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              <text>Tumor Infiltration of Microrobot using Magnetic torque and AI Technique</text>
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              <text>ANFIS; CNN; Disease Prediction; Machine learning</text>
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              <text>Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process.  2023 IEEE.</text>
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              <text>Shalini R.; Mishra L.; Athulya S.; Chimankar A.G.; Kandavalli S.R.; Keshav Kumar K.; Selvan R.S.</text>
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              <text>ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/ViTECoN58111.2023.10157336" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ViTECoN58111.2023.10157336&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165534220&amp;amp;doi=10.1109%2FViTECoN58111.2023.10157336&amp;amp;partnerID=40&amp;amp;md5=ecb0e66b1c15e33dea8bbf8b83f3e250" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165534220&amp;amp;doi=10.1109%2fViTECoN58111.2023.10157336&amp;amp;partnerID=40&amp;amp;md5=ecb0e66b1c15e33dea8bbf8b83f3e250&lt;/a&gt;</text>
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              <text>Restricted Access</text>
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              <text>ISBN: 979-835034798-2</text>
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              <text>Online</text>
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              <text>Shalini R., School of Computing Sciences, Vels Institute of Science Technology and Advanced Studies, Department of BCA &amp;amp; IT, Chennai, India; Mishra L., Khwaja Moinuddin Chishti Language University, Department of Electronics and Communication Engineering, Uttar Pradesh, India; Athulya S., Christ Deemed to Be University, Faculty of Engineering, Department of Computer Science and Engineering, Bangalore, India; Chimankar A.G., Chatrapati Shivaji BCA and BCS College, Department of B.Sc. Computer Science, Maharashtra, India; Kandavalli S.R., Sri Padmavati Mahila Visva Vidyalayam, Department of Electronics and Communication Engineering, Tirupati, India; Keshav Kumar K., G. Narayanamma Institute of Technology and Science, Department of Humanities and Mathematics, Hyderabad, India; Selvan R.S., Annamacharya Institute of Technology &amp;amp; Sciences, Department of Electronics and Communication Engineering, Tirupati, India</text>
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