Advanced Deep Learning and Mathematical Modeling for Reliability, Security and Privacy Problems in Engineering: 2nd Edition
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 30 September 2024 | Viewed by 1061
Special Issue Editors
Interests: blockchain; software reliability; services computing; machine learning; big data analytics
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; security; privacy; game theory
Special Issues, Collections and Topics in MDPI journals
Interests: mathematical modeling and statistical learning; anomaly detection and fault diagnosis; services-oriented computing; process mining; industrial Internet of Things; data management
Special Issues, Collections and Topics in MDPI journals
Interests: statistical learning theory and paradigms for modern information rich; large-scale; human-involved systems
Special Issues, Collections and Topics in MDPI journals
Interests: control over communication; network control system; cyber-physical security and privacy; distributed control; data-driven control; neural networks and control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Currently, new embedded technologies and interconnected networking advances in multiple domains based on new paradigms and heterogeneous technologies inevitably induce functional complexities in underlying engineering systems and, certainly, diverse vulnerabilities and risks may significantly grow according to new adaptations. Such trends have generated a need to further research the reliability, security and privacy of certain problems, calling for novel exploitations and guarantees for secure, resilient and dependable cohesion between theoretical algorithms and real-world engineering systems. For this reason, we are drawing special attention to the competitive advantages that recent prospering deep learning technologies and mathematical modeling approaches have to demonstrate, which can bring about original solution paths and advances related to the aforementioned challenges.
In this Special Issue, we are expecting a wide spectrum of research papers that cover relevant reliability, security and privacy issues in engineering, such as intrusion detection, cyber-attacks, critical data protection, situational awareness, incident responsiveness and system resilience. We welcome high-quality papers from both theory and application perspectives and embrace methodologies ranging from advanced deep learning technologies to efficient mathematical modeling in order to promote academic exchange between a wide array of scholars. As such, I am inviting you to submit an article to this Special Issue named “Advanced Deep Learning and Mathematical Modeling for Reliability, Security and Privacy Problems in Engineering: 2nd Edition” in Mathematics, a peer-reviewed journal. This Special Issue will focus on (but is not limited to) the following topics:
- Mathematical modeling for reliability, security and privacy problems;
- Innovative applications (healthcare, education, media, insurance, Internet of Things, smart city, industry, etc.) about risk management and reliability issues;
- Data quality and sparse learning;
- Mathematical solutions for representation learning;
- Mathematical optimization techniques and its application for quality management;
- Reliability, security and privacy analysis and requirements in engineering;
- Vulnerabilities and risk assessment in networked systems;
- Advanced threat models, cyber-crime or cyber-espionage;
- Reliable, secure and private architectures by design;
- Secure interoperability, mobility and coexistence between systems, including users;
- Prevention, awareness and resilience models for advanced threats;
- Data preservation and privacy models;
- Trust management and trusted computing models;
- Privacy issues and mitigation techniques;
- Malware and intrusion detection techniques;
- Mathematical and deep learning solutions for reliability problems in IoT and Industry 4.0.
Prof. Dr. Zibin Zheng
Dr. Ruoxi Jia
Dr. Dan Li
Dr. Yuxun Zhou
Prof. Dr. Liang Xu
Guest Editors
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. Mathematics 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.
Keywords
- deep learning
- sparse learning
- risk management
- reliability
- neural networks
- anomaly detection
- fault detection
- cyber security
- data privacy
- data quality
- mathematical modeling
- knowledge-based systems
