Nowadays, people will not stop their activity once they purchase the goods on a website. They will post their experience and emotions on a website as a review. The majority of customers search for product ratings on w...
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Waste sorting poses significant challenges because of several factors, including a lack of awareness and education about proper disposal, inadequate infrastructure and collection systems, cultural practices that disco...
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Polycystic Ovary Syndrome (PCOS) is the most prevalent hormonal disorder among women during their reproductive years. It is one of the leading causes of female infertility. Diagnosing peos is difficult because the sym...
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Functional networks(FNs)hold significant promise in understanding brain *** component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal mod...
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Functional networks(FNs)hold significant promise in understanding brain *** component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN ***,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model *** extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI *** on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to *** two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age ***,both small-scale and large-scale brain FN templates are provided as benchmarks for future *** together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.
Blockchain is a decentralized ledger system that securely records transactions across multiple nodes. A key challenge in blockchain networks is forking, where the transaction history diverges due to protocol changes, ...
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For aquaculture operations to be successful, water quality is essential. Maintaining a healthy aquaculture environment depends on the correct and timely evaluation of water quality based on both water parameters and e...
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ISBN:
(纸本)9798400708329
For aquaculture operations to be successful, water quality is essential. Maintaining a healthy aquaculture environment depends on the correct and timely evaluation of water quality based on both water parameters and environmental variables. Using deep learning and a sparse attention transformer model, this work provides a unique method for categorizing water quality in aquaculture. Aquaculture has always assessed water quality using crude rule-based techniques. This study shows how sophisticated machine learning methods, particularly sparse attention transformers, may be used to capture intricate connections between water parameter values and environmental influences. Sparse attention transformers make it possible to model lengthy sequences well and consider how several environmental variables, including temperature, dissolved oxygen, pH, and nutrient concentrations, are interdependent. A dataset that includes measurements of the water quality and the accompanying ambient condition over time is used to train the suggested model. The model may successfully filter out less significant data points by concentrating on limited windows of relevant information using a sparse attention mechanism. This dynamic attention mechanism adjusts to the temporal and geographical features of aquaculture systems, resulting in more precise and context-aware categorization of water quality. Importantly, this work makes use of IoT-based real-time data to provide the model a constant supply of input. The integration of real-time data ensures that the model's predictions are not only accurate but also timely, enabling rapid responses to changes in water quality conditions. The proposed model gives 99.79% accuracy whereas the existing DNN-LSTM gives 96.86%. The results of this study demonstrate the effectiveness of the deep learning-based sparse attention transformer model for water quality classification in aquaculture. By accurately predicting water quality status, aquaculture practitioner
This research paper presents a pioneering approach to cross-domain sentiment analysis utilizing logistic regression, a widely employed technique for binary classification tasks. Sentiment analysis, crucial for underst...
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Pattern recognition in candlestick charts poses a formidable challenge due to the intricate shapes and intrinsic noise in financial data. This study addresses the critical need of a dataset to accurate pattern identif...
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In the context of the new era, the financing needs of enterprises grow significantly with the continuous expansion of their scale. However, in the financing process, small and micro enterprises often encounter challen...
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Rapid advancements in technology has aided in early prediction of Breast cancer which is a high mortality rate characterized condition. Fuzzy and Neural network-based models have been effective in prediction of early ...
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