Kapil Omprakash Gupta
@stvincentngp.edu.in
Assistant Professor, Computer Engineering
St. Vincent Pallotti College of Engineering and Technology, Nagpur
EDUCATION
PhD in Computer Science and Engineering
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Kapil Gupta, Ruchika Sinhal, and Sagarkumar S. Badhiye
Wiley
AbstractThis article aims to predict vital signs like heart rate (HR), respiration rate, and arterial oxygen saturation using ambient light video, eliminating chronic distortions through improved frame quality with BER estimation. The study employs the cascade residual CNN‐FPNR technique for preprocessing and SNR enhancement using energy variance maximization. The image cascade network (ICNet) facilitates segmentation, achieving strong segmentation in low‐light ambient videos. Remote photoplethysmography (iPPG) enables noncontact vital sign monitoring, predicting HR and respiratory rate (RR). An innovative noninvasive temperature and cyclical algorithm, incorporating principal component analysis and fast Fourier transform, evaluate patient HR and RR. To address challenges related to involuntary movements, a dynamic time‐warping‐based optimization method is used for precise region selection. The study introduces an intensity variance‐based threshold analysis for arterial oxygen saturation level determination. Ultimately, the support vector machine (SVM) classification technique evaluates the ground truth, showcasing the system's promising potential for remote and accurate vital sign assessment.
Chinnaiah Kotadi, K. Mithun Chakravarthi, Srihari Chintha, and Kapil Gupta
Wiley
Ruchika Sinhal, Kavita R. Singh, and K. O. Gupta
IEEE
K. O. Gupta and P. N. Chatur
Springer Science and Business Media LLC
K. O. Gupta and P. N. Chatur
IEEE
Functional Magnetic Resonance imaging (fMRI) provides sequence of 3D images which contains large number of voxels as information. There are many statistical methods evolved in last few years to analyze this information. Main concern of all these techniques is huge dimensions of the data produced by these images. This paper proposes an efficient hybrid method for feature selection and classification. This method combine entropy based genetic algorithm (EGA) with Linear Collaborative Discriminant Regression Classification (LCDRC) to form feature based classification method. Entropy based genetic algorithm is applied to find maximum significance between the input and output and also it radically reduces the redundancy within the input features. Experiments’ using Star-Plus dataset to classify fMRI images shows that EGA-LCDRC reduces up to 60% features and produces 96.73% accuracy.