Leveraging Deep Learning for Automated Experimental Semivariogram Fitting by Siyu Yu, Lifang Zhao, Shaohua Li

“…To address these challenges, this paper proposes an automatic fitting method for experimental variogram functions based on deep learning. The variogram fitting process is inherently a nonlinear optimization problem, where the goal is to optimize the alignment between experimental and theoretical variogram functions. …”
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Application of machine learning in dentistry: insights, prospects and challenges by Lin Wang, Yanyan Xu, Weiqian Wang, Yuanyuan Lu

“…Conclusions: Machine Learning has demonstrated significant potential in dentistry with its intelligently assistive function, promoting diagnostic efficiency, personalised treatment plans and related streamline workflows. …”
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Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey by Milan Ganai, Sicun Gao, Sylvia L. Herbert

“…In recent years, a litany of proposed methods addresses this limitation by computing the reachability value function simultaneously with learning control policies to scale HJ reachability analysis while still maintaining a reliable estimate of the true reachable set. …”
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Deep Reinforcement Learning in Non-Markov Market-Making by Luca Lalor, Anatoliy Swishchuk

“…We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically focusing on price processes with semi-Markov and Hawkes Jump-Diffusion dynamics. …”
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Explainable Supervised Learning Models for Aviation Predictions in Australia by Aziida Nanyonga, Hassan Wasswa, Keith Joiner, Ugur Turhan, Graham Wild

“…Artificial intelligence (AI) has demonstrated success across various industries; however, its adoption in aviation remains limited due to concerns regarding the interpretability of AI models, which often function as black box systems with opaque decision-making processes. …”
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Global miniaturization of broadband antennas by prescreening and machine learning by Slawomir Koziel, Anna Pietrenko-Dabrowska, Ubaid Ullah

“…Our technique includes parameter space pre-screening and the iterative refinement of kriging surrogate models using the predicted merit function minimization as an infill criterion. Concurrently, the design task incorporates design constraints implicitly by means of penalty functions. …”
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Neuromorphic Hebbian learning with magnetic tunnel junction synapses by Peng Zhou, Alexander J. Edwards, Frederick B. Mancoff, Sanjeev Aggarwal, Stephen K. Heinrich-Barna, Joseph S. Friedman

“…Abstract Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency. …”
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Advancing the design of gold nanomaterials with machine-learned potentials by Kithma Sajini, Caroline Desgranges, Jerome Delhommelle

“…The design of nanomaterials with optimal properties hinges on our ability to understand and control their structure-function relationship, which has remained a challenge so far. …”
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Design of Reinforcement Learning Guidance Law for Antitorpedo Torpedoes by Zhong Wang, Zhiwen Wen, Weitong Cui, Daming Zhou, Pei Wang

“…Based on the proportional guidance interception law, the law incorporates a variable proportional coefficient based on the deep Q-network (DQN) algorithm from deep reinforcement learning. Integrating engineering design, the intelligent guidance law for antitorpedo torpedoes proposed in this article selects the rate of change in the line-of-sight angle as the state variable, designs a reward function based on interception results, and designs a discretized behavior space based on the commonly used proportional guidance coefficient selection range. …”
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Hierarchical contrastive learning for multi-label text classification by Wei Zhang, Yun Jiang, Yun Fang, Shuai Pan

“…This unique loss function enables our model to effectively capture both the correlations and distinctions among labels, thereby enhancing the model’s ability to learn the intricacies of the label hierarchy. …”
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Deep learning-enhanced signal detection for communication systems. by Yang Liu, Peng Liu, Yu Shi, Xue Hao

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Deep Learning Model for Analyzing EEG Signal Analysis by Varun Gupta, Vivek Kumar, Prince, Saurabh Singh, Young-Seok Lee, In-Ho Ra

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Deep Learning based Models for Drug-Target Interactions by Ali K. Abdul Raheem, Ban N. Dhannoon

“…This paper developed two deep-learning architectures to predict drug-target interactions. …”
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Multi-Agent Reinforcement Learning in Games: Research and Applications by Haiyang Li, Ping Yang, Weidong Liu, Shaoqiang Yan, Xinyi Zhang, Donglin Zhu

“…Building upon stochastic game and extensive-form game-theoretic frameworks, we establish a methodological taxonomy across three dimensions: value function optimization, policy gradient learning, and online search planning, thereby clarifying the evolutionary logic and innovation trajectories of algorithmic advancements. …”
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Machine Learning‐Enabled Drug‐Induced Toxicity Prediction by Changsen Bai, Lianlian Wu, Ruijiang Li, Yang Cao, Song He, Xiaochen Bo

“…Big data and artificial intelligence (AI), especially machine learning (ML), are robustly contributing to innovation and progress in toxicology research. …”
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Estimation of Battery SoC using Reinforcement Learning by Mortabit Idriss, Rachid Aziz, Errifai Nidale, Boudmane Bilal

“…Our work utilized a high-end adaptive learning approach called PPO, which learns from operational data directly without structural assumptions beforehand. …”
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Optimizing Knowledge Transfer Graph for Deep Collaborative Learning by Soma Minami, Naoki Okamoto, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi

“…Knowledge transfer among multiple networks, using predicted probabilities or intermediate-layer activations, has evolved significantly through extensive manual design, ranging from simple teacher—student approaches (for example, knowledge distillation) to bidirectional cohort methods (for example, deep mutual learning). However, key factors such as network size, the number of networks, transfer direction, and loss function design interact in complex ways and limit conventional methods to exploring only a narrow range of possible combinations. …”
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The Role of Basolateral Amygdalar Cholinergic Neuromodulation in Emotional Learning by Victor Manuel Torres-Garcia, Emmanuel Rodriguez-Nava, Gabriel Roldan-Roldan, Donald B. Katz, Jean-Pascal Morin

“…We integrate psychopharmacological insights with loss and gain-of-function studies to demonstrate how cholinergic signaling in the BLA shapes approach and avoidance behaviors. …”
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TO THE QUESTION OF THE USING OF INFORMATIONCOMPUTER TECHNOLOGIES IN LEARNING ENGLISH LANGUAGE by Kerimbaeva T. Botagoz, Kaya Karligash

“…The aim of the article is the using of informational-computer technologies in learning English language of future specialists very effectively, as the didactic function of these technologies is wide. …”
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Adjacent Inputs With Different Labels and Hardness in Supervised Learning by Sebastian A. Grillo, Julio Cesar Mello Roman, Jorge Daniel Mello-Roman, Jose Luis Vazquez Noguera, Miguel Garcia-Torres, Federico Divina, Pedro Esteban Gardel Sotomayor

“…An important aspect of the design of effective machine learning algorithms is the complexity analysis of classification problems. …”
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