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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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
The objective of this investigation of Internet of Things (IoT) technology in retail environments is to cause a revolution in efficiency by leading to improvements in both the efficiency of operations and the quality ...
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The investigation on AI-drive segmentation In real-time of commercial marketing, client segmentation is an essential method that enables firms to tailor their offerings to specific client groups within their target au...
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Starting rescue operations quickly after an earth-quake is the best way to save lives in such disasters. In the case of large-scale earthquakes, current post-earthquake response systems cannot determine which building...
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Relative to previous periods, the quantity of transaction frauds is sharply rising nowadays. Plagiarism and other technologies are being used by criminals to trick consumers into parting with their money. Thus, the ne...
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Federated learning is a distributed machine learning method that has important research significance in areas such as privacy, data heterogeneity, communication efficiency, and incentive mechanisms. Data imbalance is ...
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Microservices are a new approach to software architecture design that enables complex monolithic applications to be constructed as a set of independent and loosely coupled chunks of services. A critical step in migrat...
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The composition and proportion parameters of temperature sensitive paint formulation are complex. The current methods have a long development cycle and low efficiency. We design a visual formulation design system for ...
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