Machine learning-based ultrasound radiomics for predicting risk of recurrence in breast cancer by Wei Fan, Hao Cui, Xiaoxue Liu, Xudong Zhang, Xinran Fang, Junjia Wang, Zihao Qin, Xiuhua Yang, Jiawei Tian, Lei Zhang

“…The informative radiomics features were screened using the minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms. Subsequently, radiomics models were constructed with eight machine learning algorithms. …”
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Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

“…Nine machine learning algorithms were tested using internal validation (70% training, 30% testing) and external validation. …”
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Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models by Lei Tianxing

“…This study explores the application of machine learning algorithms in assessing diabetes risk, with a particular focus on Decision Trees (DT) and Ensemble Learning techniques. …”
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Application of convolutional neural networks in the intelligence security system subsystem by V. S. Demeshko, A. I. Фёдоров

“…The tasks of the intelligence subsystem for detecting and recognizing ground-based objects of observation in a complex phono-target environment are defined.The task of detecting an object of observation was solved on the basis of a previously proposed algorithm. The disadvantage of this algorithm was the presence of false positives from a flickering complex phono-target environment. …”
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Accelerating and Compressing Transformer-Based PLMs for Enhanced Comprehension of Computer Terminology by Jian Peng, Kai Zhong

“…Our method involves a pipeline parallelism algorithm designed to accelerate training. It is paired with an innovative mixed compression strategy that combines pruning and knowledge distillation to effectively reduce the model size while preserving its performance. …”
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Early Prediction of Stroke Risk Using Machine Learning Approaches and Imbalanced Data by Hassan Qassim

“… Classifying medical datasets using machine learning algorithms could help physicians to provide accurate diagnosing and suitable treatment. …”
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Optimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process. by Shiya Gao, Xin Guan, Xiaojing Cao, Zhili Bai, Caimeng Wang, Yun Zhan, Haiyang Yu

“…Moreover, the recognition accuracy of the model for oil condition on the training and testing sets reaches 90.51% and 93.08%, respectively, while the accuracy of other algorithms remains below 90%. …”
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The OPS-SAT benchmark for detecting anomalies in satellite telemetry by Bogdan Ruszczak, Krzysztof Kotowski, David Evans, Jakub Nalepa

“…The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms. They were evaluated using the training-test dataset split introduced in this work, and we suggest a set of quality metrics which should be calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. …”
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Learning EEG Representations With Weighted Convolutional Siamese Network: A Large Multi-Session Post-Stroke Rehabilitation Study by Shuailei Zhang, Kai Keng Ang, Dezhi Zheng, Qianxin Hui, Xinlei Chen, Yang Li, Ning Tang, Effie Chew, Rosary Yuting Lim, Cuntai Guan

“…Without losing generality, we also evaluated the proposed method on two publicly available datasets acquired from healthy subjects, wherein the proposed algorithm demonstrated superior performance at most cases as well. …”
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Application of Machine Learning in the Prediction of the Acute Aortic Dissection Risk Complicated by Mesenteric Malperfusion Based on Initial Laboratory Results by Zhechuan Jin, Jiale Dong, Jian Yang, Chengxiang Li, Zequan Li, Zhaofei Ye, Yuyu Li, Ping Li, Yulin Li, Zhili Ji

“…Among the six assessed machine learning algorithms, the RF model exhibited the best predictive capabilities, yielding AUROCs of 0.888 (95% CI 0.887, 0.889) and 0.797 (95% CI 0.794, 0.800) in the training and testing datasets, respectively, as well as sensitivities of 0.864 (95% CI 0.862, 0.867) and 0.811 (95% CI 0.806, 0.816), respectively, in the corresponding datasets. …”
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Development and Validation of a Prediction Model Using Sella Magnetic Resonance Imaging–Based Radiomics and Clinical Parameters for the Diagnosis of Growth Hormone Deficiency and I... by Kyungchul Song, Taehoon Ko, Hyun Wook Chae, Jun Suk Oh, Ho-Seong Kim, Hyun Joo Shin, Jeong-Ho Kim, Ji-Hoon Na, Chae Jung Park, Beomseok Sohn

“…The extreme gradient boosting algorithm was used to train the prediction models. …”
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Temporal scalability analysis of spectral remote sensing and Machine Learning-Based models for forest mapping in the tropical region of Nigeria by K. Pawłuszek-Filipiak, D. Włodarczyk, K. Stuchły, F.E. Ikuemonisan

“…Three strategies were evaluated: the first involved training RS-ML model on spectral vegetation indices generated based on datasets from single date and applying it to datasets captured in different dates; the second entailed training distinct models for each datasets captured in certain time period and the third one utilized datasets from various dates (multi-temporal) to land cover mapping. …”
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Enhancing land feature classification with the BTR Extractor: A novel software package for high-accuracy analysis of aerial laser scan data by Jamshid Talebi, Zahra Azizi

“…. – Implementing supervised algorithms for high-accuracy classification. – Evaluating the performance against existing software like TerraSolid.The user-friendly interface allows data entry, training data collection, and selection of classification methods. …”
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Robust extreme gradient boosting model for predicting the behavior of RC slabs under impact loading: key influencing factors and performance insights by Ammar Babiker

“…The XGB algorithm showed superior predictive performance, with high coefficients of determination, underscoring the model’s robustness and accuracy across both training and testing datasets. …”
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Sparse Representation of Deformable 3D Organs with Spherical Harmonics and Structured Dictionary by Dan Wang, Ahmed H. Tewfik, Yingchun Zhang, Yunhe Shen

“…This algorithm is also generalized into applications of organs with both interior and exterior surfaces. …”
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Enhanced object detection in low-visibility haze conditions with YOLOv9s. by Yang Zhang, Bin Zhou, Xue Zhao, Xiaomeng Song

“…Furthermore, the implementation of a nonmonotonic strategy for dynamically adjusting the loss function weights significantly boosts the model's detection precision and training efficiency. Comprehensive experimental evaluations of the COCO2017 fog-augmented dataset indicate that the proposed algorithm surpasses current state-of-the-art techniques in various assessment metrics, including precision, recall, and mean average precision (mAP). …”
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Discovery of Exact Equations for Integer Sequences by Boštjan Gec, Sašo Džeroski, Ljupčo Todorovski

“…In this article, we introduce <i>Diofantos</i>, an algorithm for discovering equations in the ring of integers that exactly match the training data. …”
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Cultivation Method Analysis for Teachers’ Teaching Ability Driven by Artificial Intelligence Technology by Yanfang Chen, Shasha Xu

“…Second, this work proposes a neural network (IPSO-BP) for evaluating the teaching ability of college teachers via artificial intelligence technology. …”
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Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model by Elhoucine Ouassam, Yassine Dabachine, Nabil Hmina, Belaid Bouikhalene

“…This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow management at international airports. …”
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MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort... by Yiru Wang, Fuli Chen, Zhechen Ouyang, Siyi He, Xinling Qin, Xian Liang, Weimei Huang, Rensheng Wang, Kai Hu

“…The random forest algorithm was employed to identify the most valuable features. …”
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