An improved model accuracy for forecasting risk measures: application of ensemble methods by Katleho Makatjane, Kesaobaka Mmelesi

“…Statistical-based predictions with extreme value theory improve the performance of the risk model not by choosing the model structure that is expected to predict the best but by developing a model whose results are a combination of models with different shapes. …”
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Quantum Neural Networks Approach for Water Discharge Forecast by Liu Zhen, Alina Bărbulescu

“…The lower error between the recorded values and the predicted ones in the evaluation of maxima compared to the case of the competitors mentioned shows that the algorithm best fits the extremes. …”
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Hybrid GOA and PSO optimization for load frequency control in renewable multi source dual area power systems by Muhammad Zubair Yameen, Abdul Khalique Junejo, Zhigang Lu, Rizwan Aziz Siddiqui, Fayez F. M. El-Sousy, Ibtisam Naveed

“…Similarly, in the dual-area IPS, it provides a 76.73% reduction in overshoot, an 87.62% reduction in undershoot, and a 75.68% improvement in rise time. …”
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MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models by Devlin C. Moyer, Justin Reimertz, Daniel Segrè, Juan I. Fuxman Bass

“…Abstract Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. …”
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Machine learning approaches for modelling of molecular polarizability in gold nanoclusters by Abhishek Ojha, Satya S. Bulusu, Arup Banerjee

“…Our results demonstrate the efficacy of machine-learning in accurately predicting the polarizability of gold nanoclusters. …”
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Mapping the ADDQoL to the EQ-5D-5L and SF-6Dv2 among Chinese patients with type 2 diabetes mellitus by Haoran Fang, Tianqi Hong, Xinran Liu, Chang Luo, Yuanyuan Hou, Shitong Xie

“…This study developed mapping algorithms to predict EQ-5D-5L and SF-6Dv2 utility values from ADDQoL scores in T2DM patients in China. …”
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Blending physical and artificial intelligence models to improve satellite-derived bathymetry mapping by Daniel García-Díaz, Sandra Paola Viaña-Borja, Mar Roca, Gabriel Navarro, Isabel Caballero

“…We assessed the ability of these methods to predict bathymetries over successive years subsequent to algorithm calibration, as well as their capacity to estimate depths of other areas not included in model calibration, thereby evaluating temporal and spatial independence, respectively. …”
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Automation of the Formation of a Mathematical Formulation of Kinetics for Multistage Chemical Reactions and Numerical Solution to a Direct Problem by N. A. Lysenko, K. F. Koledina

“…Kinetic analysis is a challenge in chemical technology, since it allows for optimizing synthesis processes and predicting their efficiency. Numerous chemical processes involve several stage reactions. …”
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Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production by Kristen L. Beck, Niina Haiminen, Akshay Agarwal, Anna Paola Carrieri, Matthew Madgwick, Jennifer Kelly, Victor Pylro, Ban Kawas, Martin Wiedmann, Erika Ganda

“…We also show through analysis of publicly available fluid milk microbial data that our artificial intelligence approach is able to successfully predict milk in different stages of processing. The approach could potentially be applied in the food industry for safety and quality control.…”
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Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview by Imran Ul Haq, Mustafa Mhamed, Mohammed Al-Harbi, Hamid Osman, Zuhal Y. Hamd, Zhe Liu

“…This paper analyzes current methodologies, applications, and trends in MMML while outlining challenges and predicting upcoming research directions. Beginning with an overview of the different data modalities involved in radiology, namely, imaging, text, and structured medical data, this review explains the processes of modality fusion, representation learning, and modality translation, showing how they boost diagnosis efficacy and improve patient care. …”
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Deep Ocean Learning of Small Scale Turbulence by Ali Mashayek, Nick Reynard, Fangming Zhai, Kaushik Srinivasan, Adam Jelley, Alberto Naveira Garabato, Colm‐cille P. Caulfield

“…Here, we show that supervised machine learning algorithms can be trained on the existing turbulence data to develop skillful predictions of the key properties of turbulence from T, S, Z, and topographic data. …”
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FEATURES OF THERAPEUTIC SUPPORT FOR PATIENTS WITH POLYTRAUMA by I.A. Mormol, O.A. Borzykh, I.P. Kaidashev

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Stochastic modeling of structural fatigue damage in High Strength Steel structures by Jiri Brozovsky, Martin Krejsa, Petr Lehner, Premysl Parenica, Stanislav Seitl

“…The proposed parallel Direct Optimized Probability Computation method is developed and studied on the problem of fatigue damage prediction. Description of the parallel algorithm is provided, and the functionality of the method is shown in an example case. …”
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INFLUENCE OF MICROBIAL WOUND DISSEMINATION AND MICROCIRCULATION ON THE RESULTS OF SKIN ENGRAFTMENT by Yu. V. Yurova, I. V. Shlyk

“…An analysis of capillary blood flow in the groups under test showed the information value of indicators of microcirculation obtained by Doppler laser flowmetery for determination of the granulating wound condition before autotransplantation and prediction of the results of skin engraftment. It is stated, that the disorder of microcirculation has been developed against the background of progression of wound invasive infection. …”
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Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality by Mu Yue, Jingxin Xi

“…Instead of adopting the traditional regularization approaches, we offer a novel multi-step sparse boosting algorithm to conduct model-based prediction and variable selection. …”
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Variable Selection for Additive Quantile Regression with Nonlinear Interaction Structures by Yongxin Bai, Jiancheng Jiang, Maozai Tian

“…Effective variable selection is crucial for avoiding the curse of dimensionality and enhancing the predictive performance of a model. In this paper, we introduce a nonlinear interaction structure into the additive quantile regression model and propose an innovative penalization method. …”
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The Utility of Follow-up Transthoracic Echocardiogram to Screen for Severe Portopulmonary Hypertension (POPH) in Patients Granted POPH Model for End-stage Liver Disease (MELD) Exce... by Kathryn T. del Valle, MD, Dana Kay, MD, Michael J. Krowka, MD, James R. Runo, MD, Corey Sadd, MD, Julie K. Heimbach, MD, Rodrigo Cartin-Ceba, MD, Hector R. Cajigas, MD, Charles D. Burger, MD, John E. Moss, MD, Hilary M. DuBrock, MD

“…However, echocardiograms with RVSP ≥48 mm Hg had 100% sensitivity for detecting hemodynamically severe POPH, with 100% negative predictive value. In external validation of 13 paired echocardiograms and RHCs, our algorithm had 64% specificity and 100% negative predictive value. …”
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Error-Mask-Adaptive Dynamic Filtering for Image Inpainting by Keunsoo Ko, Seunggyun Woo, Chang-Su Kim

“…Through several EMDF layers, we predict an inpainting result. Finally, we refine it to reconstruct a more faithful image. …”
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Distinctive Behaviors of Druggable Proteins in Cellular Networks. by Costas Mitsopoulos, Amanda C Schierz, Paul Workman, Bissan Al-Lazikani

“…We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. …”
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Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing by Yiyang HU, Lina QI

“…Massive multiple-input multiple-output (MIMO) is a solution for efficiently providing connection services for a variety of machine equipment in the Internet of things (IoT), and efficient connection services require accurate channel estimation.Aimed at the problems of high pilot overhead and poor performance of normalized mean square error (NMSE) estimation in downlink channel estimation of massive MIMO systems, based on the compressed sensing (CS) theory, the common sparsity of the channel space domain was combined while using the feature of lower sparsity of adjacent time slot differential channel impulse response (CIR), which leaded to a significant reduction in pilot overhead.In the reconstruction algorithm, a two-stage differential estimation algorithm, which divided the channel estimation in consecutive time slots with time correlation into two stages, was proposed and the idea of adaptive compressed sensing was combined to achieve fast and accurate CIR estimate.The simulation results show that the proposed two-stage differential channel estimation algorithm not only has a significant improvement in the estimated NMSE performance and data transmission rate compared to the existing CS-based multiple measurement vector (MMV) algorithm, but also show a certain reduction in runtime complexity.…”
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