DR. SHARAD GANGELE

@rkdf.ac.in

Professor & Dean, Department of Computer Science& Application, RKDF University, Bhopal MP
RKDF University, Bhopal MP

DR. SHARAD GANGELE


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Dr. Sharad Gangele, Science), M.Tech(CSE), M.Sc(Computer Science) and M.Sc(Statistics) is presently working as a Principal, Bhabha College of Engineering and Professor & Dean, Department of Computer Science & Application, RKDF University, Bhopal MP. He has 13 years of Experience in the field of Computer Science and Statistics. He completed his graduation from Dr. Hari Singh Gour Central University, Sagar MP in 2003, Master’s degree in Statistics from DAVV, Indore MP in 2005, M.Sc(CS) from MCRPEV, Bhopal MP in 2008, M.Tech(CSE) from People’s University Bhopal MP in 2017 and Science) from M.P. BhoJ University, Bhopal MP in 2013. He has throughout first division academic record in education. He has published more than 40 research papers in National and International Journals and presented 15 various scientific research papers at numerous National & International platform. He has published 4 books, 3 book chapters and 2 Indian patents.

EDUCATION

Ph.D. (Computer Science), MTech (CSE), M.Sc. (CS), M.Sc. (Statistics)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Statistics and Probability, Computer Science Applications, Statistics, Probability and Uncertainty
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Scopus Publications

Scopus Publications

  • Fake news detection: A RNN-LSTM, Bi-LSTM based deep learning approach
    Govind Singh Mahara, Sharad Gangele
    2022 IEEE 1st International Conference on Data Decision and Systems Icdds 2022, 2022
    Fake news is a new phenomenon that promotes misleading information and fraud via internet social media or traditional news sources. Fake news is readily manufactured and transmitted across numerous social media platforms nowadays, and it has a significant influence on the real world. It is vital to create effective algorithms and tools for detecting misleading information on social media platforms. Most modern research approaches for identifying fraudulent information are based on machine learning, deep learning, feature engineering, graph mining, image and video analysis, and newly built datasets and online services. There is a pressing need to develop a viable approach for readily detecting misleading information. The deep learning LSTM and Bi-LSTM model was proposed as a method for detecting fake news, In this work. First, the NLTK toolkit was used to remove stop words, punctuation, and special characters from the text. The same toolset is used to tokenize and preprocess the text. Since then, GLOVE word embeddings have incorporated higher-level characteristics of the input text extracted from long-term relationships between word sequences captured by the RNN-LSTM, Bi-LSTM model to the preprocessed text. The proposed model additionally employs dropout technology with Dense layers to improve the model's efficiency. The proposed RNN Bi-LSTM-based technique obtains the best accuracy of 94%, and 93% using the Adam optimizer and the Binary cross-entropy loss function with Dropout (0.1,0.2), Once the Dropout range increases it decreases the accuracy of the model as it goes 92% once Dropout (0.3).
  • Female infertility investigation and statistics
    International Journal on Emerging Technologies, 2019
  • Influence of human resource management on organizational efficiency and effectiveness within ADNOC in UAE
    International Journal on Emerging Technologies, 2019
  • Algorithm: Fuzzy Prognosis of Prolactin Hormone
    12th Indiacom 5th International Conference on Computing for Sustainable Global Development Indiacom 2018, 2018