Journal articles: 'Detector based on Bayes Theory'

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Doering, Dionísio, and Adalberto Schuck Junior. "A Novel Method for Generating Scale Space Kernels Based on Wavelet Theory." Revista de Informática Teórica e Aplicada 15, no. 2 (December 12, 2008): 121–38. http://dx.doi.org/10.22456/2175-2745.7024.

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The linear scale-space kernel is a Gaussian or Poisson function. These functions were chosen based on several axioms. This representation creates a good base for visualization when there is no information (in advanced) about which scales are more important. These kernels have some deficiencies, as an example, its support region goes from minus to plus infinite. In order to solve these issues several others scale-space kernels have been proposed. In this paper we present a novel method to create scale-space kernels from one-dimensional wavelet functions. In order to do so, we show the scale-space and wavelet fundamental equations and then the relationship between them. We also describe three different methods to generate two-dimensional functions from one-dimensional functions. Then we show results got from scale-space blob detector using the original and two new scale-space bases (Haar and Bi-ortogonal 4.4), and a comparison between the edges detected using the Gaussian kernel and Haar kernel for a noisy image. Finally we show a comparison between the scale space Haar edge detector and the Canny edge detector for an image with one known square in it, for that case we show the Mean Square Error (MSE) of the edges detected with both algorithms.

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Gotoh, Masayuki, and Shigeichi Hirasawa. "Statistical model selection based on Bayes decision theory and its application to change detection problem." International Journal of Production Economics 60-61 (April 1999): 629–38. http://dx.doi.org/10.1016/s0925-5273(98)00186-8.

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Sun, Shuang, Li Liang, Ming Li, and Xin Li. "Multidamage Detection of Bridges Using Rough Set Theory and Naive-Bayes Classifier." Mathematical Problems in Engineering 2018 (May 27, 2018): 1–13. http://dx.doi.org/10.1155/2018/6752456.

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This paper is intended to introduce a two-stage detection method to solve the multidamage problem in bridges. Vibration analysis is conducted to acquire the dynamic fingerprints which are regarded as information sources. Bayesian fusion is used to integrate these sources and preliminarily locate the damage. Then, the RSNB method which combines rough set theory and Naive-Bayes classifier is proposed to simplify the sample dimensions and fuse the remaining attributes for damage extent detection. A numerical simulation of a real structure, the Sishui Bridge in Shenyang, China, is conducted to validate the effectiveness of the proposed detection method. Data fusion based method is compared with single-valued index method at the damage localization stage. The proposed RSNB method is compared with the Back Propagation Neural Network (BPNN) method at the damage qualification stage. The results show that the proposed two-stage damage detection method has better performances in regard to transparency, accuracy, efficiency, noise robustness, and stability. Furthermore, an ambient excitation modal test was carried out on the bridge to obtain the vibration responses and assess the damage condition with the proposed method. This novel approach is applicable for early damage detection and provides a basis for bridge management and maintenance.

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Rastiveis, H. "DECISION LEVEL FUSION OF LIDAR DATA AND AERIAL COLOR IMAGERY BASED ON BAYESIAN THEORY FOR URBAN AREA CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 589–94. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-589-2015.

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Airborne Light Detection and Ranging (LiDAR) generates high-density 3D point clouds to provide a comprehensive information from object surfaces. Combining this data with aerial/satellite imagery is quite promising for improving land cover classification. In this study, fusion of LiDAR data and aerial imagery based on Bayesian theory in a three-level fusion algorithm is presented. In the first level, pixel-level fusion, the proper descriptors for both LiDAR and image data are extracted. In the next level of fusion, feature-level, using extracted features the area are classified into six classes of “Buildings”, “Trees”, “Asphalt Roads”, “Concrete roads”, “Grass” and “Cars” using Naïve Bayes classification algorithm. This classification is performed in three different strategies: (1) using merely LiDAR data, (2) using merely image data, and (3) using all extracted features from LiDAR and image. The results of three classifiers are integrated in the last phase, decision level fusion, based on Naïve Bayes algorithm. To evaluate the proposed algorithm, a high resolution color orthophoto and LiDAR data over the urban areas of Zeebruges, Belgium were applied. Obtained results from the decision level fusion phase revealed an improvement in overall accuracy and kappa coefficient.

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Chetouani, Yahya. "Model selection and fault detection approach based on Bayes decision theory: Application to changes detection problem in a distillation column." Process Safety and Environmental Protection 92, no. 3 (May 2014): 215–23. http://dx.doi.org/10.1016/j.psep.2013.02.004.

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Podlech, Steffen. "Autofocus by Bayes Spectral Entropy Applied to Optical Microscopy." Microscopy and Microanalysis 22, no. 1 (January 13, 2016): 199–207. http://dx.doi.org/10.1017/s1431927615015652.

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AbstractThis study introduces a passive autofocus method based on image analysis calculating the Bayes spectral entropy (BSE). The method is applied to optical microscopy and together with the specific construction of the opto-mechanical unit, it allows the analysis of large samples with complicated surfaces without subsampling. This paper will provide a short overview of the relevant theory of calculating the normalized discrete cosine transform when analyzing obtained images, in order to find the BSE measure. Furthermore, it will be shown that the BSE measure is a strong indicator, helping to determine the focal position of the optical microscope. To demonstrate the strength and robustness of the microscope system, tests have been performed using a 1951 USAF test pattern resolution chart determining the in focus position of the microscope. Finally, this method and the optical microscope system is applied to analyze an optical grating (100 lines/mm) demonstrating the detection of the focal position. The paper concludes with an outlook of potential applications of the presented system within quality control and surface analysis.

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Du, Na, Qiaoning Zhang, and X. Jessie Yang. "Evaluating effects of automation reliability and reliability information on trust, dependence and dual-task performance." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 174. http://dx.doi.org/10.1177/1541931218621041.

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The use of automated decision aids could reduce human exposure to dangers and enable human workers to perform more challenging tasks. However, automation is problematic when people fail to trust and depend on it appropriately. Existing studies have shown that system design that provides users with likelihood information including automation certainty, reliability, and confidence could facilitate trust- reliability calibration, the correspondence between a person’s trust in the automation and the automation’s capabilities (Lee & Moray, 1994), and improve human–automation task performance (Beller et al., 2013; Wang, Jamieson, & Hollands, 2009; McGuirl & Sarter, 2006). While revealing reliability information has been proposed as a design solution, the concrete effects of such information disclosure still vary (Wang et al., 2009; Fletcher et al., 2017; Walliser et al., 2016). Clear guidelines that would allow display designers to choose the most effective reliability information to facilitate human decision performance and trust calibration do not appear to exist. The present study, therefore, aimed to reconcile existing literature by investigating if and how different methods of calculating reliability information affect their effectiveness at different automation reliability. A human subject experiment was conducted with 60 participants. Each participant performed a compensatory tracking task and a threat detection task simultaneously with the help of an imperfect automated threat detector. The experiment adopted a 2×4 mixed design with two independent variables: automation reliability (68% vs. 90%) as a within- subject factor and reliability information as a between-subjects factor. Reliability information of the automated threat detector was calculated using different methods based on the signal detection theory and conditional probability formula of Bayes’ Theorem (H: hits; CR: correct rejections, FA: false alarms; M: misses): Overall reliability = P (H + CR | H + FA + M + CR). Positive predictive value = P (H | H + FA); negative predictive value = P (CR | CR + M). Hit rate = P (H | H + M), correct rejection rate = P (CR | CR + FA). There was also a control condition where participants were not informed of any reliability information but only told the alerts from the automated threat detector may or may not be correct. The dependent variables of interest were participants’ subjective trust in automation and objective measures of their display-switching behaviors. The results of this study showed that as the automated threat detector became more reliable, participants’ trust in and dependence on the threat detector increased significantly, and their detection performance improved. More importantly, there were significant differences in participants’ trust, dependence and dual-task performance when reliability information was calculated by different methods. Specifically, when overall reliability of the automated threat detector was 90%, revealing positive and negative predictive values of the automation significantly helped participants to calibrate their trust in and dependence on the detector, and led to the shortest reaction time for detection task. However, when overall reliability of the automated threat detector was 68%, positive and negative predictive values didn’t lead to significant difference in participants’ compliance on the detector. In addition, our result demonstrated that the disclosure of hit rate and correct rejection rate or overall reliability didn’t seem to aid human-automation team performance and trust-reliability calibration. An implication of the study is that users should be made aware of system reliability, especially of positive/negative predictive values, to engender appropriate trust in and dependence on the automation. This can be applied to the interface design of automated decision aids. Future studies should examine whether the positive and negative predictive values are still the most effective pieces of information for trust calibration when the criterion of the automated threat detector becomes liberal.

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Dr. Pullagura Priyadarsini, Ravi Kanth Motupalli, Dr Joel Sunny Deol Gosu,. "A Hybrid Approach for the Analysis of Feature Selection using Information Gain and BAT Techniques on The Anomaly Detection." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 656–66. http://dx.doi.org/10.17762/turcomat.v12i5.1063.

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Every day, millions of people in many institutions communicate with each other on the Internet. The past two decades have witnessed unprecedented levels of Internet use by people around the world. Almost alongside these rapid developments in the internet space, an ever increasing incidence of attacks carried out on the internet has been consistently reported every minute. In such a difficult environment, Anomaly Detection Systems (ADS) play an important role in monitoring and analyzing daily internet activities for security breaches and threats. However, the analytical data routinely generated from computer networks are usually of enormous size and of little use. This creates a major challenge for ADSs, who must examine all the functionality of a certain dataset to identify intrusive patterns. The selection of features is an important factor in modeling anomaly-based intrusion detection systems. An irrelevant characteristic can lead to overfitting which in turn negatively affects the modeling power of classification algorithms. The objective of this study is to analyze and select the most discriminating input characteristics for the construction of efficient and computationally efficient schemes for an ADS. In the first step, a heuristic algorithm called IG-BA is proposed for dimensionality reduction by selecting the optimal subset based on the concept of entropy. Then, the relevant and meaningful features are selected, before implementing Number of Classifiers which includes: (1) An irrelevant feature can lead to overfitting which in turn negatively affects the modeling power of the classification algorithms. Experiment was done on CICIDS-2017 dataset by applying (1) Random Forest (RF), (2) Bayes Network (BN), (3) Naive Bayes (NB), (4) J48 and (5) Random Tree (RT) with results showing better detection precision and faster execution time. The proposed heuristic algorithm outperforms the existing ones as it is more accurate in detection as well as faster. However, Random Forest algorithm emerges as the best classifier for feature selection technique and scores over others by virtue of its accuracy in optimal selection of features.

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Kabanda, Gabriel. "Bayesian Network Model for a Zimbabwean Cybersecurity System." Oriental journal of computer science and technology 12, no. 4 (January 3, 2020): 147–67. http://dx.doi.org/10.13005/ojcst12.04.02.

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The purpose of this research was to develop a structure for a network intrusion detection and prevention system based on the Bayesian Network for use in Cybersecurity. The phenomenal growth in the use of internet-based technologies has resulted in complexities in cybersecurity subjecting organizations to cyberattacks. What is required is a network intrusion detection and prevention system based on the Bayesian Network structure for use in Cybersecurity. Bayesian Networks (BNs) are defined as graphical probabilistic models for multivariate analysis and are directed acyclic graphs that have an associated probability distribution function. The research determined the cybersecurity framework appropriate for a developing nation; evaluated network detection and prevention systems that use Artificial Intelligence paradigms such as finite automata, neural networks, genetic algorithms, fuzzy logic, support-vector machines or diverse data-mining-based approaches; analysed Bayesian Networks that can be represented as graphical models and are directional to represent cause-effect relationships; and developed a Bayesian Network model that can handle complexity in cybersecurity. The theoretical framework on Bayesian Networks was largely informed by the NIST Cybersecurity Framework, General deterrence theory, Game theory, Complexity theory and data mining techniques. The Pragmatism paradigm used in this research, as a philosophy is intricately related to the Mixed Method Research (MMR). A mixed method approach was used in this research, which is largely quantitative with the research design being a survey and an experiment, but supported by qualitative approaches where Focus Group discussions were held. The performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms was discussed. Alternative improved solutions discussed include the use of machine learning algorithms specifically Artificial Neural Networks (ANN), Decision Tree C4.5, Random Forests and Support Vector Machines (SVM).

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Et.al, Kandala Srujana Kumari. "Performance Analysis of Diabetes Mellitus Using Machine Learning Techniques." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 225–30. http://dx.doi.org/10.17762/turcomat.v12i6.1297.

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Diabetes is a common disease in the human body caused by a set of metabolic disorders in which blood sugar levels are very long. It affects various organs in the human body and destroys many-body systems, especially the kidneys and kidneys. Early detection can save lives. To achieve this goal, this study focuses specifically on the use of machine learning techniques for many risk factors associated with this disease. Technical training methods achieve effective results by creating predictive models based on medical diagnostic data collected on Indian sugar. Learning from such data can help in predicting diabetics. In this study, we used four popular machine learning algorithms, namely Support Vector Machine (SVM), Naive Bayes (NB), Near Neighbor K (KNN), and Decision Tree C4.5 (DT), based on statistical data. people. adults in sugar. , preview. The results of our experiments show that the C4.5 solution tree has greater accuracy compared to other machine learning methods.

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Hadini, Fadhil Muhammad. "Detection System Milkfish Formalin Android-Based Method Based on Image Eye Using Naive Bayes Classifier." MATICS 9, no. 1 (March 21, 2017): 44. http://dx.doi.org/10.18860/mat.v9i1.4054.

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In this study was researcher trying to make an android-based application that can identify fish with formalin. The method used in researcher methods naïve Bayes classifier as a detector (detector) with the object input in the form of fish eye image. The steps in the study include the training and testing process. In the training process used to build the model naïve classifier and estimation parameters. While testing process, implement the results of the model and parameter estimation have been built to detect fish formalin or not formalin. The trial results demonstrate the ability-based applications using the naïve Bayes 98.3% for object dimensions 10x10 image

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Odongo, George, Richard Musabe, and Damien Hanyurwimfura. "A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers." Algorithms 14, no. 4 (April 20, 2021): 128. http://dx.doi.org/10.3390/a14040128.

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This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a huge number of consumers and suppresses economic activities in the vicinity. Because of this, it is important that power utility companies accord high priority to condition monitoring of critical assets. The analysis of dissolved gases is a technique popularly used for monitoring the condition of transformers dipped in oil. The interpretation of DGA data is however inconclusive as far as the determination of incipient faults is concerned and depends largely on the expertise of technical personnel. To have a coherent, accurate, and clear interpretation of DGA, this study proposes a novel multinomial classification model christened KosaNet that is based on decision trees. Actual DGA data with 2912 entries was used to compute the performance of KosaNet against other algorithms with multiclass classification ability namely the decision tree, k-NN, Random Forest, Naïve Bayes, and Gradient Boost. Investigative results show that KosaNet demonstrated an improved DGA classification ability particularly when classifying multinomial data.

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Abbood, Auss, Alexander Ullrich, Rüdiger Busche, and Stéphane Ghozzi. "EventEpi—A natural language processing framework for event-based surveillance." PLOS Computational Biology 16, no. 11 (November 20, 2020): e1008277. http://dx.doi.org/10.1371/journal.pcbi.1008277.

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According to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of public health agents sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural language processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at the RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles’ key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We extracted the key country and disease using a heuristic with good results. We trained a naive Bayes classifier to find the key date and confirmed-case count, using the RKI’s EBS database as labels which performed modestly. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using bag-of-words, document and word embeddings. The best classifier, a logistic regression, achieved a sensitivity of 0.82 and an index balanced accuracy of 0.61. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code and data are publicly available under open licenses.

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LI Peng, 李鹏. "Research of Neural Network Algorithm based on Bayes Theory." OME Information 28, no. 1 (2011): 28–32. http://dx.doi.org/10.3788/omei20112801.0028.

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Pramanik, Moumita, Ratika Pradhan, Parvati Nandy, Saeed Mian Qaisar, and Akash Kumar Bhoi. "Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection." Journal of Healthcare Engineering 2021 (August 21, 2021): 1–13. http://dx.doi.org/10.1155/2021/9957132.

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This article presents a machine learning approach for Parkinson’s disease detection. Potential multiple acoustic signal features of Parkinson’s and control subjects are ascertained. A collaborated feature bank is created through correlated feature selection, Fisher score feature selection, and mutual information-based feature selection schemes. A detection model on top of the feature bank has been developed using the traditional Naïve Bayes, which proved state of the art. The Naïve Bayes detector on collaborative acoustic features can detect the presence of Parkinson’s magnificently with a detection accuracy of 78.97% and precision of 0.926, under the hold-out cross validation. The collaborative feature bank on Naïve Bayes revealed distinguishable results as compared to many other recently proposed approaches. The simplicity of Naïve Bayes makes the system robust and effective throughout the detection process.

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Zhao, Jun, Juliang Jin, Qizhong Guo, Yaqian Chen, Mengxiong Lu, and Luis Tinoco. "Forewarning model for water pollution risk based on Bayes theory." Environmental Science and Pollution Research 21, no. 4 (November 6, 2013): 3073–81. http://dx.doi.org/10.1007/s11356-013-2222-8.

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Gao, Xing-Long, Qing-Bin Zhang, and Qian-Gang Tang. "Reliability assessment of slot-parachute inflation based on Bayes theory." Journal of Statistical Computation and Simulation 84, no. 6 (July 8, 2013): 1159–72. http://dx.doi.org/10.1080/00949655.2013.810218.

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Zhang, Lei, Taiyong Wang, and Zhanqi Hu. "Assessment method of heavy NC machine reliability based on Bayes theory." Transactions of Tianjin University 22, no. 2 (April 2016): 105–9. http://dx.doi.org/10.1007/s12209-016-2567-4.

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Chen, Hao, Kan Liu, and Tunhua Wu. "Optimal Threshold of LTE-Femtocell Network Based Bayes-Nash Equilibrium Theory." International Journal of Future Generation Communication and Networking 8, no. 6 (December 31, 2015): 169–76. http://dx.doi.org/10.14257/ijfgcn.2015.8.6.17.

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Jiang, Miao Miao, Liu Chun Hu, Yin Yu Nie, Rui Wen Zhou, and Chang Lu. "The Measurement of Composite Property Based on Bayes Formula." Advanced Materials Research 779-780 (September 2013): 243–46. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.243.

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The application of Bayesian estimation methods in the measurement of composite performance is studied in this paper. At first, the traditional measurement method of the composite performance and its disadvantages are described. In addition, the Bayesian method is reviewed and the Bayesian estimation theory is briefly introduced. Then the hardness of the compound interface of 42CrMo-ductile iron composite is obtained by applying the Bayesian estimation theory. In this way, the method has been extended.

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Liao, Yong, and Tao Wu. "The Research on Share Rate of Trip Mode Choice Based on Bayesian Theory." Applied Mechanics and Materials 373-375 (August 2013): 2262–65. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.2262.

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Based on Bayes analysis, a new model of trip mode choice is presented. Trip mode choice is divided into three phases: calculating prior distribution, obtaining conditional distribution by sampling and calculating share rate of trip modes. Supply characteristics of trip modes are taken as prior information. Unity value takes the place of unity function in MNL, and then prior distribution is achieved. Condition distribution is gained from sampling information. Bayes analysis is introduced into calculating posterior distribution. Share rates of trip modes, is calculated by total probability formula. Compared with other choice models, Model proposed in this paper improves the forecast accuracy of share rate without the need of parameter calibration like Logit model.

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Wen, Chang Ping, and Qing Qing Tian. "Assessment of Urban Road Traffic Safety Based on Bayes Discriminant Analysis Method." Advanced Materials Research 639-640 (January 2013): 544–47. http://dx.doi.org/10.4028/www.scientific.net/amr.639-640.544.

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Bayes discriminant analysis theory (BDAT) is used to create an evaluation method to determine the condition of urban road traffic safety. The resulting Bayes discriminant model (BDM) is designed to strictly adhere to BDAT. Three indexes including death ratio per ten thousand vehicles, death ratio per hundred thousand bicycles and death ratio per hundred thousand citizens are selected as the factors in the analysis of urban road traffic safety. The grade of condition of urban road traffic safety is divided into three grades that are regarded as three normal populations in Bayes discriminant analysis. Bayes discriminant functions rigorously constructed through training a set of samples are employed to compute the Bayes function values of the evaluating samples, and the maximal function value is used to judge which population the evaluating sample belongs to. The optimality of the proposed model is verified by back-substitution method. The study shows that the prediction accuracy of the proposed model is 100% and could be used in practice.

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C, Antonius Rachmat, and Yuan Lukito. "Deteksi Komentar Spam Bahasa Indonesia Pada Instagram Menggunakan Naive Bayes." Jurnal ULTIMATICS 9, no. 1 (June 16, 2017): 50–58. http://dx.doi.org/10.31937/ti.v9i1.564.

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Instagram is the most famous pictures and videos media sharing based on the web & mobile application. Instagram users can have picture posts that can be commented by their followers. Indonesian public figures such as actors, actresses, musicians use Instagram to promote their activities to their followers. Unfortunately, there are a lot of spam comments in Instagram that need special attention and have to be removed. This research grabs Instagram comments and builds the dataset from Indonesian public figures who have more than one million followers. By using preprocessing (tokenization, stop words removal, and stemming), TF-IDF weighting, and supervised learning, Naive Bayes method is used to detect spam comments in Indonesian. Naive Bayes produces 74,31% accuracy rate on unbalanced datasets and 77,25% accuracy rate on balanced datasets. This result shows that Naïve Bayes can be used to build an automatic Indonesian spam comments detector on Instagram with high accuracy rate. The novelty of this research is that Naive Bayes can be used to detect spam comment on our Indonesian Instagram comments dataset. Index Terms—Instagram, Naive Bayes, Indonesian spam comments, spam comments detection.

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Kumar Bhowmik, Tapan. "Naive Bayes vs Logistic Regression: Theory, Implementation and Experimental Validation." Inteligencia Artificial 18, no. 56 (December 18, 2015): 14. http://dx.doi.org/10.4114/intartif.vol18iss56pp14-30.

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This article presents the theoretical derivation as well as practical steps for implementing Naive Bayes (NB) and Logistic Regression (LR) classifiers. A generative learning under Gaussian Naive Bayes assumption and two discriminative learning techniques based on gradient ascent and Newton-Raphson methods are described to estimate the parameters of LR. Some limitation of learning techniques and implementation issues are discussed as well. A set of experiments are performed for both the classifiers under different learning circumstances and their performances are compared. From the experiments, it is observed that LR learning with gradient ascent technique outperforms general NB classifier. However, under Gaussian Naive Bayes assumption, both classifiers NB and LR perform similar.

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Verrall, R. J. "Bayes and Empirical Bayes Estimation for the Chain Ladder Model." ASTIN Bulletin 20, no. 2 (November 1990): 217–43. http://dx.doi.org/10.2143/ast.20.2.2005444.

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AbstractThe subject of predicting outstanding claims on a porfolio of general insurance policies is approached via the theory of hierarchical Bayesian linear models. This is particularly appropriate since the chain ladder technique can be expressed in the form of a linear model. The statistical methods which are applied allow the practitioner to use different modelling assumptions from those implied by a classical formulation, and to arrive at forecasts which have a greater degree of inherent stability. The results can also be used for other linear models. By using a statistical structure, a sound approach to the chain ladder technique can be derived. The Bayesian results allow the input of collateral information in a formal manner. Empirical Bayes results are derived which can be interpreted as credibility estimates. The statistical assumptions which are made in the modelling procedure are clearly set out and can be tested by the practitioner. The results based on the statistical theory form one part of the reserving procedure, and should be followed by expert interpretation and analysis. An illustration of the use of Bayesian and empirical Bayes estimation methods is given.

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An, Shi, Lina Ma, and Jian Wang. "Optimization of Traffic Detector Layout Based on Complex Network Theory." Sustainability 12, no. 5 (March 6, 2020): 2048. http://dx.doi.org/10.3390/su12052048.

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With the recent development of traffic networks, traffic detector layout has become very complicated, due to the complexity of traffic network structures and states. Thus, this paper presents an optimal method for traffic detector layout based on network centrality using complex network theory. It mainly depends on the topology of the traffic network, and does not depend on pre-conditions (e.g., OD (Origin Destination)) traffic, path traffic, prior matrix, and so on) or consider route-choosing behavior too much. Considering the travel time, OD demand, observation demand of urban managers, dynamic characteristic of the traffic network, detector failure, and so on, an optimization model for traffic detector layout is established, which is called the Traffic Network Centrality Model (TNCM). Numerical experiments are conducted, based on data from the Sioux Falls network, which demonstrate that the model has a strong practical value. TNCM is not only helpful in reducing the traffic detector layout cost, but also improves the monitoring revenue of the traffic network in complex scenarios, which offers a promising way of thinking about the optimization of traffic detector layout schemes.

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Jin-ping, He, Tu Yuan-yuan, and Shi Yu-qun. "Fusion Model of Multi Monitoring Points on Dam Based on Bayes Theory." Procedia Engineering 15 (2011): 2133–38. http://dx.doi.org/10.1016/j.proeng.2011.08.399.

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Wu, Bo, Shuhai Guo, Lingyan Zhang, and Fengmei Li. "Risk forewarning model for rice grain Cd pollution based on Bayes theory." Science of The Total Environment 618 (March 2018): 1343–49. http://dx.doi.org/10.1016/j.scitotenv.2017.09.248.

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Zhao, Hong-hao, Fan-bo Meng, Si-wen Zhao, Si-hang Zhao, and Yi Lu. "A Bayes Theory-Based Modeling Algorithm to End-to-end Network Traffic." ITM Web of Conferences 7 (2016): 09024. http://dx.doi.org/10.1051/itmconf/20160709024.

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Jiang, Liangxiao, Lungan Zhang, Chaoqun Li, and Jia Wu. "A Correlation-Based Feature Weighting Filter for Naive Bayes." IEEE Transactions on Knowledge and Data Engineering 31, no. 2 (February 1, 2019): 201–13. http://dx.doi.org/10.1109/tkde.2018.2836440.

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Klinger, T., F. Rottensteiner, and C. Heipke. "PROBABILISTIC MULTI-PERSON TRACKING USING DYNAMIC BAYES NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 20, 2015): 435–42. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-435-2015.

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Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach uses a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forest-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available benchmark. The results confirm that our approach is well suited for tracking pedestrians over long distances while at the same time achieving comparatively good geometric accuracy.

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Zhu, Changfeng, Gang Fang, and Qingrong Wang. "Optimization on Emergency Resources Transportation Network Based on Bayes Risk Function: A Case Study." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/5030619.

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In order to coordinate the complex relationship between supplies distribution and path selection, some influential factors must be taken into account such as the insufficient remaining capacity of the road and uncertainty of travel time during supplies distribution and transportation. After the structure of emergency logistics network is analyzed, the travel time Bayes risk function of path and the total loss Bayes risk function of the disaster area are proposed. With the emergency supplies total transportation unit loss as the goal, an emergency logistics network optimization model under crowded conditions is established by the Bayes decision theory and solved by the improved ant colony algorithm. Then, a case of the model is validated to prove that the emergency logistics network optimization model is effective in congested conditions.

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Fan, Yi-na, Bo Lang, and Hui Wei. "A Classification and Prediction Model of Multi Spike Train Based on Bayes Theory." Journal of Electronics & Information Technology 35, no. 7 (February 24, 2014): 1619–23. http://dx.doi.org/10.3724/sp.j.1146.2012.01453.

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Klinger, T., F. Rottensteiner, and C. Heipke. "A Dynamic Bayes Network for visual Pedestrian Tracking." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 145–50. http://dx.doi.org/10.5194/isprsarchives-xl-3-145-2014.

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Many tracking systems rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach suggests a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, prior scene information, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forests-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available dataset captured in a challenging outdoor scenario. Using the adaptive classifier, our system is able to keep track of pedestrians over long distances while at the same time supporting the localisation of the people. The results show that the derived trajectories achieve a geometric accuracy superior to the one achieved by modelling the image positions as observations.

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Song, Jianglong, Xi Liu, Qingqiong Deng, Wen Dai, Yibo Gao, Lin Chen, Yunling Zhang, et al. "A Network-Based Approach to Investigate the Pattern of Syndrome in Depression." Evidence-Based Complementary and Alternative Medicine 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/768249.

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In Traditional Chinese Medicine theory, syndrome is essential to diagnose diseases and treat patients, and symptom is the foundation of syndrome differentiation. Thus the combination and interaction between symptoms represent the pattern of syndrome at phenotypic level, which can be modeled and analyzed using complex network. At first, we collected inquiry information of 364 depression patients from 2007 to 2009. Next, we learned classification models for 7 syndromes in depression using naïve Bayes, Bayes network, support vector machine (SVM), and C4.5. Among them, SVM achieves the highest accuracies larger than 0.9 except for Yin deficiency. Besides, Bayes network outperforms naïve Bayes for all 7 syndromes. Then key symptoms for each syndrome were selected using Fisher’s score. Based on these key symptoms, symptom networks for 7 syndromes as well as a global network for depression were constructed through weighted mutual information. Finally, we employed permutation test to discover dynamic symptom interactions, in order to investigate the difference between syndromes from the perspective of symptom network. As a result, significant dynamic interactions were quite different for 7 syndromes. Therefore, symptom networks could facilitate our understanding of the pattern of syndrome and further the improvement of syndrome differentiation in depression.

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Pérez-Díaz, N., D. Ruano-Ordás, F. Fdez-Riverola, and J. R. Méndez. "Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory." Scientific Programming 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5945192.

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Nowadays, spam deliveries represent a major problem to benefit from the wide range of Internet-based communication forms. Despite the existence of different well-known intelligent techniques for fighting spam, only some specific implementations of Naïve Bayes algorithm are finally used in real environments for performance reasons. As long as some of these algorithms suffer from a large number of false positive errors, in this work we propose a rough set postprocessing approach able to significantly improve their accuracy. In order to demonstrate the advantages of the proposed method, we carried out a straightforward study based on a publicly available standard corpus (SpamAssassin), which compares the performance of previously successful well-known antispam classifiers (i.e., Support Vector Machines, AdaBoost, Flexible Bayes, and Naïve Bayes) with and without the application of our developed technique. Results clearly evidence the suitability of our rough set postprocessing approach for increasing the accuracy of previous successful antispam classifiers when working in real scenarios.

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Tai, Kuan-Chen, and Chih-Wei Tang. "Siamese Networks-Based People Tracking Using Template Update for 360-Degree Videos Using EAC Format." Sensors 21, no. 5 (March 1, 2021): 1682. http://dx.doi.org/10.3390/s21051682.

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Rich information is provided by 360-degree videos. However, non-uniform geometric deformation caused by sphere-to-plane projection significantly decreases tracking accuracy of existing trackers, and the huge amount of data makes it difficult to achieve real-time tracking. Thus, this paper proposes a Siamese networks-based people tracker using template update for 360-degree equi-angular cubemap (EAC) format videos. Face stitching overcomes the problem of content discontinuity of the EAC format and avoids raising new geometric deformation in stitched images. Fully convolutional Siamese networks enable tracking at high speed. Mostly important, to be robust against combination of non-uniform geometric deformation of the EAC format and partial occlusions caused by zero padding in stitched images, this paper proposes a novel Bayes classifier-based timing detector of template update by referring to the linear discriminant feature and statistics of a score map generated by Siamese networks. Experimental results show that the proposed scheme significantly improves tracking accuracy of the fully convolutional Siamese networks SiamFC on the EAC format with operation beyond the frame acquisition rate. Moreover, the proposed score map-based timing detector of template update outperforms state-of-the-art score map-based timing detectors.

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Li, Xiao Feng, and Shu Chun Ding. "Research of Transport Junctions of Flow Analysis Algorithm Based on Decision-Making Theory." Advanced Materials Research 760-762 (September 2013): 1821–24. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1821.

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Intersection traffic flow analysis and analysis of algorithms, design of algorithms, this paper, the vehicles unified identification as a standard car equivalent steps of the algorithm based on the decision-making on the flow of traffic junctions, the algorithm is applicable not only to single-coil detector or single magnetic detector such as a single detector, but also applies to dual-loop detectors. The detection process is simple and can achieve very high accuracy rate, and through case studies, to verify the effectiveness and accuracy of change algorithm

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HIROHATA, Kenji, Takashi KAWAKAMI, Minoru MUKAI, Noriyasu KAWAMURA, Qiang YU, and Masaki SHIRATORI. "Proposal for Structural Reliability Design Method Based on Response Surface Methodology and Bayes Theory." Transactions of the Japan Society of Mechanical Engineers Series A 67, no. 660 (2001): 1297–304. http://dx.doi.org/10.1299/kikaia.67.1297.

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Guoqiang, Chen, Tan Jianping, and Tao Yourui. "A Reliability-Based Multidisciplinary Design Optimization Method with Evidence Theory and Probability Theory." International Journal of Reliability, Quality and Safety Engineering 25, no. 01 (February 2018): 1850003. http://dx.doi.org/10.1142/s0218539318500031.

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Uncertainties, including aleatory and epistemic uncertainties, always exist in multidisciplinary system. Due to the discontinuous nature of epistemic uncertainty and the complex coupled relation among subsystems, the computational efficiency of reliability-based multidisciplinary design optimization (RBMDO) with mixed aleatory and epistemic uncertainties is extremely low. A novel RBMDO procedure is presented in this paper based on combined probability theory and evidence theory (ET) to deal with hybrid-uncertainties and improve the computational efficiency. Firstly, based on Bayes method, a novel method to define the probability density function of the aleatory variables is proposed. Secondly, the conventional equivalent normal method (J-C method) is modified to reliability analysis with hybrid-uncertainties. Finally, a novel RBMDO procedure is suggested by integrating the modified J-C method into the frame of sequence optimization and reliability analysis (SORA). Numerical examples and engineering example are applied to demonstrate the performance of the proposed method. The examples show the excellence of the RBMDO method both in computational efficiency and accuracy. The proposed method provides a practical and effective reliability design method for multidisciplinary system.

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Chao, J. J., and C. C. Lee. "An efficient direct-sequence signal detector based on Dempster-Shafer theory." IEEE Transactions on Communications 38, no. 6 (June 1990): 868–74. http://dx.doi.org/10.1109/26.57479.

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ZHUGE, Jian-Wei. "A Network Anomaly Detector Based on the D-S Evidence Theory." Journal of Software 17, no. 3 (2006): 463. http://dx.doi.org/10.1360/jos170463.

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Xi, Haixu, Feiyue Ye, Sheng He, Yijun Liu, and Hongfen Jiang. "Bayes Performance of Batch Data Mining Based on Functional Dependencies." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 03 (February 19, 2019): 1959011. http://dx.doi.org/10.1142/s0218001419590110.

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Batch processes and phenomena in traffic video data processing, such as traffic video image processing and intelligent transportation, are commonly used. The application of batch processing can increase the efficiency of resource conservation. However, owing to limited research on traffic video data processing conditions, batch processing activities in this area remain minimally examined. By employing database functional dependency mining, we developed in this study a workflow system. Meanwhile, the Bayesian network is a focus area of data mining. It provides an intuitive means for users to comply with causality expression approaches. Moreover, graph theory is also used in data mining area. In this study, the proposed approach depends on relational database functions to remove redundant attributes, reduce interference, and select a property order. The restoration of selective hidden naive Bayesian (SHNB) affects this property order when it is used only once. With consideration of the hidden naive Bayes (HNB) influence, rather than using one pair of HNB, it is introduced twice. We additionally designed and implemented mining dependencies from a batch traffic video processing log for data execution algorithms.

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Fahidy, Thomas Z. "Some Applications of Bayes' Rule in Probability Theory to Electrocatalytic Reaction Engineering." International Journal of Electrochemistry 2011 (2011): 1–5. http://dx.doi.org/10.4061/2011/404605.

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Bayesian methods stem from the principle of linking prior probability and conditional probability (likelihood) to posterior probability via Bayes' rule. The posterior probability is an updated (improved) version of the prior probability of an event, through the likelihood of finding empirical evidence if the underlying assumptions (hypothesis) are valid. In the absence of a frequency distribution for the prior probability, Bayesian methods have been found more satisfactory than distribution-based techniques. The paper illustrates the utility of Bayes' rule in the analysis of electrocatalytic reactor performance by means of four numerical examples involving a catalytic oxygen cathode, hydrogen evolution on a synthetic metal, the reliability of a device testing the quality of an electrocatalyst, and the range of Tafel slopes exhibited by an electrocatalyst.

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Zhang, Zi Yi, Lin Hui Zhao, Miao Miao Tan, and Hong Hong Guo. "Study on MCU Based Programmable Frequency Detector." Advanced Materials Research 328-330 (September 2011): 2036–39. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.2036.

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An MCU based programmable frequency detector was developed and relative tests were carried out. The detector used few elements which are very common and cheap, one MCU which has only 8 pins and with small dimension, one Schimidt trigger, one D type flip-flop and several resistors and capacitors. Cost for the detector is very low. Besides hardware work, software program has played very important role in the detector. By adjusting program parameters, different frequency can be detected and detecting band width can also be adjusted. Program flow chart is given in this thesis. Test shows that the detector is stable and sensitive and is suitable for different applications, e.g. over power line data transmission, etc. This paper introduces theory of the detector, hardware design and the software programming.

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Grossmann, Volker, and Aderonke Osikominu. "Let the Data Speak? On the Importance of Theory-Based Instrumental Variable Estimations." German Economic Review 20, no. 4 (December 1, 2019): e831-e851. http://dx.doi.org/10.1111/geer.12192.

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Abstract In absence of randomized-controlled experiments, identification is often aimed via instrumental variable (IV) strategies, typically two-stage least squares estimations. According to Bayes’ rule, however, under a low ex ante probability that a hypothesis is true (e.g. that an excluded instrument is partially correlated with an endogenous regressor), the interpretation of the estimation results may be fundamentally flawed. This paper argues that rigorous theoretical reasoning is key to design credible identification strategies, the foremost, finding candidates for valid instruments. We discuss prominent IV analyses from the macro-development literature to illustrate the potential benefit of structurally derived IV approaches.

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Yang, Jianwei, Chunqing Zhao, Xi Li, and Fumin Wang. "The Reliability Evaluation of Electromagnetic Valve of EMUs Based on Two-Parameter Exponential Distribution." Open Mechanical Engineering Journal 9, no. 1 (September 16, 2015): 630–36. http://dx.doi.org/10.2174/1874155x01509010630.

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In order to solve reliability evaluation of life of electromagnetic valve of EMUs, this paper evaluated the life of electromagnetic valve under small sample size based on zero-failure data. Firstly, prior distribution of the failure probability was taken into consideration and then the posteriori distribution was obtained by using the Bayes method so that the Bayes estimation could be received under the square loss. Finally, according to the pi received, the reliability parameters of two-parameter exponential distribution were estimated based on weighted least square method. In addition, this paper applied the reliability theory to the reliability life evaluation of electromagnetic valve of EMUs which shows that this method can solve the reliability assessment problem providing certain theoretical basis for the reliability of electromagnetic valve.

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Li, Yuanbin. "Fatigue Life Prediction of Gear System Based on Bayes Statistical Theory with Gamma Prior Distribution." Journal of Information and Computational Science 11, no. 15 (October 10, 2014): 5571–82. http://dx.doi.org/10.12733/jics20104737.

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Deng, Xiuqin, and Jiadi Deng. "A Study of Prisoner’s Dilemma Game Model with Incomplete Information." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/452042.

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Prisoners’ dilemma is a typical game theory issue. In our study, it is regarded as an incomplete information game with unpublicized game strategies. We solve our problem by establishing a machine learning model using Bayes formula. The model established is referred to as the Bayes model. Based on the Bayesian model, we can make the prediction of players’ choices to better complete the unknown information in the game. And we suggest the hash table to make improvement in space and time complexity. We build a game system with several types of game strategy for testing. In double- or multiplayer games, the Bayes model is more superior to other strategy models; the total income using Bayes model is higher than that of other models. Moreover, from the result of the games on the natural model with Bayes model, as well as the natural model with TFT model, it is found that Bayes model accrued more benefits than TFT model on average. This demonstrates that the Bayes model introduced in this study is feasible and effective. Therefore, it provides a novel method of solving incomplete information game problem.

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Tan, Yuegang, Li Cai, Bei Peng, and Lijun Meng. "An Investigation of Structural Damage Location Based on Ultrasonic Excitation-Fiber Bragg Grating Detection." Advances in Acoustics and Vibration 2013 (September 23, 2013): 1–6. http://dx.doi.org/10.1155/2013/525603.

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With the continuous development of mechanical automation, the structural health monitoring techniques are increasingly high requirements for damage detection. So structural health monitoring (SHM) has been playing a significant role in terms of damage prognostics. The main contribution pursued in this investigation is to establish a detection system based on ultrasonic excitation and fiber Bragg grating sensing, which combines the advantages of the ultrasonic detection and fiber Bragg grating (FBG). Differencing from most common approaches, a new way of damage detection is based on fiber Bragg grating (FBG), which can easily realize distributed detection. The basic characteristics of fiber Bragg grating sensing system are analyzed, and the positioning algorithm of structural damage is derived in theory. On these bases, the detection system was used to analyze damage localization in the aluminum alloy plate of a hole with diameters of 6 mm. Experiments have been carried out to demonstrate that the sensing system was feasible and that the estimation method of the location algorithm was easy to implement.

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Journal articles on the topic 'Detector based on Bayes Theory'

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