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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Biradar, Rajashekhar C.
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Computer Science & IT Engineering

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A terrain data collection sensor box towards a better analysis of terrains conditions Olivier Akansie, Kouame Yann; Biradar, Rajashekhar C.; Rajendra, Karthik; Devanagavi, Geetha D.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4388-4402

Abstract

Autonomous mobile robots are increasingly used across various applications, relying on multiple sensors for environmental awareness and efficient task execution. Given the unpredictability of human environments, versatility is crucial for these robots. Their performance is largely determined by how they perceive their surroundings. This paper introduces a machine learning (ML) approach focusing on land conditions to enhance a robot’s locomotion. The authors propose a method to classify terrains for data collection, involving the design of an apparatus to gather field data. This design is validated by correlating collected data with the output of a standard ML model for terrain classification. Experiments show that the data from this apparatus improves the accuracy of the ML classifier, highlighting the importance of including such data in the dataset.
Hierarchical enhanced deep encoder-decoder for intrusion detection and classification in cloud IoT networks K. M., Ramya; Biradar, Rajashekhar C.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1176-1188

Abstract

Securing cloud-based internet of things (IoT) networks against intrusions and attacks is a significant challenge due to their complexity, scale, and the diverse nature of connected devices. IoT networks consist of billions of devices, computer servers, data transmission networks, and application computers, all communicating vast amounts of data that must adhere to various protocols. This study introduces a novel approach, termed hierarchical enhanced deep encoder-decoder with adaptive frequency decomposition (HED-EDFD), and is designed to address these challenges within cloud-based IoT environments. The HED-EDFD methodology integrates adaptive frequency decomposition, specifically adaptive frequency decomposition, with a deep encoder-decoder model. This integration allows for the extraction and utilization of frequency domain features from time-sequence IoT data. By decomposing data into multiresolution wavelet coefficients, the model captures both high-frequency transient changes and low-frequency trends, essential for detecting potential intrusions. The deep encoder-decoder model, enhanced with deep contextual attention mechanisms, processes these features to identify complex patterns indicative of malicious activities. The hierarchical structure of the approach includes a hierarchical wavelet-based attention mechanism, which enhances the accuracy and robustness of feature extraction and classification. To address the issue of imbalanced intrusion data, a cosine-based SoftMax classifier is employed, ensuring effective recognition of minority class samples.
Co-Authors Devanagavi, Geetha D. K. M., Ramya Olivier Akansie, Kouame Yann Rajendra, Karthik