Artificial intelligence-based prescription of personalized scalp cosmetics improved the scalp condition: efficacy results from 100 participants by Bo Ri Kim, Min Jae Kim, Jieun Koo, Hwa-Jung Choi, Kyung Ho Paik, Soon Hyo Kwon, Hye-Ryung Choi, Chang Hun Huh, Jung Won Shin, Dong-sun Park, Jung-Im Na

“…However, it requires trained evaluators.Aim: To develop artificial intelligence (AI) algorithms for assessment of scalp conditions and to assess the feasibility of AI-based recommendations on personalized scalp cosmetics.Methods: Using EfficientNet, convolutional neural network (CNN) models (SPI-AI) ofeach scalp feature were established. 101,027 magnified scalp images graded according to the SPI scoring were used for training, validation, and testing the model Adults with scalp discomfort were prescribed shampoos and scalp serums personalized according to their SPI-AI-defined scalp types. …”
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Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study by Xiaoli Yuan, Jianbo Qing, Wenqiang Zhi, Feng Wu, Yan Yan, Yafeng Li

“…We applied 16SrRNA sequencing to identify differential microbial populations. ML algorithms were then used to classify IgAN based on these microbial differences. …”
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Oversampling and undersampling for intrusion detection system in the supervisory control and data acquisition IEC 60870‐5‐104 by M. Agus Syamsul Arifin, Deris Stiawan, Bhakti Yudho Suprapto, Susanto Susanto, Tasmi Salim, Mohd Yazid Idris, Rahmat Budiarto

“…Gradient boosting, decision tree, and random forest algorithms are used as classifiers for the intrusion detection system models. …”
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Advancing coal fire detection model for large-scale areas based on RS indices and machine learning by Jinglong Liu, Feng Zhao, Yunjia Wang, Yanan Wang, Sen Du, Libo Dang, Jordi J. Mallorqui

“…CFDM outperformed other ML algorithms, achieving Recall, Precision, F1-score, and Kappa coefficient values of 0.89, 0.94, 0.93, and 0.92, respectively. …”
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Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD by Miao-Lin Lei, Guan-Wei Bi, Xiao-Lin Yin, Xiao-Lin Yin, Yue Wang, Yue Wang, Yue Wang, Zi-Ru Sun, Zi-Ru Sun, Zi-Ru Sun, Xin-rui Guo, Hui-peng Zhang, Xiao-han Zhao, Feng Li, Feng Li, Yan-Bo Yu

“…Diagnostic models were constructed using six machine learning algorithms, and their performance was evaluated by cross-validated area under the receiver operating characteristic curve (AUC). …”
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Identification of key programmed cell death genes for predicting prognosis and treatment sensitivity in colorectal cancer by Jian-ying Ma, Jian-ying Ma, Yi-xian Wang, Zhen-yu Zhao, Zhen-yu Xiong, Zi-long Zhang, Jun Cai, Jia-wei Guo

“…Unsupervised clustering algorithms were used to identify two molecular subtypes of CRC with distinct features. …”
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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images by Yuhang Zhang, Wuxia Zhang, Songtao Ding, Siyuan Wu, Xiaoqiang Lu

“…However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multilevel features, leading to challenges in extracting LCLU features in complex scenes. …”
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The Milky Way Radial Metallicity Gradient as an Equilibrium Phenomenon: Why Old Stars Are Metal Rich by James W. Johnson, David H. Weinberg, Guillermo A. Blanc, Ana Bonaca, Gwen C. Rudie, Yuxi (Lucy) Lu, Bronwyn Reichardt Chu, Emily J. Griffith, Tawny Sit, Jennifer A. Johnson, Liam O. Dubay, Miqaela K. Weller, Daniel A. Boyea, Jonathan C. Bird

“…We quantify the evolution of the metallicity gradient in the Milky Way as traced by APOGEE red giants with age estimates from machine learning algorithms. Stars up to ages of ∼9 Gyr follow a similar relation between metallicity and Galactocentric radius. …”
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Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images by Lili Zhang, Zihan Jin, Yibo Wang, Ziyi Wang, Zeyu Duan, Taoran Qi, Rui Shi

“…Hence, we proposed an innovative, non-destructive infrared inspection method via constructed dataset and proposed deep learning algorithms. We first modeled the surface temperature field variation of concrete dams as a one-dimensional, non-stationary partial differential equation with Robin boundary. …”
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Mechanical Ventilation Strategies in Buildings: A Comprehensive Review of Climate Management, Indoor Air Quality, and Energy Efficiency by Farhan Lafta Rashid, Mudhar A. Al-Obaidi, Najah M. L. Al Maimuri, Arman Ameen, Ephraim Bonah Agyekum, Atef Chibani, Mohamed Kezzar

“…Smart, personalized ventilation strategies supported by modern control algorithms and continuous monitoring are essential for the development of resilient and health-promoting buildings. …”
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Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy by Young Kwang Chae, Emma Yu, Na Hyun Kim, Min Jeong Kim, Leeseul Kim, Hyung-Gyo Cho, Yeonggyeong Park, Yoonhee Choi, Seung Pyo Daniel Hong

“…Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). …”
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Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis by Ana Rocha, Cristina Costeira, Raul Barbosa, Florbela Gonçalves, Miguel Castelo-Branco, Joaquim Viana, Margarida Gaudêncio, Filipa Ventura

“…Statistical analyses were performed using SPSS and machine learning tools, specifically KMeans clustering and Random Forest algorithms. Results Six protective patterns against burnout were identified, characterized by conditions of permanent contracts, work-life balance, and supportive work environments. …”
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Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic... by Peng Lyu, Na Xie, Xu-peng Shao, Shuai Xing, Xiao-yue Wang, Li-yun Duan, Xue Zhao, Jia-min Lu, Rong-fei Liu, Duo Zhang, Wei Lu, Kai-liang Fan

“…Thereafter, the genes were identified utilizing machine-learning algorithms. The receiver operating characteristic curve was employed to assess the discrimination and effectiveness of the hub genes. …”
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Context guided transformer enhanced YOLOv8 for accurate juvenile abalone detection and counting by Dapeng Cheng, Ji Ruan, Xinhao Li, Feng Zhao, Shoudu Zhang, Guofan Zhang, Fucun Wu

“…However, due to the small size, dense distribution, and frequent occlusion among individuals during the rearing period, existing detection algorithms often demonstrate low precision in identifying abalones. …”
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Integrating Low-Altitude Remote Sensing and Variable-Rate Sprayer Systems for Enhanced Cassava Crop Management by Pongpith Tuenpusa, Grianggai Samseemoung, Peeyush Soni, Thirapong Kuankhamnuan, Waraphan Sarasureeporn, Warinthon Poonsri, Apirat Pinthong

“…The image processing algorithms and calibration methods used were deemed acceptable, with drones equipped with variable-rate sprayer systems outperforming RC helicopters in overall quality. …”
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A Real-Time Signal Measurement System Using FPGA-Based Deep Learning Accelerators and Microwave Photonic by Longlong Zhang, Tong Zhou, Jie Yang, Yin Li, Zhiwen Zhang, Xiang Hu, Yuanxi Peng

“…Firstly, by introducing the idea of automated framework, we propose a simplest deep neural network (DNN)-based hardware structure, which automatically maps algorithms to hardware modules, supports configurable parameters, and has the advantage of low latency, with an average inference time of only 3.5 μs. …”
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Markarian Multiwavelength Data Center (MMDC): A Tool for Retrieving and Modeling Multitemporal, Multiwavelength, and Multimessenger Data from Blazar Observations by N. Sahakyan, V. Vardanyan, P. Giommi, D. Bégué, D. Israyelyan, G. Harutyunyan, M. Manvelyan, M. Khachatryan, H. Dereli-Bégué, S. Gasparyan

“…Another important distinguishing feature of MMDC is its ability to enable precise, self-consistent theoretical modeling of the observed data using machine learning algorithms trained on leptonic and lepto-hadronic models, which consider the injection of particles and all relevant cooling processes. …”
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Prediction on rock strength by mineral composition from machine learning of ECS logs by Dongwen Li, Xinlong Li, Li Liu, Wenhao He, Yongxin Li, Shuowen Li, Huaizhong Shi, Gaojian Fan

“…This study proposes the use of Random Forest and Transformer algorithms to predict rock strength from Elemental Capture Spectroscopy (ECS) logs. …”
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Definition, management, and training in impacted fetal head at cesarean birth: a national survey of maternity professionals by Katie Cornthwaite, Pauline Hewitt, Jan W. van derScheer, Imogen A. F. Brown, Jenni Burt, Eliane Dufresne, Mary Dixon‐Woods, Tim Draycott, ABC Contributor Group, Thiscovery Authorship Group, Rachna Bahl

“…These findings can inform a program of work to improve care, including use of structured management algorithms and simulation‐based multi‐professional training.…”
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Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retro... by Li Zhang, Ling He, Guangli Zhang, Xiaoyin Tian, Haoru Wang, Fang Wang, Xin Chen, Yinglan Zheng, Man Li, Yang Li, Zhengxiu Luo

“…Combined models based on radiomic and clinical features were established via logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC). …”
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