Robust Line Feature Matching via Point–Line Invariants and Geometric Constraints by Chenyang Zhang, Yunfei Xiang, Qiyuan Wang, Shuo Gu, Jianghua Deng, Rongchun Zhang

“…Line feature matching is a crucial aspect of computer vision and image processing tasks, attracting significant research attention. …”
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Low-Complexity Receiver Using Undersampling for Guard-Band SSB-DDO-OFDM by Ming Chen, Qinghui Chen, Hui Zhou, Zhiwei Zheng, Jing He, Lin Chen

“…As a result, the implementation complexity of the receiver can be greatly reduced with the lower ADC sampling rate and reduced digital signal processing complexity. The feasibility of the proposed low-complexity receiver is investigated by numerical simulations. …”
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Maize Seed Variety Classification Based on Hyperspectral Imaging and a CNN-LSTM Learning Framework by Shuxiang Fan, Quancheng Liu, Didi Ma, Yanqiu Zhu, Liyuan Zhang, Aichen Wang, Qingzhen Zhu

“…Spectral data were acquired by hyperspectral imaging technology from five maize varieties and processed using Savitzky–Golay (SG) smoothing, along with standard normal variate (SNV) preprocessing. …”
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Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms by Quancheng Liu, Jun Zhou, Zhaoyi Wu, Didi Ma, Yuxuan Ma, Shuxiang Fan, Lei Yan

“…This study integrates hyperspectral imaging with intelligent optimization algorithms—Zebra Optimization Algorithm (ZOA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)—and a Support Vector Machine (SVM) model to classify jujube varieties. …”
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Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms by Nikolaos Servos, Xiaodi Liu, Michael Teucke, Michael Freitag

“…We apply the ML algorithms extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), and support vector regression (SVR) to this problem because of their ability to deal with low data volumes and their low processing times. …”
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Land cover change across the major proglacial regions of the sub-Antarctic islands, Antarctic Peninsula, and McMurdo Dry Valleys, during the 21st century by Christopher D. Stringer, Jonathan L. Carrivick, Duncan J. Quincey, Daniel Nývlt, Alexis Comber

“…Furthermore, we use change vector analysis (CVA) to discriminate landcover change in the twenty-first century. …”
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Neural Network Based on Dynamic Collaboration of Flows for Temporal Downscaling by Junkai Wang, Lianlei Lin, Yu Zhang, Zongwei Zhang, Sheng Gao, Hanqing Zhao

“…Time downscaling is one of the most challenging topics in remote sensing and meteorological data processing. Traditional methods often face the problems of high computing cost and poor generalization ability. …”
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An extreme forecast index-driven runoff prediction approach using stacking ensemble learning by Zhiyuan Leng, Lu Chen, Binlin Yang, Siming Li, Bin Yi

“…The approach focuses on predicting the inflow processes of the Geheyan Reservoir in the Qing River using EFI and runoff time series. …”
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Biomarker identification and gene-drug interaction prediction for breast cancer using machine learning algorithms by Raja Abhavya, Pragya Pragya, Sabitha R., Kumar Brijesh, Agastinose Ronickom Jac Fredo

“…Initially, RNA-sequencing data of normal and malignant BC tissues publicly available in the NCBI GEO database were pre-processed using a standard pipeline. Further, machine learning algorithms, such as logistic regression, support vector machine, and random forest, were used to identify the differentially expressed genes (DEGs). …”
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Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods by Bilal Cemek, Yunus Kültürel, Emirhan Cemek, Erdem Küçüktopçu, Halis Simsek

“…Soil temperature is a critical environmental factor that affects plant development, physiological processes, and overall productivity. This study compares two modeling approaches for predicting soil temperature at various depths: (i) fuzzy logic-based systems, including the Mamdani fuzzy inference system (MFIS) and the adaptive neuro-fuzzy inference system (ANFIS); (ii) supervised machine learning algorithms, such as multilayer perceptron (MLP), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGB), and k-nearest neighbors (KNN), along with multiple Linear regression (MLR) as a statistical benchmark. …”
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Framework for Groove Rating in Exercise-Enhancing Music Based on a CNN–TCN Architecture with Integrated Entropy Regularization and Pooling by Jiangang Chen, Junbo Han, Pei Su, Gaoquan Zhou

“…Our method also surpasses the performance of CNN and other machine-learning models, including long short-term memory (LSTM) networks and support vector machine (SVM) variants. This study establishes a strong foundation for the automated assessment of musical groove, with potential applications in music education, therapy, and composition. …”
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Rolling Bearing Fault Diagnosis Method Based on Improved Variational Mode Decomposition and Information Entropy by Wen FAN, Lian GE, Xiaoting XIAO, Fangji GAN, Xin LAI, Hongxia DENG, Qi HUANG

“…However, VMD has the problem of parameter selection, which directly affects the performance of VMD processing, and causes mode aliasing. Therefore, a rolling bearing fault diagnosis method based on improved VMD is proposed. …”
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Thermal error modeling of slant bed CNC lathe spindle based on BiLSTM with data augmentation and grey wolf optimizer algorithm by Musab Alataiqeh, Hu Shi, Qiangqiang Qu, Xuesong Mei, Haitao Wang

“…Experimental data are processed using fuzzy c-means clustering and grey relational analysis, which leads to the identification of four critical temperature-sensitive points. …”
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Near Infrared Spectroscopy Based on Supervised Pattern Recognition Methods for Rapid Identification of Adulterated Edible Gelatin by Hao Zhang, Haifeng Sun, Ling Wang, Shun Wang, Wei Zhang, Jiandong Hu

“…The spectral data obtained from a total of 144 samples consisting of six kinds of adulterated gelatin gels with different mixture ratios were processed with multiplicative scatter correction (MSC), Savitzky–Golay (SG) smoothing, and min-max normalization. …”
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Identifying G-Protein Coupled Receptors Using Mixed-Feature Extraction Methods and Machine Learning Methods by Chunyan Ao, Lin Gao, Liang Yu

“…GPCRs play an important role in a variety of physiological processes and are important drug targets for a variety of diseases. …”
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Identification of Critical Molecular Pathways Induced by HDAC11 Overexpression in Cardiac Mesenchymal Stem Cells by Chongyu Zhang, Neal L. Weintraub, Yaoliang Tang

“…To investigate the effects of HDAC11 overexpression on the gene regulatory networks in CMSCs, we treated mouse CMSCs with an adenoviral vector encoding human HDAC11 (Ad-HDAC11) versus adenoviral GFP (Ad-GFP) as a control. …”
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Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review by Filip Orzan, Ştefania D. Iancu, Laura Dioşan, Zoltán Bálint

“…The study emphasizes common models, including U-Net, Support Vector Machine, Random Forest, and K-Nearest Neighbors, alongside their evaluation metrics.ResultsThe analysis revealed a fragmented research landscape, with significant variation in model architectures and performance. …”
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Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards by Eyad Btoush, Xujuan Zhou, Raj Gururajan, Ka Ching Chan, Omar Alsodi

“…The methodology incorporates robust data pre-processing techniques. Experimental evaluations demonstrate the superior performance of the hybrid ML+DL model, particularly in handling class imbalances and achieving a high F1 score, achieving an F1 score of 94.63%. …”
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Calculation of hydrogen dispersion in cushion gases using machine learning by Ali Akbari, Mehdi Maleki, Yousef Kazemzadeh, Ali Ranjbar

“…Existing experimental and numerical methods for predicting hydrogen dispersion coefficients (KL) are often limited by high costs, lengthy processing times, and insufficient accuracy in dynamic reservoir conditions. …”
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A Comparative Evaluation of Machine Learning Methods for Predicting Student Outcomes in Coding Courses by Zakaria Soufiane Hafdi, Said El Kafhali

“…Educational data mining (EDM) has emerged as a pivotal AI application to transform educational environments by optimizing learning processes and identifying at-risk students. This study leverages EDM within a Moroccan university (Hassan First, University Settat, Morocco) context to augment educational quality and improve learning. …”
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