Dr I Manimozhi

Seeking a challenging career in Technical and Research Oriented field and to excel in it by determination and hard work and thereby to be a part of the esteemed organization where I can leverage my skills and knowledge in a conducive working environment which facilitate my potential advancement.

EDUCATION

Research Supervisor in EPCET R & D center under VTU, Belagavi
Ph.D in Computer Science & Engg. in Manonmaniam Sundaranar University
at Tirunelveli 2019 10526 at Tamilnadu
M.E (Computer Science & Engg.) in Manonmaniam Sundaranar University
at Tirunelveli 2004 with FCD
B.E (Electrical & Electronics Engg.) in Madurai Kamaraj University
Tamilnadu 1998 with FC

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science Applications, Multidisciplinary, Multidisciplinary, Multidisciplinary

Scopus Publications

Efficient Multimodal Retinal Image Registration for Diabetic Retinopathy Detection Using a Lightweight Neural Network and Enhanced RANSAC Algorithm
Young-Jin Jung, I. Manimozhi, Temesgen Engida Yimer
International Journal of Computational Intelligence Systems, 2026
The proposed research aims to develop an efficient multimodal retinal image registration framework for the earlier diagnosis of Diabetic Retinopathy (DR). By integrating a lightweight neural network based on a modified MobileNet architecture with an enhanced RANSAC (Random Sample Consensus) algorithm, the framework improves registration accuracy, reduces computational cost, and enhances diagnostic performance by increasing sensitivity, specificity, and robustness against outliers. Accurate detection of DR is often hindered by the limitations of unimodal imaging techniques, which may not provide enough detailed information. Traditional methods for registering multimodal images, such as color fundus images and Optical Coherence Tomography (OCT) scans, face difficulties in aligning the images accurately due to the presence of outliers. These challenges lead to suboptimal image alignment, reducing the overall effectiveness of DR diagnosis. While existing methods suffer from poor outlier handling and computational inefficiency, the integration of the proposed lightweight MobileNet and enhanced RANSAC explicitly addresses these challenges by reducing false matches and achieving faster alignment. One of the main causes of eyesight loss in the globe is diabetic retinopathy. To stop its development, early identification is essential. By combining data from many imaging modalities, multimodal retinal imaging provides a more thorough retinal structures and increases diagnostic precision. However, because these multimodal images range in scale, orientation, and noise, accurately aligning them is a challenging process. The proposed framework utilizes a modified MobileNet-based lightweight neural network for feature extraction from retinal images. It is combined with an enhanced RANSAC algorithm that optimizes image alignment by rejecting outliers and fine-tuning transformation matrices. The registration algorithm was tested on a dataset of color fundus images and OCT scans to evaluate its performance. On the proposed registration achieved MSE = 0.0045, improving by ↓0.014 compared to MI-based registration, and ALD = 0.45 px (↓0.16 px vs. standard RANSAC). On the pooled fundus + OCT test set, DR classification produced an AUC of about 0.98, representing a + 0.03 improvement over non-registered baselines. The average runtime was 12.3 ms per picture pair on CPU and ~ 8 ms on GPU. The proposed method achieves 98.8% accuracy, 97.6% precision, 97.1% recall, 97.4% F1-score, and an AUC of around 0.98. These results demonstrate the effectiveness and reliability of the proposed method for comprehensive retinal image analysis. The results demonstrate that the proposed framework significantly enhances the accuracy of DR detection, making it a valuable tool for clinical applications.
Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques
M. Mohamed Musthafa, I. Manimozhi, T. R. Mahesh, Suresh Guluwadi
BMC Medical Informatics and Decision Making, 2024
Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, and prone to ambiguous interpretations. This study proposes an advanced machine learning model designed to enhance lung cancer stage classification using CT scan images, aiming to overcome these limitations by offering a faster, non-invasive, and reliable diagnostic tool. Utilizing the IQ-OTHNCCD lung cancer dataset, comprising CT scans from various stages of lung cancer and healthy individuals, we performed extensive preprocessing including resizing, normalization, and Gaussian blurring. A Convolutional Neural Network (CNN) was then trained on this preprocessed data, and class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The model’s performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and ROC curve analysis. The results demonstrated a classification accuracy of 99.64%, with precision, recall, and F1-score values exceeding 98% across all categories. SMOTE significantly enhanced the model’s ability to classify underrepresented classes, contributing to the robustness of the diagnostic tool. These findings underscore the potential of machine learning in transforming lung cancer diagnostics, providing high accuracy in stage classification, which could facilitate early detection and tailored treatment strategies, ultimately improving patient outcomes.
Study of Deep Learning Algorithm for Uncovering Deep Fakes in Online Networking Platform
I. Manimozhi, R. Padmavathy, D. R. Anita Sofia Liz, M. Devi, D. Rajkumar, P. Kavitha
4th International Conference on Power Energy Control and Transmission Systems Harnessing Power and Energy for an Affordable Electrification of India Icpects 2024, 2024
The vital domain of using sophisticated deep learning approaches to counter deep fakes in online networking platforms is explored in this paper. The project seeks to construct a robust deep fake detection model by using Generative Adversarial Networks (GANs) to mimic deep fake production and by employing the feature extraction capabilities of Inception ResNetV2. To combat adversarial strategies used in complex deep fake creation, the suggested model is fine-tuned after extensive training and assessment. The research provides a solid method for differentiating between real and fake material, which enhances the security of online networking sites. In addition, the advice for deployment and insights into adversarial methods provide a complete strategy to tackle the growing danger of deep fakes online.
Contextual Information Based Scheduling for Service Migration in Mobile Edge Computing
Sanchari Saha, Iyappan Perumal, Niveditha V R, Mohamed Abbas, I. Manimozhi, C.Rohith Bhat
International Journal of Computers Communications and Control, 2024
Mobile Edge Computing (MEC) is a distributed computing paradigm that delivers processing and data storage capabilities closer to the network edge, which is adjacent to mobile consumers and devices. MEC lowers latency, reduces data transmission times, and improves overall performance for mobile apps by relocating computing resources to the network’s edge. But, due to higher average load and longer elapsed time, modern end devices such as smartphones and tablets cause major load challenges in mobile computing networks. Furthermore, if smartphones cause unpredictable traffic patterns, it becomes impossible to model and forecast the nature of communication. Such confusing traffic figures are caused not just by bursty Internet traffic, but also by multitasking operating systems that allow users to swiftly switch between active apps. Mobility of users and end devices impose a difficult challenge to provide continuous services in mobile computing. In this paper, this issue is addressed using the Contextual Information Based Scheduling (CIBS) technique to optimally allocate resources and provide seamless service to the users. The proposed method is implemented with NS-3, an open-source network simulator that provides a comprehensive set of modules for Mobile Edge Computing (MEC) simulations, including mobility modelling support. The experimental results show that CIBS offers migration time of 97512ms, delay time of 372115ms, execution time of 1061328ms and downtime of 98715ms. The results are compared with the existing Mobility-Aware Joint Task Scheduling (MATS) approach. The obtained results show that CIBS outperforms MATS with regard to migration time, latency, execution time and downtime.
Early Predictive Model for Detection of Plant Leaf Diseases Using MobileNetV2 Architecture
International Journal of Intelligent Systems and Applications in Engineering, 2023
A Survey on Machine Learning Algorithms for the Detection of Chronic Kidney Disease
L Saumya, I. Manimozhi, Swathi
2nd International Conference on Automation Computing and Renewable Systems Icacrs 2023 Proceedings, 2023
Artificial lntelligence and Machine learning (AIIML) can make a tremendous contribution to the early prediction and detection of various diseases. The early phases of chronic kidney disease (CKD), which has a high mortality and morbidity rate, are devoid of any outward signs of illness. This survey intends to review prior studies on using artificial intelligence and machine learning for the early-stage prediction of CKD. The accuracy and areas of application of various algorithms are studied and compared. The goal of this review of the literature is to present an overview of the studies that have recently been utilized to predict the development of CKD and to pinpoint any research gaps Comparative analysis is done on the accuracy of different machine learning algorithms for the early prediction of CKD. Cases of application of such algorithms into commercial mobile-based applications for health professionals for the detection and prediction of CKD are also being studied. Based on the study, possible research areas that could add to further developments in the application of AJNL for CKD prediction are identified.
Trustworthy Artificial Intelligence and Automatic Morse Code Based Communication Recognition with Eye Tracking
Krishnakanth Medichalam, V. Vijayarajan, V. Vinoth Kumar, I. Manimozhi Iyer, Yaswanth Kumar Vanukuri, V. B. Surya Prasath, B. Swapna
Journal of Mobile Multimedia, 2023
Morse code is one of the oldest communication techniques and used in telecommunication systems. Morse code can be transmitted as a visual signal by using reflections or with the help of flashlights, but it can also be used as a non-detectable form of communication by using the tapping of fingers or even blinking of eyes. In this paper, we develop a computer vision based approach that automatically characterizes the characters conveyed wherein a person can communicate to system or another person through Morse code with eye gestures. We can decode this visual eye tracking based language with the help of our automatic computer vision driven method. Our approach uses a normal webcam to detect the gestures made by the eyes and are interpreted as dots and dashes. These dots and dashes are used to represent the Morse code-based words. Image processing techniques-based blink and pupil detectors are employed. Blink detector helps us to detect a blink and the time that took for each blink. A blink that takes 2 to 4 seconds is acknowledged as a dot whereas a blink that takes more than 4 seconds is represented as a dash. The pupil detector helps us to detect the movement of the pupils, and if pupils move towards right with respect to a person then it is acknowledged as next letter and if the pupils are moved towards left with respect to a person then it is acknowledged as next word. In this way, we decode the Morse code which will be communicated using eyes and establish a non-detectable communication between a person and an automatic system. Our experimental results on an unconstrained visual scene with preliminary greeting words indicate the promise of an automatic eye tracking based system with success rate of 98.25% that can be of use in non-verbal communications.
Public Key Encryption with Equality Test for Industrial Internet of Things Based on Near-Ring
Muthukumaran V., Manimozhi I., Praveen Sundar P. V., Karthikeyan T., Magesh Gopu
International Journal of E Collaboration, 2021
Organizations have moved from the conventional industries to smart industries by embracing the approach of industrial internet of things (IIoT), which has provided an avenue for the integration of smart devices and communication technologies. In this context, this work presents a public key encryption with equality test based on DLP with decomposition problems over near-ring. The proposed method is highly secure, and it solves the problem of quantum algorithm attacks in industrial internet of thing systems. Further, the proposed system is highly secure, and it prevents the chosen-ciphertext attack in type-I adversary and it is indistinguishable against the random oracle model for the type-II adversary. The proposed scheme is highly secure, and the security analysis measures are comparatively stronger than existing techniques.
An Efficient Translation of Tulu to Kannada South Indian Scripts using Optical Character Recognition
I. Manimozhi, Manoj challa
Proceedings 5th International Conference on Computing Methodologies and Communication Iccmc 2021, 2021
Tulu script is not used to write the Tulu language, as it uses the Kannada script for documentation. As Tulu is not an official language of Karnataka, most people are unaware of this language. The Tulu-speaking people are larger in number than speakers of Manipuri and Sanskrit, which have the Eighth Schedule status. To enhance the readability of Tulu documents, there is a need for machine translation of Tulu scripts into Kannada Script. The motivation behind this work is to create software that can proficiently perceive written by a handwritten Tulu character and produces a yield in Kannada character. Tulu Kannada characters include a combination of needs to focus, making them difficult to recognize when written by hand. Besides, interpretation of the south Dravidian language (TULU) is the least investigations in the research field. The south-west of Karnataka state and northern Kerala with some Maharashtra state are speaking around 5 million TULU speakers in India. The programmed acknowledgment of transcribed characters from filtered images assists with changing over characters in a image into the helpful editable and comprehensible structure. This framework is utilized to map TULU to classical Kannada perceives the Tulu characters and reacquaint the precious data store in automatic recognitions for future generations.
An efficient approach for defect detection in pattern texture analysis using an improved support vector machine
I. Manimozhi, S. Janakiraman
International Journal of Business Intelligence and Data Mining, 2021
Texture defect detection can be defined as the process of determining the location and size of the collection pixels in a textured image which deviate in their intensity values or spatial in compression to a background texture. The detection of abnormalities is a very challenging problem in computer vision. In our proposed method we have designed a method for detecting the defect of pattern texture analysis. Initially, features are extracted from the input image using the grey level co-occurrence matrix (GLCM) and grey level run-length matrix (GLRLM). Then the extracted features are fed to the input of classification stage. Here the classification is done by improved support vector machine (ISVM). The proposed pattern analysis showed that the traditional support vector machine is improved by means of kernel methods. In the final stage, the classified features are segmented using the modified fuzzy c means algorithm (MFCM).
An intelligent modified approach towards synthesizing virtual human sign language text for the hearing impaired communications based on OCR
International Journal of Control and Automation, 2020
Defect detection in pattern texture analysis using improved support vector machine
I. Manimozhi, S. Janakiraman
Cluster Computing, 2019

RECENT SCHOLAR PUBLICATIONS

Efficient Multimodal Retinal Image Registration for Diabetic Retinopathy Detection Using a Lightweight Neural Network and Enhanced RANSAC Algorithm
YJ Jung, I Manimozhi, TE Yimer
International Journal of Computational Intelligence Systems , 2026
2026
Machine Learning based Lung Cancer Diagnostic System using Optimized Feature Subset Selection
R Perumal, Y Kumaran, I Manimozhi, AC Kaladevi, CR Bhat
Scalable Computing: Practice and Experience 25 (6), 4589–4603-4589–4603 , 2024
2024
Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques
MM Musthafa, I Manimozhi, TR Mahesh, S Guluwadi
BMC Medical Informatics and Decision Making 24 (1), 142 , 2024
2024
Citations: 67
Contextual Information Based Scheduling for Service Migration in Mobile Edge Computing
S Saha, I Perumal, M Abbas, I Manimozhi, CR Bhat
International Journal of Computers Communications & Control 19 (3) , 2024
2024
Citations: 13
A Method of Skin Disease Detection Using Image Processing and Machine Learning
I Manimozhi, U Shashanka, R Rahul, U Rithik, NP Nandish
2024
A survey on machine learning algorithms for the detection of chronic kidney disease
L Saumya, I Manimozhi
2023 2nd International Conference on Automation, Computing and Renewable … , 2023
2023
Citations: 3
Trustworthy artificial intelligence and automatic Morse code based communication recognition with eye tracking
K Medichalam, V Vijayarajan, VV Kumar, IM Iyer, YK Vanukuri, ...
Journal of Mobile Multimedia 19 (6), 1439-1461 , 2023
2023
Citations: 7
Early predictive model for detection of plant leaf diseases using MobileNetV2 architecture
TR Mahesh, R Sivakami, I Manimozhi, N Krishnamoorthy, B Swapna
International Journal of Intelligent Systems and Applications in Engineering … , 2023
2023
Citations: 38
Automation framework for post development medical device application (QT/QML).
I Manimozhi, Y Swathi
2022
An emotion intelligence auto music play for stress burst based on Nomogram model for quarantine and self-isolation people using deep learning in COVID 19 pandemic
I Manimozhi, TK Sateesh
Journal of Positive School Psychology 6 (3), 6467–6473-6467–6473 , 2022
2022
An Emotion Intelligence Auto Music Play for Stress Burst Based on Nomogram Model for Quarantine and Self-Isolation People Using Deep Learning in Pandemic.
I Manimozhi, TK Sateesh
Special Education 1 (43) , 2022
2022
Mucormycosis Image classification based on the severity by using Convolutional Neural Network (CNN).
C Sugunadevi, I Manimozhi, N Gopal
Special Education 1 (43) , 2022
2022
Public Key Encryption With Equality Test for Industrial Internet of Things Based on Near-Ring
V Muthukumaran, I Manimozhi, PV Praveen Sundar, T Karthikeyan
International Journal of e-Collaboration (IJeC) 17 (3), 25-45 , 2021
2021
Citations: 9
An efficient translation of Tulu to Kannada south Indian scripts using optical character recognition
I Manimozhi
2021 5th International Conference on Computing Methodologies and … , 2021
2021
Citations: 5
An efficient approach for defect detection in pattern texture analysis using an improved support vector machine
I Manimozhi, S Janakiraman
International Journal of Business Intelligence and Data Mining 18 (4), 411-434 , 2021
2021
Citations: 4
Defect detection in pattern texture analysis using improved support vector machine
I Manimozhi, S Janakiraman
Cluster Computing 22 (Suppl 6), 15223-15230 , 2019
2019
Citations: 6
Defect Detection in Pattern Texture Analysis Based on Kernel Selection in Support Vector Machine
I Manimozhi, S Janakiraman
Indian Journal of Science and Technology 9, 45 , 2016
2016
Citations: 1
ESIC: Embedded smart ID card based on Android Platforms
MN I.Manimozhi
journal of Computer Applications and Robotic 3 (2), 6 , 2015
2015
Content-based image retrieval system using feed-forward backpropagation neural network
A Nagathan, MI Manimozhi
International Journal of Computer Science and Network Security (IJCSNS) 14 … , 2014
2014
Citations: 45
"Defect Detection in Pattern texture Analysis"
DJ I.Manimozhi
IEEE International conference in communication and signal processing ICCSP … , 2014
2014

MOST CITED SCHOLAR PUBLICATIONS

Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques
MM Musthafa, I Manimozhi, TR Mahesh, S Guluwadi
BMC Medical Informatics and Decision Making 24 (1), 142 , 2024
2024
Citations: 67
Content-based image retrieval system using feed-forward backpropagation neural network
A Nagathan, MI Manimozhi
International Journal of Computer Science and Network Security (IJCSNS) 14 … , 2014
2014
Citations: 45
Early predictive model for detection of plant leaf diseases using MobileNetV2 architecture
TR Mahesh, R Sivakami, I Manimozhi, N Krishnamoorthy, B Swapna
International Journal of Intelligent Systems and Applications in Engineering … , 2023
2023
Citations: 38
Contextual Information Based Scheduling for Service Migration in Mobile Edge Computing
S Saha, I Perumal, M Abbas, I Manimozhi, CR Bhat
International Journal of Computers Communications & Control 19 (3) , 2024
2024
Citations: 13
Public Key Encryption With Equality Test for Industrial Internet of Things Based on Near-Ring
V Muthukumaran, I Manimozhi, PV Praveen Sundar, T Karthikeyan
International Journal of e-Collaboration (IJeC) 17 (3), 25-45 , 2021
2021
Citations: 9
Trustworthy artificial intelligence and automatic Morse code based communication recognition with eye tracking
K Medichalam, V Vijayarajan, VV Kumar, IM Iyer, YK Vanukuri, ...
Journal of Mobile Multimedia 19 (6), 1439-1461 , 2023
2023
Citations: 7
Defect detection in pattern texture analysis using improved support vector machine
I Manimozhi, S Janakiraman
Cluster Computing 22 (Suppl 6), 15223-15230 , 2019
2019
Citations: 6
An efficient translation of Tulu to Kannada south Indian scripts using optical character recognition
I Manimozhi
2021 5th International Conference on Computing Methodologies and … , 2021
2021
Citations: 5
An efficient approach for defect detection in pattern texture analysis using an improved support vector machine
I Manimozhi, S Janakiraman
International Journal of Business Intelligence and Data Mining 18 (4), 411-434 , 2021
2021
Citations: 4
A survey on machine learning algorithms for the detection of chronic kidney disease
L Saumya, I Manimozhi
2023 2nd International Conference on Automation, Computing and Renewable … , 2023
2023
Citations: 3
Noise reduction of an IC engine by enclosure method with 25 mm thick glass wool as noise absorbing medium
SR Patil, SS Shinde, SV Chaitanya, V Chaturvedi, G Raghavendra, ...
International Journal of Engineering Technology, Management and Applied … , 2014
2014
Citations: 3
Defect Detection in Pattern Texture Analysis Based on Kernel Selection in Support Vector Machine
I Manimozhi, S Janakiraman
Indian Journal of Science and Technology 9, 45 , 2016
2016
Citations: 1
Dynamic BTB Indexing Using Jump Offset Prediction
C Vidhya, I Manimozhi
International Journal of Information and Education Technology 2 (5), 486 , 2012
2012
Citations: 1
Efficient Multimodal Retinal Image Registration for Diabetic Retinopathy Detection Using a Lightweight Neural Network and Enhanced RANSAC Algorithm
YJ Jung, I Manimozhi, TE Yimer
International Journal of Computational Intelligence Systems , 2026
2026
Machine Learning based Lung Cancer Diagnostic System using Optimized Feature Subset Selection
R Perumal, Y Kumaran, I Manimozhi, AC Kaladevi, CR Bhat
Scalable Computing: Practice and Experience 25 (6), 4589–4603-4589–4603 , 2024
2024
A Method of Skin Disease Detection Using Image Processing and Machine Learning
I Manimozhi, U Shashanka, R Rahul, U Rithik, NP Nandish
2024
Automation framework for post development medical device application (QT/QML).
I Manimozhi, Y Swathi
2022
An emotion intelligence auto music play for stress burst based on Nomogram model for quarantine and self-isolation people using deep learning in COVID 19 pandemic
I Manimozhi, TK Sateesh
Journal of Positive School Psychology 6 (3), 6467–6473-6467–6473 , 2022
2022
An Emotion Intelligence Auto Music Play for Stress Burst Based on Nomogram Model for Quarantine and Self-Isolation People Using Deep Learning in Pandemic.
I Manimozhi, TK Sateesh
Special Education 1 (43) , 2022
2022
Mucormycosis Image classification based on the severity by using Convolutional Neural Network (CNN).
C Sugunadevi, I Manimozhi, N Gopal
Special Education 1 (43) , 2022
2022

Publications

Prof. Manoj challa , Dr. I. Manimozhi “An Intelligent Modified Approach towards Synthesizing Virtual Human Sign Language Text for the Hearing Impaired Communications based on OCR” Published in International Journal of Control and Automation , Scopus indexed Vol 13. No 2 pp 710 -
715April 2020

Dr. I. Manimozhi , Prof . Manoj challa “Enhancing QOE in Online Video Streaming based on Random Forest Regression Prediction Towards Future
Popularity of a Video” published in Test Engineering and Management “
Scopus indexed March 2020
I. Manimozhi, “Defect Detection In Pattern Texture Analysis Using Improved Support Vector Machine", Cluster Computing,
Impact: 2.040 , Springer US. Indexed in SCIE, Scopus, UGC approved
list,2018, ,
 I. Manimozhi Dr. S. Janakiraman 2017 “Automated smart Kitchen
monitoring and controlled system” is published in Journal of advanced
research in dynamical control system, Scopus (IJRDCS) 2017
 I. Manimozhi, , 2016, ‘Defect Detection in Pattern Texture
Analysis Based on Kernel Selection in Support Vector Machine’, Indian
Journal of Science & Technology,
. 9, Issue. 45, Indexed in Scopus, Listed in UGC Approved ,2017,
DOI: 10.17485/ijst/2016/v9i45/106501Vol
 I. Manimozhi ESIC: Embedded smart ID card based on Android Platforms
is published in International Journal of Computer Applications and Robotics
Vol 3 issue 2 June 2015
 I. Manimozhi, “Defect Detection in Patter

GRANT DETAILS

Dr. I. Manimozhi & Dr. T K Sateesh submitted Research proposal “Effective Antibiotic Analysis for COVID-19 Pandemic Using Randomized Multivariable
Drugs Resistance Machine Learning Model to VTU _ UPI model