• Tunable energy-efficient approximatunable energy-efficient approximate circuits for self-powered ai and autonomous edge computing systemste circuits for self-powered ai and autonomous edge computing systems 

  Chaturvedi, Nitin (IEEE, 2025-03)

  Artificial Intelligence is applied in various domains of compute-intensive applications ranging from image recognition healthcare to statistical analysis. Additionally, recent advancements in Deep Neural Network (DNN) ...
• Efficient edge AI implementation for IoT device identification for hierarchical federated learning 

  Shenoy, Meetha V. (Inder Science, 2025-03)

  As IoT devices proliferate, efficient IoT device identification is crucial for resource management, planning, and detecting anomalous traffic. Traditional ML-based identification relies on centralised training, but federated ...
• Deep q-learning framework based solar energy generation prediction 

  Gupta, Karunesh Kumar (2022)

  In the recent years, the power system market has seen a huge shift towards the utilisation of Renewable energy sources (RES) as candidates of power generation since they proved to be a great alternative of conventional ...
• A detailed comparative analysis of automatic neural metrics for machine translation: bleurt & bertscore 

  Chamola, Vinay; Gupta, Karunesh Kumar (IEEE, 2025-04)

  Bleurt a recently introduced metric that employs Bert, a potent pre-trained language model to assess how well candidate translations compare to a reference translation in the context of machine translation outputs. While ...
• Qualitative and quantitative analysis of Indian honey samples and various adulterants using near infrared spectroscopy with water as a sensing probe (aquaphotomics) and chemometrics 

  Gupta, Karunesh Kumar (Elsevier, 2025)

  Various honey samples and their adulterants from C3 plants (rice syrup) and C4 based plants (sugar syrup, corn syrup, and jaggery syrup) were analyzed using Near-Infrared Spectroscopy (NIRS) coupled with aquaphotomics and ...

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