AIartificial intelligence topic ppt.pptx

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1. Artificial Intelligence An Overview 1

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3. Traditional AI Problem solving Searching 3

4. Machine Learning Machine learning involves the development of algorithms that learn patterns and relationships from data to make predictions or decisions. 4

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6. Regression Classification Clustering Dimensionality Reduction 6

7. Algorithms Linear Regression Logistic Regression Polynomial Regression Support Vector Regression Decision Tree Regression 7 Logistic Regression Support Vector Machines K-Nearest Neighbours Kernel SVM Naïve Bayes Decision Tree Classification Random Forest Classification

8. Validate the model Data set (100%) For Training(70%) For Testing(30%) 8

9. Regression 9

10. Classification 10

11. Clustering 11 https://www.javatpoint.com/clustering-in-machine-learning

12. Dimensionality Reduction 12

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15. Rein- Forcement Learning Needs human validation 15

16. Deep Learning 16 Deep learning is a subset of machine learning that focuses on using artificial neural networks with multiple layers to learn hierarchical representations of data .

17. Biological Brain 17 By BruceBlaus - Own work, CC BY 3.0, https://commons.wikimedia.org/w/index.php?curid=28761830

18. 18 http://commons.wikimedia.org/wiki/File:Cajal_actx_inter.jpg#mediaviewer/File:Cajal_actx_inter.jpg

19. Artificial Neural Networks 19

20. Convolutional Neural networks 20

21. Architecture of CNNs Convolutional Layers Pooling Layers Fully Connected Layers 21

22. Kernels 22

23. pooling 23

24. Fully connected layers 24

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27. Generative adversial networks GANs algorithmic architectures that use two neural networks called a  Generator  and a  Discriminator 27

28. Architecture of GANs 28

29. Recurrent Neural Networks Used for speech recognition, voice recognition, time series prediction, and  natural language processing. 29

30. DeepFace DeepID FaceNet VGGNET RESNET SIAMESE NET Triplet NET 30

31. Digital Image Processing Edge Detection Morphology Thresholding 31

32. Edge Detection Canny edge detector Sobel Marr- Hildreth Robert 32

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37. Morphology Erosion Dilation Opening Closing Boundary extraction Hole filling 37

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41. Natural Language Processing 41 Text Analytics and Mining Conversational Agents

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44. Computational Genomics 44 DNA RNA PROTEINS

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46. References World Health Organization—Cancer. Available online:  https://www.who.int/news-room/fact-sheets/detail/cancer.  (accessed on 5 November 2019). Priya , V.V. An Efficient Segmentation Approach for Brain Tumor Detection in MRI.  Indian J. Sci. Technol.   2016 ,  9 , 1–6. [ Google Scholar ] Cancer Treatments Centers of America—Brain Cancer Types. Available online:  https://www.cancercenter.com/cancer-types/brain-cancer/types  (accessed on 30 November 2019). American Association of Neurological Surgeons—Classification of Brain Tumours . Available online:  https://www.aans.org/en/Media/Classifications-of-Brain-Tumors  (accessed on 30 November 2019). DeAngelis , L.M. Brain Tumors.  New Engl. J. Med.   2001 ,  344 , 114–123. [ Google Scholar ] [ CrossRef ][ Green Version ] Louis, D.N.; Perry, A.; Reifenberger , G.; Von Deimling , A.; Figarella-Branger , M.; Cavenee , W.K.; Ohgaki , H.; Wiestler , O.D.; Kleihues , P.; Ellison, D.W. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary.  Acta Neuropathol .   2016 ,  131 , 803–820. [ Google Scholar ] [ CrossRef ][ Green Version ] Afshar , P.; Plataniotis , K.N.; Mohammadi , A. Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries. In Proceedings of the ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 12–17 May 2019; pp. 1368–1372. [ Google Scholar ] Byrne, J.; Dwivedi , R.; Minks, D. Tumours of the brain. In  Nicholson T ( ed ) Recommendations Cross Sectional Imaging Cancer Management , 2nd ed.; Royal College of Radiologists: London, UK, 2014; pp. 1–20. Available online:  https://www.rcr.ac.uk/publication/recommendations-cross-sectional-imaging-cancer-management-second-edition  (accessed on 5 November 2019). Center for Biomedical Image Computing & Analytics (CBICA). Available online:  http://braintumorsegmentation.org/  (accessed on 5 November 2019). Mlynarski , P.; Delingette , H.; Criminisi , A.; Ayache , N. Deep learning with mixed supervision for brain tumor segmentation.  J. Med Imaging   2019 ,  6 , 034002. [ Google Scholar ] [ CrossRef ] Amin , J.; Sharif, M.; Yasmin , M.; Fernandes , S.L. Big data analysis for brain tumor detection: Deep convolutional neural networks.  Futur . Gener . Comput . Syst.   2018 ,  87 , 290–297. [ Google Scholar ] [ CrossRef ] Amin , J.; Sharif, M.; Raza , M.; Yasmin , M. Detection of Brain Tumor based on Features Fusion and Machine Learning.  J. Ambient. Intell . Humaniz . Comput .   2018 , 1–17. [ Google Scholar ] [ CrossRef ] Usman , K.; Rajpoot , K. Brain tumor classification from multi-modality MRI using wavelets and machine learning.  Pattern Anal. Appl.   2017 ,  20 , 871–881. [ Google Scholar ] [ CrossRef ][ Green Version ] 46

47. Thank You 47