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1,174 results on '"Bhattacharya, Abhishek"'

1. A Novel Framework for Automatic Plant Disease Detection Using Convolutional Neural Networks

Author

Sar, Ayan, Goel, Anvi, Choudhury, Tanupriya, Kotecha, Ketan, Bhattacharya, Abhishek, 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, Dutta, Soumi, editor, Bhattacharya, Abhishek, editor, Shahnaz, Celia, editor, and Chakrabarti, Satyajit, editor

Published
2024
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2. A Novel Framework for Facial Emotion Detection Using Deep Learning Algorithms for HCI-Enabled System

Author

Sharma, Hitesh Kumar, Sar, Ayan, Choudhury, Tanupriya, Kotecha, Ketan, Dutta, Soumi, Bhattacharya, Abhishek, 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, Dutta, Soumi, editor, Bhattacharya, Abhishek, editor, Shahnaz, Celia, editor, and Chakrabarti, Satyajit, editor

Published
2024
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3. An Approach for Digital-Social Network Analysis Using Twitter API

Author

Cunaku, Erita, Ndrecaj, Jona, Berisha, Shkurte, Samanta, Debabrata, Dutta, Soumi, Bhattacharya, Abhishek, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bhattacharya, Abhishek, editor, Dutta, Soumi, editor, Dutta, Paramartha, editor, and Piuri, Vincenzo, editor

Published
2023
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4. Forecasting the Growth in Covid-19 Infection Rates

Author

Dutta, Soumi, Bhattacharya, Abhishek, Das, Prithwidip, Pal, Shayan, Mandal, Ratna, Obaid, Ahmed J., Lai, Wen Cheng, Agarwal, Ambuj Kumar, Soufiene, Ben Othman, 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, Dutta, Paramartha, editor, Chakrabarti, Satyajit, editor, Bhattacharya, Abhishek, editor, Dutta, Soumi, editor, and Piuri, Vincenzo, editor

Published
2023
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5. A Comparative Analysis of Performances of Different Ensemble Approaches for Classification of Android Malwares

Author

Bhattacharya, Abhishek, Dutta, Soumi, Hasan, Mohammad Kamrul, Yadav, Kusum, Le, Dac-Nhuong, Arguelles, Pastor, Jr, 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, Dutta, Paramartha, editor, Chakrabarti, Satyajit, editor, Bhattacharya, Abhishek, editor, Dutta, Soumi, editor, and Piuri, Vincenzo, editor

Published
2023
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6. A Stacking Ensemble Framework for Android Malware Prediction

Author

Bhattacharya, Abhishek, Dutta, Soumi, Krit, Salahddine, Lai, Wen Cheng, Azzaoui, Nadjet, Burlea-Schiopoiu, Adriana, 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, Dutta, Paramartha, editor, Chakrabarti, Satyajit, editor, Bhattacharya, Abhishek, editor, Dutta, Soumi, editor, and Piuri, Vincenzo, editor

Published
2023
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7. Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence

Author

Jiang, Cheng, Bhattacharya, Abhishek, Linzey, Joseph, Joshi, Rushikesh S., Cha, Sung Jik, Srinivasan, Sudharsan, Alber, Daniel, Kondepudi, Akhil, Urias, Esteban, Pandian, Balaji, Al-Holou, Wajd, Sullivan, Steve, Thompson, B. Gregory, Heth, Jason, Freudiger, Chris, Khalsa, Siri, Pacione, Donato, Golfinos, John G., Camelo-Piragua, Sandra, Orringer, Daniel A., Lee, Honglak, and Hollon, Todd

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Background: Accurate diagnosis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative diagnosis can be challenging due to tumor diversity and lack of intraoperative pathology resources. Objective: To develop an independent and parallel intraoperative pathology workflow that can provide rapid and accurate skull base tumor diagnoses using label-free optical imaging and artificial intelligence. Method: We used a fiber laser-based, label-free, non-consumptive, high-resolution microscopy method ($<$ 60 sec per 1 $\times$ 1 mm$^\text{2}$), called stimulated Raman histology (SRH), to image a consecutive, multicenter cohort of skull base tumor patients. SRH images were then used to train a convolutional neural network (CNN) model using three representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive learning. Our trained CNN models were tested on a held-out, multicenter SRH dataset. Results: SRH was able to image the diagnostic features of both benign and malignant skull base tumors. Of the three representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to identify tumor-normal margins and detect regions of microscopic tumor infiltration in whole-slide SRH images. Conclusion: SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making., Comment: Published as journal article

Published
2021
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18. English to Bengali Neural Machine Translation Using Global Attention Mechanism

Author

Abujar, Sheikh, Masum, Abu Kaisar Mohammad, Bhattacharya, Abhishek, Dutta, Soumi, Hossain, Syed Akhter, 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, Tavares, João Manuel R. S., editor, Chakrabarti, Satyajit, editor, Bhattacharya, Abhishek, editor, and Ghatak, Sujata, editor

Published
2021
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19. Classification of Focal and Non-focal EEG Signal for Epileptic Seizure Detection with Entropy Features Using KNN Classifier

Author

Samreen Fatima, N., Mariam Bee, M. K., Bhattacharya, Abhishek, Dutta, Soumi, 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, Tavares, João Manuel R. S., editor, Chakrabarti, Satyajit, editor, Bhattacharya, Abhishek, editor, and Ghatak, Sujata, editor

Published
2021
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20. Rhythmic Finger-Striking: A Memetic Computing-Inspired Musical Rhythm Improvisation Strategy

Author

Roy, Samarjit, Chakrabarty, Sudipta, De, Debashis, Bhattacharya, Abhishek, Dutta, Soumi, Ghatak, Sujata, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hassanien, Aboul Ella, editor, Bhattacharyya, Siddhartha, editor, Chakrabati, Satyajit, editor, Bhattacharya, Abhishek, editor, and Dutta, Soumi, editor

Published
2021
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21. COVID-R: A Deep Feature Learning-Based COVID-19 Rumors Detection Framework

Author

Paul, Tulika, Roy, Samarjit, Maity, Satanu, Bhattacharya, Abhishek, Dutta, Soumi, Ghatak, Sujata, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hassanien, Aboul Ella, editor, Bhattacharyya, Siddhartha, editor, Chakrabati, Satyajit, editor, Bhattacharya, Abhishek, editor, and Dutta, Soumi, editor

Published
2021
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22. An Automated Methodology for Epileptic Seizure Detection Using Random Forest Classifier

Author

Samreen Fathima, N., Mariam Bee, M. K., Bhattacharya, Abhishek, Dutta, Soumi, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hassanien, Aboul Ella, editor, Bhattacharyya, Siddhartha, editor, Chakrabati, Satyajit, editor, Bhattacharya, Abhishek, editor, and Dutta, Soumi, editor

Published
2021
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23. A Recommendation System for Customizable Items

Author

Sharma, Dhananjai, Choudhury, Tanupriya, Dewangan, Bhupesh Kumar, Bhattacharya, Abhishek, Dutta, Soumi, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hassanien, Aboul Ella, editor, Bhattacharyya, Siddhartha, editor, Chakrabati, Satyajit, editor, Bhattacharya, Abhishek, editor, and Dutta, Soumi, editor

Published
2021
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25. Cryptanalysis of a Chaotic Key based Image Encryption Scheme

Author

Mukherjee, Pratyusa, Rarhi, Krishnendu, and Bhattacharya, Abhishek

Subjects
Computer Science - Cryptography and Security
Abstract

Security of multimedia data is a major concern due to its widespread transmission over various communication channels. Hence design and study of good image encryption schemes has become a major research topic. During the last few decades, there has been a increasing in chaos-based cryptography. This paper proposes an attack on a recently proposed chaos based image encryption scheme. The cryptosystem under study proceed by first shuffling the original image to disturb the arrangement of pixels by applying a chaotic map several times. Second, a keystream is generated using Chen's chaotic system to mix it with the shuffled pixels to finally obtain the cipher image. A chosen ciphertext attack can be done to recover the system without any knowledge of the key. It simply demands two pairs of plaintext-ciphertext to completely break the cryptosystem.

Published
2018
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26. COZMO-A New Lightweight Stream Cipher

Author

Rarhi, Krishnendu, Bonnerji, Rhea, Sarkar, Simanta, and Bhattacharya, Abhishek

Subjects
Computer Science - Cryptography and Security
Abstract

This paper deals with the merger of the two lightweight stream ciphers: A5by1 and Trivium. The idea is to make the key stream generation more secure and to remove the attacks of the individual algorithms. The bits generated by the Trivium cipher will act as the input of the A5by1 cipher. The registers used in the A5by1 cipher will be filled by the output bits of the Trivium cipher. The three registers will then be connected to generate an output which will be our required key stream.

Published
2018
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27. Systematization of a 256-bit lightweight block cipher Marvin

Author

Saha, Sukanya, Rarhi, Krishnendu, and Bhattacharya, Abhishek

Subjects
Computer Science - Cryptography and Security
Abstract

In a world heavily loaded by information, there is a great need for keeping specific information secure from adversaries. The rapid growth in the research field of lightweight cryptography can be seen from the list of the number of lightweight stream as well as block ciphers that has been proposed in the recent years. This paper focuses only on the subject of lightweight block ciphers. In this paper, we have proposed a new 256 bit lightweight block cipher named as Marvin, that belongs to the family of Extended LS designs., Comment: 12 pages,6 figures

Published
2018

30. Forecasting the Growth in Covid-19 Infection Rates

Author

Dutta, Soumi, primary, Bhattacharya, Abhishek, additional, Das, Prithwidip, additional, Pal, Shayan, additional, Mandal, Ratna, additional, Obaid, Ahmed J., additional, Lai, Wen Cheng, additional, Agarwal, Ambuj Kumar, additional, and Soufiene, Ben Othman, additional

Published
2022
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31. Mulsemedia Data Representation Based on Multi-image Concept

Author

Sulema, Yevgeniya, Bhattacharya, Abhishek, Murray, Niall, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Auer, Michael E., editor, and Tsiatsos, Thrasyvoulos, editor

Published
2021
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32. Magnetic properties of photosynthetic materials - a nano scale study

Author

Bhattacharya, Abhishek, Raja, Sufi O, Ahmed, Md. A, Bandyopadhyay, Sudip, and Dasgupta, Anjan Kr.

Subjects
Quantitative Biology - Biomolecules, Physics - Biological Physics, Physics - Chemical Physics
Abstract

Photosynthetic materials form the basis of quantum biology. An important attribute of quantum biology is correlation and coherence of spin states. Such correlated spin states are targets of static magnetic field. In this paper, we report magnetic properties and spectroscopically realizable static magnetic field effect in photosynthetic materials. Two classes of nano-scale assembly of chlorophyll (NC) are used for such a study. Magnetic measurements are made using a superconducting quantum interference device (SQUID). Both ferromagnetic and superparamagnetic states are observed in NC along with a blocking temperature around 250 K. Low temperature quantum (liquid nitrogen) spectroscopy is employed to see how optical transitions are affected in presence of static magnetic field. Plausible practical application aspects of magnetic properties of this optically active material are discussed in the text., Comment: 10 Pages 6 Figures

Published
2017

33. Cryptanalysis of a Chaotic Key-Based Image Encryption Scheme

Author

Mukherjee, Pratyusa, Rarhi, Krishnendu, Mishra, Abhishek, Bhattacharya, Abhishek, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Dutta, Paramartha, editor, Mandal, Jyotsna Kumar, editor, Bhattacharya, Abhishek, editor, and Dutta, Soumi, editor

Published
2019
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34. Community Detection Based Tweet Summarization

Author

Dutta, Soumi, Das, Asit Kumar, Bhattacharya, Abhishek, Dutta, Gourav, Parikh, Komal K., Das, Atin, Ganguly, Dipsa, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Dutta, Paramartha, editor, Mandal, Jyotsna Kumar, editor, Bhattacharya, Abhishek, editor, and Dutta, Soumi, editor

Published
2019
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35. Recent Trends of Data Mining in Cloud Computing

Author

Sarkar, Abhijit, Bhattacharya, Abhishek, Dutta, Soumi, Parikh, Komal K., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Dutta, Paramartha, editor, Mandal, Jyotsna Kumar, editor, Bhattacharya, Abhishek, editor, and Dutta, Soumi, editor

Published
2019
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37. Precision Diagnosis Of Melanoma And Other Skin Lesions From Digital Images.

Author

Bhattacharya, Abhishek, Young, Albert, Wong, Andrew, Stalling, Simone, Wei, Maria, and Hadley, Dexter

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Cancer, Clinical Research, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, 4.1 Discovery and preclinical testing of markers and technologies, Good Health and Well Being
Abstract

Melanoma will affect an estimated 73,000 new cases this year and result in 9,000 deaths, yet precise diagnosis remains a serious problem. Without early detection and preventative care, melanoma can quickly spread to become fatal (Stage IV 5-year survival rate is 20-10%) from a once localized skin lesion (Stage IA 5- year survival rate is 97%). There is no biomarker for melanoma in clinical use, and the current diagnostic criteria for skin lesions remains subjective and imprecise. Accurate diagnosis of melanoma relies on a histopathologic gold standard; thus, aggressive excision of melanocytic skin lesions has been the mainstay of treatment. It is estimated that 36 biopsies are performed for every melanoma confirmed by pathology among excised lesions. There is significant morbidity in misdiagnosing melanoma such as progression of the disease for a false negative prediction vs the risks of unnecessary surgery for a false positive prediction. Every year, poor diagnostic precision adds an estimated $673 million in overall cost to manage the disease. Currently, manual dermatoscopic imaging is the standard of care in selecting atypical skin lesions for biopsy, and at best it achieves 90% sensitivity but only 59% specificity when performed by an expert dermatologist. Many computer vision (CV) algorithms perform better than dermatologists in classifying skin lesions although not significantly so in clinical practice. Meanwhile, open source deep learning (DL) techniques in CV have been gaining dominance since 2012 for image classification, and today DL can outperform humans in classifying millions of digital images with less than 5% error rates. Moreover, DL algorithms are readily run on commoditized hardware and have a strong online community of developers supporting their rapid adoption. In this work, we performed a successful pilot study to show proof of concept to DL skin pathology from images. However, DL algorithms must be trained on very large labelled datasets of images to achieve high accuracy. Here, we begin to assemble a large imageset of skin lesions from the UCSF and the San Francisco Veterans Affairs Medical Center (VAMC) dermatology clinics that are well characterized by their underlying pathology, on which to train DL algorithms. If trained on sufficient data, we hypothesize that our approach will significantly outperform general dermatologists in predicting skin lesion pathology. We posit that our work will allow for precision diagnosis of melanoma from widely available digital photography, which may optimize the management of the disease by decreasing unnecessary office visits and the significant morbidity and cost of melanoma misdiagnosis.

Published
2017

40. VHDL Modeling of Intrusion Detection & Prevention System (IDPS) A Neural Network Approach

Author

Chatterjee, Tanusree and Bhattacharya, Abhishek

Subjects
Computer Science - Cryptography and Security
Abstract

The rapid development and expansion of World Wide Web and network systems have changed the computing world in the last decade and also equipped the intruders and hackers with new facilities for their destructive purposes. The cost of temporary or permanent damages caused by unauthorized access of the intruders to computer systems has urged different organizations to increasingly implement various systems to monitor data flow in their network. The systems are generally known as Intrusion Detection System (IDS).Our objective is to implement an artificial network approach to the design of intrusion detection and prevention system and finally convert the designed model to a VHDL (Very High Speed Integrated Circuit Hardware Descriptive Language) code. This feature enables the system to suggest proper actions against possible attacks. The promising results of the present study show the potential applicability of ANNs for developing practical IDSs.

Published
2014
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41. An Estimation Method of Measuring Image Quality for Compressed Images of Human Face

Author

Bhattacharya, Abhishek and Chatterjee, Tanusree

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Nowadays digital image compression and decompression techniques are very much important. So our aim is to calculate the quality of face and other regions of the compressed image with respect to the original image. Image segmentation is typically used to locate objects and boundaries (lines, curves etc.)in images. After segmentation the image is changed into something which is more meaningful to analyze. Using Universal Image Quality Index(Q),Structural Similarity Index(SSIM) and Gradient-based Structural Similarity Index(G-SSIM) it can be shown that face region is less compressed than any other region of the image., Comment: 6 pages

Published
2014
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47. Nonparametric Bayes Classification via Learning of Affine Subspaces

Author

Bhattacharya, Abhishek

Subjects
Statistics - Methodology
Abstract

The goal of this presentation is to build an efficient non-parametric Bayes classifier in the presence of large numbers of predictors. When analyzing such data, parametric models are often too inflexible while non-parametric procedures tend to be non-robust because of insufficient data on these high dimensional spaces. When dealing with these types of data, it is often the case that most of the variability tends to lie along a few directions, or more generally along a much smaller dimensional subspace of the feature space. Hence a class of regression models is proposed that flexibly learn about this subspace while simultaneously performing dimension reduction in classification. This methodology, allows the cell probabilities to vary non-parametrically based on a few coordinates expressed as linear combinations of the predictors. Also, as opposed to many black-box methods for dimensionality reduction, the proposed model is appealing in having clearly interpretable and identifiable parameters which provide insight into which predictors are important in determining accurate classification boundaries. Gibbs sampling methods are developed for posterior computations. The estimated cell probabilities are theoretically shown to be consistent, and real data applications are included to support the findings., Comment: Presented at the ISBA Regional Meeting and International Workshop/Conference on Bayesian Theory and Applications (IWCBTA) organized by the DST Centre for Interdisciplinary Mathematical Sciences, Banaras Hindu University, India on January 10, 2013

Published
2013

49. Resampling in Time Series Models

Author

Bhattacharya, Abhishek and Bose, Arup

Subjects
Mathematics - Statistics Theory
Abstract

This project revolves around studying estimators for parameters in different Time Series models and studying their assymptotic properties. We introduce various bootstrap techniques for the estimators obtained. Our special emphasis is on Weighted Bootstrap. We establish the consistency of this scheme in a AR model and its variations. Numerical calculations lend further support to our consistency results. Next we analyze ARCH models, and study various estimators used for different error distributions. We also present resampling techniques for estimating the distribution of the estimators. Finally by simulating data, we analyze the numerical properties of the estimators., Comment: Master's Project

Published
2012

50. Density Estimation and Classification via Bayesian Nonparametric Learning of Affine Subspaces

Author

Bhattacharya, Abhishek, Page, Garritt, and Dunson, David

Subjects
Statistics - Methodology
Abstract

It is now practically the norm for data to be very high dimensional in areas such as genetics, machine vision, image analysis and many others. When analyzing such data, parametric models are often too inflexible while nonparametric procedures tend to be non-robust because of insufficient data on these high dimensional spaces. It is often the case with high-dimensional data that most of the variability tends to be along a few directions, or more generally along a much smaller dimensional submanifold of the data space. In this article, we propose a class of models that flexibly learn about this submanifold and its dimension which simultaneously performs dimension reduction. As a result, density estimation is carried out efficiently. When performing classification with a large predictor space, our approach allows the category probabilities to vary nonparametrically with a few features expressed as linear combinations of the predictors. As opposed to many black-box methods for dimensionality reduction, the proposed model is appealing in having clearly interpretable and identifiable parameters. Gibbs sampling methods are developed for posterior computation, and the methods are illustrated in simulated and real data applications.

Published
2011

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