Dr I Manimozhi
@epcet.ac.in
Professor & HOD CSE
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
Scopus Publications
L Saumya, I. Manimozhi, and Swathi
IEEE
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.
Muthukumaran V., Manimozhi I., Praveen Sundar P. V., Karthikeyan T., and Magesh Gopu
IGI Global
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.
I. Manimozhi and Manoj challa
IEEE
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.
I. Manimozhi and S. Janakiraman
Inderscience Publishers
I. Manimozhi and S. Janakiraman
Springer Science and Business Media LLC
Manimozhi Iyer and S. Janakiraman subbaih
IEEE
The detection of abnormalities is a very challenging problem in computer vision, especially if these abnormalities must be detected in images of textured surfaces. The cast extrusion manufacturing process is the initial step which enables the creation of the raw materials, such as clear polypropylene film, needed for the flexible packaging manufacturing process. The current methodology of controlling extrusion related defects occurrences is attempted by a combination of statistical Sampling and human inspection. However, the defects are small in size and hard to visualize in a clear thin film 3 m in width moving at a speed of 50m/min/.This resulted in poor product quality and high return ratio from customers. This problem becomes even more complicated in case when testing is possible only with one surface of the part. Texture analysis plays an important role in the automated visual inspection of texture images to detect their defects. This automated classification method helps us to acquire knowledge about the pattern of defect within a very short period of time. This research investigates possible defect detection methodologies and has subsequently proposed a system that is capable of real time monitoring of defects on the cast extrusion manufacturing process.
RECENT SCHOLAR PUBLICATIONS
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