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All Outputs (5)

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.