In the era of the first Industrial Revolution, many buildings were built with red bricks, and the heritage buildings built at that time are more than 100 years old. In these old heritage buildings, damage is bound to occur due to chemical and physical effects. Technologies such as automatic damage detection can effectively manage damage, but they can be affected by other categories present in heritage buildings. Therefore, this paper proposes a CNN algorithm that can automatically detect cracks and damage that occur in heritage buildings, as well as multi-label classification, such as doors, windows, arches, artwork, brick walls, stonewalls, and vents. A total of 2400 thermal infrared images are collected for 8 categories and automatic classification was performed using the CNN algorithm. The average precision and average sensitivity for the eight categories of heritage buildings are 97.72% and 97.43%, respectively. This paper defines the causes of misclassification as the following two causes: misclassification by multiple objects and misclassification by the perception of the CNN algorithm.
This article provides research on sleep apnoea. Sleep apnoea is a capable for suspending breath or frequently pausing in period of deep sleep. This symptoms may leads to an unappropriate death that makes it a critical sleeping disorder. Periods of apnoea generally lasts for five seconds or hardly a minute which affects the sleeping pattern due to breathing. This probably happens five times of an hour or even more. Obstructive sleep apnoea (OSA),central sleep apnoea (CSA) and mixed/complex sleep apnoea(MSA) are common three types of apnoea, where mixed/complex sleep apnoea is combination of other two apnoea. Airway obstruction is caused in OSA, while in CSA airway is not blocked, but the brain dosn’t sends proper signals to the muscles that cause instability of the respiratory center. The study includes the sleep disorders, types, cause, signs and symptoms and methods of Sleep Apnoea. Considering the study; it is very much required to detection of sleep apnoea using noninvasive techniques. Machine learning algorithms based detection of sleep apnoea is a feasible solution which provides more than 90% accuracy. The study surveys the similar techniques based on machine learning.
This article provides research on sleep apnoea. Sleep apnoea is a capable for suspending breath or frequently pausing in period of deep sleep. This symptoms may leads to an unappropriate death that makes it a critical sleeping disorder. Periods of apnoea generally lasts for five seconds or hardly a minute which affects the sleeping pattern due to breathing. This probably happens five times of an hour or even more. Obstructive sleep apnoea (OSA),central sleep apnoea (CSA) and mixed/complex sleep apnoea(MSA) are common three types of apnoea, where mixed/complex sleep apnoea is combination of other two apnoea. Airway obstruction is caused in OSA, while in CSA airway is not blocked, but the brain dosn’t sends proper signals to the muscles that cause instability of the respiratory center. The study includes the sleep disorders, types, cause, signs and symptoms and methods of Sleep Apnoea. Considering the study; it is very much required to detection of sleep apnoea using noninvasive techniques. Machine learning algorithms based detection of sleep apnoea is a feasible solution which provides more than 90% accuracy. The study surveys the similar techniques based on machine learning.
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