Application of a Proposed Sustainability Checklist for Construction Projects

Ahmed, Thamir M.; Al‐Numan, Bayan

doi:10.23918/iec2018.25

Cited by 2 publications

(4 citation statements)

References 1 publication

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“…This dataset was used in the NASA Space Apps Challenge 2018 [13] as part of the team's data for fire detection. The dataset from Kaggle consists of images of fire in forest settings as well as normal looking images of forests [14].…”

Section: Kaggle-"nasa"datasetmentioning

confidence: 99%

Using Neural Networks to Detect Fire from Overhead Images

Kurasinski

Tan

Malekian

2023

Wireless Pers Commun

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The use of artificial intelligence (AI) is increasing in our everyday applications. One emerging field within AI is image recognition. Research that has been devoted to predicting fires involves predicting its behaviour. That is, how the fire will spread based on environmental key factors such as moisture, weather condition, and human presence. The result of correctly predicting fire spread can help firefighters to minimise the damage, deciding on possible actions, as well as allocating personnel effectively in potentially fire prone areas to extinguish fires quickly. Using neural networks (NN) for active fire detection has proven to be exceptional in classifying smoke and being able to separate it from similar patterns such as clouds, ground, dust, and ocean. Recent advances in fire detection using NN has proved that aerial imagery including drones as well as satellites has provided great results in detecting and classifying fires. These systems are computationally heavy and require a tremendous amount of data. A NN model is inextricably linked to the dataset on which it is trained. The cornerstone of this study is based on the data dependencieds of these models. The model herein is trained on two separate datasets and tested on three dataset in total in order to investigate the data dependency. When validating the model on their own datasets the model reached an accuracy of 92% respectively 99%. In comparison to previous work where an accuracy of 94% was reached. During evaluation of separate datasets, the model performed around the 60% range in 5 out of 6 cases, with the outlier of 29% in one of the cases.

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Section: Kaggle-"nasa"datasetmentioning

confidence: 99%

Using Neural Networks to Detect Fire from Overhead Images

Kurasinski

Tan

Malekian

2023

Wireless Pers Commun

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“…conducted by human are often characterized by non-response. [20] first discussed the issue of non-response and imputation methods to deal with non-response issues were suggested by several scholars like [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], Prasad [33], [34], [35], [36], [37], [38], are some of the most recent imputation methods. However, all the estimators of the schemes proposed by aforementioned authors are functions of population mean of auxiliary variable ( X ) and if X is unknown, the schemes can not be applied to real life situations.…”

Section: Caliphate Journal Of Science and Technology (Cajost)mentioning

confidence: 99%

“…  [23] proposed imputation scheme for population mean estimators which is applicable when the study and auxiliary variables are either positively or negatively correlated, using power transformation.…”

Section: Cajostmentioning

confidence: 99%

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On the efficiency of modified regression-type mean imputation scheme under two-phase sampling

Musa,

Audu,

Joseph

et al. 2023

CaJoST

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Human-based surveys such as medical and social science surveys are often characterized by non-response or missing observations. In this study, a new class of regression-type mean imputation method that uses X̄n as an estimate of X̄ was suggested. Using partial derivative approach, the MSEs of the class of estimators presented were derived up to first order approximation under two cases. Case I: when the secondary sample S2 of size n(n<n1) is a subset of preliminary sample S1[S2 ⸦ S1] , and Case II: is when secondary sample S2 is a subset of universal set ΩN . Conditions for which the new estimator was more efficient than the other estimators studied were derived. The results of numerical examples through simulations revealed that the suggested class of estimators is more efficient.

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Application of a Proposed Sustainability Checklist for Construction Projects

Cited by 2 publications

References 1 publication

Using Neural Networks to Detect Fire from Overhead Images

Using Neural Networks to Detect Fire from Overhead Images

On the efficiency of modified regression-type mean imputation scheme under two-phase sampling

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