掲載種別：研究論文（学術雑誌）  

<jats:sec><jats:title>Introduction</jats:title><jats:p>Retinal diseases significantly impact patients’ quality of life and increase social medical costs. Optical coherence tomography (OCT) offers high-resolution imaging for precise detection and monitoring of these conditions. While deep learning techniques have been employed to extract features from OCT images for classification, convolutional neural networks (CNNs) often fail to capture global context due to their focus on local receptive fields. Transformer-based methods, on the other hand, suffer from quadratic complexity when handling long-range dependencies.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>To overcome these limitations, we introduce the Multi-Resolution Visual Mamba (MRVM) model, which addresses long-range dependencies with linear computational complexity for OCT image classification. The MRVM model initially employs convolution to extract local features and subsequently utilizes the retinal Mamba to capture global dependencies. By integrating multi-scale global features, the MRVM enhances classification accuracy and overall performance. Additionally, the multi-directional selection mechanism (MSM) within the retinal Mamba improves feature extraction by concentrating on various directions, thereby better capturing complex, orientation-specific retinal patterns.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Experimental results demonstrate that the MRVM model excels in differentiating retinal images with various lesions, achieving superior detection accuracy compared to traditional methods, with overall accuracies of 98.98\% and 96.21\% on two public datasets, respectively.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>This approach offers a novel perspective for accurately identifying retinal diseases and could contribute to the development of more robust artificial intelligence algorithms and recognition systems for medical image-assisted diagnosis.</jats:p></jats:sec>

DOI： 10.3389/fcell.2024.1484880

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掲載種別：研究論文（学術雑誌）  

DOI： 10.3390/jmse12101802

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掲載種別：研究論文（学術雑誌）  

DOI： 10.3390/ma17215231

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掲載種別：研究論文（学術雑誌）  

DOI： 10.3390/jmse12081359

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.32604/cmc.2024.051336

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

<jats:title>Abstract</jats:title><jats:p>This paper introduces a novel meta-heuristic algorithm named Rhinopithecus Swarm Optimization (RSO) to address optimization problems, particularly those involving high dimensions. The proposed algorithm is inspired by the social behaviors of different groups within the rhinopithecus swarm. RSO categorizes the swarm into mature, adolescent, and infancy individuals. Due to this division of labor, each category of individuals employs unique search methods, including vertical migration, concerted search, and mimicry. To evaluate the effectiveness of RSO, we conducted experiments using the CEC2017 test set and three constrained engineering problems. Each function in the test set was independently executed 36 times. Additionally, we used the Wilcoxon signed-rank test and the Friedman test to analyze the performance of RSO compared to eight well-known optimization algorithms: Dung Beetle Optimizer (DBO), Beluga Whale Optimization (BWO), Salp Swarm Algorithm (SSA), African Vultures Optimization Algorithm (AVOA), Whale Optimization Algorithm (WOA), Atomic Retrospective Learning Bare Bone Particle Swarm Optimization (ARBBPSO), Artificial Gorilla Troops Optimizer (GTO), and Harris Hawks Optimization (HHO). The results indicate that RSO exhibited outstanding performance on the CEC2017 test set for both 30 and 100 dimension. Moreover, RSO ranked first in both dimensions, surpassing the mean rank of the second-ranked algorithms by 7.69% and 42.85%, respectively. Across the three classical engineering design problems, RSO consistently achieves the best results. Overall, it can be concluded that RSO is particularly effective for solving high-dimensional optimization problems.</jats:p>

DOI： 10.1038/s41598-024-66450-x

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掲載種別：研究論文（学術雑誌）  

DOI： 10.7717/peerj-cs.2048

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掲載種別：研究論文（学術雑誌）  

DOI： 10.3390/pr12061063

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1093/jcde/qwae035

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掲載種別：研究論文（学術雑誌）  

<jats:sec><jats:title>Introduction</jats:title><jats:p>The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>In this paper, a novel <jats:italic>U</jats:italic>-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a <jats:italic>U</jats:italic>-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Overall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation.</jats:p></jats:sec>

DOI： 10.3389/fncom.2024.1387004

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

<jats:title>Abstract</jats:title><jats:p>Assistive medical image classifiers can greatly reduce the workload of medical personnel. However, traditional machine learning methods require large amounts of well-labeled data and long learning times to solve medical image classification problems, which can lead to high training costs and poor applicability. To address this problem, a novel unsupervised breast cancer image classification model based on multiscale texture analysis and a dynamic learning strategy for mammograms is proposed in this paper. First, a gray-level cooccurrence matrix and Tamura coarseness are used to transfer images to multiscale texture feature vectors. Then, an unsupervised dynamic learning mechanism is used to classify these vectors. In the simulation experiments with a resolution of 40 pixels, the accuracy, precision, F1-score and AUC of the proposed method reach 91.500%, 92.780%, 91.370%, and 91.500%, respectively. The experimental results show that the proposed method can provide an effective reference for breast cancer diagnosis.</jats:p>

DOI： 10.1038/s41598-024-57891-5

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.3788/col202422.110603

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

<jats:title>Abstract</jats:title><jats:p>High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When tackling high-dimensional spaces, traditional evolutionary tools often encounter pitfalls, including dimensional catastrophes and a propensity to become trapped in local optima, ultimately compromising result accuracy. To address this issue, we introduce the Pair Barracuda Swarm Optimization (PBSO) algorithm in this paper. PBSO employs a unique strategy for constructing barracuda pairs, effectively mitigating the challenges posed by high dimensionality. Furthermore, we enhance global search capabilities by incorporating a support barracuda alongside the leading barracuda pair. To assess the algorithm’s performance, we conduct experiments utilizing the CEC2017 standard function and compare PBSO against five state-of-the-art natural-inspired optimizers in the control group. Across 29 test functions, PBSO consistently secures top rankings with 9 first-place, 13 second-place, 5 third-place, 1 fourth-place, and 1 fifth-place finishes, yielding an average rank of 2.0345. These empirical findings affirm that PBSO stands as the superior choice among all test algorithms, offering a dependable solution for high-dimensional optimization challenges.</jats:p>

DOI： 10.1038/s41598-023-43748-w

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

<jats:p>Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions.</jats:p>

DOI： 10.3390/pr11103011

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Cold Spring Harbor Laboratory  

<jats:title>Abstract</jats:title><jats:p>Given their high sociality and close evolutionary distance to humans, monkeys are an essential animal model for unraveling the biological mechanisms underlying human social behavior and elucidating the pathogenesis of diseases exhibiting abnormal social behavior. However, behavioral analysis of naturally behaving monkeys requires manual counting of various behaviors, which has been a bottleneck due to problems in throughput and objectivity. Here, we developed a three-dimensional markerless motion capture system that utilized multi-view data for robust tracking of individual monkeys and accurate reconstruction of the three-dimensional poses of multiple monkeys living in groups. Validation analysis in two monkey groups revealed that the system enabled the characterization of individual social dispositions and relationships through automated detection of various social events. Analyses of social looking facilitated the investigation of adaptive behaviors in a social group. These results suggest that this motion capture system will significantly enhance our ability to analyze primate social behavior.</jats:p>

DOI： 10.1101/2023.09.13.556332

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

<jats:title>Abstract</jats:title><jats:p>High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems.</jats:p>

DOI： 10.1038/s41598-023-37129-6

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0284170

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：{MDPI} {AG}  

<jats:p>A critical step in the visual navigation of unmanned surface vehicles (USVs) is horizon line detection, which can be used to adjust the altitude as well as for obstacle avoidance in complex environments. In this paper, a real-time and accurate detection method for the horizon line is proposed. Our approach first differentiates the complexity of navigational scenes using the angular second moment (ASM) parameters in the grey level co-occurrence matrix (GLCM). Then, the region of interest (ROI) is initially extracted using minimal human interaction for the complex navigation scenes, while subsequent frames are dynamically acquired using automatic feature point matching. The matched ROI can be maximally removed from the complex background, and the Zernike-moment-based edges are extracted from the obtained ROI. Finally, complete sea horizon information is obtained through a linear fitting of the lower edge points to the edge information. Through various experiments carried out on a classical dataset, our own datasets, and that of another previously published paper, we illustrate the significance and accuracy of this technique for various complex environments. The results show that the performance has potential applications for the autonomous navigation and control of USVs.</jats:p>

DOI： 10.3390/jmse11061130

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1109/ACCESS.2023.3250228

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DOI： 10.1007/978-3-031-36622-2_10

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1109/ACCESS.2023.3276989

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DOI： 10.1007/978-3-031-36622-2_14

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1109/ACCESS.2022.3222530

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0271925

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/s41598-022-14355-y

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0267197

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

DOI： 10.1007/s10489-019-01474-9

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/s10015-018-0440-3

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

DOI： 10.1007/978-3-319-61824-1_17

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掲載種別：研究論文（学術雑誌）  

DOI： 10.2991/ijndc.2017.5.3.3

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