulating predictive models for detecting bulk cargo theft in ports. More specifically, we apply various feature-ranking methods and classification algorithms for selecting an effective feature set of relevant risk elements. Then, implicit Bayesian networks are derived with the features to graphically present the relationship with the risk elements of fraud. Thus, various binary classifiers are compared to derive a suitable predictive model, and Bayesian network performs best overall. The resulting Bayesian networks are then comparatively analyzed based on the outcomes of model validation and testing, as well as essential domain knowledge. The experimental results show that predictive models are effective, with both accuracy and recall values greater than 0.8. These predictive models are not only useful for understanding the dependency between relevant risk elements, but also for supporting the strategy optimization of risk management.
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Liu, Yang, Limin Wang, and Minghui Sun. "Efficient Heuristics for Structure Learning of k-Dependence Bayesian Classifier." Entropy 20, no. 12 (November 22, 2018): 897. http://dx.doi.org/10.3390/e20120897.
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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-model by removing redundant features and arcs in the Bayesian network. Experimental results on 40 UCI datasets demonstrate that these two techniques are complementary and the proposed algorithm achieves competitive classification performance, and less classification time than other state-of-the-art Bayesian network classifiers like tree-augmented naive Bayes and averaged one-dependence estimators.
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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.
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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 wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN) based and multilayer feed forward (MLF) neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.
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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.
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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 then move to structured Markov processes (those arising from state spaces consisting of assignments to variables) including Kronecker, decision-diagram, and continuous-time Bayesian network representations. We provide the first connection between decision-diagrams and continuous-time Bayesian networks.
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Batenkov, Aleksandr, Kirill Batenkov, Andrey Bogachev, and Vladislav Mishin. "Mathematical Model of Object Classifier based on Bayesian Approach." Informatics and Automation 19, no. 6 (December 11, 2020): 1166–97. http://dx.doi.org/10.15622/ia.2020.19.6.2.
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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 class, and representative (reference) elements within each class. The development of the feature classifier is based on the synthesis of two-dimensional probability density in the coordinate space: classes-objects. Using generalized functions, the probabilistic problem of finding the minimum Bayesian risk is reduced to a deterministic problem on a set of non-randomized classifiers. At the same time, the use of specially introduced constraints fixes non-randomized decision rules and plunges the integer problem of nonlinear programming into a General continuous nonlinear problem. For correct synthesis of the classifier, the dispersion curve of the isotropic sample is necessary. It is necessary to use the total intra-class and inter-class variance to characterize the quality of classification. The classification problem can be interpreted as a particular problem of the theory of catastrophes. Under the conditions of limited initial data, a minimax functional was found that reflects the quality of classification for a quadratic loss function. The developed mathematical model is classified as an integer nonlinear programming problem. The model is given using polynomial constraints to the form of a General problem of nonlinear continuous programming. The necessary conditions for the bundle into classes are found. These conditions can be used as sufficient when testing the hypothesis about the existence of classes.
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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 (January 1, 2022): 012049. http://dx.doi.org/10.1088/1742-6596/2171/1/012049.
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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 classification is widely used in statistical method because of its convenience and good classification effect.
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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 (January 2016): 76–93. http://dx.doi.org/10.4018/ijehmc.2016010105.
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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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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 (March 1, 2019): 1136. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp1136-1142.
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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 classification using MLP classifier. Then, we perform two type of assessments. Firstly, the accuracy assessment of chosen classifier in relation to three different training algorithms. Secondly, the execution time comparison of the training algorithms. Based on assessment results, we outline several recommendations for future work.
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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.
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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 existing studies and (ii) the definition of an ensemble learning approach, made up of basic deep neural network classifiers, with the objective of distinguishing the different users of the considered dataset by exploiting a proper group of features able to capture their typing style. After an optimization phase, in order to find the best possible base classifier, we evaluated the ensemble super-classifier comparing different voting techniques, namely majority and Bayesian, as well as training allocation strategies, i.e., random and K-means. The approach we propose has been assessed using the created very large integrated dataset and the obtained results are very promising, achieving an accuracy of up to 0.997 under certain evaluation conditions.
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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 (December 2021): 04021041. http://dx.doi.org/10.1061/(asce)is.1943-555x.0000649.
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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 (November 25, 2016): 80–89. http://dx.doi.org/10.14445/23488387/ijcse-v3i11p114.
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Wu, Si, and Shun-ichi Amari. "Computing with Continuous Attractors: Stability and Online Aspects." Neural Computation 17, no. 10 (October 1, 2005): 2215–39. http://dx.doi.org/10.1162/0899766054615626.
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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 according to the biologically plausible Hebbian learning rule and have the computational role of memorizing and propagating stimulus information accumulated with time. As a result, the new network model responds to the history of external inputs over a period of time, and hence becomes insensitive to short-term fluctuations. Also, since dynamical interactions provide a mechanism to convey the prior knowledge of stimulus, that is, the information of the stimulus presented previously, the network effectively implements online Bayesian inference. This study also reveals some interesting behavior in neural population coding, such as the trade-off between decoding stability and the speed of tracking time-varying stimuli, and the relationship between neural tuning width and the tracking speed.
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Zhang, Guoyin, Chengyan Lin, and Yangkang Chen. "Convolutional neural networks for microseismic waveform classification and arrival picking." GEOPHYSICS 85, no. 4 (June 13, 2020): WA227—WA240. http://dx.doi.org/10.1190/geo2019-0267.1.
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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 microseismic data with low S/N. The CWT-CNN classifier has a shallow network architecture and small learning data set, and it can be trained quickly for different data sets. We have determined why CWT-CNN has better performance for noisy microseismic data. CWT can decompose the microseismic data into time-frequency spectra, where effective signals and interfering noise are easier to distinguish. With the help of CWT, CNN can focus on the specific frequency components to extract useful features and build a more effective classifier.
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Donnelly, Patrick J., and John W. Sheppard. "Classification of Musical Timbre Using Bayesian Networks." Computer Music Journal 37, no. 4 (December 2013): 70–86. http://dx.doi.org/10.1162/comj_a_00210.
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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. Bayesian networks with conditional dependencies in the time and frequency dimensions achieved 98 percent accuracy in the instrument classification task and 97 percent accuracy in the instrument family identification task. These results demonstrate a significant improvement over the previous approaches in the literature on this data set. Additionally, we tested our Bayesian approach on the widely used Iowa musical instrument data set, with similar results.
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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 (July 1, 2021): i16—i24. http://dx.doi.org/10.1093/bioinformatics/btab287.
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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 microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota. Availability and implementation The C++ and python source codes of the Umibato software are available at https://github.com/shion-h/Umibato. Supplementary information Supplementary data are available at Bioinformatics online.
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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 (July 16, 2021): 62. http://dx.doi.org/10.3390/drones5030062.
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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 a classifier, employing the Bayesian regularization backpropagation algorithm as training function. For the network, the relative angle between two sides of a corner and the wind condition are the inputs, while the flight-time over the corner is the output parameter. Then, generic paths were designed and performed to test the method. The total flight-time as resulting from the drone telemetry was compared with the flight-time predicted by the developed method based on machine learning techniques. At the end of the paper, the proposed method was demonstrated as effective in predicting possible collisions among drones flying intersecting paths, as a possible application to support the development of unmanned traffic management procedures.
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Haghayegh, Shahab, Kun Hu, Katie Stone, Susan Redline, and Eva Schernhammer. "Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study." Journal of Medical Internet Research 25 (February 10, 2023): e40211. http://dx.doi.org/10.2196/40211.
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Background Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and follow the epoch. Objective We proposed and tested a convolutional neural network called SleepInceptionNet, which allows sleep classification of a single epoch using a single-channel electroencephalogram (EEG). Methods SleepInceptionNet is based on our systematic evaluation of the effects of different EEG preprocessing methods, EEG channels, and convolutional neural networks on automatic sleep staging performance. The evaluation was performed using polysomnography data of 883 participants (937,975 thirty-second epochs). Raw data of individual EEG channels (ie, frontal, central, and occipital) and 3 specific transformations of the data, including power spectral density, continuous wavelet transform, and short-time Fourier transform, were used separately as the inputs of the convolutional neural network models. To classify sleep stages, 7 sequential deep neural networks were tested for the 1D data (ie, raw EEG and power spectral density), and 16 image classifier convolutional neural networks were tested for the 2D data (ie, continuous wavelet transform and short-time Fourier transform time-frequency images). Results The best model, SleepInceptionNet, which uses time-frequency images developed by the continuous wavelet transform method from central single-channel EEG data as input to the InceptionV3 image classifier algorithm, achieved a Cohen κ agreement of 0.705 (SD 0.077) in reference to the gold standard polysomnography. Conclusions SleepInceptionNet may allow real-time automated sleep staging in free-living conditions using a single-channel EEG, which may be useful for on-demand intervention or treatment during specific sleep stages.
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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 (April 2012): 147–54. http://dx.doi.org/10.4015/s1016237212500159.
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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 component analysis (ICA) and genetic algorithm (GA) were tested in our study. The output from each inference classifier are scaled and product in an intervention module to indicate driver aptitude in real-time. Implementation of monitoring system is practically designed in Android-based smartphone device where it can received all the sensory information from the dedicated sensors installed at the steering wheel via a small scale wireless sensor network. Device built-in front camera was utilized to capture driver facial image. No supplementary monitor is required to be installed in the vehicle as the all the information is to be displayed on the smartphone device itself. Warning system is triggered to warn driver once fatigue is suspected. System testing statistical results revealed that the manifold used of the proposed system demonstrates the advantages of performing information fusion, particularly with discrete methods, and the multi-classifier enabled a more authentic and ample driver fatigue evaluation.
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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.
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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 solution approaches. In this paper, we present an alternative approach relying on a continuous time model which avoids the state explosion caused by time stamping in the presence of action concurrency and action duration uncertainty. Time is represented as a continuous random variable. The dependency between state time variables is conveyed by a Bayesian network, which is dynamically generated by a state-based forward-chaining search based on the action descriptions. A generated plan is characterized by a probability of satisfying a goal. The evaluation of this probability is done by making a query the Bayesian network.
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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 (March 2, 2020): 249–61. http://dx.doi.org/10.2174/1573405614666180720152838.
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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 local optimal subset of features from population. The third one is Random Local Optimal (RLO) search algorithm for generating random, local optimal subset of features from the random population. Finally the Random Global and Optimal (RGLO) search algorithm for searching the continuous, global and local optimal subset of features from the random population. RGLO search algorithm combines the properties of first three stated algorithm. The subsets of features generated from the proposed four search algorithms are evaluated using the consistency based subset evaluation measure. Instance based learning algorithm is applied to the resulting feature dataset to reduce the instances that are redundant or irrelevant for classification. The model developed using naïve Bayesian classifier from the reduced features and instances is validated with the tenfold cross validation. Results: Classification accuracy based on RGLO search algorithm using naïve Bayesian classifier is 94.82% for Breast, 97.4% for DLBCL, 98.83% for SRBCT and 98.89% for Leukemia datasets. Conclusion: The RGLO search based reduced features results in the high prediction rate with less computational time when compared with the complete dataset and other proposed subset generation algorithm.
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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.
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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 model. The verification results show that the Bayesian based NAR neural network prediction model has higher adaptability to the water consumption prediction than the standard BP neural network and the Bayesian regularized BP neural network. The prediction accuracy can more accurately reflect the short-term variation of water consumption.
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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 (September 24, 2022): 1351. http://dx.doi.org/10.3390/e24101351.
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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 are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly.
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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 (December 1991): 693–714. http://dx.doi.org/10.1142/s0218001491000417.
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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 Gaussian densities for each frame and multi-templates for each word are employed to characterize each word pattern. Experimental evaluation of this system with four templates/word and five mixtures/frame, using 53 speakers (28 males, 25 females) and isolated words (10 digits and 30 city names) databases, gave average recognition accuracies of 97.3%, for the speaker-trained mode and 95.7% for the speaker-independent mode, respectively. Comparisons with K-means and DTW algorithms show that the integration of the splitting LVQ and LVQ2 algorithms makes this system well suited to speaker-independent isolated word recognition. A cookbook approach for the determination of parameters in the Hopfield time-alignment network is also described.
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Preen, Richard J., and Larry Bull. "Dynamical Genetic Programming in XCSF." Evolutionary Computation 21, no. 3 (September 2013): 361–87. http://dx.doi.org/10.1162/evco_a_00080.
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A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic representation within the XCSF learning classifier system. In particular, dynamical arithmetic networks are used to represent the traditional condition-action production system rules to solve continuous-valued reinforcement learning problems and to perform symbolic regression, finding competitive performance with traditional genetic programming on a number of composite polynomial tasks. In addition, the network outputs are later repeatedly sampled at varying temporal intervals to perform multistep-ahead predictions of a financial time series.
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Abdelwahab, Amira, and Mohamed Mostafa. "A Deep Neural Network Technique for Detecting Real-Time Drifted Twitter Spam." Applied Sciences 12, no. 13 (June 23, 2022): 6407. http://dx.doi.org/10.3390/app12136407.
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The social network is considered a part of most user’s lives as it contains more than a billion users, which makes it a source for spammers to spread their harmful activities. Most of the recent research focuses on detecting spammers using statistical features. However, such statistical features are changed over time, and spammers can defeat all detection systems by changing their behavior and using text paraphrasing. Therefore, we propose a novel technique for spam detection using deep neural network. We combine the tweet level detection with statistical feature detection and group their results over meta-classifier to build a robust technique. Moreover, we embed our technique with initial text paraphrasing for each detected tweet spam. We train our model using different datasets: random, continuous, balanced, and imbalanced. The obtained experimental results showed that our model has promising results in terms of accuracy, precision, and time, which make it applicable to be used in social networks.
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Acerbi, Enzo, Marcela Hortova-Kohoutkova, Tsokyi Choera, Nancy Keller, Jan Fric, Fabio Stella, Luigina Romani, and Teresa Zelante. "Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism." Journal of Fungi 6, no. 3 (July 14, 2020): 108. http://dx.doi.org/10.3390/jof6030108.
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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 particularly critical for novel drug target discovery designed to control pathogen determinants in invasive infections. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling l-tryptophan metabolism. The method unravels a possible novel approach to target fungal virulence factors during infection. Furthermore, this study represents the first application of continuous-time Bayesian networks as a gene network reconstruction method in Aspergillus metabolism. The experiment showed that the applied computational approach may improve the understanding of metabolic networks over traditional pathways.
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Li, Cailing, and Wenjun Li. "Automatic Classification Algorithm for Multisearch Data Association Rules in Wireless Networks." Wireless Communications and Mobile Computing 2021 (March 17, 2021): 1–9. http://dx.doi.org/10.1155/2021/5591387.
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In order to realize efficient data processing in wireless network, this paper designs an automatic classification algorithm of multisearch data association rules in a wireless network. According to the algorithm, starting from the mining of multisearch data association rules, from the discretization of continuous attributes of multisearch data, generation of fuzzy classification rules, and the design of association rule classifier and other aspects, automatic classification is completed by using the mining results. Experimental results show that this algorithm has the advantages of small classification error, good real-time performance, high coverage rate, and high feasibility.
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Kolář, Jakub, Jan Sýkora, and Petr Hron. "Update-Based Machine Learning Classification of Hierarchical Symbols in a Slowly Varying Two-Way Relay Channel." Mathematics 8, no. 11 (November 11, 2020): 2007. http://dx.doi.org/10.3390/math8112007.
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This paper presents a stochastic inference problem suited to a classification approach in a time-varying observation model with continuous-valued unknown parameterization. The utilization of an artificial neural network (ANN)-based classifier is considered, and the concept of a training process via the backpropagation algorithm is used. The main objective is the minimization of resources required for the training of the classifier in the parametric observation model. To reach this, it is proposed that the weights of the ANN classifier vary continuously with the change of the observation model parameters. This behavior is then used in an update-based backpropagation algorithm. This proposed idea is demonstrated on several procedures, which re-use previously trained weights as prior information when updating the classifier after a channel phase change. This approach successfully saves resources needed for re-training the ANN. The new approach is verified via a simulation on an example communication system with the two-way relay slowly fading channel.
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Liu, Fucong, Tongzhou Zhang, Caixia Zheng, Yuanyuan Cheng, Xiaoli Liu, Miao Qi, Jun Kong, and Jianzhong Wang. "An Intelligent Multi-View Active Learning Method Based on a Double-Branch Network." Entropy 22, no. 8 (August 17, 2020): 901. http://dx.doi.org/10.3390/e22080901.
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Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN requires a large amount of labeled data to achieve a good performance but labeling data is a time-consuming and laborious work. Since active learning can effectively reduce the labeling effort, we propose a new intelligent active learning method for deep learning, which is called multi-view active learning based on double-branch network (MALDB). Different from most existing active learning methods, our proposed MALDB first integrates two Bayesian convolutional neural networks (BCNNs) with different structures as two branches of a classifier to learn the effective features for each sample. Then, MALDB performs data analysis on unlabeled dataset and queries the useful unlabeled samples based on different characteristics of two branches to iteratively expand the training dataset and improve the performance of classifier. Finally, MALDB combines multiple level information from multiple hidden layers of BCNNs to further improve the stability of sample selection. The experiments are conducted on five extensively used datasets, Fashion-MNIST, Cifar-10, SVHN, Scene-15 and UIUC-Sports, the experimental results demonstrate the validity of our proposed MALDB.
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Rodziewicz, A., and M. Perzyk. "Application of Time-Series Analysis for Predicting Defects in Continuous Steel Casting Process." Archives of Foundry Engineering 16, no. 4 (December 1, 2016): 125–30. http://dx.doi.org/10.1515/afe-2016-0096.
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Abstract The purpose of this paper was testing suitability of the time-series analysis for quality control of the continuous steel casting process in production conditions. The analysis was carried out on industrial data collected in one of Polish steel plants. The production data concerned defective fractions of billets obtained in the process. The procedure of the industrial data preparation is presented. The computations for the time-series analysis were carried out in two ways, both using the authors’ own software. The first one, applied to the real numbers type of the data has a wide range of capabilities, including not only prediction of the future values but also detection of important periodicity in data. In the second approach the data were assumed in a binary (categorical) form, i.e. the every heat(melt) was labeled as ‘Good’ or ‘Defective’. The naïve Bayesian classifier was used for predicting the successive values. The most interesting results of the analysis include good prediction accuracies obtained by both methodologies, the crucial influence of the last preceding point on the predicted result for the real data time-series analysis as well as obtaining an information about the type of misclassification for binary data. The possibility of prediction of the future values can be used by engineering or operational staff with an expert knowledge to decrease fraction of defective products by taking appropriate action when the forthcoming period is identified as critical.
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Moura, Márcio das Chagas, and Enrique López Droguett. "A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems." Pesquisa Operacional 28, no. 2 (August 2008): 355–75. http://dx.doi.org/10.1590/s0101-74382008000200011.
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In this work it is proposed a model for the assessment of availability measure of fault tolerant systems based on the integration of continuous time semi-Markov processes and Bayesian belief networks. This integration results in a hybrid stochastic model that is able to represent the dynamic characteristics of a system as well as to deal with cause-effect relationships among external factors such as environmental and operational conditions. The hybrid model also allows for uncertainty propagation on the system availability. It is also proposed a numerical procedure for the solution of the state probability equations of semi-Markov processes described in terms of transition rates. The numerical procedure is based on the application of Laplace transforms that are inverted by the Gauss quadrature method known as Gauss Legendre. The hybrid model and numerical procedure are illustrated by means of an example of application in the context of fault tolerant systems.