Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…To achieve this goal, we compared the performance of two advanced modeling approaches, the convolutional neural network (CNN) and the XGBoost model. Our datasets included field and high-resolution topographic metrics including elevation, slope, aspect, and solar radiation and canopy height derived from lidar (1 m) and 44 vegetation indices derived from high-resolution (1 m) visible to near infrared (VNIR) hyperspectral data collected by NASA Goddard's Lidar, Hyperspectral and Thermal Imager (G-LiHT) sensor. …”
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Advances in machine learning applications to resource technology for organic solid waste by Hongzhi MA, Yichan LIU, Jihua ZHAO, Fan FEI, Ming GAO, Qunhui WANG

“…This study explores a range of commonly used ML models, including artificial neural network (ANN), support vector machine (SVM), decision tree, random forest, and extreme gradient boosting (XGBoost). …”
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Intelligent task-oriented semantic communications:theory, technology and challenges by Chuanhong LIU, Caili GUO, Yang YANG, Jiujiu CHEN, Meiyi ZHU, Lu’nan SUN

“…Furthermore, from the perspective of neural network interpretability,an interpretability-based semantic encoding method is proposed.Finally, a semantic communication platform for intelligent tasks is built based on software and hardware such as USRP and LabView,and the performance of the proposed algorithm is verified. …”
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EyeLiner by Yoga Advaith Veturi, MSc, Steve McNamara, OD, Scott Kinder, MS, Christopher William Clark, MS, Upasana Thakuria, MS, Benjamin Bearce, MS, Niranjan Manoharan, MD, Naresh Mandava, MD, Malik Y. Kahook, MD, Praveer Singh, PhD, Jayashree Kalpathy-Cramer, PhD

“…Methods: Anatomical keypoints along the retinal blood vessels were detected from the moving and fixed images using a convolutional neural network and subsequently matched using a transformer-based algorithm. …”
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HHO–LSSVM prediction model of blast casting muck pile morphology based on Gaussian distribution by Ning MA, Li MA, Yinda LI, Tianxiang LI, Sen YANG, Fuming LIU, You ZHOU, Xiaomin WANG, Qifeng ZHANG, Mengbo LI

“…And the Gaussian distribution model combined with the HHO–LSSVM algorithm was used to predict the shape of the blast casting muck pile, compare the accuracy of the LSSVM, Particle Swarm Optimization (PSO) optimized Least Squares Support Vector Machine, and Genetic Algorithm (GA) optimized BP neural network models, at the same time, compare the predicted blast casting muck pile morphology with the actual blasting muck pile morphology. …”
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Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ by HU Chengxi, TAN Lixin, WANG Wenyin, SONG Min

“…Identifying and locating the tender buds of famous and high-quality tea for picking is an important component of the modern tea picking robot. Traditional neural network methods suffer from issues such as large model size, long training times, and difficulties in dealing with complex scenes. …”
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Eye Collateral Channel Characteristic Analysis and Identification Model Construction of Mild Cognitive Impairment by WU Tiecheng, CAO Lei, YIN Lianhua, HE Youze, LIU Zhizhen, YANG Minguang, XU Ying, WU Jinsong

“…Different MCI identification models were constructed using support vector machine, decision tree, artificial neural network and random forest algorithm, with MCI eye collateral channel characteristics and TCM syndrome elements as independent variables and onset of MCI as a dependent variable. …”
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Jaringan Syaraf Tiruan Perambatan Balik untuk Klasifikasi Covid-19 Berbasis Tekstur Menggunakan Orde Pertama Berdasarkan Citra Chest X-Ray by Muchtar Ali Setyo Yudono, Eki Ahmad Zaki Hamidi, Jumadi Jumadi, Abdul Haris Kuspranoto, Aryo De Wibowo Muhammad Sidik

“…The feature extraction used is based on the first-order texture, and the classification used is a backpropagation neural network. The classification system in this study resulted in an average classification accuracy of 94.17% for the normal class and 77.5% for Covid -19. …”
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Development and validation of a prediction model for coronary heart disease risk in depressed patients aged 20 years and older using machine learning algorithms by Yicheng Wang, Yicheng Wang, Yicheng Wang, Chuan-Yang Wu, Hui-Xian Fu, Jian-Cheng Zhang, Jian-Cheng Zhang, Jian-Cheng Zhang

“…Eight machine learning algorithms were applied to the training set to construct the model, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), extreme gradient boosting (XGBoost), classification and regression tree (CART), k-nearest neighbors (KNN), and neural network (NNET). The validation set are used to evaluate the various performances of eight machine learning models. …”
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AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection by Zhiyang Chen, Tingting Xie, Shuting Chen, Zhenhui Li, Su Yao, Xuanjun Lu, Wenfeng He, Chao Tang, Dacheng Yang, Shaohua Li, Feng Shi, Huan Lin, Zipei Li, Anant Madabhushi, Xiangtian Zhao, Zaiyi Liu, Cheng Lu

“…We trained a deep neural network and a random forest model to segment tumor regions and locate CD8+ TILs in H&E and CD8-stained whole-slide images. …”
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Lightweight Daylily Grading and Detection Model Based on Improved YOLOv10 by JIN Xuemeng, LIANG Xiyin, DENG Pengfei

“…This module enhanced the model's sensitivity to the shapes and boundaries of targets, allowing the neural network to better capture the shape information of irregular objects like dried daylilies, further improving the model's feature extraction capability. …”
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Predicting Insemination Outcome in Holstein Dairy Cattle using Deep Learning by Mohammad Alishahi, Mahdi Ravakhah

“…In the problem of predicting the results of artificial insemination of livestock, the presented LSTM neural network model shows the best performance based on the stated evaluation criteria, and then the XGBoost-based classifier has better performance than MLP.…”
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