Hierarchical Machine Learning Models for Energy-Efficient Modulation and Spectrum Sensing in Cognitive Radio Environments Preeti Badhani, Nookala Venu, Arvind Kumar, Prateek Srivastava, Deepak Upadhyay, Rashmy Moray Conference Proceedings 1st International Conference on Advancing Sustainable Solutions Through Technologies Icasst 2026, 2026 This paper has introduced a novel hierarchical machine-learning based cognitive radio model which aims at reducing energy consumption by improving both spectral-sensing and adapting modulation. Simulation experiments have been conducted to test the proposed model in different signal-to-noise ratio (snr) and additive white gaussian noise (awgn), the experimental results indicated higher accuracy of detection compared to traditional approaches and fewer false alarms, the proposed approach also dynamically selects appropriate modulation techniques (such as bpsk or qam) based on the channel characteristics to reduce the energy per bit required and consequently reduces the bit-error-rate (ber). Matlab implementation of the proposed setup clearly demonstrated the stability of the hierarchical approach (using random-forest for sensing and a shallow-dnn for modulation) over other considered approaches (svm or flat models) to provide evidence of the feasibility of hierarchical designs for future networks.
An investigation of factors impacting the acceptance of telehealth in rural India Rashmy Moray Discover Social Science and Health, 2025 The study aims at investigating the factors influencing the acceptance of telehealth services and understand its inhibitors w.r.t the rural population of India. As this portion of population face healthcare challenges. Using the Technology Acceptance Model (TAM) as a theoretical base framework, the study introduced State of technology conditions, Resistance to use technology, Technology anxiety, Digital divide, Perceived risk, Privacy issue and Technology literacy as external antecedents reflecting the intention to use telehealth among the rural inhabitants. The purposive sampling technique is used to collect the primary data through a structured questionnaire from rural participants and the data was analysed by employing structural equation method (SEM). The study investigations revealed that state of technological conditions deposited significantly high impact on digital divide, privacy issue and perceived risk. Digital divide significantly influenced technology literacy and resistance to use technology confirmed its impact on perceived usefulness and usage intention of telehealth. Also, technology literacy proved to be a significant inhibitor affecting technology anxiety, perceive ease of use and perceived usefulness. All the latent factors influentially impacted intention to use telehealth. It is a unique study where a comprehensive model confirming the application of TAM with the inclusion of exhaustive variables has been investigated the usage intent of telehealth in the rural set up of developing nations. The study presents valuable insights to the healthcare service providers, policy makers and regulators for understanding the issues influencing the implementation of telehealth services in the state of technological advancements. The research findings would help in the design, structure and adequate provision of telehealth care services in developing countries.
Analyzing User Resistance in Adoption of Robo-Advisory Services Rashmy Moray, Pratap Kakde, Priya, Ravjyot Kaur Chhabra, Sradha Sajeev Proceedings of the 2025 3rd International Conference on Advances in Computation Communication and Information Technology Icaiccit 2025, 2025
