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23 results on '"Tsihrintzis, George A."'

1. Introduction to Advances in Information, Intelligence, Systems and Applications

Author

Tsihrintzis, George A., Virvou, Maria, Bourbakis, Nikolaos G., Jain, Lakhmi C., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Bourbakis, Nikolaos, editor, Tsihrintzis, George A., editor, Virvou, Maria, editor, and Jain, Lakhmi C., editor

Published
2024
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3. Augmenting Large Language Models with Rules for Enhanced Domain-Specific Interactions: The Case of Medical Diagnosis.

Author

Panagoulias, Dimitrios P., Virvou, Maria, and Tsihrintzis, George A.

Subjects
LANGUAGE models, MACHINE learning, GENERATIVE artificial intelligence, ARTIFICIAL intelligence, DIAGNOSIS
Abstract

In this paper, we present a novel Artificial Intelligence (AI) -empowered system that enhances large language models and other machine learning tools with rules to provide primary care diagnostic advice to patients. Specifically, we introduce a novel methodology, represented through a process diagram, which allows the definition of generative AI processes and functions with a focus on the rule-augmented approach. Our methodology separates various components of the generative AI process as blocks that can be used to generate an implementation data flow diagram. Building upon this framework, we utilize the concept of a dialogue process as a theoretical foundation. This is specifically applied to the interactions between a user and an AI-empowered software program, which is called "Med|Primary AI assistant" (Alpha Version at the time of writing), and provides symptom analysis and medical advice in the form of suggested diagnostics. By leveraging current advancements in natural language processing, a novel approach is proposed to define a blueprint of domain-specific knowledge and a context for instantiated advice generation. Our approach not only encompasses the interaction domain, but it also delves into specific content that is relevant to the user, offering a tailored and effective AI–user interaction experience within a medical context. Lastly, using an evaluation process based on rules, defined by context and dialogue theory, we outline an algorithmic approach to measure content and responses. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Intelligent Decision Technologies: An International Journal (IDT) heading into its 18 th year: A Short Note from the Editors-in-Chief.

Author

Tsihrintzis, George A., Phillips-Wren, Gloria, and Jain, Lakhmi C.

Subjects
ARTIFICIAL intelligence, INFORMATION technology, GENERATIVE artificial intelligence, DECISION support systems, INTERNET forums, TECHNICAL specifications
Abstract

The Intelligent Decision Technologies (IDT) Journal is a scholarly publication that focuses on the theory and application of intelligent technologies and systems for decision making. It was founded in 2007 by Professors Gloria Phillips-Wren and Lakhmi C. Jain to address the need for real-world decision support systems. The journal has gained popularity over the years and now receives over 800 submissions annually. It covers various topics related to intelligent decision support, such as artificial intelligence and data processing. The current editors-in-chief are Professors George A. Tsihrintzis, Gloria Phillips-Wren, and Lakhmi C. Jain. The text provides information about the journal's recognition, the number of submitted and published papers, special collections and issues, and future plans. It also acknowledges the contributions of associate editors and encourages readers to explore the journal's website for more information and to consider submitting their research work. The editors hope that readers will find the journal useful and inspiring. [Extracted from the article]

Published
2024
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5. VIRTSI: A novel trust dynamics model enhancing Artificial Intelligence collaboration with human users – Insights from a ChatGPT evaluation study.

Author

Virvou, Maria, Tsihrintzis, George A., and Tsichrintzi, Evangelia-Aikaterini

Subjects
*ARTIFICIAL intelligence, *GENERATIVE artificial intelligence, *LANGUAGE models, *CHATGPT, *MACHINE learning
Abstract

The rapid integration of intelligent processes and methods into information systems in the Artificial Intelligence (AI) era has led to a substantial shift towards autonomous software decision-making. This evolution necessitates robust human oversight, especially in critical domains like Healthcare, Education, and Energy. Human trust in AI plays a vital role in influencing decision-making processes of users interacting with AI. This paper presents VIRTSI (V ariability and I mpact of R eciprocal T rust S tates towards I ntelligent systems), a novel rigorous computational model for human-AI Interaction. VIRTSI simulates human trust states, spanning from overtrust to distrust, through user modelling. It comprises: 1. A trust dynamics representational model based on Deterministic Finite State Automata (DFAs), illustrating transitions among cognitive trust states in response to AI-generated replies. 2. A trust evaluation model based on Confusion Matrices, originating from machine learning and Accuracy Metrics, providing a quantitative framework for analysing human trust dynamics. As a result, this is the first time that trust dynamics have been thoroughly traced in a representational model and a method has been developed to assess the impact of possibly harmful states like overtrust and distrust. An empirical study on the recently launched Large Language Model of generative AI, ChatGPT (version 3.5), provides a radical underexplored AI-generated platform for evaluating the human-AI interaction through VIRTSI. The study involved 1200 interactions of real users as well as AI experts together with experts in two very different domains of evaluation, namely software engineering and poetry. This study traces trust dynamics and the emerging human-AI interaction, in concrete examples of real user synergies with generative AI. The research reveals the vital role of maintaining normal trust states for optimal human-AI interaction and that both AI and human users need further steps towards this goal. The real-world implications of this research can guide the creation and evaluation of user interfaces with AI and the incorporation of functionalities in the development of generative AI chatbots in terms of trust by providing a new rigorous DFA representational method of trust dynamics and a corresponding new perspective of confusion matrix evaluation method of the dynamics' impact in the efficiency of human-AI dialogues. [ABSTRACT FROM AUTHOR]

Published
2024
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6. A novel framework for artificial intelligence explainability via the Technology Acceptance Model and Rapid Estimate of Adult Literacy in Medicine using machine learning.

Author

Panagoulias, Dimitrios P., Virvou, Maria, and Tsihrintzis, George A.

Subjects
*TECHNOLOGY Acceptance Model, *ARTIFICIAL intelligence, *ADULT literacy, *TRUST, *HIERARCHICAL clustering (Cluster analysis), *COMPUTER literacy
Abstract

The significant proliferation of AI-empowered systems and machine learning (ML) across various examined domains underscores the vital necessity for comprehensive and customised explainability frameworks to lead to usable and trustworthy systems. Especially in the medical domain, where validation of methodologies and outcomes is as important as the adoption rate of such systems, the requirements of the depth and the level of abstraction of the explainability are particularly important and necessitate a systemic approach to ensure a proper definition. Explainability and interpretability are important usability and trustworthiness properties of AI-empowered systems and, as such, constitute important factors for technology acceptance. In this paper, we propose a novel framework for explainability requirements in AI-empowered systems using the Technology Acceptance Model (TAM). This framework employs targeted ML (hierarchical clustering, k-means or other) to acquire a user model for personalised, multi-layered explainability. Our novel framework integrates a rule-based system, which guides the degree of trustworthiness to be achieved based on user perception and AI literacy level. We test this methodology in the case of AI-empowered medical systems to (1) assess and quantify the doctors' abilities and familiarisation with technology and AI, (2) generate layers of personalised explainability based on user ability and user needs in terms of trustworthiness and (3) provide the necessary environment for transparency and validation. To assess and quantify the doctors' abilities we have considered Rapid Estimate of Adult Literacy in Medicine (REALM) a tool commonly used in the medical domain to bridge the communication gap between patients and doctors. • Framework for personalised explainability in Artificial intelligence, based on Levels. • Levels refer to AI literacy level and level of abstraction. • Behavioural models were used that measure perception towards an innovation. • Framework is evaluated and implemented in an AI-empowered medical application. [ABSTRACT FROM AUTHOR]

Published
2024
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17. AI Enhanced Cyber Security Methods for Anomaly Detection

Author

Shaik, Abdul Subhahan, Shaik, Amjan, Tsihrintzis, George A., Series Editor, Virvou, Maria, Series Editor, Jain, Lakhmi C., Series Editor, Dehuri, Satchidananda, editor, Cho, Sung-Bae, editor, Padhy, Venkat Prasad, editor, Shanmugam, Poonkuntrun, editor, and Ghosh, Ashish, editor

Published
2024
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