Evaluation Metrics Research for Explainable Artificial Intelligence Global Methods Using Synthetic Data

Oblizanov, Alexandr; Shevskaya, Natalya V.; Kazak, Anatoliy N.; Rudenko, Мarina; Dorofeeva, Anna

doi:10.3390/asi6010026

Cited by 10 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…• Oblizanov, A. et al, 2023 This research explores evaluation metrics for explainable AI global methods using synthetic data, shedding light on the challenges and advancements in assessing the performance of interpretable models and contributing to the understanding of their effectiveness and limitations. The authors propose the evaluation methods must be based on accuracy features, must have stable distribution and must be instance-guided.…”

Section: Local Interpretable Model-agnostic Explanationsmentioning

confidence: 99%

novel method for developing explainable machine learning framework using feature neutralization technique

Kalyanathaya,

2024

The Scientific Temper

View full text Add to dashboard Cite

The rapid advancement of artificial intelligence (AI) has led to its widespread adoption across various domains. One of the most important challenges faced by AI adoption is to justify the outcome of the AI model. In response, explainable AI (XAI) has emerged as a critical area of research, aiming to enhance transparency and interpretability in AI systems. However, existing XAI methods facing several challenges, such as complexity, difficulty in interpretation, limited applicability, and lack of transparency. In this paper, we discuss current challenges using SHAP and LIME metrics being popular methods for explainable AI and then present a novel approach for developing an explainable AI framework that addresses these limitations. This novel approach uses simple techniques and understandable human explanations to provide users with clear and interpretable insights into AI model behavior. Key components of this approach include model-agnostic interpretability, a newly developed explainable factor overcoming the challenges of current XAI methods and enabling users to understand the decision-making process of AI models. We demonstrate the effectiveness of the new approach through a case study and evaluate the framework’s performance in terms of interpretability. Overall, the new approach offers enhanced transparency and trustworthiness in AI systems across diverse applications.

show abstract

Section: Local Interpretable Model-agnostic Explanationsmentioning

confidence: 99%

novel method for developing explainable machine learning framework using feature neutralization technique

Kalyanathaya,

2024

The Scientific Temper

View full text Add to dashboard Cite

show abstract

“…A partir da ideia de um servic ¸o centralizador [Barreto 2016], propõe o H-KaaS (Health Knowledge as a Service), um servic ¸o de KaaS dedicado ao domínio da saúde, adaptado a partir da definic ¸ão de [Xu and Zhang 2016], que define os principais com-ponentes da arquitetura dedicada a saúde, que são eles: detentores de dados, servic ¸o provedor de conhecimento e consumidores de conhecimento. XAI se refere ao conjunto de técnicas que visa prover ao usuário um melhor entendimento do processo decisório dos modelos e de como foram obtidos os resultados e as conclusões [Oblizanov et al 2023]. A ausência de transparência, confianc ¸a e interpretabilidade é uma das principais barreiras para a adoc ¸ão de modelos de aprendizado de máquina na área médica.…”

Section: Fundamentac ¸ãO Teóricaunclassified

XH-KaaS (eXplanable Health-Knowledge as a Service)

Montenegro,

Lino

2024

Anais Estendidos Do XX Simpósio Brasileiro De Sistemas De Informação (SBSI 2024)

View full text Add to dashboard Cite

Os Sistemas de Suporte à Decisão Clínica (SSDC) e as técnicas de inteligência artificial, especialmente o aprendizado de máquina, tornaram-se verdadeiros aliados devido à sua precisão e eficácia. No entanto, a falta de transparência e interpretabilidade desses sistemas representa desafios para sua aplicação prática. Para mitigar tal problemática, o artigo propõe uma arquitetura de conhecimento como serviço ao domínio da saúde. Essa abordagem busca centralizar serviços e incorporar técnicas de explicabilidade, visando aprimorar a compreensão do processo decisório dos modelos de aprendizado de máquina pelos usuários e estabelecer arquiteturas de referência que minimizem os riscos associados à falta de transparência.

show abstract

“…CHAIN_APPROX_SIMPLE parameter (Figure 3b), all boundary points are preserved. It removes all unnecessary points, compresses the contour, and also saves memory [31].…”

Section: Algorithm For Contour Analysis Of the Image Of A Cut Of A Gr...mentioning

confidence: 99%

The Use of Computer Vision to Improve the Affinity of Rootstock-Graft Combinations and Identify Diseases of Grape Seedlings

et al. 2023

Self Cite

View full text Add to dashboard Cite

This study explores the application of computer vision for enhancing the selection of rootstock-graft combinations and detecting diseases in grape seedlings. Computer vision has various applications in viticulture, but publications and research have not reported the use of computer vision in rootstock-graft selection, which defines the novelty of this research. This paper presents elements of the technology for applying computer vision to rootstock-graft combinations and includes an analysis of grape seedling cuttings. This analysis allows for a more accurate determination of the compatibility between rootstock and graft, as well as the detection of potential seedling diseases. The utilization of computer vision to automate the grafting process of grape cuttings offers significant benefits in terms of increased efficiency, improved quality, and reduced costs. This technology can replace manual labor and ensure economic efficiency and reliability, among other advantages. It also facilitates monitoring the development of seedlings to determine the appropriate planting time. Image processing algorithms play a vital role in automatically determining seedling characteristics such as trunk diameter and the presence of any damage. Furthermore, computer vision can aid in the identification of diseases and defects in seedlings, which is crucial for assessing their overall quality. The automation of these processes offers several advantages, including increased efficiency, improved quality, and reduced costs through the reduction of manual labor and waste. To fulfill these objectives, a unique robotic assembly line is planned for the grafting of grape cuttings. This line will be equipped with two conveyor belts, a delta robot, and a computer vision system. The use of computer vision in automating the grafting process for grape cuttings offers significant benefits in terms of efficiency, quality improvement, and cost reduction. By incorporating image processing algorithms and advanced robotics, this technology has the potential to revolutionize the viticulture industry. Thanks to training a computer vision system to analyze data on rootstock and graft grape varieties, it is possible to reduce the number of defects by half. The implementation of a semi-automated computer vision system can improve crossbreeding efficiency by 90%. Reducing the time spent on pairing selection is also a significant advantage. While manual selection takes between 1 and 2 min, reducing the time to 30 s using the semi-automated system, and the prospect of further automation reducing the time to 10–15 s, will significantly increase the productivity and efficiency of the process. In addition to the aforementioned benefits, the integration of computer vision technology in grape grafting processes brings several other advantages. One notable advantage is the increased accuracy and precision in pairing selection. Computer vision algorithms can analyze a wide range of factors, including size, shape, color, and structural characteristics, to make more informed decisions when matching rootstock and graft varieties. This can lead to better compatibility and improved overall grafting success rates.

show abstract

Evaluation Metrics Research for Explainable Artificial Intelligence Global Methods Using Synthetic Data

Cited by 10 publications

References 31 publications

novel method for developing explainable machine learning framework using feature neutralization technique

novel method for developing explainable machine learning framework using feature neutralization technique

XH-KaaS (eXplanable Health-Knowledge as a Service)

The Use of Computer Vision to Improve the Affinity of Rootstock-Graft Combinations and Identify Diseases of Grape Seedlings

Contact Info

Product

Resources

About