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
  • 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
  • Developing Explainable Artificial Intelligence Models for Space Science Applications
    Ambuj Kumar Agarwal, Rajat Bhardwaj, Gyanendra Tiwary, Abeer A. Aljohani, Ruchi Kawatra, et al.
    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
  • 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
  • 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
  • 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