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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Coronary artery disease (CAD) is the primary cause of mortality and a key driver of healthcare expenses globally. Accurately segmenting stenotic regions from coronary angiograms is decisive in identifying and treating...
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ISBN:
(纸本)9798350389609
Coronary artery disease (CAD) is the primary cause of mortality and a key driver of healthcare expenses globally. Accurately segmenting stenotic regions from coronary angiograms is decisive in identifying and treating cardiovascular diseases. However, it is a challenging task for medical professionals to use X-ray Coronary Angiogram (XCA) due to the reduced signal quality, the existence of obstructive contextual elements, and various types of noise. Furthermore, handcrafted segmentation is arduous, laborious, and prone to inconsistencies and human errors. In this context, this research aim to develop an automatic stenosis segmentation system using a deep network. Initially, the input image is processed by Gaussian filters and the improved angiogram is filtered by Hessian-based Vessel Filtering (HVF) technique to increase the clarity of vascular components in the angiogram images. This study identifies the branch points (BP) in the angiograms based on the eigenvalues of the Hessian matrix. The proposed model employ a Mask Region-based Convolutional Neural Network (Mask R-CNN) to provide precise pixel-wise masks for every detected stenosis. The proposed Mask R-CNN includes (i) ResNet50 as the backbone network to extract significant attributes;(ii) Region Proposal Network (RPN) to identify possible Regions of Interest (RoIs) that may have stenosis;(iii) RoI Align to ensure precise alignment of the RoIs for improved mask prediction;and (iv) a mask branch to create a pixel-level segmentation mask for each RoI. The effectiveness of the model is assessed by applying an open-access ARCADE Phase 1 (Automatic Region-based CAD diagnostics using XCA images) dataset. The Mask R-CNN model achieves better results with 97.8% dice score, 92.9% sensitivity, and 96.6% specificity. Besides, it provides reduced standard deviation (SD) in the segmentation task with a 0.8% dice score, 1.0% sensitivity, and 1.0% specificity. These results shows that the Mask R-CNN model provides more relia
Lung cancer stands as a formidable and prevalent threat, necessitating urgent attention to early diagnosis and precise treatment to mitigate its high fatality rates. In this context, the utilization of computed tomogr...
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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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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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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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In the present world a lot of data is generated via twitter, Instagram, WhatsApp etc. in different languages. It is important and necessary task to detect the offensive language among those data to create healthy and ...
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In the era of a new digital world, there has been a decline in social interactions among humans in a physical setting. These concerns have made educational institutions focus on holistic development, which is recogniz...
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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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