A Comparative Study of Deep Semantic Segmentation and UAV-Based Multispectral Imaging for Enhanced Roadside Vegetation Composition Assessment by Puranjit Singh, Michael A. Perez, Wesley N. Donald, Yin Bao

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Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role by Chiranjeevi Yarramsetty, Tukaram Moger, Debashisha Jena, Veeranki Srinivasa Rao

“…A comparative examination of conventional and Machine Learning (ML)-based methods demonstrates that deep learning models, such as Convolutional Neural Networks, offer substantial reductions in computational time while maintaining reliability assessment precision. …”
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A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates by Muhammad Haris Yazdani, Muhammad Muzammil Azad, Salman Khalid, Heung Soo Kim

“…Structural health monitoring (SHM) has proven to be an effective technique to maintain the safety and reliability of laminated composites. Recently, both deep learning and machine learning methodologies have gained popularity in sensor-based SHM. …”
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Small Sample-Oriented Prediction Method of Mechanical Properties for Hot Rolled Strip Steel Based on Model Independent Element Learning by Hongyi Wu, Borui Zhang, Zhiwei Li

“…In this paper, a new method based on Model-Agnostic Meta-Learning (MAML) is proposed to address the small sample problem in predicting the mechanical properties of hot rolled strip steel. …”
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Decision Support System for the Design Process of Apatite Biopolymer Composite Parts by Panda Anton, Dyadyura Kostiantyn, Dmitrishin Dmitriy, Smorodin Andrey, Prokopovich Igor

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Identifying G-Protein Coupled Receptors Using Mixed-Feature Extraction Methods and Machine Learning Methods by Chunyan Ao, Lin Gao, Liang Yu

“…In this paper, we propose a method for identifying GPCRs based on mixed-feature vectors. …”
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Transfer learning with class activation maps in compositions driving plaque classification in carotid ultrasound by Georgia D. Liapi, Christos P. Loizou, Maura Griffin, Constantinos S. Pattichis, Constantinos S. Pattichis, Andrew Nicolaides, Efthyvoulos Kyriacou

“…In this study, the objective was to assess whether class activations maps (CAMs) can reveal which U/S grayscale-(GS)-based plaque compositions (lipid cores, fibrous content, collagen, and/or calcified areas) influence the model's understanding of the ASY and SY cases.MethodsWe used Xception via transfer learning, as a base for feature extraction (all layers frozen), whose output we fed into a new dense layer, followed by a new classification layer, which we trained with standardized B-mode U/S longitudinal plaque images. …”
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Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western populationResearch in context by Matthias Jung, Vineet K. Raghu, Marco Reisert, Hanna Rieder, Susanne Rospleszcz, Tobias Pischon, Thoralf Niendorf, Hans-Ulrich Kauczor, Henry Völzke, Robin Bülow, Maximilian F. Russe, Christopher L. Schlett, Michael T. Lu, Fabian Bamberg, Jakob Weiss

“…Summary: Background: Manually extracted imaging-based body composition measures from a single-slice area (A) have shown associations with clinical outcomes in patients with cardiometabolic disease and cancer. …”
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Image Target Detection and Recognition Method Using Deep Learning by Hongyan Sun

“…Based on the analysis of the existing theories of deep learning detection and recognition, this paper summarized the composition and working principle of the traditional image target detection and recognition system and compared the basic models of target detection and recognition, such as R-CNN network, Fast-RCNN network, and Faster-RCNN network. …”
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Association between the nutritional inflammation index and mortality among patients with sepsis: insights from traditional methods and machine learning-based mortality prediction by Yuanshuo Ge, Ding Hu, Zhe Wang, Cheng Zhang

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Machine learning methods for spectrally-resolved imaging analysis in neuro-oncology by T. A. Savelieva, I. D. Romanishkin, A. Ospanov, K. G. Linkov, S. A. Goryajnov, G. V. Pavlova, I. N. Pronin, V. B. Loschenov

“…In this paper, we focus on both the physical foundations of such methods and a very important aspect of their application – machine learning (ML) methods for image processing and tissues’ classification.…”
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Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons. by Amadeus Maes, Mauricio Barahona, Claudia Clopath

“…Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. …”
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MCAUnet: a deep learning framework for automated quantification of body composition in liver cirrhosis patients by Jiening Wang, Shuqi Xia, Jie Zhang, Xinyi Wang, Cai Zhao, Wen Zheng

“…Abstract Traditional methods for measuring body composition in CT scans rely on labor-intensive manual delineation, which is time-consuming and imprecise. …”
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Advancing Dynamic Emergency Route Optimization with a Composite Network Deep Reinforcement Learning Model by Jin Zhang, Hao Xu, Ding Liu, Qi Yu

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Wide & deep learning for predicting relative mineral compositions of sediment cores solely based on XRF scans, a case study from Pleistocene Paleolake Olduvai, Tanzania by Gayantha R.L. Kodikara, Lindsay J. McHenry, Ian G. Stanistreet, Harald Stollhofen, Jackson K. Njau, Nicholas Toth, Kathy Schick

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Abstract visual reasoning based on algebraic methods by Mingyang Zheng, Weibing Wan, Zhijun Fang

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ACCELERATING THE FORMATION OF CHILDREN’S VALUES IN A LEARNING ENVIRONMENT by A. I. Mantarova, I. A. Angelova

“…The present publication presents the results of an experiment conducted on the territory of Bulgaria with children aged 4.5 to 6 years.The aim of the research is to work out a model of interaction based on the contemporary knowledge of preschool children’ development and cognitive process peculiarities of children aged 4.5 to 6.Methodology and research methods. …”
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GENDER-SPECIFIC PREDICTORS OF VAULT PERFORMANCE IN GYMNASTICS: A MACHINE LEARNING APPROACH by Dušan Đorđević, Janez Vodičar, Robi Kreft, Edvard Kolar, Miloš Paunović, Saša Veličković, Miha Marinšek

“… This study investigated gender-specific predictors of vault performance in gymnastics by applying machine learning techniques to analyse body composition and run-up dynamics. …”
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Data‐driven prediction of phase formation in graphene–metal systems based on phase diagram insights by Leilei Chen, Changheng Li, Kai Xu, Ruonan Zhou, Ming Lou, Yujie Du, Denis Music, Keke Chang

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Crude Oil Futures Price Forecasting Based on Variational and Empirical Mode Decompositions and Transformer Model by Linya Huang, Xite Yang, Yongzeng Lai, Ankang Zou, Jilin Zhang

“…This model integrates a second decomposition and Transformer model-based machine learning method. More specifically, we employ the VMD technique to decompose the original sequence into variational mode filtering (VMF) and a residual sequence, followed by using EMD to decompose the residual sequence. …”
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