Data mining and machine learning in the context of sustainable evaluation: a literature review

Souza, Jovani Taveira de; Francisco, Antônio Carlos de; Piekarski, Cassiano Moro; Prado, Guilherme; Oliveira, Leandro Gasparello de

doi:10.1109/tla.2019.8863307

Cited by 20 publications

(3 citation statements)

References 93 publications

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“…With the machine learning capabilities, these technologies become important allies in the decision making (Yang et al, 2017) and emergence of new manufacturing models, including networking (Li et al, 2017). The adoption of AI in the business areas has primarily focused on the management of IT (Pandl et al, 2020;Zhu et al, 2019), decision-making (Ding et al, 2020)2020, evaluation of sustainable performance (Souza et al, 2019), and the future of work (Wang & Siau, 2019).…”

Section: Artificial Intelligencementioning

confidence: 99%

Artificial intelligence and internet of things adoption in operations management: Barriers and benefits

Rocha

Kissimoto

2022

RAM, Rev. Adm. Mackenzie

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Purpose: Based on the context of digital transformation and the evolution of digital technologies, this research sought to understand how artificial intelligence (AI) and internet of things (IoT) collaborate to improve the efficiency of operations management (OM). Originality/value: Digital transformation and the use of new technologies, such as AI and IoT, have impacted the management of the companies’ operation. A preliminary survey carried out in the Web of Science (WoS) database, analyzing data through the VOSviewer bibliometric software, identified an important relationship between AI, IoT, and OM through industry 4.0 (i4.0), which has as one of its main objectives the improvement in OM. The results of this research bring a practical contribution to business managers, such as the identification of the main barriers and expected benefits when adopting AI and IoT in their operations. For researchers, this study differs from studies already published by conducting a systematic review of the literature that investigates the relationship of OM with technological tools, such as AI and IoT. Design/methodology/approach: A systematic review of the literature was carried out with the objective of analyzing all articles that brought some contribution to a better understanding of how AI and IoT collaborate to improve the efficiency of operations. Findings: The results demonstrated how AI and IoT were being incorporated into OM, identifying the main barriers of its use, as well as indications of research gaps that may lead to further investigations to advance on this topic.

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Section: Artificial Intelligencementioning

confidence: 99%

Artificial intelligence and internet of things adoption in operations management: Barriers and benefits

Rocha

Kissimoto

2022

RAM, Rev. Adm. Mackenzie

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“…Com a capacidade de aprendizado das máquinas, estas se tornaram importantes aliadas nas tomadas de decisão (Yang et al, 2017) e no surgimento de novos modelos de manufatura, inclusive em rede (Li et al, 2017). A adoção da IA nas áreas de negócios tem prioritariamente focado a gestão da TI (Pandl et al, 2020;Zhu et al, 2019), a tomada de decisão (Ding et al, 2020), a avaliação do desempenho sustentável (Souza et al, 2019) e o futuro do trabalho (Wang & Siau, 2019).…”

Section: Inteligência Artificialunclassified

Barreiras e benefícios na adoção de inteligência artificial e IoT na gestão da operação

Rocha

Kissimoto

2022

RAM, Rev. Adm. Mackenzie

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Resumo Objetivo: Tomando como base o contexto de transformação digital e a evolução das tecnologias digitais, esta pesquisa buscou compreender como a inteligência artificial (IA) e a internet das coisas (internet of things – IoT) colaboram para melhorar a eficiência da gestão da operação (GO). Originalidade/valor: A transformação digital e o uso de novas tecnologias, como a IA e a IoT, têm impactado a gestão da operação das empresas. Um levantamento feito na base de dados Web of Science (WoS) e a análise deles, realizadas pelo software bibiliométrico VOSviewer, identificaram uma importante relação entre IA, IoT e GO por meio da indústria 4.0 (i4.0), que tem como um de seus principais objetivos a melhora na gestão da operação. Os resultados da presente pesquisa trazem uma contribuição prática aos gestores de negócios, como a identificação das principais barreiras e benefícios esperados ao adotarem a IA e a IoT em suas operações. Para os pesquisadores, este estudo difere de pesquisas já publicadas ao realizar uma revisão sistemática da literatura que investiga a relação da GO com as ferramentas tecnológicas IA e IoT. Design/metodologia/abordagem: Foi realizada uma revisão sistemática da literatura com o objetivo de analisar todos os artigos que trouxessem alguma contribuição no sentido de fornecer uma melhor compreensão de como a IA e a IoT colaboram para melhorar a eficiência das operações. Resultados: Os resultados demonstraram de que forma a IA e a IoT foram sendo incorporadas na gestão da operação, com destaque às barreiras e aos benefícios de seu uso. Verificaram-se ainda as indicações de lacunas de pesquisa que podem levar a novas investigações para avançar no tema.

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“…From this perspective, limitations are noted in many existing datasets, metrics employed for evaluation, and the degree to which results express the domain of a problem. As [ 18 , 19 , 20 ] state, changes are needed in the way research is conducted to increase the impact of ML, and six impact challenges are highlighted to focus explicitly on problems. Aiming to inspire further discussions and focus on the implementation of ML is the main contribution of this paper, highlighting: (1) regulatory framework for use and implementation, (2) cost reduction with rules for informed decision making, (3) avoiding conflicts of interest between nations, (4) extending cyber security through defenses, (5) saving human lives with diagnostics or recommendations, and (6) improving the Human Development Index (HDI) with at least 10% fair taxation in the country.…”

Section: Introductionmentioning

confidence: 99%

Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning

Silva

Mendes

Blas

et al. 2022

Sensors

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Due to the increasing urban development, it has become important for municipalities to permanently understand land use and ecological processes, and make cities smart and sustainable by implementing technological tools for land monitoring. An important problem is the absence of technologies that certify the quality of information for the creation of strategies. In this context, expressive volumes of data are used, requiring great effort to understand their structures, and then access information with the desired quality. This study are designed to provide an initial response to the need for mapping zones in the city of Itajaí (SC), Brazil. The solution proposes to aid object recognition employing object-based classifiers OneR, NaiveBayes, J48, IBk, and Hoeffding Tree algorithms used together with GeoDMA, and a first approach in the use of Region-based Convolutional Neural Network (R-CNN) and the YOLO algorithm. All this is to characterize vegetation zones, exposed soil zones, asphalt, and buildings within an urban and rural area. Through the implemented model for active identification of geospatial objects with similarity levels, it was possible to apply the data crossover after detecting the best classifier with accuracy (85%) and the kappa agreement coefficient (76%). The case study presents the dynamics of urban and rural expansion, where expressive volumes of data are obtained and submitted to different methods of cataloging and preparation to subsidize rapid control actions. Finally, the research describes a practical and systematic approach, evaluating the extraction of information to the recommendation of knowledge with greater scientific relevance. Allowing the methods presented to apply the calibration of values for each object, to achieve results with greater accuracy, which is proposed to help improve conservation and management decisions related to the zones within the city, leaving as a legacy the construction of a minimum technological infrastructure to support the decision.

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Data mining and machine learning in the context of sustainable evaluation: a literature review

Cited by 20 publications

References 93 publications

Artificial intelligence and internet of things adoption in operations management: Barriers and benefits

Artificial intelligence and internet of things adoption in operations management: Barriers and benefits

Barreiras e benefícios na adoção de inteligência artificial e IoT na gestão da operação

Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning

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