Memory Aware Active Learning Sensor for System Server Monitoring

Registration ID: IJNRD_204895

Published ID: IJNRD2309030

DOI: Click Here to Get

Gayathri S , Sangeetha P

Active learning, wearable computing, machine learning, activity recognition, memory retention, cognitive factors.

I propose a novel active learning framework for activity recognition and server monitoring. My work is unique in that it takes limitations of the oracle into account when selecting sensor data for annotation by the oracle. This capacity constraint is manifested not only in the number of queries that a person can respond to in a given time-frame but also in the time lag between the query issuance and the oracle response. I introduce the notion of mindful active learning and propose a computational framework, called EMMA, to maximize the active learning performance taking informativeness of system data, query budget, and human memory into account. I formulate this optimization problem, propose an approach to model memory retention, discuss the complexity of the problem, and propose a greedy heuristic to solve the optimization problem. I design an approach to perform mindful active learning in batch where multiple system observations are selected simultaneously for querying the oracle. I demonstrate the effectiveness of our approach using three publicly available activity datasets and by simulating oracles with various memory strengths. I show that the activity recognition accuracy ranges from 21% to 97% depending on memory strength, query budget, and difficulty of the machine learning task. Moreover, I show that the performance of our approach is at most 20% less than the experimental upper-bound and up to 80% higher than the experimental lower-bound. To evaluate the performance of EMMA for batch active learning, I design two instantiations of EMMA to perform active learning in batch mode. I show that these algorithms improve the algorithm training time at the cost of a reduced accuracy in performance. Another finding in our work is that integrating clustering into the process of selecting sensor observations for batch active learning improves the activity learning performance by 11.1% on average, mainly due to reducing the redundancy among the selected sensor observations. I observe that mindful active learning is most beneficial when the query budget is small and/or the oracle’s memory is weak. This observation emphasizes advantages of utilizing mindful active learning strategies in mobile health settings that involve interaction with older adults and other populations with cognitive impairments.

Published Paper   E-Certificate

Gayathri S & Sangeetha P (September-2023). Memory Aware Active Learning Sensor for System Server Monitoring. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(9), a245-a255. https://ijnrd.org/papers/IJNRD2309030.pdf

Volume 8 Issue 9, September-2023

Pages : a245-a255

Paper Reg. ID: IJNRD_204895

Published Paper Id: IJNRD2309030

Downloads: 000121979

Research Area: Engineering

Country: Erode, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2309030.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2309030

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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