Special Issue "Machine Learning Empowered Secure Computing for Intelligent Systems"

Submission Deadline: 28 February 2022
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Guest Editors
Dr. Muhammad Adnan Khan, Gachon University, Korea.
Dr. Muhammad Aamer Saleem, Hamdard University, Pakistan.
Dr. Rizwan Ali Naqvi, Sejong University, Korea.

Summary

Artificial intelligence (AI) and machine learning (ML) have been put thoroughly into practice, with more promotion being given, to enhance continuity, cybersecurity in cloud computing, Internet services, and the Internet-of-Things. Machine learning algorithms, such as AI, are used to track complex cyber threats that cannot be readily identified by conventional detection methods. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost.

By far, the most difficult challenge is how to exploit AI and machine learning algorithms for improved safe service computation while maintaining the user's privacy. The robustness of AI and deep learning as well as the reliability and privacy of data is an important part of today's modern computing. This topic aims to determine the security issues of using AI to protect systems. To be able to enforce them in reality, privacy would have to be held throughout the implementation process.

In this special issue of the journal, we are finding groundbreaking applications and undisclosed work related to artificial intelligence and machine learning for more stable and privacy cleaning computing. By reflecting on the role of machine learning in information security, we are looking to discuss recent developments in the area of machine learning and privacy-preserving strategies. To make our computation more secure and confidential, we aim to experiment, conceptualize, and theorize about issues that include AI and machine learning for improved security and privacy of information.


Keywords
Suggested topics include, but are not limited to, the following:
• Cybersecurity
• Spam Detection
• Secure online social networks
• Anomaly and intrusion detection in the network
• Malware analysis and detection
• Security models based AI for protecting IoT networks
• Intrusion Detection for IoT systems
• Distributed AI Systems and Architectures
• eBusiness, eCommerce, eHealth, eLearning
• Finance and AI
• Extreme Machine Learning
• Applications of neural networks in data analytics
• CNN, LSTM
• Automation and control system
• Smart mobility and transportation
• Signal and Image Processing

Published Papers
  • Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning
  • Abstract Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm… More
  •   Views:108       Downloads:81        Download PDF

  • A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis
  • Abstract In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53… More
  •   Views:107       Downloads:408        Download PDF