MRI-based 2.5D deep learning radiomics nomogram for the differentiation of benign versus malignant vertebral compression fractures by Wenhua Liang, Hong Yu, Lisha Duan, Xiaona Li, Ming Wang, Bing Wang, Jianling Cui

“…Among all these models, the DLR model that employed ExtraTrees algorithm performed the best, with area under the curve (AUC) values of 0.971 (95% CI: 0.948-0.995) in the training dataset and 0.828 (95% CI: 0.727-0.929) in the testing dataset. …”
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Molecular sub-classification of renal epithelial tumors using meta-analysis of gene expression microarrays. by Thomas Sanford, Paul H Chung, Ariel Reinish, Vladimir Valera, Ramaprasad Srinivasan, W Marston Linehan, Gennady Bratslavsky

“…<h4>Purpose</h4>To evaluate the accuracy of the sub-classification of renal cortical neoplasms using molecular signatures.…”
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Construction and Comparison of Machine Learning-Based Risk Prediction Models for Major Adverse Cardiovascular Events in Perimenopausal Women by Chen A, Chang X, Bian X, Zhang F, Ma S, Chen X

“…According to the principle of 10 events per Variable, the training set sample size was sufficient. In the training set, Random Forest (RF) algorithm, backpropagation neural network (BPNN) and Logistic Regression (LR) were used to construct a MACE risk prediction model for perimenopausal women, and the test set was used to verify the model. …”
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Exploring the possibilities of MADDPG for UAV swarm control by simulating in Pac-Man environment by Artem Novikov, Sergiy Yakovlev, Ivan Gushchin

“…This study evaluates the performance of MADDPG-trained model-controlled adversaries against heuristic navigation strategies, such as A* and Breadth-First Search (BFS). …”
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Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery by E. Z. Golukhova, M. A. Keren, T. V. Zavalikhina, N. I. Bulaeva, D. S. Akatov, I. Yu. Sigaev, K. B. Yakhyaeva, D. A. Kolesnikov

“…For each of these algorithms, cross-validation and hyperparameter search were performed on the training sample. …”
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Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia by Kriengsak Yothapakdee, Yosawaj Pugtao, Sarawoot Charoenkhun, Tanunchai Boonnuk, Kreangsak Tamee

“…This study focused on algorithm performance and training/testing time, evaluating the most suitable chest X-ray image size for machine learning models to predict pneumonia infection. …”
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Ultimate Compression: Joint Method of Quantization and Tensor Decomposition for Compact Models on the Edge by Mohammed Alnemari, Nader Bagherzadeh

“…The process includes training floating-point models, applying tensor decomposition algorithms, binarizing the decomposed layers, and fine tuning the resulting models. …”
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Detection and diagnosis of air compressor faults using weightless neural networks by Anubhab Bhattacharyya, Naveen Venkatesh Sridharan, Aravinth Sivakumar, Sugumaran Vaithiyanathan

“…Random access memory (RAM) devices are harnessed by WNNs to emulate the functioning of neurons. The training process employs a versatile and effective algorithm aimed at generating reliable and accurate results. …”
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Automating areas of interest analysis in mobile eye tracking experiments based on machine learning by Julian Wolf, Stephan Hess, David Bachmann, Quentin Lohmeyer, Mirko Meboldt

“…Together with a real-time capability of the mapping process after completed training, even hours of eye tracking recording can be evaluated efficiently. …”
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Graph-Based Semi-Supervised Learning with Bipartite Graph for Large-Scale Data and Prediction of Unseen Data by Mohammad Alemi, Alireza Bosaghzadeh, Fadi Dornaika

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Ultrasound Assessment in Polycystic Ovary Syndrome Diagnosis: From Origins to Future Perspectives—A Comprehensive Review by Stefano Di Michele, Anna Maria Fulghesu, Elena Pittui, Martina Cordella, Gilda Sicilia, Giuseppina Mandurino, Maurizio Nicola D’Alterio, Salvatore Giovanni Vitale, Stefano Angioni

“…Studies on diagnostic criteria, imaging modalities, stromal assessment, and machine-learning algorithms were prioritized. Additional references were identified via citation screening. …”
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Ambiguity-aware semi-supervised learning for leaf disease classification by Tri-Cong Pham, Tien-Nam Nguyen, Van-Duy Nguyen

“…Specifically, we present a per-disease ambiguity rejection algorithm that eliminates ambiguous results, thereby enhancing the precision of pseudo labels for the subsequent semi-supervised training step and improving the precision of the final classifier. …”
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An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP-Based DRDoS Attack Detection by V. Sreenivasulu, C. V. Ravikumar

“…The detection performance of DSA is increased by training using the fractional lyrebird optimization algorithm (FLOA); FLOA provides a more effective, reliable, and scalable optimization strategy for training DSAs than traditional algorithms. …”
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Intelligent Course Plan Recommendation for Higher Education: A Framework of Decision Tree by Xiaoliang Chen, Jianzhong Zheng, Yajun Du, Mingwei Tang

“…The issue of how to timely update the talent training plans for computer major in a university has received considerable critical attention. …”
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A Machine Learning Approach for User Behavior Analysis in Developing Websites by Aeesha Shaheen

“…After training, the accuracy of network assesses using test data, and evaluate the effectiveness of forecasting. …”
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Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection by Jian Yang, Zixin Tang, Zhenkai Guan, Wenjia Hua, Mingyu Wei, Chunjie Wang, Chenglong Gu

“…Problems like feature dimension explosion, low interpretability, long training time, and low detection accuracy are solved by compressing abstract and uninterpretable features to limit the depth of DFS algorithm. …”
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Optimal design and performance prediction of stepped honeycomb labyrinth seal using CFD and ANN by Geunseo Park, Min Seok Hur, Tong Seop Kim

“…Similar to geometry optimization, i-LHS was used to select the operating design points for training the ANN. A metamodel reflecting operating conditions was developed by evaluating the generalization and practicality of the ANN. …”
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Topology aware multitask cascaded U-Net for cerebrovascular segmentation. by Pierre Rougé, Nicolas Passat, Odyssée Merveille

“…Currently, differentiable skeletonization algorithms are either inaccurate or computationally demanding. …”
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CNN-Based Medical Ultrasound Image Quality Assessment by Siyuan Zhang, Yifan Wang, Jiayao Jiang, Jingxian Dong, Weiwei Yi, Wenguang Hou

“…It is also an important index in evaluating the performance of ultrasonic imaging equipment and image processing algorithms. …”
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Enhancing BVR Air Combat Agent Development With Attention-Driven Reinforcement Learning by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

“…Evaluations in single- and multi-agent BVR scenarios against adversaries with diverse tactical characteristics demonstrate superior training efficiency and enhanced agent capabilities compared to leading RL algorithms commonly applied in similar domains, including PPO, DDPG, and SAC. …”
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