Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

“…Three widely applicable machine learning algorithms (Random Forests, Gradient Boosting Machine, and XGBoost) were used to screen the metrics, and then the corresponding predictive models were constructed using discriminative analysis, and the best performing model was selected as the target model. …”
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Modulation pattern recognition method of wireless communication automatic system based on IABLN algorithm in intelligent system. by Ting Xie, Xing Han

“…The aim of this study is to address the limitations of convolutional networks in recognizing modulation patterns. These networks are unable to utilize temporal information effectively for feature extraction and modulation pattern recognition, resulting in inefficient modulation pattern recognition. …”
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The Impact of Various Filling Patterns and Building Orientations on the Mechanical Characteristics and Building Time of PLA Using FDM by M. Hamoud, Sachin Salunkhe, Lenka Cepova, H. M. A. Hussien

“…The results show that the filling pattern, orientation, and density at which the part is built significantly affect the strength, hardness, and building time. …”
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Identification of Depression Patients Using LIF Spiking Neural Network Model From the Pattern of EEG Signals by Rekha Sahu, Prasant Kumar Pattnaik, Kalaiarasi Sonai Muthu Anbananthen, Saravanan Muthaiyah

“…Interpreting electroencephalography signals and the abnormality of the signals can help to find the specific pattern for specific diseases like depression. A Spiking Neural Network is a machine learning approach that emphasizes the data value and manipulates the value to find the particular signal feature. …”
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Spatiotemporal patterns of water and vegetation in Poyang Lake from 2013 to 2021 using remote sensing data. by Zhigang Lu, Zihao Chen, Meng Zhou, Daxing Lei, Yifan Chen

“…We used a combination of Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM) to preprocess and classify 42 Landsat 8 OLI images. …”
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LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns by Mehran Motamedi, Reza Shidpour, Mehdi Ezoji

“…This LSTM-based method offers a novel approach to predicting defect percentages using simulated XRD patterns of materials.…”
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Changes in Land Use and Land Cover Patterns in Two Desert Basins Using Remote Sensing Data by Abdullah F. Alqurashi, Omar A. Alharbi

“…A combination of object-based image analysis and a support vector machine classifier was used to produce LULC thematic maps. …”
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Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection by Wafiq Azizah, Soffiana Agustin

“…The preservation of traditional batik patterns, often transmitted orally and through direct practice across generations, faces significant challenges in the modern era. …”
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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 aim of this work is to identify the adulteration of edible gelatin using near-infrared (NIR) spectroscopy combined with supervised pattern recognition methods. 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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Mitigating the Concurrent Interference of Electrode Shift and Loosening in Myoelectric Pattern Recognition Using Siamese Autoencoder Network by Ge Gao, Xu Zhang, Xiang Chen, Zhang Chen

“…The objective of this work is to develop a novel myoelectric pattern recognition (MPR) method to mitigate the concurrent interference of electrode shift and loosening, thereby improving the practicality of MPR-based gestural interfaces towards intelligent control. …”
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Commonalities and differences in gene expression patterns in major depressive disorder and chronic spontaneous urticaria: implications for comorbidity by Yibo Jiang, Wen Li, Lihao Zheng, Nan Ba

“…Results indicated shared molecular patterns between CSU and MDD, with 26 key genes in the total population, 6 in males and 7 in females. …”
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Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial Data by Clemir Abbeg Coproski, Bingqing Liang, James T. Dietrich, John DeGroote

“…With limited research on smaller cities, especially in the American Midwest, the goal of this research was to examine the spatial patterns of air temperature across multiple small to medium-sized cities in Iowa, a relatively rural US state. …”
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A Mean Weighted Squared Error-based Neural Classifier for Intelligent Pattern Recognition in Smart Grids by Mehdi Khashei, Mehrnaz Ahmadi, Fatemeh Chahkoutahi

“…Supervised learning is widely used in pattern recognition and classification due to its strong ability to enhance data accuracy. …”
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Classification patterns identification of immunogenic cell death-related genes in heart failure based on deep learning by Zhihui Ma, Shixin Ma, Bin Chen, Yongjun Zhang, Jinmei Zeng, Jianping Tao, Yu Hu

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Microbiome and metabolome patterns after lung transplantation reflect underlying disease and chronic lung allograft dysfunction by Christian Martin, Kathleen S. Mahan, Talia D. Wiggen, Adam J. Gilbertsen, Marshall I. Hertz, Ryan C. Hunter, Robert A. Quinn

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Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria by Maninder Singh Dhillon, Thomas Koellner, Sarah Asam, Jakob Bogenreuther, Stefan Dech, Stefan Dech, Ursula Gessner, Daniel Gruschwitz, Sylvia Helena Annuth, Tanja Kraus, Thomas Rummler, Christian Schaefer, Sarah Schönbrodt-Stitt, Ingolf Steffan-Dewenter, Martina Wilde, Martina Wilde, Tobias Ullmann

“…To address this, we compared biomass estimates from a semi-empirical light use efficiency (LUE) model with predictions from a machine learning–remote sensing framework that integrates environmental variables. …”
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Evaluating Spatio-Temporal Kriging with Machine Learning Considering the Sources of Spatio-Temporal Variation by Min Jeong, Hyeongmo Koo

“…However, differences in the estimated global trends and spatio-temporal interactions resulting from applying machine learning may influence the spatio-temporal variation patterns of the kriging results. …”
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A Machine Learning-Based Approach to Analyze and Visualize Time-Series Sentencing Data by Eugene Pinsky, Kandaswamy Piranavakumar

“…This work proposes a machine learning approach to associate patterns with clusters. …”
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Real-time fMRI pattern decoding and neurofeedback using FRIEND: an FSL-integrated BCI toolbox. by João R Sato, Rodrigo Basilio, Fernando F Paiva, Griselda J Garrido, Ivanei E Bramati, Patricia Bado, Fernanda Tovar-Moll, Roland Zahn, Jorge Moll

“…In combination with improved computational approaches, such as pattern recognition analysis (e.g., Support Vector Machines, SVM), fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. …”
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Anisotropic patterns of nanospikes induces anti-biofouling and mechano-bactericidal effects of titanium nanosurfaces with electrical cue by Eiji Kato, Masahiro Yamada, Eitoyo Kokubu, Hiroshi Egusa, Kazuyuki Ishihara

“…Two types of alkali-etched titanium nanosurfaces with isotropically or anisotropically patterned nanospikes had markedly denser surface protrusions, greater superhydrophilicity, and greater negative charge than machined or micro-roughened titanium surfaces. …”
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