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Archive Issue – Vol.4, Issue.3 (Jul-Sep 2024)

Archive Issue – Vol.4, Issue.3 (July-September 2024)


Text-to-Image Conversion: Advancements, Challenges, and Future Directions

Arun Pratap Singh, Sanjay Kumar Sharma

Research Paper | Journal Paper

Vol.4, Issue.3, pp.1-04, Sep-2024

Abstract References Citation Download

Abstract

Text-to-image conversion, an emerging domain in artificial intelligence (AI), focuses on synthesizing realistic images from natural language descriptions. This research paper explores the fundamental principles, recent advancements, and challenges associated with text-to-image conversion. Leveraging deep learning models, particularly generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models, researchers have made significant progress in bridging the gap between textual semantics and visual representation. Despite these advancements, challenges such as maintaining semantic accuracy, generating high-resolution images, and addressing biases persist. This paper presents a comprehensive study of related works, problem statements, proposed methodologies, results, and potential future directions in the field.

Key-Words / Index Term: Text-to-Image Generation, Deep Learning, Generative Adversarial Networks (GANs), Diffusion Models, Natural Language Processing (NLP), Computer Vision.

References

      1. Mirza, M., & Osindero, S. (2014). Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784.
      2. Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. N. (2017). StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV).
      3. Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., & He, X. (2018). AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
      4. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the International Conference on Machine Learning (ICML).
      5. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv preprint arXiv:2204.06125.
      6. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Citation

Arun Pratap Singh, Sanjay Kumar Sharma, "Text-to-Image Conversion: Advancements, Challenges, and Future Directions" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.3, pp.1-04, 2024.

A Review on Unusual Activity Detection for Prior Appraisal against Crime

Swati Bhargava, Arun Jhapate

Research Paper | Journal Paper

Vol.4, Issue.3, pp.05-08, Sep-2024

Abstract References Citation Download

Abstract

Unusual activities on public areas and personal safety are seriously endangered. Millions of video surveillance systems are used in public areas, such as roads, prisons, holy sites, airports, and supermarkets. Video surveillance cameras are not intelligent enough to detect unusual activities even in real time. It is necessary to investigate the detection and recognition of the contents of suspicious activities from surveillance video. It is necessary to identify crook status in real time from video surveillance for quick and immediate management. The purpose of this paper is to review various implemented systems and their drawbacks. Most systems use Gaussian filters to classify objects according to gestures identified from a video or frame. Some systems are based on background or foreground subtraction that work with two layers; The first one is the background and the second is the foreground. But these systems are good enough for simple backgrounds or less crowded areas. These systems are recognizing many more basic things such as walking, sitting, running, waving hands, clapping, and classifying unusual activities. A system is required that must be intelligent enough to recognize unusual activities from a crowded area in real time.

Key-Words / Index Term: Unusual Activity Recognition, Gaussian Filter, Foreground, Video Surveillance.

References

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      3. H. Xu, C. Yuan, P. Li, and Y. Wang, “Design and Implementation of Action Recognition System Based on RFID Sensor,” in Proc. 2017 13th Int. Conf. on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE, 2017.
      4. V. S. Nanaware, M. H. Nerkar, and C. M. Patil, “A Review of the Detection Methodologies of Multiple Human Tracking & Action Recognition in a Real Time Video Surveillance,” in Proc. IEEE Int. Conf. on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 2017.
      5. J. Li, H. Wu, and X. Zhou, “PeMapNet: Action Recognition from Depth Videos Using Pyramid Energy Maps on Neural Networks,” in Proc. 2017 Int. Conf. on Tools with Artificial Intelligence, IEEE, 2017.
      6. N. E. D. Elmadany, Y. He, and L. Guan, “Information Fusion for Human Action Recognition via Biset/Multiset Globality Locality Preserving Canonical Correlation Analysis,” IEEE Trans. Image Process., 2018.
      7. K. Aouaidjia, B. Sheng, P. Yang, P. Li, R. Shen, and D. D. Feng, “Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures,” IEEE Trans. Systems, Man, and Cybernetics: Systems, 2018.
      8. S. Sen, M. Dhar, and S. Banerjee, “Implementation of Human Action Recognition Using Image Parsing Techniques,” in Proc. 2018 Emerging Trends in Electronic Devices and Computational Techniques (EDCT), IEEE, 2018.
      9. Cornell University, “Human Activities Dataset,” Available: http://pr.cs.cornell.edu/humanactivities/.
      10. SlidePlayer, “Human Activity Recognition,” Available: https://slideplayer.com/slide/6932185/.
      11. Quora, “What is the easiest way to recognise gestures in OpenCV using Python?” Available: https://www.quora.com/What-is-the-easiest-way-to-recognise-gestures-in-OpenCV-using-Python.
      12. Frontiers, “Action Recognition Image Example,” Available: https://www.frontiersin.org/.../frobt-02-00028-g008.jpg.
      13. Semantic Scholar, “Motion Influence Map for Unusual Human Activity and Crowd Behavior Analysis,” Available: Link.
      14. HVSS Events, “Intro Concealed Carry,” Available: https://hvss.eventsmart.com/events/intro-concealed-carry/hqdefault/.
      15. YouTube, “Gesture Recognition Example,” Available: https://www.youtube.com/watch?v=l1Ui4U-1dNw.
      16. A. Ghasemi and C. N. R. Kumar, “A Novel Algorithm to Predict and Detect Suspicious Behaviors of People at Public Areas for Surveillance Cameras,” in Proc. Int. Conf. on Intelligent Sustainable Systems (ICISS), IEEE, 2017. [Online]. Available: IEEE Xplore, ISBN: 978-1-5386-1959-9.

Citation

Prakash Chandr Nandanwar, Ankur Taneja, "A Review on Unusual Activity Detection for Prior Appraisal against Crime" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.3, pp.05-08, 2024.

A Review on Automatic Lung Lesion Detection from Various Imaging Techniques

Ayush Bhargava, Madhu Shandilya, Vijayshri Chaurasia

Research Paper | Journal Paper

Vol.4, Issue.3, pp.09-14, Sep-2024

Abstract References Citation Download

Abstract

Lung cancer is horrible disease that may takes human life. It is the most diagnosed cancer in the world that may considered as life threatening disease. If it can be diagnosed earlier then treatment is a solution that may saves human life. There are various imaging techniques through which this disease can be diagnosed and treated accordingly. But Computed Tomographical image commonly known as CT scan image is the better option for diagnosing disease with better level of accuracy as compare to the other imaging techniques such as X-Ray, Ultrasound and many more. If a disease can be diagnosed automatically then it saves medical professional time as well as human life. A routine checkup can become easier and it can be processed in less time. The intension of this paper is to review various previously implemented systems related to the automatic diagnosis of lung cancer. There are so many researches have been done that are based on CNN, DNN, Edge Detection and various image processing or machine learning techniques. The objective of this paper is to define the limitations and drawbacks of various systems that are lacking somewhere.

Key-Words / Index Term: Lung Cancer Detection, Lesion Classification, Imaging Techniques, CNN, DNN, X-Ray, CT Scan.

References

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    17. R. K. Samala, H.-P. Chan, C. Richter, L. Hadjiiski, C. Zhou, and J. Wei, “Analysis of deep convolutional features for detection of lung nodules in computed tomography,” in Proc. SPIE Medical Imaging: Computer-Aided Diagnosis, vol. 10950, p. 109500Q, 2019.
    18. D. Kumar, A. Wong, and D. A. Clausi, “Lung nodule classification using deep features in CT images,” in Proc. Conf. Computer and Robot Vision, pp. 133–138, 2015.
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Citation

MAyush Bhargava, Madhu Shandilya, Vijayshri Chaurasia, "A Review on Automatic Lung Lesion Detection from Various Imaging Techniques" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.3, pp.09-14, 2024.

Automatic Diabetic Retinopathy Disease Diagnosis from Fundus Imaging: A Review

Mohini Rathore, Madhu Shandilya

Research Paper | Journal Paper

Vol.4, Issue.3, pp.15-21, Sep-2024

Abstract References Citation Download

Abstract

In recent decades, there are many cases can be observed which have been affected by Diabetes. This is the most common disease that can be found in people. If a person having this disease from a long time then that person may also suffer from Diabetic Retinopathy; due to that a person may lost his vision partially or completely as per the condition of the retina or how much tissues have been damaged. Diabetic Retinopathy is a disease that cannot be cured and there is no treatment to repair the retina or vision optics. It only can be prevented by taking care of it with routine checkup from medical professionals. It blocks the blood flow towards retina, due to that; blood vessels get swell and exudates started leaking that may cause partial or complete blindness. The intension of this paper is to review various researches which have been done in the field of diabetic retinopathy. Diabetic retinopathy can be automatically diagnosed through fundus imaging and there are many approaches have been made for pertaining better level of accuracy with minimal error rate. System compares machine learning approaches, classifiers and edge detection techniques that have been used for implementing Automatic Diabetic Retinopathy Disease Diagnosis.

Key-Words / Index Term: Automatic Diabetic Retinopathy Diagnosis, Fundus Imaging, Optic Disc, Optic Cup, CNN, Retinal Image, Hemorrhages.

References

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Citation

Mohini Rathore, Madhu Shandilya, "Automatic Diabetic Retinopathy Disease Diagnosis from Fundus Imaging: A Review" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.3, pp.15-21, 2024.