Machine Learning Framework for the Sustainable Maintenance of Building Facilities

The importance of sustainable building maintenance is growing as part of the Sustainable Building concept. The integration and implementation of new technologies such as the Internet of Things (IoT), smart sensors, and information and communication technology (ICT) into building facilities generate a large amount of data that will be utilized to better manage the sustainable building maintenance and staff. Anomaly prediction models assist facility managers in informing operators to perform scheduled maintenance and visualizing predicted facility anomalies on building information models (BIM). This study proposes a Machine Learning (ML) anomaly prediction model for sustainable building facility maintenance using an IoT sensor network and a BIM model. The suggested framework shows the data management technique of the anomaly prediction model in the 3D building model. The case study demonstrated the framework’s competence to predict anomalies in the heating ventilation air conditioning (HVAC) system. Furthermore, data collected from various simulated conditions of the building facilities was utilized to monitor and forecast anomalies in the 3D model of the fan coil. The faults were then predicted using a classification model, and the results of the models are introduced. Finally, the IoT data from the building facility and the predicted values of the ML models are visualized in the building facility’s BIM model and the real-time monitoring dashboard, respectively.

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Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data

Advances in Meteorology ◽

10.1155/2018/5024930 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 12

Author(s):

Changhyun Choi ◽

Jeonghwan Kim ◽

Jongsung Kim ◽

Donghyun Kim ◽

Younghye Bae ◽

...

Keyword(s):

Machine Learning ◽

Big Data ◽

Prediction Model ◽

Prediction Models ◽

Meteorological Data ◽

Heavy Rain ◽

Machine Learning Techniques ◽

Damage Prediction ◽

Explanatory Variables ◽

The Republic

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.

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Machine Learning-Based Prediction Model for Papillary Thyroid Carcinoma Recurrence

10.21203/rs.3.rs-113105/v1 ◽

2020 ◽

Author(s):

Young Min Park ◽

Byung-Joo Lee

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Tumor Size ◽

Large Scale ◽

Prediction Models ◽

Prognostic Significance ◽

Disease Recurrence ◽

Machine Learning Techniques ◽

Papillary Thyroid ◽

Recurrence Prediction

Abstract Background: This study analyzed the prognostic significance of nodal factors, including the number of metastatic LNs and LNR, in patients with PTC, and attempted to construct a disease recurrence prediction model using machine learning techniques.Methods: We retrospectively analyzed clinico-pathologic data from 1040 patients diagnosed with papillary thyroid cancer between 2003 and 2009. Results: We analyzed clinico-pathologic factors related to recurrence through logistic regression analysis. Among the factors that we included, only sex and tumor size were significantly correlated with disease recurrence. Parameters such as age, sex, tumor size, tumor multiplicity, ETE, ENE, pT, pN, ipsilateral central LN metastasis, contralateral central LNs metastasis, number of metastatic LNs, and LNR were input for construction of a machine learning prediction model. The performance of five machine learning models related to recurrence prediction was compared based on accuracy. The Decision Tree model showed the best accuracy at 95%, and the lightGBM and stacking model together showed 93% accuracy. Conclusions: We confirmed that all machine learning prediction models showed an accuracy of 90% or more for predicting disease recurrence in PTC. Large-scale multicenter clinical studies should be performed to improve the performance of our prediction models and verify their clinical effectiveness.

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Determination of Significant Features for Building an Efficient Heart Disease Prediction System

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3393.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 4499-4504

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Prediction Model ◽

Prediction Models ◽

Heart Diseases ◽

Medical Diagnostics ◽

Medical Data ◽

Machine Learning Algorithms ◽

Prediction System ◽

Early Stages

Heart diseases are responsible for the greatest number of deaths all over the world. These diseases are usually not detected in early stages as the cost of medical diagnostics is not affordable by a majority of the people. Research has shown that machine learning methods have a great capability to extract valuable information from the medical data. This information is used to build the prediction models which provide cost effective technological aid for a medical practitioner to detect the heart disease in early stages. However, the presence of some irrelevant and redundant features in medical data deteriorates the competence of the prediction system. This research was aimed to improve the accuracy of the existing methods by removing such features. In this study, brute force-based algorithm of feature selection was used to determine relevant significant features. After experimenting rigorously with 7528 possible combinations of features and 5 machine learning algorithms, 8 important features were identified. A prediction model was developed using these significant features. Accuracy of this model is experimentally calculated to be 86.4%which is higher than the results of existing studies. The prediction model proposed in this study shall help in predicting heart disease efficiently.

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Development of the Hypertension Index Model in General Adult Using the Korea National Health and Nutritional Examination Survey and the Korean Genome and Epidemiology Study (Preprint)

10.2196/preprints.31024 ◽

2021 ◽

Author(s):

Myung Jae Seo ◽

Sung Gyun Ahn ◽

Yong-Jae Lee ◽

Jong Koo Kim

Keyword(s):

Prediction Model ◽

National Health ◽

Prediction Models ◽

Classification Model ◽

Nutrition Examination Survey ◽

Epidemiology Study ◽

Index Model ◽

Health And Nutrition ◽

Specific Prediction ◽

And Control

BACKGROUND Hypertension, a risk factor for cardiovascular disease and all-cause mortality, has been increasing. Along with emphasizing awareness and control of hypertension, predicting the incidence of hypertension is important. Several studies have previously reported prediction models of hypertension. However, among the previous models for predicting hypertension, few models reflect various risk factors for hypertension. OBJECTIVE We constructed a sex-specific prediction model using Korean datasets, which included socioeconomic status, medical history, lifestyle-related variables, anthropometric status, and laboratory indices. METHODS We utilized the data from the Korea National Health and Nutrition Examination Survey from 2011 to 2015 to derive a hypertension prediction model. Participants aged 40 years or older. We constructed a sex-specific hypertension classification model using logistic regression and features obtained by literature review and statistical analysis. RESULTS We constructed a sex-specific hypertension classification model including approximately 20 variables. We estimated its performance using the Korea National Health and Nutrition Examination Survey dataset from 2016 to 2018 (AUC = 0.807 in men, AUC = 0.854 in women). The performance of our hypertension model was considered significant based on the cumulative incidence calculated from a longitudinal dataset, the Korean Genome and Epidemiology Study dataset. CONCLUSIONS We developed this hypertension prediction model using features that could be collected in a clinical office without difficulty. Individualized results may alert a person at high risk to modify unhealthy lifestyles.

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Impact of Industry 4.0 in Architecture and Cultural Heritage

Impact of Industry 4.0 on Architecture and Cultural Heritage - Advances in Civil and Industrial Engineering ◽

10.4018/978-1-7998-1234-0.ch013 ◽

2020 ◽

pp. 306-329

Author(s):

Claudio Mirarchi ◽

Alberto Pavan ◽

Beniamino Di Martino ◽

Antonio Esposito

Keyword(s):

Machine Learning ◽

Expert Systems ◽

Cultural Heritage ◽

Industry 4.0 ◽

Data Use ◽

Building Information Modelling ◽

Information Modelling ◽

Information Models ◽

Building Information ◽

Digital Environments

Building Information Modelling (BIM) is recognized as the central mean in the digitalization process of the construction sector affecting both the technological and the organizational levels. The use of information models can empower communication capabilities thus addressing one of the main development directions of industry 4.0. However, several issues can be highlighted in the representation of objects through information models especially in the case of existing and/or historical buildings. This chapter proposes an extensive analysis of the use of BIM for existing assets exploring the recent development in the area of machine learning and in the use of ontologies to overcome the existing issues. It will provide a structured presentation of existing works and of perspectives in the use of ontologies, expert systems, and machine learning application in architecture and cultural heritage focusing on communication and data use in digital environments along the industry 4.0 paradigm.

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Software Maintainability: Systematic Literature Review and Current Trends

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194016500431 ◽

2016 ◽

Vol 26 (08) ◽

pp. 1221-1253 ◽

Cited By ~ 16

Author(s):

Ruchika Malhotra ◽

Anuradha Chug

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Evaluation System ◽

Prediction Models ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Design Metrics ◽

Current Trends ◽

Software Maintainability ◽

Early Phases

Software maintenance is an expensive activity that consumes a major portion of the cost of the total project. Various activities carried out during maintenance include the addition of new features, deletion of obsolete code, correction of errors, etc. Software maintainability means the ease with which these operations can be carried out. If the maintainability can be measured in early phases of the software development, it helps in better planning and optimum resource utilization. Measurement of design properties such as coupling, cohesion, etc. in early phases of development often leads us to derive the corresponding maintainability with the help of prediction models. In this paper, we performed a systematic review of the existing studies related to software maintainability from January 1991 to October 2015. In total, 96 primary studies were identified out of which 47 studies were from journals, 36 from conference proceedings and 13 from others. All studies were compiled in structured form and analyzed through numerous perspectives such as the use of design metrics, prediction model, tools, data sources, prediction accuracy, etc. According to the review results, we found that the use of machine learning algorithms in predicting maintainability has increased since 2005. The use of evolutionary algorithms has also begun in related sub-fields since 2010. We have observed that design metrics is still the most favored option to capture the characteristics of any given software before deploying it further in prediction model for determining the corresponding software maintainability. A significant increase in the use of public dataset for making the prediction models has also been observed and in this regard two public datasets User Interface Management System (UIMS) and Quality Evaluation System (QUES) proposed by Li and Henry is quite popular among researchers. Although machine learning algorithms are still the most popular methods, however, we suggest that researchers working on software maintainability area should experiment on the use of open source datasets with hybrid algorithms. In this regard, more empirical studies are also required to be conducted on a large number of datasets so that a generalized theory could be made. The current paper will be beneficial for practitioners, researchers and developers as they can use these models and metrics for creating benchmark and standards. Findings of this extensive review would also be useful for novices in the field of software maintainability as it not only provides explicit definitions, but also lays a foundation for further research by providing a quick link to all important studies in the said field. Finally, this study also compiles current trends, emerging sub-fields and identifies various opportunities of future research in the field of software maintainability.

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A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19

Big Data and Cognitive Computing ◽

10.3390/bdcc4040033 ◽

2020 ◽

Vol 4 (4) ◽

pp. 33

Author(s):

Toni Pano ◽

Rasha Kashef

Keyword(s):

Machine Learning ◽

Social Media ◽

Prediction Model ◽

Sentiment Analysis ◽

Significant Role ◽

Prediction Models ◽

Financial Sector ◽

Research Gap ◽

Text Preprocessing ◽

The Impact

During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.

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Development of Combined Heavy Rain Damage Prediction Models with Machine Learning

Water ◽

10.3390/w11122516 ◽

2019 ◽

Vol 11 (12) ◽

pp. 2516 ◽

Cited By ~ 1

Author(s):

Changhyun Choi ◽

Jeonghwan Kim ◽

Jungwook Kim ◽

Hung Soo Kim

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Prediction Model ◽

Prediction Models ◽

Predictive Performance ◽

Heavy Rain ◽

Learning Models ◽

Damage Prediction ◽

Natural Disaster Management ◽

Machine Learning Models

Adequate forecasting and preparation for heavy rain can minimize life and property damage. Some studies have been conducted on the heavy rain damage prediction model (HDPM), however, most of their models are limited to the linear regression model that simply explains the linear relation between rainfall data and damage. This study develops the combined heavy rain damage prediction model (CHDPM) where the residual prediction model (RPM) is added to the HDPM. The predictive performance of the CHDPM is analyzed to be 4–14% higher than that of HDPM. Through this, we confirmed that the predictive performance of the model is improved by combining the RPM of the machine learning models to complement the linearity of the HDPM. The results of this study can be used as basic data beneficial for natural disaster management.

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Identification of Metabolic Syndrome Based on Anthropometric, Blood and Spirometric Risk Factors Using Machine Learning

Applied Sciences ◽

10.3390/app10217741 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7741

Author(s):

Sang Yeob Kim ◽

Gyeong Hee Nam ◽

Byeong Mun Heo

Keyword(s):

Machine Learning ◽

Metabolic Syndrome ◽

Logistic Regression ◽

Prediction Model ◽

Prediction Models ◽

Strong Association ◽

Forced Expiratory Volume ◽

Binary Logistic Regression Analysis ◽

Cross Sectional Survey ◽

Cross Sectional

Metabolic syndrome (MS) is an aggregation of coexisting conditions that can indicate an individual’s high risk of major diseases, including cardiovascular disease, stroke, cancer, and type 2 diabetes. We conducted a cross-sectional survey to evaluate potential risk factor indicators by identifying relationships between MS and anthropometric and spirometric factors along with blood parameters among Korean adults. A total of 13,978 subjects were enrolled from the Korea National Health and Nutrition Examination Survey. Statistical analysis was performed using a complex sampling design to represent the entire Korean population. We conducted binary logistic regression analysis to evaluate and compare potential associations of all included factors. We constructed prediction models based on Naïve Bayes and logistic regression algorithms. The performance evaluation of the prediction model improved the accuracy with area under the curve (AUC) and calibration curve. Among all factors, triglyceride exhibited a strong association with MS in both men (odds ratio (OR) = 2.711, 95% confidence interval (CI) [2.328–3.158]) and women (OR = 3.515 [3.042–4.062]). Regarding anthropometric factors, the waist-to-height ratio demonstrated a strong association in men (OR = 1.511 [1.311–1.742]), whereas waist circumference was the strongest indicator in women (OR = 2.847 [2.447–3.313]). Forced expiratory volume in 6s and forced expiratory flow 25–75% strongly associated with MS in both men (OR = 0.822 [0.749–0.903]) and women (OR = 1.150 [1.060–1.246]). Wrapper-based logistic regression prediction model showed the highest predictive power in both men and women (AUC = 0.868 and 0.932, respectively). Our findings revealed that several factors were associated with MS and suggested the potential of employing machine learning models to support the diagnosis of MS.

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