Effect of Intravenous Contrast on CT Body Composition Measurements in Patients with Intraductal Papillary Mucinous Neoplasm by Ranjit S. Chima, Tetiana Glushko, Margaret A. Park, Pamela Hodul, Evan W. Davis, Katelyn Martin, Aliya Qayyum, Jennifer B. Permuth, Daniel Jeong

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SimProx: A Similarity-Based Aggregation in Federated Learning With Client Weight Optimization by Ayoub El-Niss, Ahmad Alzu'Bi, Abdelrahman Abuarqoub, Mohammad Hammoudeh, Ammar Muthanna

“…First, SimProx employs a composite similarity-based weighting mechanism, integrating cosine and Gaussian similarity measures to dynamically optimize client contributions. …”
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Detecting the extent of co-existing anomalies in additively manufactured metal matrix composites through explainable selection and fusion of multi-camera deep learning features by Mutahar Safdar, Gentry Wood, Max Zimmermann, Guy Lamouche, Priti Wanjara, Yaoyao Fiona Zhao

“…Advanced in-situ monitoring coupled with modern machine learning (ML) methods can expedite defect detection and qualification of additive manufacturing (AM) parts. …”
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Hyperspectral Target Detection Based on Macro–Micro Spectrum Contrastive Learning by Jiacheng Tian, Dunbin Shen, Wenfeng Kong, Min Li, Hongyu Wang, Xiaorui Ma

“…Depending on single target example under the influence of spectral variation, deep learning-based hyperspectral target detection (HTD) methods are challenged by the problem of insufficient target knowledge. …”
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Cyber security entity recognition method based on residual dilation convolution neural network by Bo XIE, Guowei SHEN, Chun GUO, Yan ZHOU, Miao YU

“…In recent years,cybersecurity threats have increased,and data-driven security intelligence analysis has become a hot research topic in the field of cybersecurity.In particular,the artificial intelligence technology represented by the knowledge graph can provide support for complex cyberattack detection and unknown cyberattack detection in multi-source heterogeneous threat intelligence data.Cybersecurity entity recognition is the basis for the construction of threat intelligence knowledge graphs.The composition of security entities in open network text data is very complex,which makes traditional deep learning methods difficult to identify accurately.Based on the pre-training language model of BERT (pre-training of deep bidirectional transformers),a cybersecurity entity recognition model BERT-RDCNN-CRF based on residual dilation convolutional neural network and conditional random field was proposed.The BERT model was used to train the character-level feature vector representation.Combining the residual convolution and the dilation neural network model to effectively extract the important features of the security entity,and finally obtain the BIO annotation of each character through CRF.Experiments on the large-scale cybersecurity entity annotation dataset constructed show that the proposed method achieves better results than the LSTM-CRF model,the BiLSTM-CRF model and the traditional entity recognition model.…”
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Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the Sea by Zhaolong Wang, Xiaokuan Zhang, Weike Feng, Binfeng Zong, Tong Wang, Cheng Qi, Xixi Chen

“…Without suppressing interferences, the proposed method uses both target and multipath information for detection based on their distinguishable image features and deep learning (DL) techniques. …”
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A deep learning‐based approach for software vulnerability detection using code metrics by Fazli Subhan, Xiaoxue Wu, Lili Bo, Xiaobing Sun, Muhammad Rahman

“…To use code metrics for vulnerability detection, a deep learning‐based vulnerability detection approach that uses a composite neural network of convolutional neural network (CNN) with long short‐term memory (LSTM) is proposed. …”
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Enhanced random vector functional link based on artificial protozoa optimizer to predict wear characteristics of Cu-ZrO2 nanocomposites by Mamdouh I. Elamy, Mohamed Abd Elaziz, Mohammed Azmi Al-Betar, A. Fathy, M. Elmahdy

“…Owing to the absence of scientific methods for predicting nanocomposites' wear rates, a freshly updated machine learning method that uses an Artificial Protozoa Optimizer (APO) to forecast the tribological performance of Cu-ZrO2 nanocomposites was proposed. …”
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Identification Method of Dynamic Propagation Process of Rock Fracture Based on Ground Penetrating Radar by CHEN Jun, ZHANG Bo, ZHUANG Xingyue, SONG Zhishu, ZHENG Jun

“…This study addresses this gap by proposing a ground penetrating radar (GPR)-based method for identifying the dynamic propagation processes of rock fractures, aiming to provide a non-intrusive, real-time, and quantitative solution for rock mass stability assessment and disaster early warning.MethodsThis research adopts a comprehensive approach, integrating numerical simulations and physical experiments to systematically investigate the intricate relationship between rock fracture propagation and GPR signal response. …”
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Students’ Mastering of Structural Analysis of Substance as a Method to Form Future Specialists’ Scientific Thinking. Part I by L. A. Baikova, M. A. Kosareva, E. A. Nikonenko, V. V. Vaitner, A. Majee

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Students’ Mastering of Structural Analysis of Substance as a Method to Form Future Specialists’ Scientific Thinking. Part II by L. A. Baikova, M. A. Kosareva, E. A. Nikonenko, V. V. Vaitner, A. Majee

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Small-size spectral features for machine learning in voice signal analysis and classification tasks by D. S. Likhachov, M. I. Vashkevich, N. A. Petrovsky, E. S. Azarov

“…The problem of developing a method for calculating small-sized spectral features that increases the efficiency of existing machine learning systems for analyzing and classifying voice signals is being solved.Methods. …”
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A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an example by I. D. Busov, M. A. Genaev, E. G. Komyshev, V. S. Koval, T. E. Zykova, A. Y. Glagoleva, D. A. Afonnikov

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Addressing process-induced porosity variations in multiscale composite materials analysis using aggregated projection clustering and Halton sequence RVE sampling by Hamidreza Dehghani, Henri Perrin, Elias Belouettar-Mathis, Borek Patzák, Salim Belouettar

“…This work introduces the Aggregated Vertical Projection Clustering (APC) method, which applies K-means clustering to partition the data into k groups based on porosity. …”
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Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration by Hao Zou, Haochen Zhao, Mingming Lu, Jiong Wang, Zeyu Deng, Jianxin Wang

“…This approach provides significant advantages in terms of time and resource efficiency compared to traditional experimental and modeling methods. However, most existing models are constructed based on specific domain knowledge, potentially introducing biases that impact their performance. …”
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A joint data and knowledge‐driven method for power system disturbance localisation by Zikang Li, Jiyang Tian, Hao Liu

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Machine learning-derived diagnostic model of epithelial ovarian cancer based on gut microbiome signatures by Cheng Chen, Chengyuan Deng, Yanwen Li, Shuguang He, Yunhong Liu, Shuwen Pan, Wenqian Xu, Lu Fang, Yixi Zhu, Yingying Wang, Xiaoxin Jiang

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Machine learning-based prediction of scale formation in produced water as a tool for environmental monitoring by Arash Tayyebi, Ali Alshami, Erfan Tayyebi, Ademola Owoade, MusabbirJahan Talukder, Nadhem Ismail, Zeinab Rabiei, Xue Yu, Glavic Tikeri

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Unlocking public health competencies: the dose–response effect of problem-based learning on undergraduate student outcomes by Ashley Falcon, Andrew Porter, Yui Matsuda, Cynthia L. Foronda, Padideh Lovan, Beck Graefe

“…IntroductionProblem-based learning (PBL) is a student-centered pedagogical strategy that emphasizes active learning through the exploration of complex real-world problems. …”
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Study on the Influence Mechanism of Machine-Learning-Based Built Environment on Urban Vitality in Macau Peninsula by Chen Pan, Jiaming Guo, Haibo Li, Jiawei Wu, Nengjie Qiu, Shengzhen Wu

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