Evaluation of the Efficiency of the Optimization Algorithms for Transfer Learning on the Rice Leaf Disease Dataset

Quach, Luyl-Da; Quoc, Khang Nguyen; Quynh, Anh Nguyen; Ngoc, Hoang Tran

doi:10.14569/ijacsa.2022.0131011

Cited by 10 publications

(4 citation statements)

References 30 publications

(36 reference statements)

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“…Therefore, we are motivated to design a new method for self-use chatbot training, and the goal of the chatbot is it can response to the commonly used questions accurately. We collect frequently asked questions in our daily works, using TF-IDF for text vectorization [5], [8], so the chatbot can process the human language, select the answer for users by text similarity, improve the accuracy by the Markov chain [6] to predict user's intentions when the question is too short.…”

Section: Related Workmentioning

confidence: 99%

A question answer chatbot using term frequency-inverse document frequency and Markov chain

Li,

Che,

Yang

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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The Natural Language Processing has achieved great success theses years, especially in sentiment analysis, text generation, and Human-Machine Interaction. Since the chat generative pretrained transformer developed by OpenAI in November 2022, more chatbots based on Large Language Models have appeared in our life, bring us a more convenient service. This paper we developed a organization self-use chatbot, using the Markov Model and the Term Frequency-Inverse Document Frequency algorithm. We use Term Frequency-Inverse Document Frequency to vectorize user's question and calculate the text similarity to match answer, use the Markov Model to predict user's intentions when the question is too short. This kind of chatbot is suitable for some organizations, for it can be trained with less corpus and topics, and it also has a good performance on user guidance and user satisfaction.

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Section: Related Workmentioning

confidence: 99%

A question answer chatbot using term frequency-inverse document frequency and Markov chain

Li,

Che,

Yang

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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“…In recent times, transfer learning has gained widespread popularity and has been successful in solving several problems with significant achievements. Agriculture imaging tasks [32], [5], [26], [33], [31]; Medical imaging tasks [34], [2], [47], etc. To utilize the pretrained model for identifying plant diseases, this paper [49] presents the identification of apple leaf diseases with an accuracy of 93.71% using DenseNet-121.…”

Section: Related Workmentioning

confidence: 99%

Ensemble of Deep Learning Models for Multi-plant Disease Classification in Smart Farming

Vo¹,

Quach²,

Ngoc³

2023

IJACSA

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Plant disease identification at an early stage plays a crucial role in ensuring efficient management of the diseases and crop protection. The occurrence of plant ailments can result in substantial reductions in both crop yield and quality, which may cause financial setbacks for farmers and lead to food shortages for consumers. Traditional methods of disease detection rely on visual observation, which can consume a significant amount of time, be a labor-intensive, and often be inaccurate. Automated disease detection systems, based on techniques for machine learning have the potential to greatly improve the precision and speed of disease detection. This article presents a model for classifying plant diseases that combines the output of two transfer learning models, EfficientNetB0 and MobileNetV2, to improve disease classification accuracy. The PlantVillage Dataset was used to train and test the model under consideration, which contains 54,305 photos of 38 different plant disease classes, achieving an accuracy rate of 99.77% in disease classification. The use of an ensemble of deep learning models in this study shows promising results, indicating that the technique can enhance the accuracy of plant disease classification. Besides, this study contributes to the development of accurate and reliable automated disease detection systems, thereby supporting sustainable agriculture and global food security.

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“…However, statistical data from scholarly sources indicates that from 2019 to the present, there have been approximately 17,000 research studies related to the keyword "rice leaf disease detection" and about 17,400 studies related to "rice pest detection" (content related to pests and rice but not necessarily containing the complete search phrase). Some notable studies on rice diseases are [ 5 , 6 ]. This demonstrates the scientific community's keen interest in developing a dataset to meet research needs in this field.…”

Section: Introductionmentioning

confidence: 99%

Rice pest dataset supports the construction of smart farming systems

Quach,

Nguyen,

Nguyen

et al. 2024

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Evaluation of the Efficiency of the Optimization Algorithms for Transfer Learning on the Rice Leaf Disease Dataset

Cited by 10 publications

References 30 publications

A question answer chatbot using term frequency-inverse document frequency and Markov chain

A question answer chatbot using term frequency-inverse document frequency and Markov chain

Ensemble of Deep Learning Models for Multi-plant Disease Classification in Smart Farming

Rice pest dataset supports the construction of smart farming systems

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