European sovereign debt control through reinforcement learning by Tato Khundadze, Willi Semmler, Willi Semmler, Willi Semmler

“…We demonstrate that the Soft Actor-Critic algorithm provides comparable or, in some cases, better solutions to multi-objective macroeconomic optimization problems, in comparison to Nonlinear Model Predictive Control (NMPC) algorithm.…”
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Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network. by Syed Hasib Akhter Faruqui, Adel Alaeddini, Carlos A Jaramillo, Jennifer S Potter, Mary Jo Pugh

“…We also use a longest path algorithm to identify the most likely sequence of comorbidities emerging from and/or leading to specific chronic conditions. …”
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Research on GNNs with stable learning by Wenbin Li, Wenxuan Wei, Peiyang Wang, Li Pan, Bo Yang, Yanling Xu

“…While guaranteeing predictive performance on the training distribution data, it also reduces prediction bias on data from unseen test distributions. …”
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An Ensemble Learning Method for the Kernel-Based Nonlinear Multivariate Grey Model and its Application in Forecasting Greenhouse Gas Emissions by Lan Wang, Nan Li, Ming Xie

“…In order to give policy makers more power to set the specific target of GHG emission reduction, we propose an ensemble learning method with the least squares boosting (LSBoost) algorithm for the kernel-based nonlinear multivariate grey model (KGM) (1, N), and it is abbreviated as BKGM (1, N). …”
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DS-AdaptNet: An Efficient Retinal Vessel Segmentation Framework With Adaptive Enhancement and Depthwise Separable Convolutions by Shuting Chen, Chengxi Hong, Hong Jia

“…Most importantly, DS-AdaptNet achieves these results with only 1.57M parameters and 44.08 GFLOPs—a 94.9% reduction in parameters and 77.2% reduction in computational operations compared to standard U-Net. …”
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Quantitative analysis of deep learning-based denoising model efficacy on optical coherence tomography images with different noise levels by Furkan Kirik, Farid Iskandarov, Kamile Melis Erturk, Hakan Ozdemir

“…Background: To quantitatively evaluate the effectiveness of the Noise2Noise (N2N) model, a deep learning (DL)-based noise reduction algorithm, on enhanced depth imaging-optical coherence tomography (EDI-OCT) images with different noise levels. …”
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Abnormal sound detection method for coal mine belt conveyors based on convolutional autoencoder by SHEN Long, SHAN Haoran, PEI Wenliang, YANG Guixiang, WANG Yongli

“…Background noise in the signals was filtered using the WebRTC noise reduction algorithm, and Mel-Frequency Cepstral Coefficients (MFCC) were calculated from the denoised signals to obtain audio features of normal operation. …”
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Spot Position Extraction Based on High-resolution Parametric Subspace Method without Eigen decomposition by Azad Raheem Kareem

“…A simulated example is provided to evaluate the proposed method and the simulation results shows that the proposed algorithm has the advantages of reduction the computational load and superior estimation performance. …”
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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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Comparison of artificial intelligence approaches for estimating wind energy production: A real-world case study by Mohamed Bousla, Mohamed Belfkir, Ali Haddi, Youness El Mourabit, Badre Bossoufi

“…These results emphasise the need of accurate prediction for the efficient operation and maintenance of contemporary wind turbines and provide direction on choosing the most appropriate prediction techniques considering variables such as time frames, input properties, and processing needs.…”
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Neural Network Models for the Half-Cell Potential of Reinforced Slabs with Magnesium Sacrificial Anodes Subjected to Chloride Ingress by Yogesh Murthy

“…The network architecture [5-10-1] and 10 neurons in the hidden layer were used for all the prediction models. Out of all the training algorithms used, the overall performance was best with LM in all stages of modelling (>96%). …”
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Artificial Intelligence in Virtual Screening: Transforming Drug Research and Discovery—A Review by Sayantani Roy, Karuppiah Nagaraj, Amit Mittal, Flora C. Shah, Kaliyaperumal Raja

“…Virtual screening (VS) has become an essential computational tool in drug discovery that helps to identify bioactive compounds by predicting their interactions with biological targets. …”
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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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Evaluation of Machine Learning Applications for the Complex Near-Critical Phase Behavior Modelling of CO₂–Hydrocarbon Systems by Daulet Magzymov, Meruyert Makhatova, Zhasulan Dairov, Murat Syzdykov

“…The objective of this study was to evaluate the capability of machine learning models to accurately predict complex near-critical phase behavior in CO₂–hydrocarbon systems, which are crucial for enhanced oil recovery and carbon storage applications. …”
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Uncertainty Quantification in Data Fusion Classifier for Ship-Wake Detection by Maice Costa, Daniel Sobien, Ria Garg, Winnie Cheung, Justin Krometis, Justin A. Kauffman

“…Using deep learning model predictions requires not only understanding the model’s confidence but also its uncertainty, so we know when to trust the prediction or require support from a human. …”
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Development of an AI‐based support system for controlled ovarian stimulation by Yoshimasa Asada, Tomoya Shinohara, Sho Yonezawa, Tomoki Kinugawa, Emiko Asano, Masae Kojima, Noritaka Fukunaga, Natsuka Hashizume, Yoshiki Hashiba, Daichi Inoue, Rie Mizuno, Masaya Saito, Yoshinori Kabeya

“…Four models, namely the hCG prediction model, the hMG prediction model, the Cetrorelix prediction model, and the Estradiol prediction model included in the prescription inference model, achieved AUC values of 0.914, 0.937, 0.966, and 0.976, respectively. …”
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Potential Health Risks of Chloroacetanilide Herbicides: An In Silico Analysis by Nihan Akıncı Kenanoğlu, Ahmet Ali Berber, Şefika Nur Demir

“…This article proposes to use tools to predict their potential toxicities based on their chemical structure. …”
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AI-driven model for optimized pulse programming of memristive devices by Benjamin Spetzler, Markus Fritscher, Seongae Park, Nayoun Kim, Christian Wenger, Martin Ziegler

“…Here, we present a computationally efficient AI model for predicting the weight update of memristive devices and guiding device programming. …”
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Implementation of new technique for Face recognition based on quantum computing by Amjed Yousif Sahib

“…This technique is transforming the oracle of Grover's search algorithm into correlation oracle with feeding image. …”
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Insights into Nitrogen-Associated Protein 50 (NAP50) as a Tyrosyl–DNA Phosphodiesterase in Dinoflagellates by Lu Tang, Nora Fung-Yee Tam, Winnie Lam, Thomas Chun-Hung Lee, Steven Jing-Liang Xu, Fred Wang-Fat Lee

“…The three-dimensional structure and biological functions of NAP50 are predicted using deep learning algorithms. Based on evolutionary relationships and functional predictions, NAP50 may play a role in repairing plastid DNA damage and potentially contribute to the transcription of plastid genes in dinoflagellates.…”
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