English
norskBrowsing by Author "Prasad, Dilip K."
Now showing items 21-34 of 34
-
Motivation detection using EEG signal analysis by residual-in-residual convolutional neural network
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-05)While we know that motivated students learn better than non-motivated students but detecting motivation is challenging. Here we present a game-based motivation detection approach from the EEG signals. We take an original approach of using EEG-based brain computer interface to assess if motivation state is manifest in physiological EEG signals as well, and what are suitable conditions in order to ... -
Neural network based country wise risk prediction of COVID-19
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-09-16)The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the ... -
Neural Network Based Country Wise Risk Prediction of COVID-19
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-09-16)The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the ... -
Object detection neural network improves Fourier ptychography reconstruction
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-11-23)High resolution microscopy is heavily dependent on superb optical elements and superresolution microscopy even more so. Correcting unavoidable optical aberrations during post-processing is an elegant method to reduce the optical system’s complexity. A prime method that promises superresolution, aberration correction, and quantitative phase imaging is Fourier ptychography. This microscopy technique ... -
Physics-based machine learning for subcellular segmentation in living cells
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-12-15)Segmenting subcellular structures in living cells from fluorescence microscope images is a ground truth (GT)-deficient problem. The microscopes’ three-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution and the complex morphological manifestations of the structures all contribute to GT-hardness. Unsupervised segmentation approaches are quite ... -
Physics-Guided Loss Functions Improve Deep Learning Performance in Inverse Scattering
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-03-15)Solving electromagnetic inverse scattering problems (ISPs) is challenging due to the intrinsic nonlinearity, ill-posedness, and expensive computational cost. Recently, deep neural network (DNN) techniques have been successfully applied on ISPs and shown potential of superior imaging over conventional methods. In this paper, we discuss techniques for effective incorporation of important physical ... -
RS-HeRR: a rough set-based Hebbian rule reduction neuro-fuzzy system
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-06-01)Interpretabilty is one of the desired characteristics in various classification task. Rule-based system and fuzzy logic can be used for interpretation in classification. The main drawback of rule-based system is that it may contain large complex rules for classification and sometimes it becomes very difficult in interpretation. Rule reduction is also difficult for various reasons. Removing important ... -
Semi-CNN architecture for effective spatio-temporal Learning in action recognition
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-01-12)This paper introduces a fusion convolutional architecture for efficient learning of spatio-temporal features in video action recognition. Unlike 2D convolutional neural networks (CNNs), 3D CNNs can be applied directly on consecutive frames to extract spatio-temporal features. The aim of this work is to fuse the convolution layers from 2D and 3D CNNs to allow temporal encoding with fewer parameters ... -
seMLP: Self-Evolving Multi-Layer Perceptron in Stock Trading Decision Making
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-24)There is a growing interest in automatic crafting of neural network architectures as opposed to expert tuning to fnd the best architecture. On the other hand, the problem of stock trading is considered one of the most dynamic systems that heavily depends on complex trends of the individual company. This paper proposes a novel self-evolving neural network system called self-evolving Multi-Layer ... -
Single image dehazing for a variety of haze scenarios using back projected pyramid network
(Conference object; Konferansebidrag, 2020)Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple ... -
Single-shot multispectral quantitative phase imaging of biological samples using deep learning
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-16)Multispectral quantitative phase imaging (MS-QPI) is a high-contrast label-free technique for morphological imaging of the specimens. The aim of the present study is to extract spectral dependent quantitative information in single-shot using a highly spatially sensitive digital holographic microscope assisted by a deep neural network. There are three different wavelengths used in our method: 𝜆=532 , ... -
SoCodeCNN: Program Source Code for Visual CNN Classification Using Computer Vision Methodology
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-10-24)Automated feature extraction from program source-code such that proper computing resources could be allocated to the program is very difficult given the current state of technology. Therefore, conventional methods call for skilled human intervention in order to achieve the task of feature extraction from programs. This research is the first to propose a novel human-inspired approach to automatically ... -
Topic-based Video Analysis: A Survey
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-13)Manual processing of a large volume of video data captured through closed-circuit television is challenging due to various reasons. First, manual analysis is highly time-consuming. Moreover, as surveillance videos are recorded in dynamic conditions such as in the presence of camera motion, varying illumination, or occlusion, conventional supervised learning may not work always. Thus, computer ... -
Virtual labeling of mitochondria in living cells using correlative imaging and physics-guided deep learning
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-09-28)Mitochondria play a crucial role in cellular metabolism. This paper presents a novel method to visualize mitochondria in living cells without the use of fluorescent markers. We propose a physics-guided deep learning approach for obtaining virtually labeled micrographs of mitochondria from bright-field images. We integrate a microscope’s point spread function in the learning of an adversarial neural ...
