All Outputs (515)

A novel multimodal fusion network based on a joint-coding model for lane line segmentation (2021)
Journal Article
Zou, Z., Zhang, X., Liu, H., Li, Z., Hussain, A., & Li, J. (2022). A novel multimodal fusion network based on a joint-coding model for lane line segmentation. Information Fusion, 80, 167-178. https://doi.org/10.1016/j.inffus.2021.10.008

There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architecture from an information theory perspective, and demonstrate its practica... Read More about A novel multimodal fusion network based on a joint-coding model for lane line segmentation.

Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model (2021)
Journal Article
Rabhi, B., Elbaati, A., Boubaker, H., Hamdi, Y., Hussain, A., & Alimi, A. M. (2021). Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model. Memetic Computing, 13, Article 459-475. https://doi.org/10.1007/s12293-021-00345-6

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than off... Read More about Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model.

A novel few-shot learning method for synthetic aperture radar image recognition (2021)
Journal Article
Yue, Z., Gao, F., Xiong, Q., Sun, J., Hussain, A., & Zhou, H. (2021). A novel few-shot learning method for synthetic aperture radar image recognition. Neurocomputing, 465, 215-227. https://doi.org/10.1016/j.neucom.2021.09.009

Synthetic aperture radar (SAR) image recognition is an important stage of SAR image interpretation. The standard convolutional neural network (CNN) has been successfully applied in the SAR image recognition due to its powerful feature extraction capa... Read More about A novel few-shot learning method for synthetic aperture radar image recognition.

Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps (2021)
Journal Article
Li, J., Jiang, F., Yang, J., Kong, B., Gogate, M., Dashtipour, K., & Hussain, A. (2021). Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps. Neurocomputing, 465, 15-25. https://doi.org/10.1016/j.neucom.2021.08.105

Accurate high-definition maps with lane markings are often used as the navigation back-end for commercial autonomous vehicles. Currently, most high-definition maps are manually constructed by human labelling. Therefore, it is urgently required to pro... Read More about Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps.

Effectiveness of virtual and augmented reality for improving knowledge and skills in medical students: protocol for a systematic review (2021)
Journal Article
Hussain, Z., Ng, D. M., Alnafisee, N., Sheikh, Z., Ng, N., Khan, A., Hussain, A., Aitken, D., & Sheikh, A. (2021). Effectiveness of virtual and augmented reality for improving knowledge and skills in medical students: protocol for a systematic review. BMJ Open, 11(8), Article e047004. https://doi.org/10.1136/bmjopen-2020-047004

Introduction Virtual reality (VR) and augmented reality (AR) technologies are increasingly being used in undergraduate medical education. We aim to evaluate the effectiveness of VR and AR technologies for improving knowledge and skills in medical stu... Read More about Effectiveness of virtual and augmented reality for improving knowledge and skills in medical students: protocol for a systematic review.

A Hybrid-Domain Deep Learni/ng-Based BCI For Discriminating Hand Motion Planning From EEG Sources (2021)
Journal Article
Ieracitano, C., Morabito, F. C., Hussain, A., & Mammone, N. (2021). A Hybrid-Domain Deep Learni/ng-Based BCI For Discriminating Hand Motion Planning From EEG Sources. International Journal of Neural Systems, 31(9), Article 2150038. https://doi.org/10.1142/s0129065721500386

In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand movement preparation phases from electroencephalographic (EEG) recordings. The system exploits information extracted from the temporal-domain and time-fr... Read More about A Hybrid-Domain Deep Learni/ng-Based BCI For Discriminating Hand Motion Planning From EEG Sources.

Conceptual Text Region Network: Cognition-Inspired Accurate Scene Text Detection (2021)
Journal Article
Cui, C., Lu, L., Tan, Z., & Hussain, A. (2021). Conceptual Text Region Network: Cognition-Inspired Accurate Scene Text Detection. Neurocomputing, 464, 252-264. https://doi.org/10.1016/j.neucom.2021.08.026

Segmentation-based methods are widely used for scene text detection due to their superiority in describing arbitrary-shaped text instances. However, two major problems still exist: (1) current label generation techniques are mostly empirical and lack... Read More about Conceptual Text Region Network: Cognition-Inspired Accurate Scene Text Detection.

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model (2021)
Journal Article
Nazir, T., Nawaz, M., Rashid, J., Mahum, R., Masood, M., Mehmood, A., Ali, F., Kim, J., Kwon, H., & Hussain, A. (2021). Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model. Sensors, 21(16), Article 5283. https://doi.org/10.3390/s21165283

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems... Read More about Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.

Leveraging label hierarchy using transfer and multi-task learning: A case study on patent classification (2021)
Journal Article
Aroyehun, S. T., Angel, J., Majumder, N., Gelbukh, A., & Hussain, A. (2021). Leveraging label hierarchy using transfer and multi-task learning: A case study on patent classification. Neurocomputing, 464, 421-431. https://doi.org/10.1016/j.neucom.2021.07.057

When labels are organized into a meaningful taxonomy, the parent-child relationship between labels at different levels can give the classifier additional information not deducible from the data alone, especially with limited training data. As a case... Read More about Leveraging label hierarchy using transfer and multi-task learning: A case study on patent classification.

Cloud based scalable object recognition from video streams using orientation fusion and convolutional neural networks (2021)
Journal Article
Usman Yaseen, M., Anjum, A., Fortino, G., Liotta, A., & Hussain, A. (2022). Cloud based scalable object recognition from video streams using orientation fusion and convolutional neural networks. Pattern Recognition, 121, Article 108207. https://doi.org/10.1016/j.patcog.2021.108207

Object recognition from live video streams comes with numerous challenges such as the variation in illumination conditions and poses. Convolutional neural networks (CNNs) have been widely used to perform intelligent visual object recognition. Yet, CN... Read More about Cloud based scalable object recognition from video streams using orientation fusion and convolutional neural networks.

A Novel Multi-Stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-Ray Images (2021)
Journal Article
Fang, Z., Ren, J., MacLellan, C., Li, H., Zhao, H., Hussain, A., & Fortino, G. (2022). A Novel Multi-Stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-Ray Images. IEEE Transactions on Molecular, Biological and Multi-Scale Communications, 8(1), 17-27. https://doi.org/10.1109/TMBMC.2021.3099367

To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial, chest screening with radiography imaging plays an important role in addition to the real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test. Due... Read More about A Novel Multi-Stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-Ray Images.

Arabic question answering system: a survey (2021)
Journal Article
Alwaneen, T. H., Azmi, A. M., Aboalsamh, H. A., Cambria, E., & Hussain, A. (2022). Arabic question answering system: a survey. Artificial Intelligence Review, 55, 207-253. https://doi.org/10.1007/s10462-021-10031-1

Question answering is a subfield of information retrieval. It is a task of answering a question posted in a natural language. A question answering system (QAS) may be considered a good alternative to search engines that return a set of related docume... Read More about Arabic question answering system: a survey.

Public perception of the fifth generation of cellular networks (5G) on social media (2021)
Journal Article
Dashtipour, K., Taylor, W., Ansari, S., Gogate, M., Zahid, A., Sambo, Y., Hussain, A., Abbasi, Q. H., & Imran, M. A. (2021). Public perception of the fifth generation of cellular networks (5G) on social media. Frontiers in Big Data, 4, Article 640868. https://doi.org/10.3389/fdata.2021.640868

With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these types of reviews. To utilize these reviews for user perception, there is a n... Read More about Public perception of the fifth generation of cellular networks (5G) on social media.

Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning (2021)
Journal Article
Taylor, W., Dashtipour, K., Shah, S. A., Hussain, A., Abbasi, Q. H., & Imran, M. A. (2021). Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning. Sensors, 21(11), Article 3881. https://doi.org/10.3390/s21113881

The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in act... Read More about Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning.

Ten Years of Sentic Computing (2021)
Journal Article
Susanto, Y., Cambria, E., Ng, B. C., & Hussain, A. (2022). Ten Years of Sentic Computing. Cognitive Computation, 14, 5-23. https://doi.org/10.1007/s12559-021-09824-x

Sentic computing is a multi-disciplinary approach to sentiment analysis at the crossroads between affective computing and commonsense computing, which exploits both computer and social sciences to better recognize, interpret, and process opinions and... Read More about Ten Years of Sentic Computing.

Advances in machine translation for sign language: approaches, limitations, and challenges (2021)
Journal Article
Farooq, U., Rahim, M. S. M., Sabir, N., Hussain, A., & Abid, A. (2021). Advances in machine translation for sign language: approaches, limitations, and challenges. Neural Computing and Applications, 33, 14357-14399. https://doi.org/10.1007/s00521-021-06079-3

Sign languages are used by the deaf community around the globe to communicate with one another. These are gesture-based languages where a deaf person performs gestures using hands and facial expressions. Every gesture represents a word or a phrase in... Read More about Advances in machine translation for sign language: approaches, limitations, and challenges.

Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence–Enabled Social Media Analysis (2021)
Journal Article
Cresswell, K., Tahir, A., Sheikh, Z., Hussain, Z., Domínguez Hernández, A., Harrison, E., Williams, R., Sheikh, A., & Hussain, A. (2021). Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence–Enabled Social Media Analysis. Journal of Medical Internet Research, 23(5), Article e26618. https://doi.org/10.2196/26618

Background: The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to conta... Read More about Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence–Enabled Social Media Analysis.

Sentiment analysis of persian movie reviews using deep learning (2021)
Journal Article
Dashtipour, K., Gogate, M., Adeel, A., Larijani, H., & Hussain, A. (2021). Sentiment analysis of persian movie reviews using deep learning. Entropy, 23(5), Article 596. https://doi.org/10.3390/e23050596

Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning... Read More about Sentiment analysis of persian movie reviews using deep learning.

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI images (2021)
Journal Article
Masood, M., Nazir, T., Nawaz, M., Mehmood, A., Rashid, J., Kwon, H., Mahmood, T., & Hussain, A. (2021). A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI images. Diagnostics, 11(5), Article 744. https://doi.org/10.3390/diagnostics11050744

A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmenta... Read More about A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI images.

Improving generative adversarial networks with simple latent distributions (2021)
Journal Article
Zhang, S., Huang, K., Qian, Z., Zhang, R., & Hussain, A. (2021). Improving generative adversarial networks with simple latent distributions. Neural Computing and Applications, 33, 13193-13203. https://doi.org/10.1007/s00521-021-05946-3

Generative Adversarial Networks (GANs) have drawn great attention recently since they are the powerful models to generate high-quality images. Although GANs have achieved great success, they usually suffer from unstable training and consequently may... Read More about Improving generative adversarial networks with simple latent distributions.