Ultrasonic radiomics in predicting pathologic type for thyroid cancer: a preliminary study using radiomics features for predicting medullary thyroid carcinoma by Dai Zhang, Dai Zhang, Dai Zhang, Dai Zhang, Fan Yang, Fan Yang, Fan Yang, Fan Yang, Wenjing Hou, Wenjing Hou, Wenjing Hou, Wenjing Hou, Ying Wang, Ying Wang, Ying Wang, Ying Wang, Jiali Mu, Jiali Mu, Jiali Mu, Jiali Mu, Hailing Wang, Hailing Wang, Hailing Wang, Hailing Wang, Xi Wei, Xi Wei, Xi Wei, Xi Wei

“…We constructed clinical model, radiomics model and comprehensive model by executing machine learning algorithms based on baseline clinical, pathological characteristics and ultrasound image data, respectively.ResultsThe study showed that the comprehensive model observed the highest diagnostic efficacy in differentiating MTC from PTC with AUC, sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 0.93, 0.88, 0.82, 0.77, 0.91, 85.8%. …”
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Accurate and robust prediction of Amyloid-β brain deposition from plasma biomarkers and clinical information using machine learning by Jiayuan Xu, Andrew J. Doig, Sofia Michopoulou, Sofia Michopoulou, Petroula Proitsi, Petroula Proitsi, Fumie Costen, The Alzheimer's disease neuroimaging initiative

“…This study aims to develop and validate machine learning algorithms for accurately predicting brain Aβ positivity using plasma biomarkers, genetic information, and clinical data as a cost-effective alternative to PET imaging.MethodsWe analyzed 1,043 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and validated our models on 127 patients from the Center for Neurodegeneration and Translational Neuroscience (CNTN) dataset. …”
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Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool by Jianying Liu, Wei Jiang, Yahong Yu, Jiali Gong, Guie Chen, Yuxing Yang, Chao Wang, Dalong Sun, Xuefeng Lu

“…In external validation, the SVM model maintained robust performance with an AUC of 0.889. The SHAP algorithm further explained the contribution of each feature to model predictions.Conclusion The study developed an interpretable and practical machine learning model for predicting bowel preparation adequacy in elderly patients, facilitating early interventions to improve outcomes and reduce resource wastage.…”
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A Comparative Study of Hybrid Adaptive Neuro-Fuzzy Inference Systems to Predict the Unconfined Compressive Strength of Rocks by Annabelle Graham, Emma Scott

“…Performance metrics like R2, RMSE, NMSE, MAE, and n_10 index were used to assess the predictive capability of models, indicating that ANAS with maximum and minimum =3.103, has the most optimal prediction performance for practical applications.…”
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A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy by R. Sudharsan, E. N. Ganesh

Subjects: “…customer churn prediction…”
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A dynamic prediction method for surface mining subsidence based on dynamic probability integral model and Logistic model by Lei HAN, Xinxin QI, Tianjun WANG, Chunmin PEI, Xudong WANG

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Medium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential Fitting by Zihan CHEN, Wei TENG, Xuefeng XU, Xian DING, Yibing LIU

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Extreme high accuracy prediction and design of Fe-C-Cr-Mn-Si steel using machine learning by Hao Wu, Jianyuan Zhang, Jintao Zhang, Chengjie Ge, Lu Ren, Xinkun Suo

“…In this study, a data-driven model combining machine learning (ML), firefly optimization algorithm (FA) and conditional generative adversarial networks (CGANs) were proposed to predict solid solution strengthening theory of Fe-C-Cr-Mn-Si steel. …”
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The Controlling Factors and Prediction of Deep-Water Mass Transport Deposits in the Pliocene Qiongdongnan Basin, South China Sea by Jiawang Ge, Xiaoming Zhao, Qi Fan, Weixin Pang, Chong Yue, Yueyao Chen

“…Our study indicates that a random forest artificial intelligence algorithm could be useful in predicting the susceptibility of deep-water MTDs and can be applied to other study areas to predict and avoid submarine disasters caused by wasting processes.…”
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Battery Health Prediction with Singular Spectrum Analysis and Grey Wolf Optimized Long Short-Term Memory Networks by Chengti Huang, Na Li, Jianqing Zhu, Shengming Shi

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A comparative study of hybrid adaptive neuro-fuzzy inference systems to predict the unconfined compressive strength of rocks by Wei Cao

“…Abstract The accurate prediction of unconfined compressive strength (UCS) in rock samples is critical for the successful planning, design, and implementation of mining and civil engineering projects. …”
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Application of radiomics-based prediction model to predict preoperative lymph node metastasis in prostate cancer: a systematic review and meta-analysis by Yanghuang Zheng, Yuelin Du, Biao Zhang, Helin Zhang, Panfeng Shang, Zizhen Hou

“…BackgroundThis study aims to comprehensively evaluate the accuracy and efficacy of radiomics models based on imaging equipment in predicting prostate cancer (PCa) lymph node metastasis (LNM).MethodsWe systematically searched PubMed, Embase, Cochrane Library, Web of Science, and Sinomed databases from their establishment until July 2024. …”
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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

“…A classification prediction model for the haemoglobin concentration after kidney transplantation was constructed. …”
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A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF by Zenzen, Roumaissa, Ayadi, Ayoub, Benaissa, Brahim, Belaidi, Idir, Sukic, Enes, Khatir, Tawfiq

“…This study presents an innovative methodology that synergistically combines Proper Orthogonal Decomposition and Radial Basis Function interpolation for predicting structural responses based on crack parameters. …”
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Accurate Rotor Temperature Prediction of Permanent Magnet Synchronous Motor in Electric Vehicles Using a Hybrid RIME-XGBoost Model by Jianzhao Shan, Zhongyuan Che, Fengbin Liu

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Balancing accuracy and convergence rate: a hybrid optimisation algorithm for parameter identification of unmanned marine vehicles by Zheng Li, Caoyang Yu, Yiming Zhong, Yuanju Cao, Xianbo Xiang, Lian Lian

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EfficientNet-b0-Based 3D Quantification Algorithm for Rectangular Defects in Pipelines by Di Wu, Yong Hong, Jie Wang, Shaojun Wu, Zhihao Zhang, Yizhang Liu

“…This multi-task learning model utilizes a shared feature extraction layer and numerous branching networks to create individual predictions for each task: length, width, and depth. …”
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Robust prediction of tool-tissue interaction force using ISSA-optimized BP neural networks in robotic surgery by Yong-Li Yan, Teng Ren, Li Ding, Tiansheng Sun, Shandeng Huang

“…Methods The current proposal concerns a deep learning-based solution utilizing a backpropagation neural network (BPNN) optimized by improved sparrow search algorithm (ISSA) to predict clamp force on soft tissue. …”
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Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 by Morteza Abbasi, Amir Hossein Namadchi, Mehdi Abbasi, Mohsen Abbasi

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Post-extrasystolic variation of ST segment and T wave as a mortality risk predictor after myocardial infarction by Ralf J. Dirschinger, Ralf J. Dirschinger, Alexander Müller, Petra Barthel, Alexander Steger, Michael Dommasch, Axel Bauer, Karl-Ludwig Laugwitz, Georg Schmidt, Daniel Sinnecker, Daniel Sinnecker

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