Workshop on Machine Intelligence in Networked Data and Systems (MINDS)
The inter-working of machine learning and networking is set to transform and disrupt many areas of business and everyday human life. MINDS (Machine Intelligence in Networked Data and Systems) aims to bring together researchers and practitioners to understand and explain this inter-working. MINDS will discuss and present latest achievements and innovations at the cross-section of machine learning, systems, and networking.
MINDS welcomes original research submissions that define challenges, report experiences, or discuss progress toward design and solutions that integrate machine learning, deep learning, mobile systems, and networked systems in various application areas. These application areas include but are not limited to healthcare, environment, retail, transportation, life sciences etc. Contributions describing techniques applied to real-world problems and interdisciplinary research involving novel networking architectures, system designs, IoT systems, big data systems with machine learning as the core component are especially encouraged.
The topics of interest include but are not limited to:
- Design and implementation of intelligent systems for applications such as home automation, self-driving vehicles, driver assistance systems
- Cloud based machine and deep learning applications in retail and e-commerce
- Machine learning systems for healthcare, weather modelling, life sciences, and environment monitoring
- Intelligent networked systems for city-scale transportation and logistics
Internet of Things (IoT)
- Machine learning driven systems using mobile phones, embedded devices, and sensor networks
- Applications of machine learning in IoT, IIoT, manufacturing, and supply chain optimisation
- Experiences in managing wearable devices, smart-home systems and mobile sensor networks
- Root cause analysis and failure prediction using system and network logs
- Applications of machine/deep/reinforcement learning in satellite networks, cellular networks and WiFi networks
- Machine learning driven algorithms and tools for network anomaly detection and network security
- Machine learning and data mining of large-scale network measurements
- Stream-based machine learning for networked data
- Machine learning driven algorithms for network scheduling and control
- Challenges and solutions in IoT data and stream processing at the edge and in the cloud
- High dimensional big data (images, videos) analysis using machine/deep learning
Social Media Networks
- Machine learning driven analysis of text, image, and video data on social media
- Security, privacy, trust analysis, health analytics in social media and digital networks
- Information diffusion, fake news detection, and knowledge transfer in social media and digital networks
- Anomaly and outlier detection in social networks
- Computational models and agent-based simulations of social networks
- MINDS invites submission of original work not previously published, or under review at another conference or journal.
- Submissions (including title, author list, abstract, all figures, tables, and references) must be no greater than 6 pages in length.
- Reviews will be single-blind: authors name and affiliation should be included in the submission.
- Submissions must follow the formatting guidelines as given on IEEE Website; and those that do not meet the size and formatting requirements will not be reviewed.
- All papers must be in Adobe Portable Document Format (PDF) and submitted through the MINDS Workshop submission site on EDAS.
- All workshop papers will appear in conference proceedings and submitted to IEEE Xplore as well as other Abstracting and Indexing (A&I) databases.
Papers can be submitted through EDAS : Click Here
|Paper Submission||8th November 2019, 11:59 pm AoE|
|Notification of Acceptance||29th November 2019|
|Camera-ready Submission||13th December 2019|
|Workshop Date||7th January 2020|