Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine
R Hamamoto, K Takasawa, H Machino… - Briefings in …, 2022 - academic.oup.com
The increase in the expectations of artificial intelligence (AI) technology has led to machine
learning technology being actively used in the medical field. Non-negative matrix …
learning technology being actively used in the medical field. Non-negative matrix …
Machine Learning‐Based CO2 Prediction for Office Room: A Pilot Study
Air pollution is increasing profusely in Indian cities as well as throughout the world, and it
poses a major threat to climate as well as the health of all living things. Air pollution is the …
poses a major threat to climate as well as the health of all living things. Air pollution is the …
In-situ sensor calibration for building HVAC systems with limited information using general regression improved Bayesian inference
Sensors in building heating, ventilation and air-conditioning systems (HVACs) play
important roles in maintaining indoor environmental quality and energy consumption. Owing …
important roles in maintaining indoor environmental quality and energy consumption. Owing …
Augmenting energy time-series for data-efficient imputation of missing values
This study explores the applicability of data augmentation techniques for reconstructing
missing energy time-series in limited data regimes. In particular, multiple synthetic copies of …
missing energy time-series in limited data regimes. In particular, multiple synthetic copies of …
Sensor deployment configurations for building energy consumption prediction
N Bucarelli, N El-Gohary - Energy and Buildings, 2024 - Elsevier
Sensor-based data-driven building energy consumption prediction could play an important
role in designing and operating energy-efficient buildings. However, existing approaches …
role in designing and operating energy-efficient buildings. However, existing approaches …
Analysis of the building occupancy estimation and prediction process: A systematic review
The prediction of the occupancy in buildings is essential to design efficient energy control
strategies that optimize consumption and reduce losses while guaranteeing the comfort of …
strategies that optimize consumption and reduce losses while guaranteeing the comfort of …
[HTML][HTML] Unsupervised outlier detection for time-series data of indoor air quality using LSTM autoencoder with ensemble method
The proposed framework consists of three modules as an outlier detection method for indoor
air quality data. We first use a long short-term memory autoencoder (LSTM-AE) based …
air quality data. We first use a long short-term memory autoencoder (LSTM-AE) based …
[HTML][HTML] A novel Edge architecture and solution for detecting concept drift in smart environments
The proliferation of the Internet of Things (IoT), artificial intelligence (AI), the adoption of 5G,
and progress towards 6G technology have led to the accumulation of massive amounts of …
and progress towards 6G technology have led to the accumulation of massive amounts of …
Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building
MH Sulaiman, Z Mustaffa - Journal of Building Engineering, 2023 - Elsevier
This paper presents a simulation study focused on optimizing user comfort and energy
consumption in smart buildings. Managing energy efficiently in smart buildings poses a …
consumption in smart buildings. Managing energy efficiently in smart buildings poses a …
Imputation of missing values in residential building monitored data: Energy consumption, behavior, and environment information
This study compares missing value handling algorithms based on the type of missing value
occurrence for building monitored data. The study data comprised energy consumption and …
occurrence for building monitored data. The study data comprised energy consumption and …