Clinical Pathways Guided by Remotely Monitoring Cardiac Device Data: The Future of Device Heart Failure Management? by Joanne K Taylor, Fozia Zahir Ahmed

“…As standalone diagnostic tools, individual physiological metrics and algorithm-based systems have demonstrated utility in predicting future events, but the integration of remote monitoring data with existing clinical care pathways for device HF patients is not well described. …”
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Improving Accuracy and Calibration of Deep Image Classifiers With Agreement-Driven Dynamic Ensemble by Pedro Conde, Rui L. Lopes, Cristiano Premebida

“…Possible strategies to tackle this problem are two-fold: (i) models need to be highly accurate, consequently reducing this risk of failure; (ii) facing the impossibility of completely eliminating the risk of error, the models should be able to inform the level of uncertainty at the prediction level. As such, state-of-the-art DL models should be <italic>accurate</italic> and also <italic>calibrated</italic>, meaning that each prediction has to codify its confidence/uncertainty in a way that approximates the true likelihood of correctness. …”
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A GRNN based frame work to test the influence of nano zinc additive biodiesel blends on CI engine performance and emissions by Chiranjeeva Rao Seela, B. Ravisankar, B.M.V.A. Raju

“…The neural network predictions are corroborated with the experimental results and are found in good agreement. …”
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Consistency regularization for few shot multivariate time series forecasting by Yumei She, Yi Hong, Shikai Shen, Bin Yang, Liyi Zhang, Jianxiao Wang

“…Abstract Multivariate time series forecasting aims to accurately predict future trends by capturing and analyzing various features of the time series. …”
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Substrate-aware computational design of two-dimensional materials by Arslan Mazitov, Ivan Kruglov, Alexey V. Yanilkin, Aleksey V. Arsenin, Valentyn S. Volkov, Dmitry G. Kvashnin, Artem R. Oganov, Kostya S. Novoselov

“…This study presents a novel method for predicting the atomic structure of 2D materials on substrates by combining an evolutionary algorithm, a lattice-matching technique, an automated machine-learning interatomic potentials training protocol, and the ab initio thermodynamics approach. …”
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Evaluating a Procedural Content Orchestrator Gameplay Data and Classifying User Profiles by Leonardo Tórtoro Pereira, T. Yuji Teoi, Claudio Fabiano Motta Toledo

“…Our extended results suggest that we can predict player’s profiles through gameplay metrics and data augmentation, even for small samples. …”
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Mechanical Behavior of Filled Rubber Compounds: Hyper-Elastic Models Based on Strain Amplification by Mir Hamid Reza Ghoreishy, Foroud Abbassi-Sourki

“…The previous theories for the prediction of the mechanical behavior of these materials arebased on phenomenological relationships. …”
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Integrated Bidding and Battery Scheduling in a Microgrid for Sealed-Bid Double Auction Power Trading With Peer Microgrids Under Uncertainty and Its Blockchain-Based Implementation by Zubin J. B., Sunitha R., Gopakumar Pathirikkat

“…The Q-learning relies solely on its historical bidding outcomes without attempting to predict the bids of other participants. In parallel, battery operations are optimized using a hybrid method that combines Genetic Algorithm (GA) and Simulated Annealing (SA), explicitly incorporating the bid buffer capacity to align scheduling with market commitments. …”
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The Prognostic Value of Serum HBV-RNA during Hepatitis B Virus Infection is Related to Acute-on-Chronic Liver Failure by Keli Qian, Ying Xue, Hang Sun, Ting Lu, Yixuan Wang, Xiaofeng Shi

“…A nomogram was developed to formulate an algorithm incorporating serum HBV-RNA for predicting the survival of HBV-ACLF patients. …”
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Comparative performance of deep learning architectures for diabetic peripheral neuropathy detection using corneal confocal microscopy: a retrospective single-centre study by Yuyang Deng, Wenqu Chen, Weihuang Xu, Jianzhang Hu

“…For single-image predictions in the three-class classification task of CCM images, the InceptionV3 model achieved a precision of 0.8385, a recall of 0.9083, an F1 score of 0.8720 and an AUC of 0.8769 for predicting DPN+.Conclusions The InceptionV3-based DLA model achieved superior performance compared with traditional convolutional neural network architectures like ResNet and DenseNet, and the Swin transformer model, highlighting its potential for effective DPN screening.…”
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Short-term wind power forecasting method for extreme cold wave conditions based on small sample segmentation by Lin Lin, Jinhao Xu, Jianfei Liu, Hao Zhang, Pengchen Gao

“…Given the scarcity, extreme values, and high volatility of the sample data, a Sequence Variational Autoencoder (SeqVAE) algorithm is employed to generate numerical weather prediction data and corresponding power samples. …”
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Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques by Antonietta Eliana Barrasso, Claudio Perone, Roberto Romaniello

“…Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. …”
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Identification and validation of hub m7G-related genes and infiltrating immune cells in osteoarthritis based on integrated computational and bioinformatics analysis by Zhenhui Huo, Chongyi Fan, Kehan Li, Chenyue Xu, Yingzhen Niu, Fei Wang

“…Functional enrichment, drug target prediction, and target gene-related miRNA prediction were performed for these genes. …”
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An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN by Mohamed Abdelkader Aboamer, Mohamed Yacin Sikkandar, Sachin Gupta, Luis Vives, Kapil Joshi, Batyrkhan Omarov, Sitesh Kumar Singh

“…This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. …”
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Machine vision-based detection of key traits in shiitake mushroom caps by Jiuxiao Zhao, Jiuxiao Zhao, Wengang Zheng, Wengang Zheng, Yibo Wei, Yibo Wei, Qian Zhao, Qian Zhao, Jing Dong, Jing Dong, Xin Zhang, Xin Zhang, Mingfei Wang, Mingfei Wang

“…Finally,M3 group using GWO_SVM algorithm achieved optimal performance among six mainstream machine learning models tested with an R²value of 0.97 and RMSE only at 0.038 when comparing predicted values with true values. …”
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Use of deep learning in early software bug detection by Syed Mibran Hassan Zaidi, Maria Khan, Mustafa Latif, Ali Akhtar, Sallar Khan

“…This was achieved by using an ensemble parallel technique for training the Convolutional Neural Network and Random Forest algorithm in a parallel way. Then, the predictions from both models are aggregated using an ensemble voting technique. …”
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No-Reference Stereoscopic IQA Approach: From Nonlinear Effect to Parallax Compensation by Ke Gu, Guangtao Zhai, Xiaokang Yang, Wenjun Zhang

“…Third, the saliency based parallax compensation, resulted from different stereoscopic image contents, is considerably valid to improve the prediction performance of image quality metrics. Experimental results confirm that our proposed stereoscopic image quality assessment paradigm has superior prediction accuracy as compared to state-of-the-art competitors.…”
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Investigation on the Radar Scattering and Doppler Spectrum From Trimaran Based on the Motion of Six Degrees of Freedom by Yuxin Deng, Jinxing Li, Wangqiang Jiang, Min Zhang

“…First, based on the Reynolds-averaged Navier&#x2013;Stokes equations, the interpolation wave-making method is proposed for the prediction of the six degrees of freedom motion of the trimaran. …”
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Multiobjective Optimization of Milling Parameters for Ultrahigh-Strength Steel AF1410 Based on the NSGA-II Method by Jin Xu, Fuwu Yan, Yan Li, Zhenchao Yang, Long Li

“…The influence of milling parameters (milling speed, each tooth feed, radial depth of cut, and axial depth of cut) on milling force and surface roughness is studied by ANOVA and established prediction model. Multiobjective optimization of milling parameters is accomplished based on nondominated sorting genetic algorithm II (NSGA-II) with milling force, surface roughness, and material removal rate as optimization objectives. …”
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Application of Categorical Boosting to Modelling the Friction Behaviour of DC05 Steel Sheets in Strip Drawing Test by Marek Szewczyk, Krzysztof Szwajka, Sherwan Mohammed Najm, Salwa O Mohammed

“…A Shapley decision plot exhibits the coefficient of friction prediction models via cumulative SHapley Additive exPlanations (SHAP) data. …”
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