Log in

Relevant bibliographies by topics / Gaussian mixer model (GMM) / Journal articles

To see the other types of publications on this topic, follow the link: Gaussian mixer model (GMM).

Journal articles on the topic 'Gaussian mixer model (GMM)'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Gaussian mixer model (GMM).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Zhang, Lianjun, Chuangmin Liu, and Craig J. Davis. "A mixture model-based approach to the classification of ecological habitats using Forest Inventory and Analysis data." Canadian Journal of Forest Research 34, no. 5 (2004): 1150–56. http://dx.doi.org/10.1139/x04-005.

Full text

Abstract:

A Gaussian mixture model (GMM) is used to classify Forest Inventory and Analysis (FIA) plots into six ecological habitats in the northeastern USA. The GMM approach captures intra-class variation by modeling each habitat class as a mixture of subclasses of Gaussian distributions. The classification is achieved based on the appropriate posterior probability. The GMM classifier outperforms a traditional statistical method (i.e., linear discriminant analysis or LDA), and produces similar overall accuracy rates to a commonly used neural network model (i.e., multi-layer perceptrons or MLP). For the

APA, Harvard, Vancouver, ISO, and other styles

2

Jin, Qiwen, Yong Ma, Erting Pan, et al. "Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity." Remote Sensing 11, no. 20 (2019): 2434. http://dx.doi.org/10.3390/rs11202434.

Full text

Abstract:

In recent years, endmember variability has received much attention in the field of hyperspectral unmixing. To solve the problem caused by the inaccuracy of the endmember signature, the endmembers are usually modeled to assume followed by a statistical distribution. However, those distribution-based methods only use the spectral information alone and do not fully exploit the possible local spatial correlation. When the pixels lie on the inhomogeneous region, the abundances of the neighboring pixels will not share the same prior constraints. Thus, in this paper, to achieve better abundance estim

APA, Harvard, Vancouver, ISO, and other styles

3

Nagesh, A. "New Feature Vectors using GFCC for Speaker Identification." International Journal of Emerging Research in Management and Technology 6, no. 8 (2018): 243. http://dx.doi.org/10.23956/ijermt.v6i8.146.

Full text

Abstract:

The feature vectors of speaker identification system plays a crucial role in the overall performance of the system. There are many new feature vectors extraction methods based on MFCC, but ultimately we want to maximize the performance of SID system. The objective of this paper to derive Gammatone Frequency Cepstral Coefficients (GFCC) based a new set of feature vectors using Gaussian Mixer model (GMM) for speaker identification. The MFCC are the default feature vectors for speaker recognition, but they are not very robust at the presence of additive noise. The GFCC features in recent studies

APA, Harvard, Vancouver, ISO, and other styles

4

Wang, Dong, Xiaodong Wang, and Shaohe Lv. "An Overview of End-to-End Automatic Speech Recognition." Symmetry 11, no. 8 (2019): 1018. http://dx.doi.org/10.3390/sym11081018.

Full text

Abstract:

Automatic speech recognition, especially large vocabulary continuous speech recognition, is an important issue in the field of machine learning. For a long time, the hidden Markov model (HMM)-Gaussian mixed model (GMM) has been the mainstream speech recognition framework. But recently, HMM-deep neural network (DNN) model and the end-to-end model using deep learning has achieved performance beyond HMM-GMM. Both using deep learning techniques,

APA, Harvard, Vancouver, ISO, and other styles

5

Satyanand, Singh, and Singh Pragya. "High level speaker specific features modeling in automatic speaker recognition system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1859–67. https://doi.org/10.11591/ijece.v10i2.pp1859-1867.

Full text

Abstract:

Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what

APA, Harvard, Vancouver, ISO, and other styles

6

Wang, Ying. "Solving Multi-Instance Visual Scene Recognition with Classifier Ensemble Based on Unsupervised Clustering." Applied Mechanics and Materials 415 (September 2013): 338–44. http://dx.doi.org/10.4028/www.scientific.net/amm.415.338.

Full text

Abstract:

This paper proposes a new image Multi-Instance (MI) bag generating method, which models an image with a Gaussian Mixed Model (GMM). The generated GMM is treated as an MI bag, of which the color and locally stable invariant components (SIFT) are the instances. Agglomerative Information Bottleneck clustering is employed to transform the MIL problem into single-instance learning problem so that single-instance classifiers can be used for classification. Finally, ensemble learningis involved to further enhance classifiers generalization ability. Experimental results demonstrate that the performanc

APA, Harvard, Vancouver, ISO, and other styles

7

Sun, Qi, Liwen Jiang, and Haitao Xu. "Expectation-Maximization Algorithm of Gaussian Mixture Model for Vehicle-Commodity Matching in Logistics Supply Chain." Complexity 2021 (January 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/9305890.

Full text

Abstract:

A vehicle-commodity matching problem (VCMP) is presented for service providers to reduce the cost of the logistics system. The vehicle classification model is built as a Gaussian mixture model (GMM), and the expectation-maximization (EM) algorithm is designed to solve the parameter estimation of GMM. A nonlinear mixed-integer programming model is constructed to minimize the total cost of VCMP. The matching process between vehicle and commodity is realized by GMM-EM, as a preprocessing of the solution. The design of the vehicle-commodity matching platform for VCMP is designed to reduce and elim

APA, Harvard, Vancouver, ISO, and other styles

8

Bandi, Hari, Dimitris Bertsimas, and Rahul Mazumder. "Learning a Mixture of Gaussians via Mixed-Integer Optimization." INFORMS Journal on Optimization 1, no. 3 (2019): 221–40. http://dx.doi.org/10.1287/ijoo.2018.0009.

Full text

Abstract:

We consider the problem of estimating the parameters of a multivariate Gaussian mixture model (GMM) given access to n samples that are believed to have come from a mixture of multiple subpopulations. State-of-the-art algorithms used to recover these parameters use heuristics to either maximize the log-likelihood of the sample or try to fit first few moments of the GMM to the sample moments. In contrast, we present here a novel mixed-integer optimization (MIO) formulation that optimally recovers the parameters of the GMM by minimizing a discrepancy measure (either the Kolmogorov–Smirnov or the

APA, Harvard, Vancouver, ISO, and other styles

9

Chen, Gang, Binjie Hou, and Tiangang Lei. "A new Monte Carlo sampling method based on Gaussian Mixture Model for imbalanced data classification." Mathematical Biosciences and Engineering 20, no. 10 (2023): 17866–85. http://dx.doi.org/10.3934/mbe.2023794.

Full text

Abstract:

<abstract><p>Imbalanced data classification has been a major topic in the machine learning community. Different approaches can be taken to solve the issue in recent years, and researchers have given a lot of attention to data level techniques and algorithm level. However, existing methods often generate samples in specific regions without considering the complexity of imbalanced distributions. This can lead to learning models overemphasizing certain difficult factors in the minority data. In this paper, a Monte Carlo sampling algorithm based on Gaussian Mixture Model (MCS-GMM) is p

APA, Harvard, Vancouver, ISO, and other styles

10

Deng, Lei, and Yong Gao. "Gammachirp Filter Banks Applied in Roust Speaker Recognition Based GMM-UBM Classifier." International Arab Journal of Information Technology 17, no. 2 (2019): 170–77. http://dx.doi.org/10.34028/iajit/17/2/4.

Full text

Abstract:

In this paper, authors propose an auditory feature extraction algorithm in order to improve the performance of the speaker recognition system in noisy environments. In this auditory feature extraction algorithm, the Gammachirp filter bank is adapted to simulate the auditory model of human cochlea. In addition, the following three techniques are applied: cube-root compression method, Relative Spectral Filtering Technique (RASTA), and Cepstral Mean and Variance Normalization algorithm (CMVN).Subsequently, based on the theory of Gaussian Mixes Model-Universal Background Model (GMM-UBM), the simul

APA, Harvard, Vancouver, ISO, and other styles

11

Tang, Lin, Shane Halloran, Jian Qing Shi, Yu Guan, Chunzheng Cao, and Janet Eyre. "Evaluating upper limb function after stroke using the free-living accelerometer data." Statistical Methods in Medical Research 29, no. 11 (2020): 3249–64. http://dx.doi.org/10.1177/0962280220922259.

Full text

Abstract:

Accelerometer devices are becoming efficient tools in clinical studies for automatically measuring the activities of daily living. Such data provides a time series describing activity level at every second and displays a subject’s activity pattern throughout a day. However, the analysis of such data is very challenging due to the large number of observations produced each second and the variability among subjects. The purpose of this study is to develop efficient statistical analysis techniques for predicting the recovery level of the upper limb function after stroke based on the free-living a

APA, Harvard, Vancouver, ISO, and other styles

12

Li, Xi, Zhangyong Li, Dewei Yang, Lisha Zhong, Lian Huang, and Jinzhao Lin. "Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection." Sensors 21, no. 1 (2020): 132. http://dx.doi.org/10.3390/s21010132.

Full text

Abstract:

In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, f

APA, Harvard, Vancouver, ISO, and other styles

13

Zhang, Zhenyu, Jian Wang, Zhiyuan Li, Youlong Zhao, Ruisheng Wang, and Ayman Habib. "Optimization Method of Airborne LiDAR Individual Tree Segmentation Based on Gaussian Mixture Model." Remote Sensing 14, no. 23 (2022): 6167. http://dx.doi.org/10.3390/rs14236167.

Full text

Abstract:

Forests are the main part of the terrestrial ecosystem. Airborne LiDAR is fast, comprehensive, penetrating, and contactless and can depict 3D canopy information with a high efficiency and accuracy. Therefore, it plays an important role in forest ecological protection, tree species recognition, carbon sink calculation, etc. Accurate recognition of individual trees in forests is a key step to various application. In real practice, however, the accuracy of individual tree segmentation (ITS) is often compromised by under-segmentation due to the diverse species, obstruction and understory trees typ

APA, Harvard, Vancouver, ISO, and other styles

14

Guo, Yao, and Hongyan Zhu. "Joint short-time speaker recognition and tracking using sparsity-based source detection." Acta Acustica 7 (2023): 10. http://dx.doi.org/10.1051/aacus/2023004.

Full text

Abstract:

A random finite set-based sequential Monte–Carlo tracking method is proposed to track multiple acoustic sources in indoor scenarios. The proposed method can improve tracking performance by introducing recognized speaker identities from the received signals. At the front-end, the degenerate unmixing estimation technique (DUET) is employed to separate the mixed signals, and the time delay of arrival (TDOA) is measured. In addition, a criterion to select the reliable microphone pair is designed to quickly obtain accurate speaker identities from the mixed signals, and the Gaussian mixture model un

APA, Harvard, Vancouver, ISO, and other styles

15

Ren, Hang, and Taotao Hu. "An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints." Sensors 20, no. 13 (2020): 3722. http://dx.doi.org/10.3390/s20133722.

Full text

Abstract:

This paper addresses the lack of robustness of feature selection algorithms for fuzzy clustering segmentation with the Gaussian mixture model. Assuming that the neighbourhood pixels and the centre pixels obey the same distribution, a Markov method is introduced to construct the prior probability distribution and achieve the membership degree regularisation constraint for clustering sample points. Then, a noise smoothing factor is introduced to optimise the prior probability constraint. Second, a power index is constructed by combining the classification membership degree and prior probability

APA, Harvard, Vancouver, ISO, and other styles

16

Arunachalam, Manasha, Siddhaarth Sekar, Annastasia M. Erdmann, V. V. Sajith Variyar, and Ramesh Sivanpillai. "Comparative Analysis of Machine Learning Algorithms and Statistical Techniques for Data Analysis in Crop Growth Monitoring with NDVI." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-5-2024 (March 12, 2025): 15–20. https://doi.org/10.5194/isprs-archives-xlviii-m-5-2024-15-2025.

Full text

Abstract:

Abstract. We assessed the potential of Machine Learning (ML) for mapping crop growth in three flood irrigated fields. Results generated from ML algorithms were compared to the output generated by the ISODATA algorithm. Affinity Propagation (AP) identifies the number of clusters by considering all data points as potential exemplars and iteratively refine the set, while Gaussian Mixture Model (GMM) algorithm treats the data as a mixture of several Gaussian distributions, allowing for flexible cluster shapes. In contrast, ISODATA, a statistical clustering method, requires an analyst to specify th

APA, Harvard, Vancouver, ISO, and other styles

17

Othman, Khairulnizam, Mohd Norzali Mohd, Muhammad Qusyairi Abdul Rahman, Mohd Hadri Mohamed Nor, Khairulnizam Ngadimon, and Zulkifli Sulaiman. "A Mixed Gaussian Distribution Approach using the Expectation-Maximization Algorithm for Topography Predictive Modelling." WSEAS TRANSACTIONS ON COMPUTERS 24 (April 7, 2025): 29–41. https://doi.org/10.37394/23205.2025.24.4.

Full text

Abstract:

The incidence of sugarcane crop infestations at the migration stage, especially by the top borer, can lower yields substantially, which may translate to revenue losses of over 20% across many parts of the world. Traditional pest surveillance approaches tend to lack the accuracy required for timely intervention. This research introduces a new burden rate concept incorporated within a Gaussian Mixture Model (GMM), framed within a machine learning environment in order to enhance the precision of infestation pattern prediction. Through the utilization of the Expectation-Maximization (EM) algorithm

APA, Harvard, Vancouver, ISO, and other styles

18

Zhou, Yuliang, Mingxuan Chen, Guanglong Du, Ping Zhang, and Xin Liu. "Intelligent grasping with natural human-robot interaction." Industrial Robot: An International Journal 45, no. 1 (2018): 44–53. http://dx.doi.org/10.1108/ir-05-2017-0089.

Full text

Abstract:

Purpose The aim of this paper is to propose a grasping method based on intelligent perception for implementing a grasp task with human conduct. Design/methodology/approach First, the authors leverage Kinect to collect the environment information including both image and voice. The target object is located and segmented by gesture recognition and speech analysis and finally grasped through path teaching. To obtain the posture of the human gesture accurately, the authors use the Kalman filtering (KF) algorithm to calibrate the posture use the Gaussian mixture model (GMM) for human motion modelin

APA, Harvard, Vancouver, ISO, and other styles

19

Du, Zhibin, Hui Xie, Pengyu Zhai, et al. "Game-Based Flexible Merging Decision Method for Mixed Traffic of Connected Autonomous Vehicles and Manual Driving Vehicles on Urban Freeways." Applied Sciences 14, no. 16 (2024): 7375. http://dx.doi.org/10.3390/app14167375.

Full text

Abstract:

Connected Autonomous Vehicles (CAVs) have the potential to revolutionize traffic systems by autonomously handling complex maneuvers such as freeway ramp merging. However, the unpredictability of manual-driven vehicles (MDVs) poses a significant challenge. This study introduces a novel decision-making approach that incorporates the uncertainty of MDVs’ driving styles, aiming to enhance merging efficiency and safety. By framing the CAV-MDV interaction as an incomplete information static game, we categorize MDVs’ behaviors using a Gaussian Mixture Model–Support Vector Machine (GMM-SVM) method. Th

APA, Harvard, Vancouver, ISO, and other styles

20

Cai, Zhi, Jiawei Wang, Tong Li, et al. "A Novel Trajectory Based Prediction Method for Urban Subway Design." ISPRS International Journal of Geo-Information 11, no. 2 (2022): 126. http://dx.doi.org/10.3390/ijgi11020126.

Full text

Abstract:

In recent years, with the development of various types of public transportation, they are also more and more closely connected. Among them, subway transportation has become the first choice of major cities. However, the planning of subway stations is very difficult and there are many factors to consider. Besides, few methods for selecting optimal station locations take other public transport in to consideration. In order to study the relationship between different types of public transportation, the authors collected and analyzed the travel data of subway passengers and the passenger trajector

APA, Harvard, Vancouver, ISO, and other styles

21

Duan, Shaoming, Chuanyi Liu, Peiyi Han, et al. "HT-Fed-GAN: Federated Generative Model for Decentralized Tabular Data Synthesis." Entropy 25, no. 1 (2022): 88. http://dx.doi.org/10.3390/e25010088.

Full text

Abstract:

In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated generative models with differential privacy are used by the existing methods. Unfortunately, the existing models apply only to images or text data and not to tabular data. Unlike images, tabular data usually consist of mixed data types (discrete and continuous attributes) and real-world datasets with highly imbalanced data distributions. Existing methods hardly model such scenarios due to the multimodal distri

APA, Harvard, Vancouver, ISO, and other styles

22

Nguyen, Duy, Ca Hoang, Tien Truong, et al. "Multi-level phenotypic models of cardiovascular disease and obstructive sleep apnea comorbidities: A longitudinal Wisconsin sleep cohort study." PLOS One 20, no. 7 (2025): e0327977. https://doi.org/10.1371/journal.pone.0327977.

Full text

Abstract:

Cardiovascular diseases (CVDs) are prevalent among obstructive sleep apnea (OSA) patients, presenting significant challenges in predictive modeling due to the complex interplay of these comorbidities. Current methodologies predominantly lack the dynamic and longitudinal perspective necessary to accurately predict CVD progression in the presence of OSA. This study addresses these limitations by proposing a novel multi-level phenotypic model that analyzes the progression and interaction of these comorbidities over time. Our study utilizes a longitudinal cohort from the Wisconsin sleep cohort, co

APA, Harvard, Vancouver, ISO, and other styles

23

Liu, Yue, Chunying Ma, and Zhehao Huang. "Can the digital economy improve green total factor productivity? An empirical study based on Chinese urban data." Mathematical Biosciences and Engineering 20, no. 4 (2023): 6866–93. http://dx.doi.org/10.3934/mbe.2023296.

Full text

Abstract:

<abstract><p>With the new generation of technological revolution, the digital economy has progressively become a key driver of global economic development. In this context, how to promote green economic growth and improve green total factor productivity (GTFP) with the help of the digital economy is an important issue that urgently needs empirical research. We adopted the panel data of 278 Chinese prefecture-level cities from 2011 to 2020 to test whether the digital economy improves the GTFP through the Gaussian Mixed Model (GMM) dynamic panel model. The moderating effect model has

APA, Harvard, Vancouver, ISO, and other styles

24

Deng, Hui, Zhibin Ou, and Yichuan Deng. "Multi-Angle Fusion-Based Safety Status Analysis of Construction Workers." International Journal of Environmental Research and Public Health 18, no. 22 (2021): 11815. http://dx.doi.org/10.3390/ijerph182211815.

Full text

Abstract:

Hazardous accidents often happen in construction sites and bring fatal consequences, and therefore safety management has been a certain dilemma to construction managers for long time. Although computer vision technology has been used on construction sites to identify construction workers and track their movement trajectories for safety management, the detection effect is often influenced by limited coverage of single cameras and occlusion. A multi-angle fusion method applying SURF feature algorithm is proposed to coalesce the information processed by improved GMM (Gaussian Mixed Model) and HOG

APA, Harvard, Vancouver, ISO, and other styles

25

Gao, Zitian, Danlu Guo, Dongryeol Ryu, and Andrew W. Western. "Enhancing the Accuracy and Temporal Transferability of Irrigated Cropping Field Classification Using Optical Remote Sensing Imagery." Remote Sensing 14, no. 4 (2022): 997. http://dx.doi.org/10.3390/rs14040997.

Full text

Abstract:

Mapping irrigated areas using remotely sensed imagery has been widely applied to support agricultural water management; however, accuracy is often compromised by the in-field heterogeneity of and interannual variability in crop conditions. This paper addresses these key issues. Two classification methods were employed to map irrigated fields using normalized difference vegetation index (NDVI) values derived from Landsat 7 and Landsat 8: a dynamic thresholding method (method one) and a random forest method (method two). To improve the representativeness of field-level NDVI aggregates, which are

APA, Harvard, Vancouver, ISO, and other styles

26

Liu, Lin, Bingbing Wang, Yongfu Li, and Nenglong Hu. "Regular Vehicle Spatial Distribution Estimation Based on Machine Learning." Journal of Electrical and Computer Engineering 2023 (August 30, 2023): 1–11. http://dx.doi.org/10.1155/2023/4954035.

Full text

Abstract:

For the mixed traffic flow, obtaining the distribution of connected vehicles (CVs) and regular vehicles (RVs) is of great significance for road network analysis and cooperative control in intelligent transportation systems (ITSs). However, whether it is based on fixed sensors or based on CVs and traffic mechanism to estimate the spatial distribution of RVs, the implementation complexity and low estimation accuracy are the points that need to be improved. This paper proposes a regular vehicle spatial distribution estimation method using adjacent connected vehicles as mobile sensors. First, to i

APA, Harvard, Vancouver, ISO, and other styles

27

Khalifa, Othman O., Muhammad H. Wajdi, Rashid A. Saeed, Aisha H. A. Hashim, Muhammed Z. Ahmed, and Elmustafa Sayed Ali. "Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm." Journal of Advanced Transportation 2022 (March 15, 2022): 1–11. http://dx.doi.org/10.1155/2022/9189600.

Full text

Abstract:

Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histog

APA, Harvard, Vancouver, ISO, and other styles

28

Wang, Xin, Jing Yang, and Yong Luo. "Multi-Channel Coupled Variational Bayesian Framework with Structured Sparse Priors for High-Resolution Imaging of Complex Maneuvering Targets." Remote Sensing 17, no. 14 (2025): 2430. https://doi.org/10.3390/rs17142430.

Full text

Abstract:

High-resolution ISAR (Inverse Synthetic Aperture Radar) imaging plays a crucial role in dynamic target monitoring for aerospace, maritime, and ground surveillance. Among various remote sensing techniques, ISAR is distinguished by its ability to produce high-resolution images of non-cooperative maneuvering targets. To meet the increasing demands for resolution and robustness, modern ISAR systems are evolving toward wideband and multi-channel architectures. In particular, multi-channel configurations based on large-scale receiving arrays have gained significant attention. In such systems, each r

APA, Harvard, Vancouver, ISO, and other styles

29

Mattana, Sara, Alice Dal Fovo, João Luís Lagarto, et al. "Automated Phasor Segmentation of Fluorescence Lifetime Imaging Data for Discriminating Pigments and Binders Used in Artworks." Molecules 27, no. 5 (2022): 1475. http://dx.doi.org/10.3390/molecules27051475.

Full text

Abstract:

The non-invasive analysis of fluorescence from binders and pigments employed in mixtures in artworks is a major challenge in cultural heritage science due to the broad overlapping emission of different fluorescent species causing difficulties in the data interpretation. To improve the specificity of fluorescence measurements, we went beyond steady-state fluorescence measurements by resolving the fluorescence decay dynamics of the emitting species through time-resolved fluorescence imaging (TRFI). In particular, we acquired the fluorescence decay features of different pigments and binders using

APA, Harvard, Vancouver, ISO, and other styles

30

Jun, Sunghae. "Text Data Analysis Using Generalized Linear Mixed Model and Bayesian Visualization." Axioms 11, no. 12 (2022): 674. http://dx.doi.org/10.3390/axioms11120674.

Full text

Abstract:

Many parts of big data, such as web documents, online posts, papers, patents, and articles, are in text form. So, the analysis of text data in the big data domain is an important task. Many methods based on statistics or machine learning algorithms have been studied for text data analysis. Most of them were analytical methods based on the generalized linear model (GLM). For the GLM, text data analysis is performed based on the assumption of the error included in the given data and follows the Gaussian distribution. However, the GLM has shown limitations in the analysis of text data, including

APA, Harvard, Vancouver, ISO, and other styles

31

Wang, Guangwei, and Xiaomei Chen. "Evaluation of the Online and Offline Mixed Teaching Effect of MOOC Based upon the Deep Neural Network Model." Wireless Communications and Mobile Computing 2022 (March 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/2173005.

Full text

Abstract:

This article is dedicated to discussing the online and offline mixed teaching evaluation of MOOC based on deep neural networks. Deep neural networks are an important means to solve various problems in various fields. It can evaluate the teaching attitude of teachers, the teaching content in the classroom, the teacher’s narrative ability, the teaching methods used by the teachers, and whether the teaching methods are rigorous. And it can train on a large number of datasets evaluated by students on a certain course and get results. This article first explains the advantages of the neural network

APA, Harvard, Vancouver, ISO, and other styles

32

Jang, Minseok, Hyun-Cheol Jeong, Taegon Kim, and Sung-Kwan Joo. "Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs." Energies 14, no. 19 (2021): 6130. http://dx.doi.org/10.3390/en14196130.

Full text

Abstract:

Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tari

APA, Harvard, Vancouver, ISO, and other styles

33

Ma, Wei, Chao Gou, and Yunyun Hou. "Research on Adaptive 1DCNN Network Intrusion Detection Technology Based on BSGM Mixed Sampling." Sensors 23, no. 13 (2023): 6206. http://dx.doi.org/10.3390/s23136206.

Full text

Abstract:

The development of internet technology has brought us benefits, but at the same time, there has been a surge in network attack incidents, posing a serious threat to network security. In the real world, the amount of attack data is much smaller than normal data, leading to a severe class imbalance problem that affects the performance of classifiers. Additionally, when using CNN for detection and classification, manual adjustment of parameters is required, making it difficult to obtain the optimal number of convolutional kernels. Therefore, we propose a hybrid sampling technique called Borderlin

APA, Harvard, Vancouver, ISO, and other styles

34

Kozłowski, Edward, Anna Borucka, Marta Cholewa-Wiktor, and Tomasz Jałowiec. "Influence of Selected Geopolitical Factors on Municipal Waste Management." Sustainability 17, no. 1 (2024): 190. https://doi.org/10.3390/su17010190.

Full text

Abstract:

The collection and transportation of municipal solid waste create a significant energy and carbon footprint, resulting in a significant environmental impact. Proper waste management organization is necessary to minimize this impact. This research aims to identify differences and similarities in waste collection sectors, distinguish affiliation clusters for different waste types, and determine the impact of geopolitical factors on waste production in the analyzed region. Therefore, the similarities of waste production in the separated sectors for different waste types were analyzed. Instead of

APA, Harvard, Vancouver, ISO, and other styles

35

Elking, Dennis M., G. Andrés Cisneros, Jean-Philip Piquemal, Thomas A. Darden, and Lee G. Pedersen. "Gaussian Multipole Model (GMM)." Journal of Chemical Theory and Computation 6, no. 1 (2009): 190–202. http://dx.doi.org/10.1021/ct900348b.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Satyanand, Singh. "High level speaker specific features as an efficiency enhancing parameters in speaker recognition system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 2443–50. https://doi.org/10.11591/ijece.v9i4.pp2443-2450.

Full text

Abstract:

In this paper, I present high-level speaker specific feature extraction considering intonation, linguistics rhythm, linguistics stress, prosodic features directly from speech signals. I assume that the rhythm is related to language units such as syllables and appears as changes in measurable parameters such as fundamental frequency Fo, duration, and energy. In this work, the syllable type features are selected as the basic unit for expressing the prosodic features. The approximate segmentation of continuous speech to syllable units is achieved by automatically locating the vowel starting point

APA, Harvard, Vancouver, ISO, and other styles

37

Rusk, Sam, Chris Fernandez, Yoav Nygate, et al. "0710 REM Behavior Disorder Explainability in EEG via Spectral Band Cluster Prevalence." SLEEP 47, Supplement_1 (2024): A303—A304. http://dx.doi.org/10.1093/sleep/zsae067.0710.

Full text

Abstract:

Abstract Introduction Prior work has established substantial overlap in polysomnography features between synucleinopathy-associated RBD and PTSD/TASD-associated RBD (trauma-associated-sleep-disorders). However, our mechanistic understanding remains limited. To explore RBD endophenotypes, we applied a novel analysis for clustering and categorizing PSG without AI/ML or sleep scoring, Spectral-Band Cluster-Prevalence (SBCP), to examine and compare differences in EEG characteristics between patients with RBD diagnosis versus clinical controls. Methods Our data source was retrospective EEG/EOG reco

APA, Harvard, Vancouver, ISO, and other styles

38

Zhang, Yi, Miaomiao Li, Siwei Wang, et al. "Gaussian Mixture Model Clustering with Incomplete Data." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (2021): 1–14. http://dx.doi.org/10.1145/3408318.

Full text

Abstract:

Gaussian mixture model (GMM) clustering has been extensively studied due to its effectiveness and efficiency. Though demonstrating promising performance in various applications, it cannot effectively address the absent features among data, which is not uncommon in practical applications. In this article, different from existing approaches that first impute the absence and then perform GMM clustering tasks on the imputed data, we propose to integrate the imputation and GMM clustering into a unified learning procedure. Specifically, the missing data is filled by the result of GMM clustering, and

APA, Harvard, Vancouver, ISO, and other styles

39

Alqulaity, Malak, and Po Yang. "Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-Based Cardiovascular Healthcare." Sensors 24, no. 23 (2024): 7673. https://doi.org/10.3390/s24237673.

Full text

Abstract:

The generation of synthetic tabular data has emerged as a critical task in various fields, particularly in healthcare, where data privacy concerns limit the availability of real datasets for research and analysis. This paper presents an enhanced Conditional Generative Adversarial Network (GAN) architecture designed for generating high-quality synthetic tabular data, with a focus on cardiovascular disease datasets that encompass mixed data types and complex feature relationships. The proposed architecture employs specialized sub-networks to process continuous and categorical variables separatel

APA, Harvard, Vancouver, ISO, and other styles

40

Zhao, Mingliang, Fangyi Liu, Wei Sun, and Xin Tao. "The Relationship between Environmental Regulation and Green Total Factor Productivity in China: An Empirical Study Based on the Panel Data of 177 Cities." International Journal of Environmental Research and Public Health 17, no. 15 (2020): 5287. http://dx.doi.org/10.3390/ijerph17155287.

Full text

Abstract:

Promoting the coordinated development of industrialization and the environment is a goal pursued by all of the countries of the world. Strengthening environmental regulation (ER) and improving green total factor productivity (GTFP) are important means to achieving this goal. However, the relationship between ER and GTFP has been debated in the academic circles, which reflects the complexity of this issue. This paper empirically tested the relationship between ER and GTFP in China by using panel data and a systematic Gaussian Mixed Model (GMM) of 177 cities at the prefecture level. The research

APA, Harvard, Vancouver, ISO, and other styles

41

Singh, Renu, Arvind Singh, and Utpal Bhattacharjee. "A Review on Text-Independent Speaker Verification Techniques in Realistic World." Oriental journal of computer science and technology 9, no. 1 (2016): 36–40. http://dx.doi.org/10.13005/ojcst/901.07.

Full text

Abstract:

This paper presents a reviewof various speaker verification approaches in realistic world, and explore a combinational approach between Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) as well as Gaussian Mixture Model (GMM) and Universal Background Model (UBM).

APA, Harvard, Vancouver, ISO, and other styles

42

Shi, X., and Q. H. Zhao. "GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 647–50. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-647-2017.

Full text

Abstract:

For the image segmentation method based on Gaussian Mixture Model (GMM), there are some problems: 1) The number of component was usually a fixed number, i.e., fixed class and 2) GMM is sensitive to image noise. This paper proposed a RS image segmentation method that combining GMM with reversible jump Markov Chain Monte Carlo (RJMCMC). In proposed algorithm, GMM was designed to model the distribution of pixel intensity in RS image. Assume that the number of component was a random variable. Respectively build the prior distribution of each parameter. In order to improve noise resistance, used Gi

APA, Harvard, Vancouver, ISO, and other styles

43

Liu, Jialu, Deng Cai, and Xiaofei He. "Gaussian Mixture Model with Local Consistency." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 512–17. http://dx.doi.org/10.1609/aaai.v24i1.7659.

Full text

Abstract:

Gaussian Mixture Model (GMM) is one of the most popular data clustering methods which can be viewed as a linear combination of different Gaussian components. In GMM, each cluster obeys Gaussian distribution and the task of clustering is to group observations into different components through estimating each cluster's own parameters. The Expectation-Maximization algorithm is always involved in such estimation problem. However, many previous studies have shown naturally occurring data may reside on or close to an underlying submanifold. In this paper, we consider the case where the probability d

APA, Harvard, Vancouver, ISO, and other styles

44

Bakare K.A and Torentikaza I.E. "An Improved Semi-Supervised Gaussian Mixture Model (I-SGMM)." Research Briefs on Information and Communication Technology Evolution 9 (November 12, 2023): 147–59. http://dx.doi.org/10.56801/rebicte.v9i.165.

Full text

Abstract:

In the era of data-driven decision-making, the Gaussian Mixture Model (GMM) stands as a cornerstone in statistical modeling, particularly in clustering and density estimation. The Improved GMM presents a robust solution to a fundamental problem in clustering: the determination of the optimal number of clusters. Unlike its predecessor, it does not rely on a predetermined cluster count but employs model selection criteria, such as the Bayesian Information Criterion (BIC) or Akaike Information Criterion (AIC), to automatically identify the most suitable cluster count for the given data. This inhe

APA, Harvard, Vancouver, ISO, and other styles

45

Ding, Ing Jr, Chih Ta Yen, and Che Wei Chang. "Classification of Chinese Popular Songs Using a Fusion Scheme of GMM Model Estimate and Formant Feature Analysis." Applied Mechanics and Materials 479-480 (December 2013): 1006–9. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.1006.

Full text

Abstract:

In this paper, a fusion scheme that combines Gaussian mixture model (GMM) calculations and formant feature analysis, called GMM-Formant, is proposed for classification of Chinese popular songs. Generally, automatic classification of popular music could be performed by two main categories of techniques, model-based and feature-based approaches. In model-based classification techniques, GMM is widely used for its simplicity. In feature-based music recognition, the formant parameter is an important acoustic feature for evaluation. The proposed GMM-Formant method takes use of linear interpolation

APA, Harvard, Vancouver, ISO, and other styles

46

Zhang, Yan, Cun Bao Chen, and Li Zhao. "Noise Classification Based on GMM and AANN." Applied Mechanics and Materials 58-60 (June 2011): 1847–53. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1847.

Full text

Abstract:

In this paper, Gaussian Mixture model (GMM) as specific method is applied to noise classification. On this basis, a modified Gaussian Mixture Model with an embedded Auto-Associate Neural Network (AANN) is proposed. It integrates the merits of GMM and AANN. We train GMM and AANN as a whole and they are trained by means of Maximum Likelihood (ML). In the process of training, the parameter of GMM and AANN are updated alternately. AANN reshapes the distribution of the data and improves the similarity of the feature data in the same distribution type of noise. Experiments show that the GMM with emb

APA, Harvard, Vancouver, ISO, and other styles

47

Ma, Yong, Qiwen Jin, Xiaoguang Mei, et al. "Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank Representation." Remote Sensing 11, no. 8 (2019): 911. http://dx.doi.org/10.3390/rs11080911.

Full text

Abstract:

Gaussian mixture model (GMM) has been one of the most representative models for hyperspectral unmixing while considering endmember variability. However, the GMM unmixing models only have proper smoothness and sparsity prior constraints on the abundances and thus do not take into account the possible local spatial correlation. When the pixels that lie on the boundaries of different materials or the inhomogeneous region, the abundances of the neighboring pixels do not have those prior constraints. Thus, we propose a novel GMM unmixing method based on superpixel segmentation (SS) and low-rank rep

APA, Harvard, Vancouver, ISO, and other styles

48

Bhuvaneswari, M. "Gaussian mixture model: An application to parameter estimation and medical image classification." Journal of Scientific and Innovative Research 5, no. 3 (2016): 100–105. http://dx.doi.org/10.31254/jsir.2016.5308.

Full text

Abstract:

Gaussian mixture model based parameter estimation and classification has recently received great attention in modelling and processin g data. Gaussian Mixture Model (GMM) is the probabilistic model for representing the presence of subpopulations and it works well with the classification and parameter estimation strategy. Here in this work Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for the parameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality.

APA, Harvard, Vancouver, ISO, and other styles

49

Haiqin Zhu. "Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model." Journal of Electrical Systems 20, no. 3s (2024): 2588–600. http://dx.doi.org/10.52783/jes.3156.

Full text

Abstract:

Polar ice sheets, or ice cores, are among the most well-known natural archives that might provide crucial historical details about our planet's previous environment. An important factor in establishing the fundamental characteristics of ice, likes pore close-off, albedo, melt events, is the ice-core microstructure. To engulf these complications Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model (ICMCTS-WSOA-DSSDNN-GMM)is proposed. Initially, micro scale CT images are collect from Alfred Wegener Institute ice-core storage as in

APA, Harvard, Vancouver, ISO, and other styles

50

Muthahharah, Andi Shahifah, Muhammad Arif Tiro, and Aswi Aswi. "Application of Soft-Clustering Analysis Using Expectation Maximization Algorithms on Gaussian Mixture Model." Jurnal Varian 6, no. 1 (2022): 71–80. http://dx.doi.org/10.30812/varian.v6i1.2142.

Full text

Abstract:

Research on soft-clustering has not been explored much compared to hard-clustering. Soft-clustering algorithms are important in solving complex clustering problems. One of the soft-clustering methods is the Gaussian Mixture Model (GMM). GMM is a clustering method to classify data points into different clusters based on the Gaussian distribution. This study aims to determine the number of clusters formed by using the GMM method. The data used in this study is synthetic data on water quality indicators obtained from the Kaggle website. The stages of the GMM method are: imputing the Not Available

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!