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Archive Issue – Vol.3, Issue.3 (JUl-Sep 2023)

Archive Issue – Vol.3, Issue.3 (July-September 2023)


A Review on Privacy Preservation over Data Leakage in Cloud

Sanjay Kumar Sharma, Arun Pratap Singh

Research Paper | Journal Paper

Vol.3, Issue.3, pp.1-05, Sep-2023

Abstract References Citation Download

Abstract

Cloud centered computing is the conclusion of acceptance and development of present-day technologies and prototypes. There are various researches take place to achieve external and internal reviews of cloud security. The information should be conserved and protectively accessible. The dissimilar safety disputes in cloud are heterogeneity, scalability, Data Truthfulness, Data Intrusion, Non- Disclaimer, Concealment, access control, authentication and authorization. Confidentiality of information data is additional safety issue connected with cloud computing environment. The motive of this paper is to review various techniques which have been proposed till now in the reference of cloud data security along with comparing their techniques. This paper also imputes the advantages and disadvantages of data accessibility through cloud and issues related to the databases.

Key-Words / Index Term: Cloud Computing, Cloud Security, Data Concealment, Data Encryption, SQL, Data Protection.

References

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Citation

Sanjay Kumar Sharma, Arun Pratap Singh "A Review on Privacy Preservation over Data Leakage in Cloud" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.3, pp.1-05, 2023.

Image Restoration Using Deep Learning: A Comprehensive Study

Arun Pratap Singh, Sanjay Kumar Sharma

Research Paper | Journal Paper

Vol.3, Issue.3, pp.06-10, Sep-2023

Abstract References Citation Download

Abstract

Image restoration using deep learning has become an essential research direction in computer vision, enabling the recovery of high-quality images from degraded observations caused by noise, blur, low resolution, and compression artifacts. Traditional approaches relied on handcrafted priors and optimization-based algorithms, but these lacked the adaptability to complex and diverse degradations. With the emergence of convolutional neural networks (CNNs), generative adversarial networks (GANs), and more recently transformers, deep learning models have demonstrated significant improvements in tasks such as denoising, deblurring, super-resolution, and inpainting. These models learn end-to-end mappings directly from data, ensuring robust and perceptually convincing results across varied application domains, including medical imaging, surveillance, and satellite vision. This paper explores the state-of-the-art methods in image restoration using deep learning, their limitations, and promising directions for future advancements.

Key-Words / Index Term: Image Restoration, Deep Learning, CNN, GAN, Image Denoising, Image Deblurring, Super-Resolution, Inpainting.

References

  1. Dong, C., Loy, C. C., & Tang, X. (2016). Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 295–307.
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  3. Kupyn, O., Budzan, V., Mykhailych, V., & Makarov, V. (2018). DeblurGAN: Blind motion deblurring using conditional adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 8183–8192.
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  12. Ji, H., & Lu, G. (2021). U2-Former: A nested U-shaped transformer for image restoration. arXiv preprint arXiv:2112.02279.
  13. Zhang, K., & Zuo, W. (2020). Plug-and-play image restoration with deep denoiser prior. arXiv preprint arXiv:2008.13751.
  14. Zamir, S. W., & Yang, M.-H. (2021). Restormer: Efficient transformer for high-resolution image restoration. arXiv preprint arXiv:2111.09881.
  15. Chen, Z., & Zhang, Y. (2022). Cross aggregation transformer for image restoration. arXiv preprint arXiv:2211.13654.
  16. Wang, Q., & Zhang, L. (2024). A versatile wavelet-enhanced CNN-transformer for image restoration. Journal of Visual Communication and Image Representation, 80, 103401.
  17. Saini, M. M. K. (2025). Image restoration and enhancement by adapting CNN. ALAP Research Journal, 93, 75–171.
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  19. Wikipedia contributors. (2025). Residual neural network. Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Residual_neural_network
  20. Wikipedia contributors. (2025). Deep image prior. Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Deep_image_prior
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  22. Wikipedia contributors. (2025). Shrinkage Fields (image restoration). Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Shrinkage_Fields_(image_restoration)
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Citation

Arun Pratap Singh, Sanjay Kumar Sharma, "Image Restoration Using Deep Learning: A Comprehensive Study" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.3, pp.06-10, 2023.

A Review on Automatic Social Distancing Rule Violation Detection

Ravi Kumar, Mashhood Siddiqui

Research Paper | Journal Paper

Vol.3, Issue.3, pp.11-16, Sep-2023

Abstract References Citation Download

Abstract

Social distancing implies remaining at home and away from others as much as possible to prevent the spread of COVID-19. It encourages online communication instead of in-person contact. With communities reopening, the term "physical distancing" reinforces maintaining at least 6 feet of distance from others and wearing face masks. This paper reviews various research works on automatic social distancing detection in public places using computer vision techniques. Several studies utilize machine learning, deep learning, CNNs, and object detection frameworks to detect and alert social distancing violations.

Key-Words / Index Term: Social Distancing Rule Violation Detection, Physical Distancing, Machine Learning, Coronavirus, Computer Vision, Artificial Neural Network, TensorFlow.

References

    1. L. Hensley, “Social distancing is out, physical distancing is in—here’s how to do it,” Global News–Canada (27 March 2020), 2020.
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    7. Das, S. et al., “Computer Vision-based Social Distancing Surveillance Solution with Optional Automated Camera Calibration for Large Scale Deployment,” arXiv abs/2104.10891, 2021.
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Citation

Ravi Kumar, Mashhood Siddiqui, "A Review on Automatic Social Distancing Rule Violation Detection" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.3, pp.11-16, 2023.

A Review on Automatic Covid-19 Lung Infection Detection from Different Imaging Techniques

Sangeeta Singh, Prashant Kumar Jain

Research Paper | Journal Paper

Vol.3, Issue.3, pp.17-22, Sep-2023

Abstract References Citation Download

Abstract

Corona Virus Disease-2019 commonly known as COVID-19 which has been defined by the Novel Corona Virus. It is a family of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) and was first detected during respiratory outbreak. It was first reported to the World Health Organization on December 31, 2019. On January 30, 2020, the World Health Organization declared the COVID-19 eruption a global health emergency. As of 27-May-2021 169,095,283 confirmed cases have been reported in the world and 2,73,67,935 cases in India. It is required to identify the infection with high precision rate but there are lots of deficiency in the diagnosing system that may resulted false alarm rate. Initially it could be detected through throat saliva but now it can also be identified thought the impairment in lungs from computerized tomographical imaging technique. This paper reviewed various researches over COVID-19 diagnosis approach as well as the syndrome in respiratory organs. There are so many imaging techniques through which lungs impairments can be detected that may diagnose COVID-19 with high level of accuracy. CT scan image is the best alternative for diagnosing COVID-19.

Key-Words / Index Term: COVID-19, Lesion Detection, Deep Learning, CT Scans, Segmentation, SARS, Lung Tomographical Image.

References

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Citation

Sangeeta Singh, Prashant Kumar Jain "A Review on Automatic Covid-19 Lung Infection Detection from Different Imaging Techniques" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.3, pp.17-22, 2023.