Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement by Potjanee Kanchanapiboon, Pitipat Tunksook, Prinya Tunksook, Panrasee Ritthipravat, Supatchai Boonpratham, Yodhathai Satravaha, Chaiyapol Chaweewannakorn, Supakit Peanchitlertkajorn

“…Results Features that considered significant in the consensus-based datasets were consistent with a CVMS guideline. The Support Vector Machine model on the Complete Agreement dataset achieved the highest accuracy (77.4%), followed by the Multi-Layer Perceptron model on the Majority Voting dataset (69.6%). …”
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serologic markers of Epstein-Barr virus reactivation in the conditions of viral encephalitis in young patients by O. V. Goleva, E. A. Murina, Z. A. Osipova

“…The reactivation of Epstein-Barr virus on the background of some basic disease revealed during the development of encephalitis of various etiology could act as a co-factor to trigger the process of demyelinazation of central nervous system tissues. …”
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Klasifikasi Multilabel Pada Gaya Belajar Siswa Sekolah Dasar Menggunakan Algoritma Machine Learning by I Kadek Nicko Ananda, Ni Putu Novita Puspa Dewi, Ni Wayan Marti, Luh Joni Erawati Dewi

“…The machine learning algorithms used to build the model are Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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Construction exploration and application prospect of the large model in mining industry by Haijun WANG

“…Based on this, this paper sorts out the development status of generative large model technology, expounds the application status and effectiveness of large model technology in multiple fields, introduces the key technologies of industry large model such as data processing (cleaning, balancing, enhancement, etc.), text tokenization, pre-training and fine-tuning, prompt word optimization, vector embedding, alignment, retrieval enhancement generation and other large model technologies, and demonstrates that the industry large model inherits the advantages of the general large model of “general” and at the same time has the characteristics of “specialization”. …”
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Leveraging Explainable Artificial Intelligence (XAI) for Expert Interpretability in Predicting Rapid Kidney Enlargement Risks in Autosomal Dominant Polycystic Kidney Disease (ADPKD... by Latifa Dwiyanti, Hidetaka Nambo, Nur Hamid

“…We applied seven machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Tree, XGBoost, and Deep Neural Network (DNN)—to data from the Polycystic Kidney Disease Outcomes Consortium (PKDOC) database. …”
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Leveraging deep learning technology for enhancing printing press quality by Omotunde Alabi Muyiwa, B.H. Adejumo

“…The Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Gaussian filters, Local Binary Patterns (LBP), pre-trained Visual Geometry Group sixteen (VGG16) models, Neural Network Search (NNS), and Deep Forest Models (DFM) were used for defect-detections. …”
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A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers by Mehtab Ur Rahman, Cem Direkoglu

“…Abstract Recent advances in artificial intelligence-based audio and speech processing have increasingly focused on the binary and multi-class classification of voice disorders. …”
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AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search Algorithm by Xin Shi, Seyed Mohamad Javidan, Yiannis Ampatzidis, Zhao Zhang

“…A support vector machine (SVM) classifier achieved 97.5% overall accuracy after preprocessing, a substantial improvement over the initial 81.25% without image preprocessing. …”
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Combining Open-Source Machine Learning and Publicly Available Aerial Data (NAIP and NEON) to Achieve High-Resolution High-Accuracy Remote Sensing of Grass–Shrub–Tree Mosaics by Brynn Noble, Zak Ratajczak

“…We compared two machine learning models—support vector machines and random forests—implemented in R using large training and evaluation data sets. …”
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Habitat suitability modeling to improve conservation strategy of two highly-grazed endemic plant species in saint Catherine Protectorate, Egypt by Mohamed M. El-Khalafy, Eman T. El-Kenany, Alshymaa Z. Al-Mokadem, Salma K. Shaltout, Ahmed R. Mahmoud

“…In our analysis, we included the incorporation of bioclimatic variables into the SDM modeling process using four main algorithms: generalized linear model (GLM), Random Forest (RF), Boosted Regression Trees (BRT), and Support Vector Machines (SVM) in an ensemble model. …”
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Multiscale Residual Weighted Classification Network for Human Activity Recognition in Microwave Radar by Yukun Gao, Lin Cao, Zongmin Zhao, Dongfeng Wang, Chong Fu, Yanan Guo

“…This can extract the representative vector of each image and also obtain the pre-training parameters of the MRW image encoder. …”
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A case of laryngeal leishmaniasis presenting as hoarseness in an Ethiopian patient: a case report by Mohammedsefa Arusi Dari, Zelalem Tadesse Wondimu, Melaku Abay Muluneh, Martha Mekonen Gdey, Adil Fekede Ayele

“…The infection can spread through zoonotic or anthroponotic transmission, depending on the species, with the phlebotomine sandfly serving as the primary vector. Leishmaniasis is endemic in tropical regions of Asia and Africa. …”
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Analysis of junior servers approaching a senior server in the multi-server queueing-inventory system by K. Jeganathan, V. Anzen Koffer, K. Prasanna Lakshmi, N. Anbazhagan, Gyanendra Prasad Joshi, Woong Cho

“…We analyze the performance of such a system by modeling it into a quasi-birth-and-death process, and its steady-state probability vector has been computed. …”
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Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…Considering comprehensive data preliminary processing, FS, and validation, ELNET-BDE outperforms existing methods. …”
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PDAA: An End-to-End Polygon Dynamic Adjustment Algorithm for Building Footprint Extraction by Longjie Luo, Jiangchen Cai, Bin Feng, Liufeng Tao

“…Values of 75.4% AP and 84.9% PolySim were achieved on the WHU dataset, effectively solving the problems of redundant vertices and contour smoothing, and providing high-precision building vector data support for scenarios such as smart cities and emergency response.…”
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Old Drugs, New Indications (Review) by I. I. Miroshnichenko, E. A. Valdman, I. I. Kuz'min

“…The drug can be used in the treatment of one disease and for the prevention and treatment of another pathological process. This is possible due to the repurposing of medicines. …”
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Stability and transepithelial transport of oligopeptide (KRQKYD) with hepatocyte-protective activity from Jinhua ham in human intestinal Caco-2 monolayer cells by Wen Nie, Feiran Xu, Kai Zhou, Jieying Deng, Ying Wang, Baocai Xu

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A Mobile Image-Driven PM2.5 Estimation Framework Using Deep Learning Techniques by Anupam Kamble, Somrawee Aramkul, Paskorn Champrasert

“…The EfficientNet-B1 neural network is applied in the image feature vector extraction process. EfficientNet-B1, with a resolution of <inline-formula> <tex-math notation="LaTeX">$240\times 240$ </tex-math></inline-formula> pixels, was determined to be the optimal variant of EfficientNet for a small dataset of images needed for the estimation of the PM2.5 concentration value. …”
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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…This paper proposes a railway worker detection method based on improved support vector machines (ISVM), while using non-local mean noise reduction and histogram equalisation pre-processing techniques to optimise image quality to improve detection efficiency and accuracy. …”
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A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition by Qian Qiao, Quan Liu, Jiong Pu, Haixia Shi, Wenxiao Li, Zhixiong Zhu, Dawei Guo, Hongchang Qian, Dawei Zhang, Xiaogang Li, C. T. Kwok, L. M. Tam

“…Expect for the stable parameters (temperature, force and torque) in situ monitored by the self‐developed process‐aware kit during the AFSD process and the other factors (rotation speed, traverse speed, feed rate and layer thickness) are also set as input variables. …”
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