A long short‐term memory‐based model for greenhouse climate prediction

Liu, Yuwen; Li, Dejuan; Wan, Shaohua; Wang, Fan; Dou, Wanchun; Xu, Xiaolong; Li, Shancang; Ma, Rui; Qi, Lianyong

doi:10.1002/int.22620

Cited by 136 publications

(73 citation statements)

References 48 publications

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“…In addition, Mahalanobis Distance requires additional time cost to compute the covariance matrix of different dimensions; therefore, its time complexity is not very low. While time cost is critical for real world applications especially for the big data scenario [29][30][31][32][33][34][35] . Therefore, we would continuously refine our algorithm to further reduce its time costs so as to meet the quick response requirements from users.…”

Section: Discussionmentioning

confidence: 99%

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

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With the widely-adopted idea of health and longevity, sports have been becoming one of the most popular entertainment ways of the public. For the majority of sports, players need to know about their concrete physical conditions in a real time manner so as to pursue a good sport score or ranking in a competition or a race. Generally, we can achieve the above goal through analyzing and evaluating the daily training scores of each player. However, there are often multiple physical trainings for players and various correlations are existent among them, which significantly decrease the fairness and trust of player training score evaluation and ranking since traditional multi-dimensional data integration solutions are often based on a strong hypothesis, i.e., the involved multiple dimensions are independent with each other. In view of this shortcoming, we introduce the Mahalanobis Distance into the multi-dimensional player training score evaluation and further propose a correlation-aware player training score evaluation method with trust (abbreviated as CPEMD) based on Mahalanobis Distance. As Mahalanobis Distance can eliminate the hidden linear correlations among the involved multiple dimensions, we can guarantee the fairness and trust of Mahalanobis Distance-based player training score evaluation and ranking results. At last, we use a case study to show the feasibility of CPEMD in this paper.

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Section: Discussionmentioning

confidence: 99%

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

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“…These architectures use sequential patterns and associations between words by treating a sentence as a series of tokens to predict sentiments specific categories such as positive or negative. LSTM can extract text context dependencies better than RNN 10 , 37 . Still, it faces significant challenges in weighting the word order, i.e., a future text has a more significant impact on the text representation than the preceding one.…”

Section: Proposed Model Architecturementioning

confidence: 99%

“…A word embedding is a learned depiction of texts, where words with identical meanings have a similar representation. The layer learns the representation of individual input words in a text having a unique identification by initializing with random weights 10 , 37 . In this study Python Keras library that offers a framework for embedding layer was implemented.…”

Section: Proposed Model Architecturementioning

confidence: 99%

Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model

Kumar

2022

Sci Rep

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India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc.

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“…Artificial intelligence, as the theoretical basis of deep learning, has been updated by researchers in recent years. Liu et al 13 proposed A long-term memory-based model for greenhouse climate prediction. He used long short-term memory (LSTM) model to capture the dependence between historical climate data.…”

Section: Related Workmentioning

confidence: 99%

A pavement distresses identification method optimized for YOLOv5s

Guo

Yang

et al. 2022

Sci Rep

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Automatic detection and recognition of pavement distresses is the key to timely repair of pavement. Repairing the pavement distresses in time can prevent the destruction of road structure and the occurrence of traffic accidents. However, some other factors, such as a single object category, shading and occlusion, make detection of pavement distresses very challenging. In order to solve these problems, we use the improved YOLOv5 model to detect various pavement distresses. We optimize the YOLOv5 model and introduce attention mechanism to enhance the robustness of the model. The improved model is more suitable for deployment in embedded devices. The optimized model is transplanted to the self-built intelligent mobile platform. Experimental results show that the improved network model proposed in this paper can effectively identify pavement distresses on the self-built intelligent mobile platform and datasets. The precision, recall and mAP are 95.5%, 94.3% and 95%. Compared with YOLOv5s and YOLOv4 models, the mAP of the improved YOLOv5s model is increased by 4.3% and 25.8%. This method can provide technical reference for pavement distresses detection robot.

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A long short‐term memory‐based model for greenhouse climate prediction

Cited by 136 publications

References 48 publications

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model

A pavement distresses identification method optimized for YOLOv5s

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