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                <text>Conference Papers</text>
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    <name>Conference Paper</name>
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              <text>Enhanced Lumpy Cattle Skin Disease Prognosis via Deep Learning Methods</text>
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              <text>Deep Learning; ECLAT Algorithm; Lesk Algorithm; Lumpy skin disease; Prediction</text>
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              <text>Animal illness is growing in importance. Identification of the illness is important since various diseases may affect different animals, and immediate guidance will be provided. Cows with lumpy skin issues are caused by the Neethling infection. The affection of these diseases causes lasting injury to the cattle's skin. Reduced Poor growth, reversal, milk production, gravidity, and, in severe cases, mortality are the most common adverse consequences of the illness. We developed a deep learning-based architecture that can predict or recognize disease. A deep literacy system is required to identify the microorganism causing the lumpy skin disease. This system collects diverse cattle electronic medical records and uses data analysis to create an intelligent diagnosis system for cattle diseases. It involves text preprocessing to enhance data quality, and the ECLAT algorithm correlates disease names with probabilities, providing tailored treatment plans. The system ensures timely disease treatment, reducing herders' losses and promoting scientific intelligence in animal husbandry.  2024 IEEE.</text>
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              <text>Lamani M.R.; Padmaja K.; Shilpa B.L.; Asha V.</text>
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              <text>2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024</text>
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          <name>Publisher</name>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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          <name>Date</name>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/NMITCON62075.2024.10699233" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/NMITCON62075.2024.10699233&lt;/a&gt;
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          <name>Rights</name>
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              <text>Restricted Access</text>
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              <text>ISBN: 979-835037289-2</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Lamani M.R., CHRIST(Deemed to be University), Dept of Cse, Bangalore, India; Padmaja K., Gsssietw, Dept of Ise, Mysore, India; Shilpa B.L., Atmece, Dept of Cse, Mysore, India; Asha V., Rns Institute of Technology, Dept of Cse, Bangalore, India</text>
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