I have done Bachelor’s Degree(BE) in Information Technology from University of Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal. Then obtained Master’s Degree (M.Tech) in Network Management and Information Security from School of Computer Science ,DAVV Indore. I have completed Ph.D. in the Department of Computer Applications, Maulana Azad National Institute of Technology Bhopal. My Research interest in Text Data Analysis, Natural Language Processing, Machine Learning and Deep learning.
EDUCATION
Ph.D. from MANIT, Bhopal
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Multidisciplinary, Artificial Intelligence, Computational Theory and Mathematics
Post-Quantum Traceable Anonymous Cross-Domain Authentication for Blockchain-Based IoT Deepak Kumar Khare, Dharmendra Dangi, Amit Bhagat, Sunil Malviya, Dheeraj Kumar Dixit IEEE Transactions on Consumer Electronics, 2026 With the proliferation of Internet of Things (IoT) applications, secure and efficient cross-domain information sharing has become essential. Traditional cross-domain authentication approaches that rely on real device identities pose significant privacy risks, while fully anonymous schemes often lack accountability for malicious behavior. Additionally, emerging quantum computing capabilities threaten the security of classical cryptographic systems. To address these challenges, this paper proposes a post-quantum secure, traceable anonymous cross-domain authentication scheme based on blockchain technology. The scheme incorporates lattice-based certificateless cryptography and quantum-resistant hash functions to ensure resilience against quantum attacks. Devices are assigned multiple unlinkable pseudonymous identities with corresponding post-quantum public-private key pairs. A dynamic accumulator is employed to update domain information, and distinct pseudonyms are used for each authentication. Cross-domain credentials issued by a key generation center enable identity verification without exposing real identities. The system ensures both device privacy and traceability of malicious actors. Formal security proofs and BAN logic analysis confirm the scheme’s robustness against classical and quantum-era threats. Compared with existing classical approaches, the proposed scheme achieves strong post-quantum security with reduced computational and communication overhead during the authentication process.
Performance Comparison of Static and Contextual Embedding Models for Opinion Mining Dharmendra Dangi, Abhay Sharma, Dheeraj Kumar Dixit, Amit Bhagat, Chandrapal Singh Dangi IEEE Access, 2026 Transformer-based language models are widely adopted for sentiment analysis due to their strong contextual representation capabilities; however, their high computational complexity can limit practical deployment in resource-constrained environments. This work presents a systematic comparative study of static and contextual word representations for sentiment classification across architecturally distinct model families under standardized preprocessing and evaluation conditions. We evaluate Long Short-Term Memory (LSTM) networks with Word2Vec, GloVe, and FastText embeddings, and compare them with fine-tuned BERT-base and DistilBERT transformer baselines using the same preprocessing, training, and evaluation protocols across five publicly available datasets spanning formal reviews and noisy social media text. In addition to overall accuracy, we report macro- and weighted-F1 scores across three random seeds to estimate experimental variance, and apply McNemar’s test (with Bonferroni correction for multiple comparisons) to assess pairwise model differences on each dataset’s test set. Contingency tables required for each McNemar comparison are provided in the supplementary material. Detailed error analyses using confusion matrices and out-of-vocabulary (OOV) statistics examine the impact of lexical coverage on model performance. Experimental results indicate that FastText consistently outperforms other static embeddings, particularly on datasets with high lexical variability, while offering substantially lower computational overhead compared to the transformer baselines. The primary contribution of this study is a diagnostic multi-dataset evaluation framework that quantifies the relationship between lexical coverage and subword modeling benefit, and characterises the efficiency–accuracy trade-off across embedding paradigms. All architectural and training differences between model families are explicitly documented and their confounding effects discussed rather than assumed away. These findings provide practical guidance for selecting embedding strategies by jointly considering accuracy and deployment efficiency.
Computer Vision and Artificial Intelligence for Intelligence Automation Systems (IAS) Dharmendra Dangi, Vaibhav Suman, Amit Bhagat, Dheeraj Kumar Dixit Handbook of Intelligent Automation Systems Using Computer Vision and Artificial Intelligence, 2025 Intelligence automation systems (IAS), which make use of computer vision and artificial intelligence (AI), are changing the way businesses operate by simplifying tasks reducing errors, and providing insights. These systems are necessary in sectors including banking, insurance, healthcare, and retail to ensure security and maintain quality control. To analyze data, find trends, and support decision-making processes, they rely on AI systems. We may expect improvements in automation systems that will become essential for organizations as technology develops. These advancements will be fuelled by emerging technologies like machine learning, edge computing, and IoT to further enhance AI vision solutions’ capabilities. In security and surveillance applications, computer vision is taking over human monitoring duties by analyzing real-life scenarios to identify threats and deliver instant security assessments. Computer vision is transforming quality assurance techniques and production processes in manufacturing and automation settings to increase flexibility and efficiency. Additionally, it is used in agriculture to identify diseases and weeds, and it plays a critical role in the automobile sector by identifying quality flaws prior to the release of vehicles from the assembly line. AI and computer vision integration into business operations is changing how companies operate and opening doors for innovations that will increase efficiency and competitiveness in the marketplace.
Multilingual AI-Generated Text Detection with BERT and LSTM Model Jeetendra Kumar, Rashmi Gupta, Suvarna Sharma, Jatin Arora, Dharmendra Dangi, Dheeraj Kumar Dixit 3rd IEEE International Conference on Networks Multimedia and Information Technology Nmitcon 2025, 2025 The advent of AI-generated text, driven by advanced language models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) has revolutionized content creation but also raised concerns about authenticity and misuse in various domains. This research proposes a system to detect whether the supplied text is written by human-being or it generated by AI algorithms, supporting both English and Hindi languages using two datasets with mixed human-authored and AI-generated content. After pre-processing steps such as tokenization, cleaning, and normalization, trained the models including BERT, LSTM (Long Short Term Memory), Logistic Regression, SVM (Support Vector Machine), Random Forest, and Naive Bayes, with BERT finetuned for contextual understanding and LSTM for sequential data, achieving higher accuracy in both languages. A userfriendly interface was developed to allow text file uploads for instant authenticity predictions, serving academic, journalistic, and digital media contexts, with the study highlighting transformer-based models' potential in multilingual detection to enhance digital content trustworthiness and mitigate AIgenerated text misuse. Using the proposed method, accuracies of 95.76 % in English text and 95.58 % in Hindi text have been obtained. The proposed system was found to be effective in detecting text generated by AI algorithms.
A Comparative Analysis for Detecting Fake News Using Supervised Learning Algorithms Dheeraj Kumar Dixit, Amit Bhagat, Dharmendra Dangi Aip Conference Proceedings, 2024 Fake news is a type of essential problem on social media.The rapid circulate of fake news has an ability for disastrous influences on human beings and the society.Thus, it becomes more useful to detect fake news on social sites or internet.Recently, many models have been developed to detect the fake news from the publicly available datasets.In this paper discussed the various machine learning algorithms and their performance analysis on two different news data.The proposed framework contains two step process.In the first step, clean the data and extracted features by TF-IDF and Hashing Vectorizer.In the second step, machine learning algorithms (Logistic regression, Decision Tree, Random Forest, Multinomial Naive Bayes, and Passive Aggressive classifier) have been applied in an effective and efficient manner.Comparative analysis revealed that the optimal performance is achieved by the Logistic regression and Passive aggressive classifier, 95.45% and 97.35% respectively for two public datasets.
Medical Image Analysis and Morphology with Generative Artificial Intelligence for Biomedical and Smart Health Informatics Dharmendra Dangi, Arish Mallick, Amit Bhagat, Dheeraj Kumar Dixit Generative Artificial Intelligence for Biomedical and Smart Health Informatics, 2024 What does the term “Medical Image Analysis” mean? Before understanding this term, we must first understand what analysis is. Analysis is interpreting data and involves drawing conclusions based on the information gathered. Medical Image Analysis aims to extract insights using various computational techniques. This process includes exploring, manipulating, visualizing, segmenting, classifying, and registering both 2D and 3D images. In addition, it encompasses reconstructing image data from medical imaging tools, like X-rays, ultrasounds, CT scans, MRIs, PET scans (positron emission tomography), SPECT scans (single photon emission computed tomography), and microscopy. The motto is to generate visual representations of internal body parts for medical and clinical assessments. This analysis is required for automating operations of healthcare like cell counting, detecting anomalies like cancerous cells, segmenting tumor tissues, measuring oxygen saturation, and reconstructing 3D representations for diagnostic and treatment planning. These days, high-end computing is necessary for many medical image processing and analysis techniques in order to minimize the requisite runtime. This review's primary goal is to offer a comprehensive reference for medical image analysis and morphological methods that have recently benefited from high-performance computing technologies. Now, let us understand the term morphology ; it is an inspection of an organism's form, structure, and unique structural characteristics. Here, we go over some of its features and introduce the fundamental morphological transformations. We also provide a synopsis of the morphological filtering theory. In conclusion, we delineate many attributes of mathematical morphology that illustrate its relevance in the medical image processing domain.
Graph attention and multi-neural memory networks for fake news detection: FakeDetectNet framework DK Dixit, D Dangi, J Kumar, R Gupta, S Sharma, A Bhagat Evolving Systems 17 (2), 25 , 2026 2026
Performance Comparison of Static and Contextual Embedding Models for Opinion Mining D Dangi, A Sharma, DK Dixit, A Bhagat, CS Dangi IEEE Access , 2026 2026
Post-Quantum Traceable Anonymous Cross-Domain Authentication for Blockchain-based IoT DK Khare, D Dangi, A Bhagat, S Malviya, DK Dixit IEEE Transactions on Consumer Electronics , 2025 2025
Computer Vision and Artificial Intelligence for Intelligence Automation Systems (IAS) D Dangi, V Suman, A Bhagat, DK Dixit Handbook of Intelligent Automation Systems Using Computer Vision and … , 2025 2025
Multilingual AI-Generated Text Detection with BERT and LSTM Model J Kumar, R Gupta, S Sharma, J Arora, D Dangi, DK Dixit 2025 Third International Conference on Networks, Multimedia and Information … , 2025 2025
Utilization of hesitant and intuitionistic fuzzy sets (HFS-IFS) in computational intelligence for decision modeling S Sharma, D Dangi, DK Dixit, R Gupta, J Kumar, A Bhagat International Journal of Information Technology 17 (6), 3389-3396 , 2025 2025 Citations: 1
Efficient deep learning model for analyzing muscle activity patterns in biomechanical simulations D Dangi, DK Dixit, A Bhagat, D Rao, JK Gupta SN Computer Science 6 (2), 138 , 2025 2025 Citations: 4
Medical Image Analysis and Morphology with Generative Artificial Intelligence for Biomedical and Smart Health Informatics D Dangi, A Mallick, A Bhagat, DK Dixit Generative Artificial Intelligence for Biomedical and Smart Health … , 2025 2025
Fake News Detection Using ARO and LSTM Algorithms A Bhagat, D Dangi, V Suman, DK Dixit, S Sharma SN Computer Science 6 (1), 36 , 2024 2024 Citations: 2
A comparative analysis for detecting fake news using supervised learning algorithms DK Dixit, A Bhagat, D Dangi AIP Conference Proceedings 2900 (1), 020014 , 2024 2024 Citations: 1
An effective deep learning prediction model for the COVID-19 pandemic in India D Dangi, S Sharma, DK Dixit 2023 2nd International Conference on Ambient Intelligence in Health Care … , 2023 2023 Citations: 2
An Efficient Model for Sentiment Analysis using Artificial Rabbits Optimized Vector Functional Link Network D Dangi, ST Chandel, DK Dixit, S Sharma, A Bhagat Expert Systems with Applications, 119849 , 2023 2023 Citations: 36
Sentiment analysis of COVID-19 social media data through machine learning D Dangi, DK Dixit, A Bhagat Multimedia tools and applications 81 (29), 42261-42283 , 2022 2022 Citations: 64
An accurate fake news detection approach based on a Levy flight honey badger optimized convolutional neural network model DK Dixit, A Bhagat, D Dangi Concurrency and Computation: Practice and Experience, e7382 , 2022 2022 Citations: 17
Automating fake news detection using PPCA and levy flight-based LSTM DK Dixit, A Bhagat, D Dangi Soft Computing 26 (22), 12545-12557 , 2022 2022 Citations: 45
Sentiment Analysis on Social Media Using Genetic Algorithm with CNN. D Dangi, A Bhagat, DK Dixit Computers, Materials & Continua 70 (3) , 2022 2022 Citations: 13
Fake News Classification Using Fuzzy Based Deep Convolutional Neural Networks (FDNN) on Social Media Data DK Dixit, A Bhagat AIJR Abstracts, 63 , 2022 2022
Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network DD Dheeraj Kumar Dixit*, Amit Bhagat CMC-Computers, Materials & Continua 71 (2), 5733–5750 , 2022 2022 Citations: 19
Analyzing the sentiments by classifying the tweets based on COVID-19 using machine learning classifiers D Dangi, DK Dixit, A Bhagat, R Nair, N Verma 2021 IEEE International conference on technology, research, and innovation … , 2021 2021 Citations: 6
Sentiment analysis of social media data based on chaotic coyote optimization algorithm based time weight‐AdaBoost support vector machine approach D Dangi, A Bhagat, DK Dixit Concurrency and Computation: Practice and Experience 34 (3), 1532-0626 , 2021 2021 Citations: 40
MOST CITED SCHOLAR PUBLICATIONS
Sentiment analysis of COVID-19 social media data through machine learning D Dangi, DK Dixit, A Bhagat Multimedia tools and applications 81 (29), 42261-42283 , 2022 2022 Citations: 64
Automating fake news detection using PPCA and levy flight-based LSTM DK Dixit, A Bhagat, D Dangi Soft Computing 26 (22), 12545-12557 , 2022 2022 Citations: 45
Sentiment analysis of social media data based on chaotic coyote optimization algorithm based time weight‐AdaBoost support vector machine approach D Dangi, A Bhagat, DK Dixit Concurrency and Computation: Practice and Experience 34 (3), 1532-0626 , 2021 2021 Citations: 40
An Efficient Model for Sentiment Analysis using Artificial Rabbits Optimized Vector Functional Link Network D Dangi, ST Chandel, DK Dixit, S Sharma, A Bhagat Expert Systems with Applications, 119849 , 2023 2023 Citations: 36
Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network DD Dheeraj Kumar Dixit*, Amit Bhagat CMC-Computers, Materials & Continua 71 (2), 5733–5750 , 2022 2022 Citations: 19
An accurate fake news detection approach based on a Levy flight honey badger optimized convolutional neural network model DK Dixit, A Bhagat, D Dangi Concurrency and Computation: Practice and Experience, e7382 , 2022 2022 Citations: 17
Sentiment Analysis on Social Media Using Genetic Algorithm with CNN. D Dangi, A Bhagat, DK Dixit Computers, Materials & Continua 70 (3) , 2022 2022 Citations: 13
Analyzing the sentiments by classifying the tweets based on COVID-19 using machine learning classifiers D Dangi, DK Dixit, A Bhagat, R Nair, N Verma 2021 IEEE International conference on technology, research, and innovation … , 2021 2021 Citations: 6
Efficient deep learning model for analyzing muscle activity patterns in biomechanical simulations D Dangi, DK Dixit, A Bhagat, D Rao, JK Gupta SN Computer Science 6 (2), 138 , 2025 2025 Citations: 4
Fake News Detection Using ARO and LSTM Algorithms A Bhagat, D Dangi, V Suman, DK Dixit, S Sharma SN Computer Science 6 (1), 36 , 2024 2024 Citations: 2
An effective deep learning prediction model for the COVID-19 pandemic in India D Dangi, S Sharma, DK Dixit 2023 2nd International Conference on Ambient Intelligence in Health Care … , 2023 2023 Citations: 2
Utilization of hesitant and intuitionistic fuzzy sets (HFS-IFS) in computational intelligence for decision modeling S Sharma, D Dangi, DK Dixit, R Gupta, J Kumar, A Bhagat International Journal of Information Technology 17 (6), 3389-3396 , 2025 2025 Citations: 1
A comparative analysis for detecting fake news using supervised learning algorithms DK Dixit, A Bhagat, D Dangi AIP Conference Proceedings 2900 (1), 020014 , 2024 2024 Citations: 1
Cloud Based Security Analysis in Body Area Network for Health Care Applications D Dangi, D Dixit, A Bhagat Cloud Security, 203-222 , 2021 2021 Citations: 1
Graph attention and multi-neural memory networks for fake news detection: FakeDetectNet framework DK Dixit, D Dangi, J Kumar, R Gupta, S Sharma, A Bhagat Evolving Systems 17 (2), 25 , 2026 2026
Performance Comparison of Static and Contextual Embedding Models for Opinion Mining D Dangi, A Sharma, DK Dixit, A Bhagat, CS Dangi IEEE Access , 2026 2026
Post-Quantum Traceable Anonymous Cross-Domain Authentication for Blockchain-based IoT DK Khare, D Dangi, A Bhagat, S Malviya, DK Dixit IEEE Transactions on Consumer Electronics , 2025 2025
Computer Vision and Artificial Intelligence for Intelligence Automation Systems (IAS) D Dangi, V Suman, A Bhagat, DK Dixit Handbook of Intelligent Automation Systems Using Computer Vision and … , 2025 2025
Multilingual AI-Generated Text Detection with BERT and LSTM Model J Kumar, R Gupta, S Sharma, J Arora, D Dangi, DK Dixit 2025 Third International Conference on Networks, Multimedia and Information … , 2025 2025
Medical Image Analysis and Morphology with Generative Artificial Intelligence for Biomedical and Smart Health Informatics D Dangi, A Mallick, A Bhagat, DK Dixit Generative Artificial Intelligence for Biomedical and Smart Health … , 2025 2025
