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Wagh, Kavita
- Development of RP-HPLC Method for Separation of Atorvastatin Calcium, Amlodipine Besylate and Azilsartan Medoxomil and its Application to Analyze their Tablet Dosage Forms
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Authors
Affiliations
1 MET’S Institute of Pharmacy, Bhujbal Knowledge City, Adgaon, Nashik-422003, IN
1 MET’S Institute of Pharmacy, Bhujbal Knowledge City, Adgaon, Nashik-422003, IN
Source
Asian Journal of Pharmaceutical Research, Vol 7, No 3 (2017), Pagination: 148-154Abstract
A single, simple, accurate and precise RP-HPLC method has been developed for the separation amlodipine besylate in presence of azilsartan medoxomil and atorvastatin calciumand estimation in their respective combined dosage forms. The chromatographic separation was achieved on C18 column (250×4.6 mm, 5 μ) using Acetonitrile: 20 mM Phosphate buffer (pH 3) 60:40 v/v as a mobile phase at flow rate of 0.8 mL/min. The separation was achieved in isocratic mode and the detection was performed at 242 nm. Further the developed method was validated as per the ICH Q2 (R1) and applied for quantitation of atorvastatin calcium-amlodipine besylate and amlodipine besylate-azilsartan medoxomil in tablet formulations.Keywords
Amlodipine Besylate, Azilsartan Medoxomil, Atorvastatin Calcium, RP-HPLC, Analytical Method Validation.- Development of a RP-HPLC Method for Separation of Ezetimibe in Presence of Atorvastatin Caclium and Simvastatin and its Application for Qunatitation of Tablet Dosage Forms
Abstract Views :221 |
PDF Views:0
Authors
Affiliations
1 MET’S Institute of Pharmacy, Bhujbal Knowledge City, Adgaon, Nashik-422003, IN
1 MET’S Institute of Pharmacy, Bhujbal Knowledge City, Adgaon, Nashik-422003, IN
Source
Asian Journal of Pharmaceutical Analysis, Vol 7, No 3 (2017), Pagination: 169-175Abstract
A single, simple, accurate and precise RP-HPLC method has been developed for the estimation of ezetimibe in presence of atorvastatin calcium and simvastatin in bulk and marketed combined formulations. The chromatographic separation was achieved on C18 column (250 × 4.6 mm, 5 μ) using Acetonitrile: water 70:30 v/v as a mobile phase at flow rate of 1.2 mL/min. The separation was achieved in isocratic mode and the detection was performed at 242 nm. Further the developed method was validated as per the ICH Q2 (R1) and applied for quantitation of atorvastatin calcium-ezetimibe and ezetimibe – simvastatin in tablet formulations.Keywords
Ezetimibe, Simvastatin, Atorvastatin Calcium, RP-HPLC, Analytical Method Validation.References
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- Inazawa, T., et al., Research (Recognized effect of Statin and ezetimibe therapy for achieving LDL-C Goal), a randomized, doctor-oriented, multicenter trial to compare the effects of higherdose statin versus ezetimibe-plus-statin on the serum LDL-C concentration of Japanese type-2 diabetes patients design and rationale. Lipids in Health and Disease, 2013. 12(1): p. 142.
- Qutab, S.S., S.N. Razzaq, and I.U. Khan, Simultaneous determination of atorvastatin calcium and ezetimibe in pharmaceutical formulations by liquid chromatography. Journal of Food and Drug Analysis, 2007. 15(2).
- Seshachalam, U. and C.B. Kothapally, HPLC analysis for simultaneous determination of atorvastatin and ezetimibe in pharmaceutical formulations. Journal of Liquid Chromatography and Related Technologies, 2008. 31(5): p. 714-721.
- Godse, V., et al., A RP-HPLC Method for Simultaneous Estimation of Atorvastatin and Ezetimibe in Pharmaceutical formulation. International Journal of Chemical Sciences, 2009. 7(3).
- Dhaneshwar, S., et al., Development and validation of a method for simultaneous densitometric estimation of atorvastatin calcium and ezetimibe as the bulk drug and in tablet dosage forms. Acta Chromatographica, 2007. 19: p. 141.
- Saroj Kumar Raul, Durgasi Jhansi RP-HPLC method development and validation for the simultaneous estimation of atorvastatin and ezetimibe in pharmaceutical dosage form. Asian Journal of Pharmaceutical and Clinical Research 2015. 8(2).
- Baldha R. G. Simultaneous Spectrophotometric Determination of Atorvastatin Calcium and Ezetimibe in Tablet Dosage Form International Journal of Chem Tech Research 2009. 1(2): p. 233236.
- Shivshanker, K., et al., Validated simultaneous estimation of simvastatin and ezetimibe by RP-HPLC in pure and pharmaceutical dosage form. Asian Journal of Chemistry, 2007. 19(6): p. 4303.
- Jain, N., et al., Spectrophotometric method for simultaneous estimation of simvastatin and ezetimibe in bulk drug and its combined dosage form. Internet J. Pharmacy Pharm. Sci, 2009. 1(1): p. 170-175.
- Krishnaveni, G. and P. Sathyannarayana, Method development and validation for simultaneous determination of ezetimibe and simvastatin in combined pharmaceutical dosage form by RPHPLC method. International Journal of Pharmaceutical and Life Sciences, 2013. 2(2): p. 60-69.
- Rahman, M., et al., Simultaneous estimation of simvastatin and ezetimibe in pharmaceutical tablet dosage forms by RP-HPLC: A Review. Int J Pharm Res Dev, 2010. 2: p. 56-62.
- Sama, J., et al., Simultaneous estimation of simvastatin and ezetimibe in pharmaceutical formulations by RP-HPLC method. J. Pharm. Sci. Res, 2010. 2(2): p. 82-89.
- Neelima, B., et al., Simultaneous estimation of simvastatin and ezetimibe by RP-HPLC in pure and pharmaceutical dosage form. Oriental Journal of Chemistry, 2008. 24(1): p. 195.
- Kumar, D.A., et al., Simultaneous determination of simvastatin and ezetimibe in tablets by HPLC. Journal of Chemistry, 2009. 6(2): p. 541-544.
- Amit Goel, S.B., Jasjeet K. Sahni, Kona S. Srinivas, Ravi S. Gupta, Abhishek Gupta, Vinod P. Semwal and Javed Ali, Development and Validation of Stability-Indicating Assay Method by UPLC for a Fixed Dose Combination of Atorvastatin and Ezetimibe. Journal of Chromatographic Science 2013. 51: p. 222-228.
- Mane, V.B., S. Babar, and N. Kulkarni, Development of UV spectrophotometric method for the simultaneous estimation of simvastatine and ezetimibe in tablet dosage form by simultaneous equation and absorbance ratio method. International Journal of Pharm Tech Research, 2011. 3(3): p. 1459-1466.
- B. Stephen Rathinaraj, V.R., Ch .Rajveer, D.Kumaraswamy, Ganesh Shehraobanglae, A. Arunachalam Development and Validation of A HPTLC Method for the Estimation of Simvastatin and Ezetimibe.. International Journal of Pharmaceutical and Biological Archives, 2010. 1(4): p. 325-330.
- Exploring Transfer Learning in Image Analysis Using Feature Extraction with Pre-Trained Models
Abstract Views :46 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, Vasantdada Patil Pratishthan's College of Engineering and Visual Arts, IN
2 Department of Computer Science, Rajeswari Vedachalam Government Arts College, IN
3 Department of Computer Science, School of Engineering, Ajeenkya DY Patil University, IN
1 Department of Information Technology, Vasantdada Patil Pratishthan's College of Engineering and Visual Arts, IN
2 Department of Computer Science, Rajeswari Vedachalam Government Arts College, IN
3 Department of Computer Science, School of Engineering, Ajeenkya DY Patil University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 3 (2024), Pagination: 3203-3208Abstract
Transfer learning has emerged as a powerful approach in image analysis, leveraging pre-trained models to enhance performance on specific tasks. This study focuses on feature extraction using pre-trained models to address challenges in image classification. We employ state-of-the-art pre-trained models, such as ResNet and VGG, as feature extractors. The models are fine-tuned on a target dataset to adapt to the specific characteristics of the problem at hand. Extracted features are then fed into a custom classifier for task-specific learning. We explore the effectiveness of transfer learning in scenarios with limited labeled data, aiming to demonstrate the model’s ability to generalize and improve performance. Our research contributes to the understanding of transfer learning’s efficacy in image analysis, providing insights into its applicability and limitations. We propose a methodology that optimizes the use of pre-trained models for feature extraction, making them adaptable to diverse image classification tasks. Experimental results showcase significant improvements in classification accuracy compared to training models from scratch, particularly when dealing with small datasets. The study highlights the potential of transfer learning in enhancing the efficiency of image analysis tasks.Keywords
Feature Extraction, Image Analysis, Transfer Learning, Pre-Trained Models, Classification Accuracy.References
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- S. Yazdani, R. Yusuf, A. Karimian, M. Pasha and A. Hematian, “Image Segmentation Methods and Applications in MRI Brain Images”, IETE Technical Review, Vol. 32, No. 6, pp. 413-427, 2015.
- Wen Chen and Minhe Ji, “Comparative Analysis of Fuzzy Approaches to Remote Sensing Image Classification”, Proceedings of International Conference on Fuzzy Systems and Knowledge Discovery, Vol. 2, pp. 537-541, 2010.
- M. Benco and R. Hudec, “Novel Method for Color Textures Features Extraction based on GLCM”, Radioengineering, Vol. 16, No. 4, pp. 64-67, 2007.
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- Domingo Mery, Franco Pedreschi and Alvaro Soto, “Automated Design of a Computer Vision System for Visual Food Quality Evaluation”, Food and Bioprocess Technology, Vol. 6, No. 8, pp. 2093-2108, 2013.
- Nandini M. Chaudhari and Bhausaheb V. Pawar, “Light Scattering Study on Semen Analysis Methods/Techniques”, Proceedings of Nirma University International Conference on Engineering, pp.1-4, 2013.
- Yogesh Kumar Meena, Dinesh Gopalani and Ravi Nahta, “A Two-Step Hybrid Unsupervised Model with Attention Mechanism for Aspect Extraction”, Expert Systems with Applications, Vol. 161, pp. 1-18, 2020.
- Shanshan Huang, and Kenny Q. Zhu, “Knowledge Empowered Prominent Aspect Extraction from Product Reviews”, Information Processing and Management, Vol. 56, No. 3, pp. 408-423, 2019.
- T.A. Rana and Y.N. Cheah, “A Two-Fold Rule-Based Model for Aspect Extraction”, Expert Systems with Applications, Vol. 89, pp. 273-285, 2017.
- Ensemble Machine Learning Method for Detecting Deep Fakes in Social Platform
Abstract Views :40 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Telecommunication, National Institute of Electronics and Information Technology, IN
2 Department of Computer Information Systems, Texas A&M University, US
3 Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, IN
4 Department of Computer Science and Engineering, Maulana Azad National Urdu University, IN
1 Department of Electronics and Telecommunication, National Institute of Electronics and Information Technology, IN
2 Department of Computer Information Systems, Texas A&M University, US
3 Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, IN
4 Department of Computer Science and Engineering, Maulana Azad National Urdu University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 3 (2024), Pagination: 3216-3221Abstract
With the rise of deep fake technology, the detection of manipulated media has become crucial in maintaining the integrity of social platforms. In this study, we propose an ensemble machine learning approach combining Support Vector Machines (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), and Decision Trees (DT) for deep fake detection. Our contribution lies in the development of a robust ensemble method that leverages the strengths of multiple algorithms to enhance detection accuracy and resilience against evolving deep fake techniques. Through experimentation on a diverse dataset, our ensemble model demonstrated superior performance compared to individual models, achieving high accuracy and robustness in detecting deep fakes on social platforms. Keywords: Deep fakes, Ensemble learning, Machine learning, Social platforms, Detection.Keywords
Support Vector Machine, Artificial Neural Networks, k-Nearest Neighbors, Decision Trees, Deep Fake Detection.References
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- L.V. Casalo, C. Flavian.and M. Guinaliu, “Understanding the Intention to Follow the Advice Obtained in an Online Travel Community”, Computers in Human Behaviour, Vol. 27, No. 12, pp. 622-633, 2011.
- J. Bao, Y. Zheng, D. Wilkie and M. Mokbel, “Recommendations in Location-Based Social Networks: A Survey”, Geoinformatica, Vol. 19, No. 3, pp.525-565, 2015.
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- T. Xiang and N. Goharian, “ToxCCIn: Toxic Content Classification with Interpretability”, Proceedings of International Conference on Artificial Intelligence, pp. 1-8, 2021.
- S.T. Suganthi, K. Venkatachalam and T. Pavel, “Deep Learning Model for Deep Fake Face Recognition and Detection”, PeerJ Computer Science, Vol. 8, pp. 881-892, 2022.
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- S. Zobaed, A. Karim and K. Md Hasib, “Deepfakes: Detecting Forged and Synthetic Media Content using Machine Learning”, Proceedings of International Conference on Artificial Intelligence in Cyber Security: Impact and Implications: Security Challenges, 177-201, 2021.
- R. Rafique, A. Mustapha and A.H. Alshehri, “Deep Fake Detection and Classification using Error-Level Analysis and Deep Learning”, Scientific Reports, Vol. 13, No. 1, pp. 7422-7434, 2023.