Suchetha M

Electronics

School of Electronics Engineering

Chennai Campus

Navaneeth Bhaskar

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

IEEE Sensors Letters

In this paper, we propose a novel Particle Swarm Optimized (PSO) One-Dimensional Convolutional Neural Network with Support Vector Machine (1-D CNN-SVM) architecture for real-time detection and classification of diseases. The performance of the proposed architecture is validated with a novel hardware model for detecting Chronic Kidney Disease (CKD) from saliva samples. For detecting CKD, the urea concentration in the saliva sample is monitored by converting it into ammonia. The urea on hydrolysis in the presence of urease enzyme produces ammonia. This ammonia is then measured using a semiconductor gas sensor. The sensor response is given to the proposed architecture for feature extraction and classification. The performance of the architecture is optimized by regulating the parameter values using a PSO algorithm. The proposed architecture outperforms current conventional methods, as this approach is a combination of strong feature extraction and classification techniques. Optimal features are extracted directly from the raw signal, aiming to reduce the computational time and complexity. The proposed architecture has achieved an accuracy of 98.25%.

Computers in Biology and Medicine

PSO optimized 1-D CNN-SVM architecture for real-time detection and classification applications

To enhance the performance and sensitivity of continuous monitoring systems for detection of chronic diseases, selection of optimal machine learning algorithms is pivotal. Presently, the commonly used algorithms face constraints, such as high computational cost and lack of optimal feature selection on application to real time signals thereby reducing the efficiency of such analysis. Deep learning approaches, such as the convolution neural network, overcome these drawbacks by calculating automated features from raw signal and classifying the derived features. This architecture shows good merits. However, the use of fully connected multi-layer perceptron algorithms have shown low classification performance. This paper proposes to develop a modified deep learning convolution neural network algorithm integrated with support vector machines to address the drawbacks present in multi-layer perceptron and thereby improving the overall performance of real-time detection applications. The system is validated on real-time breath signals for non-invasive detection of diabetes. The performance of this proposed algorithm is evaluated and compared with the existing technique. © 2017 IEEE.

IEEE Sensors Journal

A Novel 1-D Convolution Neural Network With SVM Architecture for Real-Time Detection Applications

A Study on Radar Target Detection Based on Deep Neural Networks

Real-Time Human Motion Behavior Detection via CNN Using mmWave Radar

Transfer Learning for a Multimodal Hybrid EEG-fTCD Brain–Computer Interface

A Deep-Learning-Based System for Automated Sensing of Chronic Kidney Disease

Journal	Data powered by TypesetIEEE Sensors Letters
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers (IEEE)
Open Access	0