Machine Learning Overview

Ayodele, Taiwo

doi:10.5772/9374

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…The only definition of AI is software package systems that perform intelligently despite not being explicitly programmed. These systems learn to recognize and categorize input patterns, make and act on probabilistic forecasts, as well as operate without explicit rules or supervision [ 6 ]. AI and deep learning will identify latent patterns in encephalograms known as biomarkers that change over time and are related to the player's performance [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Athlete’s Post-Match Psychological and Emotional Changes Based on Deep Learning

Zhang

Feng-jun

2022

Computational Intelligence and Neuroscience

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Athletes have had to deal with significant shifts in the way they think about psychology and emotion before and after attending a match in their respective fields. It has become increasingly difficult for players of any sport to overcome these differences due to massive technological advancements that aid in analyzing the difficulties of an athlete. The trainer can use the results of the analysis to help motivate and prepare the athletes for the upcoming competitions. The analysis in this study is based on information about the athletes who competed in the Tokyo Olympics. Deep learning models were used to evaluate the study. Image feature detection can be accomplished through the application of a machine learning technique known as deep learning. It employs a neural network, a computer system that mimics the human brain’s multiple layers. One or more unique features can be extracted from each layer. A deep learning model called the behavior recognition algorithm is used for the research. The questionnaire from the dataset was used to generate the results of the analysis.

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Section: Introductionmentioning

confidence: 99%

Feature Extraction of Athlete’s Post-Match Psychological and Emotional Changes Based on Deep Learning

Zhang

Feng-jun

2022

Computational Intelligence and Neuroscience

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“…The categorical variables were: (1) sex, (2) nationalities represented as “Nationalities_CAT,” (3) age categorized as “Age_CAT,” (4) region, and (5) weight categorized as “Weight_CAT.” Categorization of age and weight is a common practice in the clinical field and helps provide a more nuanced understanding of risk factors across different subgroups. 9 …”

Section: Methodsmentioning

confidence: 99%

Epidemiology of prehospital emergency calls according to patient transport decision in a middle eastern emergency care environment: Retrospective cohort‐based

Farhat,

Alinier,

El Aifa

et al. 2024

Health Science Reports

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Background and AimThough emergency medical services (EMS) respond to all types of emergency calls, they do not always result in the patient being transported to the hospital. This study aimed to explore the determinants influencing emergency call‐response‐based conveyance decisions in a Middle Eastern ambulance service.MethodsThis retrospective quantitative analysis of 93,712 emergency calls to the Hamad Medical Corporation Ambulance Service (HMCAS) between January 1 and May 31, 2023, obtained from the HMCAS electronic system, was analyzed to determine pertinent variables. Sociodemographic, emergency dispatch‐related, clinical, and miscellaneous predictors were analyzed. Descriptive, bivariate, ridge logistic regression, and combination analyses were evaluated.Results23.95% (N = 21,194) and 76.05% (N = 67,285) resulted in patient nontransport and transportation, respectively. Sociodemographic analysis revealed that males predominantly activated EMS resources, and 60% of males (n = 12,687) were not transported, whilst 65% of females (n = 44,053) were transported. South Asians represented a significant proportion of the transported patients (36%, n = 24,007). “Home” emerged as the primary emergency location (56%, n = 37,725). Bivariate analysis revealed significant associations across several variables, though multicollinearity was identified as a challenge. Ridge regression analysis underscored the role of certain predictors, such as missing provisional diagnoses, in transportation decisions. The upset plot shows that hypertension and diabetes mellitus were the most common combinations in both groups.ConclusionsThis study highlights the nuanced complexities governing conveyance decisions. By unveiling patterns such as male predominance, which reflects Qatar's expatriate population, and specific temporal EMS activity peaks, this study accentuates the importance of holistic patient assessment that transcends medical histories.

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“…Types of ML can be broadly and not strictly categorised by their functionality, such as classification, prediction, or clustering or by the degree of user input, such as supervised, semi-supervised and unsupervised. 58 , 59 …”

Section: Introductionmentioning

confidence: 99%

In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses

Boswell¹,

Verga²,

Mackle³

et al. 2023

IDR

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The urgent need for SARS-CoV-2 controls has led to a reassessment of approaches to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. There are yet no clinically approved broad-spectrum antivirals available for beta-coronaviruses. Discovery pipelines for pan-virus medications against a broad range of betacoronaviruses are therefore a priority. A variety of marine natural product (MNP) small molecules have shown inhibitory activity against viral species. Access to large data caches of small molecule structural information is vital to finding new pharmaceuticals. Increasingly, molecular docking simulations are being used to narrow the space of possibilities and generate drug leads. Combining in-silico methods, augmented by metaheuristic optimization and machine learning (ML) allows the generation of hits from within a virtual MNP library to narrow screens for novel targets against coronaviruses. In this review article, we explore current insights and techniques that can be leveraged to generate broad-spectrum antivirals against betacoronaviruses using in-silico optimization and ML. ML approaches are capable of simultaneously evaluating different features for predicting inhibitory activity. Many also provide a semi-quantitative measure of feature relevance and can guide in selecting a subset of features relevant for inhibition of SARS-CoV-2.

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Machine Learning Overview

Cited by 20 publications

References 14 publications

Feature Extraction of Athlete’s Post-Match Psychological and Emotional Changes Based on Deep Learning

Feature Extraction of Athlete’s Post-Match Psychological and Emotional Changes Based on Deep Learning

Epidemiology of prehospital emergency calls according to patient transport decision in a middle eastern emergency care environment: Retrospective cohort‐based

In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses

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