The recognition of sign language holds significant utility in facilitating communication for the hearing-impaired, aiding in robotic interactions, and connecting with non-verbal communities. This field has evolved fro...
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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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Air quality emerges as a critical global concern affecting millions of individuals. This paper explores into the development and application of data-driven models for air quality prediction, Motivated by air quality&#...
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Amidst the surging demands of the thriving e-commerce industry, the intricate task of manual singulation from bulk shipments has become a critical operational challenge. This research introduces a cutting-edge automat...
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It has been associated with converters and inverters. The system has been found to be feasible in efficiently utilizing Photovoltaic energy and integrating it with the electrical grid without any disturbances. The suc...
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Heart disease is still the world's top cause of death, and becoming older is a major risk factor. Age-related risk for heart disease can be decreased by using early detection and prevention strategies. This resear...
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Urban heat islands raise surface temperatures, which has an effect on city dwellers’ health and welfare. Urbanisation-related changes to the land surface, which are especially notable right after sunset, have an impa...
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The Integrated Telemedicine Platform connects patients and doctors seamlessly, enabling video consultations, report uploads, real-time prescriptions, and nearby pharmacylocators. Enhancedbymachinelearning for early di...
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