Výsledky vyhledávání - ML: Deep Neural Network Algorithms

Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks Autor Ryu, Sung Mo, Seo, Sung Wook, Lee, Sun-Ho

“…Background We used the Surveillance, Epidemiology, and End Results (SEER) database to develop and validate deep survival neural network machine learning (ML…”
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Frost prediction using machine learning and deep neural network models Autor Talsma, Carl J., Solander, Kurt C., Mudunuru, Maruti K., Crawford, Brandon, Powell, Michelle R.

“… ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6–48 h…”
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DOA Estimation Method Based on Improved Deep Convolutional Neural Network Autor Zhao, Fangzheng, Hu, Guoping, Zhan, Chenghong, Zhang, Yule

“… The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inverse mapping problem of the array output covariance matrix…”
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Comparative Insights Into E‐Scooter Usage Prediction Through Machine Learning and Deep Learning Techniques Autor Yurdakul, Gokhan, Aydin, Nezir, Seker, Sukran, Yu, Hao

“…) algorithms, including traditional ML algorithms, neural network–based (NN) models , ANN and metaheuristic hybrid models, and ensemble models…”
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CTDN (Convolutional Temporal Based Deep‐ Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction Autor Singh, Davinder Paul, Kaushik, Baijnath

“… To the finest of our knowledge, this is the first study to evaluate optimization algorithms for selection of features and pharmacogenetics categorization using classification methods based…”
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A Performance Study of Deep Neural Network Representations of Interpretable ML on Edge Devices with AI Accelerators Autor Schauer, Julian, Goodarzi, Payman, Morsch, Jannis, Schütze, Andreas

“…). This study investigated a novel application-oriented approach to representing interpretable ML inference as deep neural networks (DNNs…”
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Hybrid Deep Learning-based Models for Crop Yield Prediction Autor Oikonomidis, Alexandros, Catal, Cagatay, Kassahun, Ayalew

“… The algorithms evaluated in our study are the XGBoost machine learning (ML) algorithm, Convolutional Neural Networks (CNN…”
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Shedding light on “Black Box” machine learning models for predicting the reactivity of HO radicals toward organic compounds Autor Zhong, Shifa, Zhang, Kai, Wang, Dong, Zhang, Huichun

“…[Display omitted] •MF-ML assisted-QSAR model was developed for 1089 compounds toward HO reactivity…”
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Practical Machine Learning Model to Predict the Recovery of Motor Function in Patients with Stroke Autor Kim, Jeoung Kun, Lv, Zhihan, Park, Donghwi, Chang, Min Cheol

“… We analyzed data from 833 consecutive stroke patients using 3 ML algorithms: deep neural network (DNN…”
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Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature Autor Tao, Lei, Varshney, Vikas, Li, Ying

“…In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the glass transition temperature Tg and other properties of polymers has attracted extensive attention…”
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Explainable AI for Bioinformatics: Methods, Tools and Applications Autor Karim, Md Rezaul, Islam, Tanhim, Shajalal, Md, Beyan, Oya, Lange, Christoph, Cochez, Michael, Rebholz-Schuhmann, Dietrich, Decker, Stefan

“…Abstract Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML…”
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PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges Autor Unke, Oliver T, Meuwly, Markus

“… Because of their computational efficiency and scalability to large data sets, deep neural networks (DNNs…”
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A catchment-scale model of river water quality by Machine Learning Autor Zanoni, Maria Grazia, Majone, Bruno, Bellin, Alberto

“… Among the available approaches, Machine Learning (ML) is promising because of its capability to detect complex nonlinear relationships and flexibility in the parameterization…”
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Development of machine learning models for the prediction of the compressive strength of calcium-based geopolymers Autor Huo, Wangwen, Zhu, Zhiduo, Sun, He, Ma, Borui, Yang, Liu

“… A total of eight algorithms in three types, traditional ML algorithms, integrated tree-based ML algorithms, and deep neural network algorithm, were employed to predict the compressive strength…”
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UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat Autor Fei, Shuaipeng, Hassan, Muhammad Adeel, Xiao, Yonggui, Su, Xin, Chen, Zhen, Cheng, Qian, Duan, Fuyi, Chen, Riqiang, Ma, Yuntao

“… algorithms including Cubist, support vector machine (SVM), deep neural network (DNN), ridge regression (RR) and random forest (RF) were used for multi-sensor data fusion and ensemble learning for grain yield prediction in wheat…”
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Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks Autor Vahdat, Vahab, Alagoz, Oguzhan, Chen, Jing Voon, Saoud, Leila, Borah, Bijan J, Limburg, Paul J

“… We then used this data set to train several ML algorithms, including deep neural network (DNN), random forest, and several gradient boosting variants…”
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Machine learning in medicine: what clinicians should know Autor Sim, Jordan Zheng Ting, Fong, Qi Wei, Huang, Weimin, Tan, Cher Heng

“… Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree…”
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A Machine Learning-Based Classification Method for Monitoring Alzheimer's Disease Using Electromagnetic Radar Data Autor Ullah, Rahmat, Dong, Yinhuan, Arslan, Tughrul, Chandran, Siddharthan

“… These studies focus on wearable and portable devices and imaging algorithms. However, microwave…”
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Deep Learning Application in Spinal Implant Identification Autor Yang, Hee-Seok, Kim, Kwang-Ryeol, Kim, Sungjun, Park, Jeong-Yoon

“… For deep learning algorithm development, radiographs were retrospectively obtained from clinical cases in which the patients had lumbar spine one-segment…”
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A multi-label deep residual shrinkage network for high-density surface electromyography decomposition in real-time Autor Ma, Jinting, Wang, Lifen, Wu, Renxiang, Zhang, Naiwen, Wei, Jing, Li, Jianjun, Li, Qiuyuan, Tan, Lihai, Li, Guanglin, Jiang, Naifu, Dan, Guo

“… Methods This study introduces a novel real-time high-density sEMG (HD-sEMG) decomposition algorithm named ML-DRSNet, which combines multi-label learning with a deep residual shrinkage network (DRSNet…”
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