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Mishra, Akhilesh
- Resilience of Indian Banking Sector:A Macro Stress Testing of Selected Banks
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Affiliations
1 Department of Management Studies, Panipat Institute of Engineering & Technology, Samalkha, Panipat, Haryana, IN
1 Department of Management Studies, Panipat Institute of Engineering & Technology, Samalkha, Panipat, Haryana, IN
Source
International Journal of Education and Management Studies, Vol 7, No 4 (2017), Pagination: 594-597Abstract
The Indian banking sector is severely suffering with the problem of growing Non Performing Assets (NPAs) of banks. This unwanted growth has a direct impact on the profitability and liquidity of banks; it also poses threat on quality of assets and survival of banks. The economic growth scenario of the country needs a sound and resilient banking industry to support it. In this regard the present paper is an attempt to examine the health of banking industry with reference to the position of Capital Adequacy Ratio (CAR) and ability of banks to absorb various shocks. The paper provides an insight on the position of Capital Adequacy Ratio (CAR) of the top 5 public sector banks and top 3 private sector banks. Stress test sensitivity analysis has been used to analyze the CAR position of the banks considered for the study. A stress test is an analysis conducted under unfavorable economic scenarios which is designed to determine whether a bank has enough capital to withstand the impact of adverse developments. These tests are meant to detect weak spots in the banking system at an early stage, so that preventive action can be taken by the banks and regulators. Under the stress test sensitivity analysis of capital adequacy ratio is to be studied by imparting shocks to the NPA levels.Keywords
Capital Adequacy Ratio, Non-Performing Assets (NPAs), Basel Norms, Stress Test, Risk Weighted Assets.
- Induction of Chronic Renal Failure in Goats Using Cisplatin: A New Animal Model
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Toxicology International (Formerly Indian Journal of Toxicology), Vol 20, No 1 (2013), Pagination: 56-60Abstract
Cisplatin was administered at the dose rate of 30 mg m‑2 daily intravenously consecutive for 7 days in goats. Blood samples (2 ml) were collected from each goat at ‘0’ hr and then at weekly interval and centrifuged immediately at 3000 rpm for 20 min to separate plasma, which were used for estimation of blood urea nitrogen (BUN), plasma creatinine (CRT), gamma glutamyltransferase (γGT), and glomerular filtration rate (GFR). Total volume of urine of each goat was recorded, and 5 ml of urine samples were collected for estimation of GFR. Blood urea nitrogen started to increase significantly from 7 days post‑dosing and achieved a peak on day 14. Higher values persisted up to 91 days. Plasma creatinine level was significantly higher in all samples on day 7 onwards, and it was maintained up to day 91 post‑dosing compared to control samples (‘0’ day) whilst GFR declined significantly from day 7 and attained a minimum values on day 70. GFR was almost <60% up to 91 days. The signs like emaciation, loss of body weight, and oliguria were observed. The values of all 4 biomarkers showed a chronic renal failure in goats.Keywords
Blood urea nitrogen, chronic renal failure, cisplatin, GGT, glomerular filtration rate, goat, plasm creatinine- Feature Extraction Using AT-ConvLSTM Based Cultural Algorithm for Image Understanding
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Authors
Affiliations
1 Department of Electronics and Communication, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
2 Department of Computer Science and Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
3 Department of Electrical and Electronics Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
1 Department of Electronics and Communication, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
2 Department of Computer Science and Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
3 Department of Electrical and Electronics Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 1 (2023), Pagination: 3066-3072Abstract
This research presents a novel approach for feature extraction in image understanding, utilizing an AT-ConvLSTM-based Cultural Algorithm. The Proposed CA-AT-ConvLSTM leverages the power of deep learning through AT-ConvLSTM architecture while optimizing the feature extraction process using Cultural Algorithms. This synergistic approach enhances the efficiency and accuracy of image understanding tasks, making it suitable for a wide range of applications, from computer vision to pattern recognition. The experimental results demonstrate the superiority of the proposed technique over traditional methods, highlighting its potential in advancing the field of image analysis.Keywords
Feature Extraction, AT-ConvLSTM, Cultural Algorithm, Image Understanding, Deep learningReferences
- D.R. Sarvamangala and R.V. Kulkarni, “Convolutional Neural Networks in Medical Image Understanding: A Survey”, Evolutionary Intelligence, Vol. 15, No. 1, pp. 1-22, 2022.
- K. He and D. Shen, “Transformers in Medical Image Analysis”, Intelligent Medicine, Vol. 3, No. 1, pp. 59-78, 2023.
- Z. Salahuddin and P. Lambin, “Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods”, Computers in Biology and Medicine, Vol. 140, pp. 105111-105124, 2022.
- J.A. Richards, “Remote Sensing Digital Image Analysis”, Vol. 5, Springer, 2022.
- M. Bhende, S. Shinde and V. Saravanan, “Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection”, BioMed Research International, Vol. 2022, pp. 1-12, 2022.
- Y. Tang and A. Hatamizadeh, “Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20730-20740, 2022.
- B.H. Van Der Velden, K.G. Gilhuijs and M.A. Viergever, “Explainable Artificial Intelligence (XAI) in Deep Learning-based Medical Image Analysis”, Medical Image Analysis, Vol. 79, pp. 102470-102478, 2022.
- X. Chen and Y. Qiu, “Recent Advances and Clinical Applications of Deep Learning in Medical Image Analysis”, Medical Image Analysis, Vol. 79, pp. 102444-10249, 2022.
- S. Gupta, M.R. Abonazel and K.S. Babu, “Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer Disease-Based Neurodegenerative Disorders”, Computational and Mathematical Methods in Medicine, Vol. 2022, pp. 1-12, 2022.
- M. Adnan and H.R. Tizhoosh, “Federated Learning and Differential Privacy for Medical Image Analysis”, Scientific Reports, Vol. 12, No. 1, pp. 1953-1965, 2022.
- X. Li and M. Grzegorzek, “A Comprehensive Review of Computer-Aided Whole-Slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification and Detection Approaches”, Artificial Intelligence Review, Vol. 55, No. 6, pp. 4809-4878, 2022.
- R. Qin and T. Liu, “A Review of Landcover Classification with very-High Resolution Remotely Sensed Optical Images-Analysis Unit, Model Scalability and Transferability”, Remote Sensing, Vol. 14, No. 3, pp. 646-657, 2022.
- B. Cassidy and M.H. Yap, “Analysis of the Isic Image Datasets: Usage, Benchmarks and Recommendations”, Medical Image Analysis, Vol. 75, pp. 102305-102313, 2022.
- Semantic Segmentation in Medical Image Analysis With Convolutional Neural Networks
Abstract Views :48 |
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
2 Department of Computer Science and Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
3 Department of Electrical and Electronics Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
1 Department of Electronics and Communication Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
2 Department of Computer Science and Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
3 Department of Electrical and Electronics Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 2 (2023), Pagination: 3143-3148Abstract
Medical image analysis plays a pivotal role in modern healthcare, aiding clinicians in accurate diagnosis and treatment planning. However, the complexity and diversity of medical images pose significant challenges for traditional image processing methods. Existing methods often struggle to precisely delineate structures in medical images, leading to suboptimal diagnostic accuracy. The demand for automated and accurate segmentation tools in medical imaging has grown, highlighting the necessity for robust and efficient algorithms capable of handling diverse anatomical variations and pathologies. While CNNs have shown promise in image analysis, their application to medical images requires customization to accommodate unique challenges. The literature lacks comprehensive studies that bridge the gap between general-purpose CNNs and the specific demands of medical image segmentation, especially concerning the diverse and intricate structures present in medical imagery. This study addresses the need for advanced techniques by leveraging Convolutional Neural Networks (CNNs) for semantic segmentation in medical image analysis. Our approach involves the design and implementation of a specialized CNN architecture tailored to the nuances of medical image data. We employ state-of-the-art techniques for data preprocessing, model training, and validation. The model is trained on a diverse dataset encompassing various medical imaging modalities, ensuring its adaptability and generalizability. The proposed CNN-based semantic segmentation model demonstrates superior performance in accurately delineating anatomical structures compared to traditional methods. Evaluation metrics, including Dice coefficient and sensitivity, indicate the model efficacy in achieving precise segmentation. The results underscore the potential of CNNs in advancing medical image analysis for improved clinical outcomes.Keywords
Convolutional Neural Networks, Medical Image Analysis, Semantic Segmentation, Anatomical Structures, Automated DiagnosisReferences
- R. Yang and Y. Yu, “Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis”, Frontiers in Oncology, Vol. 11, pp. 1-12, 2021.
- I. Qureshi and P. Szczuko, “Medical Image Segmentation using Deep Semantic-Based Methods: A Review of Techniques, Applications and Emerging Trends”, Information Fusion, Vol. 90, pp. 316-352, 2023.
- M.L. Huang and Y.Z. Wu, “Semantic Segmentation of Pancreatic Medical Images by using Convolutional Neural Network”, Biomedical Signal Processing and Control, Vol. 73, pp. 103458-103463, 2022.
- S. Niyas and J. Rajan, “Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey”, Neurocomputing, Vol. 493, pp. 397-413, 2022.
- Z. Han and G.G. Wang, “ConvUNeXt: An Efficient Convolution Neural Network for Medical Image Segmentation”, Knowledge-Based Systems, Vol. 253, pp. 1-12, 2022.
- A. Shrivastava and M.A. Shah, “A Comprehensive Analysis of Machine Learning Techniques in Biomedical Image Processing Using Convolutional Neural Network”, Proceedings of International Conference on Contemporary Computing and Informatics, pp. 1363-1369, 2022.
- H. Thisanke and D. Herath, “Semantic Segmentation using Vision Transformers: A Survey”, Engineering Applications of Artificial Intelligence, Vol. 126, pp. 1-14, 2023.
- P. Malhotra, A. Zaguia and W. Enbeyle, “Deep Neural Networks for Medical Image Segmentation”, Journal of Healthcare Engineering, Vol. 2022, pp. 1-9, 2022.
- S. Huang, W.L. Hsu, R.J. Hsu and D.W. Liu, “Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey”, Diagnostics, Vol. 12, No. 11, pp. 2765-2775, 2022.
- R. Ramadan and M. Abdel-Atty, “Color-Invariant Skin Lesion Semantic Segmentation based on Modified U-Net Deep Convolutional Neural Network”, Health Information Science and Systems, Vol. 10, No. 1, pp. 1-17, 2022.
- Y. Jiang, Y. Zhang, Y. Lin and J. Liang, “SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation using Swin Transformer”, Brain Sciences, Vol. 12, No. 6, pp. 797-812, 2022.
- A. Abdelrahman and S. Viriri, “Kidney Tumor Semantic Segmentation using Deep Learning: A Survey of State-of-the-Art”, Journal of Imaging, Vol. 8, No. 3, pp. 55-68, 2022.