Machine Learning-Based Mooring Failure Detection for FPSOs: A Two-Step ANN Approach by Omar Jebari, Do-Soo Kwon, Sung-Jae Kim, Chungkuk Jin, Moohyun Kim

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A High-Efficiency Deep-Learning-Based Antivibration Hammer Defect Detection Model for Energy-Efficient Transmission Line Inspection Systems by Fangrong Zhou, Gang Wen, Guochao Qian, Yutang Ma, Hao Pan, Jing Liu, Jiaying Li

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A Distributed Machine Learning-Based Scheme for Real-Time Highway Traffic Flow Prediction in Internet of Vehicles by Hani Alnami, Imad Mahgoub, Hamzah Al-Najada, Easa Alalwany

“…Most recent studies utilized machine learning models in traffic flow detection systems. …”
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Deep Learning-Enabled Visual Inspection of Gap Spacing in High-Precision Equipment: A Comparative Study by Xiuling Li, Fusheng Li, Huan Yang, Peng Wang

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Machine learning-based prognostic model of lactylation-related genes for predicting prognosis and immune infiltration in patients with lung adenocarcinoma by Mingjun Gao, Mengmeng Wang, Siding Zhou, Jiaqi Hou, Wenbo He, Yusheng Shu, Xiaolin Wang

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Securing Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification by Mahbub Ul Islam Khan, Md. Ilius Hasan Pathan, Mohammad Mominur Rahman, Md. Maidul Islam, Mohammed Arfat Raihan Chowdhury, Md. Shamim Anower, Md. Masud Rana, Md. Shafiul Alam, Mahmudul Hasan, Md. Shohanur Islam Sobuj, Md. Babul Islam, Veerpratap Meena, Francesco Benedetto

“…The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3-<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3-<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. …”
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Automated snow cover detection on mountain glaciers using spaceborne imagery and machine learning by R. Aberle, E. Enderlin, S. O'Neel, C. Florentine, L. Sass, A. Dickson, H.-P. Marshall, A. Flores

“…We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. …”
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Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids by Christoph Balada, Max Bondorf, Sheraz Ahmed, Markus Zdrallek, Andreas Dengel

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Spectral Fingerprinting of Tencha Processing: Optimising the Detection of Total Free Amino Acid Content in Processing Lines by Hyperspectral Analysis by Qinghai He, Yihang Guo, Xiaoli Li, Yong He, Zhi Lin, Hui Zeng

“…This study employs VNIR-HSI combined with machine learning algorithms to develop a model for visualizing the total free amino acid content in Tencha samples that have undergone different processing steps on the production line. …”
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Defect detection in photolithographic patterns using deep learning models trained on synthetic data by Prashant P. Shinde, Priyadarshini P. Pai, Shashishekar P. Adiga, K. Subramanya Mayya, Yongbeom Seo, Myungsoo Hwang, Heeyoung Go, Changmin Park

“…Due to ever-shrinking pattern size, these defects are extremely small and cause false or missed detection during inspection. Specifically, the lack of defect-annotated quality data with good representation of smaller defects has prohibited deployment of deep learning based defect detection models in fabrication lines. …”
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A Lightweight Intrusion Detection System for Internet of Things: Clustering and Monte Carlo Cross-Entropy Approach by Abdulmohsen Almalawi

“…To address these challenges, machine learning-based intrusion detection systems (IDSs)—traditionally considered a primary line of defense—have been deployed to monitor and detect malicious activities in IoT networks. …”
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Imaging‐Based Lensless Polarization‐Sensitive Fluid Stream Analyzer for Automated, Label‐Free, and Cost‐Effective Microplastic Classification by Fraser Montandon, Fred Nicolls

“…This study introduces an imaging‐based lensless polarization‐sensitive fluid stream analyzer (FSA) for automated, label‐free, and cost‐effective detection and classification of microplastics. The FSA incorporates digital in‐line holography and birefringence computation, enabling quantitative polarization‐sensitive imaging and machine‐learning‐based activities including sample classification. …”
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Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression by Joseph M. Josephides, Chun-Long Chen

“…Here, we develop MnM, an efficient machine learning-based tool that allows disentangling scRT profiles from heterogenous samples. …”
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Molecular detection of hrHPV-induced high-grade squamous intraepithelial lesions of the cervix through a targeted RNA next generation sequencing assay by Julia Faillace Thiesen, Elise Jacquemet, Pascal Campagne, Denis Chatelain, Etienne Brochot, Yves-Edouard Herpe, Nolwenn M. Dheilly, Fabrice Bouilloux, Bénédicte Rognon, Alexandre Douablin, Guillaume Leboucher, Florent Percher, Marc Eloit, Philippe Pérot

“…Conclusions HPV-RNA-SEQ improves the detection of HSIL lesions and has the potential to complete and eventually replace current molecular approaches as a first-line test. …”
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Point Cloud-Based Proactive Link Quality Prediction for Millimeter-Wave Communications by Shoki Ohta, Takayuki Nishio, Riichi Kudo, Kahoko Takahashi, Hisashi Nagata

“…Previous studies have proposed machine learning-based methods to predict received signal strength for future time periods using time series of depth images to mitigate the line-of-sight (LOS) path blockage by pedestrians in mmWave communication. …”
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Deciphering Spatially Resolved Lyman-Alpha Profiles in Reionization Analogs: The Sunburst Arc at Cosmic Noon by Erik Solhaug, Hsiao-Wen Chen, Mandy C. Chen, Fakhri Zahedy, Max Gronke, Magdalena J. Hamel-Bravo, Matthew B. Bayliss, Michael D. Gladders, Sebastián López, Nicolás Tejos

“…The hydrogen Lyman-alpha (Ly$\alpha$) emission line, the brightest spectral feature of a photoionized gas, is considered an indirect tracer of the escape of Lyman continuum (LyC) photons, particularly when the intergalactic medium is too opaque for direct detection. …”
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Reading Shakespeare sonnets: Combining quantitative narrative analysis and predictive modeling - an eye tracking study by Shuwei Xue, Jana Lüdtke, Teresa Sylvester, Arthur M. Jacobs

“…., 2017), the present study analyzed the eye movement behavior of participants reading three of the 154 sonnets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a machine learning- based predictive modeling approach five ‘surface’ features (word length, orthographic neighborhood density, word frequency, orthographic dissimilarity and sonority score) were detected as important predictors of total reading time and fixation probability in poetry reading. …”
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