M. Kalaiselvi Geetha

Manipal Academy of Higher Education

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Co-Authors

J. Arunnehru

SRM Institute of Science and Technology

Neena Aloysius

Amrita Vishwa Vidyapeetham

R.C. Suganthe

Madurai Medical College

R. Santhoshkumar

ASA College

R.S. Latha

St. Peter's Institute of Higher Education and Research

R. Sathya

SRM Institute of Science and Technology

A. Punitha

Indian Institute of Rice Research

G.R. Sreekanth

Madurai Medical College

Preetham Kumar

Manipal Academy of Higher Education

Sulatha V. Bhandary

Manipal Academy of Higher Education

Cooperative Institutions

Amrita Vishwa Vidyapeetham

Manipal Academy of Higher Education

Annamalai University

SRM Institute of Science and Technology

Vellore Institute of Technology University

Anna University, Chennai

SRM University

PES University

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH

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A Study on Discrete Action Sequences Using Deep Emotional Intelligence

Studies in big data (2021)

R. Santhoshkumar M. Kalaiselvi Geetha

10.1007/978-3-030-75855-4_1

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Citations (1)

Glaucoma Prediction Using Supervised Learning

2022 International Conference on Computer Communication and Informatics (ICCCI) (2023)

R.C. Suganthe M. Kalaiselvi Geetha R.S. Latha S. Preethi M. Rishika

Glaucoma is an eye disorder when the optic nerve suffers damage due to an increase in intraocular fluid pressure. When there is too much fluid present or the channel in the eye through which the fluid normally drains is blocked, pressure builds up. People blind due to glaucoma and estimated that 4.5 million people globally affected by glaucoma. Among all global blindness, blindness happen by glaucoma is slightly more than 12%. It is the second most frequent reason for blindness in the globe. Percent of affected people that are not even aware of having glaucoma, may up to 50% in developed countries and may rise to 90%in undeveloped parts of the world. So, detection of glaucoma is a crucial role in life Glaucoma can be detected using increased cup to disc ratio where optic cup is a cup in the center of the optic disc. The optic disk is the circular area inside the eye where the optic nerve connects to the retina. The Glaucoma prediction can be done with Machine Learning, Deep Learning Techniques. The proposed work proposes Convolutional Neural Network algorithm which gives a great classification performance when compared to other algorithms such as Support Vector Machine (SVM) and Random Forest.

Optic disc

Optic cup (embryology)

Optic disk

10.1109/iccci56745.2023.10128405

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Dhrishti: A Visual Aiding System for Outdoor Environment

2022 7th International Conference on Communication and Electronics Systems (ICCES) (2020)

Amruda Kandoth Nitin Arya Pillai Ritika Mohan Tuhina Priya M. Kalaiselvi Geetha

Visually impaired individuals face difficulties in safe and independent movement which bereave them from their day-to-day life activities in both outdoor and indoor environment. The evaluated number of visually impaired individuals in the world is said to be in millions. This paper mainly addresses the navigation of visually impaired in outdoor environment. The application developed proposes a smart portable cane that is equipped with sensors and camera to help the visually impaired to understand their surroundings, detect the obstacles and avoid them using Computer Vision, Neural networks and Internet of Things(IoT) in outdoor spaces thereby helping them to navigate safely. Our idea is to implement YOLO (You Only Look Once) algorithm for the obstacle detection using the DarkNet framework which is then used for object avoidance using an ultrasonic sensor. The main goal is to create a system which is easy, efficient and will have high computational speed as it has to work in the real time environment.

Obstacle avoidance

10.1109/icces48766.2020.9137967

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Citations (7)

A review on deep convolutional neural networks

Neena Aloysius M. Kalaiselvi Geetha

The success of traditional methods for solving computer vision problems heavily depends on the feature extraction process. But Convolutional Neural Networks (CNN) have provided an alternative for automatically learning the domain specific features. Now every problem in the broader domain of computer vision is re-examined from the perspective of this new methodology. Therefore it is essential to figure-out the type of network specific to a problem. In this work, we have done a thorough literature survey of Convolutional Neural Networks which is the widely used framework of deep learning. With AlexNet as the base CNN model, we have reviewed all the variations emerged over time to suit various applications and a small discussion on the available frameworks for the implementation of the same. We hope this piece of article will really serve as a guide for any neophyte in the area.

Feature (linguistics)

10.1109/iccsp.2017.8286426

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Citations (518)

A Vision Based Recognition of Indian Sign Language Alphabets and Numerals Using B-Spline Approximation

M. Kalaiselvi Geetha U C Manjusha

Sign language is the most natural way of expression for the deaf community. The urge to support the integration of deaf people into the hearing society made the automatic sign language recognition, an area of interest for the researchers. Indian Sign Language (ISL) is a visual-spatial language which provides linguistic information using hands, arms, facial expressions, and head/body postures. In this paper we propose a novel vision-based recognition of Indian Sign Language Alphabets and Numerals using B-Spline Approximation. Gestures of ISL alphabets are complex since it involves the gestures of both the hands together. Our algorithm approximates the boundary extracted from the Region of Interest, to a B-Spline curve by taking the Maximum Curvature Points (MCPs) as the Control points. Then the B-Spline curve is subjected to iterations for smoothening resulting in the extraction of Key Maximum Curvature points (KMCPs), which are the key contributors of the gesture shape. Hence a translation & scale invariant feature vector is obtained from the spatial locations of the KMCPs in the 8 Octant Regions of the 2D Space which is given for classification.

Numeral system

Source

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Citations (44)

Stock Movement Prediction using KNN Machine Learning Algorithm

2022 International Conference on Computer Communication and Informatics (ICCCI) (2022)

R.S. Latha G.R. Sreekanth R.C. Suganthe M. Kalaiselvi Geetha Ranjithkumar Selvaraj

The stock market refers to the buying, selling, and issuance of shares of publicly held companies. Stock market prediction is for good and successful investment. Some people think stock market prediction is impossible to predict future stock values and it involves risks it may face huge losses. To address these, a machine learning model-based stock market prediction using K Nearest Neighbor (KNN) algorithm is proposed. For predicting next day change in the stock value, KNN is very powerful in numeric prediction problems because it can process relation between the numeric data. By utilizing these algorithms in stock market prediction our proposed results with experimental results show that a higher accuracy of 70%.

Stock (firearms)

Stock Market Prediction

10.1109/iccci54379.2022.9740781

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Citations (15)

Real Time Object Identification for Intelligent Video Surveillance Applications

International Journal of Computer Applications Technology and Research (2013)

L. Menaka M. Kalaiselvi Geetha M. Chitra

Intelligent video surveillance system has emerged as a very important research topic in the computer vision field in the recent years.It is well suited for a broad range of applications such as to monitor activities at traffic intersections for detecting congestions and predict the traffic flow.Object classification in the field of video surveillance is a key component of smart surveillance software.Two robust methodology and algorithms adopted for people and object classification for automated surveillance systems is proposed in this paper.First method uses background subtraction model for detecting the object motion.The background subtraction and image segmentation based on morphological transformation for tracking and object classification on highways is proposed.This algorithm uses erosion followed by dilation on various frames.Proposed algorithm in first method, segments the image by preserving important edges which improves the adaptive background mixture model and makes the system learn faster and more accurately.The system used in second method adopts the object detection method without background subtraction because of the static object detection.Segmentation is done by the bounding box registration technique.Then the classification is done with the multiclass SVM using the edge histogram as features.The edge histograms are calculated for various bin values in different environment.The result obtained demonstrates the effectiveness of the proposed approach.

Identification

10.7753/ijcatr0206.1013

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Development of Weblog Pre-Processing System: A Parallel Approach

2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI) (2018)

Prakash K. Aithal U. Dinesh Acharya M. Kalaiselvi Geetha

Preprocessing weblog is required to weed out unwanted data and select the only information which is useful. Preprocessing involves reading the web log data, separating the fields, finding the timestamp, removing the entries which contain image files, bots, not GET and unsuccessful web page requests such as error code 304 - which stands for not modified and error code 404 - which stands for a requested resource not found. After cleaning of weblog data one will identify the users and sessions associated with the users.

Code (set theory)

Data pre-processing

10.1109/icoei.2018.8553715

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Citations (1)

An Ensembled Scale-Space Model of Deep Convolutional Neural Networks for Sign Language Recognition

Advances in intelligent systems and computing (2020)

Neena Aloysius M. Kalaiselvi Geetha

American Sign Language

10.1007/978-981-15-3514-7_29

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Citations (9)

Behavior recognition in surveillance video using temporal features

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (2013)

J. Arunnehru M. Kalaiselvi Geetha

Manual video surveillance is highly expensive and inconvenient in continuous monitoring by security personnel. So automatic video surveillance is needed. A large amount of security measure is required in public and private sectors due to terrorist activities. In this paper, an automatic activity recognition approach is proposed. The difference image is used to extract the motion information based on Region of Interest (ROI). The experiments are carried out on the Weizmann dataset considering four activities viz (walking, running, waving one hand and waving both hands) with multiclass-SVM scheme for classification. Experimental results shows an overall performance of 92% to recognize performed actions. The proposed method demonstrates good results on Weizmann dataset and results are comparable with well-known existing methods.

Public security

Activity detection

10.1109/icccnt.2013.6726526

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Citations (9)