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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 19 of 19 1 Persian Sentence-level Sentiment Polarity Classification Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander... In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03258138 ; ICOTEN, Jun 2021, Glasgow, United Kingdom (2021) BASE Show details 2 Persian Sentence-level Sentiment Polarity Classification Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander... In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03241928 ; ICOTEN, May 2021, Glasgow, France (2021) BASE Show details 3 Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander. - : Springer, 2021 BASE Show details 4 Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts Ahmed, Rami; Gogate, Mandar; Tahir, Ahsen. - : MDPI, 2021 BASE Show details 5 A novel context-aware multimodal framework for persian sentiment analysis Dashtipour, Kia; Gogate, Mandar; Cambria, Erik. - : Elsevier, 2021 BASE Show details 6 A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect Adeel, Ahsan; Guellil, Imane; Gogate, Mandar. - : Springer, 2021 BASE Show details 7 A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect Guellil, Imane; Adeel, Ahsan; Azouaou, Faical. - 2021 BASE Show details 8 A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis Dashtipour, Kia; Gogate, Mandar; Cambria, Erik. - : Elsevier BV, 2021 BASE Show details 9 Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts Ahmed, Rami; Gogate, Mandar; Tahir, Ahsen; Dashtipour, Kia; Al-tamimi, Bassam; Hawalah, Ahmad; El-Affendi, Mohammed A; Hussain, Amir. - : MDPI, 2021 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 BASE Hide details 10 Persian Sentence-level Sentiment Polarity Classification Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander. - : IEEE, 2021. : Piscataway, NJ, USA, 2021 BASE Show details 11 CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement Gogate, Mandar; Dashtipour, Kia; Adeel, Ahsan. - : Elsevier, 2020 BASE Show details 12 Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances Ahmed, Rami; Dashtipour, Kia; Gogate, Mandar. - : Springer, 2020 BASE Show details 13 Deep Neural Network Driven Binaural Audio Visual Speech Separation Gogate, Mandar; Dashtipour, Kia; Bell, Peter. - : Institute of Electrical and Electronics Engineers, 2020 BASE Show details 14 Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System Gogate, Mandar; Dashtipour, Kia; Hussain, Amir. - 2020 BASE Show details 15 A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks Dashtipour, Kia; Gogate, Mandar; Li, Jingpeng. - : Elsevier, 2019 BASE Show details 16 A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks ... Dashtipour, Kia; Gogate, Mandar; Li, Jingpeng. - : arXiv, 2019 BASE Show details 17 Lip-reading driven deep learning approach for speech enhancement Adeel, Ahsan; Gogate, Mandar; Hussain, Amir... In: abs/1808.00046 ; 1 ; 10 (2019) BASE Show details 18 A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks Dashtipour, Kia; Gogate, Mandar; Li, Jingpeng. - : Elsevier, 2019 BASE Show details 19 Persian Named Entity Recognition Dashtipour, Kia; Gogate, Mandar; Adeel, Ahsan. - : Institute of Electrical and Electronics Engineers Inc, 2017. : Piscataway, NJ, USA, 2017 BASE Show details

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