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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Visual Emotion Analysis (VEA) seeks to anticipate individuals' emotional reactions to visual stimuli. The subjective perception of visual emotion is an integrated impact of the appearance, scene, and objects prese...
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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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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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The Industrial Internet of Things (IIoT) is the result of integrating the Internet of Things (IoT) into critical industries, especially in industrial and production settings. The industrial Internet of things (IoT) ha...
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In recent decades, machine learning has its increased problem solving methodologies and applications in various fields of business, marketing, education and medical diagnostics. Among all the ML techniques, some have ...
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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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A patient's cognitive health may be better assessed using smart monitoring and assisted living technologies. Social skills, repetitive habits, verbal and nonverbal communication, and adjusting to new environments ...
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