Online monitoring data processing methods for railway slopes and its application: A case study of the Shuohuang Railway by Mu GU

“…Subsequently, the CLEAN algorithm, introduced to the field of deformation monitoring, is utilized to suppress noise, minimizing its impact on subsequent deformation trend predictions. …”
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Handling method for GPS outages based on PSO-LSTM and fading adaptive Kalman filtering by Xiaoming Li, Xianchen Wang, Can Pei

“…Considering that the predicted pseudo-position may contain outliers or accumulated errors, a robust algorithm is employed to mitigate its impact on correcting INS errors. …”
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Optimization of State Clustering and Safety Verification in Deep Reinforcement Learning Using KMeans++ and Probabilistic Model Checking by Ryeonggu Kwon, Gihwon Kwon

“…The continuous state space is clustered using the KMeans++ algorithm, enabling efficient state space reduction. …”
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Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning by Pol Suárez, Francisco Alcántara-Ávila, Jean Rabault, Arnau Miró, Bernat Font, Oriol Lehmkuhl, Ricardo Vinuesa

“…The setup involves multiple zero-net-mass-flux jets and couples a computational fluid dynamics solver with a numerical multi-agent reinforcement learning framework based on the proximal policy optimization algorithm. Our results demonstrate up to 16% drag reduction at R e D  = 400, outperforming classical periodic control strategies. …”
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Phase diagram from nonlinear interaction between superconducting order and density: toward data-based holographic superconductor by Sejin Kim, Kyung Kiu Kim, Yunseok Seo

“…We introduce positional embedding layers to improve the learning process in our algorithm, and the Adam optimization is used to predict the critical temperature data via holographic calculation with appropriate accuracy. …”
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Deep learning can reduce acquisition time of T2-weighted image in brain imaging by Eman Hassan El-Saeed Abou-ELMagd, Sabry Alameldin Elmogy, Dina Gamal Abdelzaher

“…Abstract Background We used the deep learning-based reconstruction algorithm to reduce the scan time for brain T2-weighted images (T2WI) with reduction of image noise and preservation of image quality. …”
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Sparse sensing data–based participant selection for people finding by Ye Tian, Zhirong Tang, Jian Ma

“…In order to evaluate how possible a candidate can approach lost people, the probability distribution of their tracing points should be predicted. However, the sparse sensing data problem has been a bottleneck of estimating people’s probable position. …”
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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…Abstract This research shows the utilization of various tree-based machine learning algorithms with a specific focus on predicting Salicylic acid solubility values in 13 solvents. …”
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HybridBranchNetV2: Towards reliable artificial intelligence in image classification using reinforcement learning. by Ebrahim Parcham, Mansoor Fateh, Vahid Abolghasemi

“…Many artificial intelligence (AI) algorithms struggle to adapt effectively in dynamic real-world scenarios, such as complex classification tasks and object relationship extraction, due to their predictable but non-adaptive behavior. …”
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Recent advances and controversies in head and neck reconstructive surgery by Kuriakose Moni, Sharma Mohit, Iyer Subramania

“…Standardized reconstructive algorithms for common head and neck defects have been developed with predictable results. …”
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Slicing Cuts on Food Materials Using Robotic-Controlled Razor Blade by Debao Zhou, Gary McMurray

“…Based on the blade sharpness properties and the specific materials, the required cutting force can be predicted. These formulation and experimental results explained the basic theory of blade cutting fracture and further provided the support to optimize the cutting mechanism design and to develop the force control algorithms for the automation of blade cutting operations.…”
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Artificial intelligence as one of the key drivers of the economy digital transformation by O.I. Pizhuk

“…The prospects for using AI are huge as the algorithms that allow massive amounts of information to be processed on an hourly basis can detect cause-and-effect relationships, which are not achievable for a person, and thus make predictions more accurate and make solutions more efficient. …”
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Lightweight opportunistic routing forwarding strategy based on Markov chain by Feng LI, Ya-li SI, Zhen CHEN, Li-min SHEN

“…A lightweight opportunistic routing forwarding strategy (MOR) was proposed based on Markov chain.In the scheme,the execute process of network was divided into a plurality of equal time period,and the random encounter state of node in each time period was represented by activity degree.The state sequence of a plurality of continuous time period constitutes a discrete Markov chain.The activity degree of encounter node was estimated by Markov model to predict its state of future time period,which can enhance the accuracy of activity degree estimation.Then,the method of comprehensive evaluating forwarding utility was designed based on the activity degree of node and the average encounter interval.MOR used the utility of node for making a routing forwarding decision.Each node only maintained a state of last time period and a state transition probability matrix,and a vector recording the average encounter interval of nodes.So,the routing forwarding decision algorithm was simple and efficient,low time and space complexity.Furthermore,the method was proposed to set optimal number of the message copy based on multiple factors,which can effectively balance the utilization of network resources.Results show that compared with existing algorithms,MOR algorithm can effectively increase the delivery ratio and reduce the delivery delay,and lower routing overhead ratio.…”
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Effective theory of collective deep learning by Lluís Arola-Fernández, Lucas Lacasa

“…We derive an effective theory for linear networks to show that the coarse-grained behavior of our system is equivalent to a deformed Ginzburg-Landau model with quenched disorder. This framework predicts depth-dependent disorder-order-disorder phase transitions in the parameters' solutions that reveal a depth-delayed onset of a collective learning phase and a low-rank microscopic learning path. …”
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Data reconstruction from machine learning models via inverse estimation and Bayesian inference by Agus Hartoyo, Dominika Ciupek, Maciej Malawski, Alessandro Crimi

“…Empirical results across multiple benchmark datasets and machine learning algorithms corroborate these theoretical predictions, reinforcing the validity and robustness of our theoretical framework. …”
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Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding by Ibrahim Almubark

“…Data was utilized to train the model and subsequently generate predictions by utilizing testing data following the pre-processing of the dataset. …”
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Exploring machine learning approaches for precipitation downscaling by Honglin Zhu, Qiming Zhou, Jukka M. Krisp

“…However, their coarse spatial resolution typically prevented their applicability in regional flood predictions and agricultural management. To achieve reliable and finer-scale precipitation data, many techniques and frameworks have been employed to improve the resolution of the satellite-derived precipitation data. …”
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Assessment of food toxicology by Alexander Gosslau

“…Integration of food toxicology data obtained throughout biochemical and cell-based in vitro, animal in vivo and human clinical settings has enabled the establishment of alternative, highly predictable in silico models. These systems utilize a combination of complex in vitro cell-based models with computer-based algorithms. …”
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The Value of Biomarkers in the Diagnosis and Prognosis of Heart Failure in Older Age by V. N. Larina, V. I. Lunev

“…The search for reliable algorithms for diagnosing heart failure with preserved left ventricular ejection fraction (LVEF) in elderly patients is an urgent problem due to the low specificity of clinical manifestations and the peculiarities of involutive processes occurring in the human body. …”
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Emotional intelligence in management activities аnd artificial intelligence technologies by M. S. Laschenov, R. A. Bondarenko, I. V. Slomova

“…The relevance of the sociological study of emotions in the process of social management is justified by the growing trends in the influence and development of artificial technologies, which are actively penetrating into all spheres of modern society and leading to changes, the consequences of which are still poorly predictable, and therefore require timely assessment. …”
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