Prioritizing user requirements for digital products using explainable artificial intelligence: A data-driven analysis on video conferencing apps

Bai, Shizhen; Shi, Songlin; Han, Chunjia; Yang, Mu; Gupta, Brij B.; Arya, Varsha

doi:10.1016/j.future.2024.04.037

Cited by 3 publications

(1 citation statement)

References 75 publications

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“…High-quality apples are not only a source of consumer satisfaction but also a key driver of profitability for stockholders. In a competitive market for fresh produce, consistently delivering superior apples can enhance a brand's reputation, foster long-term customer loyalty, and ensure compliance with market standard regulations [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

Ibrahim,

Khodadadi,

Khodadadi

et al. 2024

Journal of Artificial Intelligence in Engineering Practice

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This study concentrates on the evaluation of apple quality, which is a vital part of the agricultural industry. The quality of apples is examined through several factors, such as the cultivation techniques, the harvesting methods, and the post-harvest procedures. The dataset, titled "Apple Perfection," contains important characteristics such as the size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and overall quality of the apple. To make the apple quality prediction more accurate, we used different feature selection algorithms, mainly the binary Waterwheel Plant Algorithm (bWWPA), which, in fact, had the lowest average error of 0.52153, and several of the types of classification models, especially Logistic Regression, which had the highest accuracy of 0.88625. The attribute selection process found the most important attributes, which cut down the dimensionality, and hence, the model performance became better. The results of the study show that the combination of bWWPA for feature selection and logistic regression for classification can predict apple quality with high accuracy. This way of dealing with the problem gives us information that is useful for the improvement of the cultivation techniques and the post-harvest handling to the extent that we will be able to have the best quality apples. The findings of this research have a great impact on the farming industry, meaning a strong way to evaluate the quality of apples.

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Section: Introductionmentioning

confidence: 99%

Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

Ibrahim,

Khodadadi,

Khodadadi

et al. 2024

Journal of Artificial Intelligence in Engineering Practice

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The research landscape of industry 5.0: a scientific mapping based on bibliometric and topic modeling techniques

Rejeb,

Zrelli

et al. 2024

Flex Serv Manuf J

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Industry 5.0 (I5.0) marks a transformative shift toward integrating advanced technologies with human-centric design to foster innovation, resilient manufacturing, and sustainability. This study aims to examine the evolution and collaborative dynamics of I5.0 research through a bibliometric analysis of 942 journal articles from the Scopus database. Our findings reveal a significant increase in I5.0 research, particularly post-2020, yet highlight fragmented collaboration networks and a noticeable gap between institutions in developed and developing countries. Key thematic areas identified include human-robot collaboration, data management and security, AI-driven innovation, and sustainable practices. These insights suggest that a more integrated approach is essential for advancing I5.0, calling for strengthened global collaborations and a balanced emphasis on both technological and human-centric elements to fully realize its potential in driving resilient and sustainable industrial practices. This study provides the first comprehensive bibliometric analysis of I5.0, offering valuable insights for both researchers and practitioners.

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Leveraging Natural Language Processing for Enhanced Text Analysis in Business Intelligence

Hidayatullah,

Kalinaki,

Gul

et al. 2024

Advances in Computational Intelligence and Robotics

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Business intelligence (BI) is crucial for informed decision-making, optimizing operations, and gaining a competitive edge. The rapid growth of unstructured text data has created a need for advanced text analysis techniques in BI. Natural language processing (NLP) is essential for analyzing unstructured textual data. This chapter covers foundational NLP techniques for text analysis, the role of text analysis in BI, and challenges and opportunities in this area. Real-world applications of NLP in BI demonstrate how organizations use NLP-driven text analysis to gain insights, improve customer experience, and anticipate market trends. Future directions and emerging trends, including multimodal learning, contextualized embeddings, conversational AI, explainable AI, federated learning, and knowledge graph integration, were explored. These advancements enhance the scalability, interpretability, and privacy of NLP-driven BI systems, enabling organizations to derive deeper insights and drive innovation in data-driven business landscapes.

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Prioritizing user requirements for digital products using explainable artificial intelligence: A data-driven analysis on video conferencing apps

Cited by 3 publications

References 75 publications

Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

Apple Perfection: Assessing Apple Quality with Waterwheel Plant Algorithm for Feature Selection and Logistic Regression for Classification

The research landscape of industry 5.0: a scientific mapping based on bibliometric and topic modeling techniques

Leveraging Natural Language Processing for Enhanced Text Analysis in Business Intelligence

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