Manuscript Number : IJSRST523103183

Tropical Convolutional Neural Networks (TCNNs) Based Methods for Breast Cancer Diagnosis

Authors(5) :-Apoorva Dwivedi, Dr. Basant Ballabh Dumka, Dr. Nikhat Akhtar, Ms Farah Shan4, Dr. Yusuf Perwej

One of the leading causes of mortality for women worldwide is breast cancer. The likelihood of breast cancer-related mortality can be decreased by early identification and rapid treatment. Machine learning-based predictive technologies provide ways to detect breast cancer earlier. Several analytical techniques, such as breast MRI, X-ray, thermography, mammography, ultrasound, etc., may be used to find it. Accuracy metrics are the most extensively used approach for performance evaluation, and the Tropical Convolutional Neural Networks (TCNNs) model for breast cancer detection is the most precise and popular model. The proposed approach was examined using the Kaggle Breast Cancer Datasets (KBCD). The data set is partitioned into training and testing. We suggest a new class of CNNs called Tropical Convolutional Neural Networks (TCNNs), which are based on tropical convolutions and replace the multiplications and additions in traditional convolutional layers with additions and min/max operations, respectively, in order to reduce the number of multiplications. The results of the review demonstrated that the Tropical Convolutional Neural Networks (TCNNs) is the most successful and popular model for detecting breast cancer, and that accuracy metrics is the most popular approach for evaluating performance. It is amazing how deep learning is being used to so many different real-world problems. Additionally, because tropical convolution operators are basically nonlinear operators, we anticipate that TCNNs will be better at nonlinear fitting than traditional CNNs. The Kaggle Breast Cancer Datasets (KBCD) findings demonstrate that TCNN can reach more expressive power than regular convolutional layers.

Authors and Affiliations

Apoorva Dwivedi
Assistant Professor, Computer Science & Engineering, Invertis University, Bareilly, Uttar Pradesh, India
Dr. Basant Ballabh Dumka
Assistant Professor, Department of Civil Engineering, Invertis University, Bareilly, Uttar Pradesh, India
Dr. Nikhat Akhtar
Associate Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India
Ms Farah Shan4
Assistant Professor, Department of Computer Science & Engineering, Maharana Pratap College of Engineering, Kanpur, Uttar Pradesh, India
Dr. Yusuf Perwej
Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India

Machine Learning, Breast Cancer, Malignant, Classification, Tropical Convolutional Neural Networks (TCNNs), Kaggle Breast Cancer Datasets (KBCD).

  1. El-Nabawy, A., El-Bendary, N., Belal, N.A. “A feature-fusion framework of clinical, genomics, and histopathological data for METABRIC breast cancer subtype classification”, Appl. Soft Comput., 91, 20, 2020
  2. L.A. Altonen, R. Saalovra, P. Kristo, F. Canzian, A. Hemminki, P Peltomaki, R. Chadwik, A. De La Chapelle, "Incidence of hereditary nonpolyposis colorectal cancer and the feasibility of molecular screening for the disease", N Engl J Med, vol. 337, pp. 1481-1487, 1998.
  3. B. Liu, K. Yao, M. Huang, J. Zhang, Y. Li and R. Li, "Gastric Pathology Image Recognition Based on Deep Residual Networks," 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, 2018, pp. 408-412. doi: 10.1109/COMPSAC.2018.10267.
  4. N. Akhtar, S. Rahman, H. Sadia and Y. Perwej, "A Holistic Analysis of Medical Internet of Things (MIoT)", Journal of Information and Computational Science (JOICS), vol. 11, pp. 209-222, 2021
  5. I. Guyon, J. Weston, S. Barnhill, V. Vapnik, "Gene selection for cancer classification using support vector machines", Machine Learning, vol. 46, pp. 389-422, 2002
  6. Yusuf Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 2, PP. 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.420
  7. Shubham Mishra, Versha Verma, Nikhat Akhtar, Shivam Chaturvedi and Yusuf Perwej, "An Intelligent Motion Detection Using OpenCV", Journal of Scientific Research in Science Engineering and Technology, Volume 9, Issue 2, Pages 51-63, 2022, DOI: 10.32628/IJSRSET22925
  8. Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, and Alan L. Yuille. Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  9. Bhavesh Kumar Jaisawal, Dr. Yusuf Perwej, Sanjay Kumar Singh, Susheel Kumar, Jai Pratap Dixit, Niraj Kumar Singh, “An Empirical Investigation of Human Identity Verification Methods”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN: 2395-1990 , Online ISSN : 2394-4099, Volume 10, Issue 1, Pages 16-38, 2022, DOI: 10.32628/IJSRSET2310012
  10. Dawar Husain, Yusuf Perwej, Satendra Kumar Vishwakarma, Prof. (Dr.) Shishir Rastogi, Vaishali Singh, Nikhat Akhtar, “Implementation and Statistical Analysis of De-noising Techniques for Standard Image”, International Journal of Multidisciplinary Education Research (IJMER), ISSN:2277-7881, Volume 11, Issue10 (4), Pages 69-78, Nov. 2022, DOI: 10.IJMER/2022/11.10.72
  11. D. Ciregan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3642–3649, 2012
  12. Vipin Rawat, Vishal Verma, Jai Pratap Dixit, Nikhat Akhtar, Neeta Rastogi, Susheel Kumar, “Face Mask Identification Using a Machine Learning Approach”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 9, Issue 8, Pages 842-847, 2022, DOI: 10.6084/m9.jetir.JETIR2208393
  13. Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, and Alan L. Yuille. Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  14. G. Wang, W. Li, M. A. Zuluaga, R. Pratt, P. A. Patel, M. Aertsen, T. Doel, A. L. David, J. Deprest, S. Ourselin, and T. Vercauteren. Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Transactions on Medical Imaging, 37(7):1562–1573, 2018
  15. Khadeeja Naqvi, Divyanshi Gautam, Ashish Kumar Srivastava, Prof. (Dr.) Syed Qamar Abbas, Dr. Nikhat Akhtar, “A Machine Learning-Based Rational Breast Cancer Diagnosis” , Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 9, Issue 7, Pages 558-567, 2022, DOI: 10.6084/m9.jetir.JETIR2207677
  16. Liu R., Sun Z., Wang A., Yang K., Wang Y., and Sun Q., “Lightweight Efficient Network for Defect Classification of Polarizers,” Concurrency and Computation: Practice and Experience, vol. 32, no. 11, pp. e5663, 2020
  17. Z. Lv, T. Liu, C. Shi, J. A. Benediktsson and H. Du, "Novel land cover change detection method based on k-Means clustering and adaptive majority voting using bitemporal remote sensing images", IEEE Access, vol. 7, pp. 34425-34437, 2019
  18. Khuriwal N, Mishra N. Breast cancer diagnosis using adaptive voting ensemble machine learning algorithm. 2018 IEEMA Engineer Infinite Conference (eTechNxT); 2018 Mar 13-14; New Delhi, India. USA: IEEE; 2018. p. 1-5.
  19. Yusuf Perwej, Nikhat Akhtar, Firoj Parwej, “The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 98, No.11, Pages 32 – 38, 2014, DOI: 10.5120/17230-7556
  20. Fu MR, Wang Y, Li C, Qiu Z, Axelrod D, Guth AA, et al. Machine learning for detection of lymphedema among breast cancer survivors. MHealth.;4:17, 2018
  21. Nahid A. and Kong Y., “Involvement of Machine Learning for Breast Cancer Image Classification: A Survey,” Computational and Mathematical Methods in Medicine, vol. 2017, pp.1-29, 2017
  22. Yusuf Perwej, Firoj Parwej, Nikhat Akhtar, “An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm”, European Journal of Engineering Research and Science (EJERS), Bruxelles, Belgium, ISSN: 2506-8016 (Online), Vol. 3, No. 12, Pages 126 – 134, 2018, DOI: 10.24018/ejers.2018.3.12.989
  23. Salama, G.I., Abdelhalim, M.B., and Abd-elghany Zeid, M., Breast cancer diagnosis on three different datasets using multi-classifiers. Int. J. Comput. Inf. Technol. 1(Issue 01):2277–0764, 2012.
  24. Jafari-Marandi, R., Davarzani, S., Gharibdousti, M.S., and Smith, B.K., An optimum ANNbased breast cancer diagnosis: Bridging gaps between ANN learning and decision-making goals. Appl. Soft Comput. 72:108–120, 2018.
  25. Nikhat Akhtar, “Artificial Intelligence and Machine Learning in Human Resource Management for Sales research Perspective”, IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Electronic ISBN:978-1-6654-7413-9, SCOPUS, ISBN:978-1-6654-7414-6, Chennai, India, 15-16, 2022, DOI: 10.1109/ICSES55317.2022.9914086
  26. Guo, H., and Nandi, A.K.: Breast cancer diagnosis using genetic programming generated feature. 2005 IEEE Workshop on Machine Learning for Signal Processing, Mystic, CT. , pp. 215–220, 2005.
  27. F. A. Mazarbhuiya, Dr. Yusuf Perwej, “The Mining Hourly Fuzzy Patterns from Temporal Datasets”, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Volume 4, Issue 10, Pages 555-559, 2015, DOI: 10.17577/IJERTV4IS100576
  28. Zhao, J.Y., and Zhang, Z.L.: Fuzzy rough neural network and its application to feature selection. In: The Fourth International Workshop on Advanced Computational Intelligence, Wuhan. pp 684–687, 2011
  29. Yusuf Perwej, “An Optimal Approach to Edge Detection Using Fuzzy Rule and Sobel Method”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), ISSN (Print) : 2320 – 3765, ISSN (Online): 2278 – 8875, Volume 4, Issue 11, Pages 9161-9179, 2015, DOI: 10.15662/IJAREEIE.2015.0411054
  30. Xue, B., Zhang, M., and Browne, W.N.: New fitness functions in binary particle swarm optimisation for feature selection. In: WCCI 2012 IEEE World Congress on Computational Intelligence June, 10–15, 2012 - Brisbane, Australia, 2012.
  31. Nikhat Akhtar, “Perceptual Evolution for Software Project Cost Estimation using Ant Colony System”, International Journal of Computer Applications (IJCA) USA, ISSN 0975 - 8887, Volume 81, No.14, Pages 23 – 30, 2013, DOI: 10.5120/14185-2385
  32. Zen K, Zhang CY. Circulating micro RNAs: a novel class of biomarkers to diagnose and monitor human cancers, Med Res Rev, vol. 32, pp. 326-348, 2012
  33. Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. Vol. 42, pp: 60–88, 2017. https://doi.org/10.1016/j.media.2017.07.005
  34. Shamy S. and Dheeba J., “A Research on Detection and Classification of Breast Cancer using k- means GMM and CNN Algorithms,” International Journal of Engineering and Advanced Technology, vol. 8, no. e-6S, pp. 501- 505, 2019
  35. Asif Perwej, Yusuf Perwej, Nikhat Akhtar, “A FLANN and RBF with PSO Viewpoint to Identify a Model for Competent Forecasting Bombay Stock Exchange”, COMPUSOFT, An International Journal of Advanced Computer Technology, ISSN:2320-0790, 4 (1), Volume-IV, Issue-I, PP 1454-1461, 2015, DOI : 10.6084/ijact.v4i1.60
  36. Mert,A., Kılıç,N.Z.,Bilgili,E.,&Akan, A, Breast cancer detection with reduced feature set. Computational and Mathematical Methods in Medicine, pp. 1–11. 2015
  37. Liu L. Research onlogisticregression algorithm of breast cancer diagnosis data by machine learning. 2018 International Conference on Robots & Intelligent System (ICRIS); 2018 May 26-27; Changsha, China. USA: IEEE, p 157-60, 2018
  38. Wadkar K, Pathak P, Wagh N. Breast cancer detection using ANN network and performance analysis with SVM. Int J Comput Eng Tech.;10(3):75-86, 2019
  39. Cowsik A, Clark JW. Breast cancer diagnosis by higher order probabilistic perceptrons. arXiv: 1912.06969,1-17, 2019
  40. Murugan S, Kumar BM, Amudha S. Classification and prediction of breast cancer using linear regression, decision tree and random forest. 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC); Mysore, India. USA: IEEE;, p. 763-6, 2017
  41. Platania R, Shams S, Yang S, Zhang J, Lee K, Park SJ. Automated Breast Cancer Diagnosis Using Deep Learning and Region of Interest Detection (BC-DROID). In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM:. p 536–43, 2017
  42. Dhungel, N., Carneiro, G., & Bradley, A. P., November). Automated mass detection in mammograms using cascaded deep learning and random forests. In 2015 international conference on digital image computing: techniques and applications (DICTA) (pp. 1-8). IEEE, 2015
  43. Nahid AA, Mehrabi MA, Kong Y. Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed research international, 2018, PMID: 29707566 17
  44. Y, Perwej, “The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents”, Transactions on Machine Learning and Artificial Intelligence (TMLAI), Society for Science and Education, United Kingdom (UK), ISSN 2054-7390, Volume 3, Issue 1, Pages 16 - 27, 2015, DOI: 10.14738/tmlai.31.863
  45. Y. Perwej, Firoj Parwej, “A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network”, International Journal of Scientific & Engineering Research (IJSER), France, ISSN 2229 – 5518, Volume 3, Issue 6, Pages 1- 9, 2012, DOI: 10.13140/2.1.1693.2808
  46. Xie, J., Liu, R., Luttrell IV, J., & Zhang, C. , Deep learning based analysis of histopathological images of breast cancer. Frontiers in genetics, 10, 80, 2019
  47. Wu, N.; Phang, J.; Park, J.; Shen, Y.; Huang, Z.; Zorin, M.; Geras, K.J. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging, 39, 1184–1194, 2020
  48. Ting, F.F.; Tan, Y.J.; Sim, K.S. Convolutional neural network improvement for breast cancer classification. Expert Syst. Appl., 120, 103–115, 2019
  49. Nikhat Akhtar, Saima Rahman, Halima Sadia, Yusuf Perwej, “A Holistic Analysis of Medical Internet of Things (MIoT)”, Journal of Information and Computational Science (JOICS), ISSN: 1548 - 7741, Volume 11, Issue 4, Pages 209 - 222, 2021, DOI: 10.12733/JICS.2021/V11I3.535569.31023
  50. https://www.kaggle.com/datasets/utkarshx27/breast-cancer-wisconsin-diagnostic-dataset
  51. Dataset, https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic).
  52. W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Analytical and Quantitative Cytology and Histology, Vol. 17 No. 2, pages 77-87, 1995
  53. Yusuf Perwej , Asif Perwej , “ Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Networks and Genetic Algorithms ”, Journal of Intelligent Learning Systems and Applications (JILSA), Scientific Research Publishing (SRP www.scirp.org) , USA, ISSN Print: 2150-8402 , ISSN Online: 2150-8410, Volume 4, No. 2, Pages 108-119, 2012, DOI: 10.4236/jilsa.2012.42010
  54. Yusuf Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), ISSN (Online): 2278-1021, ISSN (Print): 2319-5940, Volume 4, Issue 2, Pages 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.4203
  55. Yusuf Perwej, “Unsupervised Feature Learning for Text Pattern Analysis with Emotional Data Collection: A Novel System for Big Data Analytics”, IEEE International Conference on Advanced computing Technologies & Applications (ICACTA'22), IEEE No: #54488 ISBN No Xplore: 978-1-6654-9515-8, Coimbatore, India, 2022, DOI: 10.1109/ICACTA54488.2022.9753501
  56. H. Al-Zubaidy, J. Liebeherr, and A. Burchard. A (min, ×) network calculus for multi-hop fading channels. In 2013 Proceedings IEEE INFOCOM, pages 1833–1841, 2013
  57. Ye Luo and Shiqing Fan. Min-max-plus neural networks. arXiv preprint arXiv:2102.06358, 2021
  58. Lazarevich Litvinov, Grigori and Sergej Nikolaevi Sergeev. Tropical and Idempotent Mathematics: International Workshop Tropical-07, Tropical and Idempotent Mathematics, August 25-30, 2007, Independent University of Moscow and Laboratory J.-V. Poncelet, volume 495. American Mathematical Soc., 2009
  59. Venkata K. S. Maddala, Dr. Shantanu Shahi, Dr. Yusuf Perwej, H G Govardhana Reddy, “Machine Learning based IoT application to Improve the Quality and precision in Agricultural System”, European Chemical Bulletin (ECB), ISSN: 2063-5346, SCOPUS, Hungary, Volume 12, Special Issue 6, Pages 1711 – 1722, May 2023, DOI: 10.31838/ecb/2023.12.si6.157
  60. Sachin Bhardwaj, Apoorva Dwivedi, Ashutosh Pandey, Dr. Yusuf Perwej, Pervez Rauf Khan, “Machine Learning-Based Crowd Behavior Analysis and Forecasting”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 9, Issue 3, Pages 418-429, May-June 2023-2023, DOI: 10.32628/CSEIT23903104
  61. Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. Xnor-net: Imagenet classification using binary convolutional neural networks. In Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling, editors, Computer Vision – ECCV 2016, pages 525–542, Cham, 2016
  62. Ak, M.F. A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications. Healthcare, 8, 111, 2020
  63. Y. Perwej, Firoj Parwej, Asif Perwej, “Copyright Protection of Digital Images Using Robust Watermarking Based on Joint DLT and DWT”, International Journal of Scientific & Engineering Research (IJSER), France, ISSN 2229-5518, Volume 3, Issue 6, Pages 1- 9, 2012
  64. Y. Perwej, Asif Perwej, Firoj Parwej, “An Adaptive Watermarking Technique for the copyright of digital images and Digital Image Protection”, International journal of Multimedia & Its Applications (IJMA), Academy & Industry Research Collaboration Center (AIRCC), USA, Volume 4, No.2, Pages 21- 38, 2012, DOI: 10.5121/ijma.2012.4202
  65. Y. Perwej, Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  66. K. He, X. Zhang, S. Ren and J. Sun, "Deep residual learning for image recognition", Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 770-778, Jun. 2016
  67. Rodriguez-Ruiz, A.; Lång, K.; Gubern-Merida, A.; Broeders, M.; Gennaro, G.; Clauser, P.; Helbich, T.H.; Chevalier, M.; Tan, T.; Mertelmeier, T.; et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison with 101 Radiologists. JNCI J. Natl. Cancer Inst., 111, 916–922, 2019
  68. B. Sahiner et al., "Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images", IEEE Trans. Med. Imaging, vol. 15, no. 5, pp. 598-610, 1996
  69. A Comber, P Fisher, C Brunsdon and A Khmag, "Spatial analysis of remote sensing image classification accuracy", Remote Sensing of Environment, vol. 127, pp. 237-246, 2012
  70. Xia, J.; Chen, H.; Li, Q.; Zhou, M.; Chen, L.; Cai, Z.; Fang, Y.; Zhou, H. Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach. Comput. Methods Programs Biomed., 147, 37–49, 2017
  71. J. Euzenat, "Semantic precision and recall for ontology alignment evaluation", Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), pp. 348-353, 2007
  72. Wenduan Xu, Michael Auli and Stephen Clark, "Expected f-measure training for shift-reduce parsing with recurrent neural networks", HLT-NAACL, pp. 210-220, 2016

Publication Details

Published in : Volume 10 | Issue 3 | May-June 2023
Date of Publication : 2023-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1100-1116
Manuscript Number : IJSRST523103183
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Apoorva Dwivedi, Dr. Basant Ballabh Dumka, Dr. Nikhat Akhtar, Ms Farah Shan4, Dr. Yusuf Perwej, " Tropical Convolutional Neural Networks (TCNNs) Based Methods for Breast Cancer Diagnosis", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 10, Issue 3, pp.1100-1116, May-June-2023. Available at doi : https://doi.org/10.32628/IJSRST523103183    
Journal URL : https://ijsrst.com/IJSRST523103183
Citation Detection and Elimination     |      | |
  • B. Liu, K. Yao, M. Huang, J. Zhang, Y. Li and R. Li, "Gastric Pathology Image Recognition Based on Deep Residual Networks," 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, 2018, pp. 408-412. doi: 10.1109/COMPSAC.2018.10267.
  • N. Akhtar, S. Rahman, H. Sadia and Y. Perwej, "A Holistic Analysis of Medical Internet of Things (MIoT)", Journal of Information and Computational Science (JOICS), vol. 11, pp. 209-222, 2021
  • I. Guyon, J. Weston, S. Barnhill, V. Vapnik, "Gene selection for cancer classification using support vector machines", Machine Learning, vol. 46, pp. 389-422, 2002
  • Yusuf Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 2, PP. 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.420
  • Shubham Mishra, Versha Verma, Nikhat Akhtar, Shivam Chaturvedi and Yusuf Perwej, "An Intelligent Motion Detection Using OpenCV", Journal of Scientific Research in Science Engineering and Technology, Volume 9, Issue 2, Pages 51-63, 2022, DOI: 10.32628/IJSRSET22925
  • Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, and Alan L. Yuille. Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  • Bhavesh Kumar Jaisawal, Dr. Yusuf Perwej, Sanjay Kumar Singh, Susheel Kumar, Jai Pratap Dixit, Niraj Kumar Singh, “An Empirical Investigation of Human Identity Verification Methods”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN: 2395-1990 , Online ISSN : 2394-4099, Volume 10, Issue 1, Pages 16-38, 2022, DOI: 10.32628/IJSRSET2310012
  • Dawar Husain, Yusuf Perwej, Satendra Kumar Vishwakarma, Prof. (Dr.) Shishir Rastogi, Vaishali Singh, Nikhat Akhtar, “Implementation and Statistical Analysis of De-noising Techniques for Standard Image”, International Journal of Multidisciplinary Education Research (IJMER), ISSN:2277-7881, Volume 11, Issue10 (4), Pages 69-78, Nov. 2022, DOI: 10.IJMER/2022/11.10.72
  • D. Ciregan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3642–3649, 2012
  • Vipin Rawat, Vishal Verma, Jai Pratap Dixit, Nikhat Akhtar, Neeta Rastogi, Susheel Kumar, “Face Mask Identification Using a Machine Learning Approach”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 9, Issue 8, Pages 842-847, 2022, DOI: 10.6084/m9.jetir.JETIR2208393
  • Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, and Alan L. Yuille. Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  • G. Wang, W. Li, M. A. Zuluaga, R. Pratt, P. A. Patel, M. Aertsen, T. Doel, A. L. David, J. Deprest, S. Ourselin, and T. Vercauteren. Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Transactions on Medical Imaging, 37(7):1562–1573, 2018
  • Khadeeja Naqvi, Divyanshi Gautam, Ashish Kumar Srivastava, Prof. (Dr.) Syed Qamar Abbas, Dr. Nikhat Akhtar, “A Machine Learning-Based Rational Breast Cancer Diagnosis” , Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 9, Issue 7, Pages 558-567, 2022, DOI: 10.6084/m9.jetir.JETIR2207677
  • Liu R., Sun Z., Wang A., Yang K., Wang Y., and Sun Q., “Lightweight Efficient Network for Defect Classification of Polarizers,” Concurrency and Computation: Practice and Experience, vol. 32, no. 11, pp. e5663, 2020
  • Z. Lv, T. Liu, C. Shi, J. A. Benediktsson and H. Du, "Novel land cover change detection method based on k-Means clustering and adaptive majority voting using bitemporal remote sensing images", IEEE Access, vol. 7, pp. 34425-34437, 2019
  • Khuriwal N, Mishra N. Breast cancer diagnosis using adaptive voting ensemble machine learning algorithm. 2018 IEEMA Engineer Infinite Conference (eTechNxT); 2018 Mar 13-14; New Delhi, India. USA: IEEE; 2018. p. 1-5.
  • Yusuf Perwej, Nikhat Akhtar, Firoj Parwej, “The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 98, No.11, Pages 32 – 38, 2014, DOI: 10.5120/17230-7556
  • Fu MR, Wang Y, Li C, Qiu Z, Axelrod D, Guth AA, et al. Machine learning for detection of lymphedema among breast cancer survivors. MHealth.;4:17, 2018
  • Nahid A. and Kong Y., “Involvement of Machine Learning for Breast Cancer Image Classification: A Survey,” Computational and Mathematical Methods in Medicine, vol. 2017, pp.1-29, 2017
  • Yusuf Perwej, Firoj Parwej, Nikhat Akhtar, “An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm”, European Journal of Engineering Research and Science (EJERS), Bruxelles, Belgium, ISSN: 2506-8016 (Online), Vol. 3, No. 12, Pages 126 – 134, 2018, DOI: 10.24018/ejers.2018.3.12.989
  • Salama, G.I., Abdelhalim, M.B., and Abd-elghany Zeid, M., Breast cancer diagnosis on three different datasets using multi-classifiers. Int. J. Comput. Inf. Technol. 1(Issue 01):2277–0764, 2012.
  • Jafari-Marandi, R., Davarzani, S., Gharibdousti, M.S., and Smith, B.K., An optimum ANNbased breast cancer diagnosis: Bridging gaps between ANN learning and decision-making goals. Appl. Soft Comput. 72:108–120, 2018.
  • Nikhat Akhtar, “Artificial Intelligence and Machine Learning in Human Resource Management for Sales research Perspective”, IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Electronic ISBN:978-1-6654-7413-9, SCOPUS, ISBN:978-1-6654-7414-6, Chennai, India, 15-16, 2022, DOI: 10.1109/ICSES55317.2022.9914086
  • Guo, H., and Nandi, A.K.: Breast cancer diagnosis using genetic programming generated feature. 2005 IEEE Workshop on Machine Learning for Signal Processing, Mystic, CT. , pp. 215–220, 2005.
  • F. A. Mazarbhuiya, Dr. Yusuf Perwej, “The Mining Hourly Fuzzy Patterns from Temporal Datasets”, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Volume 4, Issue 10, Pages 555-559, 2015, DOI: 10.17577/IJERTV4IS100576
  • Zhao, J.Y., and Zhang, Z.L.: Fuzzy rough neural network and its application to feature selection. In: The Fourth International Workshop on Advanced Computational Intelligence, Wuhan. pp 684–687, 2011
  • Yusuf Perwej, “An Optimal Approach to Edge Detection Using Fuzzy Rule and Sobel Method”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), ISSN (Print) : 2320 – 3765, ISSN (Online): 2278 – 8875, Volume 4, Issue 11, Pages 9161-9179, 2015, DOI: 10.15662/IJAREEIE.2015.0411054
  • Xue, B., Zhang, M., and Browne, W.N.: New fitness functions in binary particle swarm optimisation for feature selection. In: WCCI 2012 IEEE World Congress on Computational Intelligence June, 10–15, 2012 - Brisbane, Australia, 2012.
  • Nikhat Akhtar, “Perceptual Evolution for Software Project Cost Estimation using Ant Colony System”, International Journal of Computer Applications (IJCA) USA, ISSN 0975 - 8887, Volume 81, No.14, Pages 23 – 30, 2013, DOI: 10.5120/14185-2385
  • Zen K, Zhang CY. Circulating micro RNAs: a novel class of biomarkers to diagnose and monitor human cancers, Med Res Rev, vol. 32, pp. 326-348, 2012
  • Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. Vol. 42, pp: 60–88, 2017. https://doi.org/10.1016/j.media.2017.07.005
  • Shamy S. and Dheeba J., “A Research on Detection and Classification of Breast Cancer using k- means GMM and CNN Algorithms,” International Journal of Engineering and Advanced Technology, vol. 8, no. e-6S, pp. 501- 505, 2019
  • Asif Perwej, Yusuf Perwej, Nikhat Akhtar, “A FLANN and RBF with PSO Viewpoint to Identify a Model for Competent Forecasting Bombay Stock Exchange”, COMPUSOFT, An International Journal of Advanced Computer Technology, ISSN:2320-0790, 4 (1), Volume-IV, Issue-I, PP 1454-1461, 2015, DOI : 10.6084/ijact.v4i1.60
  • Mert,A., Kılıç,N.Z.,Bilgili,E.,&Akan, A, Breast cancer detection with reduced feature set. Computational and Mathematical Methods in Medicine, pp. 1–11. 2015
  • Liu L. Research onlogisticregression algorithm of breast cancer diagnosis data by machine learning. 2018 International Conference on Robots & Intelligent System (ICRIS); 2018 May 26-27; Changsha, China. USA: IEEE, p 157-60, 2018
  • Wadkar K, Pathak P, Wagh N. Breast cancer detection using ANN network and performance analysis with SVM. Int J Comput Eng Tech.;10(3):75-86, 2019
  • Cowsik A, Clark JW. Breast cancer diagnosis by higher order probabilistic perceptrons. arXiv: 1912.06969,1-17, 2019
  • Murugan S, Kumar BM, Amudha S. Classification and prediction of breast cancer using linear regression, decision tree and random forest. 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC); Mysore, India. USA: IEEE;, p. 763-6, 2017
  • Platania R, Shams S, Yang S, Zhang J, Lee K, Park SJ. Automated Breast Cancer Diagnosis Using Deep Learning and Region of Interest Detection (BC-DROID). In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM:. p 536–43, 2017
  • Dhungel, N., Carneiro, G., & Bradley, A. P., November). Automated mass detection in mammograms using cascaded deep learning and random forests. In 2015 international conference on digital image computing: techniques and applications (DICTA) (pp. 1-8). IEEE, 2015
  • Nahid AA, Mehrabi MA, Kong Y. Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed research international, 2018, PMID: 29707566 17
  • Y, Perwej, “The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents”, Transactions on Machine Learning and Artificial Intelligence (TMLAI), Society for Science and Education, United Kingdom (UK), ISSN 2054-7390, Volume 3, Issue 1, Pages 16 - 27, 2015, DOI: 10.14738/tmlai.31.863
  • Y. Perwej, Firoj Parwej, “A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network”, International Journal of Scientific & Engineering Research (IJSER), France, ISSN 2229 – 5518, Volume 3, Issue 6, Pages 1- 9, 2012, DOI: 10.13140/2.1.1693.2808
  • Xie, J., Liu, R., Luttrell IV, J., & Zhang, C. , Deep learning based analysis of histopathological images of breast cancer. Frontiers in genetics, 10, 80, 2019
  • Wu, N.; Phang, J.; Park, J.; Shen, Y.; Huang, Z.; Zorin, M.; Geras, K.J. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging, 39, 1184–1194, 2020
  • Ting, F.F.; Tan, Y.J.; Sim, K.S. Convolutional neural network improvement for breast cancer classification. Expert Syst. Appl., 120, 103–115, 2019
  • Nikhat Akhtar, Saima Rahman, Halima Sadia, Yusuf Perwej, “A Holistic Analysis of Medical Internet of Things (MIoT)”, Journal of Information and Computational Science (JOICS), ISSN: 1548 - 7741, Volume 11, Issue 4, Pages 209 - 222, 2021, DOI: 10.12733/JICS.2021/V11I3.535569.31023
  • https://www.kaggle.com/datasets/utkarshx27/breast-cancer-wisconsin-diagnostic-dataset
  • Dataset, https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic).
  • W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Analytical and Quantitative Cytology and Histology, Vol. 17 No. 2, pages 77-87, 1995
  • Yusuf Perwej , Asif Perwej , “ Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Networks and Genetic Algorithms ”, Journal of Intelligent Learning Systems and Applications (JILSA), Scientific Research Publishing (SRP www.scirp.org) , USA, ISSN Print: 2150-8402 , ISSN Online: 2150-8410, Volume 4, No. 2, Pages 108-119, 2012, DOI: 10.4236/jilsa.2012.42010
  • Yusuf Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), ISSN (Online): 2278-1021, ISSN (Print): 2319-5940, Volume 4, Issue 2, Pages 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.4203
  • Yusuf Perwej, “Unsupervised Feature Learning for Text Pattern Analysis with Emotional Data Collection: A Novel System for Big Data Analytics”, IEEE International Conference on Advanced computing Technologies & Applications (ICACTA'22), IEEE No: #54488 ISBN No Xplore: 978-1-6654-9515-8, Coimbatore, India, 2022, DOI: 10.1109/ICACTA54488.2022.9753501
  • H. Al-Zubaidy, J. Liebeherr, and A. Burchard. A (min, ×) network calculus for multi-hop fading channels. In 2013 Proceedings IEEE INFOCOM, pages 1833–1841, 2013
  • Ye Luo and Shiqing Fan. Min-max-plus neural networks. arXiv preprint arXiv:2102.06358, 2021
  • Lazarevich Litvinov, Grigori and Sergej Nikolaevi Sergeev. Tropical and Idempotent Mathematics: International Workshop Tropical-07, Tropical and Idempotent Mathematics, August 25-30, 2007, Independent University of Moscow and Laboratory J.-V. Poncelet, volume 495. American Mathematical Soc., 2009
  • Venkata K. S. Maddala, Dr. Shantanu Shahi, Dr. Yusuf Perwej, H G Govardhana Reddy, “Machine Learning based IoT application to Improve the Quality and precision in Agricultural System”, European Chemical Bulletin (ECB), ISSN: 2063-5346, SCOPUS, Hungary, Volume 12, Special Issue 6, Pages 1711 – 1722, May 2023, DOI: 10.31838/ecb/2023.12.si6.157
  • Sachin Bhardwaj, Apoorva Dwivedi, Ashutosh Pandey, Dr. Yusuf Perwej, Pervez Rauf Khan, “Machine Learning-Based Crowd Behavior Analysis and Forecasting”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 9, Issue 3, Pages 418-429, May-June 2023-2023, DOI: 10.32628/CSEIT23903104
  • Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. Xnor-net: Imagenet classification using binary convolutional neural networks. In Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling, editors, Computer Vision – ECCV 2016, pages 525–542, Cham, 2016
  • Ak, M.F. A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications. Healthcare, 8, 111, 2020
  • Y. Perwej, Firoj Parwej, Asif Perwej, “Copyright Protection of Digital Images Using Robust Watermarking Based on Joint DLT and DWT”, International Journal of Scientific & Engineering Research (IJSER), France, ISSN 2229-5518, Volume 3, Issue 6, Pages 1- 9, 2012
  • Y. Perwej, Asif Perwej, Firoj Parwej, “An Adaptive Watermarking Technique for the copyright of digital images and Digital Image Protection”, International journal of Multimedia & Its Applications (IJMA), Academy & Industry Research Collaboration Center (AIRCC), USA, Volume 4, No.2, Pages 21- 38, 2012, DOI: 10.5121/ijma.2012.4202
  • Y. Perwej, Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  • K. He, X. Zhang, S. Ren and J. Sun, "Deep residual learning for image recognition", Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 770-778, Jun. 2016
  • Rodriguez-Ruiz, A.; Lång, K.; Gubern-Merida, A.; Broeders, M.; Gennaro, G.; Clauser, P.; Helbich, T.H.; Chevalier, M.; Tan, T.; Mertelmeier, T.; et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison with 101 Radiologists. JNCI J. Natl. Cancer Inst., 111, 916–922, 2019
  • B. Sahiner et al., "Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images", IEEE Trans. Med. Imaging, vol. 15, no. 5, pp. 598-610, 1996
  • A Comber, P Fisher, C Brunsdon and A Khmag, "Spatial analysis of remote sensing image classification accuracy", Remote Sensing of Environment, vol. 127, pp. 237-246, 2012
  • Xia, J.; Chen, H.; Li, Q.; Zhou, M.; Chen, L.; Cai, Z.; Fang, Y.; Zhou, H. Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach. Comput. Methods Programs Biomed., 147, 37–49, 2017
  • J. Euzenat, "Semantic precision and recall for ontology alignment evaluation", Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), pp. 348-353, 2007
  • Wenduan Xu, Michael Auli and Stephen Clark, "Expected f-measure training for shift-reduce parsing with recurrent neural networks", HLT-NAACL, pp. 210-220, 2016
    • " target="_blank"> BibTeX     |
  • B. Liu, K. Yao, M. Huang, J. Zhang, Y. Li and R. Li, "Gastric Pathology Image Recognition Based on Deep Residual Networks," 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, 2018, pp. 408-412. doi: 10.1109/COMPSAC.2018.10267.
  • N. Akhtar, S. Rahman, H. Sadia and Y. Perwej, "A Holistic Analysis of Medical Internet of Things (MIoT)", Journal of Information and Computational Science (JOICS), vol. 11, pp. 209-222, 2021
  • I. Guyon, J. Weston, S. Barnhill, V. Vapnik, "Gene selection for cancer classification using support vector machines", Machine Learning, vol. 46, pp. 389-422, 2002
  • Yusuf Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 2, PP. 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.420
  • Shubham Mishra, Versha Verma, Nikhat Akhtar, Shivam Chaturvedi and Yusuf Perwej, "An Intelligent Motion Detection Using OpenCV", Journal of Scientific Research in Science Engineering and Technology, Volume 9, Issue 2, Pages 51-63, 2022, DOI: 10.32628/IJSRSET22925
  • Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, and Alan L. Yuille. Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  • Bhavesh Kumar Jaisawal, Dr. Yusuf Perwej, Sanjay Kumar Singh, Susheel Kumar, Jai Pratap Dixit, Niraj Kumar Singh, “An Empirical Investigation of Human Identity Verification Methods”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN: 2395-1990 , Online ISSN : 2394-4099, Volume 10, Issue 1, Pages 16-38, 2022, DOI: 10.32628/IJSRSET2310012
  • Dawar Husain, Yusuf Perwej, Satendra Kumar Vishwakarma, Prof. (Dr.) Shishir Rastogi, Vaishali Singh, Nikhat Akhtar, “Implementation and Statistical Analysis of De-noising Techniques for Standard Image”, International Journal of Multidisciplinary Education Research (IJMER), ISSN:2277-7881, Volume 11, Issue10 (4), Pages 69-78, Nov. 2022, DOI: 10.IJMER/2022/11.10.72
  • D. Ciregan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3642–3649, 2012
  • Vipin Rawat, Vishal Verma, Jai Pratap Dixit, Nikhat Akhtar, Neeta Rastogi, Susheel Kumar, “Face Mask Identification Using a Machine Learning Approach”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 9, Issue 8, Pages 842-847, 2022, DOI: 10.6084/m9.jetir.JETIR2208393
  • Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, and Alan L. Yuille. Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  • G. Wang, W. Li, M. A. Zuluaga, R. Pratt, P. A. Patel, M. Aertsen, T. Doel, A. L. David, J. Deprest, S. Ourselin, and T. Vercauteren. Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Transactions on Medical Imaging, 37(7):1562–1573, 2018
  • Khadeeja Naqvi, Divyanshi Gautam, Ashish Kumar Srivastava, Prof. (Dr.) Syed Qamar Abbas, Dr. Nikhat Akhtar, “A Machine Learning-Based Rational Breast Cancer Diagnosis” , Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 9, Issue 7, Pages 558-567, 2022, DOI: 10.6084/m9.jetir.JETIR2207677
  • Liu R., Sun Z., Wang A., Yang K., Wang Y., and Sun Q., “Lightweight Efficient Network for Defect Classification of Polarizers,” Concurrency and Computation: Practice and Experience, vol. 32, no. 11, pp. e5663, 2020
  • Z. Lv, T. Liu, C. Shi, J. A. Benediktsson and H. Du, "Novel land cover change detection method based on k-Means clustering and adaptive majority voting using bitemporal remote sensing images", IEEE Access, vol. 7, pp. 34425-34437, 2019
  • Khuriwal N, Mishra N. Breast cancer diagnosis using adaptive voting ensemble machine learning algorithm. 2018 IEEMA Engineer Infinite Conference (eTechNxT); 2018 Mar 13-14; New Delhi, India. USA: IEEE; 2018. p. 1-5.
  • Yusuf Perwej, Nikhat Akhtar, Firoj Parwej, “The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 98, No.11, Pages 32 – 38, 2014, DOI: 10.5120/17230-7556
  • Fu MR, Wang Y, Li C, Qiu Z, Axelrod D, Guth AA, et al. Machine learning for detection of lymphedema among breast cancer survivors. MHealth.;4:17, 2018
  • Nahid A. and Kong Y., “Involvement of Machine Learning for Breast Cancer Image Classification: A Survey,” Computational and Mathematical Methods in Medicine, vol. 2017, pp.1-29, 2017
  • Yusuf Perwej, Firoj Parwej, Nikhat Akhtar, “An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm”, European Journal of Engineering Research and Science (EJERS), Bruxelles, Belgium, ISSN: 2506-8016 (Online), Vol. 3, No. 12, Pages 126 – 134, 2018, DOI: 10.24018/ejers.2018.3.12.989
  • Salama, G.I., Abdelhalim, M.B., and Abd-elghany Zeid, M., Breast cancer diagnosis on three different datasets using multi-classifiers. Int. J. Comput. Inf. Technol. 1(Issue 01):2277–0764, 2012.
  • Jafari-Marandi, R., Davarzani, S., Gharibdousti, M.S., and Smith, B.K., An optimum ANNbased breast cancer diagnosis: Bridging gaps between ANN learning and decision-making goals. Appl. Soft Comput. 72:108–120, 2018.
  • Nikhat Akhtar, “Artificial Intelligence and Machine Learning in Human Resource Management for Sales research Perspective”, IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Electronic ISBN:978-1-6654-7413-9, SCOPUS, ISBN:978-1-6654-7414-6, Chennai, India, 15-16, 2022, DOI: 10.1109/ICSES55317.2022.9914086
  • Guo, H., and Nandi, A.K.: Breast cancer diagnosis using genetic programming generated feature. 2005 IEEE Workshop on Machine Learning for Signal Processing, Mystic, CT. , pp. 215–220, 2005.
  • F. A. Mazarbhuiya, Dr. Yusuf Perwej, “The Mining Hourly Fuzzy Patterns from Temporal Datasets”, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Volume 4, Issue 10, Pages 555-559, 2015, DOI: 10.17577/IJERTV4IS100576
  • Zhao, J.Y., and Zhang, Z.L.: Fuzzy rough neural network and its application to feature selection. In: The Fourth International Workshop on Advanced Computational Intelligence, Wuhan. pp 684–687, 2011
  • Yusuf Perwej, “An Optimal Approach to Edge Detection Using Fuzzy Rule and Sobel Method”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), ISSN (Print) : 2320 – 3765, ISSN (Online): 2278 – 8875, Volume 4, Issue 11, Pages 9161-9179, 2015, DOI: 10.15662/IJAREEIE.2015.0411054
  • Xue, B., Zhang, M., and Browne, W.N.: New fitness functions in binary particle swarm optimisation for feature selection. In: WCCI 2012 IEEE World Congress on Computational Intelligence June, 10–15, 2012 - Brisbane, Australia, 2012.
  • Nikhat Akhtar, “Perceptual Evolution for Software Project Cost Estimation using Ant Colony System”, International Journal of Computer Applications (IJCA) USA, ISSN 0975 - 8887, Volume 81, No.14, Pages 23 – 30, 2013, DOI: 10.5120/14185-2385
  • Zen K, Zhang CY. Circulating micro RNAs: a novel class of biomarkers to diagnose and monitor human cancers, Med Res Rev, vol. 32, pp. 326-348, 2012
  • Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. Vol. 42, pp: 60–88, 2017. https://doi.org/10.1016/j.media.2017.07.005
  • Shamy S. and Dheeba J., “A Research on Detection and Classification of Breast Cancer using k- means GMM and CNN Algorithms,” International Journal of Engineering and Advanced Technology, vol. 8, no. e-6S, pp. 501- 505, 2019
  • Asif Perwej, Yusuf Perwej, Nikhat Akhtar, “A FLANN and RBF with PSO Viewpoint to Identify a Model for Competent Forecasting Bombay Stock Exchange”, COMPUSOFT, An International Journal of Advanced Computer Technology, ISSN:2320-0790, 4 (1), Volume-IV, Issue-I, PP 1454-1461, 2015, DOI : 10.6084/ijact.v4i1.60
  • Mert,A., Kılıç,N.Z.,Bilgili,E.,&Akan, A, Breast cancer detection with reduced feature set. Computational and Mathematical Methods in Medicine, pp. 1–11. 2015
  • Liu L. Research onlogisticregression algorithm of breast cancer diagnosis data by machine learning. 2018 International Conference on Robots & Intelligent System (ICRIS); 2018 May 26-27; Changsha, China. USA: IEEE, p 157-60, 2018
  • Wadkar K, Pathak P, Wagh N. Breast cancer detection using ANN network and performance analysis with SVM. Int J Comput Eng Tech.;10(3):75-86, 2019
  • Cowsik A, Clark JW. Breast cancer diagnosis by higher order probabilistic perceptrons. arXiv: 1912.06969,1-17, 2019
  • Murugan S, Kumar BM, Amudha S. Classification and prediction of breast cancer using linear regression, decision tree and random forest. 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC); Mysore, India. USA: IEEE;, p. 763-6, 2017
  • Platania R, Shams S, Yang S, Zhang J, Lee K, Park SJ. Automated Breast Cancer Diagnosis Using Deep Learning and Region of Interest Detection (BC-DROID). In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM:. p 536–43, 2017
  • Dhungel, N., Carneiro, G., & Bradley, A. P., November). Automated mass detection in mammograms using cascaded deep learning and random forests. In 2015 international conference on digital image computing: techniques and applications (DICTA) (pp. 1-8). IEEE, 2015
  • Nahid AA, Mehrabi MA, Kong Y. Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed research international, 2018, PMID: 29707566 17
  • Y, Perwej, “The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents”, Transactions on Machine Learning and Artificial Intelligence (TMLAI), Society for Science and Education, United Kingdom (UK), ISSN 2054-7390, Volume 3, Issue 1, Pages 16 - 27, 2015, DOI: 10.14738/tmlai.31.863
  • Y. Perwej, Firoj Parwej, “A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network”, International Journal of Scientific & Engineering Research (IJSER), France, ISSN 2229 – 5518, Volume 3, Issue 6, Pages 1- 9, 2012, DOI: 10.13140/2.1.1693.2808
  • Xie, J., Liu, R., Luttrell IV, J., & Zhang, C. , Deep learning based analysis of histopathological images of breast cancer. Frontiers in genetics, 10, 80, 2019
  • Wu, N.; Phang, J.; Park, J.; Shen, Y.; Huang, Z.; Zorin, M.; Geras, K.J. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging, 39, 1184–1194, 2020
  • Ting, F.F.; Tan, Y.J.; Sim, K.S. Convolutional neural network improvement for breast cancer classification. Expert Syst. Appl., 120, 103–115, 2019
  • Nikhat Akhtar, Saima Rahman, Halima Sadia, Yusuf Perwej, “A Holistic Analysis of Medical Internet of Things (MIoT)”, Journal of Information and Computational Science (JOICS), ISSN: 1548 - 7741, Volume 11, Issue 4, Pages 209 - 222, 2021, DOI: 10.12733/JICS.2021/V11I3.535569.31023
  • https://www.kaggle.com/datasets/utkarshx27/breast-cancer-wisconsin-diagnostic-dataset
  • Dataset, https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic).
  • W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Analytical and Quantitative Cytology and Histology, Vol. 17 No. 2, pages 77-87, 1995
  • Yusuf Perwej , Asif Perwej , “ Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Networks and Genetic Algorithms ”, Journal of Intelligent Learning Systems and Applications (JILSA), Scientific Research Publishing (SRP www.scirp.org) , USA, ISSN Print: 2150-8402 , ISSN Online: 2150-8410, Volume 4, No. 2, Pages 108-119, 2012, DOI: 10.4236/jilsa.2012.42010
  • Yusuf Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), ISSN (Online): 2278-1021, ISSN (Print): 2319-5940, Volume 4, Issue 2, Pages 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.4203
  • Yusuf Perwej, “Unsupervised Feature Learning for Text Pattern Analysis with Emotional Data Collection: A Novel System for Big Data Analytics”, IEEE International Conference on Advanced computing Technologies & Applications (ICACTA'22), IEEE No: #54488 ISBN No Xplore: 978-1-6654-9515-8, Coimbatore, India, 2022, DOI: 10.1109/ICACTA54488.2022.9753501
  • H. Al-Zubaidy, J. Liebeherr, and A. Burchard. A (min, ×) network calculus for multi-hop fading channels. In 2013 Proceedings IEEE INFOCOM, pages 1833–1841, 2013
  • Ye Luo and Shiqing Fan. Min-max-plus neural networks. arXiv preprint arXiv:2102.06358, 2021
  • Lazarevich Litvinov, Grigori and Sergej Nikolaevi Sergeev. Tropical and Idempotent Mathematics: International Workshop Tropical-07, Tropical and Idempotent Mathematics, August 25-30, 2007, Independent University of Moscow and Laboratory J.-V. Poncelet, volume 495. American Mathematical Soc., 2009
  • Venkata K. S. Maddala, Dr. Shantanu Shahi, Dr. Yusuf Perwej, H G Govardhana Reddy, “Machine Learning based IoT application to Improve the Quality and precision in Agricultural System”, European Chemical Bulletin (ECB), ISSN: 2063-5346, SCOPUS, Hungary, Volume 12, Special Issue 6, Pages 1711 – 1722, May 2023, DOI: 10.31838/ecb/2023.12.si6.157
  • Sachin Bhardwaj, Apoorva Dwivedi, Ashutosh Pandey, Dr. Yusuf Perwej, Pervez Rauf Khan, “Machine Learning-Based Crowd Behavior Analysis and Forecasting”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 9, Issue 3, Pages 418-429, May-June 2023-2023, DOI: 10.32628/CSEIT23903104
  • Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. Xnor-net: Imagenet classification using binary convolutional neural networks. In Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling, editors, Computer Vision – ECCV 2016, pages 525–542, Cham, 2016
  • Ak, M.F. A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications. Healthcare, 8, 111, 2020
  • Y. Perwej, Firoj Parwej, Asif Perwej, “Copyright Protection of Digital Images Using Robust Watermarking Based on Joint DLT and DWT”, International Journal of Scientific & Engineering Research (IJSER), France, ISSN 2229-5518, Volume 3, Issue 6, Pages 1- 9, 2012
  • Y. Perwej, Asif Perwej, Firoj Parwej, “An Adaptive Watermarking Technique for the copyright of digital images and Digital Image Protection”, International journal of Multimedia & Its Applications (IJMA), Academy & Industry Research Collaboration Center (AIRCC), USA, Volume 4, No.2, Pages 21- 38, 2012, DOI: 10.5121/ijma.2012.4202
  • Y. Perwej, Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  • K. He, X. Zhang, S. Ren and J. Sun, "Deep residual learning for image recognition", Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 770-778, Jun. 2016
  • Rodriguez-Ruiz, A.; Lång, K.; Gubern-Merida, A.; Broeders, M.; Gennaro, G.; Clauser, P.; Helbich, T.H.; Chevalier, M.; Tan, T.; Mertelmeier, T.; et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison with 101 Radiologists. JNCI J. Natl. Cancer Inst., 111, 916–922, 2019
  • B. Sahiner et al., "Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images", IEEE Trans. Med. Imaging, vol. 15, no. 5, pp. 598-610, 1996
  • A Comber, P Fisher, C Brunsdon and A Khmag, "Spatial analysis of remote sensing image classification accuracy", Remote Sensing of Environment, vol. 127, pp. 237-246, 2012
  • Xia, J.; Chen, H.; Li, Q.; Zhou, M.; Chen, L.; Cai, Z.; Fang, Y.; Zhou, H. Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach. Comput. Methods Programs Biomed., 147, 37–49, 2017
  • J. Euzenat, "Semantic precision and recall for ontology alignment evaluation", Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), pp. 348-353, 2007
  • Wenduan Xu, Michael Auli and Stephen Clark, "Expected f-measure training for shift-reduce parsing with recurrent neural networks", HLT-NAACL, pp. 210-220, 2016
    • " target="_blank">RIS     |
  • B. Liu, K. Yao, M. Huang, J. Zhang, Y. Li and R. Li, "Gastric Pathology Image Recognition Based on Deep Residual Networks," 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, 2018, pp. 408-412. doi: 10.1109/COMPSAC.2018.10267.
  • N. Akhtar, S. Rahman, H. Sadia and Y. Perwej, "A Holistic Analysis of Medical Internet of Things (MIoT)", Journal of Information and Computational Science (JOICS), vol. 11, pp. 209-222, 2021
  • I. Guyon, J. Weston, S. Barnhill, V. Vapnik, "Gene selection for cancer classification using support vector machines", Machine Learning, vol. 46, pp. 389-422, 2002
  • Yusuf Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 2, PP. 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.420
  • Shubham Mishra, Versha Verma, Nikhat Akhtar, Shivam Chaturvedi and Yusuf Perwej, "An Intelligent Motion Detection Using OpenCV", Journal of Scientific Research in Science Engineering and Technology, Volume 9, Issue 2, Pages 51-63, 2022, DOI: 10.32628/IJSRSET22925
  • Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, and Alan L. Yuille. Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  • Bhavesh Kumar Jaisawal, Dr. Yusuf Perwej, Sanjay Kumar Singh, Susheel Kumar, Jai Pratap Dixit, Niraj Kumar Singh, “An Empirical Investigation of Human Identity Verification Methods”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN: 2395-1990 , Online ISSN : 2394-4099, Volume 10, Issue 1, Pages 16-38, 2022, DOI: 10.32628/IJSRSET2310012
  • Dawar Husain, Yusuf Perwej, Satendra Kumar Vishwakarma, Prof. (Dr.) Shishir Rastogi, Vaishali Singh, Nikhat Akhtar, “Implementation and Statistical Analysis of De-noising Techniques for Standard Image”, International Journal of Multidisciplinary Education Research (IJMER), ISSN:2277-7881, Volume 11, Issue10 (4), Pages 69-78, Nov. 2022, DOI: 10.IJMER/2022/11.10.72
  • D. Ciregan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3642–3649, 2012
  • Vipin Rawat, Vishal Verma, Jai Pratap Dixit, Nikhat Akhtar, Neeta Rastogi, Susheel Kumar, “Face Mask Identification Using a Machine Learning Approach”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 9, Issue 8, Pages 842-847, 2022, DOI: 10.6084/m9.jetir.JETIR2208393
  • Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, and Alan L. Yuille. Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  • G. Wang, W. Li, M. A. Zuluaga, R. Pratt, P. A. Patel, M. Aertsen, T. Doel, A. L. David, J. Deprest, S. Ourselin, and T. Vercauteren. Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Transactions on Medical Imaging, 37(7):1562–1573, 2018
  • Khadeeja Naqvi, Divyanshi Gautam, Ashish Kumar Srivastava, Prof. (Dr.) Syed Qamar Abbas, Dr. Nikhat Akhtar, “A Machine Learning-Based Rational Breast Cancer Diagnosis” , Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 9, Issue 7, Pages 558-567, 2022, DOI: 10.6084/m9.jetir.JETIR2207677
  • Liu R., Sun Z., Wang A., Yang K., Wang Y., and Sun Q., “Lightweight Efficient Network for Defect Classification of Polarizers,” Concurrency and Computation: Practice and Experience, vol. 32, no. 11, pp. e5663, 2020
  • Z. Lv, T. Liu, C. Shi, J. A. Benediktsson and H. Du, "Novel land cover change detection method based on k-Means clustering and adaptive majority voting using bitemporal remote sensing images", IEEE Access, vol. 7, pp. 34425-34437, 2019
  • Khuriwal N, Mishra N. Breast cancer diagnosis using adaptive voting ensemble machine learning algorithm. 2018 IEEMA Engineer Infinite Conference (eTechNxT); 2018 Mar 13-14; New Delhi, India. USA: IEEE; 2018. p. 1-5.
  • Yusuf Perwej, Nikhat Akhtar, Firoj Parwej, “The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 98, No.11, Pages 32 – 38, 2014, DOI: 10.5120/17230-7556
  • Fu MR, Wang Y, Li C, Qiu Z, Axelrod D, Guth AA, et al. Machine learning for detection of lymphedema among breast cancer survivors. MHealth.;4:17, 2018
  • Nahid A. and Kong Y., “Involvement of Machine Learning for Breast Cancer Image Classification: A Survey,” Computational and Mathematical Methods in Medicine, vol. 2017, pp.1-29, 2017
  • Yusuf Perwej, Firoj Parwej, Nikhat Akhtar, “An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm”, European Journal of Engineering Research and Science (EJERS), Bruxelles, Belgium, ISSN: 2506-8016 (Online), Vol. 3, No. 12, Pages 126 – 134, 2018, DOI: 10.24018/ejers.2018.3.12.989
  • Salama, G.I., Abdelhalim, M.B., and Abd-elghany Zeid, M., Breast cancer diagnosis on three different datasets using multi-classifiers. Int. J. Comput. Inf. Technol. 1(Issue 01):2277–0764, 2012.
  • Jafari-Marandi, R., Davarzani, S., Gharibdousti, M.S., and Smith, B.K., An optimum ANNbased breast cancer diagnosis: Bridging gaps between ANN learning and decision-making goals. Appl. Soft Comput. 72:108–120, 2018.
  • Nikhat Akhtar, “Artificial Intelligence and Machine Learning in Human Resource Management for Sales research Perspective”, IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Electronic ISBN:978-1-6654-7413-9, SCOPUS, ISBN:978-1-6654-7414-6, Chennai, India, 15-16, 2022, DOI: 10.1109/ICSES55317.2022.9914086
  • Guo, H., and Nandi, A.K.: Breast cancer diagnosis using genetic programming generated feature. 2005 IEEE Workshop on Machine Learning for Signal Processing, Mystic, CT. , pp. 215–220, 2005.
  • F. A. Mazarbhuiya, Dr. Yusuf Perwej, “The Mining Hourly Fuzzy Patterns from Temporal Datasets”, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Volume 4, Issue 10, Pages 555-559, 2015, DOI: 10.17577/IJERTV4IS100576
  • Zhao, J.Y., and Zhang, Z.L.: Fuzzy rough neural network and its application to feature selection. In: The Fourth International Workshop on Advanced Computational Intelligence, Wuhan. pp 684–687, 2011
  • Yusuf Perwej, “An Optimal Approach to Edge Detection Using Fuzzy Rule and Sobel Method”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), ISSN (Print) : 2320 – 3765, ISSN (Online): 2278 – 8875, Volume 4, Issue 11, Pages 9161-9179, 2015, DOI: 10.15662/IJAREEIE.2015.0411054
  • Xue, B., Zhang, M., and Browne, W.N.: New fitness functions in binary particle swarm optimisation for feature selection. In: WCCI 2012 IEEE World Congress on Computational Intelligence June, 10–15, 2012 - Brisbane, Australia, 2012.
  • Nikhat Akhtar, “Perceptual Evolution for Software Project Cost Estimation using Ant Colony System”, International Journal of Computer Applications (IJCA) USA, ISSN 0975 - 8887, Volume 81, No.14, Pages 23 – 30, 2013, DOI: 10.5120/14185-2385
  • Zen K, Zhang CY. Circulating micro RNAs: a novel class of biomarkers to diagnose and monitor human cancers, Med Res Rev, vol. 32, pp. 326-348, 2012
  • Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. Vol. 42, pp: 60–88, 2017. https://doi.org/10.1016/j.media.2017.07.005
  • Shamy S. and Dheeba J., “A Research on Detection and Classification of Breast Cancer using k- means GMM and CNN Algorithms,” International Journal of Engineering and Advanced Technology, vol. 8, no. e-6S, pp. 501- 505, 2019
  • Asif Perwej, Yusuf Perwej, Nikhat Akhtar, “A FLANN and RBF with PSO Viewpoint to Identify a Model for Competent Forecasting Bombay Stock Exchange”, COMPUSOFT, An International Journal of Advanced Computer Technology, ISSN:2320-0790, 4 (1), Volume-IV, Issue-I, PP 1454-1461, 2015, DOI : 10.6084/ijact.v4i1.60
  • Mert,A., Kılıç,N.Z.,Bilgili,E.,&Akan, A, Breast cancer detection with reduced feature set. Computational and Mathematical Methods in Medicine, pp. 1–11. 2015
  • Liu L. Research onlogisticregression algorithm of breast cancer diagnosis data by machine learning. 2018 International Conference on Robots & Intelligent System (ICRIS); 2018 May 26-27; Changsha, China. USA: IEEE, p 157-60, 2018
  • Wadkar K, Pathak P, Wagh N. Breast cancer detection using ANN network and performance analysis with SVM. Int J Comput Eng Tech.;10(3):75-86, 2019
  • Cowsik A, Clark JW. Breast cancer diagnosis by higher order probabilistic perceptrons. arXiv: 1912.06969,1-17, 2019
  • Murugan S, Kumar BM, Amudha S. Classification and prediction of breast cancer using linear regression, decision tree and random forest. 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC); Mysore, India. USA: IEEE;, p. 763-6, 2017
  • Platania R, Shams S, Yang S, Zhang J, Lee K, Park SJ. Automated Breast Cancer Diagnosis Using Deep Learning and Region of Interest Detection (BC-DROID). In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM:. p 536–43, 2017
  • Dhungel, N., Carneiro, G., & Bradley, A. P., November). Automated mass detection in mammograms using cascaded deep learning and random forests. In 2015 international conference on digital image computing: techniques and applications (DICTA) (pp. 1-8). IEEE, 2015
  • Nahid AA, Mehrabi MA, Kong Y. Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed research international, 2018, PMID: 29707566 17
  • Y, Perwej, “The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents”, Transactions on Machine Learning and Artificial Intelligence (TMLAI), Society for Science and Education, United Kingdom (UK), ISSN 2054-7390, Volume 3, Issue 1, Pages 16 - 27, 2015, DOI: 10.14738/tmlai.31.863
  • Y. Perwej, Firoj Parwej, “A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network”, International Journal of Scientific & Engineering Research (IJSER), France, ISSN 2229 – 5518, Volume 3, Issue 6, Pages 1- 9, 2012, DOI: 10.13140/2.1.1693.2808
  • Xie, J., Liu, R., Luttrell IV, J., & Zhang, C. , Deep learning based analysis of histopathological images of breast cancer. Frontiers in genetics, 10, 80, 2019
  • Wu, N.; Phang, J.; Park, J.; Shen, Y.; Huang, Z.; Zorin, M.; Geras, K.J. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging, 39, 1184–1194, 2020
  • Ting, F.F.; Tan, Y.J.; Sim, K.S. Convolutional neural network improvement for breast cancer classification. Expert Syst. Appl., 120, 103–115, 2019
  • Nikhat Akhtar, Saima Rahman, Halima Sadia, Yusuf Perwej, “A Holistic Analysis of Medical Internet of Things (MIoT)”, Journal of Information and Computational Science (JOICS), ISSN: 1548 - 7741, Volume 11, Issue 4, Pages 209 - 222, 2021, DOI: 10.12733/JICS.2021/V11I3.535569.31023
  • https://www.kaggle.com/datasets/utkarshx27/breast-cancer-wisconsin-diagnostic-dataset
  • Dataset, https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic).
  • W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Analytical and Quantitative Cytology and Histology, Vol. 17 No. 2, pages 77-87, 1995
  • Yusuf Perwej , Asif Perwej , “ Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Networks and Genetic Algorithms ”, Journal of Intelligent Learning Systems and Applications (JILSA), Scientific Research Publishing (SRP www.scirp.org) , USA, ISSN Print: 2150-8402 , ISSN Online: 2150-8410, Volume 4, No. 2, Pages 108-119, 2012, DOI: 10.4236/jilsa.2012.42010
  • Yusuf Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), ISSN (Online): 2278-1021, ISSN (Print): 2319-5940, Volume 4, Issue 2, Pages 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.4203
  • Yusuf Perwej, “Unsupervised Feature Learning for Text Pattern Analysis with Emotional Data Collection: A Novel System for Big Data Analytics”, IEEE International Conference on Advanced computing Technologies & Applications (ICACTA'22), IEEE No: #54488 ISBN No Xplore: 978-1-6654-9515-8, Coimbatore, India, 2022, DOI: 10.1109/ICACTA54488.2022.9753501
  • H. Al-Zubaidy, J. Liebeherr, and A. Burchard. A (min, ×) network calculus for multi-hop fading channels. In 2013 Proceedings IEEE INFOCOM, pages 1833–1841, 2013
  • Ye Luo and Shiqing Fan. Min-max-plus neural networks. arXiv preprint arXiv:2102.06358, 2021
  • Lazarevich Litvinov, Grigori and Sergej Nikolaevi Sergeev. Tropical and Idempotent Mathematics: International Workshop Tropical-07, Tropical and Idempotent Mathematics, August 25-30, 2007, Independent University of Moscow and Laboratory J.-V. Poncelet, volume 495. American Mathematical Soc., 2009
  • Venkata K. S. Maddala, Dr. Shantanu Shahi, Dr. Yusuf Perwej, H G Govardhana Reddy, “Machine Learning based IoT application to Improve the Quality and precision in Agricultural System”, European Chemical Bulletin (ECB), ISSN: 2063-5346, SCOPUS, Hungary, Volume 12, Special Issue 6, Pages 1711 – 1722, May 2023, DOI: 10.31838/ecb/2023.12.si6.157
  • Sachin Bhardwaj, Apoorva Dwivedi, Ashutosh Pandey, Dr. Yusuf Perwej, Pervez Rauf Khan, “Machine Learning-Based Crowd Behavior Analysis and Forecasting”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 9, Issue 3, Pages 418-429, May-June 2023-2023, DOI: 10.32628/CSEIT23903104
  • Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. Xnor-net: Imagenet classification using binary convolutional neural networks. In Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling, editors, Computer Vision – ECCV 2016, pages 525–542, Cham, 2016
  • Ak, M.F. A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications. Healthcare, 8, 111, 2020
  • Y. Perwej, Firoj Parwej, Asif Perwej, “Copyright Protection of Digital Images Using Robust Watermarking Based on Joint DLT and DWT”, International Journal of Scientific & Engineering Research (IJSER), France, ISSN 2229-5518, Volume 3, Issue 6, Pages 1- 9, 2012
  • Y. Perwej, Asif Perwej, Firoj Parwej, “An Adaptive Watermarking Technique for the copyright of digital images and Digital Image Protection”, International journal of Multimedia & Its Applications (IJMA), Academy & Industry Research Collaboration Center (AIRCC), USA, Volume 4, No.2, Pages 21- 38, 2012, DOI: 10.5121/ijma.2012.4202
  • Y. Perwej, Ashish Chaturvedi, “Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (IJCA), USA, ISSN 0975 – 8887, Volume 14, No. 2, Pages 6- 9, 2011, DOI: 10.5120/1819-2380
  • K. He, X. Zhang, S. Ren and J. Sun, "Deep residual learning for image recognition", Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 770-778, Jun. 2016
  • Rodriguez-Ruiz, A.; Lång, K.; Gubern-Merida, A.; Broeders, M.; Gennaro, G.; Clauser, P.; Helbich, T.H.; Chevalier, M.; Tan, T.; Mertelmeier, T.; et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison with 101 Radiologists. JNCI J. Natl. Cancer Inst., 111, 916–922, 2019
  • B. Sahiner et al., "Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images", IEEE Trans. Med. Imaging, vol. 15, no. 5, pp. 598-610, 1996
  • A Comber, P Fisher, C Brunsdon and A Khmag, "Spatial analysis of remote sensing image classification accuracy", Remote Sensing of Environment, vol. 127, pp. 237-246, 2012
  • Xia, J.; Chen, H.; Li, Q.; Zhou, M.; Chen, L.; Cai, Z.; Fang, Y.; Zhou, H. Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach. Comput. Methods Programs Biomed., 147, 37–49, 2017
  • J. Euzenat, "Semantic precision and recall for ontology alignment evaluation", Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), pp. 348-353, 2007
  • Wenduan Xu, Michael Auli and Stephen Clark, "Expected f-measure training for shift-reduce parsing with recurrent neural networks", HLT-NAACL, pp. 210-220, 2016
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