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发表于 2025-3-21 17:11:03
书目名称Machine Intelligence and Emerging Technologies影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0620356<br><br> <br><br>书目名称Machine Intelligence and Emerging Technologies影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0620356<br><br> <br><br>书目名称Machine Intelligence and Emerging Technologies网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0620356<br><br> <br><br>书目名称Machine Intelligence and Emerging Technologies网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0620356<br><br> <br><br>书目名称Machine Intelligence and Emerging Technologies被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0620356<br><br> <br><br>书目名称Machine Intelligence and Emerging Technologies被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0620356<br><br> <br><br>书目名称Machine Intelligence and Emerging Technologies年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0620356<br><br> <br><br>书目名称Machine Intelligence and Emerging Technologies年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0620356<br><br> <br><br>书目名称Machine Intelligence and Emerging Technologies读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0620356<br><br> <br><br>书目名称Machine Intelligence and Emerging Technologies读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0620356<br><br> <br><br>
沙文主义
发表于 2025-3-21 23:39:10
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
Obliterate
发表于 2025-3-22 03:24:33
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压迫
发表于 2025-3-22 04:42:20
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
Bridle
发表于 2025-3-22 11:05:46
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
Anticonvulsants
发表于 2025-3-22 15:50:29
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
Exclude
发表于 2025-3-22 18:20:11
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
Tartar
发表于 2025-3-23 00:04:48
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脾气暴躁的人
发表于 2025-3-23 04:40:21
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
propose
发表于 2025-3-23 06:06:01
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