301 research outputs found
Deep learning applications in medical imaging Advances in medical technologies and clinical practice book series./ [edited by] Sanjay Saxena, Sudip Paul.
No full text"Premier Reference Source" -- Cover.Includes bibliographical references and index."This book explores the application deep learning in medical imaging"--Relevance of Machine Learning to Cardiovascular Imaging / Sumesh Sasidharan, Mohammad Salmasi, Selene Pirola, Omar Jarral -- Deep Learning Applications in Medical Imaging : Artificial Intelligence, Machine Learning and Deep Learning / S. Sasikala, S.J. Subhashini, P. Alli, J. Jane Rubel Angelina -- A Survey on Prematurity Detection of Diabetic Retinopathy Based on Fundus Images using Deep Learning Techniques / Amiya Dash, Puspanjali Mohapatra -- Malaria Parasites Detection Using Deep Neural Network / Biswajit Jena, Pulkit Thakar, Vedanta Nayak, Gopal Nayak, Sanjay Saxena -- Deep Learning for Medical Image Segmentation / Kanchan Sarkar, Bohang Li -- Current Trends in Integrating the Concept of Deep Learning in Medical Imaging / Kavitha S. Velammal, Anchitaalagammai J.V., S Murali, Grace Shalini T. -- A CONVblock For Convolutional Neural Networks / Hmidi Alaeddine, Malek Jihene -- Machine Learning for Prediction of Lung Cancer / Nikita Banerjee, Subhalaxmi Das -- Conventional and Non Conventional ANN's in Medical Diagnostics A Tutorial Survey of Architectures, Algorithms and Application / Devika G., Asha Karegowda.1 online resource
Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach
Get PDF[EN] Before the advent of the Internet era, code-mixing was mainly used in the spoken form. However, with the recent popular informal networking platforms such as Facebook, Twitter, Instagram, etc., in social media, code-mixing is being used more and more in written form. User-generated social media content is becoming an increasingly important resource in applied linguistics. Recent trends in social media usage have led to a proliferation of studies on social media content. Multilingual social media users often write native language content in non-native script (cross-script). Recently Banerjee et al. [9] introduced the code-mixed cross-script question answering research problem and reported that the ever increasing social media content could serve as a potential digital resource for less-computerized languages to build question answering systems. Question classification is a core task in question answering in which questions are assigned a class or a number of classes which denote the expected answer type(s). In this research work, we address the question classification task as part of the code-mixed cross-script question answering research problem. We combine deep learning framework with feature engineering to address the question classification task and enhance the state-of-the-art question classification accuracy by over 4% for code-mixed cross-script questions.The work of the third author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project.Banerjee, S.; Kumar Naskar, S.; Rosso, P.; Bandyopadhyay, S. (2018). Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach. Journal of Intelligent & Fuzzy Systems. 34(5):2959-2969. https://doi.org/10.3233/JIFS-169481S2959296934
MSIR@FIRE: A Comprehensive Report from 2013 to 2016
Get PDF[EN] India is a nation of geographical and cultural diversity where over 1600 dialects are spoken by the people. With the technological advancement, penetration of the internet and cheaper access to mobile data, India has recently seen a sudden growth
of internet users. These Indian internet users generate contents either in English or in other vernacular Indian languages.
To develop technological solutions for the contents generated by the Indian users using the Indian languages, the Forum
for Information Retrieval Evaluation (FIRE) was established and held for the first time in 2008. Although Indian languages
are written using indigenous scripts, often websites and user-generated content (such as tweets and blogs) in these Indian
languages are written using Roman script due to various socio-cultural and technological reasons. A challenge that search
engines face while processing transliterated queries and documents is that of extensive spelling variation. MSIR track was
first introduced in 2013 at FIRE and the aim of MSIR was to systematically formalize several research problems that one must
solve to tackle the code mixing in Web search for users of many languages around the world, develop related data sets, test
benches and most importantly, build a research community focusing on this important problem that has received very little attention. This document is a comprehensive report on the 4 years of MSIR track evaluated at FIRE between 2013 and 2016.Somnath Banerjee and Sudip Kumar Naskar are supported by Media Lab Asia, MeitY, Government of India, under the Visvesvaraya PhD Scheme for Electronics & IT. The work of Paolo Rosso was partially supported by the MISMIS research project PGC2018-096212-B-C31 funded by the Spanish MICINN.Banerjee, S.; Choudhury, M.; Chakma, K.; Kumar Naskar, S.; Das, A.; Bandyopadhyay, S.; Rosso, P. (2020). MSIR@FIRE: A Comprehensive Report from 2013 to 2016. SN Computer Science. 1(55):1-15. https://doi.org/10.1007/s42979-019-0058-0S115155Ahmed UZ, Bali K, Choudhury M, Sowmya VB. Challenges in designing input method editors for Indian languages: the role of word-origin and context. In: Advances in text input methods (WTIM 2011). 2011. pp. 1–9Banerjee S, Chakma K, Naskar SK, Das A, Rosso P, Bandyopadhyay S, Choudhury M. Overview of the mixed script information retrieval (MSIR) at fire-2016. In: Forum for information retrieval evaluation. Springer; 2016. pp. 39–49.Banerjee S, Kuila A, Roy A, Naskar SK, Rosso P, Bandyopadhyay S. A hybrid approach for transliterated word-level language identification: CRF with post-processing heuristics. In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 54–59.Banerjee S, Naskar S, Rosso P, Bandyopadhyay S. Code mixed cross script factoid question classification—a deep learning approach. J Intell Fuzzy Syst. 2018;34(5):2959–69.Banerjee S, Naskar SK, Rosso P, Bandyopadhyay S. The first cross-script code-mixed question answering corpus. In: Proceedings of the workshop on modeling, learning and mining for cross/multilinguality (MultiLingMine 2016), co-located with the 38th European Conference on Information Retrieval (ECIR). 2016.Banerjee S, Naskar SK, Rosso P, Bandyopadhyay S. Named entity recognition on code-mixed cross-script social media content. Comput Sistemas. 2017;21(4):681–92.Barman U, Das A, Wagner J, Foster J. Code mixing: a challenge for language identification in the language of social media. In: Proceedings of the first workshop on computational approaches to code switching. 2014. pp. 13–23.Bhardwaj P, Pakray P, Bajpeyee V, Taneja A. Information retrieval on code-mixed Hindi–English tweets. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. 2016.Bhargava R, Khandelwal S, Bhatia A, Sharmai Y. Modeling classifier for code mixed cross script questions. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Bhattacharjee D, Bhattacharya, P. Ensemble classifier based approach for code-mixed cross-script question classification. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Chakma K, Das A. CMIR: a corpus for evaluation of code mixed information retrieval of Hindi–English tweets. In: The 17th international conference on intelligent text processing and computational linguistics (CICLING). 2016.Choudhury M, Chittaranjan G, Gupta P, Das A. Overview of fire 2014 track on transliterated search. Proceedings of FIRE. 2014. pp. 68–89.Ganguly D, Pal S, Jones GJ. Dcu@fire-2014: fuzzy queries with rule-based normalization for mixed script information retrieval. In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 80–85.Gella S, Sharma J, Bali K. Query word labeling and back transliteration for Indian languages: shared task system description. FIRE Working Notes. 2013;3.Gupta DK, Kumar S, Ekbal A. Machine learning approach for language identification and transliteration. In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 60–64.Gupta P, Bali K, Banchs RE, Choudhury M, Rosso P. Query expansion for mixed-script information retrieval. In: Proceedings of the 37th international ACM SIGIR conference on research and development in information retrieval, ACM, 2014. pp. 677–686.Gupta P, Rosso P, Banchs RE. Encoding transliteration variation through dimensionality reduction: fire shared task on transliterated search. In: Fifth forum for information retrieval evaluation. 2013.HB Barathi Ganesh, M Anand Kumar, KP Soman. Distributional semantic representation for information retrieval. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. 2016.HB Barathi Ganesh, M Anand Kumar, KP Soman. Distributional semantic representation for text classification. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Järvelin K, Kekäläinen J. Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst. 2002;20:422–46. https://doi.org/10.1145/582415.582418.Joshi H, Bhatt A, Patel H. Transliterated search using syllabification approach. In: Forum for information retrieval evaluation. 2013.King B, Abney S. Labeling the languages of words in mixed-language documents using weakly supervised methods. In: Proceedings of NAACL-HLT, 2013. pp. 1110–1119.Londhe N, Srihari RK. Exploiting named entity mentions towards code mixed IR: working notes for the UB system submission for MSIR@FIRE’16. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. 2016.Anand Kumar M, Soman KP. Amrita-CEN@MSIR-FIRE2016: Code-mixed question classification using BoWs and RNN embeddings. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Majumder G, Pakray P. NLP-NITMZ@MSIR 2016 system for code-mixed cross-script question classification. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Mandal S, Banerjee S, Naskar SK, Rosso P, Bandyopadhyay S. Adaptive voting in multiple classifier systems for word level language identification. In: FIRE workshops, 2015. pp. 47–50.Mukherjee A, Ravi A , Datta K. Mixed-script query labelling using supervised learning and ad hoc retrieval using sub word indexing. In: Proceedings of the Forum for Information Retrieval Evaluation, Bangalore, India, 2014.Pakray P, Bhaskar P. Transliterated search system for Indian languages. In: Pre-proceedings of the 5th FIRE-2013 workshop, forum for information retrieval evaluation (FIRE). 2013.Patel S, Desai V. Liga and syllabification approach for language identification and back transliteration: a shared task report by da-iict. In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 43–47.Prabhakar DK, Pal S. Ism@fire-2013 shared task on transliterated search. In: Post-Proceedings of the 4th and 5th workshops of the forum for information retrieval evaluation, ACM, 2013. p. 17.Prabhakar DK, Pal S. Ism@ fire-2015: mixed script information retrieval. In: FIRE workshops. 2015. pp. 55–58.Prakash A, Saha SK. A relevance feedback based approach for mixed script transliterated text search: shared task report by bit Mesra. In: Proceedings of the Forum for Information Retrieval Evaluation, Bangalore, India, 2014.Raj A, Karfa S. A list-searching based approach for language identification in bilingual text: shared task report by asterisk. In: Working notes of the shared task on transliterated search at forum for information retrieval evaluation FIRE’14. 2014.Roy RS, Choudhury M, Majumder P, Agarwal K. Overview of the fire 2013 track on transliterated search. In: Post-proceedings of the 4th and 5th workshops of the forum for information retrieval evaluation, ACM, 2013. p. 4.Saini A. Code mixed cross script question classification. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Salton G, McGill MJ. Introduction to modern information retrieval. New York: McGraw-Hill, Inc.; 1986.Sequiera R, Choudhury M, Gupta P, Rosso P, Kumar S, Banerjee S, Naskar SK, Bandyopadhyay S, Chittaranjan G, Das A, et al. Overview of fire-2015 shared task on mixed script information retrieval. FIRE Workshops. 2015;1587:19–25.Singh S, M Anand Kumar, KP Soman. CEN@Amrita: information retrieval on code mixed Hindi–English tweets using vector space models. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. 2016.Sinha N, Srinivasa G. Hindi–English language identification, named entity recognition and back transliteration: shared task system description. In: Working notes os shared task on transliterated search at forum for information retrieval evaluation FIRE’14. 2014.Voorhees EM, Tice DM. The TREC-8 question answering track evaluation. In: TREC-8, 1999. pp. 83–105.Vyas Y, Gella S, Sharma J, Bali K, Choudhury M. Pos tagging of English–Hindi code-mixed social media content. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014. pp. 974–979
General banking system Of IFIC Bank Ltd.
No full textThis internship report is submitted in a partial fulfillment of the requirements for the degree of Masters of Business Administration, 2015.Cataloged from PDF version of Internship report.Includes bibliographical references (page 54).The principal reason of banks chartered by the government and the central bank is to make loans
to their customers. Banks are expected to support their communities with an adequate supply of
credit for all legitimate business and financial needs of consumer and to price that credit
reasonably in line with competitively determined interest rates. Indeed, making loans is the
principal economic function of banks to fund consumption and investment spending by
businesses, individuals, and units of government. How well a bank performs its function has a
great deal to do with the economic health of any region, because banking performance support
the growth of new businesses and jobs within the banks trade territory and promote economic
vitality. Moreover, bank loans often seem to convey positive information to the marketplace
about a borrower’s credit quality, enabling a borrower to obtain more and perhaps somewhat
cheaper funds from other sources.
This report explores IFIC Bank’s activities as one of the leading non-government organization.
This report contains information about all commercial activities that the bank deals with. I have
mainly focused on General Banking system of IFIC Bank Limited in this report.
General banking operation includes all the general activities performed by the bank. I have
discussed about different types of account holder and different types of account such as Saving
Account, Current Account, Fixed Deposit Rate (FDR), Pension Saving Scheme (PSS) & other
existing accounts with their rates and other activities of General Banking in detail.Sudip BanerjeeM. Business Administration
Issues Arises after Implementation of GST in India
No full textAfter independence of India the biggest reform of indirect taxation is GST It was supposed to be implemented from April 2010 but due to economic and political reasons it was long pending After implantation of GST on 1st July 2017 there are lot of issues arises in the ground level This paper is highlighting the challenges faced by Government of India after implementatio
Scientometric Portrait of Joan C. Durrance, a Respected Researcher in the Community Focused Library Services
Get PDFThe present study attempted to prepare the scientometric portrait of Joan C Durrance, a respected researcher in the community focused library services and a pioneer in the field of community informatics. The study focused on the aspects like Year and age wise publication output, Authorship pattern, Document types, Ranking of Collaborative authors, Preferred journal for communication of research results, Distribution of citations, and Ranking of top cited papers. She has contributed 165 publications including 39 seminar presentations since 1977 to 2011. Year wise growth indicated that she contributed the maximum number of scholarly output in 1996 at the age of 58 years. The pattern and other measures of authorship displayed its strength upon single authored publication. Among the document types most of the documents were Books/ Book Chapters followed by Journal articles. The most preferred journal by Durrance for the publication of her research results was Public Libraries with 7 publications. The top ranked co-author of Joan C Durrance is K. E. Fisher who co-authored 40 papers in 10 years of contributing ages. 61 publications (48.41%) received 2595 citations with an average of 20.6 citations per paper, 65 publications (51.58%) still remained uncited. The most cited paper of Durrance is a journal article published in 2004 and received 434 citations till date. The findings of this study will be beneficial for the researchers of LIS and Scientometric domain
Library and Information Science Literature in India: An Examination of Author-Assigned Keywords
No full textThe Study of articles published in Library and Information Science Journals in recent times suggests that author assigned keywords are largely uncontrolled. Despite knowledge of controlled vocabulary they mostly use NL phrases to represent the content. This paper examines keywords assigned by authors to papers published in Library and information Science journals in India in the last fifteen years (1998-2012).</jats:p
UMBC at SemEval-2018 Task 8: Understanding Text about Malware
No full textProceedings of International Workshop on Semantic Evaluation (SemEval-2018)We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing). We participated in three of the sub-tasks: (1) classifying sentences as being relevant or irrelevant to malware, (2) predicting token labels for sentences, and (4) predicting attribute labels from the Malware Attribute Enumeration and Characterization vocabulary for defining malware characteristics. We achieved F1 scores of 50.34/18.0 (dev/test), 22.23 (test-data), and 31.98 (test-data) for Task1, Task2 and Task2 respectively. We also make our cybersecurity embeddings publicly available at https://bit.ly/cybr2vec.The research described in this paper was partially supported by gifts from IBM and Northrop Grumman. We thank Agniva Banerjee, Sudip Mittal, Sandeep Narayanan, Maithilee Prabodh, Vishal Rathod, and Arya Renjan for helping with annotations.https://www.aclweb.org/anthology/S18-1142
Combining multi-domain statistical machine translation models using automatic classifiers
Get PDFThis paper presents a set of experiments on Domain Adaptation of Statistical Machine Translation systems. The experiments focus on Chinese-English and two domain-specific
corpora. The paper presents a novel approach for combining multiple domain-trained translation models to achieve improved translation quality for both domain-specific as well as combined sets of sentences. We train a statistical
classifier to classify sentences according to the appropriate domain and utilize the corresponding domain-specific MT models to translate them. Experimental results show that the method achieves a statistically significant
absolute improvement of 1.58 BLEU (2.86% relative improvement) score over a translation model trained on combined data, and considerable improvements over a model using multiple decoding paths of the Moses decoder, for the combined domain test set. Furthermore, even for domain-specific test sets, our approach works almost as well as dedicated domain-specific models and perfect classification
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