Multi-scenario Dynamic Simulation and Optimization of Urban Ventilation Environment: A Case Study of Taiyuan Metropolitan Area by Junda HUANG, Yuncai WANG

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Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods by Minghui Ma, Oguzhan Pektezel, Vincenzo Ballerini, Paolo Valdiserri, Eugenia Rossi di Schio

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Artificial intelligence in personalized cardiology treatment by Abbas Mohammadi, Sheida Shokohyar

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A review of recent artificial intelligence for traditional medicine by Chengbin Hou, Yanzhuo Gao, Xinyu Lin, Jinchao Wu, Ning Li, Hairong Lv, William Cheng-Chung Chu

“…Artificial Intelligence (AI) has emerged as a revolutionary technology, offering exceptional capabilities in areas such as data mining, pattern recognition, and decision-making. The integration of Artificial Intelligence for Traditional Medicine (AITM) presents a promising frontier in advancing medicine and healthcare. …”
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THE CURRENT STATE OF ARTIFICIAL INTELLIGENCE IN RADIOLOGY – A REVIEW OF THE BASIC CONCEPTS, APPLICATIONS, AND CHALLENGES by Mariana Yordanova

“…Results and Discussion: Machine learning in radiology focuses on developing algorithms that analyze medical images without explicitly programmed rules, divided into supervised and unsupervised learning. …”
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Development and validation of an interpretable multi-task model to predict outcomes in patients with rhabdomyolysis: a multicenter retrospective cohort studyResearch in context by Chunli Liu, Jie Shi, Fengjuan Wang, Duo Li, Yu Luo, Bofan Yang, Yunlong Zhao, Li Zhang, Dingwei Yang, Heng Jin, Jie Song, Xiaoqin Guo, Haojun Fan, Qi Lv

“…Twenty-two clinical features available within the first 24 h of admission were selected to develop the prediction models. Ten machine learning (ML) algorithms were applied to construct multi-task prediction models. …”
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Causal estimation of the relationship between reproductive performance and the fecal bacteriome in cattle by Yutaka Taguchi, Haruki Yamano, Yudai Inabu, Hirokuni Miyamoto, Koki Hayasaki, Noriyuki Maeda, Yoshiro Kanmera, Seiji Yamasaki, Noboru Ota, Kenji Mukawa, Atsushi Kurotani, Shigeharu Moriya, Teruno Nakaguma, Chitose Ishii, Makiko Matsuura, Tetsuji Etoh, Yuji Shiotsuka, Ryoichi Fujino, Motoaki Udagawa, Satoshi Wada, Jun Kikuchi, Hiroshi Ohno, Hideyuki Takahashi

“…Subsequently, correlation analysis evaluated the interrelationships between bacteriomes, which demonstrated that the patterns exhibited distinct characteristics. Therefore, four machine-learning algorithms were employed to identify the distinctive factors between the two groups. …”
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Functional Connectivity Changes in Primary Motor Cortex Subregions of Patients With Obstructive Sleep Apnea by Lifeng Li, Qimeng Shi, Bowen Fang, Yuting Liu, Xiang Liu, Yongqiang Shu, Yingke Deng, Yumeng Liu, Haijun Li, Junjie Zhou, Dechang Peng

“…Additionally, we employed three machine learning algorithms—support vector machine (SVM), random forest (RF), and logistic regression (LR)—to distinguish patients with OSA from HC based on FC features. …”
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Multi-omics derivation of a core gene signature for predicting therapeutic response and characterizing immune dysregulation in inflammatory bowel disease by Mingming Wang, Liping Liang, Zibo Tang, Jimin Han, Lele Wu, Le Liu, Le Liu, Ye Chen, Ye Chen

“…Current precision medicine approaches lack robust molecular tools integrating transcriptomic signatures with immune dynamics for personalized treatment guidance.MethodsWe performed multi-omics analyses of GEO datasets using machine learning algorithms (LASSO/Random Forest) to derive a four-gene signature. …”
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Gene expression-based modeling of overall survival in Black or African American patients with lung adenocarcinoma by Bin Zhu, Stephanie S. McHale, Michelle Van Scoyk, Gregory Riddick, Pei-Ying Wu, Chu-Fang Chou, Ching-Yi Chen, Robert A. Winn

“…Notably, a potential B/AA-specific biomarker, C9orf64, demonstrated significant correlations with genes involved in immune response. Unsupervised machine learning algorithms stratified B/AA patients into groups with distinct survival outcomes, while supervised algorithms demonstrated a higher accuracy in predicting survival for B/AA LUAD patients compared to white patients.DiscussionIn total, this study explored OS-associated genes and pathways specific for B/AA LUAD patients. …”
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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…Background and Objective: In recent years, DNA methylation-tumor classification based on artificial intelligence algorithms has led to a notable improvement in diagnostic accuracy compared to traditional machine learning methods. …”
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Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The study focuses on the optimization of predictive maintenance as a service on the industrial Internet of Things by machine learning algorithms. The main contribution of the study is the use of optimization techniques for feature selection and RNN-LSTM for improved accuracy.   …”
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A Robust Framework for Bamboo Forest AGB Estimation by Integrating Geostatistical Prediction and Ensemble Learning by Lianjin Fu, Qingtai Shu, Cuifen Xia, Zeyu Li, Hailing He, Zhengying Li, Shaoyang Ma, Chaoguan Qin, Rong Wei, Qin Xiang, Xiao Zhang, Yiran Zhang, Huashi Cai

“…Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. …”
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Abnormal heart sound recognition using SVM and LSTM models in real-time mode by Moy’awiah A. Al-Shannaq, Areen Nasrawi, Abed Al-Raouf K. Bsoul, Ahmad A. Saifan

“…Digital signal processing methods, by applying the fast Fourier transform, filtering techniques, and the dual-tree complex wavelet transform, with machine learning classification algorithms are employed to segment the input phonocardiogram signal, extract meaningful features, and find the appropriate class for the input signal. …”
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Identification of the immune infiltration and biomarkers in ulcerative colitis based on liquid–liquid phase separation-related genes by Zhixing Hong, Shilin Fang, Haihang Nie, Jingkai Zhou, Yuntian Hong, Lan Liu, Qiu Zhao

“…We identified the hub LLPS-RGs (DE-LLPS-RGs) (HSPB3, SLC16A1, TRIM22, SRI, PLEKHG6, GBP1, PADI2) by machine learning algorithms. Hub genes were screened that displayed high prediction accuracy of UC patients. …”
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Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions by Marios Spanakis, Eleftheria Tzamali, Georgios Tzedakis, Chryssalenia Koumpouzi, Matthew Pediaditis, Aristides Tsatsakis, Vangelis Sakkalis

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Identification of atrial fibrillation using heart rate variability: a meta-analysis by Ziwei Yin, Changxin Liu, Chenggong Xie, Zixing Nie, Jiaming Wei, Wen Zhang, Hao Liang

“…Recently, artificial intelligence (AI) algorithms have leveraged heart rate variability (HRV) patterns to enhance the accuracy of AF identification.MethodsWe conducted a systematic review of the literature by searching four major biomedical databases—PubMed, Web of Science, Embase, and Cochrane Library—spanning from their inception to December 13, 2024, following the PRISMA guidelines. …”
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The Performance of an ML-Based Weigh-in-Motion System in the Context of a Network Arch Bridge Structural Specificity by Dawid Piotrowski, Marcin Jasiński, Artur Nowoświat, Piotr Łaziński, Stefan Pradelok

“…In civil and bridge engineering, they can facilitate the identification of specific patterns through the analysis of data acquired from structural health monitoring (SHM) systems. …”
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Integration of multi-omics profiling reveals an epigenetic-based molecular classification of lung adenocarcinoma: implications for drug sensitivity and immunotherapy response predi... by Ning Wang, Yinan Li, Yaoyao Wang, Wenting Wang

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Survey on Backdoor Attacks on Deep Learning: Current Trends, Categorization, Applications, Research Challenges, and Future Prospects by Muhammad Abdullah Hanif, Nandish Chattopadhyay, Bassem Ouni, Muhammad Shafique

“…Deep Neural Networks (DNNs) have emerged as a prominent set of algorithms for complex real-world applications. However, state-of-the-art DNNs require a significant amount of data and computational resources to train and generalize well for real-world scenarios. …”
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