Titlebook: Advances in Signal Processing and Intelligent Recognition Systems; 6th International Sy Sabu M. Thampi,Sri Krishnan,Jagadeesh Kannan R. Con

冷峻

Lament

https://doi.org/10.1007/978-3-031-37706-8ion without manual labor. This has been achieved by feeding the features initially to the unsupervised learning algorithm, i.e., KMeans Clustering algorithm. The classified and misclassified vowels, then became the train and test sets respectively for supervised learning algorithms and a combined re

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frozen-shoulder

Fast Termination and?Workflow Netsand was less effortful for training compared to a Speaker Dependent (SD) recognizer. Testing of the system was conducted with the UA-Speech Database and the combined algorithm produced improvements in recognition accuracy from 43% to 90% for medium to highly impaired speakers revealing its applicabi

Glaci冰

Monitoring Atomicity in Concurrent Programsodel generates sensible, diverse and personalized recommendations and is effective even on small datasets. We compare our results quantitatively against that of the popular latent factor models for music recommendation and show that our song to vector model outperforms traditional recommendation met

谷物

Minh-Thai Trinh,Duc-Hiep Chu,Joxan Jaffarpled with image post-processing has demonstrated robustness in classifying chest X-rays of external datasets, which could be used as a standalone tool for other image analysis projects..The results of the hyper-parameter tuned classification model show a dramatic improvement in overall accuracy of t

草本植物

Chih-Hong Cheng,Yassine Hamza,Harald Ruessn methods improve the resolution of images alike without taking the capture range into account and hence are not quality driven. In order to improve the recognition rate irrespective of the acquisition distance, we propose to make use of transfer learning. The novelty of our approach is that it is t

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Factorable

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