False data injection attack sample generation using an adversarial attention-diffusion model in smart grids by Kunzhan Li, Fengyong Li, Baonan Wang, Meijing Shan

“…Considering the scarcity of FDIA attack samples, the traditional FDIA detection models based on neural networks are always limited in their detection capabilities due to imbalanced training samples. To address this problem, this paper proposes an efficient FDIA attack sample generation method by an adversarial attention-diffusion model. …”
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Suboptimal but intact integration of Bayesian components during perceptual decision-making in autism by Laurina Fazioli, Bat-Sheva Hadad, Rachel N. Denison, Amit Yashar

“…Abstract Background Alterations in sensory perception, a core phenotype of autism, are attributed to imbalanced integration of sensory information and prior knowledge during perceptual statistical (Bayesian) inference. …”
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Discriminative, generative artificial intelligence, and foundation models in retina imaging by Paisan Ruamviboonsuk, Niracha Arjkongharn, Nattaporn Vongsa, Pawin Pakaymaskul, Natsuda Kaothanthong

“…Novel images generated by GAN can be applied for training AI models in imbalanced or inadequate datasets. Foundation models are also recent advances in retinal imaging. …”
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IMPLEMENTATION OF BALANCING DATA METHOD USING SMOTETOMEK IN DIABETES CLASSIFICATION USING XGBOOST by Fatwa Ratantja Kusumajati, Basuki Rahmat, Achmad Junaidi

“…A significant challenge in this study was the imbalanced nature of the dataset, which included a disproportionate number of non-diabetic samples relative to diabetic samples. …”
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Physician demography in Lebanon 2024: identifying gaps and proposing solutions for sustainable healthcare system by Jana Doghman, Salim Saba, Joseph Bakhach

“…Conclusion There is an imbalanced distribution of physicians in Lebanon based on represented specialties and practice location. …”
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A multi-dimensional student performance prediction model (MSPP): An advanced framework for accurate academic classification and analysis by V. Balachandar, K. Venkatesh

“…We developed a method that targets the common issues associated with educational datasets over imbalanced and temporal settings which is also explainable through AI features. …”
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A Vibrational Signal Fault Diagnosis Rule Extraction Method Based on DST-ACI Discriminant Criterion by Yalun Zhang, Lin He, Guo Cheng

“…First, the feature state coding method based on K-means clustering fully takes into account the imbalanced distribution of signal feature values due to the noise interference, and divide the signal feature values into several range intervals to generate the feature state code. …”
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Generalized cross-entropy for learning from crowds based on correlated chained Gaussian processes by J. Gil-González, G. Daza-Santacoloma, D. Cárdenas-Peña, A. Orozco-Gutiérrez, A. Álvarez-Meza

“…We aim to extend CCGP-GCE for future work to handle sparse and imbalanced annotations. Additionally, we plan to apply this model to multimodal tasks.…”
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Equilibrium study of logistics demand and logistics resource allocation in Guangdong Province by Liu Lianhua, Wu Yanling, Lyu Shiqi, Chen Zexian

“…However, logistics resource allocation remains imbalanced, with a certain degree of correspondence to logistics demand levels. …”
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An Adaptive Intrusion Detection System for Evolving IoT Threats: An Autoencoder-FNN Fusion by J. Jasmine Shirley, M. Priya

“…By addressing critical challenges in IoT network security, including imbalanced datasets and evolving threats, this research contributes a reliable and efficient IDS model optimized for real-world applications. …”
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Research on the Coupling Relationship between Professional Clusters and Industrial Clusters in the Pearl River Delta Region of China by Li Li, Yuzhou Hu, Sisi Zheng, Yimian Lin

“…Finally, this mismatch is evident in imbalanced resource allocation. The fit between the setting of professional clusters and the structure of industrial clusters is not high. …”
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Analysis of SO2 Pollution Changes of Beijing-Tianjin-Hebei Region over China Based on OMI Observations from 2006 to 2017 by Zhifang Wang, Fengjie Zheng, Wenhao Zhang, Shutao Wang

“…The spatial analysis indicates an imbalanced spatial distribution pattern, with higher SO2 level in the southern BTH and lower in the northern. …”
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Efficient Handling of Data Imbalance in Health Insurance Fraud Detection Using Meta-Reinforcement Learning by Supriya Seshagiri, K. V. Prema

“…This research is the first to apply Meta-RL to the problem of data imbalance in fraud detection, contributing to a generalizable and efficient framework for imbalanced learning. The findings of this research show that Meta-RL algorithms can be effectively tuned to handle data imbalance without modification to their objective functions and hence, can be considered an appropriate option for health insurance fraud detection solutions.…”
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Wafer Defect Classification Algorithm With Label Embedding Using Contrastive Learning by Jeongjoon Hwang, Somi Ha, Dohyun Kim

“…Moreover, compared to previous methods, our approach demonstrates better classification performance and computational efficiency, even in situations with imbalanced labels. This method also shows significant potential in identifying unseen defects not defined in the original classification tasks. …”
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Fat-tail allele-specific expression genes may affect fat deposition in tail of sheep. by Hossein Mansourizadeh, Mohammad Reza Bakhtiarizadeh, Luciana Correia de Almeida Regitano, Jennifer Jessica Bruscadin

“…ASE is a common phenomenon in mammals and refers to allelic imbalanced expression modified by cis-regulatory genetic variants that can be observed at heterozygous loci. …”
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Encrypted traffic classification method based on convolutional neural network by Rongna XIE, Zhuhong MA, Zongyu LI, Ye TIAN

“…Aiming at the problems of low accuracy, weak generality, and easy privacy violation of traditional encrypted network traffic classification methods, an encrypted traffic classification method based on convolutional neural network was proposed, which avoided relying on original traffic data and prevented overfitting of specific byte structure of the application.According to the data packet size and arrival time information of network traffic, a method to convert the original traffic into a two-dimensional picture was designed.Each cell in the histogram represented the number of packets with corresponding size that arrive at the corresponding time interval, avoiding reliance on packet payloads and privacy violations.The LeNet-5 convolutional neural network model was optimized to improve the classification accuracy.The inception module was embedded for multi-dimensional feature extraction and feature fusion.And the 1*1 convolution was used to control the feature dimension of the output.Besides, the average pooling layer and the convolutional layer were used to replace the fully connected layer to increase the calculation speed and avoid overfitting.The sliding window method was used in the object detection task, and each network unidirectional flow was divided into equal-sized blocks, ensuring that the blocks in the training set and the blocks in the test set in a single session do not overlap and expanding the dataset samples.The classification experiment results on the ISCX dataset show that for the application traffic classification task, the average accuracy rate reaches more than 95%.The comparative experimental results show that the traditional classification method has a significant decrease in accuracy or even fails when the types of training set and test set are different.However, the accuracy rate of the proposed method still reaches 89.2%, which proves that the method is universally suitable for encrypted traffic and non-encrypted traffic.All experiments are based on imbalanced datasets, and the experimental results may be further improved if balanced processing is performed.…”
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HGL: A hybrid global-local load balancing routing scheme for the Internet of Things through satellite networks by Ziluan Liu, Jiangsheng Li, Yanru Wang, Xin Li, Shanzhi Chen

“…However, because of the varying Internet of Things traffic density, satellite networks may endure imbalanced traffic requirements and frequent link congestion. …”
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An Optimized Deep-Learning-Based Network with an Attention Module for Efficient Fire Detection by Muhammad Altaf, Muhammad Yasir, Naqqash Dilshad, Wooseong Kim

“…Additionally, we contribute a medium-scale custom fire dataset, comprising high-resolution, imbalanced, and visually similar fire/non-fire images. …”
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Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation by Yara A. Sultan, Abdelfattah A. Eladl, Mohamed A. Hassan, Samah A. Gamel

“…However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. …”
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SynC2S: An Efficient Method for Synthesizing Tabular Data With a Learnable Pre-Processing by Jiwoo Kim, Seri Park, Junsung Koh, Dongha Kim

“…We then develop a conditional generative model with a hierarchical structure and its corresponding learning framework, called HCIWAE, to successfully capture imbalanced categorical distributions. Combining these two components, we coin our method Synthetic data generation with C2Smap (SynC2S). …”
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