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1
Persian Sentence-level Sentiment Polarity Classification
In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03258138 ; ICOTEN, Jun 2021, Glasgow, United Kingdom (2021)
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2
Persian Sentence-level Sentiment Polarity Classification
In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03241928 ; ICOTEN, May 2021, Glasgow, France (2021)
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3
Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis
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4
Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
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5
Does semantics aid syntax? An empirical study on named entity recognition and classification
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6
Arabic question answering system: a survey
Azmi, Aqil M.; Cambria, Erik; Hussain, Amir. - : Springer, 2021
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7
Advances in machine translation for sign language: approaches, limitations, and challenges
Sabir, Nabeel; Abid, Adnan; Hussain, Amir. - : Springer, 2021
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8
A novel context-aware multimodal framework for persian sentiment analysis
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9
A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect
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10
Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model
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11
A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect
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12
A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis
Dashtipour, Kia; Gogate, Mandar; Cambria, Erik. - : Elsevier BV, 2021
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13
Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
Abstract: Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model.
Keyword: Arabic Handwritten; Batch normalization; databases; DCNN; Dropout
URL: http://hdl.handle.net/1893/32386
https://doi.org/10.3390/e23030340
http://dspace.stir.ac.uk/bitstream/1893/32386/1/entropy-23-00340-v2.pdf
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14
Persian Sentence-level Sentiment Polarity Classification
Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander. - : IEEE, 2021. : Piscataway, NJ, USA, 2021
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15
Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes
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16
CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement
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17
Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances
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18
Persuasive dialogue understanding: The baselines and negative results
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19
Deep Neural Network Driven Binaural Audio Visual Speech Separation
Gogate, Mandar; Dashtipour, Kia; Bell, Peter. - : Institute of Electrical and Electronics Engineers, 2020
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20
Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System
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