Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations by Deepanshu Lakra, Deepanshu Lakra, Shobhit Pipil, Prashant K. Srivastava, Suraj Kumar Singh, Manika Gupta, Rajendra Prasad

“…Soil moisture is a fundamental variable in the Earth’s hydrological cycle and vital for development of agricultural water management practices. …”
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Two-Mode Hereditary Model of Solar Dynamo by Evgeny Kazakov, Gleb Vodinchar, Dmitrii Tverdyi

“…The feedback is represented by an integral term of the type of convolution of a quadratic form of phase variables with a kernel of a fairly general form. …”
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Evaluating soil erosion zones in the Kangsabati River basin using a stacking framework and SHAP model: a comparative study of machine learning approaches by Javed Mallick, Saeed Alqadhi, Swapan Talukdar, Md Nawaj Sarif, Tania Nasrin, Hazem Ghassan Abdo

“…The Boruta algorithm assessed the importance of these variables. Random Forest (RF), (Deep Neural Networks) DNN, Convolution Neural Network (CNN), and stacking (Meta model) models were used to map soil erosion susceptibility based on the inventory map and controlling features. …”
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Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields by Larona Keabetswe, Yiyin He, Chao Li, Zhenjiang Zhou

“…Three models were configured and compared for each CNN-RF (CNN-RF1, CNNRF2, CNNRF3) and CNN-SVM (CNN-SVM1, CNN-SVM2, CNN-SVM3), by using different combinations of variable input features derived from meteorological data (air temperature (Ta), vapour pressure deficit (VPD), net radiation (Rn)) and MODIS satellite data (land surface temperature (LST), fraction of photosynthetically active radiation (Fpar), leaf area index (LAI)). …”
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A multimodal functional structure-based graph neural network for fatigue detection by Dongrui Gao, Zhihong Zhou, Zongyao Peng, Haokai Zhang, Shihong Liu, Manqing Wang, Hongli Chang

“…An innovative intra- and inter-channel separable convolution module is designed to extract deep interaction patterns through parallel convolution operations within and across signal channels. …”
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An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN by Mohamed Abdelkader Aboamer, Mohamed Yacin Sikkandar, Sachin Gupta, Luis Vives, Kapil Joshi, Batyrkhan Omarov, Sitesh Kumar Singh

“…The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. …”
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Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study by Xiangkui Jiang, Bingquan Wang

“…MethodsIn this study, we analyzed data from 1948 patients with heart failure in a hospital in Sichuan Province between 2016 and 2019. By applying 3 variable selection strategies, 29 relevant variables were identified. …”
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RE-YOLOv5: Enhancing Occluded Road Object Detection via Visual Receptive Field Improvements by Tianyu Li, Xuanrui Xiong, Yuan Zhang, Xiaolin Fan, Yushu Zhang, Haihong Huang, Dan Hu, Mengting He, Zhanjun Liu

“…The complexity and variability of real-world road environments make the detection of densely occluded objects more challenging in autonomous driving scenarios. …”
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Laparoscopic Suture Gestures Recognition via Machine Learning: A Method for Validation of Kinematic Features Selection by Juan M. Herrera-Lopez, Alvaro Galan-Cuenca, Antonio J. Reina, Isabel Garcia-Morales, Victor F. Munoz

“…For that purpose, this work models the laparoscopic suturing manoeuvre as a set of simpler gestures to be recognized and, using the ReliefF algorithm on the JIGSAWS dataset’s kinematic data, presents a study of significance of the different kinematic variables. To validate this study, three classification models based on the multilayer perceptron and on Hidden Markov Models have been trained using both the complete set of variables and a reduced selection including only the most significant. …”
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Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry by ZHANG Wei, ZHANG Guiyu, TUO Xianguo, FU Ni, LI Xiaoping, PANG Tingting, LIU Kecai

“…After preprocessing the NIR data through 5-point 2-fold convolutional smoothing, spectral feature wavelengths were selected using the competitive adaptive reweighted sampling (CARS) algorithm; combining Spearman’s rank correlation coefficient, maximum information coefficient (MIC) and random forest (RF) variable importance, the key flavor components (KC) identified by GC-MS affecting the grading of raw Baijiu were determined. …”
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Design a new scheme for image security using a deep learning technique of hierarchical parameters by Khazaal Yasmine M., Falih Mohanaed Ajmi, Majeed Abbas Hamid

“…DL technology was used to encrypt and decrypt images, and based on hierarchical variables to complicate the encryption process. Convolutional neural networks are used in automatic learning to extract hierarchical features from an image, and to ensure adaptability, the model is trained on a variety of images. …”
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Time–frequency ensemble network for wind turbine mechanical fault diagnosis by Haiyu Guo, Xingzheng Guo, Xiaoguang Zhang, Fanfan Lu, Chuang Liang

“…Wind turbines typically operate under variable speed conditions, so the collected vibration signals are affected by non-linearity and information mixing, while also containing a large amount of noise interference. …”
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An interpretable deep learning model for the accurate prediction of mean fragmentation size in blasting operations by Baoqian Huan, Xianglong Li, Jianguo Wang, Tao Hu, Zihao Tao

“…SHapley Additive exPlanations (SHAP) analysis revealed that the modulus of elasticity (E) was a key variable influencing the prediction of mean fragmentation size. …”
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Bureaucratic Behavior and Utilization of Online Single Submission (OSS) Technology by Nur Mulyani Sari, Bachtari Alam Hidayat, Rika Destiny Sinaga

“…This study employed a descriptive quantitative approach. The independent variables were bureaucratic behavior (X1) and business actor behavior (X2), while the dependent variable was investment acceleration (Y). …”
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Toward the validation of crowdsourced experiments for lightness perception. by Emily N Stark, Terece L Turton, Jonah Miller, Elan Barenholtz, Sang Hong, Roxana Bujack

“…Here, we propose that the error due to a crowdsourced experimental design can be effectively averaged out because the crowdsourced experiment can be accommodated by the Thurstonian model as the convolution of two normal distributions, one that is perceptual in nature and one that captures the error due to variability in stimulus presentation. …”
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A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations by Muhammad Irfan Ishaq, Muhammad Adnan, Muhammad Ali Akbar, Amine Bermak, Nimra Saeed, Maaz Ansar

“…From existing literature, conventional fault diagnosis approaches in an IM struggle to reliably identify fault patterns at different speeds, particularly under variable speed and changing load conditions. To resolve this issue, this paper presents a unique hybrid Convolutional Neural Network (CNN) along with the Long Short Term Memory (LSTM) topology for diagnosing faulty patterns in an IM under loaded and unloaded variable speed settings. …”
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Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning by Haixia Li, Qian Li, Chunlai Yu, Shanjun Luo

“…Moreover, the model accuracies (MLP and CNN) before and after variable screening showed noticeable changes. Conducting variable screening contributed to a substantial improvement in the accuracy of rice LAI estimation. …”
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Comparative Study of the Performance of Two Treatment Planning Systems Using Tests from the MPPG 5b Guidelines, TECDOC 1583 and Venselaar's Confidence Limits by Carlos Vega D'Espaux, Federico Lorenzo, Franco La Paz Mastandrea, Nicolás Larragueta

“…The methodologies recommended by Venselaar, the MPPG 5b protocol by the American Association of Physicists in Medicine (AAPM), and the guidelines of the IAEA TECDOC 1583 for heterogeneity assessment were adopted for this purpose.The results indicated that the Eclipse™ system showed better overall performance in water measurements, with lower variability and greater compliance with gamma criteria (average above 95% in 3%, 2 mm profile tests). …”
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Video Visualization Technology and Its Application in Health Statistics Teaching for College Students by Chengfei Li, Yuan Xie, Shuanbao Li

“…The results show that the external model load difference between each explicit variable and latent variable is statistically significant. …”
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Hyperspectral segmentation of plants in fabricated ecosystems by Petrus H. Zwart, Petrus H. Zwart, Petrus H. Zwart, Peter Andeer, Trent R. Northen

“…To further enhance robustness, we incorporate image alignment techniques to address spatial variability in the dataset. Downstream analysis focuses on using the segmented data for processing spectral data, enabling monitoring of plant health. …”
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