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76 articles for “Bayesian”
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Bayesian Optimization–Driven Operating Parameter Tuning for Maximizing Methane Yield in Anaerobic Digestion
Abstract: To achieve maximum methane production in an anaerobic digestion (AD) process, a combination of various operational parameters must be tuned nonlinearly in the digestion ecosystem. The conventional trial and error optimization methods are slow, resource consuming, and in most instances, cannot model the intricate parameter interaction in biogas production. The current work introduces a Bayesian Optimization-based model to optimize the set of conditions to maximize the level of methane produced …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 1–8 Read article
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Predictive Modeling and Optimization of Tensile and Flexural Strength in FDM 3D Printing Using Decision Trees and Bayesian Optimization.
Abstract: This research investigates predictive modelling and optimization technique for the tensile and flexural strength of PlA (Poly Lactic Acid) in Fused Deposition Modelling (FDM) 3D printing. Employing Decision Trees and Bayesian Optimization enhances comprehension and control of 3D printing process. Precise model predicts PLA material properties based on input parameters. Methodology involves rigorous data preprocessing, encompassing, cleaning, transformation, and normalization. Hyperparameter optimization via grid search systematically explores configurations, optimizing model …
Published in Journal of Polymer & Composites · Vol. 11, Issue 12, 2023 · pp. 203–214 Read article
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Chi-Square Statistic and Principal Component Analysis Based Compressed Feature Selection Approach for Naïve Bayesian Classifier
Abstract: Many of the machine learning algorithms are based on an assumption of attribute independency and often used in domains where the assumption doesn’t hold true. Naïve Bayesian (NB) classifier makes assumption that all the features are conditionally independent given the class labels; In this paper, attribute dependencies were analyzed using Chi-Square test and the Principal Component Analysis (PCA) was carried out on the whole dataset to get a set of …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 2, Issue 2, 2015 · pp. 16–23 Read article
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TextSum: An Automatic Text Summarization Tool using Bayesian Classification
Abstract: Automatic text summarization is the process of summarizing text from one or more documents in order to help the reader to determine if [s]he has to read the document(s) in full. Although automatic summarization is around for many years, there is a renewed interest in this area from the government, industry, and academia. The reason is the availability of large quantities of information due to world wide web and the …
Published in Recent Trends in Programming languages · Vol. 1, Issue 1, 2014 · pp. 1–3 Read article
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Heteroscedasticity in Survey Data and Model Selection based on Weighted Schwarz-Bayesian Information Criterion
Abstract: AbstractThis paper proposed Weighted Schwarz Bayesian Information criterion for the purpose of selecting a best model from various competing models, when heteroscedasticity is present in the survey data. The authors found that the information loss between the true model and fitted models are equally weighted, instead of giving unequal weights. The computation of weights purely depends on the differential entropy of each sample observation and traditional Schwarz Bayesian information criterion …
Published in Research & Reviews : Journal of Statistics · Vol. 2, Issue 3, 2013 · pp. 8–23 Read article
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Bayesian Approximation Techniques of Inverse Rayleigh Distribution
Abstract: The present study is concerned with the estimation of inverse Rayleighdistribution using various Bayesian approximation techniques like normal approximation, and Tierney and Kadane (T-K) approximation. Different informative and non-informative priors are used to obtain the Baye’s estimate of inverse Rayleighdistribution under different approximation techniques. The simulation study has been conducted for comparison of Baye’s estimates obtained under different approximations using different priors. A real life example has also been discussed …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 50–59 Read article
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Bayesian Weibull Model in Survival Analysis
Abstract: In this paper, we examine the performance of Maximum Likelihood Estimation (MLE) and Bayesian estimation of survival function of Weibull distribution with two parameters to the assigning informative prior and with three loss functions, namely, Linear Exponential (LINEX) Loss, General Entropy Loss and Squared Error Loss. Here the simulation study is carried out and the statistical analysis is done. Keywords: Weibull distribution, maximum likelihood estimation, Bayesian estimation, linear exponential loss, …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 24s–29s Read article
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Finding Risk Factors in Thyroid and Cardiovascular System Using Naive Bayesian (NB) Machine Learning Technique
Abstract: Thyroid malady could be a common condition. A TSH test is carried out to check for the capacities of your thyroid organ. Illnesses of thyroid can be caused by conditions which cause thyroid organ over or under function. The test can moreover assist you recognize thyroid conditions some time recently indications happen. It is either as well (hyperthyroidism) or under-active (hypothyroidism), you might know. In case untreated, thyroid clutter may …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 8, Issue 1, 2020 · pp. 1–5 Read article
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Bayesian Analysis of Operating Characteristic Curves of Single Sampling Plans by Attributes under Mixture of Two Binomial Distributions
Abstract: In the current scenario, manufacturing of products in single stream is not enough to meet the needs of consumers and in order to increase the production, two or more streams are required and the lots are formed from various streams. The production unit differs due to input raw materials, machineries, manpower and management, there may be inherent variation among the quality of the products manufactured from different streams. In this …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 12s–23s Read article
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Reducing the Uncertainty of Marine Accidents Failure Rate in Harsh Environment; Using Bayesian Theory
Abstract: The interest in arctic activities is strong, but has not led to significant quantifying analysis over the fact of finding rescannable failure rate. To attract investors and promote the development of a marine industry, a clear concept of project risk is paramount; in particular, issues relating to human role in operation are critical. In the public domain, reliability of information is often scarce or inappropriate for such environment. Also, an …
Published in Journal of Industrial Safety Engineering · Vol. 3, Issue 2, 2016 · pp. 30–39 Read article
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Bayesian Inference to Time Series Data Mining
Abstract: The time series data mining (TSDM) framework is a fundamental contribution to the field of time series analysis and data mining in the recent past. Methods based on the TSDM framework are able to successfully characterize and predict complex, nonperiodic, irregular and chaotic time series. The TSDM methods overcome limitations including stationarity and linearity requirements of traditional time series analysis techniques by adopting data mining concepts for analyzing time series. …
Published in Journal of Advanced Database Management & Systems · Vol. 1, Issue 3, 2014 · pp. 10–14 Read article
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Bayesian Image Segmentation using HMRF Algorithm
Abstract: AbstractImage segmentation is a process of dividing the image in to some distinct regions. These regions shave specially coherent in nature and have similar attributes. This technique is widely used for image analyses and to interpret the desired feature. In this present paper, we will study about the hidden Markov random fields (HMRF) and find its expectation maximization algorithm. The main idea behind developing HMRF is to adjoin the “data …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 7, Issue 3, 2020 · pp. 1–8 Read article
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A Bayesian Case-Control Study to Study the Effect of Covariates in Gall Bladder Carcinoma
Abstract: The paper provides a Bayesian study to see the effect of different covariates in developing gallbladder carcinoma from a group of patients examined in the form of case-control scenario. The cases are those who have developed carcinoma from a group of gallbladder patients and the control are those who belong to the latter category but not in the former. The study has also been extended to evaluate the odds ratio, …
Published in Research and Reviews: A Journal of Medicine · Vol. 1, Issue 1, 2011 · pp. 1–24 Read article
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Bayesian Approach for Gender Differentiation by Finger Ridges Count
Abstract: AbstractClassification of gender from fingerprints is an important step in forensic anthropology in order to identify the gender of a person. Fingerprints are considered to be the most reliable criteria for personal identification. The present analysis was undertaken to observe the distribution of finger print pattern in males and females from U.P. The objective of the present study was to study the differences in the finger loop ridge count among …
Published in Research & Reviews : Journal of Statistics · Vol. 2, Issue 2, 2013 · pp. 12–15 Read article
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Comparison of Models of Machine Learning and Hyperparameter Optimization Methods on Various Datasets
Abstract: The most likely phase in achieving powerful and robust machine learning models is probably the hyperparameter tuning step. The traditional exhaustive methods of search (grid search and others) ensure that the search space is covered, but are computationally inexpensive; random search is less expensive and can still miss good regions; and lastly, the modern model-based and population-based methods (Bayesian optimization, tree-structured Parzen estimator (TPE), genetic algorithms) are thought to provide …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 · pp. 35–42 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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Using the R Software to Conduct Network Meta-Analysis on Diabetes Data” h
Abstract: A meta-analysis's goal is typically to quantify the total treatment effect and draw conclusions regarding the differences in effects between the two treatments. Meta-analysis is a quantitative method for combining the findings of several studies in the social and medical sciences.. A meta-analysis might be of three typical forms. Network-wide, pairwise, and multivariate meta-analyses are all possible. The integrated assessment of more than two treatments is generally made possible by …
Published in Research & Reviews : Journal of Statistics · Vol. 13, Issue 2, 2024 · pp. 57–76 Read article
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Managing the Resources of LTE Networks using Multi-orthogonal Access based on Deep Learning
Abstract: AbstractOne of the topics discussed in telecommunications systems is joint subcarrier and power allocation in the uplink of an NOMA system that we study. Due to this reason a novel radio resource management framework is presented based on code-domain and a deep learning algorithm for uplink and downlink transmissions, such that the neural network is trained by Bayesian regularization back propagation and the mean squared error )MSE) are the training …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 7, Issue 2, 2020 · pp. 19–26 Read article
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The Role of Optimization and Probability in Shaping Artificial Intelligence
Abstract: This study discusses the basic roles of optimization algorithms and the theory of probability in the process of evolution and development of Artificial intelligence (AI). First, we introduce the role played by the next generation of leading-edge optimization algorithms developed since gradient descent to evolutionary strategies with respect to the learning of high-level AI models and how to enable them to learn to effectively explore high-dimensional parameter spaces. At the …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 123–128 Read article
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Data Structure Driven Probabilistic Deadlock Resolution in Multiprocessor Systems
Abstract: Deadlock resolution in multiprocessor systems is fundamentally a graph-theoretic and probabilistic decision problem. Existing victim selection heuristics, such as youngest, oldest, and lowest priority, apply static rules that overlook the dynamic runtime state of processes, leading to unnecessary computational loss. This paper reframes the inference-guided preemption (IGP) algorithm as a data-structure-centric solution, highlighting how resource allocation graphs, wait-for graphs, adjacency lists, min-heaps, and hash-based evidence stores interact to enable efficient …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 11–20 Read article