Machine Learning or Human Education? Thoughts and Data on Artificial Intelligence and the Labor Market: Economic Sociological Aspects of Career Orientation. by Keszi, Roland

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From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods by Junhua Lu, Mei Zhang, Yongsong Hu, Wei Ma, Zhiwei Tian, Hongsen Liao, Jiawei Chen, Yuxin Yang

“…Machine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. …”
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Exploring the potential of machine learning and magnetic resonance imaging in early stroke diagnosis: a bibliometric analysis (2004–2023) by Jian-cheng Lou, Xiao-fen Yu, Jian-jun Ying, Da-qiao Song, Wen-hua Xiong

“…The development in this field is marked by a coexisting pattern of interdisciplinary integration and refinement within major disciplinary branches.ConclusionThe application of machine learning in the early prediction and personalized medical plans for stroke patients using neuroimaging characteristics offers significant value. …”
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Multi objective optimization of FDM 3D printing parameters set via design of experiments and machine learning algorithms by Antonio Panico, Alberto Corvi, Luca Collini, Corrado Sciancalepore

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Spatial Prediction of High-Risk Areas for Asthma in Metropolitan Areas: A Machine Learning Approach Applied to Tehran, Iran by Alireza Mohammadi, Elahe Pishgar, Juan Aguilera

“…Data from 1473 asthma patients, alongside demographic, socioeconomic, air quality, environmental, weather, and healthcare access variables, were analyzed using geographic information systems (GIS) and remote sensing techniques. Three ensemble machine learning algorithms—Random Forest (RF), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—were applied to model and predict asthma risk. …”
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A Novel Strategy for Automatic Selection of Cross‐Basin Data to Improve Local Machine Learning‐Based Runoff Models by Congyi Nai, Xingcai Liu, Qiuhong Tang, Liu Liu, Siao Sun, Paul P. J. Gaffney

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Transforming Building Energy Management: Sparse, Interpretable, and Transparent Hybrid Machine Learning for Probabilistic Classification and Predictive Energy Modelling by Yiping Meng, Yiming Sun, Sergio Rodriguez, Binxia Xue

“…Leveraging the REFIT Smart Home Dataset, the framework integrates occupancy pattern analysis, appliance-level energy prediction, and probabilistic uncertainty quantification. …”
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Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer by Xiaolong Liu, Pengxian Tao, He Su, Yulan Li

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Nascent flagellar basal bodies are immobilized by rod assembly in Bacillus subtilis by Caroline M. Dunn, Daniel J. Foust, Yongqiang Gao, Julie S. Biteen, Sidney L. Shaw, Daniel B. Kearns

“…We conclude that the peritrichous patterning of flagella of B. subtilis is different from the polar patterning of other bacteria, and we infer that the B. subtilis rod probes the PG for holes that can accommodate the machine.…”
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Optimum Combination of Spectral Variables for Crop Mapping in Heterogeneous Landscapes based on Sentinel-2 Time Series and Machine Learning by J. G. de Oliveira Júnior, J. C. D. M. Esquerdo, J. C. D. M. Esquerdo, R. A. C. Lamparelli, R. A. C. Lamparelli

“…This article aimed to determine a workflow for more efficient large-scale crop mapping using a time series of images from the Sentinel-2 Satellite, statistical methods of attribute selection, and machine learning. The proposed methodology explores the best possible combination of spectral variables related to vegetation (16 vegetation indices in the RGB, NIR, SWIR, and Red Edge regions) to characterize different spectro-temporal profiles of Land Use and Land Cover (LULC) in spatially heterogeneous landscapes. …”
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Harnessing machine learning for streamflow prediction: A comparative study of advanced models in the Upper Klang River Basin, Malaysia by Napisah Nasir, Dani Irwan, Ali Najah Ahmed, Saerahany Legori Ibrahim, Izihan Ibrahim, Mohsen Sherif, Ahmed El-Shafie

“…New hydrological insights for the region: This study provides valuable new insights into the streamflow pattern of the upper Klang River, Malaysia, by leveraging advanced machine learning (ML) techniques for accurate daily streamflow prediction. …”
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Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters by Gaku FUJIWARA, Yohei OKADA, Eiichi SUEHIRO, Hiroshi YATSUSHIGE, Shin HIROTA, Shu HASEGAWA, Hiroshi KARIBE, Akihiro MIYATA, Kenya KAWAKITA, Kohei HAJI, Hideo AIHARA, Shoji YOKOBORI, Motoki INAJI, Takeshi MAEDA, Takahiro ONUKI, Kotaro OSHIO, Nobukazu KOMORIBAYASHI, Michiyasu SUZUKI, Naoto SHIOMI

“…Patients were divided into 3 groups based on their daily anticoagulant medication (none, direct oral anticoagulant [DOAC], and vitamin K antagonist [VKA]), and coagulation parameters were compared in each group. We then developed a machine-learning model to predict the anticoagulant using coagulation parameters and visualized the pattern using a heat map. …”
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A flexible, channel‐minimized and large‐scale touch position sensor combining contact electrification effect and screen‐printed stagger electrode patterns by Zhihao Hu, Yuanchao Ren, Xindan Hui, Lirong Tang, Jie Chen, Hengyu Guo

“…Abstract The large‐scale touch position sensor as a key human–machine interface toolkit holds immense significance in smart city and home construction. …”
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Identification of age-specific risk factors for hyperuricemia: a machine learning-driven stratified analysis in health examination cohorts by ChuXia Tan, Yuan Liu, Lijun Li, Ying Li, Pingting Yang, Yinglong Duan, Xingxing Wang, Huiyi Zhang, Jingying Wang, Honglian Zhang

“…Abstract Background Hyperuricemia (HUA) as a global public health challenge, although its overall epidemiological characteristics have been widely reported, its age-specific risk pattern remains controversial. This study aims to reveal the risk factors of HUA in healthy physical examination populations of different age groups and construct a machine learning-driven risk prediction model to achieve precise intervention. …”
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Research on the evolution and optimization of ecological networks in arid areas based on machine learning and multi-source remote sensing data by Ruyi Pan, Jie Zhang, Wentao Xue, Qianqian Xia, Jiangxiang Liu, Junjie Yan, Hongbo Ling, Xufan Jin

“…This study proposes a new methodological framework that integrates Morphological Spatial Pattern Analysis, circuit theory, and machine learning models to explore the spatiotemporal evolution and optimization of ecological networks in Xinjiang from 1990 to 2020. …”
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Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients by Thomas Meredith, Thomas Meredith, Thomas Meredith, Farhan Mohammed, Amy Pomeroy, Sebastiano Barbieri, Sebastiano Barbieri, Erik Meijering, Louisa Jorm, David Roy, Jason Kovacic, Jason Kovacic, Jason Kovacic, Jason Kovacic, Michael Feneley, Michael Feneley, Michael Feneley, Christopher Hayward, Christopher Hayward, Christopher Hayward, David Muller, David Muller, David Muller, Mayooran Namasivayam, Mayooran Namasivayam, Mayooran Namasivayam

“…BackgroundLong-term mortality risk is seldom re-assessed in contemporary clinical practice following successful transcatheter aortic valve implantation (TAVR). Unsupervised machine learning permits pattern discovery within complex multidimensional patient data and may facilitate recognition of groups requiring closer post-TAVR surveillance.MethodsWe analysed and differentiated routinely collected demographic, biochemical, and cardiac imaging data into distinct clusters using unsupervised machine learning. k-means clustering was performed on data from 200 patients who underwent TAVR for severe aortic stenosis (AS). …”
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ML-Based Pain Recognition Model Using Mixup Data Augmentation by Raghu M. Shantharam, Friedhelm Schwenker

“…Machine learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. …”
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Hybrid ERC20 Ethereum Blockchain Multisignature Wallet 3of3 with Withdrawal Pattern, External Effects, and Mutex as Single Key and Reentrancy Mitigation. by Jason Al Hilal Sabda Dewa, Indra Waspada, Priyo Sidik Sasongko

“…The system not only applied withdrawal patterns but also integrated external effects and the principle of mutual exclusion to enhance the security of smart contracts. …”
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Fracture Resistance and Patterns of Fracture in Maxillary Complete Dentures Reinforced with Glass and Aramid Fiber Mesh under Dynamic Fatigue Loading - An In Vitro Study by Ancita Caroline Dsouza, Brijesh Shetty, Suhas Rao K

“…Fracture resistance was evaluated using a Universal testing machine. Patterns of fracture were categorized as complete incomplete fracture anterior amp posterior fracture anterior fracture posterior fracture and central deformation only loaded area. …”
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Research on the patterns, risks, and driving mechanisms of urban high-temperature expansion using a fine-grained explainable model with consideration of urban vitality by Guangqin He, Guolin Cai, Gang Liu, Yongshu Li

“…This study focused on urban high-temperature expansion (HTE) and its spatiotemporal patterns, risks, and underlying driving mechanisms, a critical yet underexplored aspect of the urban climate that differs from traditional urban heat alone. …”
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