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Ferdous Sohel

Murdoch University

South Street

Murdoch 6150, 6105

Australia

  • Murdoch University

SCHOLARLY PAPERS

4

DOWNLOADS

265

TOTAL CITATIONS

1

Scholarly Papers (4)

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1.

Lambing Event Detection Using Deep Learning from Accelerometer Data

Number of pages: 28 Posted: 09 Nov 2022
Kirk Edward Turner, Ferdous Sohel, Ian Harris, Mark Ferguson and Andrew Thompson
Murdoch University, Murdoch University, affiliation not provided to SSRN, affiliation not provided to SSRN and Murdoch University
Downloads 82 (796,622)

Abstract:

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On-animal sensors, Parturition, Machine-learning, Sheep, Behaviour classification

2.

Machine-Learning-Assisted Material of Oxygen and Phosphorous-Rich Porous Activated Carbon for Aqueous Symmetric Supercapacitors

Number of pages: 34 Posted: 16 Jan 2025
Manickam Minakshi, Apsana Sharma, Ferdous Sohel, Almantas Pivrikas, Pragati A. Shinde, Katsuhiko Ariga and Lok Kumar Shrestha
Murdoch University, Murdoch University, Murdoch University, Murdoch University, National Institute for Materials Science, National Institute for Materials Science and National Institute for Materials Science (NIMS)
Downloads 74 (860,052)

Abstract:

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biomass, Carbon, storage, energy, dopant

3.

Conditional Plane-based Multi-Scene Representation for Novel View Synthesis

Number of pages: 38 Posted: 12 Jul 2025
Uchitha Rajapaksha, Hamid Laga, Dean Diepeveen, Mohammed Bennamoun and Ferdous Sohel
Murdoch University, Murdoch University, Murdoch University, The University of Western Australia and Murdoch University
Downloads 55 (1,010,164)

Abstract:

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Scene Reconstruction, 3D Reconstruction, Nerf, Hybrid Representation, Deforming Objects

4.

Object-Level Benchmark for Deep Learning-Based Detection and Classification of Weed Species

Number of pages: 30 Posted: 15 Jul 2023
A. S. M. Mahmudul Hasan, Dean Diepeveen, Hamid Laga, Michael G.K. Jones and Ferdous Sohel
Murdoch University, Murdoch University, Murdoch University, Murdoch University and Murdoch University
Downloads 54 (1,020,567)
Citation 1

Abstract:

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Weed detection, weed classification, Digital Agriculture, deep learning