Application of deep learning and feature selection technique on external root resorption identification on CBCT images by Nor Hidayah Reduwan, Azwatee Abdul Aziz, Roziana Mohd Razi, Erma Rahayu Mohd Faizal Abdullah, Seyed Matin Mazloom Nezhad, Meghna Gohain, Norliza Ibrahim

“…The performance of four DLMs including Random Forest (RF) + Visual Geometry Group 16 (VGG), RF + EfficienNetB4 (EFNET), Support Vector Machine (SVM) + VGG, and SVM + EFNET) and four hybrid models (DLM + FST: (i) FS + RF + VGG, (ii) FS + RF + EFNET, (iii) FS + SVM + VGG and (iv) FS + SVM + EFNET) was compared. …”
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Real-Time Plant Health Detection Using Deep Convolutional Neural Networks by Mahnoor Khalid, Muhammad Shahzad Sarfraz, Uzair Iqbal, Muhammad Umar Aftab, Gniewko Niedbała, Hafiz Tayyab Rauf

“…In the twenty-first century, machine learning is a significant part of daily life for everyone. …”
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Skin Microbiota: Mediator of Interactions Between Metabolic Disorders and Cutaneous Health and Disease by Magdalini Kreouzi, Nikolaos Theodorakis, Maria Nikolaou, Georgios Feretzakis, Athanasios Anastasiou, Konstantinos Kalodanis, Aikaterini Sakagianni

“…For example, elevated butyrate levels in psoriasis have been associated with reduced Th17-mediated inflammation, while the presence of specific Lactobacillus strains has shown potential to modulate immune tolerance in atopic dermatitis. Furthermore, machine learning models are increasingly used to integrate multi-omics data, enabling personalized interventions. …”
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Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine by Yi Guo, Hua Xu, Fei Wang, Jie Xu, Jiang Bian, Robert Lucero, Mattia Prosperi

“…In stage 3, a computer-based, real-time Delphi survey will be performed to consolidate the PRECOG checklist, involving experts in causal inference, epidemiology, statistics, machine learning, informatics and protocols/standards. …”
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Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong Expressway by Yifan Cao, Zhifang Zhao, Mingchun Wen, Xin Zhao, Dingyi Zhou, Jingyi Qin, Liu Ouyang, Jingyao Cao

“…In combination with optical imagery data, a total of 521 landslide disaster points were identified. (2) In comparison to individual machine learning models, the Stacking demonstrated superior performance, with prediction capabilities and accuracy that surpassed other models. …”
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3D-augmentation of 2D ultrasound for appendicitis diagnosis: A cross-sectional pilot study by Rebecca G. Theophanous, Elias Jaffa, Matthew R. Morgan, Carl D. Herickhoff, Erica Peethumnongsin, Joao Ricardo Nickenig Vissoci, Joshua S. Broder

“…An emergency physician captured 3DUS images using a Sonosite M-Turbo machine equipped with an inertial measurement unit and customized software. …”
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Proteomic, metabolomic and lipidomic profiles in community acquired pneumonia for differentiating viral and bacterial infections by Samuel Rischke, Robert Gurke, Ann-Sophie Zielbauer, Nicole Ziegler, Lisa Hahnefeld, Michaela Köhm, Aimo Kannt, Maria JGT Vehreschild, Gerd Geisslinger, Gernot Rohde, Carla Bellinghausen, Frank Behrens, CAPNETZ Study group

“…Statistical methods, machine learning and pathway analyses were conducted investigating differences between the infection types. …”
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Caries affected Dentin biomodification using Bromelain, Riboflavin photosensitizer via UVA, and Chitosan Nanoparticles on shear bond strength and microleakage scores: A SEM evaluat... by Khold Alahdal, Ahmed Maawadh, Laila AlDeeb, Thamer Almohareb, Ahoud S Alshamrani, Ali Alrahlah

“…For SBS and failure pattern analysis, a universal testing machine and stereomicroscope were used respectively. …”
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NeuTox 2.0: A hybrid deep learning architecture for screening potential neurotoxicity of chemicals based on multimodal feature fusion by Xudi Pang, Xuejun He, Ying Yang, Ling Wang, Yuzhen Sun, Huiming Cao, Yong Liang

“…Evaluations of anti-noise ability indicated that NeuTox 2.0 has excellent noise resistance relative to traditional machine learning. We applied the NeuTox 2.0 model to predict the neurotoxicity of 315,790 compounds in the REACH database. …”
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Differed brain spontaneous neural activity between limb-onset and bulbar-onset amyotrophic lateral sclerosis patients by Si-Jie Chen, Qing-Yang Li, Jiang Zhou, Qian Wu, Yu Zhang, Qian-Qian Zhang, Hao Hu, Xiao-Quan Xu, Fei-Yun Wu, Qi Niu

“…Correlation analyses with clinical measures were conducted. Support vector machine (SVM) analysis was performed to distinguish ALS subtypes. …”
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Synthetic Data Generation and Evaluation Techniques for Classifiers in Data Starved Medical Applications by Wan D. Bae, Shayma Alkobaisi, Matthew Horak, Siddheshwari Bankar, Sartaj Bhuvaji, Sungroul Kim, Choon-Sik Park

“…With their ability to find solutions among complex relationships of variables, machine learning (ML) techniques are becoming more applicable to various fields, including health risk prediction. …”
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Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence Plots by Fei Tan, Xiuwen Gao, Hao Cang, Nianyi Wu, Ruoyu Di, Jingkun Yan, Chengkai Li, Pan Gao, Xin Lv

“…This study proposes an early detection method for cotton wilt disease using hyperspectral imaging and recurrence plots (RP) combined with machine learning techniques. First, spectral curves were collected and analyzed under three conditions of cotton plants: healthy, asymptomatic, and symptomatic. …”
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Advancing soil-structure interaction (SSI): a comprehensive review of current practices, challenges, and future directions by Imtiyaz Akbar Najar, Raudhah Ahmadi, Akeem Gbenga Amuda, Raghad Mourad, Neveen El Bendary, Idawati Ismail, Nabilah Abu Bakar, Shanshan Tang

“…Additionally, the review discusses recent innovations, including the application of machine learning and advanced computational tools, and their potential to enhance the accuracy and efficiency of SSI analysis. …”
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Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity by Caden P. Chamberlain, Garrett W. Meigs, Derek J. Churchill, Jonathan T. Kane, Astrid Sanna, James S. Begley, Susan J. Prichard, Maureen C. Kennedy, Craig Bienz, Ryan D. Haugo, Annie C. Smith, Van R. Kane, C. Alina Cansler

“…Our framework used (1) machine learning to identify key bioclimatic, topographic, and fire weather drivers of burn severity in each fire, (2) standardized workflows to statistically sample untreated control units, and (3) spatial regression modeling to evaluate the effects of treatment type and time since treatment on burn severity. …”
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Mental health phenotypes of well-controlled HIV in Uganda by Leah H. Rubin, Leah H. Rubin, Leah H. Rubin, Leah H. Rubin, Kyu Cho, Jacob Bolzenius, Julie Mannarino, Rebecca E. Easter, Raha M. Dastgheyb, Aggrey Anok, Stephen Tomusange, Deanna Saylor, Maria J. Wawer, Noeline Nakasujja, Gertrude Nakigozi, Robert Paul

“…We leverage the analytic strengths of machine learning combined with inferential methods to identify novel MH phenotypes among PWH and the underlying explanatory features.MethodsA total of 277 PWH (46% female, median age = 44; 93% virally suppressed [<50copies/mL]) were included in the analyses. …”
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Prenatal exposure to polycyclic aromatic hydrocarbons and blood pressure in the early life of children by Xiaodong Chen, Lingling Dong, Lina Yang, Yan Yang, Liyu Yang, Sijia Han

“…Additionally, we applied quantile g-computation (g-comp) and Bayesian kernel machine regression (BKMR) to assess the combined and interaction effects of multiple PAH metabolites. …”
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Bridging animal models and humans: neuroimaging as intermediate phenotypes linking genetic or stress factors to anhedonia by Huiling Guo, Yao Xiao, Shuai Dong, Jingyu Yang, Pengfei Zhao, Tongtong Zhao, Aoling Cai, Lili Tang, Juan Liu, Hui Wang, Ruifang Hua, Rongxun Liu, Yange Wei, Dandan Sun, Zhongchun Liu, Mingrui Xia, Yong He, Yankun Wu, Tianmei Si, Fay Y. Womer, Fuqiang Xu, Yanqing Tang, Jie Wang, Weixiong Zhang, Xizhe Zhang, Fei Wang

“…Using the amplitude of low-frequency fluctuations, we identified neuroimaging patterns in rodent models. We then applied a machine-learning approach to cluster neuroimaging subtypes of depression. …”
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Current update on surgical management for spinal tuberculosis: a scientific mapping of worldwide publications by Romaniyanto Romaniyanto, Muhana Fawwazy Ilyas, Aldebaran Lado, Daffa Sadewa, Daffa Sadewa, Dykall Naf'an Dzikri, Enrico Ananda Budiono, Enrico Ananda Budiono

“…The recent phase reflects a shift towards technology-driven approaches, including minimally invasive techniques, artificial intelligence, and machine learning. China emerged as the leading country with the most contributions based on author, affiliations, funding sponsors, and countries. …”
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A novel framework to predict ADHD symptoms using irritability in adolescents and young adults with and without ADHD by Saeedeh Komijani, Dipak Ghosal, Manpreet K. Singh, Julie B. Schweitzer, Julie B. Schweitzer, Prerona Mukherjee, Prerona Mukherjee

“…We utilized a hierarchical clustering technique to mitigate these collinearity issues and implemented a non-parametric machine learning (ML) model to predict the significance of symptom relations over time. …”
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Information Security and Artificial Intelligence–Assisted Diagnosis in an Internet of Medical Thing System (IoMTS) by Pi-Yun Chen, Yu-Cheng Cheng, Zi-Heng Zhong, Feng-Zhou Zhang, Neng-Sheng Pai, Chien-Ming Li, Chia-Hung Lin

“…For a symmetric cryptography scheme, this study proposed a key generator combining a chaotic map and Bell inequality and generating unordered numbers and unrepeated 256 secret keys in the key space. Then, a machine learning - based model was employed to train the encryptor and decryptor for both biosignals and image infosecurity. …”
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