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Author

Nidhi Mishra

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Open access Sep 2026

Human state digital twin architecture for physiology constrained adaptive robotic autonomy

Time-dependent human cognitive and physical conditions should be considered in adaptive autonomy in collaborative robotics to ensure safety, performance, and operator comfort. But most of the current methods depend on heuristic levels of workload or individual physiological measures and fail to combine human-state esti...

J. Nirmaladevi, S. Saranya, Nidhi Mishra et al. · 0 citations
Conference Jul 2026

Hybrid Evolutionary Optimization and Neural Network Models for Climate Adaptive Renewable Energy Forecasting

The paper gives a Hybrid Evolutionary Optimization and Neural Network Model of climate adaptive renewable energy forecasting. The need to increase renewable energy has led to the creation of more precise and flexible forecasting tools. This paper integrates evolutionary optimization techniques, including Genetic algori...

Nidhi Mishra, Aakansha Soy · 0 citations
Conference Jul 2026

Uncertainty-Aware Deep Learning Models for Robust Realtime Object Recognition in Dynamic Computational Vision

The challenge of real-time object detection in dynamic environments is complicated by issues like noise, occlusion, changes in illumination, and ambiguity of object boundaries. Non-Bayesian models of deep learning tend to be confident about their predictions. Such approach is dangerous since it renders these models ina...

Nidhi Mishra, Aakansha Soy · 0 citations
Conference Jul 2026

Optimized Parallel Computing Framework for Real-Time IoT Data Processing

Advent of Internet of Things (IoT) sensor networks of scale, the amount and speed of data produced has become a major problem in real-time data processing and analytics. Conventional computing models cannot provide real-time insights because are constrained by scalability, resource management, and computational capabil...

Nidhi Mishra, Aakansha Soy · 0 citations
Open access Jul 2026

A Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing in Cloud Systems

Experiments show that the proposed Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Qualit...

Eram Fatma, Nidhi Mishra, Mohammed Abdul Bari · 0 citations

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