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Deep Learning Proactive Approach to Blackout Prevention in Smart Grids: An Early Warning System

Abderrazak Khediri, Ayoub Yahiaoui, Mohamed Ridda Laouar, Yacine Belhocine • Acta Informatica Pragensia (2024)

Blackout events in smart grids can have significant impacts on individuals, communities and businesses, as they can disrupt the power supply and cause damage to the grid. In this paper, a new proactive approach to an early warning system fo...

DOAJ Open Access (DOAJ) DOI: 10.18267/j.aip.246

Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size

Guangyu Zhu, Xueqi Luo, Tingting Yang, Li Cai et al. • Frontiers in Physiology (2022)

The manual identification and segmentation of intracranial aneurysms (IAs) involved in the 3D reconstruction procedure are labor-intensive and prone to human errors. To meet the demands for routine clinical management and large cohort studi...

DOAJ Open Access (DOAJ) DOI: 10.3389/fphys.2022.1084202

Deep learning-enabled medical computer vision

Andre Esteva, Katherine Chou, Serena Y. Yeung, Nikhil Naik et al. • npj Digital Medicine (2021)

A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields-including medicine-to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in th...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41746-020-00376-2 PMC: 7794558

Deep Learning-Based Prediction of Wind Power for Multi-turbines in a Wind Farm

Xiaojiao Chen, Xiuqing Zhang, Mi Dong, Liansheng Huang et al. • Frontiers in Energy Research (2021)

The prediction of wind power plays an indispensable role in maintaining the stability of the entire power grid. In this paper, a deep learning approach is proposed for the power prediction of multiple wind turbines. Starting from the time s...

DOAJ Open Access (DOAJ) DOI: 10.3389/fenrg.2021.723775

Deep Learning in Medical Imaging

Mingyu Kim, Jihye Yun, Yongwon Cho, Keewon Shin et al. • Neurospine (2019)

The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the low computing power and insufficient learn...

OPENALEX cc-by-nc DOI: https://doi.org/10.14245/ns.1938396.198 PMC: 6945006

Deep learning in food category recognition

Yudong Zhang, Lijia Deng, Hengde Zhu, Wei Wang et al. • Information Fusion (2023)

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.inffus.2023.101859

A learning‐based energy management strategy for hybrid energy storage systems with compressed air and solid oxide fuel cells

Yundie Guan, Xiangyu Zhang, Zheming Liang, Tao Chen • IET Renewable Power Generation (2025)

Abstract The intermittency and volatility of renewable energy have been major challenges in modern power systems. This paper proposes a self‐adaptive energy management strategy based on deep reinforcement learning (DRL) to integrate renewab...

DOAJ Open Access (DOAJ) DOI: 10.1049/rpg2.13192

Deep learning for power quality

Roger Alves de Oliveira, Math Bollen • Electric Power Systems Research (2022)

This paper aims to introduce deep learning to the power quality community by reviewing the latest applications and discussing the open challenges of this technology. Publications covering deep learning to power quality are stratified in ter...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.epsr.2022.108887

Systemic Review and Meta-Analysis: The Application of AI-Powered Drone Technology with Computer Vision and Deep Learning Networks in Waste Management

Tyrone Bright, Sarp Adali, Cristina Trois • Drones (2025)

As the generation of Municipal Solid Waste (MSW) has exponentially increased, this poses a challenge for waste managers, such as municipalities, to effectively control waste streams. If waste streams are not managed correctly, they negative...

DOAJ Open Access (DOAJ) DOI: 10.3390/drones9080550

Deep Learning for Identifying Metastatic Breast Cancer

Wang, Dayong, Aditya Khosla, Rishab Gargeya, Humayun Irshad et al. • arXiv (Cornell University) (2016)

The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies. Our team won bo...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1606.05718

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mufti Mahmud, M. Shamim Kaiser, Amir Hussain, Stefano Vassanelli • IEEE Transactions on Neural Networks and Learning Systems (2018)

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body...

OPENALEX Open Access DOI: https://doi.org/10.1109/tnnls.2018.2790388

A Survey on Deep Learning for Human Mobility

Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, Luca Pappalardo • ACM Computing Surveys (2021)

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS t...

OPENALEX Open Access DOI: https://doi.org/10.1145/3485125

Machine and deep learning methods for radiomics

Michele Avanzo, Lise Wei, Joseph Stancanello, Martin Vallières et al. • Medical Physics (2020)

Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has...

OPENALEX other-oa DOI: https://doi.org/10.1002/mp.13678 PMC: 8965689

Deployment optimization of laser chargers in self-organizing power transfer internet of things

Junzhe Hu, Chuanwen Luo, Yi Hong, Guopeng Wang et al. • Wireless Power Transfer (2025)

Recently, the Internet of Things (IoT) has played an important role in many fields. Nevertheless, the fast and uneven energy consumption of IoT Devices (IoTDs) significantly limits the lifetime of IoT networks. One of the effective solution...

DOAJ Open Access (DOAJ) DOI: 10.48130/wpt-0025-0004

Computational Linguistics and Deep Learning

Christopher D. Manning • Computational Linguistics (2015)

Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pun...

OPENALEX Open Access DOI: https://doi.org/10.1162/coli_a_00239

A Survey on Deep Learning for Named Entity Recognition

Jing Li, Aixin Sun, Jianglei Han, Chenliang Li • IEEE Transactions on Knowledge and Data Engineering (2020)

Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language ...

OPENALEX Open Access DOI: https://doi.org/10.1109/tkde.2020.2981314

Development of Nuclear-thermal-mechanical Coupling Calculation Acceleration Method in Advanced Reactor Simulation Code FSAR

ZHANG Bin1, YIN Haoyu2, ZHANG Tengfei2, WANG Jiangyu1, LU Di1, WANG Lianjie1 • Yuanzineng kexue jishu (2026)

To meet the computational efficiency requirements for multi-physics simulations in the design and development of advanced reactor cores, a neural network-based acceleration method was investigated for multi-physics coupling calculations in ...

DOAJ Open Access (DOAJ) DOI: 10.7538/yzk.2025.youxian.0668

Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet

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

Deep Learning for Generic Object Detection: A Survey

Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth et al. • International Journal of Computer Vision (2019)

Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a ...

OPENALEX cc-by DOI: https://doi.org/10.1007/s11263-019-01247-4