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Journal articles on the topic 'Bayes power'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

McKenzie, Craig R. M. "Bayes plus environment." Behavioral and Brain Sciences 32, no. 1 (2009): 93–94. http://dx.doi.org/10.1017/s0140525x09000399.

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AbstractOaksford & Chater's (O&C's) account of deductive reasoning is parsimonious at a local level (because a rational model is used to explain a wide range of behavior) and at a global level (because their Bayesian approach connects to other areas of research). Their emphasis on environmental structure is especially important, and the power of their approach is seen at both the computational and algorithmic levels.

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Mugdadi, Abdel-Razzaq, and Min A. "Bayes estimation of the power hazard function." Journal of Interdisciplinary Mathematics 12, no. 5 (2009): 675–89. http://dx.doi.org/10.1080/09720502.2009.10700653.

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Calabria, R., and G. Pulcini. "Bayes inference for the modulated power law process." Communications in Statistics - Theory and Methods 26, no. 10 (1997): 2421–38. http://dx.doi.org/10.1080/03610929708832057.

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4

Kim, Hyungchul, and Chanan Singh. "Power system probabilistic security assessment using Bayes classifier." Electric Power Systems Research 74, no. 1 (2005): 157–65. http://dx.doi.org/10.1016/j.epsr.2004.10.004.

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Lerche, Hans Rudolf. "The Shape of Bayes Tests of Power One." Annals of Statistics 14, no. 3 (1986): 1030–48. http://dx.doi.org/10.1214/aos/1176350048.

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Ghosh, Abhik, and Ayanendranath Basu. "Robust Bayes estimation using the density power divergence." Annals of the Institute of Statistical Mathematics 68, no. 2 (2015): 413–37. http://dx.doi.org/10.1007/s10463-014-0499-0.

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Brown, Timothy M. "Automated p-mode identification using Bayes' theorem." Symposium - International Astronomical Union 123 (1988): 491–94. http://dx.doi.org/10.1017/s0074180900158590.

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The task of interpreting p-mode spectra is complicated by the presence of a very large number of oscillation modes, each of which may appear (because of aliasing) in the power spectra corresponding to several values of l and m. Identifying peaks in a power spectrum with particular modes in an interactive fashion thus quickly becomes impractical. Here I describe an automated method for doing this identification. The method is based on an application of Bayes' theorem, which provides a simple way to use prior knowledge about the oscillation spectrum. The method takes as input the observed power

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Pavithraa, G., and S. Sivaprasad. "Analysis And Comparison Of Prediction Of Heart Disease Using Novel Random Forest And Naive Bayes Algorithm." CARDIOMETRY, no. 25 (February 14, 2023): 788–93. http://dx.doi.org/10.18137/cardiometry.2022.25.788793.

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Aim : Prediction of heart disease using Novel Random Forest and comparing its accuracy with Naive Bayes algorithm. Materials and methods: Two groups are proposed for predicting the accuracy (%) of heart disease. Namely, the Novel Random Forest and Naive Bayes algorithm. Here we take 20 samples each for evaluation and compared. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result : The Novel Random Forest gives better accuracy (86.40%) compared to the Naive Bayes accuracy (80.08%). Therefore the statistical significance of Novel Random Fores

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Sugasawa, S. "Robust empirical Bayes small area estimation with density power divergence." Biometrika 107, no. 2 (2020): 467–80. http://dx.doi.org/10.1093/biomet/asz075.

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Summary A two-stage normal hierarchical model called the Fay–Herriot model and the empirical Bayes estimator are widely used to obtain indirect and model-based estimates of means in small areas. However, the performance of the empirical Bayes estimator can be poor when the assumed normal distribution is misspecified. This article presents a simple modification that makes use of density power divergence and proposes a new robust empirical Bayes small area estimator. The mean squared error and estimated mean squared error of the proposed estimator are derived based on the asymptotic properties o

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Liu, Yang, Majid Khan, Syed Masroor Anwar, Zahid Rasheed, and Navid Feroze. "Stress-Strength Reliability and Randomly Censored Model of Two-Parameter Power Function Distribution." Mathematical Problems in Engineering 2022 (June 24, 2022): 1–12. http://dx.doi.org/10.1155/2022/5509684.

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The power function distribution is a flexible waiting time model that may provide better fit for some failure data. This paper presents the Bayes estimates of two-parameter power function distribution under progressive censoring. Different progressive censoring schemes have been used for the analysis. The Bayes estimates are obtained, using conjugate priors, under five loss functions including square error, precautionary, weighted, LINEX, and DeGroot loss function. The Gibbs sampling algorithm and Tierney and Kadane’s Approximation are used for the Bayes estimates of model parameters, reliabil

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Waang Bler Tuang, Soleman, Friden Elefri Neno, and Emirensiana Dappa Ege. "PERAPAN METODE NAÏVE BAYES UNTUK DIAGNOSA KERUSAKAN KOMPUTER." JATI (Jurnal Mahasiswa Teknik Informatika) 7, no. 4 (2024): 2636–40. http://dx.doi.org/10.36040/jati.v7i4.7710.

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Perkembangan teknologi dari waktu ke waktu yang begitu cepat dengan pengguna komputer semakin tinggi dimana komputer merupakan kebutuhan yang sangat penting bagi setiap individu. Permasalahn yang terjadi pada penelitian ini adalah banyak pengguna yang memiliki komputer tetapi ketika komputer terjadi kendala kerusakan tidak mampu untuk menyelesaikan sendiri tetapi membawa ke tempat servis untuk diperbaiki, oleh karena itu penulis melakukan penelitian terhadap 10 (sepuluh) kerusakan, yaitu power IC,IC VGA,Kabel Fleksibel,LCD,Keyboard,Hardisk, VGA, Memori Komputer,Processor dan Power Supply denga

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Gravestock, Isaac, and Leonhard Held. "Adaptive power priors with empirical Bayes for clinical trials." Pharmaceutical Statistics 16, no. 5 (2017): 349–60. http://dx.doi.org/10.1002/pst.1814.

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Reddy, L. Anand Kumar, and P. Sadagopan. "Human Activity Recognition on Smartphones using Innovative Logistic Regression and Comparing Accuracy of Naive Bayes Algorithm." E3S Web of Conferences 491 (2024): 03023. http://dx.doi.org/10.1051/e3sconf/202449103023.

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The objective of this study is to compare the Naive Bayes algorithm with Innovative Logistic Regression in order to enhance human activity identification for sitting and walking. To predict human activity, Naive Bayes and Innovative Logistic Regression are used with different training and testing splits. From each group, ten sets of samples are selected, yielding a total of twenty samples. About 80% of the data from an independent sample T test were utilized in the Gpower test (g power setup parameters: α = 0.05 and power = 0.80, β = 0.2). Compared to Naive Bayes (90.7210%), Innovative Logisti

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Pak, Abbas, Arjun Kumar Gupta, and Nayereh Bagheri Khoolenjani. "On Reliability in a Multicomponent Stress-Strength Model with Power Lindley Distribution." Revista Colombiana de Estadística 41, no. 2 (2018): 251–67. http://dx.doi.org/10.15446/rce.v41n2.69621.

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In this paper we study the reliability of a multicomponent stress-strength model assuming that the components follow power Lindley model. The maximum likelihood estimate of the reliability parameter and its asymptotic confidence interval are obtained. Applying the parametric Bootstrap technique, interval estimation of the reliability is presented. Also, the Bayes estimate and highest posterior density credible interval of the reliability parameter are derived using suitable priors on the parameters. Because there is no closed form for the Bayes estimate, we use the Markov Chain Monte Carlo met

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Puspa, Sofia Debi, Fani Puspitasari, Joko Riyono, Christina Eni Pujiastuti, David Leon Bijlsma, and Joseph Andrew Leo. "Customer Segmentation Analysis Using Random Forest & Naïve Bayes Method In The Case of Multi-Class Classification at PT. XYZ." Mathline : Jurnal Matematika dan Pendidikan Matematika 8, no. 4 (2023): 1359–72. http://dx.doi.org/10.31943/mathline.v8i4.532.

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Cases of the COVID-19 pandemic are gradually decreasing every day in Indonesia, but the impact of the COVID-19 pandemic has greatly affected various sectors, especially the economy and business. Sales transactions have not yet reached the company's target due to weak public purchasing power. The accuracy of customer segmentation analysis and attractive promo voucher offers are needed to increase the opportunity for people's purchasing power for a product. This study aimed to predict the level of customer purchasing power using the random forest and naïve Bayes methods in the case of multi-clas

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Barros, Wysterlânya K. P., Matheus T. Barbosa, Leonardo A. Dias, and Marcelo A. C. Fernandes. "Fully Parallel Proposal of Naive Bayes on FPGA." Electronics 11, no. 16 (2022): 2565. http://dx.doi.org/10.3390/electronics11162565.

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This work proposes a fully parallel hardware architecture of the Naive Bayes classifier to obtain high-speed processing and low energy consumption. The details of the proposed architecture are described throughout this work. Besides, a fixed-point implementation on a Stratix V Field Programmable Gate Array (FPGA) is presented and evaluated regarding the hardware area occupation, processing time (throughput), and dynamic power consumption. In addition, a comparative design analysis was carried out with state-of-the-art works, showing that the proposed implementation achieved a speedup of up to

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Liu, Benmei, and Partha Lahiri. "Adaptive Hierarchical Bayes Estimation of Small Area Proportions." Calcutta Statistical Association Bulletin 69, no. 2 (2017): 150–64. http://dx.doi.org/10.1177/0008068317722293.

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Unit-level logistic regression models with mixed effects have been used for estimating small area proportions in the literature. Normality is commonly assumed for the random effects. Nonetheless, real data often show significant departures from normality assumptions of the random effects. To reduce the risk of model misspecification, we propose an adaptive hierarchical Bayes estimation approach in which the distribution of the random effect is chosen adaptively from the exponential power class of probability distributions. The richness of the exponential power class ensures the robustness of o

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Calabria, R., M. Guida, and G. Pulcini. "Bayes estimation of prediction intervals for a power law process." Communications in Statistics - Theory and Methods 19, no. 8 (1990): 3023–35. http://dx.doi.org/10.1080/03610929008830362.

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Quatto, Piero, Nicolò Margaritella, Isa Costantini, et al. "Brain networks construction using Bayes FDR and average power function." Statistical Methods in Medical Research 29, no. 3 (2019): 866–78. http://dx.doi.org/10.1177/0962280219844288.

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Brain functional connectivity is a widely investigated topic in neuroscience. In recent years, the study of brain connectivity has been largely aided by graph theory. The link between time series recorded at multiple locations in the brain and the construction of a graph is usually an adjacency matrix. The latter converts a measure of the connectivity between two time series, typically a correlation coefficient, into a binary choice on whether the two brain locations are functionally connected or not. As a result, the choice of a threshold τ over the correlation coefficient is key. In the pres

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De Santis, Fulvio. "Alternative Bayes factors: Sample size determination and discriminatory power assessment." TEST 16, no. 3 (2007): 504–22. http://dx.doi.org/10.1007/s11749-006-0017-7.

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Puviarasi, R., and D. Dhanasekaran. "An integrated bayes soft switching interleaved and sliding window PWM for DC-DC boost converter." International Journal of Engineering & Technology 7, no. 3.6 (2018): 249. http://dx.doi.org/10.14419/ijet.v7i3.6.14982.

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In order to develop the efficient dc-dc boost converter in high output power application, an Integrated Bayes Interleaved and Sliding Window (IBI-SW) based PWM framework is proposed. Initially, the integration of interleaving and PWM improves the power factor correction in very high output power applications (photovoltaic panels) and near optimal voltage and current losses. Multiple phase shifts with soft switched Bayes interleaving technique maximizes the power generated in photovoltaic panels and the optimization of power conversion is achieved with sliding window based PWM that performs Max

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Andrews, Isaiah, and Anna Mikusheva. "Optimal Decision Rules for Weak GMM." Econometrica 90, no. 2 (2022): 715–48. http://dx.doi.org/10.3982/ecta18678.

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This paper studies optimal decision rules, including estimators and tests, for weakly identified GMM models. We derive the limit experiment for weakly identified GMM, and propose a theoretically‐motivated class of priors which give rise to quasi‐Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi‐Bayes approach regardless of model identification status, and we recommend quasi‐Bayes for settings where identification is a concern. We further propose weighted average power‐optimal identification‐robust freq

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SHRINER, DANIEL. "Mapping multiple quantitative trait loci under Bayes error control." Genetics Research 91, no. 3 (2009): 147–59. http://dx.doi.org/10.1017/s001667230900010x.

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SummaryIn mapping of quantitative trait loci (QTLs), performing hypothesis tests of linkage to a phenotype of interest across an entire genome involves multiple comparisons. Furthermore, linkage among loci induces correlation among tests. Under many multiple comparison frameworks, these problems are exacerbated when mapping multiple QTLs. Traditionally, significance thresholds have been subjectively set to control the probability of detecting at least one false positive outcome, although such thresholds are known to result in excessively low power to detect true positive outcomes. Recently, fa

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Kim, Gyeongmin, and Jin Hur. "A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations." Sustainability 13, no. 22 (2021): 12723. http://dx.doi.org/10.3390/su132212723.

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Renewable-power-generating resources can provide unlimited clean energy and emit at most minute amounts of air pollutants and greenhouse gases, whereas fossil fuels are contributing to environmental pollution problems and climate change. The share of global power capacity comprising renewable-power-generating resources is increasing. However, due to the variability and uncertainty of wind resources, predicting the power output of these resources remains a key problem that must be resolved to establish stable power system operation and planning. In this study, we propose an ensemble prediction

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Khalyasmaa, Alexandra I., Stepan A. Dmitriev, and Sergey E. Kokin. "Assessment of Power Transformers Technical State Based on Technical Diagnostics." Applied Mechanics and Materials 492 (January 2014): 218–22. http://dx.doi.org/10.4028/www.scientific.net/amm.492.218.

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This paper deals with power transformers technical state assessment based on technical diagnostics using Bayes method. Present a model for integral power transformers technical state assessment on the three types of technical diagnostics. Determining state of power transformers is based on expert judgment using the membership functions.

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Vishnuvardhan, T., and A. Rama. "Estimation of Accuracy Rate in Predicting Cardiovascular Disease using Gaussian Naive Bayes Algorithm with Logistic Regression." CARDIOMETRY, no. 25 (February 14, 2023): 1532–37. http://dx.doi.org/10.18137/cardiometry.2022.25.15321537.

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Aim: Comparison of accuracy rate in prediction of cardiovascular disease using Naive Bayes with Logistic Regression. Materials and Methods: The Naive Bayes (N=10) and Logistic Regression Algorithm (N=10) these two algorithms are calculated by using 2 Groups and taken 20 samples for both algorithm and accuracy in this work. The sample size is determined using the G power Calculator and it’s found to be 10. Results: Based on the Results Accuracy obtained in terms of accuracy is identified by Naive Bayes (87.02%) over the Logistic Regression algorithm (92.18%). Statistical significance difference

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Neumann, P. E. "Three-locus linkage analysis using recombinant inbred strains and Bayes' theorem." Genetics 128, no. 3 (1991): 631–38. http://dx.doi.org/10.1093/genetics/128.3.631.

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Abstract Recombinant inbred (RI) strains are useful in linkage analysis and gene mapping. However, the generally small number of strains in an RI strain set limits the power of RI strains in linkage detection. Several methods for increasing the power of RI strains have been used, including summing data across RI strain sets and excluding linkage to genomic regions. In this paper, Bayesian analysis is applied to three-locus linkage data. This method further increases the power of RI strains to detect linkage and gives estimations of the probability of each of the three possible gene orders if t

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Zhao, Dengfu, Zheng Zhao, Qihong Duan, and Gongnan Xie. "A Poisson-Fault Model for Testing Power Transformers in Service." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/945258.

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This paper presents a method for assessing the instant failure rate of a power transformer under different working conditions. The method can be applied to a dataset of a power transformer under periodic inspections and maintenance. We use a Poisson-fault model to describe failures of a power transformer. When investigating a Bayes estimate of the instant failure rate under the model, we find that complexities of a classical method and a Monte Carlo simulation are unacceptable. Through establishing a new filtered estimate of Poisson process observations, we propose a quick algorithm of the Bay

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Bagchi, S. B., and P. Sarkar. "Bayes Interval Estimation for the Shape Parameter of the Power Distribution." IEEE Transactions on Reliability 35, no. 4 (1986): 396–98. http://dx.doi.org/10.1109/tr.1986.4335481.

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Rodrigues, Alexandre, Lucas Martinuzzo, Flavio Miguel Varejao, Vítor E. Silva Souza, and Thiago Oliveira-Santos. "Reducing power companies billing costs via empirical bayes and seasonality remover." Engineering Applications of Artificial Intelligence 81 (May 2019): 387–96. http://dx.doi.org/10.1016/j.engappai.2019.01.007.

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Meshram, Sameer, Shital Dongre, and Triveni Fole. "Disease Prediction System using naïve bayes." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 1492–96. http://dx.doi.org/10.22214/ijraset.2022.48002.

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Abstract: Accurate and on-time analysis of any health-re- lated problem is vital for the prevention and treatment of the illness. The standard way of diagnosis might not be suf-ficient. Developing a diagnosis system with machine learn- ing (ML) algorithms for prediction of any disease can helpina very more accurate diagnosis than the traditional method.The proposed model is an Disease Prediction System with the help of machine learning algorithm Naive Bayes which takes the symptoms as the input and it gives the output as predicted disease. It results in saving time and also makes it easy to in

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Abim Febri Hananto, Raihan Canggih Panilih, Reihan Setya Banda Syah Putra, Tariq Tariq, and Wildan Setiawan. "Analisis Sentimen Komentar Video Putusan MA Terkait Kaesang Menggunakan Metode Naive Bayes." Saturnus : Jurnal Teknologi dan Sistem Informasi 2, no. 3 (2024): 162–70. http://dx.doi.org/10.61132/saturnus.v2i3.217.

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Political dynasty is a political power exercised by a group of people who are related by family, with the aim of obtaining power and ensuring that this power remains within the group by passing it on to other family members. This study conducts a sentiment analysis on comments related to the Supreme Court decision which is believed to pave the way for Kaesang Pangarep in support of Jokowi's political dynasty. Sentiment analysis is carried out using the Naive Bayes method, a commonly used algorithm for text classification based on probability. The data used consists of comments from videos take

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Aris-Brosou, Stéphane. "Identifying sites under positive selection with uncertain parameter estimates." Genome 49, no. 7 (2006): 767–76. http://dx.doi.org/10.1139/g06-038.

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Codon-based substitution models are routinely used to measure selective pressures acting on protein-coding genes. To this effect, the nonsynonymous to synonymous rate ratio (dN/dS = ω) is estimated. The proportion of amino-acid sites potentially under positive selection, as indicated by ω > 1, is inferred by fitting a probability distribution where some sites are permitted to have ω > 1. These sites are then inferred by means of an empirical Bayes or by a Bayes empirical Bayes approach that, respectively, ignores or accounts for sampling errors in maximum-likelihood estimates of the dist

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Khrennikov, Alexander, Nikolay Aleksandrov, Konstantin Mikhailov, and Sergey Mikhailov. "Neural networks and damage pattern recognition in power transformer diagnostics." E3S Web of Conferences 584 (2024): 01043. http://dx.doi.org/10.1051/e3sconf/202458401043.

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The results of detecting deformations and damage of power transformer windings using the transformer Frequency Response Analysis (SFRA) are presented taking into account RG CIGRE A2.26, Standard IEC 60076-18, Standard IEEE C57.149. The technology of pattern recognition by signal images for diagnostics of winding damage, type of defect, its localization is implemented. Neural networks are used - where in a multilayer perceptron with backpropagation of error - the work of neurons in a hierarchical network is imitated. Parametric methods - such as the Naive Bayes classifier - a probabilistic clas

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Wan, Xin Wang, and Juan Liang. "Speaker Localization in Reverberant Noisy Environment Using Principal Eigenvector and Classifier." Applied Mechanics and Materials 433-435 (October 2013): 416–19. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.416.

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Sound source localization is essential in many microphone arrays application, ranging from speech enhancement to human-computer interface. The steered response power (SRP) using the phase transform (SRP-PHAT) method has been proved robust, but the algorithm may fail to locate the sound source in highly reverberant noisy environment. The Naive-Bayes localization algorithm based on classification of cross-correlation functions outperforms the SRP-PHAT in highly reverberant noisy environment. This paper proposes the improved Naive-Bayes localization algorithm using principal eigenvector. Simulati

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Alif Zidan Mubarok, Hidayatus Sibyan, and Nur Hasanah. "SENTIMENT ANALYSIS KINERJA KARYAWAN DI OKE GARDEN MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER." Journal of Information System and Computer 3, no. 2 (2023): 24–30. https://doi.org/10.34001/jister.v3i2.806.

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Penelitian ini dilakukan untuk mengetahui sberapa akurat algoritma Naïve Bayes Classifier dalam menentukan sentiment analysis berdasarkan data feedback dari para karyawan di OKE garden. Selain itu, ada peneltian ini juga disajikan visualisasi data dari feedback menggunakan aplikasi Microsoft Power BI. Hasil dari perhitungan algoritma Naïve Bayes dalam menentukan jenis sentiment, mendapatkan nilai akurasi yang baik yaitu sebesar 80% dengan menggunakan data latih sebesar 10%. Pada visualisasi data, Pemanfaatan aplikasi Microsoft Power BI pada analisis ini, sebagai alat visualisasi data dengan me

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Kumar, V. S., and K. Vidhya. "Heart Plaque Detection with Improved Accuracy using Naive Bayes and comparing with Least Squares Support Vector Machine." CARDIOMETRY, no. 25 (February 14, 2023): 1595–99. http://dx.doi.org/10.18137/cardiometry.2022.25.15951599.

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Aim: The main aim of this research is to detect heart plaque using the Naive Bayes algorithm with improved accuracy and comparing it with Least Squares Support Vector Machine. Materials and Methods: Naive Bayes algorithm and Least squares Support Vector Machine algorithms are two groups compared in this study. In the Kaggle dataset on Heart Plaque Disease, there were a total of 20 samples. Clincalc is used to calculate sample G power of 0.08 with 95% confidence interval. The training dataset (n = 489 (70 %)) and the test dataset (n = 277 (30 %)) are divided into two groups. Result: The accurac

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Khadse, Natesh. "Analysis of Solar Power Generation Forecasting Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 7112–16. https://doi.org/10.22214/ijraset.2025.70134.

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Abstract: Accurate solar power generation forecasting is critical for efficient grid integration and renewable energy management. This paper presents a comparative analysis of machine learning techniques for predicting solar power output using weather and historical generation data. We evaluate the performance of Naive Bayes and Artificial Neural Network (ANN) models trained on a curated dataset containing temperature, humidity, wind speed, and solar irradiance features. Our methodology emphasizes robust data preprocessing, including outlier removal, missing value imputation, and normalization

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Nassar, Mazen, Refah Alotaibi, and Ahmed Elshahhat. "Statistical Analysis of Alpha Power Exponential Parameters Using Progressive First-Failure Censoring with Applications." Axioms 11, no. 10 (2022): 553. http://dx.doi.org/10.3390/axioms11100553.

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This paper is an endeavor to investigate some estimation problems of the unknown parameters and some reliability measures of the alpha power exponential distribution in the presence of progressive first-failure censored data. In this regard, the classical and Bayesian approaches are considered to acquire the point and interval estimates of the different quantities. The maximum likelihood approach is proposed to obtain the estimates of the unknown parameters, reliability, and hazard rate functions. The approximate confidence intervals are also considered. The Bayes estimates are obtained by con

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Jiang, Yuanyuan, and Xingzhong Xu. "A Two-Sample Test of High Dimensional Means Based on Posterior Bayes Factor." Mathematics 10, no. 10 (2022): 1741. http://dx.doi.org/10.3390/math10101741.

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In classical statistics, the primary test statistic is the likelihood ratio. However, for high dimensional data, the likelihood ratio test is no longer effective and sometimes does not work altogether. By replacing the maximum likelihood with the integral of the likelihood, the Bayes factor is obtained. The posterior Bayes factor is the ratio of the integrals of the likelihood function with respect to the posterior. In this paper, we investigate the performance of the posterior Bayes factor in high dimensional hypothesis testing through the problem of testing the equality of two multivariate n

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Selvam, Ravikumar, and Akhilesh Tyagi. "Residue Number System (RNS) and Power Distribution Network Topology-Based Mitigation of Power Side-Channel Attacks." Cryptography 8, no. 1 (2023): 1. http://dx.doi.org/10.3390/cryptography8010001.

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Over the past decade, significant research has been performed on power side-channel mitigation techniques. Logic families based on secret sharing schemes, such as t-private logic, that serve to secure cryptographic implementations against power side-channel attacks represent one such countermeasure. These mitigation techniques are applicable at various design abstraction levels—algorithm, architecture, logic, physical, and gate levels. One research question is when can the two mitigation techniques from different design abstraction levels be employed together gainfully? We explore this notion

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Lu, Chi-Ken, and Patrick Shafto. "Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning." Entropy 23, no. 11 (2021): 1387. http://dx.doi.org/10.3390/e23111387.

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It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success as a deep network used for feature extraction. Then, a GP was used as the function model. Recently, it was suggested that, albeit training with marginal likelihood, the deterministic nature of a feature extractor might lead to overfitting, and replacement with a Bayesian network seemed to cure it. Here, we propose the conditional deep Gaussian process (DGP) in which the intermediate GPs in hierarchical composition are support

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Mahamdi, Yassine, Ahmed Boubakeur, Abdelouahab Mekhaldi, and Youcef Benmahamed. "Power Transformer Fault Prediction using Naive Bayes and Decision tree based on Dissolved Gas Analysis." ENP Engineering Science Journal 2, no. 1 (2022): 1–5. http://dx.doi.org/10.53907/enpesj.v2i1.63.

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Power transformers are the basic elements of the power grid, which is directly related to the reliability of the electrical system. Many techniques were used to prevent power transformer failures, but the Dissolved Gas Analysis (DGA) remains the most effective one. Based on the DGA technique, this paper describes the use of two of the most effective machine learning algorithms: Naive Bayes and Decision Tree for the identification of power transformer’s faults. In our investigation, 9 different input vectors have been developed from widely known DGA techniques. 481 samples have been used and 6

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Rahman, Habibur, M. K. Roy, and Atikur Rahman Baizid. "Bayes Estimation under Conjugate Prior for the Case of Power Function Distribution." American Journal of Mathematics and Statistics 2, no. 3 (2012): 44–48. http://dx.doi.org/10.5923/j.ajms.20120203.06.

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Chen, Zhao. "Empirical Bayes Analysis on the Power Law Process with Natural Conjugate Priors." Journal of Data Science 8, no. 1 (2021): 139–49. http://dx.doi.org/10.6339/jds.2010.08(1).552.

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Lingham, Rama T., and S. Sivaganesan. "Intrinsic Bayes factor approach to a test for the power law process." Journal of Statistical Planning and Inference 77, no. 2 (1999): 195–220. http://dx.doi.org/10.1016/s0378-3758(98)00181-5.

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Li, Naiyi, Yuan Li, Yongming Li, and Yang Liu. "Empirical Bayes Inference for the Parameter of Power Distribution Based on Ranked Set Sampling." Discrete Dynamics in Nature and Society 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/760768.

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Revathi, G., K. Nageswara Rao, and G. Sita Ratnam. "Email Spam Detection using Naïve Bayes Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 653–55. http://dx.doi.org/10.22214/ijraset.2022.46654.

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Abstract: Email Spam has become a vital issue currently, with high-speed growth of internet users. Some people are using them for illegal conducts, phishing and fraud. Sending malicious link through spam emails which can harm our system and may also they will seek into our system. The need of email spam detection is to prevent spam messages from lagging into user’s inbox so it’ll improve user experience. This project will identify those spam emails by using machine learning approach. Machine learning is one amongst the applications of Artificial Intelligence that allow systems to read and impr

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', Sriyansh, Sumit Kumar Upadhyay, Yash Varshney, and Sreenu Banoth. "Stress Detection Based on Naïve Bayes Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2510–13. http://dx.doi.org/10.22214/ijraset.2023.50716.

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Abstract: Stress is a prevalent issue that affects individuals' mental and physical well-being, leading to various health problems. The use of machine learning (ML) has been gaining popularity as a tool for stress detection. ML techniques have shown promising results in identifying patterns and features from various physiological and behavioral data sources such as heart rate, blood pressure, and speech signals. The primary goal of stress detection using ML is to provide accurate, non-invasive, and costeffective methods for early stress detection and intervention. Overall, stress detection usi

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Alenazi, Fahad S., Khalil El Hindi, and Basil AsSadhan. "Complement-Class Harmonized Naïve Bayes Classifier." Applied Sciences 13, no. 8 (2023): 4852. http://dx.doi.org/10.3390/app13084852.

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Naïve Bayes (NB) classification performance degrades if the conditional independence assumption is not satisfied or if the conditional probability estimate is not realistic due to the attributes of correlation and scarce data, respectively. Many works address these two problems, but few works tackle them simultaneously. Existing methods heuristically employ information theory or applied gradient optimization to enhance NB classification performance, however, to the best of our knowledge, the enhanced model generalization capability deteriorated especially on scant data. In this work, we propos

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