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Learning / U.S. Marine Corps.
United States. Marine Corps, author.
Electronic Government Doc | 2020
Available at Online freely available Government Documents (USU and USU Eastern)
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Learning [electronic resource].
Streaming video | 2009
Available at Available Online Academic Video Online (USU and USU Eastern) (Call number: Streaming Video)
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Online Academic Video Online (USU and USU Eastern) Streaming Video Available
 
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Learning / A. Charles Catania.
Catania, A. Charles.
Book | 1998
Available at Available Merrill-Cazier Books (2nd Floor South) (Call number: BF 318 .C37 1998)
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Learning : a survey of psychological interpretations / Winfred F. Hill.
Hill, Winfred F.
Book | 1990
Available at Available Merrill-Cazier Books (2nd Floor South) (Call number: BF 318 .H553 1990)
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Learning / A. Charles Catania.
Catania, A. Charles.
Book | 1984
Available at Available Merrill-Cazier Books (2nd Floor South) (Call number: BF 318 .C37 1984)
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Learning / A. Charles Catania.
Catania, A. Charles.
Book | 1979
Available at Available Merrill-Cazier Books (2nd Floor South) (Call number: BF 318 .C37)
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Learning : a survey of psychological interpretations / Winfred F. Hill.
Hill, Winfred F.
Book | 1977
Available at Available Merrill-Cazier Books (3rd Floor South) (Call number: LB 1051 .H524 1977)
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Learning : systems, models, and theories / William S. Sahakian.
Sahakian, William S., compiler.
Book | 1976
Available at Available Merrill-Cazier Books (3rd Floor South) (Call number: LB 1051 .S117 1976)
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Learning : animal behavior and human cognition.
Restle, Frank.
Book | 1975
Available at Available Merrill-Cazier Books (3rd Floor South) (Call number: LB 1051 .R478)
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Learning / Lloyd R. Peterson.
Peterson, Lloyd.
Book | 1975
Available at Available Merrill-Cazier Books (3rd Floor South) (Call number: LB 1051 .P4515)
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Learning : processes / [by] Melvin H. Marx, editor.
Marx, Melvin Herman, editor.
Book | 1969
Available at Available Merrill-Cazier Books (3rd Floor South) (Call number: LB 1025 .M38X)
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Learning / [by] Sarnoff A. Mednick, with the collaboration of Howard R. Pollio.
Mednick, Sarnoff A.
Book | 1964
Available at Available Merrill-Cazier BARN, Books, Circulation Desk (1st Floor) (Call number: LB 1051 .M435)
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Learning: a survey of psychological interpretations.
Hill, Winfred F.
Book | 1963
Available at Merrill-Cazier BARN, Books, Circulation Desk (1st Floor) (Call number: LB 1051 .H524) plus 1 more
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Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review.
Theodore Armand TP;Nfor KA;Kim JI;Kim HC
Academic Journal Academic Journal | Publisher: MDPI Publishing Country of Publication: Switzerland NLM ID: 101521595 Publication Model: Electronic Cited Medium: Internet ISSN: 2072-6643 (Electronic) Linking ISSN: 20726643 NLM ISO Abbreviation: Nutrients Subsets: MEDLINE Please log in to see more details
In industry 4.0, where the automation and digitalization of entities and processes are... more
Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review.
Publisher: MDPI Publishing Country of Publication: Switzerland NLM ID: 101521595 Publication Model: Electronic Cited Medium: Internet ISSN: 2072-6643 (Electronic) Linking ISSN: 20726643 NLM ISO Abbreviation: Nutrients Subsets: MEDLINE
In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.

Subject terms:

Humans - Machine Learning - Nutritional Status - Automation - Artificial Intelligence - Deep Learning

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MEDLINE
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Exploring the impact of pathogenic microbiome in orthopedic diseases: machine learning and deep learning approaches.
Shao Z;Gao H;Wang B;Zhang S
Academic Journal Academic Journal | Publisher: Frontiers Media SA Country of Publication: Switzerland NLM ID: 101585359 Publication Model: eCollection Cited Medium: Internet ISSN: 2235-2988 (Electronic) Linking ISSN: 22352988 NLM ISO Abbreviation: Front Cell Infect Microbiol Subsets: MEDLINE Please log in to see more details
Osteoporosis, arthritis, and fractures are examples of orthopedic illnesses that not o... more
Exploring the impact of pathogenic microbiome in orthopedic diseases: machine learning and deep learning approaches.
Publisher: Frontiers Media SA Country of Publication: Switzerland NLM ID: 101585359 Publication Model: eCollection Cited Medium: Internet ISSN: 2235-2988 (Electronic) Linking ISSN: 22352988 NLM ISO Abbreviation: Front Cell Infect Microbiol Subsets: MEDLINE
Osteoporosis, arthritis, and fractures are examples of orthopedic illnesses that not only significantly impair patients' quality of life but also complicate and raise the expense of therapy. It has been discovered in recent years that the pathophysiology of orthopedic disorders is significantly influenced by the microbiota. By employing machine learning and deep learning techniques to conduct a thorough analysis of the disease-causing microbiome, we can enhance our comprehension of the pathophysiology of many illnesses and expedite the creation of novel treatment approaches. Today's science is undergoing a revolution because to the introduction of machine learning and deep learning technologies, and the field of biomedical research is no exception. The genesis, course, and management of orthopedic disorders are significantly influenced by pathogenic microbes. Orthopedic infection diagnosis and treatment are made more difficult by the lengthy and imprecise nature of traditional microbial detection and characterization techniques. These cutting-edge analytical techniques are offering previously unheard-of insights into the intricate relationships between orthopedic health and pathogenic microbes, opening up previously unimaginable possibilities for illness diagnosis, treatment, and prevention. The goal of biomedical research has always been to improve diagnostic and treatment methods while also gaining a deeper knowledge of the processes behind the onset and development of disease. Although traditional biomedical research methodologies have demonstrated certain limits throughout time, they nevertheless rely heavily on experimental data and expertise. This is the area in which deep learning and machine learning approaches excel. The advancements in machine learning (ML) and deep learning (DL) methodologies have enabled us to examine vast quantities of data and unveil intricate connections between microorganisms and orthopedic disorders. The importance of ML and DL in detecting, categorizing, and forecasting harmful microorganisms in orthopedic infectious illnesses is reviewed in this work.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2024 Shao, Gao, Wang and Zhang.)

Subject terms:

Humans - Quality of Life - Machine Learning - Deep Learning - Musculoskeletal Diseases - Microbiota

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MEDLINE
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Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.
Fluetsch A;Di Lascio E;Gerebtzoff G;Rodríguez-Pérez R
Academic Journal Academic Journal | Publisher: American Chemical Society Country of Publication: United States NLM ID: 101197791 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1543-8392 (Electronic) Linking ISSN: 15438384 NLM ISO Abbreviation: Mol Pharm Subsets: MEDLINE Please log in to see more details
Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) m... more
Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.
Publisher: American Chemical Society Country of Publication: United States NLM ID: 101197791 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1543-8392 (Electronic) Linking ISSN: 15438384 NLM ISO Abbreviation: Mol Pharm Subsets: MEDLINE
Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure-property relationship models are generally trained on large data sets that include diverse chemical series (global models). In the pharmaceutical industry, these ML global models are available across discovery projects as an "out-of-the-box" solution to assist in drug design, synthesis prioritization, and experiment selection. However, drug discovery projects typically focus on confined parts of the chemical space (e.g., chemical series), where global models might not be applicable. Local ML models are sometimes generated to focus on specific projects or series. Herein, ML-based global models, local models, and hybrid global-local strategies were benchmarked. Analyses were done for more than 300 drug discovery projects at Novartis and ten absorption, distribution, metabolism, and excretion (ADME) assays. In this work, hybrid global-local strategies based on transfer learning approaches were proposed to leverage both historical ADME data (global) and project-specific data (local) to adapt model predictions. Fine-tuning a pretrained global ML model (used for weights' initialization, WI) was the top-performing method. Average improvements of mean absolute errors across all assays were 16% and 27% compared with global and local models, respectively. Interestingly, when the effect of training set size was analyzed, WI fine-tuning was found to be successful even in low-data scenarios (e.g., ∼10 molecules per project). Taken together, this work highlights the potential of domain adaptation in the field of molecular property predictions to refine existing pretrained models on a new compound data distribution.

Subject terms:

Drug Discovery methods - Drug Design - Machine Learning - Quantitative Structure-Activity Relationship - Deep Learning

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MEDLINE
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Supervised representation learning based on various levels of pediatric radiographic views for transfer learning.
Kyung S;Jang M;Park S;Yoon HM;Hong GS;Kim N
Academic Journal Academic Journal | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE Please log in to see more details
Transfer learning plays a pivotal role in addressing the paucity of data, expediting t... more
Supervised representation learning based on various levels of pediatric radiographic views for transfer learning.
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Transfer learning plays a pivotal role in addressing the paucity of data, expediting training processes, and enhancing model performance. Nonetheless, the prevailing practice of transfer learning predominantly relies on pre-trained models designed for the natural image domain, which may not be well-suited for the medical image domain in grayscale. Recognizing the significance of leveraging transfer learning in medical research, we undertook the construction of class-balanced pediatric radiograph datasets collectively referred to as PedXnets, grounded in radiographic views using the pediatric radiographs collected over 24 years at Asan Medical Center. For PedXnets pre-training, approximately 70,000 X-ray images were utilized. Three different pre-training weights of PedXnet were constructed using Inception V3 for various radiation perspective classifications: Model-PedXnet-7C, Model-PedXnet-30C, and Model-PedXnet-68C. We validated the transferability and positive effects of transfer learning of PedXnets through pediatric downstream tasks including fracture classification and bone age assessment (BAA). The evaluation of transfer learning effects through classification and regression metrics showed superior performance of Model-PedXnets in quantitative assessments. Additionally, visual analyses confirmed that the Model-PedXnets were more focused on meaningful regions of interest.
(© 2024. The Author(s).)

Subject terms:

Humans - Child - Machine Learning - Radiography - Deep Learning - Fractures, Bone

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MEDLINE
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Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification.
Tutsoy O;Koç GG
Academic Journal Academic Journal | Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE Please log in to see more details
Background: Blood test is extensively performed for screening, diagnoses and surveilla... more
Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification.
Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE
Background: Blood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has not been profoundly investigated and implemented yet.
Results: This paper proposes deep machine learning algorithms with multi-dimensional adaptive feature elimination, self-feature weighting and novel feature selection approaches. To classify the health risks based on the processed data with the deep layers, four machine learning algorithms having various properties from being utterly model free to gradient driven are modified.
Conclusions: The results show that the proposed deep machine learning algorithms can remove the unnecessary features, assign self-importance weights, selects their most informative ones and classify the health risks automatically from the worst-case low to worst-case high values.
(© 2024. The Author(s).)

Subject terms:

Supervised Machine Learning - Algorithms - Machine Learning

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MEDLINE
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