Wyniki wyszukiwania - Biblioteka Nauki

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An explainable AI approach to agrotechnical monitoring and crop diseases prediction in Dnipro region of Ukraine

100%

Laktionov I. , Diachenko G. , Rutkowska D. , Kisiel-Dorohinicki M.

Journal of Artificial Intelligence and Soft Computing Research

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2023

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tom Vol. 13, No. 4

247--272

EN

The proliferation of computer-oriented and information digitalisation technologies has become a hallmark across various sectors in today’s rapidly evolving environment. Among these, agriculture emerges as a pivotal sector in need of seamless incorporation of highperformance information technologies to address the pressing needs of national economies worldwide. The aim of the present article is to substantiate scientific and applied approaches to improving the efficiency of computer-oriented agrotechnical monitoring systems by developing an intelligent software component for predicting the probability of occurrence of corn diseases during the full cycle of its cultivation. The object of research is non-stationary processes of intelligent transformation and predictive analytics of soil and climatic data, which are factors of the occurrence and development of diseases in corn. The subject of the research is methods and explainable AI models of intelligent predictive analysis of measurement data on the soil and climatic condition of agricultural enterprises specialised in growing corn. The main scientific and practical effect of the research results is the development of IoT technologies for agrotechnical monitoring through the development of a computer-oriented model based on the ANFIS technique and the synthesis of structural and algorithmic provision for identifying and predicting the probability of occurrence of corn diseases during the full cycle of its cultivation.

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Explainable deep neural network-based analysis on intrusion-detection systems

100%

Pande S. D. , Khamparia A.

Computer Science

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2023

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tom T. 24 (1)

97--111

EN

The research on intrusion-detection systems (IDSs) has been increasing in recent years. Particularly, this research widely utilizes machine-learning concepts, and it has proven that these concepts are effective with IDSs – particularly, deep neural network-based models have enhanced the rates of the detection of IDSs. In the same instance, these models are turning out to be very complex, and users are unable to track down explanations for the decisions that are made; this indicates the necessity of identifying the explanations behind those decisions to ensure the interpretability of the framed model. In this aspect, this article deals with a proposed model that can explain the obtained predictions. The proposed framework is a combination of a conventional IDS with the aid of a deep neural network and the interpretability of the model predictions. The proposed model utilizes Shapley additive explanations (SHAPs) that mixes the local explainability as well as the global explainability for the enhancement of interpretations in the case of IDS. The proposed model was implemented by using popular data sets (NSL-KDD and UNSW-NB15), and the performance of the framework was evaluated by using their accuracy. The framework achieved accuracy levels of 99.99 and 99.96%, respectively. The proposed framework can identify the top-4 features using local explainability and the top-20 features using global explainability.

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Judicial Decision-Making and Explainable AI (XAI) – Insights from the Japanese Judicial System

67%

Yamada Y.

Studia Iuridica Lublinensia

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2023

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tom 32

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nr 4

157-173

PL

Niedawny rozwój sztucznej inteligencji w technologii informacyjnej jest niezwykły. Zmiany te doprowadziły do twierdzeń, że sztuczna inteligencja może być wykorzystywana w sądach do zastępowania sędziów. W artykule autor odnosi się do sedna tych problemów, używając koncepcji interpretowalnej sztucznej inteligencji (XAI – Explainable Artificial Intelligence). Analizie poddano to, w jaki sposób regulacja może zapewnić, że sztuczna inteligencja będzie etyczna, a także w jaki sposób ta etyczność jest ściśle powiązana z XAI. Stwierdzono, że obecnie wkład sztucznej inteligencji w proces decyzyjny jest ograniczony przez brak wystarczającej możliwości jej wyjaśnienia i interpretacji, chociaż aspekty te są odpowiednio uwzględnione i omówione. Ponadto kluczowe jest rozważenie wpływu sztucznej inteligencji na autorytet prawny, który stanowi podstawę wymiaru sprawiedliwości. Zasugerowano przy tym rozważenie przeprowadzenia badania eksperymentalnego polegającego na włączeniu sztucznej inteligencji do procesu arbitrażowego.

EN

The recent development of artificial intelligence (AI) in information technology (IT) is remarkable. These developments have led to claims that AI can be used in courts to replace judges. In the article, the author addresses a matrix of these issues using the concept of explainable AI (XAI). The article examines how regulation can ensure that AI is ethical, and how this ethicality is closely related to (XAI). It concludes that, in the current context, the contribution of AI to the decision-making process is limited by the lack of sufficient explainability and interpretability of AI, although these aspects are adequately addressed and discussed. In addition, it is crucial to consider the impact of AI’s contribution on the legal authority that forms the foundation of the justice system, and a possible approach is suggested to consider conducting an experimental study as AI arbitration.

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An end-to-end Machine Learning system for mitigating checkout abandonment in E-Commerce

67%

Rifat M. R. I. , Amin M. N. , Munna M. H. , Imran A. A.

Annals of Computer Science and Information Systems

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2022

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tom Vol. 30

129--132

EN

Electronic Commerce (E-Commerce) has become one of the most significant consumer-facing tech industries in recent years. This industry has considerably enhanced people's lives by allowing them to shop online from the comfort of their own homes. Despite the fact that many people are accustomed to online shopping, e-commerce merchants are facing a significant problem, a high percentage of checkout abandonment. In this study, we have proposed an end-to-end Machine Learning (ML) system that will assist the merchant to minimize the rate of checkout abandonment with proper decision making and strategy. As a part of the system, we developed a robust machine learning model that predicts if someone will checkout the products added to the cart based on the customer's activity. Our system also provides the merchants with the opportunity to explore the underlying reasons for each single prediction output. This will indisputably help the online merchants in business growth and effective stock management.