Journal articles on the topic 'Continuous time Bayesian network classifiers'
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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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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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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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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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Ruz, Gonzalo A., and Pamela Araya-Díaz. "Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers." Complexity 2018 (December 2, 2018): 1–14. http://dx.doi.org/10.1155/2018/4075656.
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Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization. We apply Bayesian network classifiers to the facial biotype classification problem, an important stage during orthodontic treatment planning. For this, we present adaptations of classical Bayesian networks classifiers to handle continuous attributes; also, we propose an incremental tree construction procedure for tree like Bayesian network classifiers. We evaluate the performance of the proposed adaptations and compare them with other continuous Bayesian network classifiers approaches as well as support vector machines. The results under the classification performance measures, accuracy and kappa, showed the effectiveness of the continuous Bayesian network classifiers, especially for the case when a reduced number of attributes were used. Additionally, the resulting networks allowed visualizing the probability relations amongst the attributes under this classification problem, a useful tool for decision-making for orthodontists.
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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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Shih, Andy, Arthur Choi, and Adnan Darwiche. "Compiling Bayesian Network Classifiers into Decision Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7966–74. http://dx.doi.org/10.1609/aaai.v33i01.33017966.
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We propose an algorithm for compiling Bayesian network classifiers into decision graphs that mimic the input and output behavior of the classifiers. In particular, we compile Bayesian network classifiers into ordered decision graphs, which are tractable and can be exponentially smaller in size than decision trees. This tractability facilitates reasoning about the behavior of Bayesian network classifiers, including the explanation of decisions they make. Our compilation algorithm comes with guarantees on the time of compilation and the size of compiled decision graphs. We apply our compilation algorithm to classifiers from the literature and discuss some case studies in which we show how to automatically explain their decisions and verify properties of their behavior.
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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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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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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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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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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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Mohamed, Abduljalil, Khaled Bashir Shaban, and Amr Mohamed. "Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals." International Journal of Knowledge Discovery in Bioinformatics 3, no. 2 (April 2012): 27–44. http://dx.doi.org/10.4018/jkdb.2012040103.
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Different brain states and conditions can be captured by electroencephalogram (EEG) signals. EEG-based epileptic seizure detection techniques often reduce these signals into sets of discriminant features. In this work, an evidence theory-based approach for epileptic detection, using several classifiers, is proposed. Within the framework of the evidence theory, each of these classifiers is considered a source of information and given a certain weight based on both its overall classification accuracy as well as its precision rate for the respective brain state. These sources are fused using the Dempster’s rule of combination. Experimental work is done where five time domain features are obtained from EEG signals and used by a set classifiers, namely, Bayesian, K-nearest neighbor, neural network, linear discriminant analysis, and support vector machine classifiers. Higher classification accuracy of 89.5% is achieved, compared to 75.07% and 87.71% accuracy obtained from the worst and best used classifiers.
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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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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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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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Wang, Yuxin, Svetlana Avdeenko, and Yuriy Shmidt. "Evaluating the Efficiency of the Classifier Method When Analysing the Sales Data of Agricultural Products." Asian Journal of Water, Environment and Pollution 19, no. 1 (January 19, 2022): 41–46. http://dx.doi.org/10.3233/ajw220007.
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Data classification as a method of input analysis is of the greatest interest and necessity for proper distribution and quality evaluation of agricultural products. The use of classification methods allows predicting whether a selected sample from the data set will fit into a particular class or group, which is necessary for the process of sorting products. This study presents the results of a comparative analysis of high-performance classifiers for assessing the effectiveness of further use in the sorting of agricultural products. The study was carried out utilising the classifiers of k-nearest neighbours, naive Bayesian classifiers, and artificial neural networks for data analysis during apple fruit sorting. It has been established that the greatest accuracy 99% of the results is demonstrated by the classifiers of k-nearest neighbours, but, at the same time, they show the lowest calculation speed (0.47 s). The best performance at any data size (65-100%) is shown by the neural network. A comprehensive review of the features and restrictions of the studied classification algorithms, as well as their applications in various areas of agriculture, has been performed.
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Xie, Yingmei. "Application of Unbalanced Data Classification Based on CSD-ELM in English Network Teaching Mode." Wireless Communications and Mobile Computing 2022 (February 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/8351806.
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In order to accurately predict the frame loss phenomenon, this study proposes a continuous frame loss test for CSD-ELM short-range wireless communication. First, the noise and trend direction of CSD-ELM are removed, the preprocessing of the data is completed, then, these data are injected into the BP neural network as a training sample, continuous frame loss tests are performed on the communication data, the results are output, and the calculation formula is obtained. The test results show that the method has higher accuracy, lower false alarm rate, and shorter test time and is suitable for the detection of continuous data frame loss in short-distance wireless communication. Then, this study proposes an integrated WELM algorithm, which combines the features of AdaBoost, which can generate strong classifiers through the integration of weak classifiers, so as to achieve the best performance for imbalanced data classification. The algorithm assigns different methods to update the weights of different types of samples, so as not to violate the skewness of the weight distribution. The sigmoid function uses error calculation technology, which can separately reflect the classification characteristics of the two samples, and improves the antinoise function of the algorithm. Finally, this study introduces how to analyze and design a college network English learning management system that can meet the requirements of “network system and classroom English teaching mode” in the internet environment. It solves the time limitation of existing English classrooms, expands college English courses to be student centered, creates interactive online classroom spaces, realizes English classrooms regardless of time and region, avoids teachers’ duplication of work, improves teachers’ teaching efficiency, and ultimately promotes the improvement of students’ learning efficiency.
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Zhou, Qianling, Yan Tong, Hongwei Si, and Kai Zhou. "Optimization of Choreography Teaching with Deep Learning and Neural Networks." Computational Intelligence and Neuroscience 2022 (July 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/7242637.
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To improve the development level of intelligent dance education and choreography network technology, the research mainly focuses on the automatic formation system of continuous choreography by using the deep learning method. Firstly, it overcomes the technical difficulty that the dynamic segmentation and process segmentation of the automatic generation architecture in traditional choreography cannot achieve global optimization. Secondly, it is an automatic generation architecture for end-to-end continuous dance notation with access to temporal classifiers. Based on this, a dynamic time-stamping model is designed for frame clustering. Finally, it is concluded through experiments that the model successfully achieves high-performance movement time-stamping. And combined with continuous motion recognition technology, it realizes the refined production of continuous choreography with global motion recognition and then marks motion duration. This research effectively realizes the efficient and refined production of digital continuous choreography, provides advanced technical means for choreography education, and provides useful experience for school network choreography education.
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C. D, Anisha, and Arulanand N. "EMG BASED DIAGNOSIS OF MYOPATHY AND NEUROPATHY USING MACHINE LEARNING TECHNIQUES." International Journal of Engineering Technology and Management Sciences 4, no. 4 (July 28, 2020): 38–45. http://dx.doi.org/10.46647/ijetms.2020.v04i04.007.
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Myopathy and Neuropathy are non-progressive and progressive neuromuscular disorders which weakens the muscles and nerves respectively. Electromyography (EMG) signals are bio signals obtained from the individual muscle cells. EMG based diagnosis for neuromuscular disorders is a safe and reliable method. Integrating the EMG signals with machine learning techniques improves the diagnostic accuracy. The proposed system performs analysis on the clinical raw EMG dataset which is obtained from the publicly available PhysioNet database. The two-channel raw EMG dataset of healthy, myopathy and neuropathy subjects are divided into samples. The Time Domain (TD) features are extracted from divided samples of each subject. The extracted features are annotated with the class label representing the state of the individual. The annotated features split into training and testing set in the standard ratio 70: 30. The comparative classification analysis on the complete annotated features set and prominent features set procured using Pearson correlation technique is performed. The features are scaled using standard scaler technique. The analysis on scaled annotated features set and scaled prominent features set is also implemented. The hyperparameter space of the classifiers are given by trial and error method. The hyperparameters of the classifiers are tuned using Bayesian optimization technique and the optimal parameters are obtained. and are fed to the tuned classifier. The classification algorithms considered in the analysis are Random Forest and Multi-Layer Perceptron Neural Network (MLPNN). The performance evaluation of the classifiers on the test data is computed using the Accuracy, Confusion Matrix, F1 Score, Precision and Recall metrics. The evaluation results of the classifiers states that Random Forest performs better than MLPNN wherein it provides an accuracy of 96 % with non-scaled Time Domain (TD) features and MLPNN outperforms better than Random Forest with an accuracy of 97% on scaled Time Domain (TD) features which is higher than the existing systems. The inferences from the evaluation results is that Bayesian optimization tuned classifiers improves the accuracy which provides a robust diagnostic model for neuromuscular disorder diagnosis.
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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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Jackson, Rhydon, Debra Knisley, Cecilia McIntosh, and Phillip Pfeiffer. "Predicting Flavonoid UGT Regioselectivity." Advances in Bioinformatics 2011 (June 30, 2011): 1–15. http://dx.doi.org/10.1155/2011/506583.
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Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were proposed and found to be widely distributed among existing amino acid indices and to cluster residues appropriately. UGT subsequences biochemically linked to regioselectivity were modeled as sets of index sequences. Several learning techniques incorporating these UGT models were compared with classifications based on standard sequence alignment scores. These techniques included an application of time series distance functions to protein classification. Time series distances defined on the index sequences were used in nearest neighbor and support vector machine classifiers. Additionally, Bayesian neural network classifiers were applied to the index sequences. The experiments identified improvements over the nearest neighbor and support vector machine classifications relying on standard alignment similarity scores, as well as strong correlations between specific subsequences and regioselectivities.
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Geethanjali, P., and K. K. Ray. "STATISTICAL PATTERN RECOGNITION TECHNIQUE FOR IMPROVED REAL-TIME MYOELECTRIC SIGNAL CLASSIFICATION." Biomedical Engineering: Applications, Basis and Communications 25, no. 02 (April 2013): 1350026. http://dx.doi.org/10.4015/s1016237213500269.
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The authors in this paper propose a statistical technique for pattern recognition of electromyogram (EMG) signals along with effective feature ensemble to achieve an improved classification performance with less processing time and memory space. In this study, EMG signals from 10 healthy subjects and two transradial amputees for six motions of hand and wrist is considered for identification of the intended motion. From four channels myoelectric signals, the extracted time domain features are grouped into three ensembles to identify the effectiveness of feature ensemble in classification. The three feature ensembles obtained from multichannel continuous EMG signals are applied to the new classifiers namely simple logistic regression (SLR), J48 algorithm for decision tree (DT), logistic model tree (LMT) and feature subspace ensemble using k-nearest neighbor (kNN). Novel classifiers SLR, DT and LMT, select only the dominant features during training to develop the model for pattern recognition. This selection of features reduces the processing time as well as memory space of the controller for real-time application. The performance of SLR, DT, LMT and feature subspace ensemble using kNN classifiers are compared with other conventional classifiers, such as neural network (NN), simple kNN and linear discriminant analysis (LDA). The average classification accuracy with SLR is found to be better with feature ensemble-1 compared to the other classifiers. Also, the statistical Kruscal–Wallis test shows, the classification performance of SLR is not only better but also takes less time and memory space compared to other classifiers for classification. Also the performance of the classifier is tested in real-time with transradial amputees for actuation of drive for two intended motions with TMS320F28335eZdsp controller. The experimental results show that the SLR classifier improves the controller response in real-time.
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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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Borchani, Hanen, Concha Bielza, Pablo Martinez-Martin, and Pedro Larrañaga. "PREDICTING THE EQ-5D FROM THE PARKINSON'S DISEASE QUESTIONNAIRE PDQ-8 USING MULTI-DIMENSIONAL BAYESIAN NETWORK CLASSIFIERS." Biomedical Engineering: Applications, Basis and Communications 26, no. 01 (February 2014): 1450015. http://dx.doi.org/10.4015/s101623721450015x.
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The impact of the Parkinson's disease and its treatment on the patients' health-related quality of life can be estimated either by means of generic measures such as the european quality of Life-5 Dimensions (EQ-5D) or specific measures such as the 8-item Parkinson's disease questionnaire (PDQ-8). In clinical studies, PDQ-8 could be used in detriment of EQ-5D due to the lack of resources, time or clinical interest in generic measures. Nevertheless, PDQ-8 cannot be applied in cost-effectiveness analyses which require generic measures and quantitative utility scores, such as EQ-5D. To deal with this problem, a commonly used solution is the prediction of EQ-5D from PDQ-8. In this paper, we propose a new probabilistic method to predict EQ-5D from PDQ-8 using multi-dimensional Bayesian network classifiers. Our approach is evaluated using five-fold cross-validation experiments carried out on a Parkinson's data set containing 488 patients, and is compared with two additional Bayesian network-based approaches, two commonly used mapping methods namely, ordinary least squares and censored least absolute deviations, and a deterministic model. Experimental results are promising in terms of predictive performance as well as the identification of dependence relationships among EQ-5D and PDQ-8 items that the mapping approaches are unable to detect.
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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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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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Möller, A., and T. de Boissière. "SuperNNova: an open-source framework for Bayesian, neural network-based supernova classification." Monthly Notices of the Royal Astronomical Society 491, no. 3 (December 3, 2019): 4277–93. http://dx.doi.org/10.1093/mnras/stz3312.
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ABSTRACT We introduce SuperNNova, an open-source supernova photometric classification framework that leverages recent advances in deep neural networks. Our core algorithm is a recurrent neural network (RNN) that is trained to classify light curves using only photometric information. Additional information such as host-galaxy redshift can be incorporated to improve performance. We evaluate our framework using realistic supernova simulations that include survey detection. We show that our method, for the type Ia versus non-Ia supernova classification problem, reaches accuracies greater than 96.92 ± 0.09 without any redshift information and up to 99.55 ± 0.06 when redshift, either photometric or spectroscopic, is available. Further, we show that our method attains unprecedented performance for the classification of incomplete light curves, reaching accuracies >86.4 ± 0.1 (>93.5 ± 0.8) without host-galaxy redshift (with redshift information) 2 d before maximum light. In contrast with previous methods, there is no need for time-consuming feature engineering and we show that our method scales to very large data sets with a modest computing budget. In addition, we investigate often neglected pitfalls of machine learning algorithms. We show that commonly used algorithms suffer from poor calibration and overconfidence on out-of-distribution samples when applied to supernova data. We devise extensive tests to estimate the robustness of classifiers and cast the learning procedure under a Bayesian light, demonstrating a much better handling of uncertainties. We study the benefits of Bayesian RNNs for SN Ia cosmology. Our code is open sourced and available on github1.
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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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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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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.
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Hasan, Samiul, and Satish V. Ukkusuri. "Reconstructing Activity Location Sequences From Incomplete Check-In Data: A Semi-Markov Continuous-Time Bayesian Network Model." IEEE Transactions on Intelligent Transportation Systems 19, no. 3 (March 2018): 687–98. http://dx.doi.org/10.1109/tits.2017.2700481.
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Cho, Jaeik, Seonghyeon Gong, and Ken Choi. "A Study on High-Speed Outlier Detection Method of Network Abnormal Behavior Data Using Heterogeneous Multiple Classifiers." Applied Sciences 12, no. 3 (January 19, 2022): 1011. http://dx.doi.org/10.3390/app12031011.
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As the complexity and scale of the network environment increase continuously, various methods to detect attacks and intrusions from network traffic by classifying normal and abnormal network behaviors show their limitations. The number of network traffic signatures is increasing exponentially to the extent that semi-realtime detection is not possible. However, machine learning-based intrusion detection only gives simple guidelines as simple contents of security events. This is why security data for a specific environment cannot be configured due to data noise, diversification, and continuous alteration of a system and network environments. Although machine learning is performed and evaluated using a generalized data set, its performance is expected to be similar in that specific network environment only. In this study, we propose a high-speed outlier detection method for a network dataset to customize the dataset in real-time for a continuously changing network environment. The proposed method uses an ensemble-based noise data filtering model using the voting results of 6 classifiers (decision tree, random forest, support vector machine, naive Bayes, k-nearest neighbors, and logistic regression) to reflect the distribution and various environmental characteristics of datasets. Moreover, to prove the performance of the proposed method, we experimented with the accuracy of attack detection by gradually reducing the noise data in the time series dataset. As a result of the experiment, the proposed method maintains a training dataset of a size capable of semi-real-time learning, which is 10% of the total training dataset, and at the same time, shows the same level of accuracy as a detection model using a large training dataset. The improved research results would be the basis for automatic tuning of network datasets and machine learning that can be applied to special-purpose environments and devices such as ICS environments.
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Pan, Tie Jun, Lei Na Zheng, and Cheng Qing Li. "Lube Intelligent Diagnosis System Combining Bayesian and BP Network Based on IOT Technology." Advanced Materials Research 490-495 (March 2012): 1014–18. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1014.
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The existing industrial lubrication depend on experience judgment, off-line inspection and regular oil change, whose maintenance requires rich personnel experience and still always have many errors. Line monitoring and quality diagnosis for industrial lube were studied to establish the distributed the online monitoring system based on hierarchical structure, information fusion diagnostic system based on Bayesian network and BP neural network. The filtering system for industrial lube has been developed to achieve unattended, automatic operation purposes, and trialed in the metallurgical industry. The results show monitoring data is stable, reliable, and the problem of high water content of lube in the steel industry is solved. At the same time, lube filtering is transformed from the traditional blind continuous filtering to real-time targeted filtering. In the premise of guaranteeing the lube quality, the system can save electricity more than 30%.
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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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Boudali, Hichem, and Joanne Bechta Dugan. "Corrections on “A Continuous-Time Bayesian Network Reliability Modeling and Analysis Framework” [Mar 06 86-97]." IEEE Transactions on Reliability 57, no. 3 (September 2008): 532–33. http://dx.doi.org/10.1109/tr.2008.925796.
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Dui, Hongyan, Jiaying Song, and Yun-an Zhang. "Reliability and Service Life Analysis of Airbag Systems." Mathematics 11, no. 2 (January 13, 2023): 434. http://dx.doi.org/10.3390/math11020434.
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Airbag systems are important to a car’s safety protection system. To further improve the reliability of the system, this paper analyzes the failure mechanism of automotive airbag systems and establishes a dynamic fault tree model. The dynamic fault tree model is transformed into a continuous-time Bayesian network by introducing a unit step function and an impulse function, from which the failure probability of the system is calculated. Finally, the system reliability and average life are calculated and analyzed and compared with the sequential binary decision diagram method. The results show that the method can obtain more accurate system reliability and effectively identify the weak parts of the automotive airbag system, to a certain extent compensating for the lack of computational complexity of dynamic Bayesian networks in solving system reliability problems with continuous failure processes.
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HSU, WEI-YEN. "APPLICATION OF COMPETITIVE HOPFIELD NEURAL NETWORK TO BRAIN-COMPUTER INTERFACE SYSTEMS." International Journal of Neural Systems 22, no. 01 (February 2012): 51–62. http://dx.doi.org/10.1142/s0129065712002979.
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We propose an unsupervised recognition system for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data in this study. Competitive Hopfield neural network (CHNN) clustering is used for the discrimination of left and right MI EEG data posterior to selecting active segment and extracting fractal features in multi-scale. First, we use continuous wavelet transform (CWT) and Student's two-sample t-statistics to select the active segment in the time-frequency domain. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. At last, CHNN clustering is adopted to recognize extracted features. Due to the characteristic of non-supervision, it is proper for CHNN to classify non-stationary EEG signals. The results indicate that CHNN achieves 81.9% in average classification accuracy in comparison with self-organizing map (SOM) and several popular supervised classifiers on six subjects from two data sets.
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BOXER, PAUL A. "LEARNING NAIVE PHYSICS BY VISUAL OBSERVATION: USING QUALITATIVE SPATIAL REPRESENTATIONS AND PROBABILISTIC REASONING." International Journal of Computational Intelligence and Applications 01, no. 03 (September 2001): 273–85. http://dx.doi.org/10.1142/s146902680100024x.
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Autonomous robots are unsuccessful at operating in complex, unconstrained environments. They lack the ability to learn about the physical behavior of different objects through the use of vision. We combine Bayesian networks and qualitative spatial representation to learn general physical behavior by visual observation. We input training scenarios that allow the system to observe and learn normal physical behavior. The position and velocity of the visible objects are represented as qualitative states. Transitions between these states over time are entered as evidence into a Bayesian network. The network provides probabilities of future transitions to produce predictions of future physical behavior. We use test scenarios to determine how well the approach discriminates between normal and abnormal physical behavior and actively predicts future behavior. We examine the ability of the system to learn three naive physical concepts, "no action at a distance", "solidity" and "movement on continuous paths". We conclude that the combination of qualitative spatial representations and Bayesian network techniques is capable of learning these three rules of naive physics.
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Zhang, Fei, and Jie Yan. "Cloud Image Classification Method Based on Deep Convolutional Neural Network." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 4 (August 2020): 740–46. http://dx.doi.org/10.1051/jnwpu/20203840740.
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Compared with satellite remote sensing images, ground-based invisible images have limited swath, but featured in higher resolution, more distinct cloud features, and the cost is greatly reduced, conductive to continuous meteorological observation of local areas. For the first time, this paper proposed a high-resolution cloud image classification method based on deep learning and transfer learning technology for ground-based invisible images. Due to the limited amount of samples, traditional classifiers such as support vector machine can't effectively extract the unique features of different types of clouds, and directly training deep convolutional neural networks leads to over-fitting. In order to prevent the network from over-fitting, this paper proposed applying transfer learning method to fine-tune the pre-training model. The proposed network achieved as high as 85.19% test accuracy on 6-type cloud images classification task. The networks proposed in this paper can be applied to classify digital photos captured by cameras directly, which will reduce the cost of system greatly.
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Nahid, Abdullah-Al, and Yinan Kong. "Involvement of Machine Learning for Breast Cancer Image Classification: A Survey." Computational and Mathematical Methods in Medicine 2017 (2017): 1–29. http://dx.doi.org/10.1155/2017/3781951.
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Breast cancer is one of the largest causes of women’s death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors’ and physicians’ time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.
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Li, Chunyan. "Urban Planning Design and Evaluation Based on GIS Information and Bayesian Network." Mathematical Problems in Engineering 2022 (August 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/5963133.
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In recent years, China’s urban construction has set off a climax, with the rapid increase of the number of cities and towns, the increasingly perfect functions of cities and towns, the continuous improvement of the level of urbanization, and the urban landscape changing with each passing day. Under the influence of many factors, urban planning and assessment are facing unprecedented pressure, leading to the inconsistency between the compilation results of planning. Under the guidance of system analysis and system dynamics, this paper designs and develops an urban planning information management system based on GIS (Geographic Information System) and Bayesian network, and then the system is applied to the field of urban planning. In this system, all kinds of data generated by planning are stored in order to realize the standardization of data, and then the urban planning work is guided by the effective management of the result data so as to know the implementation status of urban planning in time and provide the basis for the future planning and implementation.