Dr. Gyanendra Tiwary

@bitmesra.ac.in

Assistant Professor, Computer Science and Engineering
Birla Institute of Technology

Dr. Gyanendra Tiwary


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https://researchid.co/gynendra

EDUCATION

B Tech, M Tech and PhD in Computer Science and Engineering

RESEARCH, TEACHING, or OTHER INTERESTS

Biochemistry, Genetics and Molecular Biology
12

Scopus Publications

46

Scholar Citations

5

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Prediction of traffic congestion through Decision Tree, Queuing Theory, Deep Belief Network and Random Forest methods
    Aditi Jha, R. S. Pandey, Gyanendra Tiwary, Gaurav Vishnu Londhe
    Discover Applied Sciences, 2026
    The increase in vehicle ownership and urban population puts a strain on roads and increases traffic congestion within developed and developing nations. Congestion affects an individual’s time and increases an individual’s fuel consumption and pollution output, undercutting efforts towards smart/ sustainable cities. Outdated statistical congestion predicting methods do not account for the non-linear, dynamic nature of congestion with regards to external factors such as, weather, road incidents, and infrastructure. The adoption of machine and deep learning develops the framework towards adaptable and data-driven congestion predicting methods. This study develops a framework that combines the fundamentals of machine learning algorithms, Queuing Theory, Decision Trees, Random Forests, and Deep Belief Networks (DBN) for robust and interprefig traffic congestion forecasting. The traffic flow micro-dynamics integration with Queuing Theory and the use of historical/ real-time multimodal data such as sensor feeds, GPS trajectories, and incident reports as a basis for forecasting helps in robust congestion prediction. Polynomials of moving and rest queues with defined traffic characteristics fuel flow mechanisms. The designed framework uses the DBN and Queuing Theory for robust traffic forecasting.
  • Intelligent Document Workflows: NLP for Invoice Processing, AP Automation, and e-Discovery
    Kritika Soni, Meghna Luthra, Shweta Soni, Gyanendra Tiwary
    Natural Language Processing for Business and Organizations Research and Innovation, 2026
    Businesses are flooded with scanned invoices, purchase orders, contracts, emails, pleadings, and unstructured and semi-structured documents at the heart of the finance and legal processes. Previously, these processes were manual, brittle, and costly, which has caused delays in reconciliation, compliance risk, and litigation exposure. The last decade of progress in Natural Language Processing (NLP) and multimodal document understanding, along with Robotic Process Automation (RPA) and current integration stacks, has changed the viability of end-to-end document intelligence. The chapter is a survey and systematization of the state of practice in three high-impact domains, which are invoice processing, accounts payable (AP) automation, and legal e-discovery. In this chapter, we first review foundational literature and the latest technologies like LayoutLM models, OCR-free models, foundation models, and LLMs. We also review the enterprise-level tools like Google Document AI, Azure Form Recognizer, and Amazon Textract. We then present a layered reference architecture that integrates capture, preprocessing, representation, decisioning, and orchestration. The analysis also covers deployment of systems, evaluating regimes, and how controls for explainability, auditability, and governance are implemented. With the help of case studies, we highlight the operational and financial impact of these systems. We are also transparent about discussing limitations related to domain shift, layout variance, label scarcity, privacy, cross-border data transfer, and legal defensibility. We conclude with an agenda of autonomous agents for document workflows, trusted, and explainable AI for compliance. The future directions include the implementation of federated, privacy-preserving learning at enterprise scale.
  • Developing Explainable Artificial Intelligence Models for Space Science Applications
    Ambuj Kumar Agarwal, Rajat Bhardwaj, Gyanendra Tiwary, Abeer A. Aljohani, Ruchi Kawatra, Abhijit Das
    Space Science and Technology United States, 2025
    The integration of explainable artificial intelligence (XAI) in space science has ushered in a new era of transparency and reliability in AI-driven applications. This paper delves into the transformative role of XAI in enhancing various aspects of space missions, from satellite imagery analysis to planetary science and human–AI collaboration. The introduction highlights the imperative of explainability in AI, emphasizing the need for transparent and ethical decision-making in high-stakes space missions. In the background, this paper explores the evolution of AI in space science and the emergence of XAI as a critical field. The challenges posed by the complexity of space data and the stringent reliability and safety requirements are examined, underscoring the necessity of robust and interpretable AI systems. The paper discusses various XAI techniques, including model-agnostic approaches like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), intrinsic methods such as decision trees, and generalized additive models. Visualization tools for XAI, including feature importance plots and heatmaps, are also discussed, demonstrating their role in making AI decisions more interpretable and actionable. Three case studies illustrate the practical applications of XAI in space science: monitoring deforestation in Earth observation, facilitating discoveries in planetary science, and enhancing human–AI collaboration in space missions. These examples showcase how XAI improves transparency and reliability and enables more effective decision-making. Finally, the paper looks toward the future, discussing emerging technologies in XAI and their potential to revolutionize space science. Integrating XAI, human–AI collaboration, NLP advancements, and quantum computing is a key trend in space exploration.
  • An Enhanced Hybrid Deep Learning Model to Enhance Network Intrusion Detection Capabilities for Cybersecurity
    Abhijit Das, Shobha N, Natesh M, Gyanendra Tiwary, Karthik V
    Journal of Machine and Computing, 2024
    Recently, we have noticed tremendous growth in the field of Information Technology. This increased growth has proliferated the use of new technologies and continued advancement of networking systems. These systems are widely adopted for real-time online and offline tasks. Due to this growth in information technology, maintaining security has gained huge attention as these systems are vulnerable to various attacks. In this context, an Intrusion Detection System (IDS) plays an important role in ensuring security by detecting and preventing suspicious activities within the network. However, as technology is overgrowing, malicious activities are also increasing. Moreover, legacy IDS methods cannot handle new threats, such as traditional signature-based methods requiring a predefined rule set to detect malicious activity. Also, several new methods have been proposed earlier to address security-related issues; however, the performance of these methods is limited due to poor attack detection accuracy and increased false positive rates. In this work, we propose and compare different deep-learning (DL) models that can be used to construct IDSs to provide network security. Details on convolutional neural networks (CNNs), Multilayer Perceptron (MLP), and long short-term memories (LSTMs) are introduced. A discussion of the outcomes achieved follows an assessment of the proposed DL model known as the FOA-CNN-LSTM technique. Comparisons are made between the suggested models and other machine-learning methods. This work presents a deep-learning approach based on hybrid CNN-LSTM with Fruit fly Optimization Algorithm (FOA) by ensemble techniques to distinguish between normal and abnormal behaviors.
  • Multimodal Attention CNN for Human Emotion Recognition
    Gyanendra Tiwary, Shivani Chauhan, Krishan Kumar Goyal
    Lecture Notes in Networks and Systems, 2024
  • Facial Expression Recognition Using Expression Generative Adversarial Network and Attention CNN
    International Journal of Intelligent Systems and Applications in Engineering, 2023
  • Multimodal Depression Detection Using Audio Visual Cues
    Gyanendra Tiwary, Shivani Chauhan, Krishan Kumar Goyal
    2023 International Conference on Computer Science and Emerging Technologies Cset 2023, 2023
    Millions of people all around the world suffer from depression, which is a common mental health issue. Effective intervention and therapy depend on early and precise identification of depression. Multimodal techniques that incorporate audio and video data have recently demonstrated promising outcomes in the detection of depression. In this article, authors suggest a convolutional neural network (CNN) model for multimodal depression identification based on audio and video. The proposed model makes use of audio and visual elements to gather comprehensive and complementary data about depression. Pitch, intensity, and spectral information, among others, are derived from voice recordings for the audio modality. The video modality uses face landmarks, optical flow, and pose estimation techniques to combine facial expressions, head movements, and body gestures. The parallel branches of the CNN architecture process the audio and visual inputs individually. To learn discriminative audio representations, the audio branch applies three convolutional layers, followed by pooling and dense layers. To extract video-specific information, the video branch uses five convolutional layers with different filter widths and depths, followed by fully connected and pooling layers. Further a late fusion technique has been adopted for multimodal fusion, concatenating learned features from the two modalities and passing them through additional thick layers for depression prediction. Authors also used regularization strategies, including dropout and batch normalization, during training to alleviate the overfitting problem. Authors evaluated the proposed multimodal CNN model on a DAIC-WOZ dataset consisting of audio and video recordings of individuals with and without depression. The proposed model achieved 77% accuracy and hence demonstrate that the proposed multimodal CNN model achieves superior performance compared to unimodal approaches.
  • Automatic Depression Detection Using Multi-Modal & Late-Fusion Based Architecture
    Gyanendra Tiwary, Shivani Chauhan, K. K. Goyal
    7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions Csitss 2023 Proceedings, 2023
    Depression has become second most fatal disease after cardiac arrests. Recent lockdowns during COVID, Wars such as Ukraine-Russia and War-Like-Scenario such as Taiwan-China etc has contributed badly and people suffering from severe depression, mood swings and anxiety has gone all time high. Due to lack of opportunities to reach a real medical practitioner, an AI based, automatic depression detection system could be a saviour for millions. In the current work, authors have proposed a multimodal deep Convolutional Neural Network (CNN) based automatic depression detection system. Authors have fused two CNN networks, one Long Short Term Memory (LSTM) based CNN process Electroencephalogram (EEG) signal while other CNN process facial video data. Both the networks classify the subject to one among three classes Depressed, Mild Depressed or Not Depressed. EEG waves are taken from frontal lobe of the brain. The level of alpha component in the EEG signal is being used as bio marker for depression classification. On the other hand, frame by frame analysis is being performed on facial video. An attention-based CNN is being proposed to process each video frame and Facial Action Coding System (FACS) based Linear Binary Pattern (LBP) classifier has been developed for depression classification. Further the result of these two networks is being fed into a late fusion network. The final depression class is being decided by this final classifier. The proposed model has shown quite promising outcome. Authors have trained the EEG network on MODMA, 3-channel EEG dataset and Video A-CNN network on AVEC (Audio/Visual Emotion Challenge) 2018 dataset and tested this entire model on DEAP (a Database for Emotion Analysis using Physiological Signals). dataset and it has performed better than almost all state-of-the-art models.
  • Video Based Deep CNN Model for Depression Detection
    Gyanendra Tiwary, Shivani Chauhan, Krishan Kumar Goyal
    International Journal on Recent and Innovation Trends in Computing and Communication, 2022
    Our face reflects our feelings towards anything and everything we see, smell, teste or feel through any of our senses. Hence multiple attempts have been made since last few decades towards understanding the facial expressions. Emotion detection has numerous applications since Safe Driving, Health Monitoring Systems, Marketing and Advertising etc. We propose an Automatic Depression Detection (ADD) system based on Facial Expression Recognition (FER).
 We propose a model to optimize the FER system for understanding seven basic emotions (joy, sadness, fear, anger, surprise, disgust and neutral) and use it for detection of Depression Level in the subject. The proposed model will detect if a person is in depression and if so, up to what extent. Our model will be based on a Deep Convolution Neural Network (DCNN).
  • An Artificial Intelligence Technique for Covid-19 Detection with eXplainability using Lungs X-Ray Images
    Pranshu Saxena, Sanjay Kumar Singh, Gyanendra Tiwary, Yush Mittal, Ishika Jain
    IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics Icdcece 2022, 2022
    According to the World Health Organization, the coronavirus outbreak poses a daily threat to the global health system. Almost all countries' health resources are insufficient or unequally distributed. There are several issues, such as a lack of health care workers, beds, and intensive care units, to name a few. The key to the country's health systems overcoming this epidemic is to use limited resources at optimal levels. Disease detection is critical to averting an epidemic. The greater the success, the more tightly the covid viral spread may be managed. PCR (Polymerase chain reaction) testing is commonly used to determine whether or not a person has a virus. Deep learning approaches can be used to classify chest X-RAY images in addition to the PCR method. By analyzing multi-layered pictures in one go and establishing manually entered parameters in machine learning, deep learning approaches have become prominent in academic research. This popularity has a favorable impact on the available health datasets. The goal of this study was to detect disease in persons who had x-rays done for suspected COVID-19 (Coronavirus Disease-2019). A bi-nary categorization has been used in most COVID-19 investigations. Chest x-rays of COVID-19 patients, viral pneumonia patients, and healthy patients were obtained from IEEE [17] (Institute of Electrical and Electronics Engineers) and Kaggle [18]. Before the classification procedure, the data set was subjected to a data augmentation approach. These three groups have been classified through multiclassclassification deep learning models. We are also debating a taxonomy of recent contributions on the eXplainability of Artificial Intelligence (XAI).
  • Gyanendra Tiwary on: reducing power consumption in ASICs
    Electronic Systems Technology and Design Computer Design S, 1995
  • Below the half-micron mark
    G. Tiwary
    IEEE Spectrum, 1994

RECENT SCHOLAR PUBLICATIONS

  • Prediction of traffic congestion through Decision Tree, Queuing Theory, Deep Belief Network and Random Forest methods
    A Jha, RS Pandey, G Tiwary, GV Londhe
    Discover Applied Sciences 8 (5), 535 , 2026
    2026
  • Developing explainable artificial intelligence models for space science applications
    AK Agarwal, R Bhardwaj, G Tiwary, AA Aljohani, R Kawatra, A Das
    Space: Science & Technology 5, 0255 , 2025
    2025
    Citations: 6
  • Emotion Recognition
    G Tiwary, S Chauhan, KK Goyal
    Cryptology and Network Security with Machine Learning: Proceedings of … , 2024
    2024
  • An enhanced hybrid deep learning model to enhance network intrusion detection capabilities for cybersecurity
    A Das, N Shobha, M Natesh, G Tiwary
    Journal of Machine and Computing 4 (2), 472 , 2024
    2024
    Citations: 8
  • Automatic depression detection using multi-modal & late-fusion based architecture
    G Tiwary, S Chauhan, KK Goyal
    2023 7th International conference on computation system and information … , 2023
    2023
    Citations: 2
  • Automatic Depression Detection Using Multi-Modal & Late-Fusion Based Architecture
    GT , Shivani Chauhan, K. K. Goyal
    7th International Conference on Computation System and Information … , 2023
    2023
  • Multimodal Attention CNN for Human Emotion Recognition
    G Tiwary, S Chauhan, KK Goyal
    International Conference on Cryptology & Network Security with Machine … , 2023
    2023
  • Multimodal depression detection using audio visual cues
    G Tiwary, S Chauhan, KK Goyal
    2023 International Conference on Computer Science and Emerging Technologies … , 2023
    2023
    Citations: 5
  • Facial expression recognition using expression generative adversarial network and attention cnn
    G Tiwary, S Chauhan, KK Goyal
    Int J Intell Syst Appl Eng 11 (7s), 447-454 , 2023
    2023
    Citations: 3
  • Automatic Depression Detection Using Multi-Modal & Late-Fusion Based Architecture
    GT ,Shivani Chauhan, Krishan Kumar Goyal
    7th International Conference on "Computational Systems and Information … , 2023
    2023
  • An artificial intelligence technique for Covid-19 detection with explainability using lungs x-ray images
    P Saxena, SK Singh, G Tiwary, Y Mittal, I Jain
    2022 IEEE International Conference on Distributed Computing and Electrical … , 2022
    2022
    Citations: 17
  • Video Based Deep CNN Model for Depression Detection
    GT ,Chauhan S, Goyal KK
    International Journal on Recent and Innovation Trends in Computing and … , 2022
    2022
    Citations: 5

MOST CITED SCHOLAR PUBLICATIONS

  • An artificial intelligence technique for Covid-19 detection with explainability using lungs x-ray images
    P Saxena, SK Singh, G Tiwary, Y Mittal, I Jain
    2022 IEEE International Conference on Distributed Computing and Electrical … , 2022
    2022
    Citations: 17
  • An enhanced hybrid deep learning model to enhance network intrusion detection capabilities for cybersecurity
    A Das, N Shobha, M Natesh, G Tiwary
    Journal of Machine and Computing 4 (2), 472 , 2024
    2024
    Citations: 8
  • Developing explainable artificial intelligence models for space science applications
    AK Agarwal, R Bhardwaj, G Tiwary, AA Aljohani, R Kawatra, A Das
    Space: Science & Technology 5, 0255 , 2025
    2025
    Citations: 6
  • Multimodal depression detection using audio visual cues
    G Tiwary, S Chauhan, KK Goyal
    2023 International Conference on Computer Science and Emerging Technologies … , 2023
    2023
    Citations: 5
  • Video Based Deep CNN Model for Depression Detection
    GT ,Chauhan S, Goyal KK
    International Journal on Recent and Innovation Trends in Computing and … , 2022
    2022
    Citations: 5
  • Facial expression recognition using expression generative adversarial network and attention cnn
    G Tiwary, S Chauhan, KK Goyal
    Int J Intell Syst Appl Eng 11 (7s), 447-454 , 2023
    2023
    Citations: 3
  • Automatic depression detection using multi-modal & late-fusion based architecture
    G Tiwary, S Chauhan, KK Goyal
    2023 7th International conference on computation system and information … , 2023
    2023
    Citations: 2
  • Prediction of traffic congestion through Decision Tree, Queuing Theory, Deep Belief Network and Random Forest methods
    A Jha, RS Pandey, G Tiwary, GV Londhe
    Discover Applied Sciences 8 (5), 535 , 2026
    2026
  • Emotion Recognition
    G Tiwary, S Chauhan, KK Goyal
    Cryptology and Network Security with Machine Learning: Proceedings of … , 2024
    2024
  • Automatic Depression Detection Using Multi-Modal & Late-Fusion Based Architecture
    GT , Shivani Chauhan, K. K. Goyal
    7th International Conference on Computation System and Information … , 2023
    2023
  • Multimodal Attention CNN for Human Emotion Recognition
    G Tiwary, S Chauhan, KK Goyal
    International Conference on Cryptology & Network Security with Machine … , 2023
    2023
  • Automatic Depression Detection Using Multi-Modal & Late-Fusion Based Architecture
    GT ,Shivani Chauhan, Krishan Kumar Goyal
    7th International Conference on "Computational Systems and Information … , 2023
    2023