600 meters to VO2max: Predicting Cardiorespiratory Fitness with an Uphill Run by Kübra Stoican, Regina Oeschger

“…Discussion/Conclusion These results suggest that our short, high-intensity field test, when combined with a neural network model, can provide accurate predictions of VO2max. …”
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Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images by Zhaojiang Yan, Chong Fang, Kaishan Song, Xiangyu Wang, Zhidan Wen, Yingxin Shang, Hui Tao, Yunfeng Lyu

“…This study compared and evaluated 6 commonly used machine learning models, including extreme gradient boosting (XGBoost), support vector regression (SVR), backpropagation neural network (BP), gradient boosting decision tree (GBDT), random forest (RF), and categorical boosting (CatBoost). …”
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Fine particulate matter concentrations forecasting using long short-term memory network and meteorological inputs by T. Istiana, B. Kurniawan, S. Soekirno, A. Wihono, D.E. Nuryanto, B.A. Pertala, A. Sopaheluwakan

“…This study introduces the long short-term memory deep learning model and contrasts it with the one-dimensional convolution neural network as well as their hybrid counterpart. The dataset is split into 80 percent training and 20 percent testing data. …”
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Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors by Linan Yin, Ruibao Liu, Wei Li, Shijie Li, Xunbo Hou

“…Residual convolutional neural network (ResNet) technology was used to extract image features. …”
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Peningkatan Performa Pengelompokan Siswa Berdasarkan Aktivitas Belajar pada Media Pembelajaran Digital Menggunakan Metode Adaptive Moving Self-Organizing Maps by Onky Prasetyo, Ahmad Afif Supianto, Syaiful Anam, Hilman Ferdinandus Pardede, Vicky Zilvan, R. Budiarianto Suryo Kusumo

“…One of the most frequently used clustering methods is Self-Organizing Maps (SOM), SOM is a neural network method to maintain data topology when multidimensional input data is converted into output data with lower dimensions. …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…Again, this research used a strong methodology by incorporating Machine learning (ML) algorithms, such as: Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Linear Regression Model (LRM), were applied to forecast and confirm the quality of the water. …”
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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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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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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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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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