Article
Food Science & Technology
Shida Zhao, Zongchun Bai, Shucai Wang, Yue Gu
Summary: This paper proposes a mutton multi-part classification and detection method based on the Swin-Transformer, which uses image augmentation techniques to increase sample size and overcome dataset distribution and imbalance issues. The optimal model is obtained through comparison of three Swin-Transformer variants and transfer learning. The proposed method shows high performance in terms of accuracy, robustness, generalization, and anti-occlusion abilities, outperforming five commonly used object detection methods and meeting real-time processing requirements.
Article
Computer Science, Interdisciplinary Applications
Sanjeev Bhatta, Ji Dang
Summary: This paper presents a novel quantum convolutional neural network (QCNN) approach for detecting damage to reinforced concrete (RC) buildings from images after an earthquake. The QCNN model is developed and trained using RC building damaged images collected from past earthquakes, and its performance is evaluated based on real-world RC building damaged images from the recent earthquake in Turkey in February 2023. Furthermore, the seismic damage detection accuracy obtained from the QCNN model is compared with various CNN architecture results.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Plant Sciences
Kan Jiang, Jie You, Ulzii-Orshikh Dorj, Hyongsuk Kim, Joonwhoan Lee
Summary: This paper discusses two deep learning techniques, Open Set Recognition (OSR) and Out-of-Distribution (OoD) detection, for the detection of unknown plant diseases. The paper analyzes the models and training procedures of these techniques and demonstrates reasonable performance in detecting unknown diseases. Accurate detection of unknown diseases is crucial for continued learning.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Engineering, Aerospace
Yefei Huang, Zexu Zhang, Hutao Cui
Summary: With the development of convolutional neural networks, learning-based methods in aerospace are gaining attention. The lack of diversity in model structure of aerospace datasets hampers generalization ability in on-orbital tasks, such as satellite detection. This article addresses this drawback by creating a synthetic dataset with extended structure diversity from the YCB dataset and realistic satellites, enabling the detection of unknown satellites in synthetic images. The generalization is evaluated by combining this dataset with other public satellite datasets, and a light-weighted refinement pipeline is proposed to improve performance.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Agriculture, Dairy & Animal Science
Xingze Zheng, Feiyi Li, Bin Lin, Donghang Xie, Yang Liu, Kailin Jiang, Xinyao Gong, Hongbo Jiang, Ran Peng, Xuliang Duan
Summary: This study established the world's first manually marked sex classification dataset for hemp ducks and used deep neural network models to accurately detect and classify the sex of ducks. The evaluation of the algorithm's performance suggests that this automated method is feasible for sex classification of ducks in the farming environment, serving as a reliable tool for sex ratio estimation.
Article
Multidisciplinary Sciences
Navneet Kaur, Lakhwinder Kaur, Sikander Singh Cheema
Summary: Swarm intelligence techniques have a wide range of real-world applications, particularly in medical data mining for the prediction and classification of diseases like breast cancer. The DLHO approach, which integrates dimension learning-based hunting strategy with HHO, has been developed to address the diversity and convergence issues in optimization algorithms, showing promising results in experiments with biomedical databases.
SCIENTIFIC REPORTS
(2021)
Article
Computer Science, Hardware & Architecture
Jun Li, Devkishen Sisodia, Shad Stafford
Summary: Self-propagating worms can quickly infect millions of computers on the Internet. The recent Mirai and WannaCry worms serve as evidence that worm attacks are real, destructive, and persistent. Existing worm detectors have limitations in terms of considering countermeasures from worm authors, addressing inbound worms, and requiring bi-directional traffic. This paper proposes a new worm detector called SWORD, which focuses on the fundamental behavior of worms and overcomes the drawbacks of existing detectors. Experimental results using simulated and real-world worm traffic show that SWORD outperforms existing detectors in detecting both classic and evasive outbound worms, as well as inbound worms.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Joana Fogaca, Tomas Brandao, Joao C. Ferreira
Summary: This research work aims to develop a system that can automatically detect illegal graffiti in real-time in Lisbon using cars equipped with cameras. A classification model with an overall accuracy of 81.4% is used to classify images into street art, illegal graffiti, or no graffiti. Another model is trained to detect the coordinates of graffiti on an image, achieving an Intersection over Union (IoU) of 70.3% for the test set.
APPLIED SCIENCES-BASEL
(2023)
Article
Mathematics, Interdisciplinary Applications
Annie Julie Joseph, P. N. Pournami
Summary: This study investigates various phases of mammogram image analysis and different abnormality detection techniques using Multifractal theory. The findings suggest that multifractal parameters could serve as valuable biomarkers for quantitative assessment of breast tissue in mammograms, aiding in the early diagnosis of breast cancer.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Computer Science, Information Systems
Daekyeong Park, Sangsoo Kim, Hyukjin Kwon, Dongil Shin, Dongkyoo Shin
Summary: As cyberattacks evolve, traditional intrusion detection systems face challenges in detecting advanced attacks. A deep learning-based model, Siamese-CNN, has shown improved performance in identifying attack patterns, outperforming Vanilla-CNN by achieving approximately 6% higher recall rate.
Article
Ecology
Sarkhan Badirli, Christine Johanna Picard, George Mohler, Frannie Richert, Zeynep Akata, Murat Dundar
Summary: Machine learning can be used to create an accurate and efficient method for classifying insect species, including both described and undescribed species. A deep hierarchical Bayesian model is proposed, which can classify samples based on the taxonomic hierarchy of insects. The combination of image and DNA data in the model leads to significant improvement in classification accuracy.
METHODS IN ECOLOGY AND EVOLUTION
(2023)
Editorial Material
Parasitology
Zachary L. Nikolakis, Elizabeth J. Carlton, David D. Pollock, Todd A. Castoe
Summary: Luo et al. identified genes associated with host-switching in Schistosoma japonicum, providing a guide for studying selected chromosomal genes using population genetics.
TRENDS IN PARASITOLOGY
(2022)
Review
Computer Science, Interdisciplinary Applications
Mehak Mengi, Deepti Malhotra
Summary: The article investigates the development of automated diagnostic systems based on quantitative parameters for early detection of ASD or ADHD, presenting a survey of AI-based diagnostic systems for ASD and ADHD. Additionally, studies of various automated AI-based diagnostic systems for ASD, ADHD and comorbid ASD are discussed, highlighting open issues in the literature that need further exploration.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2022)
Article
Neurosciences
Anastasia O. Ovchinnikova, Anatoly N. Vasilyev, Ivan P. Zubarev, Bogdan L. Kozyrskiy, Sergei L. Shishkin
Summary: Researchers demonstrate the potential of discriminating voluntary and spontaneous eye fixations using segments of MEG data. Applying CNN for binary classification of MEG signals related to eye fixations shows promising results in distinguishing voluntary from spontaneous fixations, supporting the improvement of gaze-based interfaces.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Multidisciplinary Sciences
Meng Qi, Hongxiang Shao, Nianfeng Shi, Guoqiang Wang, Yifei Lv
Summary: Cardiovascular diseases are the leading cause of deaths worldwide. This study focuses on optimizing the training of neural networks by conducting an in-depth analysis of existing ECG databases and proposing a unified ECG arrhythmia classification database called Hercules-3. The trained neural network achieved an accuracy rate of up to 98.67%, surpassing other data processing methods in terms of classification recall, accuracy, and F1-score.
Article
Computer Science, Interdisciplinary Applications
Blair Robertson, Chris Price
Summary: Spatial sampling designs are crucial for accurate estimation of population parameters. This study proposes a new design method that generates samples with good spatial spread and performs favorably compared to existing designs.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Hiroya Yamazoe, Kanta Naito
Summary: This paper focuses on the simultaneous confidence region of a one-dimensional curve embedded in multi-dimensional space. An estimator of the curve is obtained through local linear regression on each variable in multi-dimensional data. A method to construct a simultaneous confidence region based on this estimator is proposed, and theoretical results for the estimator and the region are developed. The effectiveness of the region is demonstrated through simulation studies and applications to artificial and real datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Cheng Peng, Drew P. Kouri, Stan Uryasev
Summary: This paper introduces a novel optimal experimental design method for quantifying the distribution tails of uncertain system responses. The method minimizes the variance or conditional value-at-risk of the upper bound of the predicted quantile, and estimates the data uncertainty using quantile regression. The optimal design problems are solved as linear programming problems, making the proposed methods efficient even for large datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiaofei Wu, Hao Ming, Zhimin Zhang, Zhenyu Cui
Summary: This paper proposes a model that combines quantile regression and fused LASSO penalty, and introduces an iterative algorithm based on ADMM to solve high-dimensional datasets. The paper proves the global convergence and comparable convergence rates of the algorithm, and analyzes the theoretical properties of the model. Numerical experimental results support the superior performance of the model.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xin He, Xiaojun Mao, Zhonglei Wang
Summary: This paper proposes a nonparametric imputation method with sparsity to estimate the finite population mean, using an efficient kernel method and sparse learning for estimation. An augmented inverse probability weighting framework is adopted to achieve a central limit theorem for the proposed estimator under regularity conditions.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Christian H. Weiss, Fukang Zhu
Summary: This study introduces a multiplicative error model (CMEMs) for discrete-valued count time series, which is closely related to the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models. It derives the stochastic properties and estimation approaches of different types of INGARCH-CMEMs, and demonstrates their performance and application through simulations and real-world data examples.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Ming-Hung Kao, Ping-Han Huang
Summary: Optimal designs for sparse functional data under the functional empirical component (FEC) settings are investigated. New computational methods and theoretical results are developed to efficiently obtain optimal exact and approximate designs. A hybrid exact-approximate design approach is proposed and demonstrated to be efficient through simulation studies and a real example.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Mateus Maia, Keefe Murphy, Andrew C. Parnell
Summary: The Bayesian additive regression trees (BART) model is a powerful ensemble method for regression tasks, but its lack of smoothness and explicit covariance structure can limit its performance. The Gaussian processes Bayesian additive regression trees (GP-BART) model addresses this limitation by incorporating Gaussian process priors, resulting in superior performance in various scenarios.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xichen Mou, Dewei Wang
Summary: Human biomonitoring is a method of monitoring human health by measuring the accumulation of harmful chemicals in the body. To reduce the high cost of chemical analysis, researchers have adopted a cost-effective approach that combines specimens and analyzes the concentration of toxic substances in the pooled samples. To effectively interpret these aggregated measurements, a new regression framework is proposed by extending the additive partially linear model (APLM). The APLM is versatile in capturing the complex association between outcomes and covariates, making it valuable in assessing the complex interplay between chemical bioaccumulation and potential risk factors.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Lili Yu, Yichuan Zhao
Summary: The classical accelerated failure time model is a linear model commonly used for right censored survival data, but it cannot handle heteroscedastic survival data. This paper proposes a Laplace approximated quasi-likelihood method with a continuous estimating equation to address this issue, and provides estimation bias and confidence interval estimation formulas.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaobo Jin, Youngjo Lee
Summary: Hierarchical generalized linear models are widely used for fitting random effects models, but the standard error estimators receive less attention. Current standard error estimation methods are not necessarily accurate, and a sandwich estimator is proposed to improve the accuracy of standard error estimation.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Rebeca Pelaez, Ingrid Van Keilegom, Ricardo Cao, Juan M. Vilar
Summary: This article proposes an estimator for the probability of default (PD) in credit risk, derived from a nonparametric conditional survival function estimator based on cure models. The asymptotic expressions for bias, variance, and normality of the estimator are presented. Through simulation and empirical studies, the performance and practical behavior of the nonparametric estimator are compared with other methods.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
L. M. Andre, J. L. Wadsworth, A. O'Hagan
Summary: This paper proposes a dependence model that captures the entire data range in multi-variable cases. By blending two copulas with different characteristics and using a dynamic weighting function for smooth transition, the model is able to flexibly capture various dependence structures.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Niwen Zhou, Xu Guo, Lixing Zhu
Summary: The paper investigates hypothesis testing regarding the potential additional contributions of other covariates to the structural function, given the known covariates. The proposed distance-based test, based on Neyman's orthogonality condition, effectively detects local alternatives and is robust to the influence of nuisance functions. Numerical studies and real data analysis demonstrate the importance of this test in exploring covariates associated with AIDS treatment effects.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Blake Moya, Stephen G. Walker
Summary: A full posterior analysis method for nonparametric mixture models using Gibbs-type prior distributions, including the well known Dirichlet process mixture (DPM) model, is presented. The method removes the random mixing distribution and enables a simple-to-implement Markov chain Monte Carlo (MCMC) algorithm. The removal procedure reduces some of the posterior uncertainty and introduces a novel replacement approach. The method only requires the probabilities of a new or an old value associated with the corresponding Gibbs-type exchangeable sequence, without the need for explicit representations of the prior or posterior distributions. This allows the implementation of mixture models with full posterior uncertainty, including one introduced by Gnedin. The paper also provides numerous illustrations and introduces an R-package called CopRe that implements the methodology.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)