Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke by Ye Jin Jeon, Hong Gee Roh, Sumin Jung, Hyun Yang, Hee Jong Ki, Jeong Jin Park, Taek-Jun Lee, Na Il Shin, Ji Sung Lee, Jin Tae Kwak, Hyun Jeong Kim

“…We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-Net, to generate DL-driven MRA collateral maps. …”
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The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis by Samer Tohme, Hamza O Yazdani, Amaan Rahman, Sanah Handu, Sidrah Khan, Tanner Wilson, David A Geller, Richard L Simmons, Michele Molinari, Christof Kaltenmeier

“…The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC. …”
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A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

“…In addition, both the XGBoost classifier and the neural network classifier showed high accuracy and reliability at each prediction stage. …”
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Multispectral Inversion of Starch Content in Rice Grains from Yingjiang County Based on Feature Band Selection Algorithms by Xiaotong Su, Zhifang Zhao, Min Zeng, Fei Zhao, Ziyang Li, Yu Zheng

“…First and second derivative transformations were applied to the multispectral reflectance data, followed by the use of three feature selection algorithms to identify key spectral bands. BP neural networks and ELM neural network regression models were then integrated to quantitatively estimate starch content across the study area. …”
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Implemantasi Mask R-CNN pada Perhitungan Tinggi dan Lebar Karang untuk Memantau Pertumbuhan Transplantasi Karang by Naufal Alkhalis, Husaini Husaini, Haekal Azief Haridhi, Cut Nadilla Maretna, Nur Fadli, Yudi Haditiar, Muhammad Nanda, Maria Ulfah, Kris Handoko, Intan Malayana, Arsa Cindy Safitri

“…Penelitian ini mengimplementasikan algoritma Mask Region-based Convolutional Neural Network (Mask R-CNN) dengan Pustaka Detectron2 dalam deteksi dan segmentasi objek untuk menghitung tinggi dan lebar karang transplantasi melalui citra. …”
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Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model by Yohei Kakimoto, Yuto Omae, Hirotaka Takahashi

“…We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. …”
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Monitoring the Maize Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature Fusion by Wenfeng Li, Kun Pan, Yue Huang, Guodong Fu, Wenrong Liu, Jizhong He, Weihua Xiao, Yi Fu, Jin Guo

“…Images of maize canopies during the jointing, tasseling, and grouting stages were captured using unmanned aerial vehicle (UAV) remote sensing to extract color, texture, and wavelet features and to construct a color and texture feature dataset and a fusion of wavelet, color, and texture feature datasets. Backpropagation neural network (BP), Stacked Ensemble Learning (SEL), and Gradient Boosting Decision Tree (GBDT) models were employed to develop CHL monitoring models for the maize canopy. …”
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A multi-task model for failure identification and GPS assessment in metro trains by Pratik Vinayak Jadhav, Sairam V. A, Siddharth Sonkavade, Shivali Amit Wagle, Preksha Pareek, Ketan Kotecha, Tanupriya Choudhury

“…A multi-task artificial neural network was developed for the simultaneous identification of failures and GPS quality assessment. …”
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Applying in machine learning and deep learning in finance industry: A case study on repayment prediction by Nguyễn Phát Đạt, Hồ Mai Minh Nhật, Trương Công Vinh, Lê Quang Chấn Phong, Lê Hoành Sử

“…Employed techniques encompass Logistic Regression (LR), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), in conjunction with deep learning architectures such as Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN). Following methodological refinement, it becomes apparent that ensemble learning approaches, exemplified by XGB and LGBM, exhibit markedly superior predictive performance, surpassing conventional models with an accuracy rate exceeding 85%. …”
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Data mining and safety analysis of voriconazole in patients with a hematological malignant tumor based on the FAERS database: differences between children and adults by Hao Li, Hao Li, Manxue Jiang, Manxue Jiang, Xiaona Pan, Lingti Kong, Lingti Kong, Lingti Kong

“…Data mining was done using reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS).ResultsA total of 605 ADEs were included: 116 (19.17%) in children and 489 (80.83%) in adults. …”
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Correction of CAMS PM₁₀ Reanalysis Improves AI-Based Dust Event Forecast by Ron Sarafian, Sagi Nathan, Dori Nissenbaum, Salman Khan, Yinon Rudich

“…To evaluate the contribution, we train a deep neural network to predict city-scale dust events (0–72 h) over the Balkans using PM₁₀ fields. …”
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G-UNETR++: A Gradient-Enhanced Network for Accurate and Robust Liver Segmentation from Computed Tomography Images by Seungyoo Lee, Kyujin Han, Hangyeul Shin, Harin Park, Seunghyon Kim, Jeonghun Kim, Xiaopeng Yang, Jae Do Yang, Hee Chul Yu, Heecheon You

“…Accurate liver segmentation from computed tomography (CT) scans is essential for liver cancer diagnosis and liver surgery planning. Convolutional neural network (CNN)-based models have limited segmentation performance due to their localized receptive fields. …”
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Rancidity Estimation of Perilla Seed Oil by Using Near-Infrared Spectroscopy and Multivariate Analysis Techniques by Suk-Ju Hong, Shin-Joung Rho, Ah-Yeong Lee, Heesoo Park, Jinshi Cui, Jongmin Park, Soon-Jung Hong, Yong-Ro Kim, Ghiseok Kim

“…Preprocessing methods were applied to the transmittance spectra of perilla seed oil, and multivariate analysis techniques, such as principal component regression (PCR), partial least squares regression (PLSR), and artificial neural network (ANN) modeling, were employed to develop the models. …”
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A machine learning model using the snapshot ensemble approach for soil respiration prediction in an experimental Oak Forest by S.N. Ferdous, J.P. Ahire, R. Bergman, L. Xin, E. Blanc-Betes, Z. Zhang, J. Wang

“…We then used the Artificial Neural Network (ANN) regression model to correct the forecasting model errors and perform the final prediction using the snapshot ensemble approach. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…This study employed the Informer model to forecast drought and conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), and Convolutional Neural Network (CNN) models. The findings indicate that the Informer model outperforms the other three models in terms of drought forecasting accuracy across all time scales. …”
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Prior Knowledge-Based Two-Layer Energy Management Strategy for Fuel Cell Ship Hybrid Power System by Lin Liu, Xiangguo Yang, Xin Li, Xingwei Zhou, Yufan Wang, Telu Tang, Qijia Song, Yifan Liu

“…Distribution results are then used to train an SSA-BP neural network, creating an offline strategy library. …”
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Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database by Shengwei Lin, Wenbin Lu, Ting Wang, Ying Wang, Xueqian Leng, Lidan Chi, Peipei Jin, Jinjun Bian

“…Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). …”
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DITC control strategy for semi-direct drive system with switched reluctance motor in coal mine belt conveyor by LIU Peng, ZHANG Lei, BAO Jiusheng, CHEN Huaxin, WEI Chunji, MA Chuanming, WANG Lei, WANG Xiaolong

“…To address these limitations, the drive system was retrofitted with a 2×400 kW switched reluctance motor semi-direct drive(SRSD) system utilizing a switched reluctance motor(SRM) for the belt conveyor. A BP neural network was used to predict the flux linkage and torque of the SRM, and a highly accurate SRM nonlinear model was developed based on the predictions. …”
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QoE-Driven Big Data Management in Pervasive Edge Computing Environment by Qianyu Meng, Kun Wang, Xiaoming He, Minyi Guo

“…Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. …”
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Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms by D. Ciani, C. Fanelli, B. Buongiorno Nardelli

“…To address these issues, we developed and tested different deep learning methodologies, specifically convolutional neural network (CNN) models that were originally proposed for single-image super resolution. …”
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