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Lee J, Kim Y, Kim S. The Study of an Adaptive Bread Maker Using Machine Learning. Foods 2023; 12:4160. [PMID: 38002216 PMCID: PMC10670275 DOI: 10.3390/foods12224160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/09/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023] Open
Abstract
Bread is one of the most consumed foods in the world, and modern food processing technologies using artificial intelligence are crucial in providing quality control and optimization of food products. An integrated solution of sensor data and machine learning technology was determined to be appropriate for identifying real-time changing environmental variables and various influences in the baking process. In this study, the Baking Process Prediction Model (BPPM) created by data-based machine learning showed excellent performance in monitoring and analyzing real-time sensor and vision data in the baking process to predict the baking stages by itself. It also has the advantage of improving the quality of bread. The volumes of bread made using BPPM were 127.54 ± 2.54, 413.49 ± 2.59, 679.96 ± 1.90, 875.79 ± 2.46, and 1260.70 ± 3.13, respectively, which were relatively larger than those made with fixed baking time (p < 0.05). The developed system is evaluated to have great potential to improve precision and efficiency in the food production and processing industry. This study is expected to lay the foundation for the future development of artificial intelligence and the food industry.
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Affiliation(s)
| | | | - Sangoh Kim
- Department of Plant and Food Engineering, Sangmyung University, Cheonan 31066, Republic of Korea; (J.L.); (Y.K.)
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Wilkens U, Lupp D, Langholf V. Configurations of human-centered AI at work: seven actor-structure engagements in organizations. Front Artif Intell 2023; 6:1272159. [PMID: 38028670 PMCID: PMC10664146 DOI: 10.3389/frai.2023.1272159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 09/29/2023] [Indexed: 12/01/2023] Open
Abstract
Purpose The discourse on the human-centricity of AI at work needs contextualization. The aim of this study is to distinguish prevalent criteria of human-centricity for AI applications in the scientific discourse and to relate them to the work contexts for which they are specifically intended. This leads to configurations of actor-structure engagements that foster human-centricity in the workplace. Theoretical foundation The study applies configurational theory to sociotechnical systems' analysis of work settings. The assumption is that different approaches to promote human-centricity coexist, depending on the stakeholders responsible for their application. Method The exploration of criteria indicating human-centricity and their synthesis into configurations is based on a cross-disciplinary literature review following a systematic search strategy and a deductive-inductive qualitative content analysis of 101 research articles. Results The article outlines eight criteria of human-centricity, two of which face challenges of human-centered technology development (trustworthiness and explainability), three challenges of human-centered employee development (prevention of job loss, health, and human agency and augmentation), and three challenges of human-centered organizational development (compensation of systems' weaknesses, integration of user-domain knowledge, accountability, and safety culture). The configurational theory allows contextualization of these criteria from a higher-order perspective and leads to seven configurations of actor-structure engagements in terms of engagement for (1) data and technostructure, (2) operational process optimization, (3) operators' employment, (4) employees' wellbeing, (5) proficiency, (6) accountability, and (7) interactive cross-domain design. Each has one criterion of human-centricity in the foreground. Trustworthiness does not build its own configuration but is proposed to be a necessary condition in all seven configurations. Discussion The article contextualizes the overall debate on human-centricity and allows us to specify stakeholder-related engagements and how these complement each other. This is of high value for practitioners bringing human-centricity to the workplace and allows them to compare which criteria are considered in transnational declarations, international norms and standards, or company guidelines.
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Affiliation(s)
- Uta Wilkens
- Institute of Work Science, Ruhr University Bochum, Bochum, Germany
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Sinde R, Diwani S, Leo J, Kondo T, Elisa N, Matogoro J. AI for Anglophone Africa: Unlocking its adoption for responsible solutions in academia-private sector. Front Artif Intell 2023; 6:1133677. [PMID: 37113649 PMCID: PMC10126471 DOI: 10.3389/frai.2023.1133677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/17/2023] [Indexed: 04/29/2023] Open
Abstract
In recent years, AI technologies have become indispensable in social and industrial development, yielding revolutionary results in improving labor efficiency, lowering labor costs, optimizing human resource structure, and creating new job demands. To reap the full benefits of responsible AI solutions in Africa, it is critical to investigate existing challenges and propose strategies, policies, and frameworks for overcoming and eliminating them. As a result, this study investigated the challenges of adopting responsible AI solutions in the Academia-Private sectors for Anglophone Africa through literature reviews, expert interviews, and then proposes solutions and framework for the sustainable and successful adoption of responsible AI.
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Affiliation(s)
- Ramadhani Sinde
- School of Computational and Communication Science and Engineering, Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania
- *Correspondence: Ramadhani Sinde
| | - Salim Diwani
- Department of Computer Science and Engineering at the College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania
| | - Judith Leo
- School of Computational and Communication Science and Engineering, Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania
| | - Tabu Kondo
- Department of Computer Science and Engineering at the College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania
| | - Noe Elisa
- Department of Computer Science and Engineering at the College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania
| | - Jabhera Matogoro
- Department of Computer Science and Engineering at the College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania
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Golovianko M, Gryshko S, Terziyan V, Tuunanen T. Responsible cognitive digital clones as decision-makers: A design science research study. EUR J INFORM SYST 2022. [DOI: 10.1080/0960085x.2022.2073278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Mariia Golovianko
- Department of Artificial Intelligence, Kharkiv National University of Radioelectronics, Ukraine
| | - Svitlana Gryshko
- Department of Economic Cybernetics, Kharkiv National University of Radioelectronics, Ukraine
| | - Vagan Terziyan
- Faculty of Information Technology, University of Jyväskylä, Finland
| | - Tuure Tuunanen
- Faculty of Information Technology, University of Jyväskylä, Finland
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Leal Filho W, Yang P, Eustachio JHPP, Azul AM, Gellers JC, Gielczyk A, Dinis MAP, Kozlova V. Deploying digitalisation and artificial intelligence in sustainable development research. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2022; 25:4957-4988. [PMID: 35313685 PMCID: PMC8927747 DOI: 10.1007/s10668-022-02252-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 02/25/2022] [Indexed: 05/16/2023]
Abstract
Many industrialised countries have benefited from the advent of twenty-first century technologies, especially automation, that have fundamentally changed manufacturing and industrial production processes. The next step in the evolution of automation is the development of artificial intelligence (AI), i.e. intelligence which is demonstrated by machines and systems, which cannot only perform tasks but also work synergistically with humans and nature. Intelligent systems that can see, analyse situations and respond sensitively to real-time cues, from human gestures and facial expressions to pedestrians crossing a busy street, will reshape transportation, precision agriculture, biodiversity conservation, environmental modelling, public health, construction and manufacturing, as well as initiatives designed to promote prosperity on Earth. This paper explores the connections between AI systems and sustainable development (SD) research. By means of a literature review, world survey, and case studies, ways in which AI can support research on SD and, inter alia, contribute to a more sustainable and equitable world, are identified.
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Affiliation(s)
- Walter Leal Filho
- European School of Sustainability Science and Research (ESSSR), Hamburg University of Applied Sciences, Hamburg, Germany
- Department of Natural Sciences, Manchester Metropolitan University, Chester Street, Manchester, M1 5GD UK
| | - Peter Yang
- Case Western Reserve University, 11112 Bellflower Road, Cleveland, OH 44106 USA
| | | | - Anabela Marisa Azul
- University of Coimbra, CIBB-Centre for Innovative Biomedicine and Biotechnology, CNC-Center for Neuroscience and Cell Biology, Rua Larga, 3004-504 Coimbra, Portugal
- University of Coimbra, IIIUC-Institute for Interdisciplinary Research, 3030-789 Coimbra, Portugal
| | - Joshua C. Gellers
- Department of Political Science and Public Administration, University of North Florida, 1 UNF Drive, Jacksonville, FL 32224 USA
| | | | - Maria Alzira Pimenta Dinis
- UFP Energy, Environment and Health Research Unit (FP-ENAS), University Fernando Pessoa (UFP), Praça 9 de Abril 349, 4249-004 Porto, Portugal
| | - Valerija Kozlova
- Faculty of Business and Economics, RISEBA University of Applied Sciences, 3 Meža Street, Riga, LV 1048 Latvia
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Costello FJ, Kim C, Kang CM, Lee KC. Identifying High-Risk Factors of Depression in Middle-Aged Persons with a Novel Sons and Spouses Bayesian Network Model. Healthcare (Basel) 2020; 8:healthcare8040562. [PMID: 33333799 PMCID: PMC7765214 DOI: 10.3390/healthcare8040562] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/06/2020] [Accepted: 12/11/2020] [Indexed: 11/24/2022] Open
Abstract
It has been reported repeatedly that depression in middle-aged people may cause serious ramifications in public health. However, previous studies on this important research topic have focused on utilizing either traditional statistical methods (i.e., logistic regressions) or black-or-gray artificial intelligence (AI) methods (i.e., neural network, Support Vector Machine (SVM), ensemble). Previous studies lack suggesting more decision-maker-friendly methods, which need to produce clear interpretable results with information on cause and effect. For the sake of improving the quality of decisions of healthcare decision-makers, public health issues require identification of cause and effect information for any type of strategic healthcare initiative. In this sense, this paper proposes a novel approach to identify the main causes of depression in middle-aged people in Korea. The proposed method is the Sons and Spouses Bayesian network model, which is an extended version of conventional TAN (Tree-Augmented Naive Bayesian Network). The target dataset is a longitudinal dataset employed from the Korea National Health and Nutrition Examination Survey (KNHANES) database with a sample size of 8580. After developing the proposed Sons and Spouses Bayesian network model, we found thirteen main causes leading to depression. Then, genetic optimization was executed to reveal the most probable cause of depression in middle-aged people that would provide practical implications to field practitioners. Therefore, our proposed method can help healthcare decision-makers comprehend changes in depression status by employing what-if queries towards a target individual.
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Affiliation(s)
- Francis Joseph Costello
- SKK Business School, Sungkyunkwan University, Seoul 03063, Korea; (F.J.C.); (C.K.); (C.M.K.)
| | - Cheong Kim
- SKK Business School, Sungkyunkwan University, Seoul 03063, Korea; (F.J.C.); (C.K.); (C.M.K.)
- Airports Council International (ACI) World, Montreal, QC H4Z 1G8, Canada
| | - Chang Min Kang
- SKK Business School, Sungkyunkwan University, Seoul 03063, Korea; (F.J.C.); (C.K.); (C.M.K.)
| | - Kun Chang Lee
- SKK Business School, Sungkyunkwan University, Seoul 03063, Korea; (F.J.C.); (C.K.); (C.M.K.)
- Department of Health Sciences & Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Korea
- Correspondence:
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Predictive Insights for Improving the Resilience of Global Food Security Using Artificial Intelligence. SUSTAINABILITY 2020. [DOI: 10.3390/su12156272] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Unabated pressures on food systems affect food security on a global scale. A human-centric artificial intelligence-based probabilistic approach is used in this paper to perform a unified analysis of data from the Global Food Security Index (GFSI). The significance of this intuitive probabilistic reasoning approach for predictive forecasting lies in its simplicity and user-friendliness to people who may not be trained in classical computer science or in software programming. In this approach, predictive modeling using a counterfactual probabilistic reasoning analysis of the GFSI dataset can be utilized to reveal the interplay and tensions between the variables that underlie food affordability, food availability, food quality and safety, and the resilience of natural resources. Exemplars are provided in this paper to illustrate how computational simulations can be used to produce forecasts of good and bad conditions in food security using multi-variant optimizations. The forecast of these future scenarios is useful for informing policy makers and stakeholders across domain verticals, so they can make decisions that are favorable to global food security.
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Artificial Intelligence-Enhanced Predictive Insights for Advancing Financial Inclusion: A Human-Centric AI-Thinking Approach. BIG DATA AND COGNITIVE COMPUTING 2020. [DOI: 10.3390/bdcc4020008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
According to the World Bank, a key factor to poverty reduction and improving prosperity is financial inclusion. Financial service providers (FSPs) offering financially-inclusive solutions need to understand how to approach the underserved successfully. The application of artificial intelligence (AI) on legacy data can help FSPs to anticipate how prospective customers may respond when they are approached. However, it remains challenging for FSPs who are not well-versed in computer programming to implement AI projects. This paper proffers a no-coding human-centric AI-based approach to simulate the possible dynamics between the financial profiles of prospective customers collected from 45,211 contact encounters and predict their intentions toward the financial products being offered. This approach contributes to the literature by illustrating how AI for social good can also be accessible for people who are not well-versed in computer science. A rudimentary AI-based predictive modeling approach that does not require programming skills will be illustrated in this paper. In these AI-generated multi-criteria optimizations, analysts in FSPs can simulate scenarios to better understand their prospective customers. In conjunction with the usage of AI, this paper also suggests how AI-Thinking could be utilized as a cognitive scaffold for educing (drawing out) actionable insights to advance financial inclusion.
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Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach. AI 2020. [DOI: 10.3390/ai1010004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
According to the World Health Organization (WHO) and the World Bank, malnutrition is one of the most serious but least-addressed development challenges in the world. Malnutrition refers to the malfunction or imbalance of nutrition, which could be influenced not only by under-nourishment, but also by over-nourishment. The significance of this paper is that it shows how artificial intelligence (AI) can be democratized to enable analysts who are not trained in computer science to also use human-centric explainable-AI to simulate the possible dynamics between malnutrition, health and population indicators in a dataset collected from 180 countries by the World Bank. This AI-based human-centric probabilistic reasoning approach can also be used as a cognitive scaffold to educe (draw out) AI-Thinking in analysts to ask further questions and gain deeper insights. In this study, a rudimentary beginner-friendly AI-based Bayesian predictive modeling approach was used to demonstrate how human-centric probabilistic reasoning could be utilized to analyze the dynamics of global malnutrition and optimize conditions for achieving the best-case scenario. Conditions of the worst-case “Black Swan” scenario were also simulated, and they could be used to inform stakeholders to prevent them from happening. Thus, the nutritional and health status of vulnerable populations could be ameliorated.
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