Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals by Kang Yan, Weidong Jin, Yingkun Huang, Zhenhua Li, Pucha Song, Ligang Huang

“…However, in practice, anomalous samples (multipath interference signals) may be scarce and highly imbalanced (i.e., mostly normal samples). To address this problem, we propose a deep anomaly detection with attention (DADA) for semisupervised detection of multipath radar signals. …”
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Studying and Analyzing Regional Inequalities (case study: cities of Ardabil province, Iran 2006 – 2011) by Bahram Imani, Roghayyeh Farshi, Reza Hashemi Masoomabad

“…Furthermore, according to the conducted analyses it is concluded that there is an imbalanced regional inequality on city scale in Ardabil province; there is a meaningful and positive relationship between the population of a city and the degree of its development; finally there is a meaningful and positive relationship between the distance from the provincial capital and the degree of its development.…”
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Vulnerability Assessment of Fallen Trees Along Arterial Road of Bukit Soeharto Grand Forest Park Using Google Street View by Ali Suhardiman, Frans Milan, Yosep Ruslim, Ariyanto Ariyanto, Hari Siswanto

“…Leaning trees, trees with imbalanced canopy proportion, dying trees, and trees in rough topography became the criteria specified from GSV photos to determine potentially fallen trees. …”
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Improving Model Performance for Predicting Exfiltration Attacks Through Resampling Strategies by Arif Rahman Hakim, Kalamullah Ramli, Muhammad Salman, Esti Rahmawati Agustina

“…This study provides a comprehensive framework for selecting resampling strategies to enhance classification efficacy in cybersecurity tasks with imbalanced data. …”
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Unveiling the power of language models in chemical research question answering by Xiuying Chen, Tairan Wang, Taicheng Guo, Kehan Guo, Juexiao Zhou, Haoyang Li, Zirui Song, Xin Gao, Xiangliang Zhang

“…This dataset reflects typical real-world challenges, including an imbalanced data distribution and a substantial amount of unlabeled data that can be potentially useful. …”
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Gut Dysbiosis and Muscle Aging: Searching for Novel Targets against Sarcopenia by Anna Picca, Francesca Fanelli, Riccardo Calvani, Giuseppina Mulè, Vito Pesce, Alex Sisto, Cecilia Pantanelli, Roberto Bernabei, Francesco Landi, Emanuele Marzetti

“…“Inflammaging,” an age-related chronic inflammatory process, is a common trait of several conditions, including sarcopenia. Interestingly, imbalanced intestinal microbial community has been suggested to contribute to inflammaging. …”
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Optimization of Job Scheduling in the Cloud Computing Environment Using the Fuzzy Particle Swarm Optimization Algorithm by Shabnam Gharaeian, Khosrow Amirizadeh

“…Hence, total load on the cloud environment could become imbalanced, which reduces system’s efficiency. In this research, PSO and fuzzy logic is used for job scheduling. …”
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Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction. by Muhammad Rizwan Khurshid, Sadaf Manzoor, Touseef Sadiq, Lal Hussain, Mohammed Shahbaz Khan, Ashit Kumar Dutta

“…While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent advancements in machine learning offer potential solutions. …”
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Arrhythmia Classification Techniques Using Deep Neural Network by Ali Haider Khan, Muzammil Hussain, Muhammad Kamran Malik

“…The primary concerns that affect the success of the developed arrhythmia detection systems are (i) manual features selection, (ii) techniques used for features extraction, and (iii) algorithm used for classification and the most important is the use of imbalanced data for classification. This study focuses on the recent trends in arrhythmia classification techniques, and through extensive simulations, the performance of the various arrhythmia classification and detection models has been evaluated. …”
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Schedule-based analysis of airborne transmission risk in public transportation systems by Jiali Zhou, Haris N. Koutsopoulos

“…Increasing frequency can mitigate risk, but cannot fully compensate for increases in infection rates. Imbalanced passenger distribution on different cars of a train increases the overall system-wide infection probability. …”
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Application of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosis by Xiaoyan Sun

“…Developing the LightGBM hybrid integration model to ensure the accuracy and stability of model diagnosis on diverse and imbalanced datasets. The effectiveness of the proposed method has been comprehensively compared and verified through the dataset in the UCI machine learning repository, and the results show that the proposed method has achieved good diagnostic performance in all indicators. …”
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Normalization-Guided and Gradient-Weighted Unsupervised Domain Adaptation Network for Transfer Diagnosis of Rolling Bearing Faults Under Class Imbalance by Hao Luo, Xinyue Wang, Li Zhang

“…Experiments conducted across three imbalanced scenarios using the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets demonstrate that NG-UDAN is effective in both single-source and mixed-source domain adaptation. …”
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StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality by Alexandra Kapp, Edith Hoffmann, Esther Weigmann, Helena Mihaljević

“…As the frequency distribution of road surface types and qualities is highly imbalanced, we propose a sampling strategy incorporating various external label prediction resources to ensure sufficient images per class while reducing manual annotation. …”
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Unbalanced protocol recognition method based on improved residual U-Net by Jisheng WU, Zheng HONG, Tiantian MA

“…An unbalanced protocol recognition method based on the improved Residual U-Net was proposed to solve the challenge of network security posed by the increasing network attacks with the continuous development of the Internet.In the captured network traffic, a small proportion is constituted by malicious traffic, typically utilizing minority protocols.However, existing protocol recognition methods struggle to accurately identify these minority protocols when the class distribution of the protocol data is imbalanced.To address this issue, an unbalanced protocol recognition method was proposed, which utilized the improved Residual U-Net, incorporating a novel activation function and the Squeeze-and-Excitation Networks (SE-Net) to enhance the feature extraction capability.The loss function employed in the proposed model was the weighted Dice loss function.In cases where the recognition accuracies of the minority protocols were low, the loss function value would be high.Consequently, the optimization direction of the model would be dominated by the minority protocols, resulting in improved recognition accuracies for them.During the protocol recognition process, the network flow was extracted from the network traffic and preprocessed to convert it into a one-dimensional matrix.Subsequently, the protocol recognition model extracted the features of the protocol data, and the Softmax classifier predicted the protocol types.Experimental results demonstrate that the proposed protocol recognition model achieves more accurate recognition of the minority protocols compared to the comparison model, while also improving the recognition accuracies of the majority protocols.…”
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Semantic-Based Classification of Long Texts on Higher Education in China by Chun Li, Yanying Fei

“…Besides, the effect of direct classification is rather poor because HE texts tend to be long and exist as an imbalanced dataset. To solve these problems, this paper improves the convolutional neural network (CNN) into the HE-CNN classification model for HE texts. …”
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STI/HIV risk prediction model development—A novel use of public data to forecast STIs/HIV risk for men who have sex with men by Xiaopeng Ji, Zhaohui Tang, Sonya R. Osborne, Thi Phuoc Van Nguyen, Amy B. Mullens, Judith A. Dean, Yan Li

“…Classification and generalization experiments are conducted to test the accuracy, F1-score, precision, and area under the curve (AUC) performance of these four algorithms. Two imbalanced data solutions are also applied to reduce bias for classification performance improvement. …”
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Adaptive Hierarchical Text Classification Using ERNIE and Dynamic Threshold Pruning by Han Chen, Yangsen Zhang, Yuru Jiang, Ruixue Duan

“…This paper proposes an Adaptive Hierarchical Text Classification method, EDTPA (ERNIE and Dynamic Threshold Pruning-based Adaptive classification), which leverages Large Language Models (LLMs) for data augmentation to mitigate imbalanced datasets. The model first uses Graph Attention Networks (GAT) to capture hierarchical dependencies among labels, effectively modeling structured relationships. …”
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Gut Microbiota Composition and Structure of the Ob/Ob and Db/Db Mice by Mingsheng Yang, Yixin Liu, Hengchang Xie, Zhengzheng Wen, Yunxia Zhang, Changjing Wu, Li Huang, Jie Wu, Chensheng Xie, Tao Wang, Weifeng Peng, Shangqi Liu, Long Chen, Xiaomeng Liu

“…The high abundance of Bacteroidetes and Firmicutes in microbiota composition and their imbalanced ratio in both model mice reflect the state of metabolic disorders of these mice. …”
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GLBR: A novel global load balancing routing scheme based on intelligent computing in partially disconnected wireless sensor networks by Zeyu Sun, Guisheng Liao, Cao Zeng, Lan Lan, Guozeng Zhao

“…Load balancing is of great significance to extend the longevity of wireless sensor networks, due to the inherent imbalanced energy overhead in such networks. However, existing solutions cannot balance the load distribution in partially disconnected wireless sensor networks. …”
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Loading and Skeletal Development and Maintenance by P. Bergmann, J. J. Body, S. Boonen, Y. Boutsen, J. P. Devogelaer, S. Goemaere, J. Kaufman, J. Y. Reginster, S. Rozenberg

“…Disuse results in osteocytes apoptosis and rapid imbalanced bone resorption, leading to severe osteoporosis. …”
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