6 citations · 8 across the 6 of their papers we have counts for
6 papers
Edge-centric Optimization of Multi-modal ML-driven eHealth Applications
Anil Kanduri, Sina Shahhosseini, Emad Kasaeyan Naeini +4
Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics. Smart eHeal…
Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing
Sina Shahhosseini, Yang Ni, Hamidreza Alikhani +4
Health monitoring applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings. Considering the signif…
Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks
Sina Shahhosseini, Tianyi Hu, Dongjoo Seo +4
Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Collaborative end-edge-cloud computing for dee…
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks
Sina Shahhosseini, Dongjoo Seo, Anil Kanduri +5
Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Deploying deep-learning-based intelligence nea…
AMSER: Adaptive Multi-modal Sensing for Energy Efficient and Resilient eHealth Systems
Emad Kasaeyan Naeini, Sina Shahhosseini, Anil Kanduri +3
eHealth systems deliver critical digital healthcare and wellness services for users by continuously monitoring physiological and contextual data. eHealth applications use multi-mod…
Partition Pruning: Parallelization-Aware Pruning for Deep Neural Networks
Sina Shahhosseini, Ahmad Albaqsami, Masoomeh Jasemi +1
Parameters of recent neural networks require a huge amount of memory. These parameters are used by neural networks to perform machine learning tasks when processing inputs. To spee…