Suchergebnisse - ML: Deep Learning Algorithms

CT image reconstruction: integrating iterative methods with Ml-EM algorithm and deep learning models von Pham, Cong Thang, Tran, Thi Thu Thao, Huynh, Duc Anh Bao, Nguyen, Quoc Cuong, Nguyen, Tien Hung

“… Sinograms represent X-ray absorption throughout the body, and sophisticated image reconstruction methods, including machine learning algorithms and generative adversarial networks (GANs …”
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Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge von Zhang, Wengang, Gu, Xin, Tang, Libin, Yin, Yueping, Liu, Dongsheng, Zhang, Yanmei

“… On the other hand, Machine learning (ML), Deep Learning (DL) and Optimization Algorithm (OA) provide the ability to learn …”
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Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models von Issaiy, Mahbod, Zarei, Diana, Kolahi, Shahriar, Liebeskind, David S.

“… Recent research has explored machine learning (ML) and deep learning (DL) algorithms for stroke management …”
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Comparing machine and deep learning‐based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging von Smucny, Jason, Davidson, Ian, Carter, Cameron S.

“… ) algorithms and deep learning (DL) to predict “Improver” status (>20% improvement on Brief Psychiatric Rating Scale [BPRS] total score at 1‐year follow‐up vs. baseline …”
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Glaucoma diagnosis using multi-feature analysis and a deep learning technique von Akter, Nahida, Fletcher, John, Perry, Stuart, Simunovic, Matthew P., Briggs, Nancy, Roy, Maitreyee

“… ) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL …”
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SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm von Bahraini, Masoud S., Rad, Ahmad B., Bozorg, Mohammad

“… We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments …”
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Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete von Pakzad, Seyed Soroush, Roshan, Naeim, Ghalehnovi, Mansour

“… The presented paper aims to use machine learning (ML) and deep learning (DL) algorithms to predict the CS of steel fiber reinforced concrete (SFRC …”
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Benchmarking Machine Learning Algorithms on Blood Glucose Prediction for Type I Diabetes in Comparison With Classical Time-Series Models von Xie, Jinyu, Wang, Qian

“… ) levels using time-series data of patients with Type 1 diabetes (T1D). Methods: The ML algorithms include ML-based regression models and deep learning models such as a vanilla Long-Short-Term-Memory (LSTM …”
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Artificial intelligence and machine learning in design of mechanical materials von Guo, Kai, Yang, Zhenze, Yu, Chi-Hua, Buehler, Markus J

“… Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials …”
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Mapping wetland habitat health in moribund deltaic India using machine learning and deep learning algorithms von Paul, Satyajit, Pal, Swades

“… Researchers have increasingly integrated machine learning (ML) and deep learning (DL) algorithms to forecast the risk, vulnerability, and susceptibility of various geo-environmental challenges …”
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Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review von Missaoui, Rim, Hechkel, Wided, Saadaoui, Wajdi, Helali, Abdelhamid, Leo, Marco

“… ), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis …”
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Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes von Yeung, Hon Wah, Stolicyn, Aleks, Buchanan, Colin R., Tucker‐Drob, Elliot M., Bastin, Mark E., Luz, Saturnino, McIntosh, Andrew M., Whalley, Heather C., Cox, Simon R., Smith, Keith

“… algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI …”
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Review of Machine Learning Algorithms for Diagnosing Mental Illness von Cho, Gyeongcheol, Yim, Jinyeong, Choi, Younyoung, Ko, Jungmin, Lee, Seoung-Hwan

“… , ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm …”
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Applications of machine and deep learning to patient‐specific IMRT/VMAT quality assurance von Osman, Alexander F. I., Maalej, Nabil M.

“… Over the past 5 years, machine learning (ML) and deep learning (DL) algorithms for predictions of IMRT/VMAT QA outcome have been investigated …”
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Diagnostic accuracy of machine learning algorithms in electrocardiogram-based sleep apnea detection: A systematic review and meta-analysis von Kilic, Mustafa Eray, Arayici, Mehmet Emin, Turan, Oguzhan Ekrem, Yilancioglu, Yigit Resit, Ozcan, Emin Evren, Yilmaz, Mehmet Birhan

“… This meta-analysis aims to evaluate the diagnostic accuracy of machine learning (ML) and deep learning (DL) algorithms in detecting sleep apnea patterns from single-lead ECG data …”
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Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases von Bakrania, Anita, Joshi, Narottam, Zhao, Xun, Zheng, Gang, Bhat, Mamatha

“… A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer …”
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Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions von Dhakal, Ashwin, McKay, Cole, Tanner, John J, Cheng, Jianlin

“… Next, we review databases that are commonly used in the domain of protein–ligand interactions. Finally, we survey and analyze the machine learning …”
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A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications von Avci, Onur, Abdeljaber, Osama, Kiranyaz, Serkan, Hussein, Mohammed, Gabbouj, Moncef, Inman, Daniel J.

“… •The transition from traditional methods to ML and DL has never been discussed for vibration-based SDD …”
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Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities von Bhatnagar, Saheba, Gill, Laurence, Ghosh, Bidisha

“… The mapping, carried out through image segmentation or semantic segmentation, was performed using machine learning (ML) and deep learning (DL) algorithms …”
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Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review von Afsaneh, Elaheh, Sharifdini, Amin, Ghazzaghi, Hadi, Ghobadi, Mohadeseh Zarei

“… This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier …”
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