MAM-IncNet: an end-to-end deep learning detector for Camellia pest recognition

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(11), С. 31379 - 31394

Опубликована: Сен. 16, 2023

Язык: Английский

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A review of image features extraction techniques and their applications in image forensic

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(40), С. 87801 - 87902

Опубликована: Март 20, 2024

Язык: Английский

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Path analysis of energy economic management standardization in the context of carbon neutralization and carbon peak

Frontiers in Ecology and Evolution, Год журнала: 2023, Номер 11

Опубликована: Март 13, 2023

Carbon neutrality and carbon peak are two important measures to control climate change. They have a huge impact on many companies in the fields of energy, industry, construction, transportation, etc. can change development pattern related industries increase new investment opportunities. This paper proposes path analysis standardization energy economic management under background peak, aiming study forecast low-carbon conditions. The algorithm proposed this is an consumption based IPAT model, which be combined with model analyze process data. In addition, by analyzing evaluating contribution various factors, people better understand environment formulate corresponding solutions. experimental results show that, from 2013 2017, baseline scenario, emissions increased year year, 9.25 billion tons 10.48 tons. Under neutral its 9.22 tons, 9.24 9.19 9.21 respectively. Obviously, controlled through strategies. Through these prediction results, it proved that peaking excellent effects promoting management. At same time, also provides valuable reference information for further research peaks.

Язык: Английский

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Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition

Frontiers in Neuroscience, Год журнала: 2023, Номер 17

Опубликована: Сен. 20, 2023

In the medical field, electronic records contain a large amount of textual information, and unstructured nature this information makes data extraction analysis challenging. Therefore, automatic entity from has become significant issue in healthcare domain.To address problem, paper proposes deep learning-based model called Entity-BERT. The aims to leverage powerful feature capabilities learning pre-training language representation BERT(Bidirectional Encoder Representations Transformers), enabling it automatically learn recognize various types records, including terminologies, disease names, drug more, providing more effective support for research clinical practices. Entity-BERT utilizes multi-layer neural network cross-attention mechanism process fuse at different levels types, resembling hierarchical distributed processing human brain. Additionally, employs pre-trained sequence models data, sharing similarities with semantic understanding Furthermore, can capture contextual long-term dependencies, combining handle complex diverse expressions method brain many aspects. exploring how utilize competitive learning, adaptive regulation, synaptic plasticity optimize model's prediction results, adjust its parameters, achieve dynamic adjustments perspective neuroscience brain-like cognition is interest.Experimental results demonstrate that achieves outstanding performance recognition tasks within surpassing other existing models. This not only provides efficient accurate natural technology health field but also introduces new ideas directions design optimization

Язык: Английский

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AI-driven optimization of agricultural water management for enhanced sustainability

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 28, 2024

Optimizing agricultural water resource management is crucial for food production, as effective can significantly improve irrigation efficiency and crop yields. Currently, precise demand forecasting have become key research focuses; however, existing methods often fail to capture complex spatial temporal dependencies. To address this, we propose a novel deep learning framework that combines remote sensing technology with the UNet-ConvLSTM (UCL) model effectively integrate features from MODIS GLDAS datasets. Our leverages high-resolution data UNet dependencies captured by ConvLSTM prediction accuracy. Experimental results demonstrate our UCL achieves best $$R^2$$ compared methods, reaching 0.927 on dataset 0.935 dataset. This approach highlights potential of AI technologies in addressing critical challenges management, contributing more sustainable efficient production systems.

Язык: Английский

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