Geological hazard susceptibility assessment under land use change: a case study of Dongchuan District, Kunming, Yunnan, China by Mengling Zhang, Ping Duan, Cheng Huang, Shunxing Peng, Shasha Feng, Kang Zhao, Jia Li

“…Land use types were extracted using the Support Vector Machine (SVM) method. Geological hazard susceptibility was assessed using machine learning, and impacts were comprehensively analyzed. …”
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Towards EEG-based Emotion Recognition During Video Viewing by Christoforos Christoforou, Maria Theodorou

“…Our results provide novel empirical evidence that the neural components extracted by our method can serve as an informative metric in EEG-based emotion recognition during video viewing and achieving a 4-fold increase in predictive power compared to traditional frequency-based metrics. …”
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Improving protection reliability of series‐compensated transmission lines by a fault detection method through an ML‐based model by Hossein Ebrahimi, Sajjad Golshannavaz, Amin Yazdaninejadi, Edris Pouresmaeil

“…Moreover, to make the proposed method harmonics‐robust and improve the correlation interpretation between the features for the Bi‐LSTM model, the 3‐phase raw measurement signals are passed through a discrete Fourier transform (DFT) which extracts their fundamental frequency component magnitudes and angles. …”
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An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form by Zaher Salah, Hamza Abu Owida, Esraa Abu Elsoud, Esraa Alhenawi, Suhaila Abuowaida, Nawaf Alshdaifat

“…Our empirical experiments demonstrates that using ensemble learning to merge attributes in the evolution of phishing emails showcases the competitive performance of ensemble learning over other machine learning algorithms. This superiority is underscored by achieving an F1-score of 0.90 in the weighted ensemble method and 0.85 in the soft voting method, showcasing the effectiveness of this approach.…”
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A Novel High-Precision Workpiece Self-Positioning Method for Improving the Convergence Ratio of Optical Components in Magnetorheological Finishing by Yiang Zhang, Pengxiang Wang, Chaoliang Guan, Meng Liu, Xiaoqiang Peng, Hao Hu

“…Further, based on these thresholds, a hybrid self-positioning method combining machine vision and a probing module is proposed. …”
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Norm-Based Outlier Filtering and Consensus Aggregation for Robust Federated Learning by Hasung Yeo, Joon-Woo Lee

“…Federated Learning (FL) enables collaborative training of machine learning models across distributed clients without sharing private data, making it ideal for privacy-sensitive applications. …”
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A global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology based on deep-learning methods by Y. Wu, J. Liu, J. Liu, Y. Zhu, Y. Zhu, Y. Zhu, Y. Zhang, Y. Cao, Y. Cao, K.-E. Huang, B. Zheng, Y. Wang, Y. Li, Q. Wang, C. Zhou, Y. Liang, J. Sun, M. Wang, M. Wang, D. Rosenfeld, D. Rosenfeld

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Comparative study of state-based neural networks for virtual analog audio effects modeling by Riccardo Simionato, Stefano Fasciani

“…In this article, we explore the application of recent machine learning advancements for virtual analog modeling. …”
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A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers by Yadong Yao, Jixuan Yan, Guang Li, Weiwei Ma, Xiangdong Yao, Miao Song, Qiang Li, Jie Li

“…To address these challenges, this study proposes a multi-factor SMC inversion method. Six GNSS stations from the Plate Boundary Observatory (PBO) were selected as study sites. …”
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Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia: further analysis of the 2011 and 2016 Ethiopian Demographic and... by Mequannent Sharew Melaku, Lamrot Yohannes, Eliyas Addisu Taye, Nebebe Demis Baykemagn

“…Therefore, this study aimed to predict khat chewing practices and their determinant factors among men aged 15 to 59 years in Ethiopia using a machine learning algorithm.MethodsThis study used data from the 2011 and 2016 Ethiopian Demographic and Health Surveys (EDHS). …”
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Highly Sensitive Glucose Sensors Based on Gated Graphene Microwave Waveguides by Patrik Gubeljak, Tianhui Xu, Jan Wlodarczyk, William Eustace, Oliver J. Burton, Stephan Hofmann, George G. Malliaras, Antonio Lombardo

“…A sensitivity of 7.30 dB(mg/L)−1 is achieved, significantly higher than metallic state‐of‐the‐art RF sensors. Different machine learning methods are applied to the raw, multidimensional datasets to infer concentrations of the analyte, without the need for parasitic effect removals via de‐embedding or circuit modeling, and a classification accuracy of 100% is achieved for aqueous glucose solutions with a concentration variation of 0.09 mgL−1.…”
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Evaluating Supervised Learning Classifier Performance for OFDM Communication in AWGN-Impacted Systems by Lavanya Vaishnavi D A, Anil Kumar C

“…Hence the present research work is an attempt to recommend a Machine Learning (ML) methodology for the performance analysis of multiplexed communication channel under Orthogonal frequency division technique. …”
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Classification of Individuals With COVID-19 and Post–COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near–Real-Time Monitoring C... by Carlos Alberto Sanches, Andre Felipe Henriques Librantz, Luciana Maria Malosá Sampaio, Peterson Adriano Belan

“…A secondary objective was to assess the feasibility of a near–real-time health monitoring system based on these patterns using wearable-derived HRV data. MethodsHRV indexes (SD of the normal-to-normal intervals [SDNN], root mean square of successive differences [RMSSD], low-frequency relative power [LF%], and high-frequency relative power [HF%]) were collected from 61 participants (n=21, 34% with active COVID-19; n=20, 33% with post–COVID-19 condition; and n=20, 33% healthy controls) using 2 standardized datasets. …”
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Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level by Chiara Romano, Emanuele Maiorana, Annunziata Nusca, Simone Circhetta, Sergio Silvestri, Schena Emiliano, Gian Paolo Ussia, Carlo Massaroni

“…Signal frames underwent feature extraction in frequency and time-frequency domains. Then, binary classification was performed through three machine learning and three deep learning methods, considering SCG, GCG, and their combination. …”
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Investigating the role of environmental factors in the French highly pathogenic avian influenza epizootic in 2022–2023 by Maryem Ben Salem, Mathieu Andraud, Stéphanie Bougeard, Virginie Allain, Morgane Salines, Rodolphe Thomas, Audrey Schmitz, Legrand Saint-Cyr, Karine Fiore, Sophie Le Bouquin, Axelle Scoizec

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Ceasing sampling at wastewater treatment plants where viral dynamics are most predictableMendeley Data by Mo Liu, Devan G. Becker

“…We apply machine learning methods to predict the mutation frequencies from wastewater sites on the next day in one location based on the frequencies on previous days in other locations, then record the prediction error. …”
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Whole‑exome evolutionary profiling of osteosarcoma uncovers metastasis‑related driver mutations and generates an independently validated predictive classifier by Zhen Wang, Zhe Wang, Ruoyu Wang, Zumin Wang, Xiangyang Cao, Rui Chen, Zebing Ma, Shanshan Liang, Shuai Tao

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TET2 gene mutation status associated with poor prognosis of transition zone prostate cancer: a retrospective cohort study based on whole exome sequencing and machine learning model... by Yutong Wang, Yutong Wang, Yutong Wang, Yutong Wang, Yutong Wang, Ailing Yu, Ailing Yu, Ailing Yu, Ailing Yu, Ailing Yu, Ziping Gao, Ziping Gao, Ziping Gao, Ziping Gao, Xiaoying Yuan, Xiaochen Du, Peng Shi, Haoyun Guan, Shuang Wen, Honglong Wang, Liang Wang, Liang Wang, Liang Wang, Liang Wang, Bo Fan, Bo Fan, Bo Fan, Bo Fan, Zhiyu Liu, Zhiyu Liu, Zhiyu Liu, Zhiyu Liu

“…BackgroundProstate cancer (PCa) in the transition zone (TZ) is uncommon and often poses challenges for early diagnosis, but its genomic determinants and therapeutic vulnerabilities remain poorly characterized.MethodsTumor mutational landscape was characterized in nine patients with TZ PCa, identifying somatic variants through whole-exome sequencing (WES). …”
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Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach by Shu Xu, Tao Wang, Zenghui Ding, Yu Wang, Tongsheng Wan, Dezhang Xu, Xianjun Yang, Ting Sun, Meng Li

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Altered static and dynamic spontaneous brain activity in patients with dysthyroid optic neuropathy: a resting-state fMRI study by Jinling Lu, Hao Hu, Jiang Zhou, Wenhao Jiang, Xiongying Pu, Huanhuan Chen, Xiaoquan Xu, Feiyun Wu

“…PurposeTo investigate static and dynamic brain functional alterations in dysthyroid optic neuropathy (DON) using resting-state functional MRI (rs-fMRI) with the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo).Materials and methodsFifty-seven thyroid-associated ophthalmopathy (TAO) patients (23 DON and 34 non-DON) and 27 healthy controls (HCs) underwent rs-fMRI scans. …”
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