STATE PREDICTION OF WIND TURBINE GENERATOR BASED ON K-CNN AND N-GRU (MT) by CHAI Tong, YUAN YiPing, MA JunYan, FAN PanPan

“…The feature extraction results after dimensionality reduction were input into N-GRU for prediction and reconstruction error was obtained, then the state evaluation was realized by setting the alarm threshold. …”
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Prediction for Tunnelling-Induced Ground Settlement in Multilayered Soils: An Improved Gradient Boosting Approach by Hongbin An, Yangyang Chen, Jingjie Lei, Hanbin Luo, Elton J. Chen

“…The research is based on the machine learning algorithm to establish a prediction model of stratum settlement caused by shield tunneling, which provides a new idea for real time prediction of the ground response caused by shield tunneling and risk reduction. …”
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Optimized deep learning models for stress-based stroke prediction from EEG signals by Sivasankaran Pichandi, Gomathy Balasubramanian, Venkatesh Chakrapani, J. Samuel Manoharan

“…The proposed research aims to classify stress-induced emotions and predict stroke risk using advanced deep learning algorithms. …”
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Evaluation of machine learning techniques for real-time prediction of implanted lower limb mechanics by Chase Maag, Clare K. Fitzpatrick, Paul J. Rullkoetter

“…The models were trained on joint alignment data, ligament information, and external boundary conditions. Several predictive algorithms were explored, including linear regression (LRM), multilayer perceptron (MLP), bi-directional long short-term memory (biLSTM), convolutional neural network (CNN), and transformer-based approaches. …”
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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data by Rui Li MS, Xiaoyan Hao MS, Yanjun Diao MD, Liu Yang MS, Jiayun Liu MD

“…This study aims to develop machine learning (ML) models for CRC risk prediction using clinical laboratory data. Methods This retrospective, single-center study analyzed laboratory examination data from healthy controls (HC), polyp patients (Polyp), and CRC patients between 2013 and 2023. …”
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Specificity and Areas of Usage of Cardiovascular Prediction Models Among Athletes—State-of-the-art Review by Tomasz Chomiuk, Przemysław Kasiak, Artur Mamcarz, Daniel Śliż

“…Athletes with confirmed or suspected cardiovascular disease should be guided to perform training in carefully adjusted safe zones. Indirect prediction algorithms are feasible and easy-to-apply methods of individual cardiovascular disease risk estimation. …”
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Tool wear prediction based on XGBoost feature selection combined with PSO-BP network by Zhangwen Lin, Yankun Fan, Jinling Tan, Zhen Li, Peng Yang, Hua Wang, Weiwei Duan

“…Experimental results show that PSO outperforms other algorithms in training the tool wear prediction model, with XGBoost feature selection reducing model construction time by 57.4% and increasing accuracy by 63.57%, demonstrating superior feature selection capabilities over Decision Tree, Random Fores, Adaboost and Extra Trees. …”
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Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…After identifying the best-performing model, Shapley Additive Explanations (SHAP) were employed to interpret its predictions. This approach provides insights into the model’s decision-making process, clarifying the complex nature of machine learning algorithms. …”
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Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel by Xiaochang Xie, Mutong Liu, Ping Yang, Zenan Yang, Chengbo Pan, Chenchong Wang, Xiaolu Wei

“…In this study, we collected 216 creep data of austenitic heat-resistant steel, selected a variety of different machine learning algorithms to establish creep life prediction models, calculated and introduced a large amount of physical metallurgy knowledge highly related to creep based on Thermo-Calc, and converted the creep life into the form of the Larson–Miller parameter to optimize the data distribution, which effectively improved the prediction accuracy and interpretability of the model. …”
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Influenza virus genotype to phenotype predictions through machine learning: a systematic review by Laura K. Borkenhagen, Martin W. Allen, Jonathan A. Runstadler

“…Machine learning techniques have demonstrated promise in addressing this critical need for other pathogens because the underlying algorithms are especially well equipped to uncover complex patterns in large datasets and produce generalizable predictions for new data. …”
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Development of data driven machine learning models for the prediction and design of pyrimidine corrosion inhibitors by Aeshah H. Alamri, N. Alhazmi

“…In the present work, machine learning algorithms were utilized to develop predictive models for fifty-four (54) pyrimidines derivatives whose experimentally determined inhibition efficiencies data as corrosion inhibitors for carbon steel in hydrochloric acid medium are available in the literature utilizing the partial least square regression (PLS) and the random forest (RF). …”
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Prediction of Myocardial Infarction Based on Non-ECG Sleep Data Combined With Domain Knowledge by Changyun Li, Yonghan Zhao, Qihui Mo, Zhibing Wang, Xi Xu

“…Prediction of myocardial infarction (MI) is crucial for early intervention and treatment. …”
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Cancer-Associated Fibroblast Risk Model for Prediction of Colorectal Carcinoma Prognosis and Therapeutic Responses by Yan Wang, Zhengbo Chen, Gang Zhao, Qiang Li

“…Then, we evaluated whether the risk score could predict CAF infiltrations and immunotherapy in CRC and confirmed the expression of the risk model in CAFs. …”
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Prediction of remaining parking spaces based on EMD-LSTM-BiLSTM neural network by Changxi Ma, Xiaoting Huang, Ke Wang, Yongpeng Zhao

“…The results may provide some potential insights for parking prediction.…”
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Online Purchase Behavior Prediction Model Based on Recurrent Neural Network and Naive Bayes by Chaohui Zhang, Jiyuan Liu, Shichen Zhang

“…The contributions of this paper are as follows: (1) By constructing an online purchasing behavior model RNN-NB, which integrates the N vs 1 structure Recurrent Neural Network and naive Bayesian model, the validity limitations of some single-architecture recommendation algorithms are solved. (2) Based on the existing naive Bayesian model, the prediction accuracy of online purchasing behavior is further improved. (3) The analysis based on the features of the time series provides new ideas for the research of later scholars and new guidance for the marketing of platform merchants.…”
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Deep-Learning-Based Solar Flare Prediction Model: The Influence of the Magnetic Field Height by Lei Hu, Zhongqin Chen, Long Xu, Xin Huang

“…With the accumulation of solar observation data and the development of data-driven algorithms, deep learning methods have been widely used to build solar flare prediction models. …”
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Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies by Mohamed Ahwiadi, Wilson Wang

“…Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. …”
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Bayesian compositional generalized linear mixed models for disease prediction using microbiome data by Li Zhang, Xinyan Zhang, Justin M. Leach, A. K. M. F. Rahman, Carrie R. Howell, Nengjun Yi

“…We fitted the proposed models using Markov Chain Monte Carlo (MCMC) algorithms with rstan. The performance of the proposed method was evaluated through extensive simulation studies, demonstrating its superiority with higher prediction accuracy compared to existing methods. …”
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Mechanical Design of McKibben Muscles Predicting Developed Force by Artificial Neural Networks by Michele Gabrio Antonelli, Pierluigi Beomonte Zobel, Muhammad Aziz Sarwar, Nicola Stampone

“…The latter was used to train 27 artificial neural networks (ANNs) to identify the best algorithm for predicting the developed forces. The best ANN was tested on three numerical models and a prototype with a combination of parameters not included in the dataset, comparing predicted and numerical responses. …”
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Enhancing Crop Yield Prediction Using IoT-Based Soil Moisture and Nutrient Sensors by Alsalami Zaid, Mohammed G., Srinivas Tummala

“…Crop yield prediction is crucial for ensuring food security by enabling farmers to optimize resource use, manage risks, and plan for market demands, ultimately leading to increased agricultural productivity and sustainability..The IoT-based crop yield prediction system integrates advanced sensing technologies, communication protocols, machine learning algorithms, and real-time monitoring to optimize crop production. …”
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