Bayesian Rule Research Articles

Lithofacies identification of deep coalbed methane reservoir based on high-resolution seismic inversion

During the exploration and development of deep coalbed methane (CBM), delineating the thickness of coal seam and lithofacies of the roof and floor is one of the major challenging tasks. In past attempts, the prediction methods of these parameters have been limited to the conventional inversion. However, the effect of coal shielding on adjacent reflecting layers restricts the identification of underlying sand effectively by conventional inversion. Also, the depth at which the deep CBM zone is located (1,500–2000 m) produces a significant overlap of P-wave impedance and Vp/Vs of sands and shale which increases classification uncertainty between these two lithofacies. We proposed a new workflow for high-precision quantitative seismic interpretation of deep CBM reservoir. Not only P-wave impedance but also GR is selected as the optimized attributes for lithofacies classification. To reduce the effect of strong reflection of coal seam and identifying thin coal layers, the seismic waveform indication inversion method is used to obtain high-resolution results of P-wave impedance and GR. It uses horizontal changes in seismic waveforms to reflect lithological assemblage characteristics for facies-controlled constraints. Then, Bayesian classification theory is used to achieve three-dimensional lithofacies classification with multi-source data. To improve the continuity and accuracy of the interpreted results, a Markov chain is applied in the Bayesian rule as the spatial prior constraint. A well-associated synthetic test and field data application in Ordos Basin demonstrates the accuracy of the proposed workflow. Compared with conventional inversion, the results of proposed workflow showed higher resolution and accuracy. By providing a new solution for the identification of roof and floor lithofacies of deep CBM reservoir, this workflow aims to contribute to the better exploration and development of deep CBM.

A Bayesian wavelet shrinkage rule under LINEX loss function

A Bayesian wavelet shrinkage rule under LINEX loss function

Navigating Skepticism: Cognitive Insights and Bayesian Rationality in Pinillos’ Why We Doubt

Abstract Pinillos’ Why We Doubt presents a powerful critique of such global skeptical assertions as “I don’t know I am not a brain-in-a-vat (biv)” by introducing a cognitive mechanism that is sensitive to error possibilities and a Bayesian rule of rationality that this mechanism is designed to approximate. This multifaceted argument offers a novel counter to global skepticism, contending that our basis for believing such premises is underminable. In this work, we engage with Pinillos’ adoption of Bayesianism, questioning whether the Bayesian principle that he invokes truly does fail to generate the verdict that we don’t we aren’t biv s, contrary to what Pinillos asserts. Furthermore, we argue that Pinillos’ empirical argument is not empirical enough; we need a lot more empirical work if we are going to counter global skepticism and win over neutral bystanders.

Bayesian Interim Analysis and Efficiency of Phase III Randomized Trials.

Improving the efficiency of interim assessments in phase III trials should reduce trial costs, hasten the approval of efficacious therapies, and mitigate patient exposure to disadvantageous randomizations. We hypothesized that in silico Bayesian early stopping rules improve the efficiency of phase III trials compared with the original frequentist analysis without compromising overall interpretation. Cross-sectional analysis. 230 randomized phase III oncology trials enrolling 184,752 participants. Individual patient-level data were manually reconstructed from primary endpoint Kaplan-Meier curves. Trial accruals were simulated 100 times per trial and leveraged published patient outcomes such that only the accrual dynamics, and not the patient outcomes, were randomly varied. Early stopping was triggered per simulation if interim analysis demonstrated ≥ 85% probability of minimum clinically important difference/3 for efficacy or futility. Trial-level early closure was defined by stopping frequencies ≥ 0.75. A total of 12,451 simulations (54%) met early stopping criteria. Trial-level early stopping frequency was highly predictive of the published outcome (OR, 7.24; posterior probability of association, >99.99%; AUC, 0.91; P < 0.0001). Trial-level early closure was recommended for 82 trials (36%), including 62 trials (76%) which had performed frequentist interim analysis. Bayesian early stopping rules were 96% sensitive (95% CI, 91% to 98%) for detecting trials with a primary endpoint difference, and there was a high level of agreement in overall trial interpretation (Bayesian Cohen's κ, 0.95; 95% CrI, 0.92 to 0.99). However, Bayesian interim analysis was associated with >99.99% posterior probability of reducing patient enrollment requirements ( P < 0.0001), with an estimated cumulative enrollment reduction of 20,543 patients (11%; 89 patients averaged equally over all studied trials) and an estimated cumulative cost savings of 851 million USD (3.7 million USD averaged equally over all studied trials). Bayesian interim analyses may improve randomized trial efficiency by reducing enrollment requirements without compromising trial interpretation. Increased utilization of Bayesian interim analysis has the potential to reduce costs of late-phase trials, reduce patient exposures to ineffective therapies, and accelerate approvals of effective therapies. Question: What are the effects of Bayesian early stopping rules on the efficiency of phase III randomized oncology trials?Findings: Individual-patient level outcomes were reconstructed for 184,752 patients from 230 trials. Compared with the original interim analysis strategy, in silico Bayesian interim analysis reduced patient enrollment requirements and preserved the original trial interpretation. Meaning: Bayesian interim analysis may improve the efficiency of conducting randomized trials, leading to reduced costs, reduced exposure of patients to disadvantageous treatments, and accelerated approval of efficacious therapies.

On Entropic Learning from Noisy Time Series in the Small Data Regime.

In this work, we present a novel methodology for performing the supervised classification of time-ordered noisy data; we call this methodology Entropic Sparse Probabilistic Approximation with Markov regularization (eSPA-Markov). It is an extension of entropic learning methodologies, allowing the simultaneous learning of segmentation patterns, entropy-optimal feature space discretizations, and Bayesian classification rules. We prove the conditions for the existence and uniqueness of the learning problem solution and propose a one-shot numerical learning algorithm that-in the leading order-scales linearly in dimension. We show how this technique can be used for the computationally scalable identification of persistent (metastable) regime affiliations and regime switches from high-dimensional non-stationary and noisy time series, i.e., when the size of the data statistics is small compared to their dimensionality and when the noise variance is larger than the variance in the signal. We demonstrate its performance on a set of toy learning problems, comparing eSPA-Markov to state-of-the-art techniques, including deep learning and random forests. We show how this technique can be used for the analysis of noisy time series from DNA and RNA Nanopore sequencing.

Analysis of the Gas Safety Situation Based on the Schweizer-Sklar Rule.

In the coal mining process, a large amount of harmful gases will be produced, which we call "gas". The main component of gas is methane. After the methane concentration reaches a certain limit, an explosion will occur, seriously affecting the safety of the production of coal mines. Gas safety situational awareness is an important basis for gas early warning. In order to take appropriate measures for different levels of risk, the safety situation level is classified into five levels, which can be attributed to the classification problem in machine learning. We propose a gas security situation analysis model based on the Schweizer-Sklar rule (GSSAM-SSR) using the multiweighted Schweizer-Sklar triangular norm array combination rule (SSR). First, the raw data are preprocessed. The model uses K-means clustering with feature preference to determine the label column of the data set and principal component analysis to reduce the dimensionality of the collected high-dimensional gas-related data. Ten basic classification models are constructed, and the best performing models are selected as the basic classifier set by using the preorder method. Then, the SSR combination rule is proposed to combine the basic classifiers. Finally, considering the dynamic and quasi dynamic data of the mine, the basic classifier set and SSR combination rule are combined to construct the GSSAM-SSR model and applied to the gas security situation analysis. Experimental results show that the SSR combination rule achieves the best classification performance, exhibiting an accuracy of 94.43%, which is 2.51% higher than the accuracy of the gradient boosting decision tree model, which was the highest performing basic classifier and higher than the accuracies of the voting method and Bayesian combination rule.

Joint multi-Gaussian mixture model and its application to multi-model multi-bernoulli filter

The existing multi-model Gaussian mixture-multi-Bernoulli (MM-GM-MB) filter lacks the ability to maintain parallel state estimates under different models. As a result, the change of model-related likelihoods lags behind target maneuvers. To address this problem, a joint multi-Gaussian mixture MB (JMGM-MB) filter is proposed. We first propose a JMGM model. Each single-target state estimate is expressed as a set of parallel model-related Gaussian functions with model probabilities, and a weight characterizes the probability of this state estimate. Based on the Bayesian rule, we derive the updating methods of the weights, model probabilities and model-related means, covariances that are collectively called JMGM components. Then, we approximate the state densities of MB filter using the JMGM model. Based on the interactive multi-model (IMM) method, we derive the interacting, prediction and estimation methods of JMGM components. Then, we provide matched association and merging methods for JMGM components. In nonlinear tracking scenarios where sensors simultaneously carry out translations and rotations, we derive a method based on the derivative rule for composite functions to compute the linearized observation matrix. Simulations demonstrate that whether in active or passive tracking, the proposed JMGM-MB filter overcomes the likelihood lag problem and owns higher accuracy of uncertain maneuvering target tracking than the existing MM-GM-MB filter, requiring lower computational expense.

Dynamic Bayesian game optimization method for multi-energy hub systems with incomplete load information

The interconnection of Energy Hub can enhance the energy efficiency and reliability associated with the independent operation of energy systems. However, the traditional optimal scheduling methods are hard to tackle the situation with the incomplete load information and competitive constraints of a multi-Energy Hub. This paper proposes a dynamic Bayesian game optimization scheduling strategy considering incomplete information on users’ load-side demand. First, a load forecast error coefficient is introduced to handle the conditional probability problem caused by incomplete information through the Bayesian rule. The constraint relationship among multi-Energy Hub under the dynamic price mechanism is analyzed to meet the economic and environmental coordination goals under load uncertainty. Second, extending from Nash equilibrium to Bayes Nash equilibrium proves the existence and uniqueness of Nash equilibrium solutions in Bayesian game models. The decision game algorithm is used to optimize the scheduling of the multi-Energy Hub and subsequently ensure the autonomous scheduling of the system and solve the information barriers. Finally, an Integrated Energy System composed of three Energy Hubs was used to validate the effectiveness and superiority of the proposed optimization method. Results show that under the Bayesian game decision, the multi-Energy Hub game model has reduced the total cost of the Integrated Energy System by 0.78 %, which can improve the economic benefit and address the environmental pollution problem.

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

Equilibrium Portfolio Selection for Smooth Ambiguity Preferences

This paper investigates the equilibrium portfolio selection for smooth ambiguity preferences in a continuous-time market. The investor is uncertain about the risky asset’s drift term and updates the subjective belief according to the Bayesian rule. A verification theorem is established, and an equilibrium strategy can be decomposed into a myopic demand and two hedging demands. When the prior is Gaussian, we provide an equilibrium solution in closed form. Moreover, a puzzle in the numerical results is interpreted via an alternative representation of the smooth ambiguity preferences. Funding: This work was supported by the National Key R&amp;D Program of China [Grant 2020YFA0712700], the National Natural Science Foundation of China [Grants 11871036, 11901574, 12071146, 12271290, and 12371477], and the MOE Project of Key Research Institute of Humanities and Social Sciences [Grant 22JJD910003].

Weighted Bayesian Belief Network for diabetics: a predictive model.

Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the "diabetes capital of the world" owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

CTGGAN: Controllable Text Generation with Generative Adversarial Network

Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user’s dissatisfaction or complaints. This imposes significant demands on controlling language model output. However, demerits exist among traditional methods. Promoting and fine-tuning language models exhibit the “hallucination” phenomenon and cannot guarantee complete adherence to constraints. Conditional language models (CLM), which map control codes into LM representations or latent space, require training the modified language models from scratch and a high amount of customized dataset is demanded. Decoding-time methods employ Bayesian Rules to modify the output of the LM or model constraints as a combination of energy functions and update the output along the low-energy direction. Both methods are confronted with the efficiency sampling problem. Moreover, there are no methods that consider the relation between constraints weights and the contexts, as is essential in actual applications such as customer service scenarios. To alleviate the problems mentioned above, we propose Controllable Text Generation with Generative Adversarial Networks (CTGGAN), which utilizes a language model with logits bias as the Generator to produce constrained text and employs the Discriminator with learnable constraint weight combinations to score and update the generation. We evaluate the method in the text completion task and Chinese customer service dialogues scenario, and our method shows superior performance in metrics such as PPL and Dist-3. In addition, CTGGAN also exhibits efficient decoding compared to other methods.

Abstract 3605: Phase II study of pembrolizumab in conjunction with lymphodepletion, TIL ACT, and high- or low-dose IL-2 in patients with metastatic melanoma

Abstract Background: In metastatic melanoma (MM) patients (pts), adoptive cell transfer (ACT) using tumor-infiltrating lymphocytes (TIL) can yield lasting responses. However, TIL ACT with high-dose IL-2 has notable toxicities. Anti-PD1 (pembrolizumab) is FDA approved for MM treatment. Our study aimed to merge TIL infusion with anti-PD1 in two arms, comparing high (HD, arm 1) and low (LD, arm 2) IL-2 doses. Methods: After tumor harvest and TIL expansion, pts were lymphodepleted with cyclophosphamide and fludarabine, followed by infusion with their ex-vivo expanded TIL combined with IL-2 (arm 1: 720,000 IU/kg IV q 8 hrs up to 15 doses; arm 2: 2 million IU SC for 14 days). Pts were given 200 mg of IV anti-PD1 starting 21 days post-TIL infusion, followed by doses every 3 weeks for up to 2 years. Blood was taken before lymphodepletion and before the first two anti-PD1 doses for flow cytometry and cytokine analysis The primary endpoint of the study was determination of overall response rate by RECISTS 1.1 criteria. Enrollment aimed for 18 pts per arm, monitoring the primary ORR endpoint using a Bayesian rule. Enrollment would halt in any arm if the ORR dropped below 40%. Results: The median age was 50 years (n=14: 6 F and 8 M). Of these 14 pts, 8, 2, 2, and 2 had cutaneous, uveal, mucosal, or unknown primary melanoma. Pts had a median of 3 lines of prior therapies (range 1-6), including anti-PD-1 (n=13). In arm 1, one pt had a partial response (PR) for 10 months, 2 had stable disease (SD), 3 had progressive disease (PD), and 1 was not evaluable (NE). In arm 2, one pt had an ongoing PR for over 76 months, 1 had SD, and 5 had PD. Due to ORR &amp;lt;40% in each arm, enrollment was stopped after treatment of 14 pts. There was no significant different in progression-free survival or overall survival between the two arms (p=0.99 and p=0.71, respectively). In general, toxicity levels were comparable in both arms; however, pts in arm 2 had slightly lower grade 3 febrile neutropenia (57% vs. 71%) and shorter hospitalization (median 16 days vs. 18 days) than pts in Arm 1. While no correlation was observed between the phenotype of the TIL product and clinical response, both PR pts received high numbers of TIL (38.6 × 10^9 and 93.7 × 10^9; mean: 32.6 × 10^9 cells), with a high CD8+/CD4+ T cell ratio (284.7 and 2.7; median: 2.7). IL-2 dose did not affect circulating Treg frequency, phenotype, or proliferation by flow cytometry 3 weeks post TIL infusion. IL-2 dose was not associated with CD4+ non-Treg and CD8+ T cell phenotype or proliferation. Addition of anti-PD1 reduced expression of checkpoints but did not boost T cell proliferation. Conclusion: Our treatment strategy did not yield any distinct difference between low and high dose IL-2, suggesting the potential of low-dose IL-2 as an alternative. Anti-PD1 did not have any appreciable effect on immune states potentially due to the cohorts being comprised of a heavily pre-treated heterogenous pt population. Citation Format: Merve Hasanov, Simin Kiany, Marie-Andrée Forget, Roland L. Bassett, Michael A. Davies, Adi Diab, Jeffrey E. Gershenwald, Isabella C. Glitza, Jeffrey E. Lee, Anthony Lucci, Jennifer L. McQuade, Sapna P. Patel, Merrick I. Ross, Hussein A. Tawbi, Jennifer Wargo, Michael K. Wong, Chantale Bernatchez, Patrick Hwu, Cara Haymaker, Rodabe N. Amaria. Phase II study of pembrolizumab in conjunction with lymphodepletion, TIL ACT, and high- or low-dose IL-2 in patients with metastatic melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3605.

Cerebellar interpositus nucleus exhibits time-dependent errors and predictive responses

Learning is a functional state of the brain that should be understood as a continuous process, rather than being restricted to the very moment of its acquisition, storage, or retrieval. The cerebellum operates by comparing predicted states with actual states, learning from errors, and updating its internal representation to minimize errors. In this regard, we studied cerebellar interpositus nucleus (IPn) functional capabilities by recording its unitary activity in behaving rabbits during an associative learning task: the classical conditioning of eyelid responses. We recorded IPn neurons in rabbits during classical eyeblink conditioning using a delay paradigm. We found that IPn neurons reduce error signals across conditioning sessions, simultaneously increasing and transmitting spikes before the onset of the unconditioned stimulus. Thus, IPn neurons generate predictions that optimize in time and shape the conditioned eyeblink response. Our results are consistent with the idea that the cerebellum works under Bayesian rules updating the weights using the previous history.

Trip Purpose Inference and Spatio-Temporal Characterization Based on Anonymized Trip Data: Empirical Study from Dockless Shared Bicycle Dataset in Xi’an, China

The rapid and widespread deployment and applications of the Internet of Things (IoT) and big data technology has generated massive personal travel data. Although most of the data are anonymized, they still provide possibilities for analyzing individual travel behavior. It is worthwhile to explore how to more accurately infer travel purposes based on trajectory data, smart card fare data, or shared bicycle data. In this paper, an improved research framework is proposed for travel purpose inference by applying the gravity model, Bayesian criterion, and spatial clustering method. The gravity model and Bayesian rule are used to calculate the probability of users traveling to nearby POIs (points of interest), and the clustering algorithm is used to identify the locations regularly visited by users. Through the identification of maximum probability POI and regular trips, different travel purposes represented by POI can be distinguished. The results showed that the identification of regular trips could verify and complement the recognition of trip purposes by POIs. Using Xi’an city of China as an example for the study, the results show that: 1) regular trips accounted for 32% of the week’s trips; 2) there are 10% of the trips reflect different trip purposes for the same POI; 3) it is also important to note that 70% of the users have a parking error of fewer than 100 m when going to the exact location. This distance can provide a reference for the study of bicycle commuting. Finally, we analyzed the spatial and temporal distribution characteristics of different trip purposes. The analysis of the spatial and temporal distribution can provide suggestions for the operation of bike-sharing enterprises and the management of regulators.

Modeling the effect of observational social learning on parental decision-making for childhood vaccination and diseases spread over household networks.

In this paper, we introduce a novel model for parental decision-making about vaccinations against a childhood disease that spreads through a contact network. This model considers a bilayer network comprising two overlapping networks, which are either Erdős-Rényi (random) networks or Barabási-Albert networks. The model also employs a Bayesian aggregation rule for observational social learning on a social network. This new model encompasses other decision models, such as voting and DeGroot models, as special cases. Using our model, we demonstrate how certain levels of social learning about vaccination preferences can converge opinions, influencing vaccine uptake and ultimately disease spread. In addition, we explore how two different cultures of social learning affect the establishment of social norms of vaccination and the uptake of vaccines. In every scenario, the interplay between the dynamics of observational social learning and disease spread is influenced by the network's topology, along with vaccine safety and availability.

Learning mechanism for a collective classifier based on competition driven by training examples

The purpose of this work is to modify the learning mechanism of a collective classifier in order to provide learning by population dynamics alone, without requiring an external sorting device. A collective classifier is an ensemble of non-identical simple elements, which do not have any intrinsic dynamics neither variable parameters; the classifier admits learning by adjusting the composition of the ensemble, which was provided in the preceding literature by selecting the ensemble elements using a sorting device. Methods. The population dynamics model of a collective classifier is extended by adding a “learning subsystem”, which is controlled by a sequence of training examples and, in turn, controls the strength of intraspecific competition in the population dynamics. The learning subsystem dynamics is reduced to a linear mapping with random parameters expressed via training examples. The solution to the mapping is an asymptotically stationary Markovian random process, for which we analytically find asymptotic expectation and show its variance to vanish in the limit under the specified assumptions, thus allowing an approximate deterministic description of the coupled population dynamics based on available results from the preceding literature. Results. We show analytically and illustrate it by numerical simulation that the decision rule of our classifier in the course of learning converges to the Bayesian rule under assumptions which are essentially in line with available literature on collective classifiers. The implementation of the required competitive dynamics does not require an external sorting device. Conclusion. We propose a conceptual model for a collective classifier, whose learning is fully provided by its own population dynamics. We expect that our classifier, similarly to the approaches taken in the preceding literature, can be implemented as an ensemble of living cells equipped with synthetic genetic circuits, when a mechanism of population dynamics with synthetically controlled intraspecific competition becomes available.

Remaining Useful Life Prediction for Two-Phase Nonlinear Degrading Systems with Three-Source Variability.

Recently, the estimation of remaining useful life (RUL) for two-phase nonlinear degrading devices has shown rising momentum for ensuring their safe and reliable operation. The degradation processes of such systems are influenced by the temporal variability, unit-to-unit variability, and measurement variability jointly. However, current studies only consider these three sources of variability partially. To this end, this paper presents a two-phase nonlinear degradation model with three-source variability based on the nonlinear Wiener process. Then, the approximate analytical solution of the RUL with three-source variability is derived under the concept of the first passage time (FPT). For better implementation, the offline model parameter estimation is conducted by the maximum likelihood estimation (MLE), and the Bayesian rule in conjunction with the Kalman filtering (KF) algorithm are utilized for the online model updating. Finally, the effectiveness of the proposed approach is validated through a numerical example and a practical case study of the capacitor degradation data. The results show that it is necessary to incorporate three-source variability simultaneously into the RUL prediction of the two-phase nonlinear degrading systems.

Noise estimation based on optimal smoothing and minimum controlled through recursive averaging for speech enhancement

One of the most significant challenges in the real time speech processing applications is the elimination of noise in the corrupted speech data. This noise can significantly impact the efficacy/performance of the applications of speech processing. However, developing a robust noise reduction algorithm is crucial for improving the accuracy of automatic speech recognition (ASR) and other speech processing systems under uncontrolled conditions. In this paper, we propose an algorithm to reduce the background noise in the degraded speech data under highly non-stationary conditions. The proposed optimal smoothing and minima controlled (OSMC) technique uses recursive averaging to enhance degraded speech data. Initially, a smoothed periodogram and local minima of the degraded speech data are computed and determined the time-frequency dependent threshold factor. The ratio of smoothed periodogram to local minima is used to find the active regions of speech in the degraded speech data by adapting the Bayesian minimum cost decision rule. To calculate the estimated noise spectrum for each frequency bin, a time-frequency smoothing factors are used. The perceptual evaluation of speech quality (PESQ) and normalized covariance metric (NCM) are used to evaluate the speech quality and intelligibility of the proposed technique over competing algorithms after speech enhancement. The experimental results demonstrated that the proposed algorithm has given a significant improvement in terms of average values of PESQ by 15.03% and 16.71% and NCM by 3.45% and 5.73% for NOIZEUS and Kannada speech databases at 5 dB and 10 dB respectively, over unprocessed speech under highly non-stationary noisy environments.

Three-way decision-based conditional probabilities by opinion scores and Bayesian rules in circular-Pythagorean fuzzy sets for developing sustainable smart living framework

Three-way decision-based conditional probabilities by opinion scores and Bayesian rules in circular-Pythagorean fuzzy sets for developing sustainable smart living framework