Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review

Ciaburro, Giuseppe; Iannace, Gino

doi:10.3390/data6060055

Cited by 24 publications

(9 citation statements)

References 179 publications

(161 reference statements)

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“…The input data for the model were carried out from data-sheet of materials manufacturers or calculated with models published in scientific literature. 12,33 –36…”

Section: Methodsmentioning

confidence: 99%

The influence of the acoustic performance in the certification of a school buildings according to the ITACA protocol

et al. 2022

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The study and analysis of the processes that determine environmental sustainability of buildings are nowadays topics of great interest for the sustainable, economic and social development of the cities. The international context presents guidelines and protocols for the energy-environmental sustainability, often aimed to assigning a sustainability label to the buildings. In Italy one of the most used rating systems is the ITACA Protocol, recently updated by the Italian Standard UNI/Pdr 2019, which through a series of criteria identifies global indicators of the sustainability of the building examined. This paper presents the potential of the ITACA protocol which could be a method for designing innovative and efficient architectures. In particular, the aspects related to the acoustic comfort are examined and how these criteria change the overall performance of a building. The evaluation of the sustainability performance was applied to a school building. Results show that an optimal acoustic quality improve over to 2 point of the final protocol score.

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“…The input data for the model were carried out from data-sheet of materials manufacturers or calculated with models published in scientific literature. 12,33 –36…”

Section: Methodsmentioning

confidence: 99%

The influence of the acoustic performance in the certification of a school buildings according to the ITACA protocol

et al. 2022

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“…Future work in this area should focus more on the realization of the simulation on both edge and fog platforms, as well as on the translation to digital farm solutions such as mobile and web apps for local farmers who, in most cases, could not afford the high cost of sensors and hardware installation on their farms. Readers are referred to the literature [31,83,[129][130][131][132][133] for more details about unsupervised learning. The system made use of data from soil moisture content, air temperature, and leaf wetness and compares it with predetermined threshold values of various soil and specific crops to guide irrigation decisions.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

et al. 2022

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Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

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“…Among these multiple DL frameworks, due to parameter efficiency and spatial invariance property, CNN has gained adoption for many problems, including time series [18]. Further, Wang et al [19] and Yang et al [20] demonstrated that encoding time series as images using Gramian Angular Field (GAF) and Markov Transition Fields (MTF) for CNNs leads to superior performance and in the identification of patterns not found in the one-dimensional sequence methods.…”

Section: Introductionmentioning

confidence: 99%

Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network

Sharma¹,

Coronato²,

Brown³

2022

Preprint

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Exercise testing has been available for more than a half-century and is a remarkably versatile tool for diagnostic and prognostic information of patients for a range of diseases, especially cardiovascular and pulmonary. With rapid advancements in technology, wearables, and learning algorithm in the last decade, its scope has evolved. Specifically, Cardiopulmonary exercise testing (CPX) is one of the most commonly used laboratory tests for objective evaluation of exercise capacity and performance levels in patients. CPX provides a non-invasive, integrative assessment of the pulmonary, cardiovascular, and skeletal muscle systems involving the measurement of gas exchanges. However, its assessment is challenging, requiring the individual to process multiple time series data points, leading to simplification to peak values and slopes. But this simplification can discard the valuable trend information present in these time series. In this work, we encode the time series as images using the Gramian Angular Field and Markov Transition Field and use it with a convolutional neural network and attention pooling approach for the classification of heart failure and metabolic syndrome patients. Using GradCAMs, we highlight the discriminative features identified by the model.Clinical relevance -The proposed framework can process multivariate exercise testing time-series data and accurately predict cardiovascular diseases. Interpretable Grad-CAMs can be obtained to explain the prediction.

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Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review

Cited by 24 publications

References 179 publications

The influence of the acoustic performance in the certification of a school buildings according to the ITACA protocol

The influence of the acoustic performance in the certification of a school buildings according to the ITACA protocol

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network

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