An interpretable fault diagnosis method for aeroengine bearings based on belief rule based with a dynamic power set by Jinyuan Li, Wei He, Hailong Zhu

“…Moreover, the models can suffer from local ignorance in the prediction process. These problems can lead to a decrease in the prediction accuracy of the model. …”
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Epistolution: a new principle necessary to a learning-first theory of life by Charlie Munford

“…By “understand” I mean neither association nor prediction but Karl Popper’s concept of explanation through conjecture and refutation. …”
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A non-linear procedure for the numerical analysis of crack development in beams failing in shear by P. Bernardi, R. Cerioni, E. Michelini, A. Sirico

“…In more details, a constitutive model originally proposed by Ottosen and based on non-linear elasticity has been here incorporated into 2D-PARC in order to improve the numerical efficiency of the adopted algorithm, providing at the same time an accurate prediction of the structural response. …”
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An Explainable Fuzzy Framework for Assessing Preeclampsia Classification by Matías Salinas, Daira Velandia, Leondry Mayeta-Revilla, Ayleen Bertini, Marvin Querales, Fabian Pardo, Rodrigo Salas

“…There is a critical need for predictive systems that not only perform accurately but also provide interpretable insights for clinical decision-making. …”
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Drilling dynamics measurement of drilling motors and its application in recognition of motor operation states through machine learning by Fei Li, Haolan Song, Yifan Wang

“…Due to the increased non-productive time and drilling costs brought about by accidental damage to drilling motors, predictive maintenance for drilling motors is necessary to optimize asset utilization. …”
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Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations by Ibrahim Antoun, Ibrahim Antoun, Ahmed Abdelrazik, Mahmoud Eldesouky, Xin Li, Georgia R. Layton, Georgia R. Layton, Georgia R. Layton, Mustafa Zakkar, Mustafa Zakkar, Mustafa Zakkar, Riyaz Somani, Riyaz Somani, G. André Ng, G. André Ng, G. André Ng

“…This scoping review examines the applications of AI across key domains: detection, risk prediction, treatment optimisation, and remote monitoring. …”
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A data-driven group retrosynthesis planning model inspired by neurosymbolic programming by Xuefeng Zhang, Haowei Lin, Muhan Zhang, Yuan Zhou, Jianzhu Ma

“…Inspired by human learning, our algorithm, akin to neurosymbolic programming, builds upon commonly used multi-step concepts such as cascade and complementary reactions and can evolve from practical experiences, enhancing the prediction model for fundamental and compositional reaction templates. …”
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Integrated phenotypic and transcriptomic characterization of desmin-related cardiomyopathy in hiPSC-derived cardiomyocytes and machine learning-based classification of disease feat... by Vivien Batoumeni, Yeranuhi Hovhannisyan, Bénédicte Gobert, Keyhan Alvandipour, Jennifer Arthur Ataam, Ombline Conrad, David Hoffmann, Nicolas Wiest-Daesslé, Jochen Dobner, José Américo Nabuco Leva Ferreira Freitas, Hakim Hocini, Zhenlin Li, Laurent Brino, Andrea Rossi, Onnik Agbulut, Peter Sommer, Pierre Joanne, Konstantinos Gkatzis

“…Finaly, we developed a machine learning prediction model to classify cellular phenotypes, which can be used for translational research, including drug candidate screening. …”
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Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan Plateau by Yonghong Liu, Fuzhong Weng, Fei Tang, Rui Li, Yongming Xu, Yang Han, Jun Yang, Qingyang Liu

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Development experience of information system for ranking of academic and pedagogical staff by A. A. Chernousov, E. V. Vavilova

“…The aim of this work is research of algorithms for quantitative assessment of intellectual potential (rating) of academic and pedagogical staff in higher educational institutions, as well as the development of technology for the application of these algorithms in practice.Materials and methods. …”
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Fuzzy AHP Based Optimal Design Building-Attached Photovoltaic System for Academic Campus by Mega Ardisa Hapsari, Subiyanto Subiyanto

“…Several algorithms have been developed for building-attached photovoltaic system (BAPV) planning in educational institute based on PV capacity. …”
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Optimalisasi Prediksi Harga Ihsg Menggunakan Hybrid Weighted Fuzzy Time Series Hidden Markov Model Dengan Algoritma Evolusi Differensial by Alya Fitri Syalsabilla, Suci Astutik, Agus Fachrur Rozy

“…Forecasting from the Hybrid WFTS-HMM Model with the DE Algorithm has lower prediction error (1.45%) compared to the model without DE (1.49%). …”
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Optimal design strategy of traditional courtyard based on performance and data-driven method—A case study of Yanshen ancient town, China by Zhixin Xu, Xin Zheng, Xiangfeng Li

“…Based on the learning rates and various evaluation indicators, XGBoost is ultimately selected to classify and predict the overall building performance. Results indicate that the model achieves an average prediction accuracy of 83.6%. …”
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Substituted 1,4-naphthoquinones for potential anticancer therapeutics: In vitro cytotoxic effects and QSAR-guided design of new analogs by Veda Prachayasittikul, Prasit Mandi, Ratchanok Pingaew, Supaluk Prachayasittikul, Somsak Ruchirawat, Virapong Prachayasittikul

“…Four QSAR models were successfully constructed using multiple linear regression (MLR) algorithm providing good predictive performance (R: training set = 0.8928–0.9664; testing set = 0.7824–0.9157; RMSE: training set = 0.1755–0.2600; testing set = 0.2726–0.3748). …”
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The quantification of percentage filling of gutta-percha in obturated root canal using image processing and analysis by Pravin R. Lokhande, S. Balaguru

“…Percentage filling of the obturated root canal using X-ray radiography (Dentist's prediction) and proposed algorithm results of the present study were compared. …”
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Error Data Analytics on RSS Range-Based Localization by Shuhui Yang, Zimu Yuan, Wei Li

“…The significance of our discovery has two folds: First, we present a general expression for localization error data analytics, which can explain and predict the accuracy of range-based localization algorithms; second, the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms.…”
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Machine Learning-Based Cost Estimation Models for Office Buildings by Guolong Chen, Simin Zheng, Xiaorui He, Xian Liang, Xiaohui Liao

“…This paper explores the application of algorithm-optimized back propagation neural networks and support vector machines in predicting the costs of office buildings. …”
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Computer Viewing Model for Classification of Erythrocytes Infected with <i>Plasmodium</i> spp. Applied to Malaria Diagnosis Using Optical Microscope by Eduardo Rojas, Irene Cartas-Espinel, Priscila Álvarez, Matías Moris, Manuel Salazar, Rodrigo Boguen, Pablo Letelier, Lucia San Martín, Valeria San Martín, Camilo Morales, Neftalí Guzmán

“…<i>Conclusions:</i> Based on the results, we propose a convolutional neural network model (VGG-19) for malaria diagnosis that can be applied in low-complexity laboratories thanks to its ease of implementation and high predictive performance.…”
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Remote sensing inversion of nitrogen content in silage maize plants based on feature selection by Kejing Cheng, Kejing Cheng, Jixuan Yan, Jixuan Yan, Guang Li, Guang Li, Weiwei Ma, Weiwei Ma, Zichen Guo, Zichen Guo, Wenning Wang, Wenning Wang, Haolin Li, Qihong Da, Qihong Da, Xuchun Li, Xuchun Li, Yadong Yao, Yadong Yao

“…In studies on nitrogen content inversion in the maize canopy, the random forest (RF) algorithm, coupled with PLSR, demonstrated superior predictive performance. …”
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An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change by J. D. Jakeman, M. Perego, D. T. Seidl, T. A. Hartland, T. R. Hillebrand, M. J. Hoffman, S. F. Price

“…This significant reduction in computational cost was achieved despite the low-fidelity models used being incapable of capturing the local features of the ice-flow fields predicted by the high-fidelity model. The MFSE algorithms were able to effectively leverage the high correlation between each model's predictions of mass change, which all responded similarly to perturbations in the model inputs. …”
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