Many of these articles may also be found at:
Y. Li, X. Fu, G. Verma, P. Buitelaar, and M. Liu, “Mitigating Hallucination in Large Language Models(LLMs): An Application-Oriented Survey on RAG, Reasoning, and Agentic Systems”, ACM Computing Surveys (Under Review) (2025).
G. Verma, D. Rebholz-Schuhmann, and M. G. Madden, “Enabling personalised disease diagnosis by combining a patient’s time-specific gene expression profile with a biomedical knowledge base”, BMC Bioinformatics 25, 62, 2024.
M. Arcan, S. Manjunath, C. Robin, G. Verma et al., “Intent classification by the use of automatically generated knowledge graphs”, in Journal of Information, vol. 14, no. 05, pp. 288, May 2023.
A. Jha, Y. Khan, G. Verma, et al., “GenomicsKG: A knowledge graph to visualize poly-omics data,” in Journal of Advances in Health, vol. 01, no. 02, pp. 70-84, 2019.
K. B. Soni, K. Rupapara, A. Rana, G. Verma, and P. Buitelaar, “EvalQAG: A Framework for Automatic Complex QA Generation and a Benchmark QA Dataset for Policy Documents,” in The Proceedings of the 40th AAAI Conference on Artificial Intelligence (AAAI 2026), Singapore, Jan. 2026.
G. Verma, S. Sarkar, H. Chen, et al., “Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning,” in Conference on Language, Data and Knowledge (LDK), Naples, Italy, Sep. 2025
G. Verma, S. Sarkar, D. Pillai, et al., “HybridContextQA: A Hybrid Approach for Complex Question Answering using Knowledge Graph Construction and Context Retrieval with LLMs,” in Proceedings of ISWC workshop on Knowledge Base Construction from Pre-trained Language Models (ISWC KBC-LM), Maryland, US, Nov. 2024.
G. Verma, A. Jha, D. Rebholz-Schuhmann, and M. G. Madden, “Ranked MSD: A new feature ranking and feature selection approach for biomarker identification,” in International Cross- Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), pp. 147- 167, Canterbury, UK, Aug. 2019.
A. Jha, G. Verma, Y. Khan, et al., “Deep convolution neural network model to predict relapse in breast cancer,” in 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 351-358, Orlando, Florida, USA, Dec. 2018.
G. Verma, A. Jha, D. Rebholz-Schuhmann, and M. G. Madden, “Using machine learning to distinguish infected from non-infected subjects at an early stage based on viral inoculation,” in 13th International Conference on Data Integration in the Life Sciences, pp. 105-121, Hanover, Germany, Nov. 2018.
R. Adhikari, and G. Verma, “Time Series Forecasting Through a Dynamic Weighted Ensemble Approach,” in Smart Innovation, Systems and Technologies, Springer, vol. 43, pp. 455-465, Oct. 2015.
I. Khandelwal, R. Adhikari, and G. Verma, “Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition,” in Procedia Computer Science (Elsevier), vol. 48, pp. 173-179, December 2014.
R. Adhikari, G. Verma, and I. Khandelwal,“A Model Ranking Based Selective Ensemble Approach for Time Series Forecasting,” in Procedia Computer Science (Elsevier), vol. 48, pp. 14-21, December 2014.