RMixDA: A Random Compound Data Augmentation Operation Framework for Neural Machine Translation by Huijun Wang, Xiaojing Du, Xinkun Hao

“…Typically, the quality of augmented data is evaluated based on its similarity and diversity to training data. …”
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TBESO-BP: an improved regression model for predicting subclinical mastitis by Kexin Han, Yongqiang Dai, Huan Liu, Junjie Hu, Leilei Liu, Zhihui Wang, Liping Wei

“…The algorithm employs three strategies to rectify issues related to insufficient population diversity, susceptibility to local optimization, and reduced accuracy in snake optimization. …”
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Comprehensive datasets for RNA design, machine learning, and beyond by Jan Badura, Agnieszka Rybarczyk, Tomasz Zok

“…While machine learning methodologies have been proposed to enhance prediction capabilities, they require high-quality training data. The lack of standardized benchmark datasets further hinders the development and evaluation of these tools. …”
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Learning new surgical techniques in low and middle income countries, approval processes, and the impact of artificial intelligence by Long Cong Duy Tran, Long Cong Duy Tran, Helal Metwalli, Helal Metwalli, Dat Tien Le, Dat Tien Le, Samuel Amo-Afful, Samuel Amo-Afful, Mario Salib Todry Gerges, Mario Salib Todry Gerges, Hajer Hatim Hassan Ahmed, Hajer Hatim Hassan Ahmed, Dinh Thi Kim Quyen, Dinh Thi Kim Quyen, Abdelrahman Gamil Gad, Abdelrahman Gamil Gad, Phillip Tran, Nguyen Tien Huy, Nguyen Tien Huy, Nguyen Tien Huy

“…However, implementing AI in LMICs faces some challenges, including inadequate resources, financial constraints and ethical issues related to data security and Equitable algorithms. This review compares usual surgical training and approval processes in LMICs and evaluates the promising role of AI to fill existing gaps and compares both approaches in terms of applicability, cost-effectiveness and impact on patient outcomes.…”
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A novel model for predicting immunotherapy response and prognosis in NSCLC patients by Ting Zang, Xiaorong Luo, Yangyu Mo, Jietao Lin, Weiguo Lu, Zhiling Li, Yingchun Zhou, Shulin Chen

“…Methods Patients were randomly divided into training cohort and validation cohort at a ratio of 2:1. …”
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Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era by Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li

“…Image preprocessing, which involves standardizing images for training or deployment of various AI algorithms, encompasses essential steps such as image quality evaluation, alignment, stacking, background extraction, gray-scale transformation, cropping, source detection, astrometry, and photometry. …”
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Prediction of copper contamination in soil across EU using spectroscopy and machine learning: Handling class imbalance problem by Chongchong Qi, Nana Zhou, Tao Hu, Mengting Wu, Qiusong Chen, Han Wang, Kejing Zhang, Zhang Lin

“…To address this limitation, we conducted a comprehensive evaluation of three basic machine learning (ML) algorithms and four imbalanced ML algorithms. …”
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Deep neural network application in real-time economic dispatch and frequency control of microgrids by Liu Jun

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Using active learning selection approach for cross-project software defect prediction by Wenbo Mi, Yong Li, Ming Wen, Youren Chen

“…In this article, we selected 10 open-source projects by using common defect prediction models, active learning algorithms, and common evaluation metrics. The results show that the proposed algorithm can effectively filter the data, solve the class imbalance problem in cross-project data, and improve the defect prediction performance.…”
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Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions by Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll

“…To advance DL‐based hydrogeological modeling, future research should focus on enhancing data availability through data fusion and public databases, improving model interpretability using physics‐informed and explainable DL techniques, and developing more efficient algorithms for training large‐scale models. Additionally, exploring new computational paradigms, such as quantum computing, could provide revolutionary solutions for handling the computational challenges associated with training complex models. …”
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TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds by Jonathan Henrich, Jan van Delden, Dominik Seidel, Thomas Kneib, Alexander S. Ecker

“…An evaluation on the benchmark dataset shows that TreeLearn performs as well as the algorithm used to generate its training data. …”
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THANTD: Triplet Hybrid Attention Network for Hyperspectral Target Detection by Ju Liu, Haoqian Wang, Xiangai Cheng, Zhongyang Xing, Zhongjie Xu

“…In recent years, deep learning has made significant progress in the HTD field, leading to the development of many effective algorithms. However, as a data-driven approach, deep learning methods still face challenges, such as the lack of sufficient labeled samples for training and the difficulty of capturing the diversity of spectral variations. …”
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Augmenting brain-computer interfaces with ART: An artifact removal transformer for reconstructing multichannel EEG signals by Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Anne-Mei Bessas

“…Tackling this problem demands advanced algorithms, extensive noisy-clean training data, and thorough evaluation strategies. …”
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M18K: A Multi-Purpose Real-World Dataset for Mushroom Detection, 3D Pose Estimation, and Growth Monitoring by Abdollah Zakeri, Mulham Fawakherji, Jiming Kang, Bikram Koirala, Venkatesh Balan, Weihang Zhu, Driss Benhaddou, Fatima A. Merchant

“…This paper details the dataset’s characteristics, presents detailed statistics on mushroom growth and yield, evaluates algorithmic performance, and, for broader applicability, makes all resources publicly available, including images, code, and trained models, via our GitHub repository. …”
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Facial Recognition Databases: Recent Developments and Review of Methods by Waleed Ayad, Siraj Qays, Asanka G. Perera, Ali Al-Naji

“…Also, this review discussed the evaluation metrics for face recognition and their two types of tasks (identification and verification). …”
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A deep learning-orchestrated garlic routing architecture for secure telesurgery operations in healthcare 4.0 by Kavit Shah, Nilesh Kumar Jadav, Rajesh Gupta, Sucheta Gupta, Sudeep Tanwar, Joel J.P.C. Rodrigues, Fayez Alqahtani, Amr Tolba

“…The proposed architecture is evaluated using different evaluation metrics, such as statistical analysis (training accuracy, training loss, optimizer performance, activation function performance), data compromisation rate (0.346), network throughput (1.44 Mbps), error rate, and latency comparison.…”
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Buoy and Winch Collaborative Control System Based on Deep Reinforcement Learning by Yang Gu, Jianjun Ni, Zaiming Geng, Bing Zhao, Haowen Yang

“…The main contributions include the development of two simulation environments for training DRL agents, designing a reward function to guide the exploration process, proposing a buoy control algorithm based on the discrete soft actor-critic (SAC) algorithm, and proposing a winch cable length prediction algorithm based on a lightweight transformer model. …”
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The value of radiomics features of white matter hyperintensities in diagnosing cognitive frailty: a study based on T2-FLAIR imaging by Qinmei Liao, Xihao Hu, Zhiqiong Jiang, Xiaoyun Huang, Jiacheng Guo, Yuanzhong Zhu, Wenjing He

“…Following an 8:2 ratio, the patients were randomly divided into training and testing sets. Repeated 5-fold cross-validation was adopted for model training and evaluation. …”
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Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning by Georgios Liapis, Ioannis Vlahavas

“…The Multi-Agent Posthumous Credit Assignment (MA-POCA) algorithm facilitates continuous learning, allowing agents to emulate behaviors through self-play. …”
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Online Purchase Behavior Prediction Model Based on Recurrent Neural Network and Naive Bayes by Chaohui Zhang, Jiyuan Liu, Shichen Zhang

“…In the current competition process of e-commerce platforms, the technical and algorithmic wars that can quickly grasp user needs and accurately recommend target commodities are the core tools of platform competition. …”
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