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Titlebook: Machine Intelligence and Emerging Technologies; First International Md. Shahriare Satu,Mohammad Ali Moni,Mohammad Sham Conference proceedi

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發(fā)表于 2025-3-21 17:11:03 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Machine Intelligence and Emerging Technologies
副標(biāo)題First International
編輯Md. Shahriare Satu,Mohammad Ali Moni,Mohammad Sham
視頻videohttp://file.papertrans.cn/621/620356/620356.mp4
叢書名稱Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engi
圖書封面Titlebook: Machine Intelligence and Emerging Technologies; First International  Md. Shahriare Satu,Mohammad Ali Moni,Mohammad Sham Conference proceedi
描述The two-volume set LNICST 490 and 491 constitutes the proceedings of the First International Conference on Machine Intelligence and Emerging Technologies, MIET 2022, hosted by Noakhali Science and Technology University, Noakhali, Bangladesh, during September 23–25, 2022.?.The 104 papers presented in the proceedings were carefully reviewed and selected from 272 submissions. This book focuses on theoretical, practical, state-of-art applications, and research challenges in the field of artificial intelligence and emerging technologies.? It will be helpful for active researchers and practitioners in this field. These papers are organized in the following topical sections: imaging for disease detection; pattern recognition and natural language processing; bio signals and recommendation systems for wellbeing; network, security and nanotechnology; and emerging technologies for society and industry..
出版日期Conference proceedings 2023
關(guān)鍵詞access control; architecting; architecture analyis; artificial intelligence; classification methods; clus
版次1
doihttps://doi.org/10.1007/978-3-031-34619-4
isbn_softcover978-3-031-34618-7
isbn_ebook978-3-031-34619-4Series ISSN 1867-8211 Series E-ISSN 1867-822X
issn_series 1867-8211
copyrightICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2023
The information of publication is updating

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發(fā)表于 2025-3-21 23:39:10 | 只看該作者
False Smut Disease Detection in?Paddy Using Convolutional Neural Networkpathogen, Villosiclava virens, epidemics result in yield loss and poor grain quality (anamorph: Ustilaginoidea virens). As a result, the farmers’ main concern is disease management measures that are effective, simple, and practical. Because of this, we look at the image of the RFS to understand and
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Potato Disease Detection Using Convolutional Neural Network: A Web Based Solutionato (.) is Bangladesh’s second-most popular and in-demand crop. But deadly diseases late blight and early blight cause an enormous loss in potato production. To increase plant yields, it is important to identify the symptoms of these diseases in plants in the early stage and advise farmers on how to
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發(fā)表于 2025-3-22 11:05:46 | 只看該作者
Device-Friendly Guava Fruit and Leaf Disease Detection Using Deep Learningdels are used in this research, which achieved high accuracy in detecting plant disease. The problem with such models with high accuracy is that the models are of larger size which do not allow them to be applied to end-user devices. In this research, model quantization techniques such as float16 an
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發(fā)表于 2025-3-22 15:50:29 | 只看該作者
Cassava Leaf Disease Classification Using Supervised Contrastive Learning especially in southern Africa. Cassava production is most common in South Africa because it can survive well in a harsh environment. Sometimes the cassava crop gets affected by leaf disease, which infects its overall production, and?reduces the farmers’ income. And manual leaf disease detection may
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發(fā)表于 2025-3-22 18:20:11 | 只看該作者
Diabetes Mellitus Prediction Using Transfer Learningachine Learning has proven to be effective for diabetes prediction. However, in the literature, barely any methods used the high learning capacities of Deep Learning (DL) techniques for diabetes prediction. Hence, in this study, we have proposed methods for diabetes diagnosis using Deep Learning (DL
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Improved and Intelligent Heart Disease Prediction System Using Machine Learning Algorithmf machine learning, there are many classification algorithms for predicting heart disease. This paper presents the probability of heart disease prediction by some machine learning classifiers which are processed by feature engineering techniques on datasets. Feature engineering is used for building
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發(fā)表于 2025-3-23 06:06:01 | 只看該作者
PreCKD_ML: Machine Learning Based Development of?Prediction Model for?Chronic Kidney Disease and?Iden of CKD and exact risk factors should be known to ensure proper treatment. The study mainly aims to address the issue by building a predictive model and discovers the most significant risk factors employing machine learning (ML) approach for CKD patients. Four individual machine learning classifier
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