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.
Novel approaches to pill dispensing tackling non-adherence in the management of chronic diseases Inderpreet Kaur, Ujjwal Matoliya, Dharmendra Dangi Ambient Assisted Living Aal Technologies Transitioning from Healthcare 4 0 to Healthcare 5 0, 2025 Medication non-adherence is a major issue in healthcare, especially among older patients with chronic conditions who live at home. The purpose of this review is to comprehensively investigate automated dispenser treatments proposed in the literature to increase patient medication adherence. We emphasize the human-centered approach, taking into account patient acceptability and proper use, as well as caregiver and physician views. Our assessment emphasizes the need for resolving issues such as direct engagement with drug prescriptions, significant infrastructure requirements, and accessibility for the elderly. We also look into the possibility of cutting-edge technology like RFID tags and Arduino Mega controllers to create pharmaceutical dispensers that can store user data, track medication adherence, and send alerts and reminders. We also examine the role of artificial intelligence (AI) in healthcare, including its applications in personalized medicine, disease detection, clinical decision-making, and patient outcomes. Our analysis sheds light on the development of efficient automated dispenser treatments, as well as the potential for artificial intelligence to improve prescription adherence and healthcare outcomes in the older population.
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.
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
Harnessing CNNs and Embedding Techniques for Enhanced Sentiment Classification: D. Dangi, A. Sharma D Dangi, A Sharma National Academy Science Letters, 1-5 , 2025 2025 Citations: 1
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
Novel Approaches to Pill Dispensing: Tackling Non-Adherence in the Management of Chronic Diseases I Kaur, U Matoliya, D Dangi Ambient Assisted Living (AAL) Technologies, 119-133 , 2025 2025
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
EEG-based mental workload estimation using bidirectional LSTM S Sharma, R Gupta, J Kumar, D Dangi 2024 4th International Conference on Technological Advancements in … , 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
Review on sentiment analysis of movie reviews using machine learning techniques based on data available on Twitter D Dangi, A Bhagat, JK Gupta International Journal of Engineering Systems Modelling and Simulation 15 (5 … , 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
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 225, 119849 , 2023 2023 Citations: 36
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 35 (1), e7382 , 2023 2023 Citations: 17
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
Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network. DK Dixit, A Bhagat, D Dangi Computers, Materials & Continua 71 (3) , 2022 2022 Citations: 19
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), e6581 , 2022 2022 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 225, 119849 , 2023 2023 Citations: 36
Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network. DK Dixit, A Bhagat, D Dangi Computers, Materials & Continua 71 (3) , 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 35 (1), e7382 , 2023 2023 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
EEG-based mental workload estimation using bidirectional LSTM S Sharma, R Gupta, J Kumar, D Dangi 2024 4th International Conference on Technological Advancements in … , 2024 2024 Citations: 2
Review on sentiment analysis of movie reviews using machine learning techniques based on data available on Twitter D Dangi, A Bhagat, JK Gupta International Journal of Engineering Systems Modelling and Simulation 15 (5 … , 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
Efficient framework for sentiment and pattern analysis on movie data D Dangi, A Bhagat, B Bakariya 2021 IEEE International Conference on Technology, Research, and Innovation … , 2021 2021 Citations: 2
Analysis of shared memory in distributed and non distributed environment D Dangi, S Bhandari, A Bhagat 2016 Fifth International Conference on Eco-friendly Computing and … , 2016 2016 Citations: 2
Harnessing CNNs and Embedding Techniques for Enhanced Sentiment Classification: D. Dangi, A. Sharma D Dangi, A Sharma National Academy Science Letters, 1-5 , 2025 2025 Citations: 1
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
