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

“…Machine learning applications heavily depend on labeled data provided by domain experts to train accurate models. …”
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Learning Dendritic-Neuron-Based Motion Detection for RGB Images: A Biomimetic Approach by Tianqi Chen, Yuki Todo, Zhiyu Qiu, Yuxiao Hua, Delai Qiu, Xugang Wang, Zheng Tang

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Eff-ReLU-Net: a deep learning framework for multiclass wound classification by Sifat Ullah, Ali Javed, Muteb Aljasem, Abdul Khader Jilani Saudagar

“…More precisely, we introduced the ReLU activation function over the Swish in our Eff-ReLU-Net because of its simplicity, reliability, and efficiency. …”
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Collaborative Federated Learning of Unmanned Aerial Vehicles in Space–Air–Ground Integrated Network by Huibo Li, Peng Gong, Siqi Li, Weidong Wang, Yu Liu, Xiang Gao, Dapeng Oliver Wu, Duk Kyung Kim, Guangwei Zhang, Jihao Zhang

“…In this paper, a collaborative federated learning (FL) scheme based on device-to-device (D2D) communication in unmanned aerial vehicle (UAV)-assisted SAGIN is proposed to address the issue of heterogeneity. …”
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Parkinson’s disease detection using inceptionV3: A Deep learning approach by Pallavi M. Shanthappa, Madhwesh Bayari, G.B. Abhilash, K.V. Gokul, P.J. Ashish

“…Parkinson's disease (PD) is a neurodegenerative condition that progressively affects motor function and causes tremors, rigidity, and bradykinesia. …”
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Enhancement of rime algorithm using quadratic interpolation learning for parameters identification of photovoltaic models by Shazly A. Mohamed, Abdullah M. Shaheen, Mohammed H. Alqahtani, Badr M. Al Faiya

“…QIL’s capacity to adjust its quadratic function in a flexible and non-linear way makes it easier to navigate complex terrain. …”
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UCN-YOLOv5: Traffic Sign Object Detection Algorithm Based on Deep Learning by Peilin Liu, Zhaoyang Xie, Taijun Li

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Machine learning model for predicting in-hospital cardiac mortality among atrial fibrillation patients by Huasheng Lv, Xuehua Bi, Shuai Shang, Meng Wei, Xianhui Zhou, Kai Wang, Baopeng Tang, Yanmei Lu

“…Abstract This study developed and validated a machine learning (ML) model to predict in-hospital cardiac mortality in 18,727 atrial fibrillation (AF) patients using electronic medical record data. …”
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Cross-domain topic transfer learning method based on multiple balance and feature fusion by Zhenshun Xu, Zhenbiao Wang, Wenhao Zhang, Zengjin Tang

“…In transfer learning, traditional homogeneous transfer learning assumes similar data and feature distributions between the source and target domains, focusing primarily on parameter sharing to enhance model performance. …”
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Best practices for developing microbiome-based disease diagnostic classifiers through machine learning by Peikun Li, Min Li, Wei-Hua Chen

“…At the batch effect removal step, we identified the “ComBat” function from the sva R package as an effective batch effect removal method and compared the performance of various algorithms. …”
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Machine learning in stream and river water temperature modeling: a review and metrics for evaluation by C. R. Corona, T. S. Hogue, T. S. Hogue

“…Most recently, the use of artificial intelligence, specifically machine learning (ML) algorithms, has garnered significant attention and utility in hydrologic sciences, specifically as a novel tool to learn undiscovered patterns from complex data and try to fill data streams and knowledge gaps. …”
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Cognitive performance classification of older patients using machine learning and electronic medical records by Monika Richter-Laskowska, Ewelina Sobotnicka, Adam Bednorz

“…Developing efficient diagnostic tools is critical, and machine learning (ML) algorithms have shown potential for improving the accuracy of cognitive impairment classification. …”
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A Fortran–Python interface for integrating machine learning parameterization into earth system models by T. Zhang, C. Morcrette, C. Morcrette, M. Zhang, W. Lin, S. Xie, Y. Liu, K. Van Weverberg, K. Van Weverberg, J. Rodrigues

“…This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and scikit-learn. We demonstrate the interface's modularity and reusability through two cases: an ML trigger function for convection parameterization and an ML wildfire model. …”
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Arrhythmia detection with transfer learning architecture integrating the developed optimization algorithm and regularization method by Fatma Akalın, Pınar Dervişoğlu Çavdaroğlu, Mehmet Fatih Orhan

“…MobileNetv2 transfer learning architecture was used to classify this balanced dataset. …”
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hvEEGNet: a novel deep learning model for high-fidelity EEG reconstruction by Giulia Cisotto, Giulia Cisotto, Alberto Zancanaro, Italo F. Zoppis, Sara L. Manzoni

“…Therefore, in this paper, we present a novel deep learning model, called hvEEGNet, designed as a hierarchical variational autoencoder and trained with a new loss function. …”
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Supervised Reinforcement Learning-Based Collaborative Master–Slave Harvest Control Study in Wheat by Zhikai Ma, Chao Zhang, Wei Wang, Hao Wang, Helong Yu, Chunjiang Zhao

“…Firstly, to improve the algorithm training success rate, a supervisor trained on actual driving data is introduced into the actor–critic reinforcement learning method. Secondly, in order to improve the effect of agricultural machine operation, considering the actual grain unloading operation scene and combining the smoothness of operation and the safety of unloading, a new reward function in the supervised reinforcement learning algorithm is designed. …”
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Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach by Dan Ben Ami, Kobi Cohen, Qing Zhao

“…Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. …”
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Deep Metric Learning for Fine-Grained Ship Classification in SAR Images with Sidelobe Interference by Haibin Zhu, Yaxin Mu, Wupeng Xie, Kang Xing, Bin Tan, Yashi Zhou, Zhongde Yu, Zhiying Cui, Chuang Zhang, Xin Liu, Zhenghuan Xia

“…Then, a hybrid loss function is proposed to strengthen intra-class correlation and inter-class separability. …”
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Physics-Guided Self-Supervised Learning Full Waveform Inversion with Pretraining on Simultaneous Source by Qiqi Zheng, Meng Li, Bangyu Wu

“…Full waveform inversion (FWI) is an established precise velocity estimation tool for seismic exploration. Machine learning-based FWI could plausibly circumvent the long-standing cycle-skipping problem of traditional model-driven methods. …”
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Sampled-data control through model-free reinforcement learning with effective experience replay by Bo Xiao, H.K. Lam, Xiaojie Su, Ziwei Wang, Frank P.-W. Lo, Shihong Chen, Eric Yeatman

“…Reinforcement Learning (RL) based control algorithms can learn the control strategies for nonlinear and uncertain environment during interacting with it. …”
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