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Şu anda burada saat 2:53 ÖS
Ocak 16, 2016 tarihinde katıldı
2 Tavsiye

Bhargav D.

@bhargavdave

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4,9 (27 değerlendirme)
4,0
4,0
$15 USD / saat
   INDIA bayrağı
nadiad, india
$15 USD / saat
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Tekrar İşe Alınma Oranı

Machine Learning and Deep Learning Expert

I just passed [login to view URL] with a Machine learning specialization from DAIICT, Gandhinagar. Currently, I am a member of the IRNLP lab at DAIICT and Junior Research Fellow (JRF) in a project MHRD sponsored project with a joint consortium of IIT Kharagpur, IIT Bombay, IIT Patna, DA-IICT and AU-KBC. I also published a paper, "Query specific graph-based query reformulation using UMLS for clinical information access" in the Journal of Biomedical Informatics. Recently I got 3rd rank in shared task on offensive language identification in Dravidian languages in DravidianLangTech@EACL2021 and 2nd rank in shared task on hope speech detection for equality, Diversity, and Inclusion. I am one of the organizers of the shared task on event detection from news in Indian languages (EDNIL) @ FIRE2020. Also I am organizer of Forum for Information Retrieval Evaluation (FIRE).
Freelancer Machine Learning Experts India

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Portföy Ögeleri

Sentiment Analysis of Review of Review using Logistic Regression, Random Forest, SVM, XgBoost, LSTM etc
Sentiment Analysis of Review
Sentiment Analysis of Review of Review using Logistic Regression, Random Forest, SVM, XgBoost, LSTM etc
Sentiment Analysis of Review
Sentiment Analysis of Review of Review using Logistic Regression, Random Forest, SVM, XgBoost, LSTM etc
Sentiment Analysis of Review
Customer Churn Prediction using Machine Learning and Deep Learning Algo
Customer Churn Prediction
Customer Churn Prediction using Machine Learning and Deep Learning Algo
Customer Churn Prediction
Below SVM Kernal implemented form scrach in this Project
1)Gaussian kernel
2)inear kernel
3)Polynomial Kernel
4)Sigmoid Kernel
5)Multiquadric Kernel
6)Cauchy kernel
7)TStudent Kernel
SVM Kernal Kernal From Scrach
For Fake User Detection happen in three part, 
1) Extraction  of profile features 
2)  Random Forest (RF), Naïve Bayes (NB) and Decision Tree (DT) used to determine the fake and genuine User. 
3) Calculate and Compare  of the accuracy rates across the results of all techniques.
Fake User Classfication
For Fake User Detection happen in three part, 
1) Extraction  of profile features 
2)  Random Forest (RF), Naïve Bayes (NB) and Decision Tree (DT) used to determine the fake and genuine User. 
3) Calculate and Compare  of the accuracy rates across the results of all techniques.
Fake User Classfication
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Website for Supplier Compney
Supplier static website
Website for Supplier Compney
Supplier static website
Website for Supplier Compney

Değerlendirmeler

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$10,00 USD
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T
    bayrağı Olea Jacquelyn P. @tori100
2 ay önce
5,0
$20,00 USD
He is good and too professional
Python Machine Learning (ML) Deep Learning
Kullanıcı Avatarı
    bayrağı Neha G. @gtmneha
2 ay önce
5,0
₹14.000,00 INR
Best support and timely work
Python Web Scraping Machine Learning (ML) NLP
Kullanıcı Avatarı
    bayrağı GSFGMICROLOG PVT LTD @GSFG123
3 ay önce
5,0
₹800,00 INR
simply awesome and quality work.
Research Scientific Research Research Writing LaTeX
M
    bayrağı Moulesh M. @mouleshm1710
4 ay önce
4,8
₹4.500,00 INR
Good work , executed timely
Python Machine Learning (ML)
A
    bayrağı Avinash J. @avi6555
4 ay önce

Deneyim

Junior Research Assistant

Dhirubhai Ambani Institute of Information and Communication Technology
Ağu 2019 - Şu anda
A platform for Cross-lingual and Multilingual Event Monitoring in Indian Languages. The project aims to build a cross-lingual, multi-lingual, multi-source and multi-domain platform for information extraction (IE) in Indian languages (ILs) to extract and classify events of interest from news media and to demonstrate the application in a few domains. This project is subpart of the project named “Imprint” which is funded by MHRD (Ministry of Human Resource and Development)

Web Devloper

A.live Mind
Oca 2017 - Tem 2018 (1 yıl, 6 ay)
I am working in a.live mind as Web Developer from last six month.I work on many websites as front end and backend developer in the company.Mainly i use Html,CSS,Javascript,PHP,Jquery for web design. I use PHP codigneter framework in back end.I am very well in it now.

Eğitim

M.tech in ICT with Machine Learning Specialization

Dhirubhai Ambani Institute of Information and Communication Technology, India 2018 - 2020
(2 yıl)

B.tech Information Tecnology

Dharmsinh Desai Institute of Technology, India 2013 - 2017
(4 yıl)

Yayınlar

IRNLP_DAIICT@DravidianLangTech-EACL2021:Offensive Language identification in Dravidian Languages

2021
This paper presents the participation of the IRNLPDAIICT team from the Information Retrieval and Natural Language Processing lab at DA-IICT, India in DravidianLangTech-EACL2021 Offensive Language identification in Dravidian Languages. https://www.aclweb.org/anthology/2021.dravidianlangtech-1.37/

Overview of the FIRE 2020 EDNIL track: Event Detection from News in Indian Languages

2020
The goal of FIRE 2020 EDNIL track was to create a framework which could be used to detect events from news articles in English, Hindi, Bengali, Marathi and Tamil. The track consisted of two tasks:(i) Identifying a piece of text from news articles that contains an event (Event Identification).(ii) Creating an event frame from the news article (Event Frame Extraction).

IRNLP_DAIICT@LT-EDI-EACL2021: Hope Speech detection in Code Mixed text using TF-IDF & MuRIL

2021
This paper presents the participation of the IRNLP_DAIICT team from the Information Retrieval and Natural Language Processing lab at DA-IICT, India in LT-EDI@EACL2021 Hope Speech Detection task. https://www.aclweb.org/anthology/2021.ltedi-1.15/

FIRE 2020 EDNIL Track: Event Detection from News in Indian Languages

FIRE 2020: Forum for Information Retrieval Evaluation
The goal of the FIRE 2020 EDNIL track was to create a framework that could be used to detect events from news articles in English, Hindi, Bengali, Marathi, and Tamil. The track consisted of two tasks: (I) Identifying a piece of text from news articles that contain an event(Event Identification). (ii) Creating an event frame from the news article (Event Frame Extraction). The events that were identified in the Event Identification task were Man-made Disasters and Natural disasters.

Query specific graph-based query reformulation using UMLS for clinical information access

Journal of Biomedical Informatics
This paper explores the usage and impact of UMLS for entity-based query reformulation in biomedical document retrieval. A novel graph-based approach for query reformulation using UMLS is described herein which queries are expanded using biomedical entities. The proposed method considers UMLS entities from a query with their related entities identified by UMLS and constructs a query-specific graph of biomedical entities for term selection.

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Machine Learning (ML) 21 Python 15 Natural Language 6 Deep Learning 4 LaTeX 3

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