scholarly journals A Machine Learning approach to enhance indoor thermal comfort in a changing climate

2021 ◽  
Vol 2042 (1) ◽  
pp. 012070
Author(s):  
Tobias Kramer ◽  
Veronica Garcia-Hansen ◽  
Sara Omrani Vahid M. Nik ◽  
Dong Chen
Keyword(s):  
Thermal Comfort ◽  
Data Science ◽  
Prediction Models ◽  
Computational Design ◽  
Learning Approach ◽  
Indoor Thermal Comfort ◽  
Machine Learning Approach ◽  
Early Case ◽  
Building Characteristics

Abstract This paper presents an alternative workflow for thermal comfort prediction. By using the leverage of Data Science & AI in combination with the power of computational design, the proposed methodology exploits the extensive comfort data provided by the ASHRAE Global Thermal Comfort Database II to generate more customised comfort prediction models. These models consider additional, often significant input parameters like location and specific building characteristics. Results from an early case study indicate that such an approach has the potential for more accurate comfort predictions that eventually lead to more efficient and comfortable buildings.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

Satisfaction and Consumption Emotions of Library Users at a Public University in Mexico: A Case Study

Libri ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Blanca-Lidia Miranda-Valencia
Keyword(s):  
Education Institution ◽  
Public University ◽  
Library Services ◽  
Learning Approach ◽  
Business Research ◽  
Consumption Emotions ◽  
Library Users ◽  
Machine Learning Approach ◽  
The Relationship

Abstract Consumption emotions are not always considered when satisfaction with library services is assessed. In this research, consumption emotions perceived by users of eight different libraries of a Mexican higher education institution are identified when using library services. Laros and Steenkamp. 2005. “Emotions in Consumer Behavior: A Hierarchical Approach.” Journal of Business Research 58: 1437–45. https://doi.org/10.1016/j.jbusres.2003.09.013 hierarchical scale was used to assess library users’ consumption emotions. The relationship between those emotions and the users’ satisfaction is then established and analyzed using both descriptive statistics analysis and an entropy-oriented machine learning approach. The first approach suggests that users feel more positive consumption emotions (contentment and happiness) than negative emotions (anger). The entropy analysis shows that the identified consumption emotions have a great prediction power over the satisfaction level that users will manifest. This research contributes to the issue of satisfaction assessment by including library users’ consumption emotions in Mexico.

scholarly journals Indoor Thermal Comfort Analysis: A Case Study of Modern and Traditional Buildings in Hot-Arid Climatic Region of Ethiopia

Urban Science ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 53
Author(s):  
Haven Hailu ◽  
Eshetu Gelan ◽  
Yared Girma
Keyword(s):  
Thermal Comfort ◽  
Significant Role ◽  
Indoor Environment ◽  
Thermal Sensation ◽  
Climatic Region ◽  
Contributing Factors ◽  
Objective Measurements ◽  
Indoor Thermal Comfort ◽  
Adaptive Comfort

Indoor thermal comfort is an essential aspect of sustainable architecture and it is critical in maintaining a safe indoor environment. Expectations, acceptability, and preferences of traditional and modern buildings are different in terms of thermal comfort. This study, therefore, attempts to evaluate the indoor thermal comforts of modern and traditional buildings and identify the contributing factors that impede or facilitate indoor thermal comfort in Semera city, Ethiopia. This study employed subjective and objective measurements. The subjective measurement is based on the ASHRAE seven-point thermal sensation scale. An adaptive comfort model was employed according to the ASHRAE standard to evaluate indoor thermal comfort. The results revealed that with regards to thermal sensational votes between −1 and +1, 88% of the respondents are satisfied with the indoor environment in traditional houses, while in modern houses this figure is 22%. Likewise, 83% of occupants in traditional houses expressed a preference for their homes to remain the same or be only slightly cooler or warmer. Traditional houses were, on average, in compliance with the 80% acceptability band of the adaptive comfort standard. The study investigated that traditional building techniques and materials, in combination with consideration of microclimate, were found to play a significant role in regulating the indoor environment.

scholarly journals COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model

2019 ◽  
Vol 1 (1) ◽  
pp. 32-44
Author(s):  
Joseph Simonian ◽  
Chenwei Wu ◽  
Daniel Itano ◽  
Vyshaal Narayanam
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Learning Approach ◽  
Machine Learning Approach
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