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Outputs (319)

A novel explainable machine learning approach for EEG-based brain-computer interface systems (2021)
Journal Article
Ieracitano, C., Mammone, N., Hussain, A., & Morabito, F. C. (2022). A novel explainable machine learning approach for EEG-based brain-computer interface systems. Neural Computing and Applications, 34, 11347-11360. https://doi.org/10.1007/s00521-020-05624-w

Electroencephalographic (EEG) recordings can be of great help in decoding the open/close hand’s motion preparation. To this end, cortical EEG source signals in the motor cortex (evaluated in the 1-s window preceding movement onset) are extracted by s... Read More about A novel explainable machine learning approach for EEG-based brain-computer interface systems.

Discriminative Dictionary Design for Action Classification in Still Images and Videos (2021)
Journal Article
Roy, A., Banerjee, B., Hussain, A., & Poria, S. (2021). Discriminative Dictionary Design for Action Classification in Still Images and Videos. Cognitive Computation, 13, 698-708. https://doi.org/10.1007/s12559-021-09851-8

In this paper, we address the problem of action recognition from still images and videos. Traditional local features such as SIFT and STIP invariably pose two potential problems: 1) they are not evenly distributed in different entities of a given cat... Read More about Discriminative Dictionary Design for Action Classification in Still Images and Videos.

A novel context-aware multimodal framework for persian sentiment analysis (2021)
Journal Article
Dashtipour, K., Gogate, M., Cambria, E., & Hussain, A. (2021). A novel context-aware multimodal framework for persian sentiment analysis. Neurocomputing, 457, 377-388. https://doi.org/10.1016/j.neucom.2021.02.020

Most recent works on sentiment analysis have exploited the text modality. However, millions of hours of video recordings posted on social media platforms everyday hold vital unstructured information that can be exploited to more effectively gauge pub... Read More about A novel context-aware multimodal framework for persian sentiment analysis.

A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect (2021)
Journal Article
Guellil, I., Adeel, A., Azouaou, F., Benali, F., Hachani, A., Dashtipour, K., Gogate, M., Ieracitano, C., Kashani, R., & Hussain, A. (2021). A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect. SN Computer Science, 2, Article 118. https://doi.org/10.1007/s42979-021-00510-1

In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach c... Read More about A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect.

A Multipath Fusion Strategy Based Single Shot Detector (2021)
Journal Article
Qu, S., Huang, K., Hussain, A., & Goulermas, Y. (2021). A Multipath Fusion Strategy Based Single Shot Detector. Sensors, 21(4), Article 1360. https://doi.org/10.3390/s21041360

Object detection has wide applications in intelligent systems and sensor applications. Compared with two stage detectors, recent one stage counterparts are capable of running more efficiently with comparable accuracy, which satisfy the requirement of... Read More about A Multipath Fusion Strategy Based Single Shot Detector.

Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition (2021)
Journal Article
Rahal, N., Tounsi, M., Hussain, A., & Alimi, A. M. (2021). Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition. IEEE Access, 9, 18569-18584. https://doi.org/10.1109/access.2021.3053618

One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to t... Read More about Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition.

Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures (2021)
Journal Article
Varone, G., Hussain, Z., Sheikh, Z., Howard, A., Boulila, W., Mahmud, M., Howard, N., Morabito, F. C., & Hussain, A. (2021). Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures. Sensors, 21(2), Article 637. https://doi.org/10.3390/s21020637

Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulatio... Read More about Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures.

iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings (2021)
Journal Article
Kaiser, M. S., Mahmud, M., Noor, M. B. T., Zenia, N. Z., Mamun, S. A., Mahmud, K. M. A., Azad, S., Aradhya, V. N. M., Stephan, P., Stephan, T., Kannan, R., Hanif, M., Sharmeen, T., Chen, T., & Hussain, A. (2021). iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings. IEEE Access, 9, 13814-13828. https://doi.org/10.1109/access.2021.3050193

The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an atte... Read More about iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings.

A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis (2021)
Journal Article
El-Affendi, M. A., Alrajhi, K., & Hussain, A. (2021). A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis. IEEE Access, 9, 7508-7518. https://doi.org/10.1109/access.2021.3049626

Over the past few years, much work has been done to develop machine learning models that perform Arabic sentiment analysis (ASA) tasks at various levels and in different domains. However, most of this work has been based on shallow machine learning,... Read More about A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis.