Enhanced Wind Power Forecasting Using a Hybrid Multi-Strategy Coati Optimization Algorithm and Backpropagation Neural Network by Hua Yang, Zhan Shu, Zhonger Li

Subjects: “…wind power prediction…”
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Prediction model of water inrush risk level of coal seam floor based on KPCA-DBO-SVM by Wei Wang, Huangrui Wang, Xuping Li, Yun Qi, Xinchao Cui, Chenhao Bai

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Comprehensive flexible framework for using multi-machine learning methods to optimal dynamic transient stability prediction by considering prediction accuracy and time by Ali Abdalredha, Alireza Sobbouhi, Abolfazl Vahedi

“…In recent years, Machine/Deep Learning (ML/DL) techniques have been widely applied to predict transient stability conditions. This paper presents a flexible framework for using the desired number of ML algorithms and combines the results of them to extract the final optimal transient stability perdition (TSP). …”
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Association between the (neutrophil + monocyte)/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according... by Lulu Liu, Qian Ma, Guangzan Yu, Xuhou Ji, Hua He

“…Moreover, we employed Boruta algorithm to evaluate the predictive potential of the NMa ratio and established the prediction models utilizing machine learning algorithms. …”
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Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models by Sughashini Murugesu, Kristofer Linton-Reid, Emily Braun, Jennifer Barcroft, Nina Cooper, Margaret Pikovsky, Alex Novak, Nina Parker, Catriona Stalder, Maya Al-Memar, Srdjan Saso, Eric O. Aboagye, Tom Bourne

“…Data pre-processing derived 14 features for predictive modelling. A combination of eight linear, Bayesian, neural-net and tree-based machine learning algorithms were applied to ten different feature sets. …”
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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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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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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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Applications of Recent Metaheuristic Algorithms for Loss Reduction in Distribution Power Systems considering Maximum Penetration of Photovoltaic Units by Le Duy Luan Nguyen, Phuc Khai Nguyen, Viet Cuong Vo, Ngoc Dieu Vo, Thang Trung Nguyen, Tan Minh Phan

“…Photovoltaic units (PVUs) are placed optimally by implementing the Coot optimization algorithm (COOA), the archimedes optimization algorithm (AOA), the transient search optimization algorithm (TSOA), the crystal structure algorithm (CrSA), the war strategy optimization algorithm (WSA), and the average and subtraction-based optimizer (ASBO). …”
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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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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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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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Interactive Mitigation of Biases in Machine Learning Models for Undergraduate Student Admissions by Kelly Van Busum, Shiaofen Fang

“…Because these issues are intrinsically subjective and context-dependent, creating trustworthy software requires human input and feedback. (1) Introduction: This work introduces an interactive method for mitigating the bias introduced by machine learning models by allowing the user to adjust bias and fairness metrics iteratively to make the model more fair in the context of undergraduate student admissions. (2) Related Work: The social implications of bias in AI systems used in education are nuanced and can affect university reputation and student retention rates motivating a need for the development of fair AI systems. (3) Methods and Dataset: Admissions data over six years from a large urban research university was used to create AI models to predict admissions decisions. …”
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