J. Manikandan

National University of Singapore

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Peter Natesan Pushparaj

King Abdulaziz University

Alirio J. Melendez

National University of Singapore

B. Venkataramani

National Institute of Technology Tiruchirappalli

M. Prakash Hande

National University of Singapore

Nam Sang Cheung

Deakin University

Alirio Jose Melendez

National University of Singapore

Dinesh Kumar Srinivasan

Command Hospital Air Force

Minghui Jessica Chen

University of Tasmania

Peng Zhao

China Academy of Chinese Medical Sciences

Y J Pavitra

PES University

Cooperative Institutions

National University of Singapore

153

PES University

123

National University Health System

King Abdulaziz University

Agency for Science, Technology and Research

Saveetha University

National University Hospital

Rajalakshmi Engineering College

SRM Institute of Science and Technology

Anna University, Chennai

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

A comparative analysis of transformer based models for figurative language classification

Computers & Electrical Engineering (2022)

Taha Junaid D. Sumathi Arun Sasikumar S. Suthir J. Manikandan

Literal (mathematical logic)

10.1016/j.compeleceng.2022.108051

Cite

Citations (15)

Optimizing reactive power using a hybrid improved shuffled bat algorithm

International Journal of Mathematical Modelling and Numerical Optimisation (2023)

D Chandrakala J. Manikandan Bala Sundaram J. Gowrishankar P. Munisekhar

10.1504/ijmmno.2023.10058301

Cite

Citations (0)

Proteasome inhibition by lactacystin in primary neuronal cells induces both potentially neuroprotective and pro‐apoptotic transcriptional responses: a microarray analysis

Journal of Neurochemistry (2005)

Elaine Hau Jin Yew Nam Sang Cheung M.S. Choy Robert Z. Qi Alan Yiu Wah Lee

Abstract Although inhibition of the ubiquitin proteasome system has been postulated to play a key role in the pathogenesis of neurodegenerative diseases, studies have also shown that proteasome inhibition can induce increased expression of neuroprotective heat‐shock proteins (HSPs). The global gene expression of primary neurons in response to treatment with the proteasome inhibitor lactacystin was studied to identify the widest range of possible pathways affected. Our results showed changes in mRNA abundance, both at different time points after lactacystin treatment and at different lactacystin concentrations. Genes that were differentially up‐regulated at the early time point but not when most cells were undergoing apoptosis might be involved in an attempt to reverse proteasome inhibitor‐mediated apoptosis and include HSP70, HSP22 and cell cycle inhibitors. The up‐regulation of HSP70 and HSP22 appeared specific towards proteasome inhibitor‐mediated cell death. Overexpression of HSP22 was found to protect against proteasome inhibitor‐mediated loss of viability by up to 25%. Genes involved in oxidative stress and the inflammatory response were also up‐regulated. These data suggest an initial neuroprotective pathway involving HSPs, antioxidants and cell cycle inhibitors, followed by a pro‐apoptotic response possibly mediated by inflammation, oxidative stress and aberrant activation of cell cycle proteins.

Lactacystin

Viability assay

10.1111/j.1471-4159.2005.03220.x

Cite

Citations (97)

FPGA Implementation of D8PSK Demodulator

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (2021)

M Shilpashree Sourabh KS S Chetan J. Manikandan

Differential phase shift keying (DPSK) is a modulation scheme that facilitates non coherent demodulation and is employed for various applications such as Wireless Local Area Networks (WLANs), Bluetooth and RFID communication. In this paper, design, development and hardware implementation of a new demapping scheme for Differential 8-PSK (D8PSK) demodulator on a Zynq 7000 FPGA based ZED board is proposed using the concepts of model based design. The proposed work can be easily extended to other M-ary DPSK schemes.

Differential coding

Modulation (music)

10.1109/icccnt51525.2021.9580120

Cite

Citations (0)

Deep Dive on Oversampling and Under Sampling Techniques in Machine Learning

CRC Press eBooks (2023)

K. Vijay J. Manikandan R. Babu K. R. Sowmia Eugene I. Berna

Data imbalance is a term used in Computer Vision to refer to an unbalanced distribution between classes throughout a dataset. In classification tasks, where the dispersion of classes or labels in a given data is not regular, this problem is most frequently encountered. The resampling approach, which involves adding data to the minority class and removing entries from majority class, is the most feasible approach for solving this problem. In this study, we conducted experiments with two frequently used resampling techniques: oversampling and under sampling, both of which have been widely accepted. In order to investigate both resampling methodologies, we used a publicly available imbalanced dataset from the Kaggle website and a collective of very well ML algorithms with distinct hyperparameters that produced the best outcomes for both resampling methodologies. One of the most important discoveries of this study is the observation that oversampling outperforms under sampling for various classifiers and results in increased scores in many evaluation metrices.

Oversampling

10.1201/9781003388913-56

Cite

Citations (10)

Design and evaluation of a fast half-against-half support vector machine classifier

J. Manikandan

SVM classifiers with Half Against Half (HAH) architecture are reported to be the fastest classifier amongst other SVM classification architectures reported in literature. An attempt is made to enhance the speed of HAH SVM classifier and is named as Fast HAH (F-HAH) classifier. The performance of proposed F-HAH classifier is evaluated using speaker dependent and multi-speaker dependent isolated digits from TI46 database. 1.05×-1.61× improvement in speed is achieved on using Fast HAH classifier for various experiments carried out.

Margin classifier

10.1109/spcom.2016.7746674

Cite

Citations (0)

Design and Evaluation of a Real-Time Character Recognition System

Shruthi Sarathy J. Manikandan

Real-time character recognition systems are in huge demand due to the technological developments in the areas of humanoid robots, autonomous vehicles, voice-based assistive systems, natural language processing, image processing and many more. In this paper, design of a real-time character recognition system using Histogram of Gradient features and Support Vector Machine (SVM) classifier is proposed. An attempt is made to enhance the recognition accuracy by selecting the optimum HOG cell size and scaling factor of resized image for HOG feature extraction. The computation time required for each stage of the proposed real-time system is also analyzed and reported. In order to assess the performance of proposed system, samples from standard datasets are used for evaluation and their recognition accuracies are reported. Recognition accuracies ranging from 91.11% - 100.0% are obtained by the system based on the dataset and type of classifier employed.

Contextual image classification

10.1109/icacci.2018.8554539

Cite

Citations (3)

Design of a Real-Time Human Emotion Recognition System

Springer eBooks (2017)

D. V. Ashwin Abhinav Kumar J. Manikandan

10.1007/978-3-319-73423-1_16

Cite

Citations (3)

Design and Development of a Real-Time Traffic Congestion Analysis System

R Aswathi U Jahnavi H Yukta A Ayush J. Manikandan

The number of vehicles on the road has drastically increased over the years, and it has become the need of the hour to monitor and analyze traffic congestion in real-time. With modern technology, high-resolution closed-circuit television has been installed on several major junctions to monitor traffic violations. In this paper, an attempt is made to design a traffic congestion analysis system that uses a combination of cameras with the concepts of computer vision and machine learning to analyze realtime traffic congestion. The standard you look only once (YOLO) algorithm is considered for the proposed work, and the evaluation of the proposed system was carried out using videos collected from a few skywalks in Bangalore. Samples from Indian driving datasets with additional annotated images of vehicles from Google images were used for training and validation. The performance evaluation of the proposed real-time model was carried out on videos captured from a mobile phone and real-time input from a laptop camera. Recognition accuracies ranging between 76.70 – 89.24% and 83.08 – 92.65% are obtained using the proposed model with YOLO v3 and YOLO v4, respectively. The proposed system can generate analysis reports providing information on total traffic congestion in a particular stretch or junction with additional details, including the type of vehicle contributing to the total traffic congestion and the traffic congestion for an entire day.

10.1109/csase51777.2022.9759708

Cite

Citations (0)

SAR image compression using Relevance Vector Machines

V.A. Nishanth J. Manikandan

Synthetic Aperture Radar (SAR) images are built on-board an aircraft or spacecraft with the help of backscatters and these images are displayed on the cockpit, transmitted to ground station or stored in on-board storage disks based on the system where it is employed. SAR images represent vital information for a large variety of applications which includes automatic target recognition. Hence there is an urge to compress these images with negligible degradation in image quality. In this paper, a novel attempt is made to compress SAR images using RVM for aerospace and satellite applications. Also an optimum threshold based RVM image compressor is proposed and its performance is evaluated. In order to assess the effectiveness of the proposed system, datasets from USC-SIPI image databases are used. It is observed that the images are compressed by 40.36% to 88.53% with a PSNR ranging from 24.34 dB to 33.81 dB on using the proposed optimum threshold technique based RVM model.

10.1109/indicon.2015.7443397

Cite

Citations (5)