Machine learning: Algorithms, real-world applications and research directions

IH Sarker - SN computer science, 2021 - Springer
In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world
has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data …

Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective

IH Sarker - SN Computer Science, 2021 - Springer
The digital world has a wealth of data, such as internet of things (IoT) data, business data,
health data, mobile data, urban data, security data, and many more, in the current age of the …

Smartphone app usage analysis: datasets, methods, and applications

T Li, T **a, H Wang, Z Tu, S Tarkoma… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
As smartphones have become indispensable personal devices, the number of smartphone
users has increased dramatically over the last decade. These personal devices, which are …

Mobile data science and intelligent apps: concepts, AI-based modeling and research directions

IH Sarker, MM Hoque, MK Uddin… - Mobile Networks and …, 2021 - Springer
Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of
computing with smart mobile phones that typically allows the devices to function in an …

Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

IH Sarker, ASM Kayes, P Watters - Journal of Big Data, 2019 - Springer
Due to the increasing popularity of recent advanced features and context-awareness in
smart mobile phones, the contextual data relevant to users' diverse activities with their …

Ageism in the era of digital platforms

A Rosales, M Fernández-Ardèvol - Convergence, 2020 - journals.sagepub.com
Ageism is the most invisible form of discrimination. While there is some awareness of
gender, racial, and socioeconomic discrimination on digital platforms, ageism has received …

Refl: Resource-efficient federated learning

AM Abdelmoniem, AN Sahu, M Canini… - Proceedings of the …, 2023 - dl.acm.org
Federated Learning (FL) enables distributed training by learners using local data, thereby
enhancing privacy and reducing communication. However, it presents numerous challenges …

Context-aware rule learning from smartphone data: survey, challenges and future directions

IH Sarker - Journal of Big Data, 2019 - Springer
Smartphones are considered as one of the most essential and highly personal devices of
individuals in our current world. Due to the popularity of context-aware technology and …

Sensors of smart devices in the internet of everything (IoE) era: big opportunities and massive doubts

M Masoud, Y Jaradat, A Manasrah… - Journal of …, 2019 - Wiley Online Library
Smart device industry allows developers and designers to embed different sensors,
processors, and memories in small‐size electronic devices. Sensors are added to enhance …

Discovering different kinds of smartphone users through their application usage behaviors

S Zhao, J Ramos, J Tao, Z Jiang, S Li, Z Wu… - Proceedings of the …, 2016 - dl.acm.org
Understanding smartphone users is fundamental for creating better smartphones, and
improving the smartphone usage experience and generating generalizable and …