A systematic review and comprehensive analysis of building occupancy prediction

T Li, X Liu, G Li, X Wang, J Ma, C Xu, Q Mao - Renewable and Sustainable …, 2024‏ - Elsevier
Buildings account for a significant portion of the global energy consumption. Forecasting
personnel occupancy is critical for reducing energy consumption in buildings. This study …

Occupancy prediction in iot-enabled smart buildings: Technologies, methods, and future directions

I Khan, O Zedadra, A Guerrieri, G Spezzano - Sensors, 2024‏ - mdpi.com
In today's world, a significant amount of global energy is used in buildings. Unfortunately, a
lot of this energy is wasted, because electrical appliances are not used properly or …

[HTML][HTML] Modeling industrial hydrocyclone operational variables by SHAP-CatBoost-A “conscious lab” approach

SC Chelgani, H Nasiri, A Tohry, HR Heidari - Powder Technology, 2023‏ - Elsevier
Undoubtedly hydrocyclones play a critical role in powder technology, which can
considerably affect the plants' process efficiency. However, hydrocyclones were rarely …

Toward secured iot-based smart systems using machine learning

MS Abdalzaher, MM Fouda, HA Elsayed… - IEEE access, 2023‏ - ieeexplore.ieee.org
Machine learning (ML) and the internet of things (IoT) are among the most booming
research directions. Smart cities, smart campuses (SCs), smart homes, smart cars, early …

Dynamic prediction and optimization of tunneling parameters with high reliability based on a hybrid intelligent algorithm

H Chen, QG Shen, MJ Skibniewski, Y Cao, Y Liu - Information Fusion, 2025‏ - Elsevier
In this paper, a hybrid intelligent framework comprising Bayesian optimization (BO), gradient
boosting with categorical features (CatBoost) and the nondominated sorting genetic …

Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed

D Rajković, AM Jeromela, L Pezo, B Lončar… - Journal of Food …, 2023‏ - Elsevier
With the aid of models used in artificial intelligence, a wide range of data can be processed
quickly with high accuracy. The quality of rapeseed oil from 40 genotypes cultivated during …

Machine learning benchmarking for secured iot smart systems

MS Abdalzaher, MM Salim, HA Elsayed… - … on Internet of Things …, 2022‏ - ieeexplore.ieee.org
Smartness and IoT along with machine learning (ML) lead the research directions
nowadays. Smart city, smart campus, smart home, smart vehicle, etc; or if we call it “Smart x” …

Edge-based real-time occupancy detection system through a non-intrusive sensing system

AN Sayed, F Bensaali, Y Himeur, M Houchati - Energies, 2023‏ - mdpi.com
Building automation and the advancement of sustainability and safety in internal spaces
benefit significantly from occupancy sensing. While particular traditional Machine Learning …

Predicting road traffic accidents—Artificial neural network approach

D Gatarić, N Ruškić, B Aleksić, T Đurić, L Pezo… - Algorithms, 2023‏ - mdpi.com
Road traffic accidents are a significant public health issue, accounting for almost 1.3 million
deaths worldwide annually, with millions more experiencing non-fatal injuries. A variety of …

Development of aggregated random intelligent approach for the modeling of desalination processes

A Mahdavi-Meymand, W Sulisz - Desalination, 2023‏ - Elsevier
The boiling point rise (BPR) is a critical parameter in the operation and optimal design of a
multi-stage flash desalination system. Accurate prediction of BPR would increase the …