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Complex-Valued Neural Networks With Nonparametric Activation Functions (2018)
Journal Article
Scardapane, S., Van Vaerenbergh, S., Hussain, A., & Uncini, A. (2020). Complex-Valued Neural Networks With Nonparametric Activation Functions. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(2), 140-150. https://doi.org/10.1109/tetci.2018.2872600

Complex-valued neural networks (CVNNs) are a powerful modeling tool for domains where data can be naturally interpreted in terms of complex numbers. However, several analytical properties of the complex domain (such as holomorphicity) make the design... Read More about Complex-Valued Neural Networks With Nonparametric Activation Functions.

Adaptation of sentiment analysis techniques to Persian language (2018)
Conference Proceeding
Dashtipour, K., Hussain, A., & Gelbukh, A. (2018). Adaptation of sentiment analysis techniques to Persian language. In Computational Linguistics and Intelligent Text Processing (129-140). https://doi.org/10.1007/978-3-319-77116-8_10

In the recent years, people all around the world share their opinions about different fields with each other over Internet. Sentiment analysis techniques have been introduced to classify these rich data based on the polarity of the opinion. Sentiment... Read More about Adaptation of sentiment analysis techniques to Persian language.

Benchmarking multimodal sentiment analysis (2018)
Conference Proceeding
Cambria, E., Hazarika, D., Poria, S., Hussain, A., & Subramanyam, R. (2018). Benchmarking multimodal sentiment analysis. In Computational Linguistics and Intelligent Text Processing (166-179). https://doi.org/10.1007/978-3-319-77116-8_13

We propose a deep-learning-based framework for multimodal sentiment analysis and emotion recognition. In particular, we leverage on the power of convolutional neural networks to obtain a performance improvement of 10% over the state of the art by com... Read More about Benchmarking multimodal sentiment analysis.

Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection (2018)
Conference Proceeding
Ieracitano, C., Adeel, A., Gogate, M., Dashtipour, K., Morabito, F., Larijani, H., …Hussain, A. (2018). Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection. . https://doi.org/10.1007/978-3-030-00563-4_74

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology (ICT) systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially... Read More about Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection.

Style Neutralization Generative Adversarial Classifier (2018)
Conference Proceeding
Jiang, H., Huang, K., Zhang, R., & Hussain, A. (2018). Style Neutralization Generative Adversarial Classifier. In BICS: International Conference on Brain Inspired Cognitive Systems (3-13). https://doi.org/10.1007/978-3-030-00563-4_1

Breathtaking improvement has been seen with the recently proposed deep Generative Adversarial Network (GAN). Purposes of most existing GAN-based models majorly concentrate on generating realistic and vivid patterns by a pattern generator with the aid... Read More about Style Neutralization Generative Adversarial Classifier.

SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis (2018)
Conference Proceeding
Guellil, I., Adeel, A., Azouaou, F., & Hussain, A. (2018). SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis. . https://doi.org/10.1007/978-3-030-00563-4_54

Data annotation is an important but time-consuming and costly procedure. To sort a text into two classes, the very first thing we need is a good annotation guideline, establishing what is required to qualify for each class. In the literature, the dif... Read More about SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis.

Saliency Detection via Bidirectional Absorbing Markov Chain (2018)
Conference Proceeding
Jiang, F., Kong, B., Adeel, A., Xiao, Y., & Hussain, A. (2018). Saliency Detection via Bidirectional Absorbing Markov Chain. . https://doi.org/10.1007/978-3-030-00563-4_48

Traditional saliency detection via Markov chain only consider boundaries nodes. However, in addition to boundaries cues, background prior and foreground prior cues play a complementary role to enhance saliency detection. In this paper, we propose an... Read More about Saliency Detection via Bidirectional Absorbing Markov Chain.

A Novel Semi-supervised Classification Method Based on Class Certainty of Samples (2018)
Conference Proceeding
Gao, F., Yue, Z., Xiong, Q., Wang, J., Yang, E., & Hussain, A. (2018). A Novel Semi-supervised Classification Method Based on Class Certainty of Samples. . https://doi.org/10.1007/978-3-030-00563-4_30

The traditional classification method based on supervised learning classifies remote sensing (RS) images by using sufficient labelled samples. However, the number of labelled samples is limited due to the expensive and time-consuming collection. To e... Read More about A Novel Semi-supervised Classification Method Based on Class Certainty of Samples.