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i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 11 Issue 2

Most Cited

Volume 9 Issue 1 January - June 2022

Research Paper

Blur Image Detection and Classification using Edge Detection Techniques

Bhuvaneswari P.* , Hema M.**

*-** Department of Electronics and Communication Engineering, University College of Engineering, Vizianagaram, Andhra Pradesh, India.

Bhuvaneswari, P., and Hema, M. (2022). Blur Image Detection and Classification using Edge Detection Techniques. i-manager’s Journal on Pattern Recognition, 9(1), 1-7. https://doi.org/10.26634/jpr.9.1.18704

Abstract

Blur classification is important for blind image restoration. It is a challenging task to detect blur in a single image without any information. In this paper, we used edge detection techniques and deep learning convolutional neural network named ResNet 50 for the classification of blur-type images. ResNet 50 model effectively reduces gradient disappearance problem and uses skip connection to train the dataset. Generally, images are subjected to defocus and motion blur, which is caused by the improper depth of focus and movement of objects at the time of capture. Kaggle's blur dataset is used in this paper, which consists of sharp, defocus and motion blur images. Edge detection techniques are applied on images using Laplacian, Sobel, Prewitt, and Roberts filters to obtain features like mean, variance, maximum signal-to-noise ratio (SNR), which are used to train the system and classify the images using classification algorithm.

References

[1]. Aizenberg, I. N., Butakoff, C., Karnaukhov, V. N., Merzlyakov, N. S., & Milukova, O. (2002, May). Blurred image restoration using the type of blur and blur parameter identification on the neural network. In Image Processing: Algorithms and Systems (Vol. 4667, pp. 460-471). SPIE. https://doi.org/10.1117/12.468009

[2]. Bhavani, S, L., & Hema, M. (2021). Detection and classification of blur images using Multi-Class Support Vector Machine. International Journal of Engineering Research & Technology (IJERT), 10(11). https://doi.org/10.17577/IJERTV10IS110150

[3]. Ciancio, A., da Silva, E. A., Said, A., Samadani, R., & Obrador, P. (2010). No-reference blur assessment of digital pictures based on multifeature classifiers. IEEE Transactions on Image Processing, 20(1), 64-75. https://doi.org/10.1109/TIP.2010.2053549

[4]. Da Rugna, J., & Konik, H. (2003, December). Automatic blur detection for meta-data extraction in content-based retrieval context. Proceedings of SPIE: Internet Imaging V, 5304, 285-294. https://doi.org/10.1117/12.526949

[5]. Gonzalez, R. C., & Woods, R. E. (2002). Digital Image Processing (pp. 550-570). Pearson Education India

[6]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778).

[7]. Jiang, Y., Yang, F., Zhu, H., Zhou, D., & Zeng, X. (2020). Nonlinear CNN: Improving CNNs with quadratic convolutions. Neural Computing and Applications, 32(12), 8507-8516. https://doi.org/10.1007/s00521-019-04316-4

[8]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.

[9]. Marziliano, P., Dufaux, F., Winkler, S., & Ebrahimi, T. (2002, September). A no-reference perceptual blur metric. In Proceedings of the International Conference on Image Processing (IEEE IClP 2002) (pp. III-57 – III-60). https://doi.org/10.1109/ICIP.2002.1038902

[10]. Prishchepov, A. V., Radeloff, V. C., Dubinin, M., & Alcantara, C. (2012). The effect of Landsat ETM/ETM+ image acquisition dates on the detection of agricultural land abandonment in Eastern Europe. Remote Sensing of Environment, 126, 195-209. https://doi.org/10.1016/j.rse.2012.08.017

[11]. Qiao, J., & Liu, J. (2006, October). A SVM-based blur identification algorithm for image restoration and resolution enhancement. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (pp. 28-35). https://doi.org/10.1007/11893004_4

[12]. Roberts, L. G. (1965). Machine perception of threedimensional solids, Optical and Electro-Optical Information Processing. MIT Press, Cambridge, MA, 159-197.

[13]. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-9).

[14]. Tong, H., Li, M., Zhang, H., & Zhang, C. (2004, June). Blur detection for digital images using wavelet transform. In 2004, IEEE International Conference on Multimedia and Expo (ICME) (pp. 17-20). IEEE. https://doi.org/10.1109/ICME.2004.1394114

[15]. Wang, R., Li, R., & Sun, H. (2016). Haze removal based on multiple scattering model with superpixel algorithm. Signal Processing, 127, 24-36. https://doi.org/10.1016/j.sigpro.2016.02.003

[16]. Wang, R., Li, W., Qin, R., & Wu, J. (2017, October). Blur image classification based on deep learning. In 2017, Proceedings of IEEE International Conference on Imaging Systems and Techniques (IST). IEEE. https://doi.org/10.1109/IST.2017.8261503

[17]. Yang, D., & Qin, S. (2015, August). Restoration of degraded image with partial blurred regions based on blur detection and classification. In 2015, Proceedings of IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 2414-2419). IEEE. https://doi.org/10.1109/ICMA.2015.7237865

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Research Paper

Breast Cancer Detection using Machine Learning and Image Processing

K. Maheswari* , Tadipathri Sayed Afreen**

*-** Department of Electronics and Communication Engineering, Sanskrithi School of Engineering, Puttaparthi, Andhra Pradesh, India.

Maheswari, K., and Afreen, T. S. (2022). Breast Cancer Detection using Machine Learning and Image Processing. i-manager’s Journal on Pattern Recognition, 9(1), 8-14. https://doi.org/10.26634/jpr.9.1.18805

Abstract

Nowadays breast cancer is the frequent type of cancer in women which leads to death. Mammography and ultrasound are the common ways to detect breast cancer. This paper employs Machine Learning for identification of breast cancer using mammography images. Ultrasound and Elastography are the combined imaging techniques used to separate benign and malignant breast lesions. Support vector machine is a classifier which is used for the classification of combined B-mode and Elastography image. This paper helps the physician to detect breast cancer earlier.

References

[1]. Indian Cancer Society. (2018). Retrieved from https:// www.indiancancersociety.org/pdf/ics-report-18-19.pdf

[2]. Padmanabhan, S., & Sundararajan, R. (2012). Enhanced accuracy of breast cancer detection in digital mammograms using wavelet analysis. In 2012 International Conference on Machine Vision and Image Processing (MVIP), 153-156. https://doi.org/10.1109/MVIP.2012.6428783

[3]. Spandana, P., & Rao, K. M., Rao, B. V. V. S. N. P., & Jwalasrikala. (2013). Novel image processing techniques for early detection of breast cancer, mat lab and lab view implementation. In 2013 IEEE Point-of-Care Healthcare Technologies (PHT), 105-108. https://doi.org/10.1109/PHT.2013.6461295

[4]. Alto, H., Rangayyan, R. M., & Desautels, J. L. (2005). Content-based retrieval and analysis of mammographic masses. Journal of Electronic Imaging, 14(2). https://doi.org/10.1117/1.1902996

[5]. Tao, Y., Lo, S. C. B., Freedman, M. T., & Xuan, J. (2007). A preliminary study of content-based mammographic masses retrieval. In Medical Imaging 2007: Computer- Aided Diagnosis, 6514, 624-635. https://doi.org/10.1117/12.711528

[6]. Zheng, B., Lu, A., Hardesty, L. A., Sumkin, J. H., Hakim, C. M., Ganott, M. A., & Gur, D. (2006). A method to improve visual similarity of breast masses for an interactive computer aided diagnosis environment. Medical Physics, 33(1), 111-117. https://doi.org/10.1118/1.2143139

[7]. Wei, C. H., Li, Y., & Huang, P. J. (2011). Mammogram retrieval through machine learning within BI-RADS standards. Journal of Biomedical Informatics, 44(4), 607-614. https://doi.org/10.1016/j.jbi.2011.01.012

[8]. Narvaez, F., Díaz, G., & Romero, E. (2011). Multi-view information fusion for automatic BI-RADS description of mammographic masses. In Medical Imaging 2011: Computer-Aided Diagnosis. 7963, 81-87.

[9]. Liu, J., Zhang, S., Liu, W., Zhang, X., & Metaxas, D. N. (2014). Scalable mammogram retrieval using anchor graph hashing. In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 898-901.

[10]. Liu,W., Wang, J.,Kumar S., & ChangS. (2011). Hashing with graphs. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), 1–8.

[11]. Guo, H., & Nandi, A. K. (2006). Breast cancer diagnosis using genetic programming generated feature. Pattern Recognition, 39(5), 980-987. https://doi.org/10.1016/j.patcog.2005.10.001

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Research Paper

Sensor Based Sign Language Recognition System

Alka Mishra* , Abhilash Jacob Mathew, Abhyudaya Agrawal*, Adnan Quarishi**, Amiy Vaishnava*, Sparsh Kumar****

*-******Department of Electrical and Electronics Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India.

Mishra, A., Mathew, A. J., Agrawal, A., Quarishi, A., Vaishnava, A., and Kumar, S. (2022). Sensor Based Sign Language Recognition System. i-manager’s Journal on Pattern Recognition, 9(1), 15-22. https://doi.org/10.26634/jpr.9.1.18757

Abstract

Sign language is used as a primary form of communication by many people who are deaf, deafened and non-verbal. Communication barriers exist for members of these populations during daily interactions with those who are unable to understand or use sign language. Advancements in technology and machine learning techniques have enabled development of innovative approaches to translate these sign languages to spoken languages. This paper proposes an intelligent system for translating sign language into text. This approach consists of hardware as well as software. The hardware consists of flex, contact, inertial sensors and SD card module mounted on a synthetic glove, additionally If-Else (Rule based learning) based learning is performed by Arduino nano (Atmega328p) to represent the proposed system. This system is able to recognize static letters, numbers and translating 26 letters from A to Z and 10 numbers from 0 to 9 from the American sign language. The database with the use of alphabet and numbers is prepared, and tested by kNN and CN2 rule inducer, where kNN has shown promising result. This experiment is done by Orange software. Experimental results demonstrate that our system is effective, cheaper and has high classification accuracy as compared to other technology available in market.

References

[1]. Ahmed, M. A., Zaidan, B. B., Zaidan, A. A., Salih, M. M., & Lakulu, M. M. B. (2018). A review on systems-based sensory gloves for sign language recognition state of the art between 2007 and 2017. Sensors, 18(7), 2208. https://doi.org/10.3390/s18072208

[2]. Clark, P., & Niblett, T. (1989). The CN2 induction algorithm. Machine Learning, 3(4), 261-283. https://doi.org/10.1023/A:1022641700528

[3]. Dutta, K. K., & Bellary, S. A. S. (2017). Machine learning techniques for Indian sign language recognition. In 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), 333-336. https://doi.org/10.1109/CTCEEC.2017.8454988

[4]. Lopez, I. V. (2017). Hand Gesture Recognition for Sign Language Transcription.

[5]. Ma, H., Wang, Q., Ma, X., & Salem, M. E. (2020). A sign language interaction system based on pneumatic soft hand. In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 568-573. https://doi.org/10.1109/ICIEA48937.2020.9248247

[6]. Nazib, R. A., Ahmed, T., Siddique, N., & Tusher, J. H. (2017). Sign Language Conversion and Training by Machine Learning with an IOT approach (Doctoral dissertation, BRAC University).

[7]. Nimisha, K. P., & Jacob, A. (2020). A brief review of the recent trends in sign language recognition. In 2020 International Conference on Communication and Signal Processing (ICCSP), 186-190. https://doi.org/10.1109/ICCSP48568.2020.9182351

[8]. Ramli, S. (2012). GMT feature extraction for representation of BIM sign language. In 2012 IEEE Control and System Graduate Research Colloquium, 43-48. https://doi.org/10.1109/ICSGRC.2012.6287133

[9]. Weiss, S. M., & Indurkhya, N. (1995). Rule-based machine learning methods for functional prediction. Journal of Artificial Intelligence Research, 3, 383-403. https://doi.org/10.1613/jair.199

[10]. Welch, G., & Bishop, G. (2006). An introduction to the Kalman filter. University of North Carolina at Chapel Hill, 1-16.

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Research Paper

Contour Based X-Ray Image Classification System for Detection of Covid-19

Neelapala Anil Kumar* , Ravuri Daniel, Prudhvi Kiran Pasam*

* Department of Electronics and Communication Engineering, ACED, Alliance University, Bangalore, Karnataka, India.

Department of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, India.

* Department of Information Technology, Sagi Ramakrishnam Raju Engineering College, Bhimavaram, Andhra Pradesh, India.

Kumar, N. A., Daniel, R., and Pasam, P. K. (2022). Contour Based X-Ray Image Classification System for Detection of Covid-19. i-manager’s Journal on Pattern Recognition, 9(1), 23-32. https://doi.org/10.26634/jpr.9.1.18631

Abstract

COVID-19 is a worldwide epidemic in recent times, as announced by the World Health Organization in early 2020. The disease is extremely contagious, affecting the respiratory system and affecting oxygen saturation in the circulatory system. In the current scenario, there are many testing procedures and processes to diagnose COVID-19. These procedures majorly face the challenges of accuracy and delay response because of their testing mechanisms. Machine learning techniques make it easy to support effective diagnostics with images and text data, but it requires a huge amount of test and training data, which requires a huge amount of memory and processing time. In support of efficient quality inference and minimal latency as the main challenge of COVID-19 detection, there are several methods that can provide a better solution to overcome these challenges through the use of cutting-edge trending technologies such as digital imaging techniques. In this study, as a first step to demonstrate machine learning algorithms to achieve high performance, digital imaging techniques were tested through the analysis, classification, and segmentation of chest x-ray (CXR) images. The proposed model starts with feature extraction from input chest x-ray images to determine if the image is COVID-19 or non-COVID-19. This diagnostic model delivers the most interesting results with 96.5% accuracy, minimal latency, and cost-effective standardized COVID-19 tests.

References

[1]. Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid- 19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43(2), 635-640. https://doi.org/10.1007/s13246-020-00865-4

[2]. Arora, P., Kumar, H., & Panigrahi, B. K. (2020). Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India. Chaos, Solitons & Fractals, 139, 110017. https://doi.org/10.1016/j.chaos.2020.110017

[3]. Das, A. K., Ghosh, S., Thunder, S., Dutta, R., Agarwal, S., & Chakrabarti, A. (2021). Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network. Pattern Analysis and Applications, 24(3), 1111-1124. https://doi.org/10.1007/s10044-021-00970-4

[4]. Dougherty, G. (2009). Digital image processing for medical applications. Cambridge University Press.

[5]. Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395. https://doi.org/10.1145/358669.358692

[6]. Hassanzadeh, S., & Pourghassem, H. (2011). A novel logo detection and recognition framework for separated part logos in document images. Australian Journal of Basic and Applied Sciences, 5(9), 936-946.

[7]. Hung, M. H., Hsieh, C. H., & Kuo, C. M. (2006). Similarity retrieval of shape images based on database classification. Journal of Visual Communication and Image Representation, 17(5), 970-985. https://doi.org/10.1016/j.jvcir.2005.09.002

[8]. Jung, K., Kim, K. I., & Jain, A. K. (2004). Text information extraction in images and video: a survey. Pattern Recognition, 37(5), 977-997. https://doi.org/10.1016/j.patcog.2003.10.012

[9]. Kumar, N. A., & Pakki, M. N. (2012). Analysing the severity of the diabetic retinopathy and its corresponding treatment. International Journal of Soft Computing and Engineering (IJSCE), 2(2), 313-315.

[10]. Kumar, N. A., Anuradha, M. S., Srinivas, P., & Daniel, R. (2013). Automatic detection of adenocarcinoma using active Contours. International Journal of Advanced Computer Research, 3(12), 76-79.

[11]. Kumar, N. A., Anuradha, M. S., Vepa, P. S., & Daniel, R. (2012). Active contours techniques for automatic detection of glaucoma. International Journal of Recent Technology and Engineering, 1(4), 29-31.

[12]. Lin, X., Rocha, I. C. N., Shen, X., Ahmadi, A., & Lucero-Prisno, D. E. (2021). Challenges and strategies in controlling COVID-19 in mainland China: lessons for future public health emergencies. Journal of Social Health, 4(2), 57-61.

[13]. Rao, V., Priyanka, M. S., Lakshmi, A., Faheema, A. G. J., Thomas, A., Medappa, K., ... & Masood, S. (2020). Predicting COVID-19 pneumonia severity on chest X-ray with convolutional neural network: A retrospective study. Indian Journal of Medical Sciences, 72(3), 132-140. htpps://doi.org/10.25259/IJMS_349_2020

[14]. Sanche, S., Lin, Y. T., Xu, C., Romero-Severson, E., Hengartner, N., & Ke, R. (2020). High contagiousness and rapid spread of severe acute respiratory syndrome coronavirus 2. Emerging Infectious Diseases, 26(7), 1470-1477. https://doi.org/10.3201/eid2607.200282

[15]. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4510-4520.).

[16]. Saxena, S. K. (Ed.) (2020). Coronavirus Disease 2019 (COVID-19): Epidemiology, Pathogenesis, Diagnosis, and Therapeutics. Springer Nature. https:/doi.org/10.1007/978-981-15-4814-7

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[21]. Zhong, Y., & Jain, A. K. (2000). Object localization using color, texture and shape. Pattern Recognition, 33(4), 671-684. https://doi.org/10.1016/S0031-3203(99)00079-5

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Survey Paper

A Survey on Deep Learning Techniques in Real-Time Applications

Kanthi Rekha Miriyala* , Dhana Lakshmi Gorle, Suneetha Eluri*

*-*** Department of Computer Science and Engineering, University College of Engineering Kakinada, Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.

Miriyala, K. R., Gorle, D. L., and Eluri, S. (2022). A Survey on Deep Learning Techniques in Real-Time Applications. i-manager’s Journal on Pattern Recognition, 9(1), 33-40. https://doi.org/10.26634/jpr.9.1.18858

Abstract

In recent years, machine learning and Deep Learning have increased and gathered epic success in traditional application domains and new areas of Artificial Intelligence. The performance using Deep Learning has dominated experimental results compared to conventional machine learning algorithms. This paper presents an overview of the progress that has occurred in Deep Learning (DL) concerning some application domains like Autonomous Driving, Healthcare, Voice Recognition, Image Recognition, Advertising, Predicting Natural Calamities, National Stock Exchange and many more. Additionally, deeper insights into several Deep Learning techniques, their working principles, and experimental results are scrutinized. The survey covers Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).

References

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[2]. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., ... & Asari, V. K. (2019). A state-ofthe- art survey on deep learning theory and architectures. Electronics, 8(3), 292. https://doi.org/10.3390/electronics8030292

[3]. Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 1-127. http://dx.doi.org/10.1561/2200000006

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[5]. Canziani, A., Paszke, A., & Culurciello, E. (2016). An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678. https://doi.org/10.48550/arXiv.1605.07678

[6]. Deng, L., Abdel-Hamid, O., & Yu, D. (2013). A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 6669-6673. https://doi.org/10.1109/ICASSP.2013.6638952

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[10]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, 1097-1105.

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[13]. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236

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i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 11 Issue 2

Most Read

Most Cited

Volume 9 Issue 1 January - June 2022

Blur Image Detection and Classification using Edge Detection Techniques

Bhuvaneswari P.* , Hema M.**

*-** Department of Electronics and Communication Engineering, University College of Engineering, Vizianagaram, Andhra Pradesh, India.

Bhuvaneswari, P., and Hema, M. (2022). Blur Image Detection and Classification using Edge Detection Techniques. i-manager’s Journal on Pattern Recognition, 9(1), 1-7. https://doi.org/10.26634/jpr.9.1.18704

Abstract

References

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Breast Cancer Detection using Machine Learning and Image Processing

K. Maheswari* , Tadipathri Sayed Afreen**

*-** Department of Electronics and Communication Engineering, Sanskrithi School of Engineering, Puttaparthi, Andhra Pradesh, India.

Maheswari, K., and Afreen, T. S. (2022). Breast Cancer Detection using Machine Learning and Image Processing. i-manager’s Journal on Pattern Recognition, 9(1), 8-14. https://doi.org/10.26634/jpr.9.1.18805

Abstract

References

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Sensor Based Sign Language Recognition System

Alka Mishra* , Abhilash Jacob Mathew**, Abhyudaya Agrawal***, Adnan Quarishi****, Amiy Vaishnava*****, Sparsh Kumar******

*-******Department of Electrical and Electronics Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India.

Mishra, A., Mathew, A. J., Agrawal, A., Quarishi, A., Vaishnava, A., and Kumar, S. (2022). Sensor Based Sign Language Recognition System. i-manager’s Journal on Pattern Recognition, 9(1), 15-22. https://doi.org/10.26634/jpr.9.1.18757

Abstract

References

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Contour Based X-Ray Image Classification System for Detection of Covid-19

Neelapala Anil Kumar* , Ravuri Daniel**, Prudhvi Kiran Pasam***

Kumar, N. A., Daniel, R., and Pasam, P. K. (2022). Contour Based X-Ray Image Classification System for Detection of Covid-19. i-manager’s Journal on Pattern Recognition, 9(1), 23-32. https://doi.org/10.26634/jpr.9.1.18631

Abstract

References

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A Survey on Deep Learning Techniques in Real-Time Applications

Kanthi Rekha Miriyala* , Dhana Lakshmi Gorle**, Suneetha Eluri***

*-*** Department of Computer Science and Engineering, University College of Engineering Kakinada, Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.

Miriyala, K. R., Gorle, D. L., and Eluri, S. (2022). A Survey on Deep Learning Techniques in Real-Time Applications. i-manager’s Journal on Pattern Recognition, 9(1), 33-40. https://doi.org/10.26634/jpr.9.1.18858

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