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Archive Issue – Vol.2, Issue.2 (April-June 2022)

Archive Issue – Vol.2, Issue.2 (April-June 2022)


COVID-19: Automatic Social Distancing Rule Voilation Detection using PP-Yolo & Tensorflow in OpenCV

Srishti Verma, Prashant Kumar Jain

Research Paper | Journal Paper

Vol.2, Issue.2, pp.1-06, June-2022

Abstract References Citation Download

Abstract

In this critical situations where people are fighting with dangerous pandemic disease; it is required to maintain the situation by indulging with social distancing or it can also be pronounced as physical distancing. Social or physical distancing may reflects to reduce the virus from spreading. There are several places where it should be followed properly to stop spreading COVID-19 like railway stations, malls, marts, airports and many more. It is advised to maintain at least 6 feet of social distancing as per the WHO guidelines. Various researches have been done to automatically detect the physical distancing violations but an ideal system should be available to detect it effectively with high level of accuracy. Here the system is based on PP-Yolo (PaddlePaddle – You only look once) and Tensorflow library. Tensorflow is an object detection or pattern recognition tool through which pedestrian can be detected automatically and then PP-Yolo classifies the distance between the pedestrians or classifying whether persons are following the physical distancing rule or not. Violation detection is bit challenging for any system because a crowd may have uncertain structures that can hardly classified distance among them. This challenge can be accepted through various researchers but not met the desired precision. Proposed system is intended to detect the physical distancing rule violations effectively and acquiring high level of accuracy with minimal false alarm rate.

Key-Words / Index Term: Physical Distancing Rule Violation Detection, Social Distancing, PP-Yolo, Machine Learning, Tensorflow, Computer Vision Library, CORONA Virus, Artificial Neural Network.

References

    • World Health Organization. “Corona Viruses -19 (COVID-19).” WHO Website , [Online; accessed November 26, 2021].
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    • Hopkins Medicine. “What is Social distancing.” Johns Hopkins Medicine , 2020, [Online; accessed November 15, 2021].
    • Singh Punn, Sanjay Kumar, Sonali, & Rai. (2020). Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques.
    • Sreetama, Anirban, Dhruba, Ramswaroop, Aravind, Sujit, & Guruprasad. (2021). Computer Vision-based Social Distancing Surveillance Solution with Optional Automated Camera Calibration for Large Scale Deployment.
    • Hendra, Edi, Suryadi, & Jalu. (2020). Physical Distancing Monitoring with Background Subtraction Methods. International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications, pp. 45–50.
    • Zhenfeng, Gui, Jiayi, Zhongyuan, Jiaming, & Deren. (2021). Real-time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic. IEEE Transactions on Multimedia.
    • Afiq, Norliza, & Mohd Fuad. (2020). Person Detection for Social Distancing and Safety Violation Alert based on Segmented ROI. 10th IEEE International Conference on Control System, Computing and Engineering, pp. 113–118.
    • Savyasachi, Rudraksh, Goutham, Shreyas, & Aniruddha. (2020). SD-Measure: A Social Distancing Detector. 12th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 306–311.
    • Yew, Mohd Zafri, Salman, & Sumayyah. (2020). Social Distancing Detection with Deep Learning Model. 8th International Conference on Information Technology and Multimedia, pp. 334–338.
    • Mohd Aquib, & Dushyant. (2021). Monitoring social distancing through human detection for preventing/reducing COVID spread. International Journal of Information Technology, 13, 1255–1264.
    • Z. Chen et al. (2021). Autonomous Social Distance in Urban Environments Using a Quadruped Robot. IEEE Access, vol. 9, pp. 8392–8403.
    • Qian, Meirui, & Jiang, Jianli. (2020). COVID-19 & Physical distancing. Journal of Public Health.

Citation

Srishti Verma, Prashant Kumar Jain "COVID-19: Automatic Social Distancing Rule Voilation Detection using PP-Yolo & Tensorflow in OpenCV" International Journal of Scientific Research in Technology & Management, Vol.2, Issue.2, pp.1-06, 2022.

Decisive Gaming CAPTCHA using Game Theory

Apoorva Dubey, Meenakshi Patel

Research Paper | Journal Paper

Vol.2, Issue.2, pp.07-12, June-2022

Abstract References Citation Download

Abstract

In this advanced period, web security is frequently expected to be careful from fraudulent activities. There are a few programmers who attempt to fabricate a program that can communicate with site pages automatically and attempt to break the information or make a few garbage passages because of that web servers get hanged. To stop the garbage passages; CAPTCHA is an answer through which bots can be distinguished and denied the machine-based program to intercede with. CAPTCHA stands for Completely Automated Public Turing test to tell Computers and Humans Apart. In the movement of CAPTCHA; there are a few techniques accessible, for example, distorted text, picture recognition, math solving, and gaming-based CAPTCHA. Game-based Turing test is a lot well known now daily however there are a few strategies through which the game can be broken on the grounds that game isn't intellectual. In this way, there is an expected characteristic of CAPTCHA. The proposed framework depends on the Intrinsic Decision-based Situation Reaction Challenge. The proposed framework can more readily characterize the humans and bots by its inborn issue. It has been considered as a human is fit to manage the genuine issues and machine is bit poor to understand what is happening or the way that the issue can be settled. Thus, proposed framework challenges with basic circumstances which is simpler for human yet extremely difficult for bots. Human is expected to utilize his presence of mind just and the issue can be addressed with few moments.

Key-Words / Index Term: CAPTCHA, Web Security, Turing Test, Reaction Test, Game Theory, Hard AI Problem, Bots.

References

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  6. S. Ezhilarasi and P. U. Maheswari. Image Recognition and Annotation based Decision Making of CAPTCHAs for Human Interpretation. 2020 International Conference on Innovative Trends in Information Technology (ICITIIT), 2020, pp. 1–6. doi:10.1109/ICITIIT49094.2020.9071558
  7. Aldwairi, M., Mohammed, S., & Padmanabhan, M. L. (2020). Efficient and secure flash-based gaming CAPTCHA. Journal of Parallel and Distributed Computing. doi:10.1016/j.jpdc.2020.03.020
  8. Cakmak, Ahmet & Balcilar, Muhammet. (2019). Audio Captcha Recognition Using RastaPLP Features by SVM.
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  14. Aadhirai R, Sathish Kumar P J and Vishnupriya S. Image CAPTCHA: Based on Human Understanding of Real World Distances. Proceedings of 4th International Conference on Intelligent Human Computer Interaction, IEEE 2012.
  15. Ibrahim Furkan Ince, Yucel Batu Salman, Mustafa Eren Yildirim and Tae-Cheon Yang. Execution Time Prediction For 3D Interactive CAPTCHA By Keystroke Level Model. Fourth International Conference on Computer Sciences and Convergence Information Technology, IEEE 2009.
  16. JingSong Cui, LiJing Wang, JingTing Mei, Da Zhang, Xia Wang, Yang Peng, WuZhou Zhang. CAPTCHA Design Based on Moving Object Recognition Problem. IEEE, 2009.
  17. Jing-Song Cui, Jing-Ting Mei, Xia Wang, Da Zhang, Wu-Zhou Zhang. A CAPTCHA Implementation Based on 3D Animation. International Conference on Multimedia Information Networking and Security, IEEE 2009.
  18. S. Bera, S. Misra, J. J. P. C. Rodrigues. Cloud computing applications for smart grid: A survey. IEEE Transactions on Parallel and Distributed Systems, 26 (5) (2015) 1477–1494. doi:10.1109/TPDS.2014.2321378
  19. N. Komninos, E. Philippou, A. Pitsillides. Survey in smart grid and smart home security: Issues, challenges and countermeasures. IEEE Communications Surveys & Tutorials, 16 (2014) 1933–1954. doi:10.1109/COMST.2014.2320093
  20. S. Asri, B. Pranggono. Impact of Distributed Denial-of-Service Attack on Advanced Metering Infrastructure. Wireless Personal Communications, 83 (3) (2015) 2211–2223. doi:10.1007/s11277-015-2510-3
  21. T. Baker, B. Aldawsari, M. Asim, H. Tawfik, Z. Maamar, R. Buyya. Cloud-senergy: A bin-packing based multi-cloud service broker for energy efficient composition and execution of data-intensive applications. Sustainable Computing: Informatics and Systems, 19 (2018) 242–252. doi:10.1016/j.suscom.2018.05.011
  22. B. Al-Duwairi, A. Al-Hammouri, M. Aldwairi, V. Paxson. Gflux: A google-based system for fast flux detection. 2015 IEEE Conference on Communications and Network Security (CNS), pp. 755–756. doi:10.1109/CNS.2015.7346920
  23. M. Aldwairi, A. M. Abu-Dalo, M. Jarrah. Pattern matching of signature-based IDS using Myers algorithm under MapReduce framework. EURASIP Journal on Information Security, 2017 (1), 9. doi:10.1186/s13635-017-0062-7
  24. M. Shirali-Shahreza, S. Shirali-Shahreza. Question-based CAPTCHA. Conference on Computational Intelligence and Multimedia Applications, 2007, Vol. 4, pp. 54–58. PDF on ResearchGate . Accessed 16 July 2022.

Citation

Apoorva Dubey, Meenakshi Patel, "Decisive Gaming CAPTCHA using Game Theory" International Journal of Scientific Research in Technology & Management, Vol.2, Issue.2, pp.07-12, 2022.

Automatic Soil Classification using Polynomial Support Vector Machine

Abhishek Dubey, Shrikant Zade

Research Paper | Journal Paper

Vol.2, Issue.2, pp.13-20, June-2022

Abstract References Citation Download

Abstract

Soil classification is an approach that can classifies the soil on the basis of its texture. Geographically there are various types of soil present in the earth that can be classified on the basis of its patterns and physical characteristics. There are various combinations of soil properties available such as- its structure, color, texture and porosity. In machine learning approaches; machines target these factors and train the model accordingly to classify the soil type. Many of the systems are based on machine learning approaches where they use deep neural networks. But the problem with the deep neural network is that if utilized network has been not used then weight of the network might be bulky that increase the size of the network and system get slower to execute and training and testing may take long time. The proposed system is based on polynomial support vector machine (P-SVM) that can classify the soil by dealing with its non-linear textures or data. SVM has great potential to classify the grouped cluster on the basis of their patterns. Patterns are the feature mapping that may belong from different particles. For other classification algorithm; it is difficult to draw a hyper plane for non-linear data but SVM can classifies the data by transforming it to the linear data and then hyper plane can be drawn easily. It has been managed through kernel trick where high dimensional feature space can be mapped. SVM can also solve the optimization problem by utilizing its polynomial feature. System perceived high level of accuracy as compare to the previous model. System pertained __ percent of accuracy.

Key-Words / Index Term: Soil Classification, Soil Identification, Image Processing, Textural Data, Soil Analysis, Feature Extraction, Clustering.

References

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Citation

Abhishek Dubey, Shrikant Zade "Automatic Soil Classification using Polynomial Support Vector Machine" International Journal of Scientific Research in Technology & Management, Vol.2, Issue.2, pp.13-20, 2022.

Automatic Indian Sign Language Recognition using Sobel Edge Detection

Shrikant Singh, Devendra Rewadikar, Ankur Taneja

Research Paper | Journal Paper

Vol.2, Issue.2, pp.20-25, June-2022

Abstract References Citation Download

Abstract

Sign language is a visual language with its own grammar and gesture that bit differs from the spoken language. It is a hand and facial expression based language that generally used by dumb and deaf people to communicate with each other. Automatic sign language recognition is a challenging task where sign can be recognized by its gesture and body posture. Sign language is different in various countries with its own gesture assigned for visual communication. There are various methods that can achieve goal but the only difference is the precision rate that is directly proportional to the correct recognition and error rate. System can be prefabricated using Sobel Edge Detection with morphological dilation in an effective manner. Sobel Edge Detection is a modern edge detection tool that can sharply extract the outlines of any gesture in an image. Technically, it is a discrete discriminant operator, computing an estimate of the gradients of the image intensity function. At each point of the image, the result of the Sobel – Feldmann operator is either the corresponding gradient vector or the ideal of this vector. System uses various preprocessing or filters to enhance the subject visibility through which a gesture can easily recognizable. System achieved the accuracy as 92.00 % which is bit higher than the previous implementations.

Key-Words / Index Term: Sign Language Recognition, Indian Sign Language, Sobel Edge Detection, Morphological Dilation, Discriminant Operator.

References

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Citation

Shrikant Singh, Devendra Rewadikar, Ankur Taneja "Automatic Indian Sign Language Recognition using Sobel Edge Detection" International Journal of Scientific Research in Technology & Management, Vol.2, Issue.2, pp.20-26, 2022.

Touch-less Biometric Fingerprint Authentication

Anushri Chourasiya, Rakesh Pandey

Research Paper | Journal Paper

Vol.2, Issue.2, pp.27-31, Dec-2021

Abstract References Citation Download

Abstract

In recent years, there is new technique which has been introduced i.e. touch-less fingerprint authentication system which may replaces the biometric device or scanner which is considered as unhygienic as well as costly. As we know that authentication system possess by a combination of username and password and this combination may be hacked because anyone of your friends or relative may perform brute force attack by guessing your password which may related to your personal details. But fingerprint cannot be stolen or copied that is why fingerprint authentication is the best authentication system. Everyone has distinct fingerprint and no one can authenticate or unauthorized access without your existence. So, on having this feature biometric device or scanner has been introduced in past years and now it is to be replaced by the touch-less fingerprint authentication or recognition system. This recognition can be perform either by webcam or by hand held device i.e. mobile. Here the system which has been proposed in this paper is able to enhance your fingerprint image through camera’s features such as autofocus, conversion through 3D print to 2D or resolve the curved area to flat image through which features can be extracted and make authentication system more powerful and portable.

Key-Words / Index Term: Touch-less, Fingerprint, Mobile, Minutiae, Authentication.

References

  1. H. Ravi and S. K. Sivanath, “A novel method for touch-less fingerprint authentication,” IEEE Transactions, 2013.
  2. G. Vinoth Kumar, K. Prasanth, S. Govinth Raj, and S. Sarathi, “Fingerprint based authentication system with keystroke dynamics for realistic user,” IEEE Transactions, 2014.
  3. A. Mohan and S. P. Kodgire, “Touchless fingerprint recognition using MATLAB,” IEEE Transactions, 2014.
  4. R. D. Labati, “Toward unconstrained fingerprint recognition: A fully touchless 3-D system based on two views on the move,” IEEE Transactions, 2015.
  5. K. Tiwari and P. Gupta, “A touch-less fingerphoto recognition system for mobile hand-held devices,” IEEE Transactions, 2015.
  6. M. Piekarczyk and M. R. Ogiela, “On using palm and finger movements as a gesture-based biometrics,” IEEE Transactions, 2015.
  7. M. Piekarczyk and M. R. Ogiela, “Usability of the fuzzy vault scheme applied to predetermined palm-based gestures as a secure behavioral lock,” IEEE Transactions, 2015.
  8. E. Ozan, “Password-free authentication for social networks,” IEEE Transactions, 2017.

Citation

Anushri Chourasiya, Rakesh Pandey "Touch-less Biometric Fingerprint Authentication" International Journal of Scientific Research in Technology & Management, Vol.2, Issue.2, pp.27-31, 2022.