International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 2 April-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 16 Issue 2
2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Machine Learning Integration in Android Photo Editing: Architectures, Challenges, and User Experience

Author(s) Kamal Gupta
Country United States
Abstract This article examines the integration of machine learning techniques in Android-based photo editing applications, focusing on implementing advanced features such as intelligent object removal, neural style transfer, and automated color grading. The article analyzes the architectural considerations for deploying deep learning models on mobile platforms with inherent resource constraints, particularly through frameworks like TensorFlow Lite and PyTorch Mobile. The article identifies optimization strategies that balance computational efficiency with output quality through comparative case studies of leading applications. The investigation further explores the user experience implications of these technologies, highlighting how interface design must evolve to make complex ML-powered capabilities accessible to novice users. Technical challenges, including battery consumption, thermal management, and memory utilization are addressed alongside their respective mitigation approaches. The article suggests that ML-enhanced photo editing represents a significant shift in mobile creativity tools, with broader implications for democratizing professional-grade image manipulation capabilities and establishing new paradigms in human-computer interaction for visual content creation.
Keywords Mobile machine learning, computational photography, generative adversarial networks, photo editing applications, user experience design.
Field Computer
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-08
Cite This Machine Learning Integration in Android Photo Editing: Architectures, Challenges, and User Experience - Kamal Gupta - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.2445
DOI https://doi.org/10.71097/IJSAT.v16.i2.2445
Short DOI https://doi.org/g9fchk
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4Cover Page Vol 15 Isu 3Cover Page Vol 15 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJSAT
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijsat.org