Deep Learning Approaches for Image Recognition and Classification

References

[1] Navaneetha Krishnan Rajagopal, Mankeshva Saini, Rosario Huerta-Soto, Rosa Vílchez-Vásquez, J. N. V. R. Swarup Kumar, Shashi Kant Gupta, Sasikumar Perumal, "Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network", Computational Intelligence and Neuroscience, vol. 2022, Article ID 5613407, 11 pages, 2022. https://doi.org/10.1155/2022/5613407

[2] Navaneetha Krishnan Rajagopal, Naila Iqbal Qureshi, S. Durga, Edwin Hernan Ramirez Asis, Rosario Mercedes Huerta Soto, Shashi Kant Gupta, S. Deepak, "Future of Business Culture: An Artificial Intelligence-Driven Digital Framework for Organization Decision-Making Process", Complexity, vol. 2022, Article ID 7796507, 14 pages, 2022. https://doi.org/10.1155/2022/7796507

[3] Kamavisdar, P., Saluja, S., & Agrawal, S. (2013). A survey on image classification approaches and techniques. Nternational Journal of Advanced Research in Computer and Communication Engineering, 2(1), 1005–1009. https://doi.org/10.23883/IJRTER.2017.3033.XTS7Z

[4] Pasolli, E., Melgani, F., Tuia, D., Pacifici, F., & Emery, W. J. (2014). SVM active learning approach for image classification using spatial information. IEEE Transactions on Geoscience and Remote Sensing, 52(4), 2217–2223. https://doi.org/10.1109/TGRS.2013.2258676

[5] Korytkowski, M., Rutkowski, L., & Scherer, R. (2016). Fast image classification by boosting fuzzy classifiers. Information Sciences, 327, 175–182. https://doi.org/10.1016/j.ins.2015.08.030

[6] Deep Learning with MATLAB – matlab expo2018

[7] Introducing Deep Learning with the MATLAB – Deep Learning E -Book provided by the mathworks.

[8] Berg, J. Deng, and L. Fei -Fei. Large scale visual recognition challenge 2010. www.imagenet.org/challenges. 2010.

[9] Fei -Fei Li, Justin Johnson and Serena Yueng, “Lecture 9: CNN Architectures” May 2017.

[10] L. Fei -Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding, 106(1):59–70, 2007.

[11] J. Sánchez and F. Perronnin. High -dimensional signature compression for large-scale image classification. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1665 –1672. IEEE, 2011.

[12] A. Krizhevsky. Learning multiple layers of features from tiny images. Master’s thesis, Department of Computer Science, University of Toronto, 2009.

[13] KISHORE, P.V.V., KISHORE, S.R.C. and PRASAD, M.V.D., 2013. Conglomeration of hand shapes and texture information for recognizing gestures of indian sign language using feed forward neural networks. International Journal of Engineering and Technology, 5(5), pp. 3742-3756.

[14] H. Lee, R. Grosse, R. Ranganath, and A.Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 609–616. ACM, 2009

[15] RAMKIRAN, D.S., MADHAV, B.T.P., PRASANTH, A.M., HARSHA, N.S., VARDHAN, V., AVINASH, K., CHAITANYA, M.N. and NAGASAI, U.S., 2015. Novel compact asymmetrical fractal aperture Notch band antenna. Leonardo Electronic Journal of Practices and Technologies, 14(27), pp. 1 -12.

[16] Ballester, P., &de Araújo, R. M. (2016). “On the Performance of GoogLeNet and AlexNet Applied to Sketches”, In Association for the Advancement of Artificial Intelligence (AAAI), pp. 1124-1128.

[17] KARTHIK, G.V.S., FATHIMA, S.Y., RAHMAN, M.Z.U., AHAMED, S.R. and LAY -EKUAKILLE, A., 2013. Efficient signal conditioning techniques for brain activity in remote health monitoring network. IEEE Sensors Journal, 13(9), pp. 3273 -3283.

[18] KISHORE, P.V.V., PRASAD, M.V.D., PRASAD, C.R. and RAHUL, R., 2015. 4-Camera model for sign language recognition using elliptical fourier descriptors and ANN, International Conference on Signal Processing and Communication Engineering Systems - Proceedings of SPACES 2015, in Association with IEEE 2015, pp. 34 -38.

[19] Pak, M., & Kim, S. (2017).”A review of deep learning in image recognition”. In Computer Applications and Information Processing Technology (CAIPT), 4th International Conference on IEEE, pp. 1-3.

[20] Hussain, M., Bird, J. J.,&Faria, D. R. (2018). “A Study on CNN Transfer Learning for Image Classification”. In UK Workshop on Computational Intelligence, Springer, Cham, pp. 191-202.

[21] Rawat, W., & Wang, Z. (2017). “Deep convolutional neural networks for image classification: A comprehensive review”. Neural computation, Vol. 29(9), pp. 2352-2449.

[22] Loussaief, S., &Abdelkrim, A. (2018). “Deep learning vs. bag of features in machine learning for image classification”, In 2018, International Conference on Advanced Systems and Electric Technologies (IC_ASET), IEEE, pp. 6-10.

[23] Maggiori, E., Tarabalka, Y., Charpiat, G., &Alliez, P. (2017). “High-resolution image classification with convolutional networks”. In Geoscience and Remote Sensing Symposium (IGARSS), 2017 IEEE International, IEEE, pp. 5157-5160.

[24] Eshrag Refaee, Shabana Parveen, Khan Mohamed Jarina Begum, Fatima Parveen, M. Chithik Raja, Shashi Kant Gupta, Santhosh Krishnan, "Secure and Scalable Healthcare Data Transmission in IoT Based on Optimized Routing Protocols for Mobile Computing Applications", Wireless Communications and Mobile Computing, vol. 2022, Article ID 5665408, 12 pages, 2022. https://doi.org/10.1155/2022/5665408

[25] Rajesh Kumar Kaushal, Rajat Bhardwaj, Naveen Kumar, Abeer A. Aljohani, Shashi Kant Gupta, Prabhdeep Singh, Nitin Purohit, "Using Mobile Computing to Provide a Smart and Secure Internet of Things (IoT) Framework for Medical Applications", Wireless Communications and Mobile Computing, vol. 2022, Article ID 8741357, 13 pages, 2022. https://doi.org/10.1155/2022/8741357

[26] Bramah Hazela et al 2022 ECS Trans. 107 2651 https://doi.org/10.1149/10701.2651ecst

[27] Ashish Kumar Pandey et al 2022 ECS Trans. 107 2681 https://doi.org/10.1149/10701.2681ecst

[28] G. S. Jayesh et al 2022 ECS Trans. 107 2715 https://doi.org/10.1149/10701.2715ecst

[29] Shashi Kant Gupta et al 2022 ECS Trans. 107 2927 https://doi.org/10.1149/10701.2927ecst

[30] S. Saxena, D. Yagyasen, C. N. Saranya, R. S. K. Boddu, A. K. Sharma and S. K. Gupta, "Hybrid Cloud Computing for Data Security System," 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021, pp. 1-8, doi: 10.1109/ICAECA52838.2021.9675493.

[31] S. K. Gupta, B. Pattnaik, V. Agrawal, R. S. K. Boddu, A. Srivastava and B. Hazela, "Malware Detection Using Genetic Cascaded Support Vector Machine Classifier in Internet of Things," 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA), 2022, pp. 1-6, doi: 10.1109/ICCSEA54677.2022.9936404.

[32] Natarajan, R.; Lokesh, G.H.; Flammini, F.; Premkumar, A.; Venkatesan, V.K.; Gupta, S.K. A Novel Framework on Security and Energy Enhancement Based on Internet of Medical Things for Healthcare 5.0. Infrastructures2023, 8, 22. https://doi.org/10.3390/infrastructures8020022

[33] V. S. Kumar, A. Alemran, D. A. Karras, S. Kant Gupta, C. Kumar Dixit and B. Haralayya, "Natural Language Processing using Graph Neural Network for Text Classification," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-5, doi: 10.1109/ICKECS56523.2022.10060655.

[34] M. Sakthivel, S. Kant Gupta, D. A. Karras, A. Khang, C. Kumar Dixit and B. Haralayya, "Solving Vehicle Routing Problem for Intelligent Systems using Delaunay Triangulation," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-5, doi: 10.1109/ICKECS56523.2022.10060807.

[35] S. Tahilyani, S. Saxena, D. A. Karras, S. Kant Gupta, C. Kumar Dixit and B. Haralayya, "Deployment of Autonomous Vehicles in Agricultural and using Voronoi Partitioning," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-5, doi: 10.1109/ICKECS56523.2022.10060773.

[36] V. S. Kumar, A. Alemran, S. K. Gupta, B. Hazela, C. K. Dixit and B. Haralayya, "Extraction of SIFT Features for Identifying Disaster Hit areas using Machine Learning Techniques," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-5, doi: 10.1109/ICKECS56523.2022.10060037.

[37] V. S. Kumar, M. Sakthivel, D. A. Karras, S. Kant Gupta, S. M. Parambil Gangadharan and B. Haralayya, "Drone Surveillance in Flood Affected Areas using Firefly Algorithm," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-5, doi: 10.1109/ICKECS56523.2022.10060857.

[38] Parin Somani, Sunil Kumar Vohra, Subrata Chowdhury, Shashi Kant Gupta. "Implementation of a Blockchain-based Smart Shopping System for Automated Bill Generation Using Smart Carts with Cryptographic Algorithms." CRC Press, 2022. https://doi.org/10.1201/9781003269281-11.

[39] Shivlal Mewada, Dhruva Sreenivasa Chakravarthi, S. J. Sultanuddin, Shashi Kant Gupta. "Design and Implementation of a Smart Healthcare System Using Blockchain Technology with A Dragonfly Optimization-based Blowfish Encryption Algorithm." CRC Press, 2022. https://doi.org/10.1201/9781003269281-10.

[40] Ahmed Muayad Younus, Mohanad S.S. Abumandil, Veer P. Gangwar, Shashi Kant Gupta. " AI-Based Smart Education System for a Smart City Using an Improved Self-Adaptive Leap-Frogging Algorithm." CRC Press, 2022. https://doi.org/10.1201/9781003252542-14.

[41] Rosak-Szyrocka, J., Żywiołek, J., & Shahbaz, M. (Eds.). (2023). Quality Management, Value Creation and the Digital Economy (1st ed.). Routledge. https://doi.org/10.4324/9781003404682

[42] Dr. Shashi Kant Gupta, Hayath T M., Lack of it Infrastructure for ICT Based Education as an Emerging Issue in Online Education, TTAICTE. 2022 July; 1(3): 19-24. Published online 2022 July, doi.org/10.36647/TTAICTE/01.03.A004

[43] Hayath T M., Dr. Shashi Kant Gupta, Pedagogical Principles in Learning and Its Impact on Enhancing Motivation of Students, TTAICTE. 2022 October; 1(2): 19-24. Published online 2022 July, doi.org/10.36647/TTAICTE/01.04.A004

[44] Shaily Malik, Dr. Shashi Kant Gupta, “The Importance of Text Mining for Services Management”, TTIDMKD. 2022 November; 2(4): 28-33. Published online 2022 November doi.org/10.36647/TTIDMKD/02.04.A006

[45] Dr. Shashi Kant Gupta, Shaily Malik, “Application of Predictive Analytics in Agriculture”, TTIDMKD. 2022 November; 2(4): 1-5. Published online 2022 November doi.org/10.36647/TTIDMKD/02.04.A001

[46] Dr. Shashi Kant Gupta, Budi Artono, “Bioengineering in the Development of Artificial Hips, Knees, and other joints. Ultrasound, MRI, and other Medical Imaging Techniques”, TTIRAS. 2022 June; 2(2): 10–15. Published online 2022 June doi.org/10.36647/TTIRAS/02.02.A002

[47] Dr. Shashi Kant Gupta, Dr. A. S. A. Ferdous Alam, “Concept of E Business Standardization and its Overall Process” TJAEE 2022 August; 1(3): 1–8. Published online 2022 August

[48] A. Kishore Kumar, A. Alemran, D. A. Karras, S. Kant Gupta, C. Kumar Dixit and B. Haralayya, "An Enhanced Genetic Algorithm for Solving Trajectory Planning of Autonomous Robots," 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, 2023, pp. 1-6, doi: 10.1109/ICICACS57338.2023.10099994

[49] S. K. Gupta, V. S. Kumar, A. Khang, B. Hazela, N. T and B. Haralayya, "Detection of Lung Tumor using an efficient Quadratic Discriminant Analysis Model," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-6, doi: 10.1109/ICRTEC56977.2023.10111903.

[50] S. K. Gupta, A. Alemran, P. Singh, A. Khang, C. K. Dixit and B. Haralayya, "Image Segmentation on Gabor Filtered images using Projective Transformation," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-6, doi: 10.1109/ICRTEC56977.2023.10111885.

[51] S. K. Gupta, S. Saxena, A. Khang, B. Hazela, C. K. Dixit and B. Haralayya, "Detection of Number Plate in Vehicles using Deep Learning based Image Labeler Model," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-6, doi: 10.1109/ICRTEC56977.2023.10111862.

[52] S. K. Gupta, W. Ahmad, D. A. Karras, A. Khang, C. K. Dixit and B. Haralayya, "Solving Roulette Wheel Selection Method using Swarm Intelligence for Trajectory Planning of Intelligent Systems," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-5, doi: 10.1109/ICRTEC56977.2023.10111861.

[53] Shashi Kant Gupta, Olena Hrybiuk, NL Sowjanya Cherukupalli, Arvind Kumar Shukla (2023). Big Data Analytics Tools, Challenges and Its Applications (1st Ed.), CRC Press. ISBN 9781032451114

[54] Shobhna Jeet, Shashi Kant Gupta, Olena Hrybiuk, Nupur Soni (2023). Detection of Cyber Attacks in IoT-based Smart Cities using Integrated Chain Based Multi-Class Support Vector Machine (1st Ed.), CRC Press. ISBN 9781032451114

[55] Parin Somani, Shashi Kant Gupta, Chandra Kumar Dixit, Anchal Pathak (2023). AI-based Competency Model and Design in the Workforce Development System (1st Ed.), CRC Press. https://doi.org/10.1201/9781003357070

[56] Shashi Kant Gupta, Alex Khang, Parin Somani, Chandra Kumar Dixit, Anchal Pathak (2023). Data Mining Processes and Decision-Making Models in Personnel Management System (1st Ed.), CRC Press. https://doi.org/10.1201/9781003357070

[57] Alex Khang, Shashi Kant Gupta, Chandra Kumar Dixit, Parin Somani (2023). Data-driven Application of Human Capital Management Databases, Big Data, and Data Mining (1st Ed.), CRC Press. https://doi.org/10.1201/9781003357070

[58] Chandra Kumar Dixit, Parin Somani, Shashi Kant Gupta, Anchal Pathak (2023). Data-centric Predictive Modelling of Turnover Rate and New Hire in Workforce Management System (1st Ed.), CRC Press. https://doi.org/10.1201/9781003357070

[59] Anchal Pathak, Chandra Kumar Dixit, Parin Somani, Shashi Kant Gupta (2023). Prediction of Employee’s Performance Using Machine Learning (ML) Techniques (1st Ed.), CRC Press. https://doi.org/10.1201/9781003357070

[60] Worakamol Wisetsri, Varinder Kumar, Shashi Kant Gupta, “Managerial Autonomy and Relationship Influence on Service Quality and Human Resource Performance”, Turkish Journal of Physiotherapy and Rehabilitation, Vol. 32, pp2, 2021.

@article{1704997,
      author = {Dr. Parin Somani},
      title = {Deep Learning Approaches for Image Recognition and Classification},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {2},
      pages = {417-425},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704997.pdf},
      abstract = {Picture processing, computer vision, and machine learning all have their own unique approaches to solving the age-old issue of picture categorization. In this research, we investigate the use of deep learning to classify images. For this, we make use of the Alex Net architecture in conjunction with convolution neural networks. For the purpose of categorization, the Image Net database provides the choice of four photos to use as test cases. In addition to carrying out studies, we cropped the photos to isolate different part locations. The findings demonstrate that Alex Net's deep learning-based picture categorization method produces accurate results. Women Deep learning is a subfield of machine learning that has seen increasing use in recent years. Depending on the kind of learning being done, many methodologies, such as unsupervised, semi-supervised, and supervised learning, have been presented as possible solutions. In the areas of image processing, computer vision, and pattern recognition, the deep learning systems performed far better than their more traditional machine learning counterparts, as shown by a number of the experimental findings. This article offers a concise summary of the Deep Learning field, starting with Deep Neural Networks (DNN). The Convolution Neural Network (CNN) and its designs are the next topic to be discussed in this study. Some examples of CNN architectures are LeNet, AlexNet, GoogleNet, VGG16, VGG19, Resnet50, and others. Transfer learning has been included into the CNN via the use of its pre-trained architectures. These designs are evaluated using significant amounts of data from ImageNet.},
      keywords = {Deep Learning, Image Recognition},
      month = {August},
  }