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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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Blog Post number 1
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publications
Simulation Comparison and Analysis of DSR and DYMO Protocols in MANETs
Published in 2016 International Conference on Industrial Informatics and Computer Systems (CIICS), 2016
Ad-hoc network has opened a new dimension in wireless networks. It allows wireless nodes to communicate with each other in the absence of centralized support. It does not follow any fixed infrastructure because of the mobility of nodes and multi-path propagations. Link instability and node mobility make routing a core issue in MANETs. A suitable and effective routing mechanism helps to extend the successful deployment of MANETs. In this paper, we have simulated and analyzed two routing protocols: DSR (Dynamic Source Routing Protocol) and DYMO (Dynamic MANET On-demand Routing). Our simulations were conducted using the OMNET simulation tool. Simulation results showed a better performance of DYMO over DSR in terms of throughput, packet delay, packet dropping, and collision ratio.
Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification
Published in Coling, 2016
In this paper, a scheme of Arabic sentiment classification, which evaluates and detects the sentiment polarity from Arabic reviews and Arabic social media, is studied. We investigated in several architectures to build a quality neural word embeddings using a 3.4 billion words corpus from a collected 10 billion words web-crawled corpus. Moreover, a convolutional neural network trained on top of pre-trained Arabic word embeddings is used for sentiment classification to evaluate the quality of these word embeddings.
Deep Knowledge Representation based on Compositional Semantics for Chinese Geography
Published in The 9th International Conference on Agents and Artificial Intelligence ICAART, 2017
In this paper, we propose a novel directed acyclic graph (DAG) deep knowledge representation built upon the theorem of combinational semantics. Knowledge is decomposed into nodes and edges which are then inserted into the ontology knowledge base. Experimental results demonstrate the superiority of the proposed method on question answering, especially when the syntax of question is complex, and its representation is fuzzy.
Multi-channel Embedding Convolutional Neural Network Model for Arabic Sentiment Classification
Published in TALLIP, 2019
In this paper, a multi-channel embedding convolutional neural network (MCE-CNN) is proposed to improve Arabic sentiment classification by learning sentiment features from different text domains, word and character n-grams levels. MCE-CNN encodes a combination of different pre-trained word embeddings into the embedding block at each embedding channel and trains these channels in parallel. Besides, a separate feature extraction module implemented in a CNN block is used to extract more relevant sentiment features. These channels and blocks help to start training on high-quality WEVs and fine-tuning them. The performance of MCE-CNN is evaluated on several standard balanced and imbalanced datasets to reflect real-world using cases. Experimental results show that MCE-CNN provides a high classification accuracy and benefits from the second embedding channel on both standard Arabic and dialectal Arabic text, which outperforms state-of-the-art methods.
Arabic Sentiment Classification Using Convolutional Neural Network and Differential Evolution Algorithm
Published in Computational Intelligence and Neuroscience, 2019
In this paper, we address this problem by combining differential evolution (DE) algorithm and CNN, where DE algorithm is used to automatically search the optimal configuration including CNN architecture and network parameters. In order to achieve the goal, five CNN parameters are searched by the DE algorithm which include convolution filter sizes that control the CNN architecture, number of filters per convolution filter size (NFCS), number of neurons in fully connected (FC) layer, initialization mode, and dropout rate. In addition, the effect of the mutation and crossover operators in DE algorithm were investigated. The performance of the proposed framework DE-CNN is evaluated on five Arabic sentiment datasets. Experiments’ results show that DE-CNN has higher accuracy and is less time consuming than the state-of-the-art algorithms.
A Study of the Effects of Stemming Strategies on Arabic Document Classification
Published in IEEE Access, 2019
This paper aims to study the impact of stemming techniques, namely Information Science Research Institute (ISRI), Tashaphyne, and ARLStem on Arabic DC. The classification algorithms, namely Naïve Bayesian (NB), support vector machine (SVM), and K-nearest neighbors (KNN), are used in this paper. In addition, the chi-square feature selection is used to select the most relevant features. Experiments are conducted on CNN Arabic corpus, which is collected from Arabic websites to assess the performance of the classification system. In order to evaluate the classifiers, the K-fold cross-validation method and Micro-F1 are used. Findings of this paper indicate that the ARLStem outperforms the ISRI and Tashaphyne stemmers. The outcomes clearly showed the effectiveness of the SVM over the KNN and NB classifiers, which achieved 94.64% Micro-F1 value when using the ARLStem stemmer.