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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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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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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Learning Analytics for Bridging the Skills Gap: A Data-Driven Study of Undergraduate Aspirations and Skills Awareness for Career Preparedness by Joel Weijia Lai, Lei Zhang, Chun Chau Sze, Fun Siong Lim

“…To address this issue, a comprehensive survey was conducted among undergraduate students to gather data on their career aspirations, their awareness of the skills required for their chosen careers, and their perceptions of how well their university supports their skill development. Using machine learning methods such as hierarchical clustering and <i>k</i>-nearest neighbors for classification, coupled with non-parametric statistical analysis such as the Mann–Whitney <i>U</i> and Chi-squared (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula>) tests to understand students’ perceptions of their career preparedness, the findings from this study provide valuable insights into how higher education institutions can prepare students for the workforce and highlight areas where improvements are needed to better support students in achieving their career goals.…”
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Early Detection of Pancreatic Cancer: Opportunities Provided by Cancer-induced Paraneoplastic Phenomena and Artificial Intelligence by Wei-Chih Liao

“…Novel computer-aided detection tools based on AI technologies, including deep learning and radiomic analysis with machine learning, have achieved accurate detection and might supplement human interpretation to improve the sensitivity for early PDAC on CT images. …”
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Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization by Rajeev Goel, Sandhya Bansal, Kavita Gupta

“…This paper proposes technique that integrates machine learning and swarm intelligence to effectively address this issue. …”
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Generating context-specific sports training plans by combining generative adversarial networks. by Juquan Tan, Jingwen Chen

“…Personalized sports training plans are essential for addressing individual athlete needs, but traditional methods often need to integrate diverse data types, limiting adaptability and effectiveness. Existing machine learning (ML) and rule-based approaches cannot dynamically generate context-specific training programs, reducing their applicability in real-world scenarios. …”
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Exploring ethical considerations in medical research: Harnessing pre-generated transformers for AI-powered ethics discussions. by Takuya Mori, Takuya Watanabe, Shinji Kosugi

“…This limitation may be attributed to biased opinions resulting from machine learning optimization, preventing comprehensive identification of certain ethical issues.…”
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Prediction of High-ozone Events Using GAM, SMOTE, and Tail Dependence Approaches in Texas (2005–2019) by Benjamin Brown-Steiner, Xiong Zhou, Matthew J. Alvarado, Brook T. Russell

“…Finally, the feature selection via the tail dependence method performs comparably to other forms of machine learning-based feature selection and we find that there are multiple parameter sets that can predict MDA8 O3 with equal success.…”
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