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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Advanced Methods for Disease Outbreak Prediction using Python & Sci-Kit Learn: Insights from COVID-19 |
| Authors Name: | Rahul Prasad , Swastik Kumar , Harsh Vishnoi , Prabhneet Singh |
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IJNRD_208438
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| Published Paper Id: | IJNRD2311130 |
| Published In: | Volume 8 Issue 11, November-2023 |
| DOI: | |
| Abstract: | In response to the critical demand for advanced disease outbreak prediction methodologies, this study delves into an extensive investigation centered around the COVID-19 pandemic. Drawing from the wealth of insights and experiences garnered through the analysis of COVID-19 outbreak data, the research endeavors to shed light on state-of-the-art predictive models utilizing advanced machine learning and artificial intelligence techniques. A meticulous review of relevant literature lays the groundwork, encompassing advanced techniques such as deep convolutional neural networks (CNNs) used for highly accurate classification of COVID-19 from X-ray images, and hybrid ensemble modular nonlinear autoregressive neural networks, showcasing exceptional proficiency in predicting COVID-19 transmission rates with remarkable precision. The research methodology involves a multi-faceted approach, leveraging diverse data sources, including COVID-19 cases data, to calibrate these models and employing recurrent neural network models for precise prediction, particularly in time series forecasting. The outcomes derived from the predictive modeling endeavors unravel the potential of these techniques in accurately forecasting the spread of diseases, thereby enabling timely public health interventions. Furthermore, the study diligently examines the critical challenges and limitations entrenched within current methodologies, underscoring the imperative for ongoing research and innovative approaches to enhance prediction accuracy and efficacy in the proactive management of future outbreaks. |
| Keywords: | Disease Outbreak Prediction, COVID-19, Machine Learning, Artificial Intelligence, Predictive Modeling, Deep Convolutional Neural Networks, Recurrent Neural Networks, Hybrid Ensemble Models. |
| Cite Article: | "Advanced Methods for Disease Outbreak Prediction using Python & Sci-Kit Learn: Insights from COVID-19", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 11, page no.b231-b236, November-2023, Available :http://www.ijnrd.org/papers/IJNRD2311130.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2311130 Registration ID: 208438 Published In: Volume 8 Issue 11, November-2023 DOI (Digital Object Identifier): Page No: b231-b236 Country: Moahli, Punjab, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2311130 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2311130 |
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