Shear Strength of Internal Reinforced Concrete Beam-Column Joints: Intelligent Modeling Approach and Sensitivity Analysis by De-Cheng Feng, Bo Fu

“…The proposed approach is established based on the famous boosting-family ensemble machine learning (ML) algorithms, i.e., gradient boosting regression tree (GBRT), which generates a strong predictive model by integrating several weak predictors, which are obtained by the well-known individual ML algorithms, e.g., DT, ANN, and SVM. …”
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Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies by Nazik Jebur, Wafa Soud

“…Various methodologies employed in these recent studies include vibration analysis, machine learning, deep learning, and both numerical and experimental simulations. …”
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Masked and Unmasked Face Recognition Model Using Deep Learning Techniques. A case of Black Race. by Mabirizi, Vicent, Ampaire, Ray Brooks, Muhoza, Gloria B.

“…The model was trained and tested on 1000 images taken from students of Kabale University using Nikon d850 camera. Machine learning techniques such as Principal Component Analysis, Geometric Feature Based Methods and double threshold techniques were used in the development phase while results were classified using CNN pre-trained models. …”
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Masked and unmasked Face Recognition Model Using Deep Learning Techniques. A case of Black Race. by Mabiriz,I, Vicent, Ampaire, Ray Brooks, Muhoza, B. Gloria

“…The model was trained and tested on 1000 images taken from students of Kabale University using a Nikon d850 camera. Machine learning techniques such as Principal Component Analysis, Geometric Feature-Based Methods, and double threshold techniques were used in the development phase while results were classified using CNN pre-trained models. …”
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Analisis Sentimen Kebijakan Penerapan Kurikulum Merdeka Sekolah Dasar dan Sekolah Menengah pada Media Sosial Twitter dengan Menggunakan Metode Word Embedding dan Long Short Term Me... by Alif Rizal Maulana, Satrio Hadi Wijoyo, Yusi Tyroni Mursityo

“…Analisis sentimen dilakukan pada opini siswa yang diutarakan di media sosial Twitter dengan menggunakan pendekatan machine learning. Arsitektur yang digunakan adalah Long Short-Term Memory Networks (LSTM). …”
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Flow Cytometric Assessment of FcγRIIIa-V158F Polymorphisms and NK Cell Mediated ADCC Revealed Reduced NK Cell Functionality in Colorectal Cancer Patients by Phillip Schiele, Stefan Kolling, Stanislav Rosnev, Charlotte Junkuhn, Anna Luzie Walter, Jobst Christian von Einem, Sebastian Stintzing, Wenzel Schöning, Igor Maximilian Sauer, Dominik Paul Modest, Kathrin Heinrich, Lena Weiss, Volker Heinemann, Lars Bullinger, Marco Frentsch, Il-Kang Na

“…Samples were collected from healthy donors and metastatic colorectal cancer (mCRC) patients from the FIRE-6-Avelumab phase II study. The machine learning model accurately predicted the FcγRIIIa-V158F polymorphism in 94% of samples. …”
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Enhanced fibrotic potential of COL1A1hiNR4A1low fibroblasts in ischemic heart revealed by transcriptional dynamics heterogeneity analysis at both bulk and single-cell levels by Cheng Luo, Cheng Luo, Cheng Luo, Baoping Tan, Luoxiang Chu, Liqiang Chen, Xinglong Zhong, Yangyang Jiang, Yuluan Yan, Fanrui Mo, Hong Wang, Fan Yang, Fan Yang

“…Gene set enrichment analysis (GSEA) shows that the gene expression pattern of COL1A1hiNR4A1low FB was closer to pathways associated with cardiac fibrosis. Through machine learning, ten feature genes from COL1A1hiNR4A1low FB were selected to construct a diagnostic tool for IHD. …”
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Accessible moderate-to-severe obstructive sleep apnea screening tool using multidimensional obesity indicators as compact representations by Xiaoyue Zhu, Chenyang Li, Xiaoting Wang, Zhipeng Yang, Yupu Liu, Lei Zhao, Xiaoman Zhang, Yu Peng, Xinyi Li, Hongliang Yi, Jian Guan, Shankai Yin, Huajun Xu

“…We trained, validated, and tested models with logistic regression and other 5 machine learning algorithms on the clinical dataset and a community dataset. …”
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Development and Evaluation of a Low-Jitter Hand Tracking System for Improving Typing Efficiency in a Virtual Reality Workspace by Tianshu Xu, Wen Gu, Koichi Ota, Shinobu Hasegawa

“…This study addresses this obstacle by introducing a novel machine learning-based solution, namely, the two-stream long short-term memory typing method, to enhance text entry performance in virtual reality. …”
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Dissipation Alters Modes of Information Encoding in Small Quantum Reservoirs near Criticality by Krai Cheamsawat, Thiparat Chotibut

“…Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet, identifying the mechanisms that underlie enhanced performance remains challenging, particularly in many-body open systems where nonlinear interactions and dissipation intertwine in complex ways. …”
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Blinding HT: Hiding Hardware Trojan signals traced across multiple sequential levels by Ying Zhang, Minghui Ge, Xin Chen, Jiaqi Yao, Zhiming Mao

“…As shown in the experimental results, the proposed HTs are hardly detected even by the novel HT detection approach based on machine learning algorithm. These HTs have small footprints on the design in terms of area and power to resist the side‐channel effect analysis. …”
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A stacked ensemble approach with resampling techniques for highly effective fraud detection in imbalanced datasets by Idongesit E. Eteng, Udeze L. Chinedu, Ayei E. Ibor

“… In several earlier studies, machine learning (ML) has been widely explored for fraud detection. …”
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Scenario-adaptive wireless fall detection system based on few-shot learning by Yuting ZENG, Suzhi BI, Lili ZHENG, Xiaohui LIN, Hui WANG

“…A scenario robust fall detection system based on few-shot learning (FDFL) in wireless environment was designed.The performance of existing fall detection methods based on Wi-Fi channel state information (CSI) degrades significantly across scenarios, which requires collecting and marking a large number of CSI samples in each application scenario, resulting in high cost for large-scale deployment.Therefore, the method of few-shot learning was introduced, which can maintain the performance of fall detection with high accuracy when the number of annotated samples in unfa-miliar scenes is insufficient.The proposed FDFL was mainly divided into two stages, source domain meta-training and target domain meta-learning.The meta training stage of the source domain consists of two parts: data preprocessing and classification training.In the data preprocessing stage, the collected original CSI amplitude and phase data were denoised and segmented.In the classification training stage, a large number of processed source domain data samples were used to train a CSI feature extractor based on convolutional neural network.In the meta-learning stage of the target domain, the limited labeled data sampled in the target domain was effectively extracted based on the feature extractor trained in the meta-training module, and then a lightweight machine learning classifier was trained to detect the fall behavior under the cross-scene.Through several experiments in different scenarios, FDFL can achieve an average accuracy of 95.52% for the four classification tasks of falling, sitting, walking and sit down with only a small number of samples in the target domain, and maintain robust detection accuracy for changes in test environment, personnel target and equipment location.…”
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The Role of Artificial Intelligence in Aviation Construction Projects in the United Arab Emirates: Insights from Construction Professionals by Mariam Abdalla Alketbi, Fikri Dweiri, Doraid Dalalah

“…AI tools can predict delays, optimize workflows, and enhance safety through real-time data analytics and machine learning algorithms, reducing risks and human error. …”
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Globality-Locality Preserving Maximum Variance Extreme Learning Machine by Yonghe Chu, Hongfei Lin, Liang Yang, Yufeng Diao, Dongyu Zhang, Shaowu Zhang, Xiaochao Fan, Chen Shen, Deqin Yan

“…An extreme learning machine (ELM) is a useful technique for machine learning; however, the existing extreme learning machine methods cannot exploit the geometric structure information or discriminate information of the data space well. …”
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English Grammar Discrimination Training Network Model and Search Filtering by Juan Zhao

“…This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training model and situation model and the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. …”
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Transboundary Impacts of NO₂ on Soil Nitrogen Fixation and Their Effects on Crop Yields in China by Jinhui Xie, Peiheng Yu, Xiangzheng Deng

“…This study combines 15 global datasets to assess nitrogen’s transboundary impacts on crop yields and soil health. We use machine learning to develop yield prediction models for major grain crops (maize, rice, soybean, and wheat) affected by NO₂. …”
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Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope by Zhike Zhao, Xiaoguang Zhang

“…In order to verify the effect of the hidden layer nodes on the performance of ELM, an open-source machine learning database (University of California, Irvine (UCI)) is provided by the performance test data sets. …”
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An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning by S. M. Taslim Uddin Raju, Amlan Sarker, Apurba Das, Md. Milon Islam, Mabrook S. Al-Rakhami, Atif M. Al-Amri, Tasniah Mohiuddin, Fahad R. Albogamy

“…Bagging (random forest regression (RFR)), boosting (gradient boosting regression (GBR) and extreme gradient boosting regression (XGBR)), and stacking (STACK) are employed as ensemble models. Different machine learning (ML) approaches, including support vector regression (SVR), extreme learning machine (ELM), and multilayer perceptron neural network (MLP), are adopted as reference models. …”
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