Outputs (45)

Towards intelligibility-oriented audio-visual speech enhancement (2021)
Presentation / Conference Contribution
Hussain, T., Gogate, M., Dashtipour, K., & Hussain, A. (2021, September). Towards intelligibility-oriented audio-visual speech enhancement. Presented at The Clarity Workshop on Machine Learning Challenges for Hearing Aids (Clarity-2021), Online

Existing deep learning (DL) based approaches are generally optimised to minimise the distance between clean and enhanced speech features. These often result in improved speech quality however they suffer from a lack of generalisation and may not deli... Read More about Towards intelligibility-oriented audio-visual speech enhancement.

An Attribute Weight Estimation Using Particle Swarm Optimization and Machine Learning Approaches for Customer Churn Prediction (2021)
Presentation / Conference Contribution
Kanwal, S., Rashid, J., Kim, J., Nisar, M. W., Hussain, A., Batool, S., & Kanwal, R. (2021, November). An Attribute Weight Estimation Using Particle Swarm Optimization and Machine Learning Approaches for Customer Churn Prediction. Presented at 2021 International Conference on Innovative Computing (ICIC), Lahore, Pakistan

One of the most challenging problems in the telecommunications industry is predicting customer churn (CCP). Decision-makers and business experts stressed that acquiring new clients is more expensive than maintaining current ones. From current churn d... Read More about An Attribute Weight Estimation Using Particle Swarm Optimization and Machine Learning Approaches for Customer Churn Prediction.

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques (2021)
Journal Article
Kaur, I., Doja, M. N., Ahmad, T., Ahmad, M., Hussain, A., Nadeem, A., & Abd El-Latif, A. A. (2021). An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques. Computational Intelligence and Neuroscience, 2021, Article 6342226. https://doi.org/10.1155/2021/6342226

Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes.... Read More about An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques.

A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls (2021)
Journal Article
Varone, G., Boulila, W., Lo Giudice, M., Benjdira, B., Mammone, N., Ieracitano, C., Dashtipour, K., Neri, S., Gasparini, S., Morabito, F. C., Hussain, A., & Aguglia, U. (2022). A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls. Sensors, 22(1), Article 129. https://doi.org/10.3390/s22010129

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigate... Read More about A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls.

FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning (2021)
Journal Article
Spinelli, I., Scardapane, S., Hussain, A., & Uncini, A. (2022). FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning. IEEE Transactions on Artificial Intelligence, 3(3), 344-354. https://doi.org/10.1109/tai.2021.3133818

Graph representation learning has become a ubiquitous component in many scenarios, ranging from social network analysis to energy forecasting in smart grids. In several applications, ensuring the fairness of the node (or graph) representations with r... Read More about FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning.

Attributes Guided Feature Learning for Vehicle Re-Identification (2021)
Journal Article
Li, H., Lin, X., Zheng, A., Li, C., Luo, B., He, R., & Hussain, A. (2022). Attributes Guided Feature Learning for Vehicle Re-Identification. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(5), 1211-1221. https://doi.org/10.1109/tetci.2021.3127906

Vehicle Re-ID has recently attracted enthusiastic attention due to its potential applications in smart city and urban surveillance. However, it suffers from large intra-class variation caused by view variations and illumination changes, and inter-cla... Read More about Attributes Guided Feature Learning for Vehicle Re-Identification.

Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis (2021)
Journal Article
Dashtipour, K., Gogate, M., Gelbukh, A., & Hussain, A. (2022). Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis. Social Network Analysis and Mining, 12(1), Article 9. https://doi.org/10.1007/s13278-021-00840-1

Nowadays, it is important for buyers to know other customer opinions to make informed decisions on buying a product or service. In addition, companies and organizations can exploit customer opinions to improve their products and services. However, th... Read More about Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis.

A Novel Fingerprinting Technique for Data Storing and Sharing through Clouds (2021)
Journal Article
Fatima, M., Nisar, M. W., Rashid, J., Kim, J., Kamran, M., & Hussain, A. (2021). A Novel Fingerprinting Technique for Data Storing and Sharing through Clouds. Sensors, 21(22), Article 7647. https://doi.org/10.3390/s21227647

With the emerging growth of digital data in information systems, technology faces the challenge of knowledge prevention, ownership rights protection, security, and privacy measurement of valuable and sensitive data. On-demand availability of various... Read More about A Novel Fingerprinting Technique for Data Storing and Sharing through Clouds.

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.