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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A Semi-Supervised Attention-Temporal Ensembling Method for Ground Penetrating Radar Target Recognition by Li Liu, Dajiang Yu, Xiping Zhang, Hang Xu, Jingxia Li, Lijun Zhou, Bingjie Wang

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Legal Analysis of AI-Generated Creations: Copyright Law Perspectives by Praja Chrisna Bagus Edhita, Hakim Hary Abdul, Basri, Kurniaty Yulia, Sari Elvira Purnama

“…Legal analysis shows that AI is just a computer programming tool that performs tasks based on human commands. AI uses algorithms and computer training for recognition, prediction, and decision-making. …”
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Sparse sensing data–based participant selection for people finding by Ye Tian, Zhirong Tang, Jian Ma

“…Simulation results demonstrate that the proposed mechanism outperforms the other algorithms both in accuracy of positioning and quality of uploaded sensing data.…”
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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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Vital node searcher: find out critical node measure with deep reinforcement learning by Guanting Du, Fei Zhu, Quan Liu

“…To solve this problem, an algorithm, referred to as Vital Node Searcher (VNS), is proposed, which discovers critical nodes from a network based on deep reinforcement learning. …”
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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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Uncertainty of industrial noise measurement at distant locations from the source by Tadeusz Wszołek

“…The paper deals with some real word data of partial uncertainties of noise prediction and measurement from large industry and impulse sound sources, taken in different meteorological conditions. …”
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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

“…Here we introduce a minimal model of interacting deep neural nets that condenses several recent decentralized algorithms by considering a competition between two terms: the local learning dynamics in the parameters of each neural network unit, and a diffusive coupling among units that tends to homogenize the parameters of the ensemble. …”
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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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An Improved Kernelized Correlation Filter for Extracting Traffic Flow in Satellite Videos by Dudu Guo, Hongbo Shuai, Jie Zhang, Yang Wang, Miao Sun

“…Second, we utilized the Kalman filter for trajectory prediction to reduce the loss of target during vehicle tracking. …”
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Enhancing Visual–Inertial Odometry Robustness and Accuracy in Challenging Environments by Alessandro Minervini, Adrian Carrio, Giorgio Guglieri

“…Visual–Inertial Odometry (VIO) algorithms are widely adopted for autonomous drone navigation in GNSS-denied environments. …”
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Exploring machine learning approaches for precipitation downscaling by Honglin Zhu, Qiming Zhou, Jukka M. Krisp

“…This study critically reviewed existing spatial downscaling approaches, specifically focusing on machine learning (ML)-based algorithms. Insights into the accuracy of these downscaling techniques were provided based on findings from published validation studies. …”
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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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On the effect of sampling frequency on the electricity theft detection performance by Fatemeh Soleimani Nasab, Foad Ghaderi

“…To investigate the effect of sampling frequency on the performance of detection methods, we designed a processing framework to evaluate different classification algorithms on versions of a challenging dataset obtained by down‐sampling the original data at various rates. …”
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