AC-YOLO: A lightweight ship detection model for SAR images based on YOLO11. by Rui He, Dezhi Han, Xiang Shen, Bing Han, Zhongdai Wu, Xiaohu Huang

“…It has become a fundamental technological support in marine science research and maritime management. However, existing SAR ship detection algorithms encounter two major challenges: limited detection accuracy and high computational cost, primarily due to the wide range of target scales, indistinct contour features, and complex background interference. …”
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Magnetic soliton-based LIF neurons for spiking neural networks (SNNs) in multilayer spintronic devices by Kishan K. Mishra

“…Neuromorphic computing, inspired by biological nervous systems, is gaining traction due to its advantages in latency, energy efficiency, and algorithmic complexity compared to traditional artificial neural networks. …”
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Chaotic billiards optimized hybrid transformer and XGBoost model for robust and sustainable time series forecasting by Reham H. Mohammed, Asmaa Mohamed El-saieed

“…The use of CBO ensures efficient convergence with minimal parameter tuning, making the model suitable for large-scale datasets compared to conventional optimizers, including Adam, Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). …”
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A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security by Ersin Enes Eryılmaz, Erdal Kılıç, Yankı Ertek, Sedat Akleylek

“…The primary objective of this systematic survey is to address research questions by discussing the advantages and disadvantages of DL and ML algorithms used in IoT security. …”
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Innovations of Contemporary Artificial Intelligence: Value-Based Approach by R. I. Mamina, A. V. Ilina

“…The sources used in the article are scientific research of domestic and foreign authors, documents, publications and websites devoted to the current state of AI and its problems.Results and discussion. …”
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Early detection of bloodstream infection in critically ill children using artificial intelligence by Hye-Ji Han, Kyunghoon Kim, June Dong Park

“…Variables selected for model development were age, white blood cell count with segmented neutrophil count, C-reactive protein, bilirubin, liver enzymes, glucose, body temperature, heart rate, and respiratory rate. Algorithms compared were extra trees, random forest, light gradient boosting, extreme gradient boosting, and CatBoost. …”
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Predictive modelling of hexagonal boron nitride nanosheets yield through machine and deep learning: An ultrasonic exfoliation parametric evaluation by Jerrin Joy Varughese, Sreekanth M․S․

“…The DNN with the Adam optimizer achieved the highest accuracy (R² = 0.98423), demonstrating superior predictive capability compared to other ML and DL models. This novel research optimizes hBN exfoliation and establishes a new framework for yield prediction using machine and deep learning, empowering researchers for targeted hBNNs production.…”
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Inversion model of stress state reconstruction for geological hazard pipelines based on digital twin by Xue Luning, Tian Mingliang, Zhao Juncheng

“…The accuracy of the inversion model was 95.14%, which was an average improvement of 11.06% compared to other models. The calculation time was 5.62 s, which was an average reduction of 18.95%. …”
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Prefrontal meta-control incorporating mental simulation enhances the adaptivity of reinforcement learning agents in dynamic environments by JiHun Kim, Jee Hang Lee, Jee Hang Lee, Jee Hang Lee

“…We evaluated this approach through comprehensive experimental simulations across three distinct paradigms: the two-stage Markov decision task, which frequently serves in human learning and decision-making research; stochastic GridWorldLoCA, an established benchmark suite for model-based reinforcement learning; and a stochastic Atari Pong variant incorporating multiple goals under uncertainty.ResultsExperimental results demonstrate Meta-Dyna's superior performance compared with baseline reinforcement learning algorithms across multiple metrics: average reward, choice optimality, and a number of trials for success.DiscussionsThese findings advance our understanding of computational reinforcement learning whilst contributing to the development of brain-inspired learning agents capable of flexible, goal-directed behavior within dynamic environments.…”
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Task shifting for non-communicable disease management in low and middle income countries--a systematic review. by Rohina Joshi, Mohammed Alim, Andre Pascal Kengne, Stephen Jan, Pallab K Maulik, David Peiris, Anushka A Patel

“…Since the majority of study designs reviewed were of inadequate quality, future research methods should include robust evaluations of such strategies.…”
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Wetland Scene Segmentation of Remote Sensing Images Based on Lie Group Feature and Graph Cut Model by Canyu Chen, Guobin Zhu, Xiliang Chen

“…Experimental analysis and field exploration validation of wetland images conducted in this study demonstrate that compared to algorithms that only use RGB features for segmentation or those that do not consider the relationship between multiple features, SceneCut performs well in wetland scene extraction, especially in extracting boundaries with significant blending.…”
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Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process by Aron Csato, Florin Mariasiu

“…A vision-based lane detection system is difficult to implement in vehicles due to the unique characteristics of algorithms, neural network architectures, limitations, and strict hardware/software requirements. …”
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Year-round daily wildfire prediction and key factor analysis using machine learning: a case study of Gangwon State, South Korea by Chanjung Lee, Eun Hyoung Choi, Youngju Han, Yohan Lee

“…We integrate meteorological elements (e.g., temperature, humidity, precipitation), forest-related variables (e.g., coniferous forest ratio, forest growing stock volume), and socioeconomic indicators (e.g., agricultural and cemetery land ratios) to identify salient predictors. We compare multiple algorithms, including Logistic Regression, XGBoost, and Random Forest, and use SHAP (SHapley Additive exPlanations) to enhance interpretability. …”
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Anomaly Detection of Highway Vehicle Trajectory under the Internet of Things Converged with 5G Technology by Ketao Deng

“…The semantic representation is analyzed, and then the moving target detection and moving target tracking algorithms needed to extract the vehicle trajectory are introduced. …”
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A Joint Approach for Energy Replenishment and Data Collection with Two Distinct Types of Mobile Chargers in WRSN by Yuxiang Li, Tianyi Shao, Weixin Gao, Feng Lin

“…By combining the capabilities of two distinct MC types, the workload and the travel distance of MCs are reduced. When compared to the conventional joint algorithms, the simulation results demonstrate that the proposed approach successfully decreases the delay it takes to gather data and recharge nodes.…”
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Human-in-the-loop control strategy for IoT-based smart thermostats with Deep Reinforcement Learning by Payam Fatehi Karjou, Fabian Stupperich, Phillip Stoffel, Drk Müller

“…Smart TRVs can provide significant energy savings, often ranging from 20–40% compared to conventional heating systems. They use sensors and algorithms to learn user behavior and optimize heating schedules accordingly. …”
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STRATEGIC MANAGEMENT OF THE ECONOMIC SUSTAINABILITY OF A COMPANY IN THE PARADIGM OF FUZZY LOGIC by Yu. A. Malyukov, A. O. Nedosekin, Z. I. Abdoulaeva

“…It is necessary to develop optimal algorithms for managing economic systems under adverse effects (AE) of a natural, technological, or military nature. …”
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Using machine learning for the assessment of ecological status of unmonitored waters in Poland by Andrzej Martyszunis, Małgorzata Loga, Karol Przeździecki

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Machine vision approach for monitoring and quantifying fish school migration by Feng Lin, Jicheng Zhu, Aiju You, Lei Hua

“…Evaluation on an extensive fish migration dataset demonstrates that DVE-YOLO outperforms YOLOv8 and other mainstream detection algorithms, showcasing superior detection accuracy with higher AP50 and AP50−95 metrics. …”
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Activity cliff-aware reinforcement learning for de novo drug design by Xiuyuan Hu, Guoqing Liu, Yang Zhao, Hao Zhang

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