Codeine 3-O-demethylase catalyzed biotransformation of morphinan alkaloids in Escherichia coli: site directed mutagenesis of terminal residues improves enzyme expression, stability... by Garrick W. K. Spencer, Xu Li, Kenny W. L. Lam, George Mutch, Fiona H. Fry, Sally L. Gras

“…Statistical analysis of biotransformation yield, enzyme expression and stability, predicted using changes in Gibbs free energy (ΔΔG) with deep-learning-based model DDmut, suggested that altered enzyme stability and/or expression of soluble protein may contribute to the observed improvements in biotransformation. …”
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Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. by Gülcan Gencer, Kerem Gencer

“…This paper offers a hybrid SE (Squeeze-and-Excitation)-Enhanced Hybrid Model for detecting retinal disorders from OCT images, including DME, Drusen, and CNV, using artificial intelligence and deep learning.<h4>Methods</h4>The model integrates SE blocks with EfficientNetB0 and Xception architectures, which provide high success in image classification tasks. …”
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Advances in antiviral strategies targeting mosquito-borne viruses: cellular, viral, and immune-related approaches by Ayyaz Khan, Zakirullah, Shahid Wahab, Seong-Tshool Hong

“…Additionally, it explores immunomodulatory therapies to enhance antiviral responses and emphasizes the potential of drug repurposing, bioinformatics, artificial intelligence, and deep learning in identifying novel antiviral candidates. …”
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Communication System Using Natural Language for Robotic Laparoscope Guidance by Pan Yan, Bernhard Lukas, Fan Cheng, Beckendorf Lukas, Wilhelm Dirk, Feußner Hubertus, Groh Georg

“…To solve these challenges, our proposed communication system uses the fine-tuned deep learning model to recognize the speaker information, and the robot assistant takes action only when it detects the commands from the responsible clinician. …”
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Human lineage mutations regulate RNA-protein binding of conserved genes NTRK2 and ITPR1 involved in human evolution by Min Zhao, Weichen Song, Shunying Yu, Wenxiang Cai, Guan Ning Lin

“…Background The role of human lineage mutations (HLMs) in human evolution through post-transcriptional modification is unclear.Aims To investigate the contribution of HLMs to human evolution through post-transcriptional modification.Methods We applied a deep learning model Seqweaver to predict how HLMs impact RNA-binding protein affinity.Results We found that only 0.27% of HLMs had significant impacts on RNA-binding proteins at the threshold of the top 1% of human common variations. …”
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Mask-Pix2Pix Network for Overexposure Region Recovery of Solar Image by Dong Zhao, Long Xu, Linjie Chen, Yihua Yan, Ling-Yu Duan

“…This paper makes effort to retrieve/recover missing information of overexposure by exploiting deep learning for its powerful nonlinear representation which makes it widely used in image reconstruction/restoration. …”
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Pervasive glacier retreats across Svalbard from 1985 to 2023 by Tian Li, Stefan Hofer, Geir Moholdt, Adam Igneczi, Konrad Heidler, Xiao Xiang Zhu, Jonathan Bamber

“…Using 124919 calving front positions for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, generated with deep learning, we identify pervasive calving front retreats for non-surging glaciers over the past 38 years. …”
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A Review of AI Applications in Unconventional Oil and Gas Exploration and Development by Feiyu Chen, Linghui Sun, Boyu Jiang, Xu Huo, Xiuxiu Pan, Chun Feng, Zhirong Zhang

“…This paper reviews how deep learning helps predict gas distribution and classify rock types. …”
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VulMPFF: A Vulnerability Detection Method for Fusing Code Features in Multiple Perspectives by Xiansheng Cao, Junfeng Wang, Peng Wu, Zhiyang Fang

“…Source code vulnerabilities are one of the significant threats to software security. Existing deep learning-based detection methods have proven their effectiveness. …”
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A Computational Approach to Understanding Agglutinative Structures in Urdu by Muhammad Shoaib Tahir, Mahnoor Amjad

“…Furthermore, the research explores the limitations of traditional rule-based models in handling Urdu’s morphological complexity and advocates for the adoption of machine learning and deep learning techniques. These modern approaches, particularly Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), show promise in accurately modeling Urdu's agglutinative morphology, though they require extensive linguistic data and computational resources. …”
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Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets by Viet Anh Truong, Ngoc Sang Dinh, Thanh Long Duong, Ngoc Thien Le, Cong Dinh Truong, Linh Tung Nguyen

“…Past research has predominantly focused on utilizing meta-heuristic algorithms to optimize neural network structures, while the exploration of deep learning in optimization has remained relatively limited. …”
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TopoQual polishes circular consensus sequencing data and accurately predicts quality scores by Minindu Weerakoon, Sangjin Lee, Emily Mitchell, Haynes Heaton

“…TopoQual leverages techniques including partial order alignments (POA), topologically parallel bases, and deep learning algorithms to polish consensus sequences. …”
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Food Sales Prediction Using MLP, RANSAC, and Bagging by Hussam Mezher Merdas, Ayad Hameed Mousa

“…Some studies used classical machine learning algorithms, some used deep learning algorithms, and others used ensemble techniques. …”
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…The development of automated models requires extensive labeled and incredibly abnormal data to accurately identify and analyze abnormalities, which are difficult to obtain in sufficient quantities. Although the deep learning-based architecture has yielded state-of-the-art performance in medical image anomaly detection, it cannot be generalized well because of the lack of anomalous datasets. …”
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Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review by Kaelan Lockhart, Juan Sandino, Narmilan Amarasingam, Richard Hann, Barbara Bollard, Felipe Gonzalez

“…Despite the potential of established Machine-Learning (ML) classifiers such as Random Forest, K Nearest Neighbour, and Support Vector Machine, and gradient boosting in the semantic segmentation of UAV-captured images, there is a notable scarcity of research employing Deep Learning (DL) models in these extreme environments. …”
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Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning by Brandon K. Phan, Kuan-Hsuan Shen, Rishi Gurnani, Huan Tran, Ryan Lively, Rampi Ramprasad

“…By amalgamating high throughput generated simulation data with available experimental data for gas permeability, diffusivity, and solubility for various gases, we construct multi-task deep learning models. These models can simultaneously predict all three properties for all gases under consideration, with markedly enhanced predictive accuracy, particularly compared to traditional models reliant solely on experimental data for a singular property. …”
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Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN by Ali Erbey, Necaattin Barışçı

“…With the growing success of deep learning architectures, research on lip reading has gained momentum. …”
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An online intelligent electronic medical record system via speech recognition by Xin Xia, Yunlong Ma, Ye Luo, Jianwei Lu

“…The rapid development of deep learning–based speech recognition technology promises to improve this situation. …”
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Force Measurement Technology of Vision‐Based Tactile Sensor by Bin Fang, Jie Zhao, Nailong Liu, Yuhao Sun, Shixin Zhang, Fuchun Sun, Jianhua Shan, Yiyong Yang

“…According to the learning approach, force measurement methods are classified into physical and deep learning models. Further, branches of each method are analyzed in terms of input types. …”
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Web-based platform to collect, share and manage technical data of historical systemic architectures: the Telegraphic Towers along the Madrid-Valencia path by Margherita Lasorella, Pasquale de-Dato, Elena Cantatore

“…The platform takes advantage of digital models, machine and deep learning procedures and relational databases, in a GIS-based environment, for the recognition and categorisation of prevalent physical and qualitative features of systemic architectures, the recognition and qualification of dominant and recurrent decays and the management of recovery activities in a semi-automatic way. …”
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