Foundation to Fly, Metaverse to Thrive

Scholarly Journals

• ‘Weight and bias initialization routines for Sigmoidal Feedforward Network’, Applied Intelligence, pp 2651-2671.(Mittal, M.Singh, A. P., and Chandra, P., 2021).
• ‘Improving Learning in Neural Networks through Weight Initializations’, Journal of Information & Optimization Sciences.(Mittal, M.Singh, A. P., and Chandra, P., 2021).
• ‘A New Weight Initialization using Statistically Resilient Method and Moore-Penrose Inverse based Method for SFANN ’, International Journal of Recent Research Aspects, 4(2), pp 98-105. (Mittal, M. Singh, A. P., and Chandra, P., 2017).
• ‘A Modification to Nyugen-Widrow Weight Initialization Method’, Intelligent Systems, Technologies and Applications, Advances in Intelligent Systems and Computing, Springer 910, pp. 141-153. (Mittal, M. Singh, A. P., and Chandra, P., 2019).
• ‘Comparison of Random Weight Initialization to New Weight Initialization CONEXP’, International Conference on Recent Developments in Science, Engineering and Technology, Data Science and Analytics, Springer, 1230, pp. 279-289. (Mittal, M. Singh, A. P., and Chandra, P., 2017).
• ‘A statistically resilient method of weight initialization for SFANN’, International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, pp. 1371-1376. (Mittal, M. Singh, A. P., and Chandra, P., 2015).
• ‘Comparison of Statistically Resilient Weight Initialization Method and Random Weight Initialization Method for Benchmark Datasets’, Poster presentation in 3rd Indian Workshop on Machine Learning, IIT(BHU). (Mittal, M. Singh, A. P., and Chandra, P., 2018).

Research

Apeksha Mittal’s research involves development of new weight initialization algorithms to improve the training of Sigmoidal Feedforward Artificial Neural Networks, that has a wide range of applications in the areas of function approximation, predictions, regression and classification.

Research:

• Publications
  • A. Mittal, A. P. Singh and P. Chandra, “Weight and bias initialization routines for Sigmoidal Feedforward Network” in Applied Intelligence, Springer, ISSN: 1573-7497, November 2020, pp. 1-21, Indexed in Science Citation Index (SCI), Impact Factor: 3.325.
  • A. Mittal, A. P. Singh and P. Chandra, “Improving learning in neural networks through weight initializations” in Journal of Information & Optimization Sciences, Taylor & Francis, ISSN: 2169- 0103, June 2021, Indexed in Emerging Sources Citation Index (ESCI).
  • A. Mittal, A. P. Singh and P. Chandra, “A New Weight Initialization using Statistically Resilient Method and Moore-Penrose Inverse based Method for SFANN” in International Journal of Recent Research Aspects ISSN: 2349-7688, Vol. 4, Issue 2, June 2017, pp. 98-105.
  • K. Middha and A. Mittal, “Discovery of type 2 diabetes mellitus with correlation and optimization driven hybrid deep learning approach” in Computer Methods in Biomechanics and Biomedical Engineering, Pre-Press, Pg. 1-13, October 2023. (SCI)
  • K. Middha and A. Mittal, “An effective feature selection method for type 2 diabetes mellitus detection using gene expression data” in Intelligent Decision Technologies, Pre-Press, Pg. 1-9, November 2022. (SCOPUS)
  • Savita, G. Rani and A. Mittal, “Detection of CAD using optimization approach with machine learning classification techniques” in International Journal of Systematic Innovation, Vol. 7, Issue 3, September 2022. (SCOPUS).
  • Savita, G. Rani and A. Mittal, “An Optimized Machine Learning Approach for Coronary Artery Disease Detection” in Journal of Advances in Information Technology, Vol. 14, Issue 1, Pg. 66-76, February 2023. (SCOPUS)
  • A. Mittal and P. Chandra, “Improving learning in Artificial Neural Networks using better weight initializations” in International Journal of Information Technology, January 2024 (Scopus)
  • A. Gupta, A. Mittal and R. Jain, “Sarcasm Detection on News Headlines, Twitter and SARC Dataset: A Detailed Evaluation of Shallow and Deep Models”, International Journal of Intelligent Engineering Informatics, 2024 (SCOPUS)
  • A. Gupta, A. Mittal and R. Jain, A novel sarcasm detection approach for text-image data: Leveraging multimodal fusion and weighted latent factors”, Information Fusion, 2025 (SCIE).
• Research Interests Artificial Neural Networks, Artificial Intelligence, Machine Learning, Deep Learning
• Case Studies N/a
• Books/ Book Chapters
  • A. Mittal, A. P. Singh and P. Chandra, “A Modification to Nyugen-Widrow Weight Initialization Method”, in Intelligent Systems, Technologies and Applications, Vol. 910, Springer, February 2019, pp 141-153, ISBN 978-981-13-6095-4.
  • A. Mittal, R. Raj, A. Jain and L. Raghav, “Sign Language Detection using Machine Learning”, in Progressive Paradigms: Unveiling the Trends in Science and Engineering, 2022, pp 207-216, ISBN 978-93-844344-3-4.
  • A. Mittal and Vinay, “Analysis for Cricket Match Outcomes using Machine Learning Techniques” in From Lab to Life: A Compilation of Cutting-Edge Science and Engineering Trends, 2022, pp 82-85, ISBN 978-81-968195-5-2.
  • A. Mittal and J. Martino, “A Sustainable Approach to Plastic Upcycling using E-Commerce Initiatives” in Innovations at the Nexus: Trends in Science and Engineering, 2022, pp 115-128, ISBN 978-81-971182- 4-1.
  • A. Mittal, Deepak, G. Gaur, S. R. Barik, V. Garg and N. Kumar, “Tourism Place Recommendation Systems for Personalized Travel Experiences” in Bridging the Future: Contemporary Perspective in Science and Engineering, 2022, pp 211-219, ISBN 978-93-844345- 1-9.
  • S. Sharma, P. Sharma, Y. Kumar and A. Mittal, “Analysis of Adverse Impacts of Crypto Mining”, in Scientific Synergy: Interdisciplinary Perspectives on Trends in Science and Engineering, 2022, pp 141-153, ISBN 978-81-968195-7-6
• Conferences
  • A. Mittal, A. P. Singh and P. Chandra, “Comparison of Random Weight Initialization to New Weight Initialization CONEXP,” Communications in Computer and Information Science, Vol 1230. Springer, 2019.
  • A. Mittal, P. Chandra and A. P. Singh, “A Statistically Resilient Method of Weight Initialization for SFANN” in proc. of Advances in Computing, Communications and Informatics (ICACCI), IEEE, 2015, pp 1371-1376.
  • K. Middha and A. Mittal, “Comparative Analysis of deep learning with different optimization techniques for Type 2 Diabetes Mellitus detection using Gene expression Data”, in International Conference on Innovative Computing and Communication (ICICC2023), Springer, 2023
• Featured in NA

Teaching:

• Courses/Programmes : Artificial Neural Networks, Artificial Intelligence, Machine Learning, Soft Computing, Deep Learning, Design and Analysis of Algorithms, Data Structures in B.Tech. and M.Tech in CSE (Core, Artificial Intelligence and Machine Learning, Data Science)
• Teaching interests : Artificial Neural Networks, Artificial Intelligence, Machine Learning, Soft Computing, Deep Learning, Design and Analysis of Algorithms, Generative AI Applications

Practice:

• Work Experience 10 years
• MDPs/FDPs N/A
• Corporate Mentoring N/A

Service:

• Board Memberships Member Secretary, Board of Studies
• Editorial Positions N/A
• Reviewer Experience: 2

Others:

• Professional Service NA
• Professional Memberships NA