Needs and Importance of Reliability Prediction: An Industrial Perspective

Sahu, Kavita; Srivastava, R. Κ.

doi:10.18576/isl/090105

Cited by 69 publications

(8 citation statements)

References 8 publications

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“…It is necessary to clarify that both the criticality and storage impact criteria are qualitative criteria that were evaluated with a defned scale (Table 5). Once the decision matrix is obtained, it is normalized as indicated by (4). Ten, the resulting matrix is multiplied by the weight of each criterion.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

“…In today's global competitive business environment, companies develop tools to support the decision-making process [1][2][3], looking to anticipate that diferent situations may afect their performance in market [4][5][6]. Logistics decisions in organizations have a direct infuence on proftability and competitiveness, so mechanisms must be established to ensure efective decision-making [7].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy AHP TOPSIS Methodology for Multicriteria ABC Inventory Classification

Paredes Rodríguez,

Bravo Bastidas,

Osorio Gómez

et al. 2023

Journal of Engineering

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Products’ classification according to their importance has been a topic addressed by academia and industry for many years, mainly due to the great importance of this process to obtain efficient inventory policies that reduce lost sales while reducing inventory maintenance costs. This research has to perform an ABC inventory classification in a medium-sized company that sells hardware goods and construction materials, considering multiple quantitative and qualitative criteria. AHP fuzzy TOPSIS multicriteria tool was used as a methodological approach which implies the definition and initial weighting of a set of relevant criteria for the study based on the AHP fuzzy methodology, to obtain an inventory products’ importance assessment according TOPSIS technique procedure. After applying the technique, it is possible to obtain that 0.26% of the inventory was classified as highly critical. Likewise, 5.45% represents products of medium relevance to the organization. Finally, it is observed that many of the products (approximately 94%) have little or almost no impact within the company under study. This methodology was used in a practical case where some criteria were taken from the reviewed literature. In addition, the criticality criterion was used from a financial perspective.

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Section: Results Analysis and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy AHP TOPSIS Methodology for Multicriteria ABC Inventory Classification

Paredes Rodríguez,

Bravo Bastidas,

Osorio Gómez

et al. 2023

Journal of Engineering

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show abstract

“…We expect the use of our metric to help in more user-focused system designs, which in turn may improve the adoption of AI-driven CDSS by clinicians [24]. However, as the CDSS involves use of sensitive patient data, reliability, security and ethical implications must also be considered in practice [1], [62], [63].…”

Section: Discussionmentioning

confidence: 99%

Evaluating Explanations from AI Algorithms for Clinical Decision-Making: A Social Science-based Approach

Ghanvatkar,

Rajan

2024

Preprint

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Explainable Artificial Intelligence (XAI) techniques generate explanations for predictions from AI models. These explanations can be evaluated for (i) faithfulness to the prediction, i.e., its correctness about the reasons for prediction, and (ii) usefulness to the user. While there are metrics to evaluate faithfulness, to our knowledge, there are no automated metrics to evaluate the usefulness of explanations in the clinical context. Our objective is to develop a new metric to evaluate usefulness of AI explanations to clinicians. Usefulness evaluation needs to consider both (a) how humans generally process explanations and (b) clinicians' specific requirements from explanations presented by clinical decision support systems (CDSS). Our new scoring method can evaluate the usefulness of explanations generated by any XAI method that provides importance values for the input features of the prediction model. Our method draws on theories from social science to gauge usefulness, and uses literature-derived biomedical knowledge graphs to quantify support for the explanations from clinical literature. We evaluate our method in a case study on predicting onset of sepsis in intensive care units. Our analysis shows that the scores obtained using our method corroborate with independent evidence from clinical literature and have the required qualities expected from such a metric. Thus, our method can be used to evaluate and select useful explanations from a diverse set of XAI techniques in clinical contexts, making it a fundamental tool for future research in the design of AI-driven CDSS.

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“…However, an important point to be highlighted in the technologies/solutions presented in Table 13 is the concern with the reliability of the systems used in the management of provenance data in HISs. For [ 130 , 131 ], reliability plays a very important role in obtaining quality software. Analyzing the study [ 130 , 131 ] in the context of provenance data management in HISs, it is a necessary factor in the medical industry to guarantee the quality of stored and shared information and the reliability of health data.…”

Section: Data Provenance In the Medical Systems Industrymentioning

confidence: 99%

“…For [ 130 , 131 ], reliability plays a very important role in obtaining quality software. Analyzing the study [ 130 , 131 ] in the context of provenance data management in HISs, it is a necessary factor in the medical industry to guarantee the quality of stored and shared information and the reliability of health data.…”

Section: Data Provenance In the Medical Systems Industrymentioning

confidence: 99%

Provenance Data Management in Health Information Systems: A Systematic Literature Review

Sembay

Macedo

Júnior

et al. 2023

JPM

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Aims: This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results. Method: An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied. Results: Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs. Conclusion: It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs.

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Needs and Importance of Reliability Prediction: An Industrial Perspective

Cited by 69 publications

References 8 publications

Fuzzy AHP TOPSIS Methodology for Multicriteria ABC Inventory Classification

Fuzzy AHP TOPSIS Methodology for Multicriteria ABC Inventory Classification

Evaluating Explanations from AI Algorithms for Clinical Decision-Making: A Social Science-based Approach

Provenance Data Management in Health Information Systems: A Systematic Literature Review

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