Proposed distance and entropy measures of picture fuzzy sets in decision support systems

Pham, Hai Van; Thai, Kim Phung; Nguyen, Quoc Hung; Le, Duy Dong; Le, Thanh; Nguyen, Thi Xuan Dao; Phan, Thi Thuy Kieu; Thảo, Nguyễn Xuân

doi:10.3233/jifs-221556

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…GenAI models have gained traction in multiple domains of interest, and the influence exerted by GenAI is clear as shown by research studies published in the literature. The design and development of GenAI models is highly resource intensive, requiring a large investment in financial, technological, computational, social analysis, and human resources [4][5][6]. Additionally, data corpus-assisted data-driven learning remains a critical element [7] and there is a need for a suitable large language model (LLM) [8].…”

Section: Introductionmentioning

confidence: 99%

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A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

Tuan,

Moore,

Thanh

et al. 2024

Applied Sciences

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ChatGPT plays significant roles in the third decade of the 21st Century. Smart cities applications can be integrated with ChatGPT in various fields. This research proposes an approach for developing large language models using generative artificial intelligence models suitable for small- and medium-sized enterprises with limited hardware resources. There are many generative AI systems in operation and in development. However, the technological, human, and financial resources required to develop generative AI systems are impractical for small- and medium-sized enterprises. In this study, we present a proposed approach to reduce training time and computational cost that is designed to automate question–response interactions for specific domains in smart cities. The proposed model utilises the BLOOM approach as its backbone for using generative AI to maximum the effectiveness of small- and medium-sized enterprises. We have conducted a set of experiments on several datasets associated with specific domains to validate the effectiveness of the proposed model. Experiments using datasets for the English and Vietnamese languages have been combined with model training using low-rank adaptation to reduce training time and computational cost. In comparative experimental testing, the proposed model outperformed the `Phoenix’ multilingual chatbot model by achieving a 92% performance compared to `ChatGPT’ for the English benchmark.

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Section: Introductionmentioning

confidence: 99%

“…The creation of a chatbot that can adapt to multiple languages; in this study, our focus is on Vietnamese and English. 4.…”

mentioning

confidence: 99%

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

Tuan,

Moore,

Thanh

et al. 2024

Applied Sciences

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“…However, Knowledge Graphs have difficulty representing knowledge and inferring output labels on medical symptom datasets with some characteristics such as amplitude and phase term, uncertain or incomplete input information. Some applications of Picture Fuzzy Set in disease diagnosis [15][16][17][18] or fuzzy techniques based on Fuzzy Inference System, such as Fuzzy Inference System [19][20][21][22][23][24][25][26][27], Complex Fuzzy Inference System [28][29][30], and Mamdani Complex Fuzzy Inference System [31,32] have overcome the limitations mentioned in Knowledge Graph models. These techniques can represent knowledge for datasets containing ambiguous and unclear information, but these models cannot find output labels for new samples that are not in the Fuzzy Rules Base.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Knowledge Graph Pairs-based Model in extreme cases for Hepatitis Sign Diagnosis

Khanh,

Long,

Hai

2023

Preprint

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Recently, machine learning models based on fuzzy inference systems have effectively solved problems in medical diagnosis and received much attention from researchers around the world. For example, Fuzzy Inference System (FIS), Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R), Mamdani Complex Fuzzy Inference System with Fuzzy Knowledge Graph (M-CFIS-FKG), and Fuzzy Knowledge Graph Pairs (FKG-Pairs) have been proposed. These machine-learning models have successfully solved most of the disease diagnosis problems based on complex symtom datasets with some characteristics such as amplitude and phase term, uncertain or incomplete input information. However, these models have revealed limitations when dealing with decision-making or disease diagnosis problems in extreme cases (where training datasets are too small or input datasets are large). To overcome this challenge, the FKG-Extreme model, based on FKG-Pairs combined with Q-learning techniques, has been proposed. In order to promote the practical application of the theoretical model FKG-Extreme, a model for Hepatitis Sign Diagnosis has been proposed in this paper. A simulation application of the proposed model has been built, and experimental results have been carried out based on the Liver dataset collected from the UCI machine learning repository, with results in terms of accuracy 13.3% higher than the FKG-Pairs model. Furthermore, some measures (Precision, Recall, and F1-Score) have also been used to evaluate performance and demonstrate that the proposed model has high reliability.

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A Robust Component-Based Template Matching Approach Using Document Layout Graph for Extracting Information

Nguyen Vu,

Nguyen,

Pham

2024

Advances and Trends in Artificial Intelligence. Theory and Applications

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Proposed distance and entropy measures of picture fuzzy sets in decision support systems

Cited by 4 publications

References 40 publications

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

A Novel Fuzzy Knowledge Graph Pairs-based Model in extreme cases for Hepatitis Sign Diagnosis

A Robust Component-Based Template Matching Approach Using Document Layout Graph for Extracting Information

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