Search
43 articles for “Bayesian modelling”
-
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
-
Health Disaster Risk Assessment Using GIS and Remote Sensing Techniques: A Case Study of Malaria
Abstract: Malaria, curable and preventable, is a life threatening disease and also can be regarded as a concurrent health disaster. This disease is greatly influenced by environmental factors such as temperature, rainfall, humidity and altitude which affect the survival and longevity of the vector mosquito, and the presence of water bodies which are breeding sites. Advancement in GIS and remote sensing has brought about an improved spatial analysis through the integrated …
Published in Journal of Remote Sensing & GIS · Vol. 7, Issue 2, 2016 · pp. 50–59 Read article
-
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
-
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
-
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
-
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
-
Blind Image Quality Assessment using NSS Approach in the DCT Domain
Abstract: We have develop an efficient model for improving image quality using IQA and NSS based on blind image Quality Assessment.This algorithm does computation for the parameters which user expect at output. The certain extracted features approach depends on a simple Bayesian inference model to dipict image quality scores. The project features are based on statistic scenes of discrete cosine transform for images. The resultant parameters of the model are used …
Published in Recent Trends in Electronics Communication Systems · Vol. 8, Issue 3, 2021 · pp. 1–6 Read article
-
Blind Image Quality Assessment using NSS Approach in the DCT Domain
Abstract: We have develop an efficient model for improving image quality using IQA and NSS based on blind image Quality Assessment. This algorithm does computation for the parameters which user expect at output. The certain extracted features approach depends on a simple Bayesian inference model to dipict image quality scores. The project features are based on statistic scenes of discrete cosine transform for images. The resultant parameters of the model are …
Published in Recent Trends in Electronics Communication Systems · pp. 1–6 Read article
-
Blind Image Quality Assessment: An Overview
Abstract: We have develop an efficient model for improving image quality using IQA and NSS based on blind image Quality Assessment.. This algorithm does computation for the parameters which user expect at output. The certain extracted features approach relies on a simple Bayesian inference model to predict image quality scores. The project features are based on statistic scenes of discrete cosine transform for images. The estimated parameters of the model are …
Published in Journal of Electronic Design Technology · Vol. 12, Issue 3, 2021 · pp. 1–3 Read article
-
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
-
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
-
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
-
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
-
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
-
Improving Polymer Composite Properties Through Reinforcement Learning Guided Prototyping A Novel Approach for Material Engineering
Abstract: Innovative approaches integrating reinforcement learning (RL) and machine learning (ML) into the fields of polymer composite prototyping and soft actuator manufacturing for applications. This new an algorithm utilizing RL optimizes polymer composite fabrication parameters to enhance material properties efficiently. By iteratively adjusting parameters based on predefined objectives, the RL agent guides the prototyping process, promising to revolutionize polymer composite engineering. A finest control method for locked loop control of Shape …
Published in Journal of Polymer & Composites · Vol. 12, Issue 4, 2024 · pp. 208–218 Read article
-
Research Frame work of Missing Data Analysis using Mathematical Models
Abstract: AbstractThis article deals with providing an overall framework of missing data analysis using mathematical models. Mathematical models serves at large for missing data imputation. Several algorithms in machine learning techniques like Mean, Median, Standard Deviation, Regression and Naïve Bayesian classifier use Mathematical models for analysis. The performance of above mathematical models has been compared by using correlation statistics analysis gives the imputed values are positively related or negatively related or …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 7, Issue 3, 2020 · pp. 36–44 Read article
-
Seasonal ARIMA for Forecasting Sea Surface Temperature of the North Zone of the Bay of Bengal
Abstract: The behavior of the sea surface temperature (SST) of the north zone of the Bay of Bengal plays an important role for understanding climate changes over Bangladesh. The monthly average of SST of this zone is used in this study which is obtained from January 1900 to December 2009. Box and Jenkins method is used to fit a seasonal auto regressive integrated moving average (ARIMA) model used in forecasting SST …
Published in Research & Reviews : Journal of Statistics · Vol. 2, Issue 2, 2013 · pp. 23–31 Read article
-
Thermo-Structural Machine Learning Framework for Malicious Sample Detection and Validation in Polymer Composite Materials
Abstract: The increased use of polymer composites in aerospace, automotive, biomedical and industrial applications has increased the urgency of developing dependable methods to detect malicious samples with counterfeited resins, unauthorized additives, recycled components, hidden flaws, or purposefully degraded physical properties. Most current machine learning techniques have focused either on isolated spectral analysis or detecting flaws in materials; as such, they are unable to perform joint verification of both chemical authenticity and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
-
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
-
Reliability Analysis of an Infinite Range Failure Model under Different Prior Distributions
Abstract: AbstractA failure rate model has been selected for the present study and Bayesian estimates of reliability and hazard rate functions of that model have been obtained. The prior distributions considered for this study are, uniform and inverted gamma distributions. Since, the same mathematics is required for the study of survival analysis that is why the estimate of survival function has also been obtained in this paper.Keywords: Reliability function, hazard function, …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 4, Issue 3, 2017 · pp. 51–73 Read article