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Najla Al-Nabhan
Researcher at King Saud University
Publications - 62
Citations - 503
Najla Al-Nabhan is an academic researcher from King Saud University. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 8, co-authored 51 publications receiving 182 citations. Previous affiliations of Najla Al-Nabhan include University of Tabuk & Nanjing Institute of Technology.
Papers
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Recognition of plant leaf diseases based on computer vision
TL;DR: The approach of combined segmentation and classification is effective for plant disease identification, and the empirical research validates the advantages of the proposed method.
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An enhanced pairing-based authentication scheme for smart grid communications
TL;DR: This study finds that the PAuth scheme proposed by Chen et al. still suffers from some security defects, and an enhanced scheme based on PAuth is proposed, and ProVerif, an automatic cryptographic protocol verifier, is used to analyze the enhanced scheme.
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T-BERTSum: Topic-Aware Text Summarization Based on BERT
TL;DR: A topic-aware extractive and abstractive summarization model named T-BERTSum, based on Bidirectional Encoder Representations from Transformers (BERTs), which achieves new state-of-the-art results while generating consistent topics compared with the most advanced method.
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Analysis and comparison of machine learning classifiers and deep neural networks techniques for recognition of Farsi handwritten digits
TL;DR: Two types of neural networks, known as deep neural networks in its expansion form, a convolutional neural network (CNN) and an auto-encoder, are implemented and by using a new combination of CNN layers one can obtain improved results in classifying Farsi digits.
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Graph classification based on structural features of significant nodes and spatial convolutional neural networks
TL;DR: Experimental results on two kinds of real-world datasets, bioinformatics and social network datasets, indicate that the proposed spatial convolutional neural network architecture for graph classification is superior to some classic kernels and similar deep learning-based algorithms on 6 out of 8 benchmark data sets.