Machine learning methods in smart lighting toward achieving user comfort: a survey
Smart lighting has become a universal smart product solution, with global revenues of up to
US 5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) …
US 5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) …
Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges
With the rapid development of single-cell RNA-sequencing techniques, various
computational methods and tools were proposed to analyze these high-throughput data …
computational methods and tools were proposed to analyze these high-throughput data …
Divclust: Controlling diversity in deep clustering
IM Metaxas, G Tzimiropoulos… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Clustering has been a major research topic in the field of machine learning, one to which
Deep Learning has recently been applied with significant success. However, an aspect of …
Deep Learning has recently been applied with significant success. However, an aspect of …
[HTML][HTML] A methodology for urban planning generation: A novel approach based on generative design
I Pérez-Martínez, M Martínez-Rojas… - … Applications of Artificial …, 2023 - Elsevier
The construction sector is undergoing a digital transformation that aims to increase
productivity, improve processes and take advantage of the new advances in digitalization …
productivity, improve processes and take advantage of the new advances in digitalization …
Resilience analysis in road traffic systems to rainfall events: Road environment perspective
W Gao, X Hu, N Wang - Transportation research part D: transport and …, 2024 - Elsevier
The road traffic system is frequently disrupted by rainfall events, significantly impacting road
system efficiency. It is challenging to capture the resilience of road traffic systems due to …
system efficiency. It is challenging to capture the resilience of road traffic systems due to …
Adaptive weighted ensemble clustering via kernel learning and local information preservation
T Li, X Shu, J Wu, Q Zheng, X Lv, J Xu - Knowledge-Based Systems, 2024 - Elsevier
Ensemble clustering refers to learning a robust and accurate consensus result from a
collection of base clustering results. Despite extensive research on this topic, it remains …
collection of base clustering results. Despite extensive research on this topic, it remains …
Impact of climate change induced future rainfall variation on dynamics of arid-humid zone transition in the western province of India
The transition of the Earth's climate from one zone to another is one of the major causes
behind biodiversity loss, rural-urban migration, and increasing food crises. The rising rate of …
behind biodiversity loss, rural-urban migration, and increasing food crises. The rising rate of …
Multi-population Black Hole Algorithm for the problem of data clustering
The retrieval of important information from a dataset requires applying a special data mining
technique known as data clustering (DC). DC classifies similar objects into a groups of …
technique known as data clustering (DC). DC classifies similar objects into a groups of …
An algorithmic approach to identification of gray areas: Analysis of sleep scoring expert ensemble non agreement areas using a multinomial mixture model
Abstract Machine learning (ML) models have become a key component in modern world
services. In decision-making domains where human expertise is crucial, for example, for …
services. In decision-making domains where human expertise is crucial, for example, for …
Robust and fuzzy ensemble framework via spectral learning for random projection-based fuzzy-c-means clustering
The ensembles of random projection-based fuzzy-c-means (RP-FCM) can handle high-
dimensional data efficiently. However, the performance of these ensemble frameworks is still …
dimensional data efficiently. However, the performance of these ensemble frameworks is still …