Peeking inside the black-box: Explainable machine learning applied to household transportation energy consumption

Amiri, Shideh Shams; Mottahedi, Sam; Lee, Earl Rusty; Hoque, Shamia

doi:10.1016/j.compenvurbsys.2021.101647

Cited by 33 publications

(23 citation statements)

References 19 publications

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“…The emergence of advanced machine learning methods can also provide unprecedented opportunities to model complex processes in shaping the cities of today [38]. Amiri et al [39] apply machine learning to household transportation energy consumption, while Byon and Liang [40] focus on realtime transportation mode detection. Moreover, numerous studies [38,41,42] confirm that, in various prediction tasks, machine learning models can provide higher accuracy and efficiency than classic statistics.…”

Section: Background: Urban Change and The Opportunity To Use Big Data Analytics And Ai-based Toolsmentioning

confidence: 99%

Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities

Kamrowska-Załuska

2021

Land

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Wide access to large volumes of urban big data and artificial intelligence (AI)-based tools allow performing new analyses that were previously impossible due to the lack of data or their high aggregation. This paper aims to assess the possibilities of the use of urban big data analytics based on AI-related tools to support the design and planning of cities. To this end, the author introduces a conceptual framework to assess the influence of the emergence of these tools on the design and planning of the cities in the context of urban change. In this paper, the implications of the application of artificial-intelligence-based tools and geo-localised big data, both in solving specific research problems in the field of urban planning and design as well as on planning practice, are discussed. The paper is concluded with both cognitive conclusions and recommendations for planning practice. It is directed towards urban planners interested in the emerging urban big data analytics based on AI-related tools and towards urban theorists working on new methods of describing urban change.

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Section: Background: Urban Change and The Opportunity To Use Big Data Analytics And Ai-based Toolsmentioning

confidence: 99%

Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities

Kamrowska-Załuska

2021

Land

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“…Shideh et al [258] described a transportation energy model (TEM) that forecasts home transportation energy use using XAI technique LIME. Data from Household Travel Survey (HTS), which is utilized to train the artificial neural network accurately, has been deployed in TEM and high validation accuracy (83.4%) was developed.…”

Section: ) Xai For Cyber Security Of Smart Transportationmentioning

confidence: 99%

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

et al. 2022

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This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internetconnected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most ML-based techniques and DL-based techniques are deployed in the ''black-box'' manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users' confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security.

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“…Froemelt et al [37] Neural network Guo et al [38] Support Vector Machine Wang et al [39] Artificial neural network Shams Amiri et al [40] To bridge this knowledge gap, this study firstly investigates the importance of factors from aspects of housing conditions, economic income, demographic structure, household appliances, and energy use habits on household carbon emissions based on the household survey data in Japan. We use machine learning methods including LASSO, Decision Tree, Random Forest and XGBoost to precisely identify driving factors, which fit well with our multi-dimensional data.…”

Section: Random Forestmentioning

confidence: 99%

“…In order to infer the overall picture of household carbon emissions, machine learning methods are also useful to estimate based on the recognized patterns and rules from surveyed households when given strong driving factors. But, limited by the data quality, few households' energy survey support the basic data demand of machine learning, only limited interests tries have been made in household electricity consumption of Hong Kong [39], household transportation energy consumption in Delaware Valley region [40] and consumption-induced environmental impacts in Switzerland [37]. The conclusions derived from literature review are shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Exploring household emission patterns and driving factors in Japan using machine learning methods

Chen

Zhong

et al. 2022

Applied Energy

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Peeking inside the black-box: Explainable machine learning applied to household transportation energy consumption

Cited by 33 publications

References 19 publications

Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities

Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Exploring household emission patterns and driving factors in Japan using machine learning methods

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