Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate

Cha, Gi-Wook; Hong, Won‐Hwa; Kim, Young-Chan

doi:10.3390/su15043691

Cited by 7 publications

(3 citation statements)

References 75 publications

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“…SVMs utilize the auxiliary hazard minimization. Regression and classification are the two main uses of support vector machines one of the primary features of SVMR is the use of support vector regression (SVR), which uses prediction rather than minimizing the training error [28]. To attain good performance, the SVMR therefore makes an effort to recognize and reduce the generalized error bound as shown in Figure 2.…”

Section: Support Vector Machine Regressionmentioning

confidence: 99%

Using support vector machine regression to reduce cloud security risks in developing countries

Dhahi

Khadhim

et al. 2023

IJEECS

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The use of the cloud by governments throughout the world is being aggressively investigated to increase efficiency and reduce costs. The majority of cloud computing risk management programs prioritize addressing cloud security issues that government organizations may face when they choose to adopt cloud computing systems, but these programs lack evidence of security risks, and problems with using cloud computing in developing nations are uncommon, so they called for more research in this area. The objective of this paper is to use quantitative models namely Spearman's Rank correlation coefficient, simple regression, and support vector machine regression (SVMR) for estimating cloud security issues based on cloud control factors for improving the mitigation of cloud computing security issues based on control factors using intelligent models in a government organization. Identify the proper cloud control factors for every cloud security issue from estimation errors using a standard for performance measurement like mean square error (MSE) and root mean square error (RMSE), performance measurement to evaluate and validate proposed models. SVMR is an approach to enhance practices for cloud security platforms to mitigate risks and infrastructure for cloud adoption in developing countries in this paper.

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Section: Support Vector Machine Regressionmentioning

confidence: 99%

Using support vector machine regression to reduce cloud security risks in developing countries

Dhahi

Khadhim

et al. 2023

IJEECS

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“…Ref. [80] created a unique hybrid AI model that predicted building destruction in South Korean redevelopment zones by combining standalone algorithms with architectural and engineering technologies. Ref.…”

Section: Infrastructure Developmentmentioning

confidence: 99%

Roles of Artificial Intelligence and Machine Learning in Enhancing Construction Processes and Sustainable Communities

2023

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Machine Learning (ML), a subset of Artificial Intelligence (AI), is gaining popularity in the architectural, engineering, and construction (AEC) sector. This systematic study aims to investigate the roles of AI and ML in improving construction processes and developing more sustainable communities. This study intends to determine the various roles played by AI and ML in the development of sustainable communities and construction practices via an in-depth assessment of the current literature. Furthermore, it intends to predict future research trends and practical applications of AI and ML in the built environment. Following the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, this study highlights the roles that AI and ML technologies play in building sustainable communities, both indoors and out. In the interior environment, they contribute to energy management by optimizing energy usage, finding inefficiencies, and recommending modifications to minimize consumption. This contributes to reducing the environmental effect of energy generation. Similarly, AI and ML technologies aid in addressing environmental challenges. They can monitor air quality, noise levels, and waste management systems to quickly discover and minimize pollution sources. Likewise, AI and ML applications in construction processes enhance planning, scheduling, and facility management.

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“…Additionally, the decoder decompresses the representation into new input data (X ), reconstructed according to the relationship between the input variables [34]. Thus, the features of the input values regenerated by the AE exhibit numerical differences [35]. Therefore, we intend to implement accident identification using an autoencoder.…”

Section: Autoencodermentioning

confidence: 99%

The Identification of Intersection Entrance Accidents Based on Autoencoder

Sun

Jiao

et al. 2023

Sustainability

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Traffic collisions are one of the leading causes of traffic congestion. In the case of urban intersections, traffic accidents can even result in widespread traffic paralysis. To solve this problem, we developed an autoencoder-based model for identifying intersection entrance accidents by analyzing the characteristics of traffic volume. The model uses the standard deviation of the intersection entrance lanes’ traffic volume as an input parameter and identifies intersection entrance accidents by comparing predicted data to actual measured data. In this paper, the detection rate and average detection time are chosen to evaluate the effectiveness of algorithms. The detection rate of the autoencoder model reaches 94.33%, 95.47%, and 81.64% during the morning peak, evening peak, and daylight off-peak periods, respectively. Compared to the support vector machine and the random forest, autoencoder has better performance. It is evident that the research presented in this paper can effectively enhance the detection effect and has a shorter detection time of intersection entrance accidents.

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Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate

Cited by 7 publications

References 75 publications

Using support vector machine regression to reduce cloud security risks in developing countries

Using support vector machine regression to reduce cloud security risks in developing countries

Roles of Artificial Intelligence and Machine Learning in Enhancing Construction Processes and Sustainable Communities

The Identification of Intersection Entrance Accidents Based on Autoencoder

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