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All Outputs (10)

A Two-branch Edge Guided Lightweight Network for infrared image saliency detection (2024)
Journal Article
Liu, Z., Li, X., Zhang, T., Zhang, X., Sun, C., Rehman, S. U., & Ahmad, J. (2024). A Two-branch Edge Guided Lightweight Network for infrared image saliency detection. Computers and Electrical Engineering, 118, 109296. https://doi.org/10.1016/j.compeleceng.2024.109296

In the dynamic landscape of saliency detection, convolutional neural networks have emerged as catalysts for innovation, but remain largely tailored for RGB imagery, falling short in the context of infrared images, particularly in memory-restricted en... Read More about A Two-branch Edge Guided Lightweight Network for infrared image saliency detection.

Advancements in intrusion detection: A lightweight hybrid RNN-RF model (2024)
Journal Article
Khan, N., Mohmand, M. I., Rehman, S. U., Ullah, Z., Khan, Z., & Boulila, W. (in press). Advancements in intrusion detection: A lightweight hybrid RNN-RF model. PloS one, 19(6), e0299666. https://doi.org/10.1371/journal.pone.0299666

Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leve... Read More about Advancements in intrusion detection: A lightweight hybrid RNN-RF model.

ENSO dataset & comparison of deep learning models for ENSO forecasting (2024)
Journal Article
Mir, S., Arbab, M. A., & Rehman, S. U. (2024). ENSO dataset & comparison of deep learning models for ENSO forecasting. Earth Science Informatics, 17(3), 2623-2628. https://doi.org/10.1007/s12145-024-01295-6

Forecasting the El Nino-Southern Oscillation (ENSO) is a challenging task in climatology. It is one of the main factors responsible for the Earth’s interannual climatic fluctuation and can result in many climatic anomalies. The impacts include natura... Read More about ENSO dataset & comparison of deep learning models for ENSO forecasting.

Deep learning-based forecasting of electricity consumption (2024)
Journal Article
Qureshi, M., Arbab, M. A., & Rehman, S. U. (in press). Deep learning-based forecasting of electricity consumption. Scientific Reports, 14(1), 6489. https://doi.org/10.1038/s41598-024-56602-4

Building energy management systems (BEMS) are integrated computerized systems that track and manage the energy use of many pieces of building-related machinery and equipment, including lighting, power systems, and HVAC systems. Modern buildings must... Read More about Deep learning-based forecasting of electricity consumption.

A novel routing optimization strategy based on reinforcement learning in perception layer networks (2023)
Journal Article
Tan, H., Ye, T., Rehman, S. U., ur Rehman, O., Tu, S., & Ahmad, J. (2023). A novel routing optimization strategy based on reinforcement learning in perception layer networks. Computer Networks, 237, 110105. https://doi.org/10.1016/j.comnet.2023.110105

Wireless sensor networks have become incredibly popular due to the Internet of Things’ (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement... Read More about A novel routing optimization strategy based on reinforcement learning in perception layer networks.

Physical Layer Authentication Based on Transformer (2023)
Conference Proceeding
Ai, X., Yue, Q., Li, H., Li, W., Tu, S., & Rehman, S. U. (2023). Physical Layer Authentication Based on Transformer. In ICCNS '23: Proceedings of the 2023 13th International Conference on Communication and Network Security (203–208). https://doi.org/10.1145/3638782.3638813

With the rapid proliferation of wireless devices, effectively authenticating legitimate users has become a pivotal challenge in wireless communication. Amongst various approaches, physical layer authentication technology based on deep learning has ga... Read More about Physical Layer Authentication Based on Transformer.

A Hybrid Deep Learning Model for Breast Cancer Detection and Classification (2023)
Conference Proceeding
Tu, S., Li, W., Ai, X., Li, H., Yue, Q., & Rehman, S. U. (2023). A Hybrid Deep Learning Model for Breast Cancer Detection and Classification. In ICCNS '23: Proceedings of the 2023 13th International Conference on Communication and Network Security (350–353). https://doi.org/10.1145/3638782.3638836

One of the main areas of study in diagnostic radiology and medical imaging is computer-aided diagnosis (CAD). In reality, a significant number of CAD systems have been used to help doctors identify breast tumours early on mammograms. Medical image an... Read More about A Hybrid Deep Learning Model for Breast Cancer Detection and Classification.

Two-stage Domain Adaptation for Infrared Ship Target Segmentation (2023)
Journal Article
Zhang, T., Shen, H., Rehman, S. U., Liu, Z., Li, Y., & Rehman, O. U. (2023). Two-stage Domain Adaptation for Infrared Ship Target Segmentation. IEEE Transactions on Geoscience and Remote Sensing, 61, https://doi.org/10.1109/tgrs.2023.3325298

Ship target segmentation in infrared scenes has always been a hot topic, since it is an important basis and prerequisite for infrared-guided weapons to reliably capture and recognize ship targets in the sea-level background. However, given the small... Read More about Two-stage Domain Adaptation for Infrared Ship Target Segmentation.

Cyberattacks and Security of Cloud Computing: A Complete Guideline (2023)
Journal Article
Dawood, M., Tu, S., Xiao, C., Alasmary, H., Waqas, M., & Rehman, S. U. (in press). Cyberattacks and Security of Cloud Computing: A Complete Guideline. Symmetry, 15(11), 1981. https://doi.org/10.3390/sym15111981

Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-... Read More about Cyberattacks and Security of Cloud Computing: A Complete Guideline.