Prediction of metal oxide nanoparticles for anticancer drug delivery using machine learning Michel Ashick A, Eben Sophia P, Stewart Kirubakaran S, Narmadha D 2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024 This work uses machine learning to predict how well metal oxide nanoparticles (MONPs) will deliver anticancer medications in a novel attempt to transform nanomedicine. In order to determine the ideal metal oxide nanoparticles (MONPs) for efficient anticancer drug delivery, this project explores a variety of machine learning techniques. Using an extensive dataset that includes toxicity profiles, drug transport efficiency assessments, and MONP physicochemical properties, the research examines the predictive power of many machine learning techniques. By means of rigorous validation procedures and careful experimentation, the research aims to identify the most effective models for precisely forecasting the optimal MONPs for anticancer medication delivery. Through comparative evaluations of several algorithms, the study seeks to offer important insights into the best methods for this crucial application. The results of this study have the potential to significantly advance the area of nanomedicine by enabling the logical design and selection of MONPs for enhanced anticancer drug delivery, thereby contributing to the ongoing efforts in combating cancer with precision and efficacy.
Monitoring of underground infrastructure of iot and analysis of path loss International Journal of Advanced Science and Technology, 2020
PIR and IR sensor based smart home automation system using IOT for energy saving applications International Journal of Innovative Technology and Exploring Engineering, 2019
Image enhancement technique using mean quantization transforms and equalization International Journal of Innovative Technology and Exploring Engineering, 2019
Five point feature recognition of face and body for driver safety in real time mobile applications International Journal of Innovative Technology and Exploring Engineering, 2019
Contextual Medical Image Compression using Normalized Wavelet-Transform Coefficients and Prediction Paul Eben Sophia, Jude Anitha IETE Journal of Research, 2017 Context-based compression plays a vital role in digital communication systems, since a particular region alone can be preserved using high bit rate and the other regions can be compressed using low bit rate compressions. Such methods are of great interest in tele-radiology applications requiring large storage. This paper presents an enhanced method for compression of medical images using wavelet transformation, normalization, and prediction. The compression method can be tuned to reproduce a good quality image close to the original image for the selected contextual area. Initially, the image undergoes 2D wavelet transform to obtain the approximate and the detailed coefficients. To ease the process of prediction, normalization is done for each sub-band separately, followed by mask-based prediction of the normalized coefficients. Finally, the prediction error coefficients are entropy-encoded using arithmetic coding technique. The proposed algorithm utilizes prediction as well as transformation to achieve a better compression along with good quality. The performance of the proposed system is compared with JPEG2000 and other conventional and contextual compression algorithms. The results show better performance quantitatively and visually.
A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction P. Eben Sophia, J. Anitha International Journal of Imaging Systems and Technology, 2017 Contextual compression is an essential part of any medical image compression since it facilitates no loss of diagnostic information. Although there are many techniques available for contextual image compression still there is a need for developing an efficient and optimized technique which would produce good quality images at lower bit rates. This article presents an efficient contextual compression algorithm using wavelet and contourlet transforms to capture the fine details of the image, along with directional information to produce good quality at high Compression Ratio (CR). The 2D discrete wavelet transform, which uses the simplest Daubechies wavelets, db1, or haar wavelet, is chosen and used to get the subband coefficients. The approximate coefficients of the higher subbands undergo contourlet transform employing length N ladder filters for capturing the directional information of the subbands at different scale and orientations. An optimized approach is used for predicting the quantized and the normalized subband coefficients resulting in improved compression performance. The proposed contextual compression approach was evaluated for its performance in terms of CR, Peak Signal to Noise Ratio, Feature SIMilarity index, Structure SIMilarity Index, and Universal quality (Q) after reconstruction. The results clarify the efficiency of the proposed method over other compression techniques.
Automated Cardiovascular Disease Diagnosis using Honey Badger Optimization with Modified Deep Learning Model AP Reddy, PE Sophia, SS Kirubakaran Biomedical Materials & Devices, 1-8 , 2025 2025.0 Citations: 1
Hybrid Convolutional Neural Network Model for Enhanced Detection of Diabetic Retinopathy TK Reddy, AP Reddy, PE Sophia International Conference on Web Intelligence and Human-Machine Interaction … , 2025 2025.0
Unravelling Protein-DNA Interaction Predictions: A State-of-the-Art review N Thomas, PE Sophia 2025 2nd International Conference on Trends in Engineering Systems and … , 2025 2025.0
Performance enhanced ripplet transform based compression method for medical images J Anitha, PE Sophia, VHC de Albuquerque Measurement 144, 203-213 , 2019 2019.0 Citations: 9
An Optimized Predictive Coding Algorithm for Medical Image Compression PE Sophia, DJ Hemanth¹ Artificial Intelligence: Second International Conference, SLAAI-ICAI 2018 … , 2019 2019.0
PIR and IR Sensor-Based Smart Home Automation System Using IOT for Energy Saving Applications PE Sophia, R Prithvirajan, S Thirunavukarasu, K Muthuraj, S Sarmila International Journal of Innovative Technology and Exploring Engineering … , 2019 2019.0 Citations: 3
Enhanced method of using contourlet transform for medical image compression PE Sophia, J Anitha International Journal of Advanced Intelligence Paradigms 14 (1-2), 107-121 , 2019 2019.0 Citations: 6
Analysis of transform-based compression techniques for MRI and CT images ES Paul, J Anitha Intelligent Data Analysis for Biomedical Applications, 103-120 , 2019 2019.0 Citations: 10
An optimized predictive coding algorithm for medical image compression J Anitha, P Eben Sophia, D Jude Hemanth International Conference of the Sri Lanka Association for Artificial … , 2018 2018.0 Citations: 4
Contextual medical image compression using normalized wavelet-transform coefficients and prediction P Eben Sophia, J Anitha IETE Journal of Research 63 (5), 671-683 , 2017 2017.0 Citations: 31
A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction PE Sophia, J Anitha International Journal of Imaging Systems and Technology 27 (2), 171-181 , 2017 2017.0 Citations: 5
Contourlet transform based subband normalization for region based medical image compression PE Sophia, J Anitha Intelligent Decision Technologies 10 (4), 385-391 , 2016 2016.0 Citations: 3
Region-based prediction and quality measurements for medical image compression P Eben Sophia, J Anitha Proceedings of Fifth International Conference on Soft Computing for Problem … , 2016 2016.0 Citations: 6
A systematic review on advances and perspectives of image compression in telemedicine PE Sophia, J Anitha International Journal of Advanced Intelligence Paradigms 7 (2), 136-155 , 2015 2015.0 Citations: 7
Implementation of region based medical image compression for telemedicine application PE Sophia, J Anitha 2014 IEEE international conference on computational intelligence and … , 2014 2014.0 Citations: 20
Design of low power and high speed configurable booth multiplier DJ Moni, PE Sophia 2011 3rd International Conference on Electronics Computer Technology 6, 338-342 , 2011 2011.0 Citations: 18
Prithvirajan PE Sophia R, Thirunavukarasu. S, Muthuraj. K, S. Sarmila, Karpagam College of … , 0 Citations: 2
Design of Low Power and High Speed Configurable Booth Multiplier D JackulineMoni, PE Sophia proc. IEEE, 978-1 , 0 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
Contextual medical image compression using normalized wavelet-transform coefficients and prediction P Eben Sophia, J Anitha IETE Journal of Research 63 (5), 671-683 , 2017 2017.0 Citations: 31
Implementation of region based medical image compression for telemedicine application PE Sophia, J Anitha 2014 IEEE international conference on computational intelligence and … , 2014 2014.0 Citations: 20
Design of low power and high speed configurable booth multiplier DJ Moni, PE Sophia 2011 3rd International Conference on Electronics Computer Technology 6, 338-342 , 2011 2011.0 Citations: 18
Analysis of transform-based compression techniques for MRI and CT images ES Paul, J Anitha Intelligent Data Analysis for Biomedical Applications, 103-120 , 2019 2019.0 Citations: 10
Performance enhanced ripplet transform based compression method for medical images J Anitha, PE Sophia, VHC de Albuquerque Measurement 144, 203-213 , 2019 2019.0 Citations: 9
A systematic review on advances and perspectives of image compression in telemedicine PE Sophia, J Anitha International Journal of Advanced Intelligence Paradigms 7 (2), 136-155 , 2015 2015.0 Citations: 7
Enhanced method of using contourlet transform for medical image compression PE Sophia, J Anitha International Journal of Advanced Intelligence Paradigms 14 (1-2), 107-121 , 2019 2019.0 Citations: 6
Region-based prediction and quality measurements for medical image compression P Eben Sophia, J Anitha Proceedings of Fifth International Conference on Soft Computing for Problem … , 2016 2016.0 Citations: 6
A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction PE Sophia, J Anitha International Journal of Imaging Systems and Technology 27 (2), 171-181 , 2017 2017.0 Citations: 5
An optimized predictive coding algorithm for medical image compression J Anitha, P Eben Sophia, D Jude Hemanth International Conference of the Sri Lanka Association for Artificial … , 2018 2018.0 Citations: 4
PIR and IR Sensor-Based Smart Home Automation System Using IOT for Energy Saving Applications PE Sophia, R Prithvirajan, S Thirunavukarasu, K Muthuraj, S Sarmila International Journal of Innovative Technology and Exploring Engineering … , 2019 2019.0 Citations: 3
Contourlet transform based subband normalization for region based medical image compression PE Sophia, J Anitha Intelligent Decision Technologies 10 (4), 385-391 , 2016 2016.0 Citations: 3
Prithvirajan PE Sophia R, Thirunavukarasu. S, Muthuraj. K, S. Sarmila, Karpagam College of … , 0 Citations: 2
Design of Low Power and High Speed Configurable Booth Multiplier D JackulineMoni, PE Sophia proc. IEEE, 978-1 , 0 Citations: 2
Automated Cardiovascular Disease Diagnosis using Honey Badger Optimization with Modified Deep Learning Model AP Reddy, PE Sophia, SS Kirubakaran Biomedical Materials & Devices, 1-8 , 2025 2025.0 Citations: 1
Hybrid Convolutional Neural Network Model for Enhanced Detection of Diabetic Retinopathy TK Reddy, AP Reddy, PE Sophia International Conference on Web Intelligence and Human-Machine Interaction … , 2025 2025.0
Unravelling Protein-DNA Interaction Predictions: A State-of-the-Art review N Thomas, PE Sophia 2025 2nd International Conference on Trends in Engineering Systems and … , 2025 2025.0
An Optimized Predictive Coding Algorithm for Medical Image Compression PE Sophia, DJ Hemanth¹ Artificial Intelligence: Second International Conference, SLAAI-ICAI 2018 … , 2019 2019.0
Publications
1. P. Eben Sophia, J. Anitha. (2019) “Performance enhanced Ripplet transform based compression method for medical images”, Journal of the International Measurement Confederation, DOI: 10.1016/j.. [Impact factor: 5.13]
2. P. Eben Sophia, J. Anitha. (2017) “A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction”, International journal of imaging systems and technology. DOI: 10.1002/ [Impact factor: 2.2]
3. P. Eben Sophia, J. Anitha. (2017) “Contextual MRI image compression using normalized Wavelet-transform coefficients and prediction”, IETE Journal of Research. DOI 10.1080/03772063.2017.1309998. [Impact factor: 1.877]
4. P. Eben Sophia, J. Anitha. (2016) “Contourlet transform based sub-band normalization for region based medical image compression” Intelligent Decision Technologies, Vol. 1, Preprint: 1-7. DOI 10.3233/IDT-160265. [Scopus Indexed]
5. P. Eben Sophia, J. Anitha. (2016) “Enhanced method of using contourlet transform for Medical image compression”, International Journal of Advanced Intelligence Paradigms, Accepted. [Scopus Indexed]
6. P. Eben Sophia, J. Anitha. (2015) “A systematic review on advances and perspectives of image compression in telemedicine” International Journal of Advanced Intelligence Paradigms, 7(2), 136-155. [Scopus Indexed]
