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Academic Articles & Research Papers
Showing 20 of 67 academic articles
Lisi Zhang, Qiancheng Yu, Beijing Zhou, Yifan Zhang et al. • IEEE Access
(2023)
The efficacy of credit risk assessment models is pivotal to the risk management capacity of financial institutions. Traditional credit risk models often suffer from inadequate predictive accuracy due to overlooked feature combinations and w...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2023.3322429
Swarnendu Ghosh, Nibaran Das, Ishita Das, Ujjwal Maulik • ACM Computing Surveys
(2019)
The machine learning community has been overwhelmed by a plethora of deep learning--based approaches. Many challenging computer vision tasks, such as detection, localization, recognition, and segmentation of objects in an unconstrained envi...
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Open Access
DOI: https://doi.org/10.1145/3329784
Xing Su, Shan Xue, Fanzhen Liu, Jia Wu et al. • IEEE Transactions on Neural Networks and Learning Systems
(2022)
Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis...
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Open Access
DOI: https://doi.org/10.1109/tnnls.2021.3137396
Alexander Selvikvåg Lundervold, Arvid Lundervold • Zeitschrift für Medizinische Physik
(2018)
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when...
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DOI: https://doi.org/10.1016/j.zemedi.2018.11.002
Mihalj Bakator, Dragica Radosav • Multimodal Technologies and Interaction
(2018)
In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 ...
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DOI: https://doi.org/10.3390/mti2030047
Xinxin Yu, Yimeng Wang, Long Chen, Weihua Li et al. • Journal of Pharmaceutical Analysis
(2025)
Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid m...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.jpha.2025.101317
Swaraja K., Akshitha V., Pranav K., Vyshnavi B. et al. • E3S Web of Conferences
(2021)
Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision s...
DOAJ
Open Access (DOAJ)
DOI: 10.1051/e3sconf/202130901069
Ahmad Mohsenimanesh, Evgueniy Entchev, Filip Bosnjak • Applied Sciences
(2022)
Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/app12189288
Lei Zhou, Chu Zhang, Fei Liu, Zhengjun Qiu et al. • Comprehensive Reviews in Food Science and Food Safety
(2019)
Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food sci...
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DOI: https://doi.org/10.1111/1541-4337.12492
ZHANG Lei, LI Yuewen, SHI Yi • Guangtongxin yanjiu
(2026)
Due to the dynamic monitoring capability, Distributed Acoustic Sensing (DAS) has been widely deployed in various industrial scenarios. Deep learning greatly boosts the event detection accuracy of fiber sensing systems. Nevertheless, practic...
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Open Access (DOAJ)
Md Mijanur Rahman, Ashik Uzzaman, Sadia Islam Sami, Fatema Khatun et al. • IET Image Processing
(2024)
Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory”...
DOAJ
Open Access (DOAJ)
DOI: 10.1049/ipr2.13287
Josh Patterson, Gibson Adam (2017)
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learningespecially deep neural networksmake a real difference in your organization? This han...
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Erik Gawehn, Jan A. Hiss, Gisbert Schneider • Molecular Informatics
(2015)
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical ...
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DOI: https://doi.org/10.1002/minf.201501008
Polina Mamoshina, Armando S. Vieira, Eugene Lane, Alex A. Zhavoronkov • Molecular Pharmaceutics
(2016)
Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this ...
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DOI: https://doi.org/10.1021/acs.molpharmaceut.5b00982
Justin M. Johnson, Taghi M. Khoshgoftaar • Journal Of Big Data
(2019)
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in...
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DOI: https://doi.org/10.1186/s40537-019-0192-5
Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu • Journal of Computational Chemistry
(2017)
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning,...
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Open Access
DOI: https://doi.org/10.1002/jcc.24764
François Chollet (2017)
DESCRIPTION Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. D...
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Seonwoo Min, Byunghan Lee, Sungroh Yoon • Briefings in Bioinformatics
(2016)
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-th...
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Open Access
DOI: https://doi.org/10.1093/bib/bbw068
Yunji Chen, Tao Luo, Shaoli Liu, Shijin Zhang et al. • IEEE/ACM International Symposium on Microarchitecture (MICRO)
(2014)
Many companies are deploying services, either for consumers or industry, which are largely based on machine-learning algorithms for sophisticated processing of large amounts of data. The state-of-the-art and most popular such machine-learni...
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DOI: https://doi.org/10.1109/micro.2014.58
Yernazar Bolat, Iain Murray, Yifei Ren, Nasim Ferdosian • Sensors
(2025)
The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challeng...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/s25154595