Search Results - ML: Deep Neural Network Algorithms

Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks by 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…”
Get full text

Frost prediction using machine learning and deep neural network models by 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…”
Get full text

DOA Estimation Method Based on Improved Deep Convolutional Neural Network by 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…”
Get full text

Comparative Insights Into E‐Scooter Usage Prediction Through Machine Learning and Deep Learning Techniques by 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…”
Get full text

CTDN (Convolutional Temporal Based Deep‐ Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction by 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…”
Get full text

A Performance Study of Deep Neural Network Representations of Interpretable ML on Edge Devices with AI Accelerators by 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…”
Get full text

Hybrid Deep Learning-based Models for Crop Yield Prediction by Oikonomidis, Alexandros, Catal, Cagatay, Kassahun, Ayalew

“… The algorithms evaluated in our study are the XGBoost machine learning (ML) algorithm, Convolutional Neural Networks (CNN…”
Get full text

Shedding light on “Black Box” machine learning models for predicting the reactivity of HO radicals toward organic compounds by Zhong, Shifa, Zhang, Kai, Wang, Dong, Zhang, Huichun

“…[Display omitted] •MF-ML assisted-QSAR model was developed for 1089 compounds toward HO reactivity…”
Get full text

Practical Machine Learning Model to Predict the Recovery of Motor Function in Patients with Stroke by 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…”
Get more information

Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature by 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…”
Get more information

Explainable AI for Bioinformatics: Methods, Tools and Applications by 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…”
Get full text

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges by Unke, Oliver T, Meuwly, Markus

“… Because of their computational efficiency and scalability to large data sets, deep neural networks (DNNs…”
Get more information

A catchment-scale model of river water quality by Machine Learning by 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…”
Get full text

Development of machine learning models for the prediction of the compressive strength of calcium-based geopolymers by 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…”
Get full text

UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat by 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…”
Get full text

Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks by 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…”
Get more information

Machine learning in medicine: what clinicians should know by 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…”
Get full text

A Machine Learning-Based Classification Method for Monitoring Alzheimer's Disease Using Electromagnetic Radar Data by Ullah, Rahmat, Dong, Yinhuan, Arslan, Tughrul, Chandran, Siddharthan

“… These studies focus on wearable and portable devices and imaging algorithms. However, microwave…”
Get full text

Deep Learning Application in Spinal Implant Identification by 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…”
Get more information

A multi-label deep residual shrinkage network for high-density surface electromyography decomposition in real-time by 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…”
Get full text