activity
20172020
most citedBehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model

127 citations · 164 across the 9 of their papers we have counts for

collaborators

9 papers

cs.DC2020★ 11 cited

SDSN@RT: a middleware environment for single-instance multi-tenant cloud applications

Indika Kumara, Jun Han, Alan Colman +3

With the Single-Instance Multi-Tenancy (SIMT) model for composite Software-as-a-Service (SaaS) applications, a single composite application instance can host multiple tenants, yiel…

cs.SE2020★ 5 cited

FM4SN: A Feature-Oriented Approach to Tenant-Driven Customization of Multi-Tenant Service Networks

Indika Kumara, Jun Han, Alan Colman +2

In a multi-tenant service network, multiple virtual service networks (VSNs), one for each tenant, coexist on the same service network. The tenants themselves need to be able to dyn…

cs.DC2020★ 8 cited

Blockchain Consensus Algorithms: A Survey

Md Sadek Ferdous, Mohammad Jabed Morshed Chowdhury, Mohammad A. Hoque +1

In recent years, blockchain technology has received unparalleled attention from academia, industry, and governments all around the world. It is considered a technological breakthro…

cs.LG2019★ 127 cited

BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model

Iqbal H. Sarker, Alan Colman, Jun Han +3

This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and dat…

cs.CY2019

CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data

Iqbal H. Sarker, Alan Colman, Jun Han +2

The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. R…

cs.CY2018

Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior

Iqbal H. Sarker, Alan Colman, MA Kabir +1

Mobile phones can record individual's daily behavioral data as a time-series. In this paper, we present an effective time-series segmentation technique that extracts optimal time s…