Journal of Autonomous Intelligence

Selected Papers from Global Conference on Computer Applications, Sustainable Engineering, Manufacturing Practices, Mathematical Science and Environment

Submission deadline: 2023-12-10
Special Issue Editors

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to the Global Conference on computer applications, sustainable engineering, manufacturing practices, Mathematical Science and Environment (15th and 16th July’2023). The authors are invited to submit the revised and extended papers.

The topics relevant to this Special Issue include, but are not limited to:

· Machine learning algorithms for big data analytics

· Artificial neural network, neural systems and its applications

· Hybrid intelligent systems for cloud security and privacy

· Big data analytics in the cloud for decision-making and predictive modeling

· Novel computational intelligence paradigms for cloud forensics and incident response

· Cloud-based anomaly detection and intrusion detection systems using computational intelligence techniques

· Fuzzy systems and other soft computing techniques for cloud resource management and optimization

· Cloud-based intelligent systems for real-time security threat detection and mitigation

· Novel applications of big data analytics and computational intelligence in the cloud

· Privacy-preserving big data analytics in the cloud using cryptographic techniques

· Performance optimization of big data analytics using cloud-based computing and distributed processing

· Computational intelligence solutions to security and privacy issues in mobile cloud computing

· Privacy concepts and applications in cloud platforms

· Cloud computing security data analysis tools and services

Prof. Anuj Kumar

Dr. Nishu Ayedee

Dr. Surya Kant Pal

Dr. Jayanta Banerjee

Dr. Prabha Kiran

Dr. Altaf Yousuf Mir

Guest Editors


Artificial Intelligence; Machine Learning; Cloud Computing; Physics Chemistry; Cloud Computing; Big Data Analytics; Computational Intelligence; Engineering Applications; Electronics; Mathematical Science

Published Paper

Adaptive transport technologies based on vehicular ad hoc networks (VANET) has proven considerable potential in light of the developing expansion of driver assistance and automobile telecommunication systems. However, confidentiality and safety are the vital challenges invehicular adhoc networks which could be seriously impaired by malicious attackers. While protecting vehicle privacy from threats, it is imperative to stop internal vehicles from putting out bogus messages. Considering these issues, a novel machine learning based message authentication combined with blockchain and inter planetary file system(IPFS) is proposed to achieve message dissemination in a secured way. Blockchain is the emerging technology whichattempts to solve these problems by producing tamper proof events of records in a distributed environment and inter planetary file system used in the framework is a protocol designed to store the event with content addressability. Along with this combinedtechnology, the source metadata information collected from the inter planetary file systemis stored via a smart contract and uploaded to the distributed ledger technology (DLT). For performing event authentication, K-means clustering and support vector machine (SVM) classifier is employed in this framework. K-means clustering performs clustering of vehicles and it is marked malicious or not malicious. After clustering, support vector machine classifier detects the malicious event messages. By this way, the malicious messages are identified and it is dropped. Only the secure messages are forwarded in the network. Finally, our approach is capable of creating a safe and decentralizedvehicular ad hoc network architecture with accountability and confidentiality through theoretical study and simulations.