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Archive Issue – Vol.1, Issue.1 (October-December 2021)

Archive Issue – Vol.1, Issue.1 (October-December 2021)


Image Steganography for Data Hiding using Dual Layers AES with LSB

Aditya Singh Sikarwar, Vineeta Saxena Nigam

Research Paper | Journal Paper

Vol.1, Issue.1, pp.1-06, Dec-2021

Abstract References Citation Download

Abstract

The technique for hiding a message within an images cover is called image steganography. You can use an image or a text message but it’s much more common and useful to hide text inside an image. To maintain national security or personal dependability in this digital age it is necessary to communicate covertly or transmit the message in a confidential manner. The system in this case is based on the Dual layer Advanced Encryption Standard (AES) and Least Significant Bit (LSB) which is also known as a hybrid technique in which two approaches can be used to improve the systems security patch. AES has been used in this work to encrypt a secret message which has subsequently been concealed in an image using LSB. In this case k-LSB has been used in accordance with the LSB methodology to effectively conceal the secret message within an image without significantly enlarging it. In order to decipher the secret message a region-based detection technique was employed to extract or unhide the hidden box. In order to conceal a message in a complex and arbitrary string and then conceal it in an image in accordance with the standards the system here employs the encryption technique.

Key-Words / Index Term: Image Steganography, Advanced Encryption Standard, Least Significant Bit, K-LSB, Data Hiding, Region Detection Method.

References

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    • Balvinder Singh et al., “A Steganography Algorithm for Hiding Secret Message inside Image using Random Key”, International Journal of Engineering Research & Technology (IJERT), Vol. 3 Issue 12, December-2014.
    • Atta, Randa & Ghanbari, Mohammed & Elnahry, Ibrahim. (2021). Advanced image steganography based on exploiting modification direction and neutrosophic set. Multimedia Tools and Applications. 80. 10.1007/s11042-021-10784-5.
    • O. Elharrouss, N. Almaadeed and S. Al-Maadeed, "An image steganography approach based on k-least significant bits (k-LSB)," 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), 2020, pp. 131-135, doi: 10.1109/ICIoT48696.2020.9089566.
    • Bloisi, Domenico Daniele, and Luca Iocchi. "Image based steganography and cryptography." VISAPP (1). 2007.
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    • M. Warkentin, M.B. Schmidt, E. Bekkering, Steganography and steganalysis, Premier reference Source – Intellectual Property Protection for Multimedia Information technology, Chapter XIX, 2008, pp. 374-380.
    • Chen, Po-Yueh, and Wei-En Wu. "A modified side match scheme for image steganography." International Journal of Applied Science and Engineering 7.1 (2009): 53-60.
    • Wu, Da-Chun, and Wen-Hsiang Tsai. "A steganographic method for images by pixel-value differencing." Pattern Recognition Letters 24.9-10 (2003): 1613-1626.
    • Kunal Ashok Shinde, Omkar Rajendra Gandhi, Somanath Rohidas Langute, Nagaraj V. Dharwadkar, "ADAPTIVE IMAGE STEGANOGRAPHY USING PIXEL INTENSITY DIFFERENCE", IJIERT - International Journal of Innovations in Engineering Research and Technology, ICCCES-16, ISSN : 2394-3696.
    • Muhammad, Khan, et al. "A novel image steganographic approach for hiding text in color images using HSI color model." arXiv preprint arXiv:1503.00388 (2015).
    • Ibraheem, Noor A., et al. "Understanding color models: a review." ARPN Journal of science and technology 2.3 (2012): 265-275.
    • Celik, Turgay, and Hasan Demirel. "Fire detection in video sequences using a generic color model." Fire Safety Journal 44.2 (2009): 147-158.
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    • Hore, Alain, and Djemel Ziou. "Image quality metrics: PSNR vs. SSIM." 2010 20th International Conference on Pattern Recognition. IEEE, 2010. Huynh-Thu, Quan, and Mohammed Ghanbari. "Scope of validity of PSNR in image/video quality assessment." Electronics letters 44.13 (2008): 800-801.

Citation

Aditya Singh Sikarwar, Vineeta Saxena Nigam "Image Steganography for Data Hiding using Dual Layers AES with LSB" International Journal of Scientific Research in Technology & Management, Vol.1, Issue.1, pp.1-06, 2021.

Brain Tumor Classification from MRI Imaging using Polynomial SVM

Rishab Moza, Mangrolia Jayandrath, Nikesha Patel

Research Paper | Journal Paper

Vol.1, Issue.1, pp.07-13, Dec-2021

Abstract References Citation Download

Abstract

Brain tumor is an uncontrollable growth of cells that may spread in different tissues. It can be recognized through Magnetic Resonance Imaging (MRI) which is a non-surgical investigation of organ for diagnosing any disease related to the symptoms. Tumors may be cancerous or non-cancerous or it can be considered as life threatening or less dangerous. A tumor belongs to two distinct categories such as benign or malignant. Benign tumor is considered as non-cancerous or less dangerous and it does not spread to the other part of the brain. It has solid boundaries or contouring that indicates the particular shade of the tumor but malignant is the cancerous tumor which is highly dangerous and it can be spread to the other part of the brain by itself. The boundaries of the malignant tumor are not solid in appearance, instead of that it appears as faded in nature. Here the proposed system is able to classify the tumor type along with brain tumor diagnosis. Here the proposed system uses polynomial Support Vector Machine (SVM) for dealing with the impairments and diagnose the disease accordingly. System perceived high level of accuracy as compare to the previous model.

Key-Words / Index Term: Polynomial Support Vector Machine, Brain Tumor, Segmentation, Cell Classification, Malignant, Benign, MRI, Brain Cells.

References

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  2. Healthline, Brain Tumor, https://www.healthline.com/health/brain-tumor, Accessed- 04 Jun 2022.
  3. Gurbina, Mircea; Lascu, Mihaela; Lascu, Dan (2019). [IEEE 2019 42nd International Conference on Telecommunications and Signal Processing (TSP)] Tumor Detection and Classification of MRI Brain Image using Different Wavelet Transforms and Support Vector Machines, pp. 505–508. doi:10.1109/TSP.2019.8769040.
  4. Jemimma, T. A.; Vetharaj, Y. Jacob (2018). [IEEE 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT)] Watershed Algorithm based DAPP features for Brain Tumor Segmentation and Classification, pp. 155–158. doi:10.1109/ICSSIT.2018.8748436.
  5. Lavanyadevi, R.; Machakowsalya, M.; Nivethitha, J.; Kumar, A. Niranjil (2017). [IEEE 2017 International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE)] Brain tumor classification and segmentation in MRI images using PNN, pp. 1–6. doi:10.1109/ICEICE.2017.8191888.
  6. Zaw, Hein Tun; Maneerat, Noppadol; Win, Khin Yadanar (2019). [IEEE 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST)] Brain tumor detection based on Naïve Bayes Classification, pp. 1–4. doi:10.1109/ICEAST.2019.8802562.
  7. R. Ezhilarasi and P. Varalakshmi, "Tumor Detection in the Brain using Faster R-CNN," 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), Palladam, India, 2018, pp. 388-392.
  8. L. J. Rao, R. Challa, D. Sudarsa, C. Naresh and C. Z. Basha, "Enhanced Automatic Classification of Brain Tumours with FCM and Convolution Neural Network," 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), 2020, pp. 1233-1237, doi: 10.1109/ICSSIT48917.2020.9214199.
  9. Manisha, B. Radhakrishnan and L. P. Suresh, "Tumor region extraction using edge detection method in brain MRI images," 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Kollam, 2017, pp. 1-5.
  10. Mubaris, Support Vector Machines for Classification, https://mubaris.com/posts/svm/, Accessed- 23 Aug 2021.
  11. G. Singh and M. Ansari, “Efficient detection of brain tumor from MRIs using K-means segmentation and normalized histogram”, 2016 1st India International Conference on Information Processing (IICIP), 2016.
  12. T. Goh, S. Basah, H. Yazid, M. Aziz Safar and F. Ahmad Saad, “Performance analysis of image thresholding: Otsu technique”, Measurement, vol. 114, pp. 298-307, 2018.
  13. Dena Nadir George, Hashem B. Jehlol, Anwer Subhi Abdulhussein Oleiwi, “Brain Tumour Detection Using Shape features and Machine Learning Algorithms”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 10, October-2015.
  14. Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins, “Digital Image Processing Using MATLAB”, 2nd Ed. Hardcover – 2009.
  15. Eric Olson, “Particle Shape Factors and Their Use In Image Analysis”, Journal of GXP Compliance, Vol. 15 Issue 4, 2011.
  16. Sazzad, T. M. Shahriar et al. “An automated approach to detect human ovarian tissues using type P63 counter stained histopathology digitized color images.” 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) (2016): 25-28.
  17. Sazzad, T. M. Shahriar et al. “An Automated Detection Process to Detect Ovarian Tissues Using Type P63 Digitized Color Images.” 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI) (2015): 278-285.
  18. A. Hazra, A. Dey, S. Gupta and M. Ansari, “Brain tumor detection based on segmentation using MATLAB”, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 2017.
  19. K. Bhima and A. Jagan, “Analysis of MRI based brain tumor identification using segmentation technique”, 2016 International Conference on Communication and Signal Processing (ICCSP), 2016.
  20. S. Prajapati and K. Jadhav, “Brain Tumor Detection By Various Image Segmentation Techniques With Introduction To Non Negative Matrix Factorization”, IJARCCE, vol. 4, no. 3, pp. 599-603, 2015.
  21. R., “Brain Tumor MRI Image Segmentation and Detection in Image Processing”, International Journal of Research in Engineering and Technology, vol. 03, no. 13, pp. 1-5, 2014.
  22. R. G. Selkar and P. Tumor, “Brain Tumor Detection and Segmentation by Using Thresholding and Watershed Algorithm”, vol. 1, no. 3, pp. 321–324, 2014.
  23. N. Gordillo, E. Montseny, and P. Sobrevilla, “State of the art survey on MRI brain tumor segmentation,” Magnetic Resonance Imaging, vol. 31, pp. 1426–1438, 2013.
  24. P. Gibbs, D.L. Buckley, S. j. Blackb, and A. Horsman, “Tumour determination from MR images by morphological segmentation,” Phys Med Biol, vol. 41, pp. 2437–2446, 1996.
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  27. K. K. Singh, M. K. Bajpai, and R. K. Pandey, “A Novel Approach for Edge Detection of Low Contrast Satellite Images,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-3/W2, pp. 211–217, 2015.

Citation

Rishab Moza, Mangrolia Jayandrath, Nikesha Patel, "Brain Tumor Classification from MRI Imaging using Polynomial SVM" International Journal of Scientific Research in Technology & Management, Vol.1, Issue.1, pp.07-13, 2021.

A Review on Implementation of Biometric Iris Recognition

Monika Singh, Sanjeev Kumar Sharma

Research Paper | Journal Paper

Vol.1, Issue.1, pp.13-19, Dec-2021

Abstract References Citation Download

Abstract

Biometric is considered as an authentic system to recognize a human with respect to their behavior and body features. Automatic verification of features like finger print, palm print, iris recognition is considered a proficient way to grant an access to any system. Among all those, iris is taken as one of the admired technique of recognition which needs precise recognition to execute the whole system. To extract those features which exists in the texture of eye and identify it with the existing database requires various methods to get performed like segmentation, preprocessing, normalization etc. For all those methods, various algorithms have been developed and their effectiveness varies according to the circumstances in which they have been applied. This paper proposes a review on various systems and their developed technique on which researchers have previously worked. Due to several issues, methods which have been developed, till now, can’t consider for wide implementation. So, the system which has been proposed in this paper provides an iris recognition or authentication system using Savitzky-Golay filter for iris feature extraction. A Savitzky–Golay filter is a digital filter that can be applied to a set of digital data points for the purpose of smoothing or enhancing the data without distorting the information. The approach also proves that the symbolic representation effectively handles noise and degradations, including low resolution, specular reflection, and occlusion of eyelids present in the eye images and uses minimum number of features to represent iris image. This system can be implemented in various fields such as banking, security concern areas and many more. Major Canadian Airports have been using Iris recognition systems to expedite passengers through customs

Key-Words / Index Term: Biometric System, IRIS recognition, Savitzky-Golay Filter, Eye Lids, Feature Extraction.

References

  1. López, F. R. J., et al. (2013). Biometric Iris Recognition Using Hough Transform. IEEE.
  2. Dehkordi, A. B., et al. (2013). Noise Reduction in Iris Recognition Using Multiple Thresholding. International Conference on Signal and Image Processing Applications. IEEE.
  3. Thirumurugan, P., et al. (2014). Iris Recognition Using Wavelet Transformation Techniques. International Journal of Computer Science and Mobile Computing, 3(1).
  4. Kaur, N., & Juneja, M. (2014). A Review on Iris Recognition. IEEE.
  5. Khatun, A., Haque, A. K. M. F., et al. (2015). Design and Implementation of Iris Recognition Based Attendance Management System. IEEE.
  6. Trokielewicz, M., et al. (2016). Iris Recognition with a Database of Iris Images Obtained in Visible Light Using Smartphone Camera. IEEE.
  7. Solanke, S. B., et al. (2016). Biometrics: Iris Recognition System, A Study of Promising Approaches for Secure Authentication. IEEE.
  8. Jagadeesh, N., et al. (2017). Iris Recognition System Development Using MATLAB. International Conference on Computing Methodologies and Communication. IEEE.
  9. Montgomery, T. (n.d.). The Eye – Iris. Retrieved from http://www.tedmontgomery.com/the_eye/iris.html
  10. Ghazi94. (n.d.). IRIS-Segmentation (GitHub repository). Retrieved from https://github.com/ghazi94/IRIS-Segmentation
  11. Jillela, R. R., et al. (2014). Segmenting Iris Images in the Visible Spectrum with Applications in Mobile Biometrics. Pattern Recognition Letters.
  12. Mattoo, I. A., & Agarwal, P. (2017). Iris Biometric Modality: A Review. Oriental Journal of Computer Science and Technology (OJCST).
  13. Geektimes. (n.d.). How Iris Recognition Works. Retrieved from https://geektimes.ru/post/247634/

Citation

Monika Singh, Sanjeev Kumar Sharma "A Review on Implementation of Biometric Iris Recognition" International Journal of Scientific Research in Technology & Management, Vol.1, Issue.1, pp.14-19, 2021.

A Review on Different Techniques of Parking Automation

Roma Jain, Jijo S. Nair

Research Paper | Journal Paper

Vol.1, Issue.1, pp.20-24, Dec-2021

Abstract References Citation Download

Abstract

In today's era, the problem of parking is also increasing due to the increase in the number of vehicles. For this, a reliable and accurate system is required which is able to manage parking by detecting the vacant slots and maintains the occupancy of slot. From the last decade, there are various researches took place with an objective to develop an ideal automatic parking slot system. The systems which have been designed so far having various flaws in different criteria. This paper has been designed for reviewing various techniques which have been used for automated parking slot detection till now. The existing systems lack somewhere to implement an effective system that would be cost effective. The proposed system is able to recognize free parking space as well as occupied parking using OpenCV. OpenCV is the latest computer vision technique or a library through which a system can be developed with high level of accuracy. Proposed system is based on Laplacian Edge Detection method which is able to recognize the occupied and free space for smart parking which may reduces the human efforts. This system is also useful for alarming if a vehicle is parked in no parking area and inform about the parking space availability at real time with high level of accuracy. In the field of intelligent vehicle and parking management system, accuracy is often important as human convenience required. It is required to get accurate outcomes at real time through which an intelligent parking slot or space detection can be implemented with newly introduced technique.

Key-Words / Index Term: Automatic Parking, Smart Parking, OpenCV, Space Detection, Edge, Computer Vision.

References

  1. Suhr, J. K., & Jung, H. G. (2012, September 16–19). Fully-automatic recognition of various parking slot markings in around view monitor (AVM) image sequences. 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, Alaska, USA. IEEE.
  2. Suhr, J. K., & Jung, H. G. (2013). Sensor fusion-based vacant parking slot detection and tracking. IEEE Transactions on Intelligent Transportation Systems. IEEE.
  3. Šolić, P., et al. (2015). RFID-based efficient method for parking slot car detection. IEEE.
  4. Suhr, J. K., & Jung, H. G. (2016). Automatic parking space detection and tracking for underground and indoor environments. IEEE Transactions on Industrial Electronics. IEEE.
  5. Malarvizhi, K., Kayathiri, A., & Subadra, K. G. (2017). Survey paper on vehicle parking slot detection using Internet of Things. International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC). IEEE.
  6. Mendiratta, S., Dey, D., & Sona, D. R. (2017). Automatic car parking system with visual indicator along with IoT. IEEE.
  7. Bibi, N., & Majid, M. N. (2017). Automatic parking space detection system. 2nd International Conference on Multimedia and Image Processing. IEEE.
  8. Chen, J. Y. (2017, October 5–8). A visual method for the detection of available parking slots. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff Center, Banff, Canada. IEEE.
  9. Li, L., & Li, C. (2017). Automatic parking slot detection based on around view monitor (AVM) systems. IEEE.

Citation

Roma Jain, Jijo S. Nair "A Review on Different Techniques of Parking Automation" International Journal of Scientific Research in Technology & Management, Vol.1, Issue.1, pp.20-24, 2021.

Automatic Facial Mask Rule Violation Detection: A Review

Eayan Francis, R.K. Chidar

Research Paper | Journal Paper

Vol.1, Issue.1, pp.25-29, Dec-2021

Abstract References Citation Download

Abstract

As per the concern of COVID-19 spread; there are several precautions through which it can be reduced either following social distancing and wearing facial mask. Facial mask is mandatory to keep away from virus droplets that also help to reduce the positive cases. Facial mask detection is bit challenging for the researchers because it has distinct postures of wearing mask that may correct or incorrect. System is to detect all these postures and declare if it has violations or not. There are various researches have been done in this field but they didn’t met the desired accuracy because of partial conditions of wearing masks. This paper intended to review the implemented researches where system suffers somehow. Most of the systems are based on Convolutional neural network and uses precompiled library for detecting facial mask automatically for real time or offline benchmarks.

Key-Words / Index Term: COVID-19, Facial Mask, Convolutional Neural Network, Classifiers, Machine Learning, Image Processing, Pattern Recognition.

References

  1. World Health Organization. (2020). Coronavirus disease 2019 (COVID-19): Situation report 205.
  2. Centers for Disease Control and Prevention. (2020). Coronavirus Disease 2019 (COVID-19) – Symptoms. [Online]. Available: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html
  3. Centers for Disease Control and Prevention. (2020). Coronavirus — Human Coronavirus Types. [Online]. Available: https://www.cdc.gov
  4. Das, M., Ansari, W., & Basak, R. (2020). Covid-19 face mask detection using TensorFlow, Keras and OpenCV. 2020 IEEE 17th India Council International Conference (INDICON), pp. 1–5. doi:10.1109/INDICON49873.2020.9342585
  5. World Health Organization. (2020). Advice on the use of masks in the context of COVID-19: Interim guidance.
  6. Bu, W., Xiao, J., Zhou, C., Yang, M., & Peng, C. (2017). A cascade framework for masked face detection. 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) & IEEE Conference on Robotics, Automation and Mechatronics (RAM), pp. 458–462. doi:10.1109/ICCIS.2017.8274819
  7. Jian, W., & Lang, L. (2021). Face mask detection based on Transfer Learning and PP-YOLO. 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). doi:10.1109/ICBAIE52039.2021.9389
  8. Negi, A., Kumar, K., Chauhan, P., & Rajput, R. S. (2021). Deep neural architecture for face mask detection on simulated masked face dataset against COVID-19 pandemic. 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). doi:10.1109/ICCCIS51004.2021.9397
  9. Srinivasan, S., Singh, R. R., Biradar, R. R., & Revathi, S. (2021). COVID-19 monitoring system using social distancing and face mask detection on surveillance video datasets. 2021 International Conference on Emerging Smart Computing and Informatics (ESCI). doi:10.1109/ESCI50559.2021.939678
  10. Venkateswarlu, I. B., Kakarla, J., & Prakash, S. (2020). Face mask detection using MobileNet and Global Pooling Block. 2020 IEEE 4th Conference on Information & Communication Technology (CICT). doi:10.1109/CICT51604.2020.931208
  11. Vijitkunsawat, W., & Chantngarm, P. (2020). Study of the performance of machine learning algorithms for face mask detection. 2020 5th International Conference on Information Technology (InCIT). doi:10.1109/INCIT50588.2020.93109
  12. Vinh, T. Q., & Anh, N. T. N. (2020). Real-time face mask detector using YOLOv3 algorithm and Haar Cascade Classifier. 2020 International Conference on Advanced Computing and Applications (ACOMP). doi:10.1109/ACOMP50827.2020.00029
  13. Xu, M., Wang, H., Yang, S., & Li, R. (2020). Mask wearing detection method based on SSD-Mask algorithm. 2020 International Conference on Computer Science and Management Technology (ICCSMT). doi:10.1109/ICCSMT51754.2020.0003
  14. Ejaz, M. S., & Islam, M. R. (2019). Masked face recognition using convolutional neural network. 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), pp. 1–6. doi:10.1109/STI47673.2019.9068044
  15. Asif, S., Wenhui, Y., Tao, Y., Jinhai, S., & Amjad, K. (2021). Real-time face mask detection system using transfer learning with machine learning method in the era of COVID-19 pandemic. 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 70–75. doi:10.1109/ICAIBD51990.2021.9459008

Citation

Eayan Francis, R.K. Chidar "Automatic Facial Mask Rule Violation Detection: A Review" International Journal of Scientific Research in Technology & Management, Vol.1, Issue.1, pp.25-29, 2021.