Titlebook: Computer, Communication, and Signal Processing; 6th IFIP TC 5 Intern Erich J. Neuhold,Xavier Fernando,Aravindan Chandra Conference proceedi

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書(shū)目名稱(chēng)Computer, Communication, and Signal Processing影響因子(影響力)




書(shū)目名稱(chēng)Computer, Communication, and Signal Processing影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Computer, Communication, and Signal Processing網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Computer, Communication, and Signal Processing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Computer, Communication, and Signal Processing被引頻次




書(shū)目名稱(chēng)Computer, Communication, and Signal Processing被引頻次學(xué)科排名




書(shū)目名稱(chēng)Computer, Communication, and Signal Processing年度引用




書(shū)目名稱(chēng)Computer, Communication, and Signal Processing年度引用學(xué)科排名




書(shū)目名稱(chēng)Computer, Communication, and Signal Processing讀者反饋




書(shū)目名稱(chēng)Computer, Communication, and Signal Processing讀者反饋學(xué)科排名

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Predicting Students’ Satisfaction Towards Online Courses Using Aspect-Based Sentiment Analysisrence to the online courses using machine learning algorithm. The proposed system has attained improved accuracy than the existing model. From this study, the specific aspects are obtained using both unsupervised and semi-supervised LDA algorithms. Students’ satisfaction with specific aspects of the

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Detection and Classification of Paddy Leaf Diseases Using Deep Learning (CNN)af blight, rice blast, brown spot, rymv and rice tungro for processing the image. In pre-processing, background exclusion is done based on the hue value to extract the non-diseased and diseased part. Feature Extraction is executed by applying Convolutional Neural Network. The proposed system achieve

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REIGN

Automated Annotation and?Classification of?Catheters in?Chest X-Raystional number of classes, custom loss function, and attention heads introduced in the model is a unique attempt to ensure robust results in the radiological inferences. It is evident that the qualitative and quantitative results of annotation and classification are viable which resembles how humans

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Trust Aware Secure Routing Model in MANET: Self-improved Particle Swarm Optimization for Optimal Rou considering certain parameters like trust rate (security), Packet Drop Ratio (PDR) distance, congestion, energy, and as well. The performance of the adopted work is examined to the existing schemes regarding Energy, Delay, and Network Lifetime.

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