Titlebook: Artificial Intelligence and Digitalization for Sustainable Development; 10th EAI Internation Bereket H. Woldegiorgis,Kibret Mequanint,Abdul

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tympanometry

Semblance

Amharic Character Recognition Using Deep Convolutional Neural Network,e resemblance among shapes of the different symbols available in the language which challenged the task of machine-based optical character recognition systems in the language. The absence of a standardized labeled dataset for the Amharic language created additional barriers for different researchers

暫時(shí)過來

Assistive Smart Cane Technology for Visually Impaired Peoples: A Review,us researchers’ works on cane development methodologies. This review’s goal is to determine the full cane configuration of hardware parts, software architecture, and cane structure. We discussed object detection methods, object identification methods, flame detection methods, water detection methods

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GROSS

Amharic Sentence-Level Word Sense Disambiguation,sing Transfer, retrieval, and machine translation. The manual disambiguation process by humans is tedious, prone to errors, and expensive. Recent research in Amharic WSD used mostly handcrafted rules. Such works do not help to learn different representations of the target word from data automatically. Moreover, s

explicit

,Deep Complex-Valued Neural Networks for?Massive MIMO Signal Detection,ial. The main challenge in designing detectors is the trade-off between performance and computational complexity; that is, efficient detectors incur higher computational costs, while computationally cheaper detectors have lower efficiency. Recently, many deep learning-based detectors have been propo

招待

漂亮才會(huì)豪華

增強(qiáng)