Article
Mathematics
Julia Garcia Cabello, Pedro A. Castillo, Maria-del-Carmen Aguilar-Luzon, Francisco Chiclana, Enrique Herrera-Viedma
Summary: This research introduces a new method using a decision model for redesigning networks without geographical constraints, applicable globally. The approach can be used in various fields, such as physical and non-physical networks, and its effectiveness is demonstrated through case studies.
Article
Computer Science, Artificial Intelligence
Hamideh Sadat Fatemighomi, Mousa Golalizadeh, Meisam Amani
Summary: Efficient analysis of hyperspectral datasets using the latent block model (LBM) has been enhanced by replacing finite mixture model (FMM) with hidden Markov random field (HMRF) and developing a new object-based classification algorithm. The proposed algorithm, named LBMHMRF, achieves higher spectral information utilization, lower parameter estimation requirements, and faster computation compared to alternative algorithms. It has shown the highest potential in terms of classification accuracy and computation time.
PATTERN ANALYSIS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Addisson Salazar, Luis Vergara, Gonzalo Safont
Summary: The study introduces a new method called Generative Adversarial Network Synthesis for Oversampling (GANSO), which utilizes Generative Adversarial Networks (GAN) and vector Markov Random Field (vMRF) to oversample the training set of a classifier in scenarios of extreme scarcity, leading to improved classifier performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Xiao Li, Yashas Malur Saidutta, Faramarz Fekri
Summary: Event-based social networks (EBSNs) have gained popularity as online platforms for event organizers, with the challenge of planning events to attract maximum attendance. This paper focuses on the social event planning problem, proposing a hybrid pairwise Markov random field (H-PMRF) model to maximize participant numbers. Through experiments on real-world data, the model outperforms several baselines, demonstrating its effectiveness in decision-making support for event organizers.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Biochemistry & Molecular Biology
Angelica M. Walker, Ashley Cliff, Jonathon Romero, Manesh B. Shah, Piet Jones, Joao Gabriel Felipe Machado Gazolla, Daniel A. Jacobson, David Kainer
Summary: Gene-to-gene networks are important tools for studying relationships between genes. Random Forest and iterative Random Forest methods can produce high-quality gene-to-gene networks. The study validates the use of synthetic and empirical data to compare these methods.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Biochemistry & Molecular Biology
Ataul Haleem, Selina Klees, Armin Otto Schmitt, Mehmet Gueltas
Summary: This study systematically analyzed the pleiotropic signatures of rSNPs in a global maize population using multi-omics data, revealing complex interactions among multiple agronomic phenotypes and providing new breeding targets for improving several agronomic traits simultaneously.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Geosciences, Multidisciplinary
C. Ghiringhelli, F. Bartolucci, A. Mira, G. Arbia
Summary: The assumption of second-order stationarity in spatial statistics may not be reasonable for modeling certain data. Therefore, a latent process is introduced to provide a more realistic representation. Markov chain Monte Carlo procedures are used for model comparison and parameter estimation, demonstrating the advantages of the proposed modeling strategy.
SPATIAL STATISTICS
(2021)
Article
Computer Science, Artificial Intelligence
Muhammad Hameed Siddiqi
Summary: This study focuses on improving the emotional speech classifier by introducing a novel methodology to address the limitations of existing classifiers, achieving significant improvement in emotional recognition. The proposed method has been validated and evaluated on two datasets, showing significantly improved classification performance. In terms of computation, the technique is also more cost-effective compared to state of the art works.
EGYPTIAN INFORMATICS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Wangxiang Ding, Wenzhong Li, Zhijie Zhang, Chen Wan, Jianhui Duan, Sanglu Lu
Summary: This article proposes a novel clustering approach for multivariate time series (MTS) data based on time-varying features. It introduces a time-varying Gaussian Markov Random Fields (T-GMRF) model to capture the correlation structure between MTS variables and formulates the feature extraction problem as a convex optimization problem. The proposed method outperforms existing techniques on various clustering performance metrics, as demonstrated through extensive experiments on 33 open MTS datasets.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Hongjun Li, Miguel Barao, Luis Rato, Shengjun Wen
Summary: This paper focuses on the mapping problem for mobile robots in dynamic environments, using a Markov chain and Gaussian random fields to model the dynamic behavior of points and capture spatial correlation. The proposed method allows for learning unobserved space and provides a map that describes occupancy probabilities and dynamics.
Article
Economics
Levent Onural, Mustafa Celebi Pinar, Can Firtina
Summary: A Gibbs random field (GRF) is proposed to model the complicated economic behavior of entities in a population, even with simple models. A computer simulator is developed to run empirical experiments for evaluating different coupling structures and parameters, testing various economic and financial models and policies for their consequences.
COMPUTATIONAL ECONOMICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Victor Freguglia, Nancy Lopes Garcia
Summary: This paper presents the mrf2d package, which provides tools for statistical inference on Markov random field models on two-dimensional lattices, including popular models such as the Potts model and texture image models. The package introduces representations of dependence structures and parameters, visualization functions and efficient implementations of sampling algorithms, common estimation methods, and other key features of the model, providing a useful framework for algorithm implementation and working with the model in general.
JOURNAL OF STATISTICAL SOFTWARE
(2022)
Article
Biochemistry & Molecular Biology
Jacob Karlstrom, Mattias Aine, Johan Staaf, Srinivas Veerla
Summary: The study introduces a novel unsupervised clustering method called SRIQ, which can address some issues in commonly used unsupervised analysis methods. Using RNA sequencing data from lung adenocarcinomas, the technical reproducibility and performance of SRIQ are demonstrated and compared to the commonly used consensus clustering method. With differential gene expression analysis and auxiliary molecular data, SRIQ is able to define new tumor subsets that are biologically relevant and consistent with existing subtypes.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Mechanics
Kun Xue, Xiaoxia Han, Jinde Wu, Yadi Shen, Xinying Xu, Gang Xie
Summary: This paper proposes a network embedding framework that explicitly considers the community structure feature extraction. The competitive walking and skip-gram training are used to extract node representation vectors. Experimental results show that the proposed method is more effective in detecting community structure in networks.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2022)
Article
Geography, Physical
Ting Li, Kasper Johansen, Matthew F. McCabe
Summary: In this study, a convolutional neural network and two clustering techniques were used to analyze the number and extent of agricultural fields using high-resolution satellite imagery. The results showed that the method can accurately identify and analyze the dynamics of agricultural fields.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)