Dr. Venkatesan C

HKBK College of Engineering, Bengaluru, India

Dr. Venkatesan C

HKBK College of Engineering, Bengaluru, India

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Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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23 January 2018 in IEEE Access

Journal Article

ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications

Venkatesan C

Karthigaikumar Palanivel

Anand Paul

S. Satheeskumaran

R. Kumar

Medical expert systems are part of the portable and smart healthcare monitoring devices used in day-to-day life. Arrhythmic beat classification is mainly used in electrocardiogram (ECG) abnormality detection for identifying heart related problems. In this paper, ECG signal preprocessing and support vector machine-based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. In ECG signal preprocessing, a delayed error normalized LMS adaptive filter is used to achieve high speed and low latency design with less computational elements. Since the signal processing technique is developed for remote healthcare systems, white noise removal is mainly focused. Discrete wavelet transform is applied on the preprocessed signal for HRV feature extraction and machine learning techniques are used for performing arrhythmic beat classification. In this paper, SVM classifier and other popular classifiers have been used on noise removed feature extracted signal for beat classification. Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers.

3 March 2023 in Sensors

Journal Article

Energy-Efficient Clustering and Routing Using ASFO and a Cross-Layer-Based Expedient Routing Protocol for Wireless Sensor Networks

Today’s critical goals in sensor network research are extending the lifetime of wireless sensor networks (WSNs) and lowering power consumption. A WSN necessitates the use of energy-efficient communication networks. Clustering, storage, communication capacity, high configuration complexity, low communication speed, and limited computation are also some of the energy limitations of WSNs. Moreover, cluster head selection remains problematic for WSN energy minimization. Sensor nodes (SNs) are clustered in this work using the Adaptive Sailfish Optimization (ASFO) algorithm with K-medoids. The primary purpose of research is to optimize the selection of cluster heads through energy stabilization, distance reduction, and latency minimization between nodes. Because of these constraints, achieving optimal energy resource utilization is an essential problem in WSNs. An energy-efficient cross-layer-based expedient routing protocol (E-CERP) is used to determine the shortest route, dynamically minimizing network overhead. The proposed method is used to evaluate the packet delivery ratio (PDR), packet delay, throughput, power consumption, network lifetime, packet loss rate, and error estimation, and the results were superior to existing methods. PDR (100%), packet delay (0.05 s), throughput (0.99 Mbps), power consumption (1.97 mJ), network lifespan (5908 rounds), and PLR (0.5%) for 100 nodes are the performance results for quality-of-service parameters.

24 March 2018 in Multimedia Tools and Applications

Journal Article

A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection

Venkatesan C

Karthigaikumar Palanivel

R. Varatharajan

29 June 2022 in Energies

Journal Article

Secure Routing-Based Energy Optimization for IoT Application with Heterogeneous Wireless Sensor Networks

Traian Candin Mihaltan

Wireless sensor networks (WSNs) and the Internet of Things (IoT) are increasingly making an impact in a wide range of domain-specific applications. In IoT-integrated WSNs, nodes generally function with limited battery units and, hence, energy efficiency is considered as the main design challenge. For homogeneous WSNs, several routing techniques based on clusters are available, but only a few of them are focused on energy-efficient heterogeneous WSNs (HWSNs). However, security provisioning in end-to-end communication is the main design challenge in HWSNs. This research work presents an energy optimizing secure routing scheme for IoT application in heterogeneous WSNs. In our proposed scheme, secure routing is established for confidential data of the IoT through sensor nodes with heterogeneous energy using the multipath link routing protocol (MLRP). After establishing the secure routing, the energy and network lifetime is improved using the hybrid-based TEEN (H-TEEN) protocol, which also has load balancing capacity. Furthermore, the data storage capacity is improved using the ubiquitous data storage protocol (U-DSP). This routing protocol has been implemented and compared with two other existing routing protocols, and it shows an improvement in performance parameters such as throughput, energy efficiency, end-to-end delay, network lifetime and data storage capacity.

25 March 2022

Conference Paper

Efficient Machine Learning Technique for Tumor Classification Based on Gene Expression Data

Venkatesan C

D. Balamurugan

T. Thamaraimanalan

M. Ramkumar

In bioinformatics research, cancer classification is a crucial domain. The use of microarray technology to identify specific illnesses is common. A small number of genes uncovered in clinical applications can lead to low-cost medicines that can help estimate a patient's survival time or diagnose cancer. Because there are more genes and fewer samples in microarray data, high dimensionality is a serious concern. The genes in the microarray data were evaluated using F-statistics, T-Statistics, and Signal-to-Noise Ratio (SNR) in this study. The top-m rated genes are analyzed using optimization approaches to retrieve useful information. The genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and shuffling frog leaping with rapid flying are among the methods employed (SFLLF). Classification is done using the Support vector machine (SVM), the K-Nearest Neighbor classifier (KNN), and the Naive Bayes classifier (NBC). Lung Cancer Michigan, AMLALL, Colon Tumour, Lung Harvard2, and others are among the datasets utilized for experimental analysis. The classifiers are assessed using a 5-fold cross-validation approach. The findings demonstrate that the suggested two-step feature selection approaches are effective in selecting relevant genes from microarray data for cancer classification.

1 July 2018 in Biomedical Signal Processing and Control

Journal Article

Mobile cloud computing for ECG telemonitoring and real-time coronary heart disease risk detection

Venkatesan C

Karthigaikumar Palanivel

S. Satheeskumaran

About Me

Prof. Dr. C. Venkatesan is currently working as a Professor in the Department of Electronics and Communication Engineering, HKBK College of Engineering, Bangalore, Karnataka, India. He received a Ph.D. in Information and Communication Engineering at Anna University, Chennai. He received a Bachelor of Engineering and a Master of Technology from Anna University, India, and the Government College of Technology, Coimbatore, India, respectively. Furthermore, he is the author/co-author of more than 50 papers in conferences, book chapters, and SCIE/Scopus/EI-indexed journals. He is a reviewer for many journals, such as the IEEE, Elsevier, Springer, Taylor & Francis, Wiley, and IGI Global Journals, and has reviewed more than 200 research papers. He is serving as Lead Guest Editor in the International Journal of Pervasive Computing and Communications, Emerald, and many more. Not only that, he served as a Volume Editor for past Springer conference proceedings. He also served as a keynote speaker, advisory committee member, and session chair for IEEE/Springer international conferences. He is a professional society member of ISTE, IET, WASET, and IAENG. Furthermore, he has received research grants from various government funding agencies and was a recipient of the Best Faculty award and the Emerging Researcher award for his contribution to teaching and research. His current research interests include wireless sensor networks, future internet technologies, and machine learning.

Dr. Venkatesan C

Dr. Venkatesan C

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About Me