Robust InceptionV3 with Novel EYENET Weights for Di-EYENET Ocular Surface Imaging Dataset: Integrating Chain Foraging and Cyclone Aging Techniques by Muhammad Ahmad Khan, Saif Ur Rehman Khan, Hafeez Ur Rehman, Suliman Aladhadh, Ding Lin

“…Abstract Predicting diabetic types from ocular surface eye images is a challenging task due to subtle variations in features and the potential overlap in presentations among different diabetic types. …”
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Accurate Chemistry Identification of Lithium-Ion Batteries Based on Temperature Dynamics with Machine Learning by Ote Amuta, Jiaqi Yao, Dominik Droese, Julia Kowal

“…Experimental results showed that the unique characteristics in the surface temperature measurement during the full charge or discharge of the different chemistry types can accurately carry out the classification task in both experimental setups, where the model is trained on data under different cycling conditions separately and jointly. …”
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Segmentation of Spatial Crack Structures in Concrete by Deep Learning Enabling Image Based Characterization by Anna Nowacka, Katja Schladitz, Szymon Grzesiak, Matthias Pahn

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Precise Sizing and Collision Detection of Functional Nanoparticles by Deep Learning Empowered Plasmonic Microscopy by Jingan Wang, Yi Sun, Yuting Yang, Cheng Zhang, Weiqiang Zheng, Chen Wang, Wei Zhang, Lianqun Zhou, Hui Yu, Jinghong Li

“…Image sequences are recorded by the state‐of‐the‐art plasmonic microscopy during single nanoparticle collision onto the sensor surface. Deep‐SM can enhance signal detection and suppresses noise by leveraging spatio‐temporal correlations of the unique signal and noise characteristics in plasmonic microscopy image sequences. …”
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Rapid mapping of landslides using satellite SAR imagery: A progressive learning approach by Nikhil Prakash, Andrea Manconi, Alessandro Cesare Mondini

“…Previous works have typically used machine learning-based methods, including the recently popular deep-learning approaches, to identify characteristics surface features from satellite remote sensing data, especially from optical images. …”
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Research on Optimized Algorithm for Deep Learning Based Recognition of Sediment Particles in Turbulent Flow by WANG Hao, YANG Feiqi, ZHANG Lei, WU Wei, XIE Haonan, ZHAO Lin

“…Characteristics of coherent turbulent structures provide a more comprehensive explanation for the mechanism underlying the formation of sediment transport belts.ConclusionThis study concludes the following: 1) The grayscale subtraction technique effectively identifies particles with significant motion distances, while deep learning methods excel at recognizing particles with smaller motion distances. …”
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Prediction of contact resistance of electrical contact wear using different machine learning algorithms by Zhen-bing Cai, Chun-lin Li, Lei You, Xu-dong Chen, Li-ping He, Zhong-qing Cao, Zhi-nan Zhang

“…The wear behavior of electrical contacts is influenced by factors such as load, sliding speed, displacement amplitude, current intensity, and surface roughness during operation. Machine learning algorithms can predict the electrical contact performance after wear caused by these factors. …”
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Crack of a helicopter main rotor actuator attachment: failure analysis and lessons learned by L. Allegrucci, F. De Paolis, A. Coletta, M. Bernabei

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Crack of a helicopter main rotor actuator attachment: failure analysis and lessons learned by L. Allegrucci, F. De Paolis, A. Coletta, M. Bernabei

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MSPT: A Transformer-Based Model Using Multiscale Periodic Information for 10–30 d Subseasonal Daily Sea Surface Temperature Forecasting by Qi He, Zhenfeng Lan, Wei Song, Wenbo Zhang, Yanling Du, Wei Zhao

“…Each periodic scale features an independent branch composed of patch embedding and Transformer encoder, dedicated to specifically learning temporal variations at that scale. Only the outputs of critical branches are weighted and aggregated to obtain effective multiperiodic scale characteristics. …”
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Deep learning-based detection of bacterial swarm motion using a single image by Yuzhu Li, Hao Li, Weijie Chen, Keelan O’Riordan, Neha Mani, Yuxuan Qi, Tairan Liu, Sridhar Mani, Aydogan Ozcan

“…Motility is a fundamental characteristic of bacteria. Distinguishing between swarming and swimming, the two principal forms of bacterial movement, holds significant conceptual and clinical relevance. …”
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Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition by Ramón Alberto Mena-Almonte, Ekaitz Zulueta, Ismael Etxeberria-Agiriano, Unai Fernandez-Gamiz

“…This paper introduces an efficient localization algorithm for robotic systems, utilizing deep learning to identify and exploit natural fiduciary patterns within the environment. …”
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Machine learning framework for investigating nano- and micro-scale particle diffusion in colonic mucus by Marco Tjakra, Kristína Lidayová, Christophe Avenel, Christel A.S. Bergström, Shakhawath Hossain

“…In conclusion, the machine-learning-driven fingerprinting approach, incorporating microrheological features, successfully differentiated the microstructural characteristics and rheological properties of the three mucus models. …”
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Do School Exclusions and Attainment Outcomes Disproportionately Impact Minority Ethnic Pupils? Analysis of Pupil Characteristics, Segregation, and Outcomes in England by Stephen Gorard, Nadia Siddiqui, Beng Huat See, Yiyang Gao

“…We present the outcomes and other characteristics for each ethnic category available. The analyses then modelled the attainment and exclusion outcomes via multivariate regression, in terms of individual pupil characteristics and school-level figures including school segregation by pupil ethnicity and disability. …”
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Deep learning-based InSAR time-series deformation prediction in coal mine areas by Chuanzeng Shu, Zhiguo Meng, Ying Yang, Yongzhi Wang, Shanjun Liu, Xiaoping Zhang, Yuanzhi Zhang

“…In this paper, we construct a multivariate deep learning model framework for precise surface deformation prediction. …”
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Implementation of Extreme Learning Machine Based on HSV Color Features for Marine Animal Image Classification by Dzil Hidayati, Yuliana Pertiwi, Agung Ramadhanu

“…This is due to the diverse visual characteristics of marine animals, including morphological shapes, body surface colors, and textures displayed in images. …”
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Bayesian Unsupervised Machine Learning Approach to Segment Arctic Sea Ice Using SMOS Data by Christoph Herbert, Adriano Camps, Florian Wellmann, Mercedes Vall‐llossera

“…The approach considers both statistical characteristics and spatial correlations of the observations. …”
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High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks by Haoyi Tan, Yukun Teng, Guangcun Shan

“…Initially, the relationship between the structural characteristics of MOF materials (including density, surface area and pore features) and their adsorption properties was explored, with the aim of identifying the optimal structural parameters for iodine capture. …”
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Utilization of Ensemble Techniques in Machine Learning to Predict the Porosity and Hardness of Plasma-Sprayed Ceramic Coating by N. Radhika, M. Sabarinathan, S. Sivaraman

“…The performance of these coatings critically depends on surface characteristics, such as porosity and hardness, which are traditionally assessed through time-consuming and labour-intensive experimental methods. …”
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Strawberry Fruit Deformity Detection and Symmetry Quantification Using Deep Learning and Geometric Feature Analysis by Lili Jiang, Yunfei Wang, Haohao Yan, Yingzi Yin, Chong Wu

“…However, current research primarily emphasizes ripeness and surface defects, with limited attention given to the quantitative analysis of geometric characteristics such as deformity and symmetry. …”
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