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Murray DG

As seen in: PeerJ
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Articles by Murray DG

Brain virtual histology and volume measurement of a lizard species (Podarcis bocagei) using X-ray micro-tomography and deep-learning segmentation

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By Murray DG, Stewart TM, Bruce LL, Christopher Kirk E
| PeerJ
Brain virtual histology and volume measurement of a lizard species (Podarcis bocagei) using X-ray micro-tomography and deep-learning segmentation 1SUBIC, Stockholm University, Stockholm, Sweden 2KTH Royal Institute of Technology, Stockholm, Sweden 3Nuclear Medicine and Medical Physics, Karolinska University Hospital, Stockholm, Sweden 4Department of Zoology, Stockholm University, Stockholm, Sweden 5Institute of Biology, University of Neuchâtell, Neuchâtel, Switzerland DOI 10.7717/peerj.19672...
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REDf: a deep learning model for short-term load forecasting to facilitate renewable integration and attaining the SDGs 7, 9, and 13

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By Murray DG, Ali ES, Al Mansur A, Keras Collaborators
| PeerJ
REDf: a deep learning model for short-term load forecasting to facilitate renewable integration and attaining the SDGs 7, 9, and 13 1Department of Computer Science, American International University—Bangladesh, Dhaka, Dhaka, Bangladesh 2Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia 3Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia 4Department of Degroote School of...
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Enhancing skin lesion classification: a CNN approach with human baseline comparison

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By Byun YC, Gil Jaurena JM, Murray DG, Ali SF
| PeerJ
Early and precise detection of skin lesions is essential for effective treatment and improved patient outcomes (Houssein et al., 2024; Ali et al., 2022). Despite advancements in medical imaging technologies, significant challenges remain in achieving high diagnostic accuracy and efficiency in real-world clinical environments (Jackson et al., 2025; Esteva et al., 2019).
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RARE: right algorithm for the right errand; a multi-model machine learning-based approach for tourism routes and spots recommendation

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By Murray DG, Kim D-H, Kim DH, Marín Herrera MA
| PeerJ
Tourism is not a mere hobby; it is a form of entertainment that enhances the quality of people’s lives (Lyu & Han, 2022). Tourists prefer to visit scenic and historical spots for serenity, solace, non-formal education, revolutionary feelings and adventurous experiences. Whether it is honeymoon tours, island tours, or red tourism, people prefer to enjoy their leisure fully, full of natural calmness and free from worldly worries.
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Auditory-GAN: deep learning framework for improved auditory spatial attention detection

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By Murray DG, Simon JZ, El KIA, Ahmed YK
| PeerJ
Auditory attention is a cognitive process in which the brain selectively focuses on specific auditory stimuli while filtering out irrelevant or distracting sounds in a multi-speaker situation, commonly termed “cocktail party scenario” (Mesgarani & Chang, 2012). This ability to selectively focus on specific sounds is critical for daily communication, but individuals with hearing loss face challenges in such environments (Das, Francart & Bertrand, 2019).
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Machine learning and natural language processing to assess the emotional impact of influencers’ mental health content on Instagram

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By Murray DG, Kim SH, Di Cara NH, Francis DB
| PeerJ
Table S11 shows a comparison of the algorithms for the global accuracy metric. The best performance is obtained by the BERT models, RoBERTuito (90%) closely followed by Danevi (86%), and then the deep learning model with very good values of 72.5%. In contrast, the worst performance is obtained by RF, as expected.
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Classification of Curcuma longa and Curcuma zanthorrhiza using transfer learning

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By Murray DG, Ben A, Al Riza DF, Agus Pratondo
| PeerJ
Classification of Curcuma longa and Curcuma zanthorrhiza using transfer learning 1Department of Multimedia Engineering, School of Applied Sciences, Telkom University, Bandung, West Java, Indonesia 2Department of Pharmaceutical Biology, School of Pharmacy, Bandung Institute of Technology, Bandung, West Java, Indonesia 3Department of Computer Engineering, School of Electrical Engineering, Telkom University, Bandung, West Jawa, Indonesia DOI 10.7717/peerj-cs.1168 Published 2022-12-15 Accepted...
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ScanFold 2.0: a rapid approach for identifying potential structured RNA targets in genomes and transcriptomes

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By Murray DG, Allan MF, Reid DW, Leonard CW
| PeerJ
Mono vs Di nucleotide shuffling of ScanFold 2.0 When comparing SF1 and SF2 results for mononucleotide shuffling there is an average difference in percent paired of 2.00% (0.03% to 4.5%) and in the majority of cases SF2 is predicting more pairs than SF1. For all results other than HIV and SARS-CoV-2 all pairs (no filter), SF2 consistently predicts more pairs than SF1.
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