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    Modeling responsible technologies using multiagent system for climate crisis and sustainability
    (Elsevier, 2025)
    Nanda, Pragyan
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    Sahoo, Sipra
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    Marmolejo-Saucedo, José Antonio
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    Behera, Itishree
    This chapter delves into the dynamic landscape of responsible technologies and their crucial role in addressing the climate crisis and advancing sustainability. It highlights the need to understand the intricate connections between technology adoption, environmental impact, and societal behavior across various dimensions of sustainability, including environmental, social, economic, cultural, technological, political, ethical, health, educational, and adaptive aspects. The chapter introduces the concept of responsible technologies and their key principles, emphasizing real-world experimentation limitations and simulation advantages, particularly through a multiagent system (MAS). It explores different modeling approaches, focusing on the suitability of MAS for studying responsible technologies, along with considerations in its implementation. A step-by-step guide is provided for constructing a tailored MAS model for responsible technologies, incorporating real-world data and environmental factors. Findings and insights from MAS simulations are analyzed, shedding light on the implications of responsible technology adoption across these sustainability dimensions. The chapter also underscores the pivotal role of a MAS in comprehensively modeling responsible technologies and invites further research exploration in this expansive domain, serving as a comprehensive guide for researchers, policymakers, and stakeholders committed to leveraging responsible technologies for climate change mitigation and sustainability advancement. ©The authors ©Elsevier.
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    Stochastic parallel extreme artificial hydrocarbon networks: An implementation for fast and robust supervised machine learning in high-dimensional data
    (2020)
    De Campos Souza, Paulo V.
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    Junio Guimarães, Augusto
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    González Mora, José Guillermo
    Artificial hydrocarbon networks (AHN) – a supervised learning method inspired on organic chemical structures and mechanisms – have shown improvements in predictive power and interpretability in comparison with other well-known machine learning models. However, AHN are very time-consuming that are not able to deal with large data until now. In this paper, we introduce the stochastic parallel extreme artificial hydrocarbon networks (SPE-AHN), an algorithm for fast and robust training of supervised AHN models in high-dimensional data. This training method comprises a population-based meta-heuristic optimization with defined individual encoding and objective function related to the AHN-model, an implementation in parallel-computing, and a stochastic learning approach for consuming large data. We conducted three experiments with synthetic and real data sets to validate the training execution time and performance of the proposed algorithm. Experimental results demonstrated that the proposed SPE-AHN outperforms the original-AHN method, increasing the speed of training more than 10,000x times in the worst case scenario. Additionally, we present two case studies in real data sets for solar-panel deployment prediction (regression problem), and human falls and daily activities classification in healthcare monitoring systems (classification problem). These case studies showed that SPE-AHN improves the state-of-the-art machine learning models in both engineering problems. We anticipate our new training algorithm to be useful in many applications of AHN like robotics, finance, medical engineering, aerospace, and others, in which large amounts of data (e.g. big data) is essential. © 2019 Elsevier Ltd.
    Scopus© Citations 21  10  5
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    A Nested Unsupervised Learning Model for Classification of SKU’s in a Transnational Company: A Big Data Model
    (2021)
    Loy-García, Gabriel
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    Marmolejo Saucedo, José Antonio
    This work seeks to develop a nested non-supervised model that allows a transnational soft drink company to improve its decision-making for the discontinuation of products from its portfolio with the use of unsupervised models from a database with commercial and financial information for all your product line in your most important operation. The integration of different cluster methodologies through a nested non-supervised model allowed to generate a correct identification of the products that should be refined from the catalog due to financial and operational factors. Given the magnitude of the information, a cluster was integrated into a platform for data processing as well as the generation of automatic reports that could be consulted automatically through the cloud. The products identified through the nested unsupervised model made it possible to identify products that had low demand and a low contribution to the utility of the company. Removing said products from the catalog will allow maximizing the profit of the business in addition to not incurring sunk costs related to the production and distribution of low-demand products. The platform developed will allow continuous monitoring of business performance in order to automatically identify the products likely to leave the catalog. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
      23  1
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    A proposal for the supply chain design : a digitization approach
    (2018)
    Marmolejo Saucedo, José Antonio
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    Retana-Blanco, Brenda
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    Pedraza-Arroyo, Erika
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    The logistics network of an automotive company in Mexico, was analyzed to propose a better logistics network in the country to improve delivery times to customers. The network design considers elements of digitization of Greenfield Analysis and Network Optimization processes. Taking into account the information given by the company, it was possible to obtain optimal scenarios for better operation, which involved the construction of distribution centers throughout Mexico. © 2020 Jose Antonio Marmolejo-Saucedo et al., licensed to EAI.
    Scopus© Citations 3  34  1
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    Stakeholder perceptions and word-of-mouth on CSR dynamics : a big data analysis from Twitter
    (2018)
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    Burgos Silva, Zamira
    Corporate social responsibility is a strategy by which firms address social issues whilst tending to their profit enhancing objectives. However, is a socially responsible firm fulfilling its objectives if current and potential stakeholders perceive it to be unethical, engaging in poor and questionable practices? The article analyzes Big Data retrieved from Twitter related to five firms that have stated to be socially responsible but have yet to obtain stakeholders’ legitimacy granted by the engagement in corporate social responsibility. The article contributes to the understanding and effects of firm dynamics in corporate social responsibility or lack thereof, on social networking sites by means of Big Data analysis. Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
      13  1