5 citations · 5 across the 6 of their papers we have counts for
10 papers
A Collaborative Artificial Intelligence as a Service Composition Dataset
Deepak Kanneganti, Sajib Mistry, Sheik Fattah +1
Artificial Intelligence as a Service (AIaaS) composition is an emerging research area that enables clients to combine multiple AI services to meet complex requirements. A recent ex…
Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments
Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah +3
Machine Learning as a Service (MLaaS) is a powerful cloud paradigm enabling data-driven intelligent applications in Internet of Things (IoT) environments, widely adopted across hea…
Designing Sustainable Federated Learning as a Service using Neural Architecture Search
Keya Patel, Sajib Mistry, Sheik Fattah +4
The sustainability constraints of FLaaS consumers pose significant challenges to maintaining carbon-feasible federated training in FLaaS environments. These constraints often lead…
Leakage-Safe Empirical Benchmarking of EEG-Based Machine Learning Pipelines for Dementia Classification
Haitian Wang, Chamara Madarasingha, Redowan Mahmud +2
Electroencephalography (EEG) is a low-cost and non-invasive signal source for dementia screening, yet existing EEG-based studies remain difficult to compare because preprocessing,…
OrchNAS: Orchestrated Neural Architecture Search Service for Personalised Federated Edge Intelligence
Keya Patel, Sajib Mistry, Sheik Mohammad Mostakim Fattah +1
We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptiv…
Test-Time Adaptive Composition for Machine Learning as a Service (MLaaS) in IoT Environments
Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah +1
The dynamic nature of Internet of Things (IoT) environments affects the long-term effectiveness of Machine Learning as a Service (MLaaS) compositions. Existing adaptive composition…
Machine Learning as a Service (MLaaS) Dataset Generator Framework for IoT Environments
Deepak Kanneganti, Sajib Mistry, Sheik Fattah +2
We propose a novel MLaaS Dataset Generator (MDG) framework that creates configurable and reproducible datasets for evaluating Machine Learning as a Service (MLaaS) selection and co…
FedPoisonTTP: A Threat Model and Poisoning Attack for Federated Test-Time Personalization
Md Akil Raihan Iftee, Syed Md. Ahnaf Hasan, Amin Ahsan Ali +3
Test-time personalization in federated learning enables models at clients to adjust online to local domain shifts, enhancing robustness and personalization in deployment. Yet, exis…
Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments
Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah +2
The dynamic nature of Internet of Things (IoT) environments challenges the long-term effectiveness of Machine Learning as a Service (MLaaS) compositions. The uncertainty and variab…
Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing
Minod Perera, Sheik Mohammad Mostakim Fattah, Sajib Mistry +1
Industrial Internet of Things (IIoT) applications demand efficient task offloading to handle heavy data loads with minimal latency. Mobile Edge Computing (MEC) brings computation c…