DHARMENDRA DANGI
@iiitbhopal.ac.in
ASSISTANT PROFESSOR
IIIT Bhopal
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Dharmendra Dangi, Sonal Telang Chandel, Dheeraj Kumar Dixit, Suvarna Sharma, and Amit Bhagat
Elsevier BV
Dheeraj Kumar Dixit, Amit Bhagat, and Dharmendra Dangi
Wiley
SummaryPeople can quickly acquire the news through a variety of sources, including websites, blogs, and social media, among others. The spread of fake news has become easier as a result of the availability of these platforms. Anybody with access to these networks generates and distributes fake news for professional or personal gain. Numerous studies relying on supervised and unsupervised learning techniques are available to address the issue of recognizing fake news. All of those studies, though, have one flaw: they all deliver mostly inaccurate or unmatched results. Poor accuracy is attributed to a variety of factors, including imbalanced datasets, inefficient parameter tuning, poor feature selection, and so on. To tackle these issues, we proposed a novel approach for fake news detection. Initially, the data were obtained from the ISOT dataset and data cleaning is performed. After that, preprocessing is done which includes three major steps such as stemming, stop word removal, and tokenization are carried out. Next to preprocessing, various features that involve name entity recognition‐based features are selected during feature extraction. From this, the short dimensionality features are selected with the help of the ensemble modified independent component analysis model. Finally, the hybrid convolutional neural network‐based Levy flight‐based honey badger algorithm detects fake news. The experiments are simulated using python software with various performance metrics such as accuracy, specificity, sensitivity, precision, and F‐scores to validate the performance of the proposed method. The proposed model offers a precision, recall, and accuracy value of 95%, 97%, and 98% when evaluated with the ISOT dataset. When compared to the existing state‐of‐art methods, the proposed method yielded superior detection results and higher accuracy rates.
Dharmendra Dangi, Suvarna Sharma, and Dheeraj Kumar Dixit
IEEE
The 2019 worldwide corona-virus disease pandemic (COVID-19) is becoming worse. It is an inflammatory disorder brought on by a unique virus, and symptoms including fever, coughing up blood, colds, and breathing problems may be quite deadly. The COVID-19 data set, which covers the months of January and August 2020, is used in this study's Long-Short- Term Memory (LSTM) Networks Model to generate predictions about the future. Long short-term memory (LSTM) networks are well-liked because they can be employed with deep learning and artificial intelligence. The order of the data may be learned from and predicted by this model. The number of daily confirmed cases, daily dead cases, daily recovered patients, and other statistics may all be predicted using the COVID-19 data for India. This prediction method can predict new cases, recoveries, and deceased patients during the next 30 days. According to the findings, it can be used to handle the COVID- 19 pandemic situation that is now in effect.
Dharmendra Dangi, Dheeraj K. Dixit, and Amit Bhagat
Springer Science and Business Media LLC
Dheeraj Kumar Dixit, Amit Bhagat, and Dharmendra Dangi
Springer Science and Business Media LLC
Dharmendra Dangi, Amit Bhagat, and Dheeraj Kumar Dixit
Wiley
SummarySentiment analysis or opinion mining is exploited in business, customer services, and so forth, where people provide their opinions in the form of reviews. However, the people's opinions are in a perplexing form such as, sarcasm, irony, and implied meaning which can cause an impact on sentiment analysis. The only way to analyze these words is through context. Nevertheless, there still exist some issues, to tackle those issues, a lot of research has been conducted by focusing the feature engineering. However, the optimized output has not been acquired yet. Hence, we propose a novel method known as chaotic coyote optimization algorithm (COA) based time weight‐AdaBoost support vector machine (SVM) approach which can be used to attain the precise classifications in context. The proposed time weight‐AdaBoost SVM can be used to circumvent the drift concept issues and can be utilized for the perfect learning of data for further classifications. Further, the class imbalance issues can be overcome by adopting a modified CO algorithm, that is, the chaotic COA. Furthermore, the proposed work performance is analyzed with state‐of‐art works such as DICE, ABCDM, and SVM approaches. The comparative analysis shows that our proposed work classifies the social media content acquired from Twitter more accurately than the other works. Thus our work outperforms all the existing approaches in terms of accuracy, precision, recall, and F1 score.
Dheeraj Kumar Dixit, Amit Bhagat, and Dharmendra Dangi
Computers, Materials and Continua (Tech Science Press)
: In recent years, social media platforms have gained immense popu-larity. As a result, there has been a tremendous increase in content on social media platforms. This content can be related to an individual’s sentiments, thoughts, stories, advertisements, and news, among many other content types. With the recent increase in online content, the importance of identifying fake and real news has increased. Although, there is a lot of work present to detect fake news, a study on Fuzzy CRNN was not explored into this direction. In this work, a system is designed to classify fake and real news using fuzzy logic. The initial feature extraction process is done using a convolutional recurrent neural network (CRNN). After the extraction of features, word indexing is done with high dimensionality. Then, based on the indexing measures, the ranking process identifies whether news is fake or real. The fuzzy CRNN model is trained to yield outstanding results with 99.99 ± 0.01% accuracy. This work utilizes three different datasets (LIAR, LIAR-PLUS, and ISOT) to find the most accurate model. This paper describes the development of an algorithm combining a CRNN and fuzzy c-means algorithm. This combination led to high generalization, the CRNNs ability to process high-dimensional data, and the fuzzy c-means’ ability to allow a data sample to belong to more than one class into a single architecture simultaneously. The proposed approach has been examined using LIAR, LIAR-PLUS, and ISOT datasets. The results showed that the proposed approach yielded 65%, 70%, and 99.99 ± 0.01% accuracy on the LIAR, LIAR-PLUS, and ISOT datasets, respectively. Although, in this work nearly 100% accuracy has been achieved but there are various other methods which have been already given almost same result. So, the importance of doing this work, lies in exploring the Fuzzy CRNN method in fake news classification and testing its accuracy on three different datasets which have been never done. From this all, it can be said that this research interest lies into exploring the possibility to enhance the efficiency or providing theoretical evidence of FCRNN into fake news classification.
Dharmendra Dangi, Amit Bhagat, and Dheeraj Kumar Dixit
Computers, Materials and Continua (Tech Science Press)
Dharmendra Dangi, Dheeraj Kumar Dixit, Amit Bhagat, Rajit Nair, and Nilesh Verma
IEEE
In the current scenario, almost all the countries face one of the biggest disasters in COVID-19. This paper has to analyze the tweets related to COVID 19 and discuss the various machine learning algorithms and their performance analysis on the tweets associated with COVID-19. The implemented classification algorithms are applied to classify the sentiments to predict whether they relate to COVID-19 or non-COVID-19. Ten most popular classification algorithms implemented. The Linear Support Vector Machine (LSVM) achieved the highest test accuracy in these algorithms with 90.3%. Logistic regression has performed better in recall with 96.06%, F1 score of 90.46%, ROC_AUC with 90.48%. Random forest classifier has achieved the better specificity and precision of 99.16% and 96.3%, respectively. Out of all, stochastic gradient descent (SGD) has attained better results in all the computational parameters.
Dharmendra Dangi, Amit Bhagat, and Brijesh Bakariya
IEEE
Nowadays, many social networks are available such as LinkedIn, Twitter, Facebook, etc. These types of networks are based on an opinion by the people. Opinion mining predicts the result based on opinion. Opinion mining is also called Sentiment Analysis. There are various opinion mining applications like Machine Learning (ML), Artificial Intelligence (AI), Data Mining, Web Mining, Text Mining, etc. It can also be analyzed sentiment on movies data. It can predict various types of intrinsic information using movies data. Here we proposed an efficient approach for sentiment and pattern analysis on movie data. It is based on people’s ratings on movies, and it can predict the behavior of movie users.
Dharmendra Dangi, Sachin Bhandari, and Amit Bhagat
IEEE
Shared memory, one of the most popular models for programming parallel platforms, is becoming ubiquitous both in low-end workstations and high-end servers. With the advent of low-latency networking hardware, clusters of workstations strive to offer the same processing power as high-end servers for a fraction of the cost. In such environments, shared memory has been limited to page-based systems that control access to shared memory using the memory' s page protection to implement shared memory coherence protocols. Unfortunately, In distributed and parallel computing system, efficient memory sharing, that is free from false-sharing and fragmentation issue and consistent, of data intensive applications is one of the most essential and difficult issues without degrading the system efficiency. Therefore proposed analysis is to simulating the behavior of the existing distributed shared memory programming model to find out some way to improve the efficiency. In Distributed shared memory environment, a process may require access to remotely available data. So the aim is to propose a distributed shared memory programming model which will maximize the utilization of shared memory, at the same time, a high degree of parallelism and efficiency.