Insulator Defect Recognition Based on Vision Big-Model Transfer Learning and Stochastic Configuration Network by Siyuan Liu, Yihua Ma, Zedong Zheng, Xinfu Pang, Bingyou Li

“…YOLOv7 accurately detects insulator positions, and the use of the DINOv2 feature extraction method increases the accuracy of insulator defect recognition by 28.6%. Compared with machine learning classification methods, the SCN classifier achieves the highest accuracy improvement of 17.4%. …”
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NeuroSight: A Deep‐Learning Integrated Efficient Approach to Brain Tumor Detection by Shafayat Bin Shabbir Mugdha, Mahtab Uddin

“…This has led to exploring deep learning and machine learning in biomedical imaging, particularly in processing and analyzing Magnetic Resonance Imaging (MRI) data. …”
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Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review by Jianqiang Gong, Bin Xu, Fanghua Chen, Gang Zhou

“…Results highlight that data-driven methods, particularly those utilizing machine learning techniques, offer superior accuracy and adaptability but often require extensive datasets. …”
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Healthy core: Harmonizing brain MRI for supporting multicenter migraine classification studies. by Hyunsoo Yoon, Todd J Schwedt, Catherine D Chong, Oyekanmi Olatunde, Teresa Wu

“…However, multicenter studies, incorporating varying research participant characteristics, MRI scanners, and imaging acquisition protocols, may introduce confounding factors, potentially hindering the creation of generalizable machine learning models. Models developed using one dataset may not readily apply to another, emphasizing the importance of classification model generalizability in multi-scanner and multicenter studies for producing reproducible results. …”
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Reducing Training Time in Skin Cancer Classification Using Convolutional Neural Network with Mixed Precision Implementation by Raka Ryandra Guntara, Hendriyana, Indira Syawanodya

“…In the field of skin cancer classification, machine learning and deep learning have been extensively utilized, particularly with convolutional neural network (CNN) architectures. …”
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Open challenges and opportunities in federated foundation models towards biomedical healthcare by Xingyu Li, Lu Peng, Yu-Ping Wang, Weihua Zhang

“…Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions. …”
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Drug-target binding affinity prediction based on power graph and word2vec by Jing Hu, Shuo Hu, Minghao Xia, Kangxing Zheng, Xiaolong Zhang

“…In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions. …”
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Synthetic Dataset for Analyzing Geometry-Dependent Optical Properties of All-Pass Micro-Ring Resonators by Sebastian Valencia-Garzon, Esteban Gonzalez-Valencia, Nelson Gómez-Cardona, Andres Calvo-Salcedo, J. A. Jaramillo-Villegas, Jorge Montoya-Cardona, Erick Reyes-Vera

“…The results confirm that variations in geometric configurations can significantly affect MRR performance, and the dataset provides valuable insights into the optimization process. Furthermore, machine learning techniques can be applied to the dataset to automate and improve the design process, reducing simulation times and increasing accuracy. …”
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Observation of a rare beta decay of the charmed baryon with a Graph Neural Network by The BESIII Collaboration

“…This work highlights a new approach to further understand fundamental interactions in the charmed baryon sector, and showcases the power of modern machine learning techniques in experimental high-energy physics.…”
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English Grammar Error Correction Algorithm Based on Classification Model by Shanchun Zhou, Wei Liu

“…Classification model is the core of machine learning and data mining, which can be applied to extracting information from English text data and constructing a reliable grammar correction method. …”
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Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain by Yi Yin, Jianwei Pan, Fang Wang, Peihang Li, Zhen Cai, Xin Xu

“…However, their complex microstructure presents significant challenges for image analysis. Traditional machine learning methods often fall short in capturing both global feature dependencies and detailed representations. …”
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Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output Power by Bo Xiao, Hai Zhu, Sujun Zhang, Zi OuYang, Tandong Wang, Saeed Sarvazizi

“…The photovoltaic output power variable is evaluated in this study using a powerful machine learning approach called the support vector machine model based on gray-wolf optimization. …”
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Herramientas para la gestión sostenible de la sedimentación en el embalse de Marmolejo (España) by David López-Gómez, Miguel de Blas-Moncalvillo, Vicente Cuéllar-Moro

“…Ha sido necesario el empleo de técnicas de supercomputación y machine learning, así como un nuevo modelo de transporte de sedimento en suspensión, para calibrar un modelo numérico bidimensional del embalse, que ha permitido reproducir la evolución histórica del proceso de sedimentación. …”
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Secure Two-Party Decision Tree Classification Based on Function Secret Sharing by Kun Liu, Chunming Tang

“…Overall, the paper delivers an efficient, modular framework for practical two-party secure decision tree evaluation that advances the capability of privacy-preserving machine learning.…”
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Quantum-limited stochastic optical neural networks operating at a few quanta per activation by Shi-Yuan Ma, Tianyu Wang, Jérémie Laydevant, Logan G. Wright, Peter L. McMahon

“…We show that by using a physics-based probabilistic model of the neuron activations in training, it is possible to perform accurate machine-learning inference in spite of the extremely high shot noise (SNR  ~ 1). …”
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Computer Vision-Based Drowsiness Detection Using Handcrafted Feature Extraction for Edge Computing Devices by Valerius Owen, Nico Surantha

“…These are integrated with traditional machine learning models, such as Support Vector Machines, Random Forests, and XGBoost. …”
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Physical Reservoir Computing Utilizing Ion‐Gating Transistors Operating in Electric Double Layer and Redox Mechanisms by Takashi Tsuchiya, Daiki Nishioka, Wataru Namiki, Kazuya Terabe

“…Abstract The enormous energy consumption of modern machine learning technologies, such as deep learning and generative artificial intelligence, is one of the most critical concerns of the time. …”
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ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph by Muhammad Ajmal Azad, Junaid Arshad, Farhan Riaz

“…The trust score of the callers is then used along with machine learning models to classify them as legitimate or robo-caller. …”
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Fast binary logistic regression by Nurdan Ayse Saran, Fatih Nar

“…This study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community. Our method achieves training times an order of magnitude faster than traditional logistic regression by employing a novel Soft-Plus approximation, which enables reformulation of logistic regression parameter estimation into matrix-vector form. …”
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Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations by Pasqual Martí, Alejandro Ibáñez, Vicente Julian, Paulo Novais, Jaume Jordán

“…This paper introduces an innovative approach to predicting bus ridership andanalysing transportation scenarios through a fusion of machine learning (ML) techniques and multi-agent simulations. …”
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