五个
发表于 2025-3-21 19:10:37
书目名称Artificial Intelligence: Theory and Applications影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0162584<br><br> <br><br>书目名称Artificial Intelligence: Theory and Applications影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0162584<br><br> <br><br>书目名称Artificial Intelligence: Theory and Applications网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0162584<br><br> <br><br>书目名称Artificial Intelligence: Theory and Applications网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0162584<br><br> <br><br>书目名称Artificial Intelligence: Theory and Applications被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0162584<br><br> <br><br>书目名称Artificial Intelligence: Theory and Applications被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0162584<br><br> <br><br>书目名称Artificial Intelligence: Theory and Applications年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0162584<br><br> <br><br>书目名称Artificial Intelligence: Theory and Applications年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0162584<br><br> <br><br>书目名称Artificial Intelligence: Theory and Applications读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0162584<br><br> <br><br>书目名称Artificial Intelligence: Theory and Applications读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0162584<br><br> <br><br>
无可非议
发表于 2025-3-21 22:54:32
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Eructation
发表于 2025-3-22 02:04:10
Biogas Generation from Animal Waste: A Case Study of Village Wazirpur,es 334.8 to 428 m. biogas daily. This biogas is used as fuel for biogas genset, and the generation system is simulated in Homer in grid connected mode, giving 58,692 kWh energy annually. Net present cost and cost of energy are having negative values indicating the costs are recovered in project’s li
哀求
发表于 2025-3-22 08:31:01
Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory,ner model ., . of Kalman filter which filters the noise out with container measurement . applying other forecasting models. Thus, a Kalman filter has been applied to improve the output of LSTM by correcting the noise of it. The prediction results of KALSTM demonstrate improved predictive performance
遗弃
发表于 2025-3-22 09:27:03
Agricultural Crop Yield Prediction for Indian Farmers Using Machine Learning,nd humidity, soil characteristics like macronutrients, soil type, topography, irrigation, and fertilizer management, all influence crop output. Remote and proximal sensing technologies are being used more often as a result of the requirement for timely and precise sensing of these inputs for big agr
N防腐剂
发表于 2025-3-22 14:23:01
Application of Artificial Intelligence on Camera-Based Human Pose Prediction for Yoga: A Methodologich achieved the accuracy of 81.59% and compares to previous works. This study and research has been done on the data that is available publicly and has been sourced from online resources and references mentioned.
Confidential
发表于 2025-3-22 17:57:20
Investigations on Deep Learning Pre-trained Model VGG-19 Using Transfer Learning for Remote Sensingnce in feature extraction using the VGG-19 deep learning model is achieved with the PatterNet dataset. The evaluation metrics indicate a precision of 92%, recall of 91%, F1 score of 90%, accuracy of 91% and a minimum validation loss of 0.3. These results highlight the effectiveness of the PatterNet
cathartic
发表于 2025-3-23 01:14:51
Complexity Analysis of Legal Documents,e to understand them. Given that there are no specific ways for defining the complexity of legal documents, we performed human evaluation and achieved the best value for the recommended approaches with the assistance of legal experts.
HATCH
发表于 2025-3-23 04:52:37
Review of Classification and Detection for Insects/Pests Using Machine Learning and Deep Learning A methods and detection methods based on parameters such as accuracy, F1 score, precision, and recall. A comprehensive survey, observations drawn from the existing research, and its analysis are presented in this paper. The survey will lead directions for future research.
相信
发表于 2025-3-23 08:56:53
Sentiment Analysis of Product Reviews Using Deep Learning and Transformer Models: A Comparative Studels to analyze product review sentiment. This paper presents a comparative investigation of different techniques used for sentiment analysis in product reviews to discover which AI-based technique works pre-eminent for review datasets.