Chapter 7 Data model recommendations for real-time machine learning applications: a suggestive approach

Mahrishi, Mehul; Morwal, Sudha; Jain, Vipin; Kalla, Mukesh

doi:10.1515/9783110702514-007

Cited by 12 publications

(5 citation statements)

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“…The state-of-the-art AI models vary from traditional machine learning models (such as Support Vector Machines, Decision Trees, etc) to advanced deep learning-based models. 46 Traditional machine learning models have the capability to train static features for the development of explanatory models, whereas deep learning models can train raw ECG signals based on spatial and temporal context. The deep learning models will be built upon earlier work developed for healthy subjects.…”

Section: Data Modellingmentioning

confidence: 99%

Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions

et al. 2023

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IntroductionHypoglycaemia is a harmful potential complication in people with type 1 diabetes mellitus (T1DM) and can be exacerbated in patients receiving treatment, such as insulin therapies, by the very interventions aiming to achieve optimal blood glucose levels. Symptoms can vary greatly, including, but not limited to, trembling, palpitations, sweating, dry mouth, confusion, seizures, coma, brain damage or even death if untreated. A pilot study with healthy (euglycaemic) participants previously demonstrated that hypoglycaemia can be detected non-invasively with artificial intelligence (AI) using physiological signals obtained from wearable sensors. This protocol provides a methodological description of an observational study for obtaining physiological data from people with T1DM. The aim of this work is to further improve the previously developed AI model and validate its performance for glycaemic event detection in people with T1DM. Such a model could be suitable for integrating into a continuous, non-invasive, glucose monitoring system, contributing towards improving surveillance and management of blood glucose for people with diabetes.Methods and analysisThis observational study aims to recruit 30 patients with T1DM from a diabetes outpatient clinic at the University Hospital Coventry and Warwickshire for a two-phase study. The first phase involves attending an inpatient protocol for up to 36 hours in a calorimetry room under controlled conditions, followed by a phase of free-living, for up to 3 days, in which participants will go about their normal daily activities unrestricted. Throughout the study, the participants will wear wearable sensors to measure and record physiological signals (eg, ECG and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep learning methods.Ethics and disseminationThis study has received ethical approval from National Research Ethics Service (ref: 17/NW/0277). The findings will be disseminated via peer-reviewed journals and presented at scientific conferences.Trial registration numberNCT05461144.

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Section: Data Modellingmentioning

confidence: 99%

Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions

et al. 2023

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“…Over the past decade, machine learning has been used to model real-life problems and successfully assist humanity in handling those [115][116][117]. State-of-the-art development of concrete mixtures and their sophisticated applications have spawned a necessity to use more precise and numerical models to predict their properties.…”

Section: Regression Through Machine Learning Approachesmentioning

confidence: 99%

“…This study comprehensively analyzed 77 mix designs to generate a model for flexural strength [44,107,[152][153][154][155][156][157][158][159][160][161][162][163][164][165][166][167]. Additionally, 49 mix designs were examined to develop a model for tensile strength [117][118][119][120][121][122]129,[132][133][134][135]. The data collected from these mix designs were used to train and test the models to predict novel mix designs' flexural and tensile strengths accurately.…”

Section: Overview Of Datasetmentioning

confidence: 99%

Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete

et al. 2023

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The additive manufacturing of concrete, also known as 3D-printed concrete, is produced layer by layer using a 3D printer. The three-dimensional printing of concrete offers several benefits compared to conventional concrete construction, such as reduced labor costs and wastage of materials. It can also be used to build complex structures with high precision and accuracy. However, optimizing the mix design of 3D-printed concrete is challenging, involving numerous factors and extensive hit-and-trail experimentation. This study addresses this issue by developing predictive models, such as the Gaussian Process Regression model, Decision Tree Regression model, Support Vector Machine model, and XGBoost Regression models. The input parameters were water (Kg/m3), cement (Kg/m3), silica fume (Kg/m3), fly ash (Kg/m3), coarse aggregate (Kg/m3 & mm for diameter), fine aggregate (Kg/m3 & mm for diameter), viscosity modifying agent (Kg/m3), fibers (Kg/m3), fiber properties (mm for diameter and MPa for strength), print speed (mm/sec), and nozzle area (mm2), while target properties were the flexural and tensile strength of concrete (MPa data from 25 literature studies were collected. The water/binder ratio used in the dataset ranged from 0.27 to 0.67. Different types of sands and fibers have been used, with fibers having a maximum length of 23 mm. Based upon the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) for casted and printed concrete, the SVM model performed better than other models. All models’ cast and printed flexural strength values were also correlated. The model’s performance has also been checked on six different mix proportions from the dataset to show its accuracy. It is worth noting that the lack of ML-based predictive models for the flexural and tensile properties of 3D-printed concrete in the literature makes this study a novel innovation in the field. This model could reduce the computational and experimental effort required to formulate the mixed design of printed concrete.

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“…Deep learning is defined as a branch of machine learning that gives artificial intelligence the upper hand. It will attempt to become closer to its ultimate goal [14].…”

Section: Introductionmentioning

confidence: 99%

A Promising Smart Healthcare Monitoring Model based on Internet of Things and Deep Learning Techniques

Al-Hadi,

Al-Gaphari,

Al-Baltah

et al. 2024

jast

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Using Internet of Things (IoT) and deep learning techniques in the healthcare sector has gained significant momentum in recent years. These technologies have the potential to transform traditional healthcare monitoring systems into real-time data collection, analysis, and decision-making capabilities. While several models have been developed to assist people with heart diseases, numerous obstacles still impede the effectiveness of the current solutions, such as power consumption, latency, accuracy, and scalability. Therefore, this study aims to develop a promising smart healthcare monitoring model that integrates IoT and deep learning techniques for saving patient lives. In addition, it clarifies the current research gap. The methodology used in this study was a literature review, which was conducted to identify relevant studies on IoT and deep learning applications in healthcare and find the gaps in each. This model consists of three main components: data acquisition through IoT devices, data processing using deep learning algorithms, and decision-making based on analyzed data. Moreover, it showed an unstable rate of accuracy in the current studies, which were taken from 2021 to 2023. In the future, our proposed smart healthcare monitoring model will solve that gap, which is already available in the current studies, and that will be proven using real-time materials such as Arduino and heart disease sensors.

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Chapter 7 Data model recommendations for real-time machine learning applications: a suggestive approach

Cited by 12 publications

References 0 publications

Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions

Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions

Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete

A Promising Smart Healthcare Monitoring Model based on Internet of Things and Deep Learning Techniques

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