Artificial intelligence in green building
Abstract The Architecture, Engineering and Construction (AEC) sector faces severe
sustainability and efficiency challenges. The application of artificial intelligence in green …
sustainability and efficiency challenges. The application of artificial intelligence in green …
Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges
Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment
process is long and very costly due to its high recurrence and mortality rates. Accurate early …
process is long and very costly due to its high recurrence and mortality rates. Accurate early …
[HTML][HTML] Comparing different supervised machine learning algorithms for disease prediction
Supervised machine learning algorithms have been a dominant method in the data mining
field. Disease prediction using health data has recently shown a potential application area …
field. Disease prediction using health data has recently shown a potential application area …
Artificial intelligence and machine learning in precision and genomic medicine
S Quazi - Medical Oncology, 2022 - Springer
The advancement of precision medicine in medical care has led behind the conventional
symptom-driven treatment process by allowing early risk prediction of disease through …
symptom-driven treatment process by allowing early risk prediction of disease through …
Evolutionary transfer optimization-a new frontier in evolutionary computation research
The evolutionary algorithm (EA) is a nature-inspired population-based search method that
works on Darwinian principles of natural selection. Due to its strong search capability and …
works on Darwinian principles of natural selection. Due to its strong search capability and …
Machine learning for survival analysis: A survey
Survival analysis is a subfield of statistics where the goal is to analyze and model data
where the outcome is the time until an event of interest occurs. One of the main challenges …
where the outcome is the time until an event of interest occurs. One of the main challenges …
AdaBoost ensemble methods using K‐fold cross validation for survivability with the early detection of heart disease
TR Mahesh, V Dhilip Kumar… - Computational …, 2022 - Wiley Online Library
As a result of technology improvements, various features have been collected for heart
disease diagnosis. Large data sets have several drawbacks, including limited storage …
disease diagnosis. Large data sets have several drawbacks, including limited storage …
A support vector machine-based ensemble algorithm for breast cancer diagnosis
This research studies a support vector machine (SVM)-based ensemble learning algorithm
for breast cancer diagnosis. Illness diagnosis plays a critical role in designating treatment …
for breast cancer diagnosis. Illness diagnosis plays a critical role in designating treatment …
Using machine learning algorithms for breast cancer risk prediction and diagnosis
H Asri, H Mousannif, H Al Moatassime… - Procedia Computer …, 2016 - Elsevier
Breast cancer represents one of the diseases that make a high number of deaths every year.
It is the most common type of all cancers and the main cause of women's deaths worldwide …
It is the most common type of all cancers and the main cause of women's deaths worldwide …
[HTML][HTML] Machine learning applications in cancer prognosis and prediction
Cancer has been characterized as a heterogeneous disease consisting of many different
subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in …
subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in …