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598 results on '"Agrawal, R. K."'

9. IterMiUnet: A lightweight architecture for automatic blood vessel segmentation

Author

Kumar, Ashish, Agrawal, R. K., and Joseph, Leve

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The automatic segmentation of blood vessels in fundus images can help analyze the condition of retinal vasculature, which is crucial for identifying various systemic diseases like hypertension, diabetes, etc. Despite the success of Deep Learning-based models in this segmentation task, most of them are heavily parametrized and thus have limited use in practical applications. This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model that requires significantly fewer parameters and yet delivers performance similar to existing models. The model makes use of the excellent segmentation capabilities of Iternet architecture but overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet model within it. Thus, the new model reduces parameters without any compromise with the network's depth, which is necessary to learn abstract hierarchical concepts in deep models. This lightweight segmentation model speeds up training and inference time and is potentially helpful in the medical domain where data is scarce and, therefore, heavily parametrized models tend to overfit. The proposed model was evaluated on three publicly available datasets: DRIVE, STARE, and CHASE-DB1. Further cross-training and inter-rater variability evaluations have also been performed. The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases.

Published
2022
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13. Kullback–Leibler Distance-Based Fuzzy K-Plane Clustering Approach for Noisy Human Brain MRI Image Segmentation

Author

Kumar, Puneet, Agrawal, R. K., Kumar, Dhirendra, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tistarelli, Massimo, editor, Dubey, Shiv Ram, editor, Singh, Satish Kumar, editor, and Jiang, Xiaoyi, editor

Published
2023
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14. Bi-stage QWOA-Based Efficient Feature Selection for Enhanced Depression Detection Based on Facial Cues

Author

Rathi, Swati, Kaur, Baljeet, Agrawal, R. K., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Hanne, Thomas, editor, Gandhi, Niketa, editor, Manghirmalani Mishra, Pooja, editor, Bajaj, Anu, editor, and Siarry, Patrick, editor

Published
2023
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17. Back Analyses of Jack-up Rig Penetration for Punch Through Case

Author

Nanda Kishore, Y., Pradeep, M., Agrawal, R. K., Faris, K., Oussama, T., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Haldar, Sumanta, editor, Patra, Shantanu, editor, and Ghanekar, Ravindra K., editor

Published
2020
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18. Deep Learning Models for Medical Image Analysis: Challenges and Future Directions

Author

Agrawal, R. K., Juneja, Akanksha, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Madria, Sanjay, editor, Fournier-Viger, Philippe, editor, Chaudhary, Sanjay, editor, and Reddy, P. Krishna, editor

Published
2019
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19. Enhanced Depression Detection from Facial Cues Using Univariate Feature Selection Techniques

Author

Rathi, Swati, Kaur, Baljeet, Agrawal, R. K., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Deka, Bhabesh, editor, Maji, Pradipta, editor, Mitra, Sushmita, editor, Bhattacharyya, Dhruba Kumar, editor, Bora, Prabin Kumar, editor, and Pal, Sankar Kumar, editor

Published
2019
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22. Stabilization of betatron tune in Indus-2

Author

Jena, Saroj, Yadav, S., Agrawal, R. K., Ghodke, A. D., Fatnani, Pravin, and Puntambekar, T. A.

Subjects
Physics - Accelerator Physics
Abstract

Indus-2 is a synchrotron radiation source which is operational at RRCAT, Indore; India. It is essentially pertinent in any synchrotron radiation facility to store the electron beam without beam loss. During the day to day operation of Indus-2 storage ring difficulty was being faced in accumulating higher beam current. After examining, it was found that the working point was shifting from its desired value during accumulation. For smooth beam accumulation, a fixed desired tune in both horizontal and vertical plane plays a great role in avoiding the beam loss via resonance process. This demanded a betatron tune feedback system to be put in storage ring and after putting ON this feedback, the beam accumulation was smooth. The details of this feedback and its working principle are described in this paper., Comment: submitted to Chinese Physics C

Published
2013

23. An Introductory Study on Time Series Modeling and Forecasting

Author

Adhikari, Ratnadip and Agrawal, R. K.

Subjects
Computer Science - Learning, Statistics - Machine Learning, 68T01
Abstract

Time series modeling and forecasting has fundamental importance to various practical domains. Thus a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and effectiveness of time series forecasting. The aim of this dissertation work is to present a concise description of some popular time series forecasting models used in practice, with their salient features. In this thesis, we have described three important classes of time series models, viz. the stochastic, neural networks and SVM based models, together with their inherent forecasting strengths and weaknesses. We have also discussed about the basic issues related to time series modeling, such as stationarity, parsimony, overfitting, etc. Our discussion about different time series models is supported by giving the experimental forecast results, performed on six real time series datasets. While fitting a model to a dataset, special care is taken to select the most parsimonious one. To evaluate forecast accuracy as well as to compare among different models fitted to a time series, we have used the five performance measures, viz. MSE, MAD, RMSE, MAPE and Theil's U-statistics. For each of the six datasets, we have shown the obtained forecast diagram which graphically depicts the closeness between the original and forecasted observations. To have authenticity as well as clarity in our discussion about time series modeling and forecasting, we have taken the help of various published research works from reputed journals and some standard books., Comment: 67 pages, 29 figures, 33 references, book

Published
2013

24. Combining Multiple Time Series Models Through A Robust Weighted Mechanism

Author

Adhikari, Ratnadip and Agrawal, R. K.

Subjects
Computer Science - Artificial Intelligence, Statistics - Applications, 68T05
Abstract

Improvement of time series forecasting accuracy through combining multiple models is an important as well as a dynamic area of research. As a result, various forecasts combination methods have been developed in literature. However, most of them are based on simple linear ensemble strategies and hence ignore the possible relationships between two or more participating models. In this paper, we propose a robust weighted nonlinear ensemble technique which considers the individual forecasts from different models as well as the correlations among them while combining. The proposed ensemble is constructed using three well-known forecasting models and is tested for three real-world time series. A comparison is made among the proposed scheme and three other widely used linear combination methods, in terms of the obtained forecast errors. This comparison shows that our ensemble scheme provides significantly lower forecast errors than each individual model as well as each of the four linear combination methods., Comment: 6 pages, 3 figures, 2 tables, conference

Published
2013
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25. PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting

Author

Adhikari, Ratnadip, Agrawal, R. K., and Kant, Laxmi

Subjects
Computer Science - Neural and Evolutionary Computing, 68T05
Abstract

Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. The forecasting performances of ANNs are further improved through combining the outputs from the three PSO based models., Comment: 4 figures, 4 tables, 31 references, conference proceedings

Published
2013
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26. A Homogeneous Ensemble of Artificial Neural Networks for Time Series Forecasting

Author

Adhikari, Ratnadip and Agrawal, R. K.

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Learning, 68T05
Abstract

Enhancing the robustness and accuracy of time series forecasting models is an active area of research. Recently, Artificial Neural Networks (ANNs) have found extensive applications in many practical forecasting problems. However, the standard backpropagation ANN training algorithm has some critical issues, e.g. it has a slow convergence rate and often converges to a local minimum, the complex pattern of error surfaces, lack of proper training parameters selection methods, etc. To overcome these drawbacks, various improved training methods have been developed in literature; but, still none of them can be guaranteed as the best for all problems. In this paper, we propose a novel weighted ensemble scheme which intelligently combines multiple training algorithms to increase the ANN forecast accuracies. The weight for each training algorithm is determined from the performance of the corresponding ANN model on the validation dataset. Experimental results on four important time series depicts that our proposed technique reduces the mentioned shortcomings of individual ANN training algorithms to a great extent. Also it achieves significantly better forecast accuracies than two other popular statistical models., Comment: 8 pages, 4 figures, 2 tables, 26 references, international journal

Published
2013
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27. Nitrogen Fertilization Strategies for Forage Maize Genotypes: Agronomic Performance, Nitrogen use Efficiency and Economic Assessment and Determination of Economical N Fertilizer Rate.

Author

Nanda, Gangadhar, Agrawal, R. K., and Nilanjaya

Abstract

This article presents the findings of a field investigation conducted in Bihar, India, which aimed to assess the efficiency, productivity, and profitability of different forage maize genotypes under varying nitrogen levels. The study determined that the genotype J-1006, when applied with 120 kg N/ha, resulted in the highest yields of green and dry fodder, as well as crude protein. The economic optimum nitrogen dose for the tested genotypes was found to be 139 kg/ha. The article highlights the importance of increasing forage production to meet the growing demands of the livestock population in Bihar. The study provides valuable data and analysis on the performance of different genotypes of fodder maize under different nitrogen levels, and establishes positive correlations between various parameters such as dry matter yield, nitrogen uptake, crude protein yield, and green forage yield. [Extracted from the article]

Published
2024
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29. Selection of Relevant Electrodes Based on Temporal Similarity for Classification of Motor Imagery Tasks

Author

Kirar, Jyoti Singh, Choudhary, Ayesha, Agrawal, R. K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Shankar, B. Uma, editor, Ghosh, Kuntal, editor, Mandal, Deba Prasad, editor, Ray, Shubhra Sankar, editor, Zhang, David, editor, and Pal, Sankar K., editor

Published
2017
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40. Clustering in Conjunction with Quantum Genetic Algorithm for Relevant Genes Selection for Cancer Microarray Data

Author

Sardana, Manju, Agrawal, R. K., Kaur, Baljeet, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Li, Jiuyong, editor, Cao, Longbing, editor, Wang, Can, editor, Tan, Kay Chen, editor, Liu, Bo, editor, Pei, Jian, editor, and Tseng, Vincent S., editor

Published
2013
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41. Performance Evaluation of Ranking Methods for Relevant Gene Selection in Cancer Microarray Datasets

Author

Sardana, Manju, Kaur, Baljeet, Agrawal, R. K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Batyrshin, Ildar, editor, and González Mendoza, Miguel, editor

Published
2013
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42. Salient Features Selection for Multiclass Texture Classification

Author

Rana, Bharti, Agrawal, R. K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Batyrshin, Ildar, editor, and González Mendoza, Miguel, editor

Published
2013
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43. Statistical Framework for Facial Pose Classification

Author

Jaiswal, Ajay, Kumar, Nitin, Agrawal, R. K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Batyrshin, Ildar, editor, and González Mendoza, Miguel, editor

Published
2013
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46. A Hybrid of Principal Component Analysis and Partial Least Squares for Face Recognition across Pose

Author

Jaiswal, Ajay, Kumar, Nitin, Agrawal, R. K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Alvarez, Luis, editor, Mejail, Marta, editor, Gomez, Luis, editor, and Jacobo, Julio, editor

Published
2012
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47. Computer Aided Diagnosis of Alzheimer’s Disease from MRI Brain Images

Author

Aggarwal, Namita, Bharti, Agrawal, R. K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Campilho, Aurélio, editor, and Kamel, Mohamed, editor

Published
2012
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48. Relevant Feature Selection from EEG Signal for Mental Task Classification

Author

Gupta, Akshansh, Agrawal, R. K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Tan, Pang-Ning, editor, Chawla, Sanjay, editor, Ho, Chin Kuan, editor, and Bailey, James, editor

Published
2012
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49. A Novel Weighted Ensemble Technique for Time Series Forecasting

Author

Adhikari, Ratnadip, Agrawal, R. K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Tan, Pang-Ning, editor, Chawla, Sanjay, editor, Ho, Chin Kuan, editor, and Bailey, James, editor

Published
2012
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