Publication Success in Knowledge-based Systems by Dr Sudhakar K

#HighImpactFactor #Q1 #SCI #KnowledgeBasedSystems #ScienceDirect

Dr Sudhakar K, LGPR Postdoctoral Candidate published at Knowledge-Based Systems, a High Impact Factor Journal that falls under 95 percentile.

Knowledge Based-Systems, 331, 2025

Journal Indexes: Impact factor 7.6

Indexing: SCI, SCIE, SCOPUS

H-Index: 188

Cite Score: 15

Quartile: Q1

Knowledge-based Systems is an international and interdisciplinary journal in the field of artificial intelligence. The journal will publish original, innovative and creative research results in the field, and is designed to focus on research in knowledge-based and other artificial intelligence techniques-based systems with the following objectives and capabilities: to support human prediction and decision-making through data science and computation techniques; to provide a balanced coverage of both theory and practical study in the field; and to encourage new development and implementation of knowledge-based intelligence models, methods, systems, and software tools, with applications in business, government, education, engineering and healthcare.

This journal's current leading topics are but not limited to:

• Machine learning theory, methodology and algorithms

• Data science theory, methodologies and techniques

• Knowledge presentation and engineering

• Recommender systems and E-service personalization

• Intelligent decision support systems, prediction systems and warning systems

• Computational Intelligence systems

• Data-driven optimization

• Cognitive interaction and brain–computer interface

• Knowledge-based computer vision techniques

Anomaly detection based self-healing mechanism using dynamic diffusion spatial-temporal graph convolutional network in industrial IoT

https://doi.org/10.1016/j.knosys.2025.114812

Sudhakar K

Lincoln University College, Petaling Jaya, Malaysia

AI & DS Department, Nitte Meenakshi Institute of Technology (NMIT), Nitte (Deemed to be University),Bengaluru, India

Arun Kumar

Computer Science & Engineering Department, Vidyavardhaka College of Engineering, Mysore, Karnataka, India.

Archana RA

Computer Science and Business Systems Department, BMS Institute of Technology and Management,Bengaluru, India.

Sivakumar N

Artificial Intelligence and Data Science Department, Panimalar Engineering college, Chennai, India

Eugenio Vocaturo

CNR (National Research Council) - NANOTEC, Rende (Cs), Italy.

Sai Kiran Oruganti*

Faculty of Built Science & Engineering, Lincoln University College, Petaling Jaya 47810, Malaysia

https://orcid.org/0000-0003-4601-2907

Abstract

The Industrial Internet of Things (IIoT) has been viewed by the public as the key component of Industry intelligent digital factories. To guarantee the security and resilience of the IIoT, which has many susceptible IIoT devices, effective anomaly detection is essential. In this paper, Anomaly Detection Based Self-Healing Mechanism Using Dynamic Diffusion Spatial-temporal Graph Convolutional Network in Industrial IoT (AD-SHM-DDSGCN-IIoT) is proposed. At first, Input data is collected from WUSTL-IIOT-2021 Dataset. Then, using Sparse Regression Unscented Kalman Filter (SRUKF) which is used to reduce the noise and standardize from the collected data. The pre-processed data are given into Dynamic Diffusion Spatial-temporal Graph Convolutional Network (DDSGCN) for detecting the anomaly as Normal Traffic, Total Attack Traffic, DoS Traffic, Reconnaissance Traffic, Command Injection Traffic, and Backdoor Traffic. To ensure precise anomaly detection from Industrial IoT, DDSGCN generally does not express any adaptation of optimization strategies for figuring out the ideal parameters. Starfish Optimization Algorithm (SFOA) is proposed in this work to optimize the weight parameter of DDSGCN classifier, which precisely classifies the anomaly from Industrial IoT. The proposed AD-SHM-DDSGCN-IIoT is implemented and analyzed under performance metrics, such asAccuracy, Recall, Precision, Fault Recovery Time, Network Throughput and ROC. The performance of the AD-SHM-DDSGCN-IIoT approach attains24.18 %, 31.66 % and 18.46 % higher accuracy and 18.16 %, 25.49 % and 30.68 % lower fault recovery time with existing methods respectively‎.

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