Data analysis in complex biomolecular systems by M. M. Yatskou, V. V. Apanasovich

“…The idea of an integrated approach is to use simulation modelling of biophysical processes occurring in the object of study, comparing simulated and most relevant experimental data selected by dimension reduction methods, determining the characteristics of the investigated processes using data analysis algorithms. …”
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A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data by Ovais A Jaffery, Lea Melki, Gregory Slabaugh, Wilson W Good, Caroline H Roney

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Improving Model-Based Deep Reinforcement Learning with Learning Degree Networks and Its Application in Robot Control by Guoqing Ma, Zhifu Wang, Xianfeng Yuan, Fengyu Zhou

“…This model’s performance also suffers due to low utilization of training data, while the model-based reinforcement learning (MBRL) algorithm improves the efficiency of the data, MBRL locks into low prediction accuracy. …”
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Classification of Some Fruits using Image Processing and Machine Learning by Dilara Gerdan Koç, Mustafa Vatandaş

“…On the other hand, the size and color values read in fruits with the image processing algorithm were evaluated using predictive techniques used in data mining. …”
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Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting by Jaemyung Shin, Ryan Kang, Kinam Hyun, Zhangkang Li, Hitendra Kumar, Kangsoo Kim, Simon S. Park, Keekyoung Kim

“…., bioink viscosity, nozzle size, printing time, printing pressure, and cell concentration), and develop algorithms capable of immediate cellular droplet size prediction. …”
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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…AI technologies, including predictive modeling and ML algorithms such as random forests and convolutional neural networks (CNNs), can analyze diverse data sources—such as meteorological, environmental, and health records—to detect patterns and predict outbreaks. …”
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Combined label matrix with the conditional generative adversarial network for secret image restoration by Jianzhong Yang, Xianquan Zhang, Chunqiang Yu, Guoxiang Li, Zhenjun Tang

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An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics by Emir Žunić, Dženana Đonko, Emir Buza

“…Moreover, as the performance of the algorithm depends on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constants according to historical data is proposed. …”
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Enhanced estimation of reference evapotranspiration using hybrid deep learning models and remote sensing variables by Tze Ying Fong, Yuk Feng Huang, Ren Jie Chin, Chai Hoon Koo

“…They managed to improve the accuracy of the prediction in most of the cases, with the highest R2 = 0.805 and the lowest prediction errors, MAE = 0.265 mm/day, RMSE = 0.343 mm/day and NRMSE = 0.096. …”
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Pinna anthropometry in sex estimation: a machine learning-based approach by Emre Söylemez, Suna Tokgöz Yılmaz

“…We aimed to investigate the feasibility of sex prediction using anthropometric measurements of the pinna in machine learning. …”
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Artificial intelligence and chordoma: A scoping review of the current landscape and future directions by Eddie Guo, Rafael D. Sanguinetti, Lyndon Boone, Jiawen Deng, Husain Shakil, Mehul Gupta

“…The studies addressed diverse clinical tasks: 7 focused on differentiating chordomas from other tumours or classifying subtypes, 6 on survival prediction, 2 on tumour segmentation, 2 on outcome prediction, and 4 miscellaneous tasks. …”
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Identifying emergency presentations of chronic liver disease using routinely collected administrative hospital data by Jessica King, Vikram Bains, James Doidge, Jan Van Der Meulen, Kate Walker, William Bernal

“…We validated these in 2018–2019 data by assessing the distributions of predictive factors, treatments, and outcomes associated with CLD in the patients captured by each algorithm. …”
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Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion by Tiantian Xu, Xuedong Zhang, Wenlei Sun

“…A digital twin system architecture is developed, comprising a high-precision geometric model and a dynamic mechanism model, enabling real-time interaction and data fusion between the physical transmission system and its virtual model. At the algorithmic level, a CNN-LSTM-Attention fault prediction model is proposed, which innovatively integrates the spatial feature extraction capabilities of a convolutional neural network (CNN), the temporal modeling advantages of long short-term memory (LSTM), and the key information-focusing characteristics of an attention mechanism. …”
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Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models by Edyta Okupska, Dariusz Gozdowski, Rafał Pudełko, Elżbieta Wójcik-Gront

“…This study performed in-season yield prediction, about 2–3 months before the harvest, for cereals and rapeseed at the province level in Poland for 2009–2024. …”
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Failure Management Overview in Optical Networks by Sergio Cruzes

“…This study surveys ML techniques for early-warning and failure prediction, failure detection, identification, localization, magnitude estimation, and soft failure detection and prediction. …”
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TransUNet Image 3D Reconstruction with Hyperparameter Optimization by Xiaofang Wang, Zhihao Luo, Mingrui Gou, Kerui Mao, Liang Zhou

“…The method employs Restricted Boltzmann Machine (RBM) for depth prediction and an optimized ant colony algorithm for network parameter optimization. …”
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Validity of the International Classification of Diseases 10th revision code for hospitalisation with hyponatraemia in elderly patients by Amit X Garg, Salimah Z Shariff, Sonja Gandhi, Jamie L Fleet, Matthew A Weir, Arsh K Jain

“…Objective To evaluate the validity of the International Classification of Diseases, 10th Revision (ICD-10) diagnosis code for hyponatraemia (E87.1) in two settings: at presentation to the emergency department and at hospital admission.Design Population-based retrospective validation study.Setting Twelve hospitals in Southwestern Ontario, Canada, from 2003 to 2010.Participants Patients aged 66 years and older with serum sodium laboratory measurements at presentation to the emergency department (n=64 581) and at hospital admission (n=64 499).Main outcome measures Sensitivity, specificity, positive predictive value and negative predictive value comparing various ICD-10 diagnostic coding algorithms for hyponatraemia to serum sodium laboratory measurements (reference standard). …”
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Performance Evaluation of Data Compression Systems Applied to Satellite Imagery by Lilian N. Faria, Leila M. G. Fonseca, Max H. M. Costa

“…This paper presents an overview and evaluation of some compression algorithms suitable for remote sensing applications. …”
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Generating Deeply-Engineered Technical Features for Basketball Video Understanding by Shaohua Fang, Guifeng Wang, Yongbin Li, Yue Yu, Jun Li

“…Our main contributions include: 1) an LSTM-based deep learning architecture for player action recognition and prediction; 2) a clustering-based algorithm for basketball court and line detection; and 3) a keyframe selection technique for basketball videos based on spatial-temporal scoring. …”
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AI illuminates paths in oral cancer by Devesh U. Kapoor, Pushpendra Kumar Saini, Narendra Sharma, Ankul Singh, Bhupendra G. Prajapati, Gehan M. Elossaily, Summya Rashid

“…The role of artificial intelligence in prognosis of oral cancer including predictive modeling, identification of prognostic factors and risk stratification also discussed significantly in this review. …”
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