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36 articles for “Bayesian method”
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Parameter Estimation of Nakagami Distribution under Weighted Loss Function
Abstract: In this paper, the Nakagami distribution is considered for Bayesian analysis. The expressions for Bayes estimators of the scale parameter have been derived under squared error and weighted loss functions by using quasi and inverted gamma priors.
Published in Research & Reviews : Journal of Statistics · Vol. 10, Issue 2, 2021 · pp. 23–28 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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Indirect Treatment Comparison in Meta-Analysis Using Three Methods for Rheumatoid Arthritis
Abstract: Background: The data were extracted from the comparative effectiveness review (CER) on pharmacological treatments for rheumatoid arthritis (RA) developed by the International University of North Carolina Evidence Based Practice Center (UNC EPC). The included studies enrolled patients with active RA despite oral disease-modifying antirheumatic drugs (DMARD) therapy. The outcome measures of choice were American College of Rheumatology (ACR) 20/50/70 response rates.Objective: To compare and find the best treatment for RA …
Published in Research and Reviews : Journal of Computational Biology · Vol. 7, Issue 1, 2018 · pp. 22–27 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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Statistical Modeling: New Trends and Applications
Abstract: Statistical modeling plays a crucial role in various fields, aiding in decision-making, prediction, and inference. This article presents a comprehensive review of recent literature to identify and discuss the emerging trends in statisticaL modeling. The methodology involved a systematic search of relevant databases using specific keywords and selection criteria. The review highlights the limitations of traditional statistical modeling techniques and emphasizes the need for advancements in the field. The findings …
Published in Research & Reviews : Journal of Statistics · Vol. 12, Issue 1, 2023 · pp. 39–43 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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Experimental Validation and Implementation Framework for Optimized Methane Yield Prediction in Anaerobic Digestion
Abstract: The correct validation and realistic application of optimized anaerobic digestion (AD) models are essential steps in transferring biogas production systems to real-life. This paper outlines an experimental validation and deployment pipeline of an AI-optimized model of the methane yield prediction model based on the application of more advanced machine learning and Bayesian optimization methods. Others The validated surrogate-assisted optimization model was tested with controlled laboratory-scale AD experiments at optimized operating …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 4, Issue 1, 2026 · pp. 25–32 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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Evaluation and Scientific Investigation: Stock Market Forecasting Techniques
Abstract: Analysts and scholars have consistently shown interest in predicting stock market trends, a complex task given the multitude of variables influencing stock values. This article includes a thorough analysis of 50 research papers that propose methodology for stock market prediction, including Bayesian models, fuzzy classifiers, artificial neural networks (ANNs), support vector machines (SVMs) classifiers, neural networks (NNs), and machine learning techniques. The collected papers are categorized using various prediction, clustering …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 3, 2023 · pp. 26–40 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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Use of Intelligent Control Systems to Manage Traffic Effectively and Productively
Abstract: Intelligent control system is the advanced technology in which the collected data is provided to A.I. and with the help ofvarious methods like machine learning, neural networks, Bayesian probability, fuzzy logic the solution to a particularproblem is found.In a country’s growth the traffic management plays a great role as the maximum industries depends on the highways orroads and when it is well planned it can reduce the commute time and …
Published in Trends in Mechanical Engineering & Technology · Vol. 13, Issue 1, 2023 · pp. 22–27 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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Geospatial Depiction of Groundwater Level Disparities on a Temporal Component over Portions of Cuddapah District, India Using Contemporary Geo Tools and Interrelated Statistics
Abstract: This paper mainly focuses on comparing methods such as Deterministic methods, Geostatistical methods and Interpolation methods in order to obtain a good fit to represent groundwater level disparities existed in the study area from 1996 to 2015. Inverse Distance Weighting, Global Polynomial Interpolation, Radial Basis Functions, Local Polynomial Interpolation, Kriging, Empirical Bayesian Kriging, Kernel smoothing and Diffusion Kernel were the methods tested for the present study. The data set procured …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 6, Issue 2, 2017 · pp. 13–29 Read article
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A Comprehensive Method for Sentiment Analysis on Twitter Posts
Abstract: AbstractIn recent years, with ascent of users of social media, researchers get drawn to sentiment analysis. The challenge is that sentiment-dependent info from multiple sources does not seem to be thought of usually in existing sentiment classification techniques. Sentiment analysis is incredibly vital as a result of it helps folks and organizations in their decision-making process. There is vast explosion of “sentiments” on the market from social media like Twitter, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 4, Issue 2, 2017 · pp. 1–5 Read article
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Applicability of Integrating BN and Resilience Engineering on Qualitative to Quantitative Description of Human Error
Abstract: Human activities are predominant part of everyday tasks from design, cAbstract Human activities are predominant part of everyday tasks from design, construction, operation, management, maintenance, system upgrade, medical and social life as well as individual human performance. It is not surprising then to see that a major means of failures are related to human errors. But there is still lack of heeds to qualify as well as quantify such errors. …
Published in Journal of Offshore Structure and Technology · Vol. 3, Issue 2, 2016 · pp. 6–16 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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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 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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Advance in Spatial Statistics for Environmental Data Analysis
Abstract: Recent advancements in spatial statistics have significantly enhanced the analysis of environmental data, addressing complex spatial dependencies and offering robust methodologies for environmental monitoring, modeling, and decision-making. This abstract highlights key developments in the field, emphasizing their applications and impact. Geostatistical Methods, Improved geostatistical techniques, such as kriging and co-kriging, provide more accurate predictions of environmental variables by incorporating spatial correlation structures. Advanced kriging variants, including Bayesian kriging and machine …
Published in Research & Reviews : Journal of Statistics · Vol. 12, Issue 3, 2023 · pp. 35–39 Read article
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Implementing Machine Learning in Data Classification
Abstract: Data classification forms an essential aspect of artificial intelligence (AI) and soft computing, helping a great deal in the transformation of raw data into knowledge that forms the basis of numerous applications, such as fraud detection, medical diagnostics, and natural language processing. This study discusses the challenges and the state of the art in data classification, as far as scalability, noise handling, and feature selection optimization are concerned. It gives …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 15–22 Read article