A deep learning approach to real-time Markov modeling of ion channel gating by Efthymios Oikonomou, Yannick Juli, Rajkumar Reddy Kolan, Linda Kern, Thomas Gruber, Christian Alzheimer, Patrick Krauss, Andreas Maier, Tobias Huth

“…In addition, we propose a method to evaluate the goodness of a predicted model by re-simulating the prediction. Finally, we tested the algorithm with data recorded on a patch-clamp setup. …”
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Stacking data analysis method for Langmuir multi-probe payload by Jin Wang, Jin Wang, Duan Zhang, Qinghe Zhang, Qinghe Zhang, Xinyao Xie, Fangye Zou, Qingfu Du, Qingfu Du, V. Manu, Yanjv Sun

“…This study uses a stacking algorithm to process m-NLP data and incorporates the International Reference Ionosphere (IRI) model to correct the predicted electron density (Ne) values. …”
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Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens by Cheng Han, Shiying Lu, Pan Hu, Jiang Chang, Deying Zou, Feng Li, Yansong Li, Qiang Lu, Honglin Ren

“…Integrating these genes into an ML model based on the support vector machine (SVM) algorithm allows us to predict the zoonotic potential of various Brucella strains with high accuracy. …”
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Toward Intelligent Financial Advisors for Identifying Potential Clients: A Multitask Perspective by Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu

“…Thus, extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent. However, two critical problems encountered in real practice make this prediction task challenging, i.e., sample selection bias and data sparsity. …”
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Creating prognostic systems for cancer patients: A demonstration using breast cancer by Mathew T. Hueman, Huan Wang, Charles Q. Yang, Li Sheng, Donald E. Henson, Arnold M. Schwartz, Dechang Chen

“…In the approach, an unsupervised learning algorithm was used to create dendrograms and the C‐index was used to cut dendrograms to generate prognostic groups. …”
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Identification of mesoscale eddies based on improved YOLOv8 model: a case study in the South China Sea by Jianhao Gao, Jianhao Gao, Jianhao Gao, Feng Zhou, Feng Zhou, Feng Zhou, Di Tian, Di Tian, Muping Zhou, Muping Zhou, Hailong Guo

“…Accurate identification of mesoscale eddies is crucial for a deeper understanding of ocean internal dynamics, the development of marine resources, and the prediction of changes in the marine environment. This study utilizes Absolute Dynamic Topography (ADT) data provided by AVISO and the YOLOv8 algorithm model to investigate the identification of mesoscale eddies in the South China Sea (SCS). …”
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Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospec... by Kohji Nishida, Ryo Kawasaki, Yiming Qian, Liangzhi Li, Yuta Nakashima, Hajime Nagahara

“…In MACE prediction, our model outperformed the traditional score-based models, with 8.2% higher AUC than Systematic COronary Risk Evaluation (SCORE), 3.5% for SCORE 2 and 7.1% for the Framingham Risk Score (with p value<0.05 for all three comparisons).Conclusions Our algorithm estimates the 5-year risk of MACE based on retinal images, while explicitly presenting which risk factors should be checked and intervened. …”
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Flood Routing of Tigris River in Baiji Station and Makhoul Dam Reservoir under Supposed Operation of the Dam by Ghassan Shalan Nida Al-Shahry, Sabbar Abdullah Saleh

“…Two mathematical models were used for flood routing purpose, the first is a relationship of discharge-level to predict the level in Baiji station depended on outflow from gates, and the second is the relationship of storage-level to predict the level in the reservoir depended on storage volume when the leave of the flood wave, an algorithm and flow chart were developed to describe and explain the steps of the flood routing program, which can be modified and applied for any dam reservoir in the world, and it is used in current study, also to calculate inflow discharges then inflow volumes, either outflow discharges may assumed for eleven operating scenarios (at 11 supposed levels to receive the flood wave) and predict the equivalent level in Baiji, and the change in storage, and then the accumulated volume and the equivalent level in reservoir, when the end of flood wave. …”
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Time series forecasting of infant mortality rate in India using Bayesian ARIMA models by Anuj Singh, Tripti Tripathi, Rakesh Ranjan, Abhay K. Tiwari

“…Forecasts based on this model predict a steady decline in IMR from 2024 to 2033. …”
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Risk Assessment of High-Voltage Power Grid Under Typhoon Disaster Based on Model-Driven and Data-Driven Methods by Xiao Zhou, Jiang Li

“…Additionally, a power grid failure risk assessment model is built based on Light Gradient Boosting Machine (LightGBM), and the Borderline-Smoothing Algorithm (BSA) is used for the modeling of power grid faults. …”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The approach assists in predicting key values of the dependent variable associated with defect occurrence, enabling foundries to enhance product quality, reduce waste, and augment overall production process efficiency. …”
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Bias-aware degradation models for reinforced concrete bridges based on XAI by Francesca Marsili, Filippo Landi, Rade Hajdin, Sylvia Kessler

“…The analysis comprises four steps: (1) cluster analysis of damage transition times using the k-means algorithm to identify damage patterns with similar damage evolution rates (fast, normal, slow, corresponding to bridge components with a fragile, normal, and robust deterioration behavior); (2) Random Forest classification to predict the cluster based on bridge inventory data; (3) SHAP analysis to explain the predictions of the Random Forest classifier; (4) application of the gamma process to the grouped damage transition times to assess damage evolution. …”
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Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network by Liming Li, Shubin Zheng, Chenxi Wang, Shuguang Zhao, Xiaodong Chai, Lele Peng, Qianqian Tong, Ji Wang

“…The sleeper is inputted into CEDNet for crack feature extraction to predict the coarse crack saliency map. The prediction graph is inputted into CRRNet to improve its edge information and local region to achieve optimization. …”
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Structure, short-range order, and phase stability of the Al x CrFeCoNi high-entropy alloy: insights from a perturbative, DFT-based analysis by Christopher D. Woodgate, George A. Marchant, Livia B. Pártay, Julie B. Staunton

“…When the underlying lattice is fcc, at low concentrations of Al, depending on the value of x, we predict either an L12 or D022 ordering emerging below approximately 1000 K. …”
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Preliminary Research on Intelligent Baking Room Dehydration and Drying Technology for Rice Sterile Seeds by Man Luo

“…An adaptive integral sliding mode control algorithm based on Smith prediction was proposed for intelligent baking room temperature. …”
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Travel route recommendation with a trajectory learning model by Xiangping Wu, Zheng Zhang, Wangjun Wan

“…Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. …”
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Processivity and coupling in messenger RNA transcription. by Stuart Aitken, Marie-Cécile Robert, Ross D Alexander, Igor Goryanin, Edouard Bertrand, Jean D Beggs

“…We demonstrate that these alternatives have a significant impact on the predicted distributions. Models are simulated by the Gillespie algorithm, and the third and fourth moments of the resulting distribution are computed in order to characterise the length of the tail, and sharpness of the peak. …”
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Identification of differentially expressed genes associated with ferroptosis in ulcerative colitis. by Fang Zhang, Xin Jiang, Xuyu Chen, Zheng Wang, Jianlei Xia, Bingcheng Wang, Mei Wang, Yanbing Ding

“…<h4>Results</h4>Eleven ferroptosis-related DEGs were identified (nine upregulated and two downregulated genes) in UC, with eight genes chosen from the PPI network. MCC algorithm demonstrated that SLC7A11, PSAT1, SLC7A5, ACSF2, and ACSL4 were hub genes, predicting TFs, miRNAs and drugs. …”
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Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques by Rami Mustafa A. Mohammad, Malak Aljabri, Menna Aboulnour, Samiha Mirza, Ahmad Alshobaiki

“…The best performance was demonstrated by the Bagging algorithm with an accuracy of 83.55% when using all the dataset features. …”
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The global invasion risk of rice yellow stem borer Scirpophaga incertulas Walker (Lepidoptera:Crambidae) under current and future climate scenarios. by Shravani Sanyal, A V M Subba Rao, H Timmanna, G Baradevanal, Santanu Kumar Bal, M A Sarath Chandran, P R Shashank, V K Singh, P K Ghosh

“…The pest identity was confirmed with morphological taxonomy, and the possible habitat distribution and further spread in future climate scenarios were modelled using the MaxEnt algorithm. The climate niche for S. incertulas was also established by analyzing the correlation between the pest occurrence data of 143 locations in India and seven bioclimatic variables viz., bio01, bio02, bio03, bio05, bio12, bio13, and bio15, were chosen for predicting the distribution of S. incertulas. …”
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