Power Efficient AI Technologies for Sensor-Based Content Moderation

Dear Colleagues,

In the wake of increasing digital content proliferation, safeguarding the integrity of social media platforms has become paramount. The proposed Special Issue, "Power Efficient AI Technologies for Sensor-Based Content Moderation", seeks original contributions that focus on innovative, energy-efficient AI methodologies tailored for the real-time moderation of sensor-generated content; this encompasses detecting inappropriate imagery, videos, and abusive speech patterns. As sensor-based platforms burgeon, powered by IoT devices and mobile technology, the demand for low-power, high-performance AI solutions becomes critical. This issue will serve as a convergence point for research that not only enhances the safety of online communities by filtering harmful content but also by minimizing the energy footprint of the AI systems employed. We invite studies that present advancements in AI algorithms, hardware optimization, and system integrations that significantly reduce power consumption without compromising the accuracy and speed necessary for effective content moderation.

I cordially invite you to contribute to this Special Issue.

Topics of interest include but are not limited to:

• Energy-efficient AI algorithms for image and video analysis;
• Low-power deep learning models for speech recognition and processing;
• Innovations in edge computing for real-time content moderation;
• Power optimization techniques for AI in embedded systems;
• Neuromorphic computing approaches for sensor data filtering;
• On-device AI for detecting explicit content sustainable AI models for large-scale data moderation;
• Energy-aware AI systems for mobile and IoT devices;
• Hardware accelerators for efficient content moderation AI;
• Adaptive AI frameworks for dynamic power management;
• Techniques for balancing performance and power in AI systems;
• Lightweight neural networks for multimedia content analysis;
• Energy-efficient transfer learning for content moderation tasks;
• Benchmarking and metrics for evaluating power efficiency in AI models;
• Case studies and real-world implementations of low-power AI content moderation.

Dr. Somdip Dey
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial intelligence
• image recognition
• content moderation
• nudity detection
• deep learning
• computer vision
• social media
• neural networks
• machine learning
• adult content filtering
• digital ethics
• image classification
• safe browsing
• algorithmic content control
• visual content analysis
• online safety
• automated moderation
• ethics in AI
• data privacy