AI Under Attack: Metric-Driven Analysis of Cybersecurity Threats in Deep Learning Models for Healthcare Applications by Sarfraz Brohi, Qurat-ul-ain Mastoi

“…However, it has also revealed critical cybersecurity vulnerabilities in Deep Learning (DL) models, which raise significant risks to patient safety and their trust in AI-driven applications. …”
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Accelerated discovery of high-density pyrazole-based energetic materials using machine learning and density functional theory by Muhammad Tukur Ibrahim, Muktar Musa Ibrahim, Adamu Uzairu, Gideon Adamu Shallangwa, Sani Uba

“…The performance of four machine learning algorithms including: multilinear regression, artificial neural network, support vector machines, and random forest algorithms were evaluated. …”
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Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction by Xin Huang, Xin Huang, Di Ouyang, Weiming Xie, Huawei Zhuang, Siyu Gao, Pan Liu, Lizhong Guo

“…Nine machine learning algorithms (Decision Tree, Gradient Boosting Machine, K-Nearest Neighbors, Linear Discriminant Analysis, Logistic Regression, Multilayer Perceptron, Naive Bayes, Random Forest, and Support Vector Machine) were combined with selected features, generating 45 unique model combinations. …”
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Machine Learning Modeling of Foam Concrete Performance: Predicting Mechanical Strength and Thermal Conductivity from Material Compositions by Leifa Li, Wangwen Sun, Askar Ayti, Wangping Chen, Zhuangzhuang Liu, Lauren Y. Gómez-Zamorano

“…For thermal conductivity, support vector regression achieved the best predictive performance with R² = 0.933. …”
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Predicting the Toxicity of Drug Molecules with Selecting Effective Descriptors Using a Binary Ant Colony Optimization (BACO) Feature Selection Approach by Yuanyuan Dan, Junhao Ruan, Zhenghua Zhu, Hualong Yu

“…Predicting the toxicity of drug molecules using in silico quantitative structure–activity relationship (QSAR) approaches is very helpful for guiding safe drug development and accelerating the drug development procedure. …”
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Prediction and Mapping of Soil Total Nitrogen Using GF-5 Image Based on Machine Learning Optimization Modeling by LIU Liqi, WEI Guangyuan, ZHOU Ping

“…[Conclusions]This study demonstrates the clear feasibility of using GF-5 satellite hyperspectral remote sensing data and machine learning algorithm for large-scale quantitative detection and visualization analysis of soil TN content. …”
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Cross-Context Stress Detection: Evaluating Machine Learning Models on Heterogeneous Stress Scenarios Using EEG Signals by Omneya Attallah, Mona Mamdouh, Ahmad Al-Kabbany

“…Although numerous studies have investigated stress detection through machine learning (ML) techniques, there has been limited research on assessing ML models trained in one context and utilized in another. …”
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Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus by Hongwei Zhou, Xiaoqing Li, Yanyu Zhang, Feng Wei, Zhiyu Liu, Yan Zhao, Xubo Zhuang, Xia Liu, Haizhou Zhou

“…This study explores critical oxidative stress (OS) features and their interrelationships in SLE pathogenesis.MethodsThree transcriptomic datasets from the Gene Expression Omnibus (GEO) were analyzed to identify SLE- and OS-associated pathways via Gene Set Variation Analysis (GSVA). Multiple machine learning methods—including deep learning (DL), random forest (RF), XGBoost, support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO)—were deployed to build OS-related gene prediction frameworks. …”
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Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic rhinosinusitis with nasal polyps by Zhaonan Xu, Qing Hao, Bingrui Yan, Qiuying Li, Xuan Kan, Qin Wu, Hongtian Yi, Xianji Shen, Lingmei Qu, Peng Wang, Yanan Sun

“…The least absolute shrinkage and selection operator (LASSO) regression model and multivariate support vector machine recursive feature elimination (mSVM-RFE) were used to identify potential biomarkers, which were validated using the real time quantitative polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC). …”
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Identify the potential target of efferocytosis in knee osteoarthritis synovial tissue: a bioinformatics and machine learning-based study by Shangbo Niu, Shangbo Niu, Shangbo Niu, Mengmeng Li, Mengmeng Li, Mengmeng Li, Jinling Wang, Jinling Wang, Jinling Wang, Peirui Zhong, Peirui Zhong, Peirui Zhong, Xing Wen, Xing Wen, Xing Wen, Fujin Huang, Fujin Huang, Fujin Huang, Linwei Yin, Linwei Yin, Linwei Yin, Yang Liao, Yang Liao, Yang Liao, Jun Zhou, Jun Zhou, Jun Zhou

“…Public datasets and quantitative real-time PCR experiments were employed for validation. …”
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Chemical space-informed machine learning models for rapid predictions of x-ray photoelectron spectra of organic molecules by Susmita Tripathy, Surajit Das, Shweta Jindal, Raghunathan Ramakrishnan

“…We explore transfer learning by utilizing the atomic environment feature vectors learned using a graph neural network framework in kernel-ridge regression. …”
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A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa) by Li-Tang Qin, Xue-Fang Tian, Jun-Yao Zhang, Yan-Peng Liang, Hong-Hu Zeng, Ling-Yun Mo

“…Quantitative structure–activity relationships (QSARs) have been used to predict mixture toxicity. …”
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A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic data by Xiufeng Zhang, Guoying Li, Yang Chen, Hao Wang, Haikuan Zhang, Haitao Li, Weisheng Du, Xiao Li, Xuewei Xu, Yuze He

“…Therefore, based on the quantitative study of the relationship between the performance of a deep learning prediction algorithm and a rockburst prediction vector, a rockburst risk and type prediction algorithm based on a convolutional neural network (CNN)-gated recurrent unit (GRU) model with prototype-based prediction is proposed. …”
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Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study by Min Yang, Huiqin Zhang, Minglan Yu, Yunxuan Xu, Bo Xiang, Xiaopeng Yao

“…Logistic regression (LR), support vector machine (SVM), random forest (RF) and eXtreme gradient boosting (XGBoost) algorithm models were used to construct risk prediction models in the training set, and the model performance was verified in the test set. …”
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Optimized Machine Learning-Augmented Hybrid Empirical Models for AlGaN/GaN HEMTs: A Comprehensive Analysis by Ahmad Khusro, Saddam Husain, Mohammad Hashmi

“…Thereafter, six extensively optimized ML regression models, namely decision tree (DT), ensemble learning (EL), support vector regression (SVR), kernel approximation regression (KAR), Gaussian process regression (GPR), and neural networks (NN) are employed to simulate the intrinsic behavior of GaN HEMTs. …”
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Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study by Kanika Godani, Vishma Prabhu, Priyanka Gandhi, Ayushi Choudhary, Shubham Darade, Rupal Kathare, Prathiba Hande, Ramesh Venkatesh

“…Data from electronic medical records and OCT scans were extracted, with OCT-derived qualitative and quantitative MH characteristics recorded. Six supervised ML models—ANCOVA, Random Forest (RF) regression, K-Nearest Neighbor, Support Vector Machine, Extreme Gradient Boosting, and Lasso regression—were trained using an 80:20 training-to-testing split. …”
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An explainable radiomics-based machine learning model for preoperative differentiation of parathyroid carcinoma and atypical tumors on ultrasound: a retrospective diagnostic study by Chunrui Liu, Wenxian Li, Baojie Wen, Haiyan Xue, Yidan Zhang, Shuping Wei, Jinxia Gong, Li Huang, Jian He, Jing Yao, Zhengyang Zhou

“…A radiomic signature derived from 544 quantitative ultrasound features was developed using three machine learning classifiers: Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). …”
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Durum Wheat (<i>Triticum durum</i> Desf.) Grain Yield and Protein Estimation by Multispectral UAV Monitoring and Machine Learning Under Mediterranean Conditions by Giuseppe Badagliacca, Gaetano Messina, Emilio Lo Presti, Giovanni Preiti, Salvatore Di Fazio, Michele Monti, Giuseppe Modica, Salvatore Praticò

“…Today, new techniques for monitoring fields using uncrewed aerial vehicles (UAVs) can detect crop multispectral (MS) responses, while advanced machine learning (ML) models can enable accurate predictions. …”
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Development of an interpretable machine learning model for frailty risk prediction in older adult care institutions: a mixed-methods, cross-sectional study in China by Weizi Wu, Li Jing, Qing Peng, Peng Hua, Zeng Shumei, Luofang Lv, Liqing Yue, Hu Jian zhong, Huang Weihong

“…A total of 586 older adults were included in the assessment data collection stage, and 15 participants (10 healthcare professionals and five data scientists) were involved in the model evaluation stage.Methods A collaborative requirements analysis involving healthcare professionals and data scientists guided the design of an interpretable frailty risk prediction model. Five machine learning models were developed and evaluated: logistic regression, support vector machines (SVM), random forest, extreme gradient boosting (XGBoost) and a multimodel ensemble approach. …”
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Machine-Learning-Based Integrated Mining Big Data and Multi-Dimensional Ore-Forming Prediction: A Case Study of Yanshan Iron Mine, Hebei, China by Yuhao Chen, Gongwen Wang, Nini Mou, Leilei Huang, Rong Mei, Mingyuan Zhang

“…Therefore, this study, based on artificial intelligence technology, focuses on geoscience big data mining and quantitative prediction, with the goal of achieving multi-scale, multi-dimensional, and multi-modal precise positioning of the Yanshan Iron Mine and establishing its intelligent mine technology system. …”
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