Titlebook: Intelligent Systems Design and Applications; 21st International C Ajith Abraham,Niketa Gandhi,Weiping Ding Conference proceedings 2022 The

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書目名稱Intelligent Systems Design and Applications影響因子(影響力)




書目名稱Intelligent Systems Design and Applications影響因子(影響力)學(xué)科排名




書目名稱Intelligent Systems Design and Applications網(wǎng)絡(luò)公開度




書目名稱Intelligent Systems Design and Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Intelligent Systems Design and Applications被引頻次




書目名稱Intelligent Systems Design and Applications被引頻次學(xué)科排名




書目名稱Intelligent Systems Design and Applications年度引用




書目名稱Intelligent Systems Design and Applications年度引用學(xué)科排名




書目名稱Intelligent Systems Design and Applications讀者反饋




書目名稱Intelligent Systems Design and Applications讀者反饋學(xué)科排名

凝視

Lecture Notes in Networks and Systemshttp://image.papertrans.cn/i/image/470018.jpg

exquisite

AWL

PRO

Lifetime Optimization of Sensor Networks with Mobile Sink and Solar Energy Supply,her mobile sink nor solar energy supply can achieve the maximal possible sensor network lifetime when used independently. We show by computational experiments on the optimization problem that using jointly these two techniques lead to a significant increase of the network lifetime.

過于平凡

Astigmatism

Induction

Semantic Representation Driven by a Musculoskeletal Ontology for Bone Tumors Diagnosis,ethod of semantic modeling ontological basis medical knowledge necessary for the diagnosis of bone tumors diseases from medical images to provide an automatic support for decision making and diagnosis of these diseases.

Dissonance

Extracting Emotion and Sentiment Quotient of Viral Information Over Twitter,data asserts significant insights, as generated sentiments and emotional effects reveals co-relations caused by viral/trending information. The algorithm delivers an efficient, robust, and adaptable solution for the sentiment analysis also.

EXALT

Transfer Learning with 2D Vibration Images for Fault Diagnosis of Bearings Under Variable Speed,ally, a Transfer Learning embedded Convolutional Neural Network (TL-CNN) is proposed to inspect the comprehensive structure of the 2D vibration images for final classification. The experimental results show that the proposed method achieved 100% classification accuracy on a publicly available dataset.