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All Journal International Journal of Electrical and Computer Engineering Journal of Applied Data Sciences
Srivatsa, Anand
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Secure data transmission in power systems using blockchain technology Srivatsa, Anand; Thammaiah, Ananthapadmanabha; Kumar MV, Likith; D, Rajeshwari; AP, Suma
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6170-6181

Abstract

Recent advances in intelligent systems have significantly improved power management, load distribution, and resource management capabilities, far beyond past constraints. Despite these gains, the development of internet-connected technology has brought various vulnerabilities, leading to negative results. The integration of intelligent technology has unintentionally offered chances for hackers to enter networks and modify data sent to central systems for analysis. One of the most serious risks is the false data injection attack (FDIA), which may drastically impair analytical outcomes. Previous research has shown that standard approaches for recovering data affected by FDIA are unreliable and inefficient. This paper investigates the use of the proof of stake (PoS) consensus method in this framework improves data integrity and makes it easier to identify illegal changes. Participating nodes may reject or change block transactions, ensuring the ledger's correctness. Our results show that the PoS consensus method is exceptionally successful in creating and adding transactions to the blockchain. Furthermore, the PoS mechanism's simplicity in block formation enhances both time and energy efficiency, resulting in considerable benefits in operational performance.
DeepCog: Classification of Mild Cognitive Impairment Using Structural MRI S, Lavanya M; Arun, Vanishri; Dhananjaya, Shashank; M, Nandini B; Srivatsa, Anand; R, Lokesha H
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1301

Abstract

Early identification of Mild Cognitive Impairment (MCI) is essential for preventing or delaying the progression of severe neurodegenerative disorders. The primary objective of this study is to develop an automated and computationally efficient framework for detecting MCI using structural brain imaging. The proposed research focuses on improving early diagnostic capability through a deep learning–based classification system that analyzes structural changes in brain images. The major contribution of this work lies in combining region-focused morphometric analysis with lightweight convolutional neural network architecture to achieve accurate classification while maintaining computational efficiency suitable for clinical environments. The methodology involves extracting anatomically meaningful features from structural brain scans using a region-of-interest based morphometric approach. Brain images undergo several preprocessing procedures including skull stripping, normalization, spatial alignment and data augmentation to ensure consistency and robustness of the dataset. After preprocessing, the images are used to train a lightweight deep learning model that performs binary classification between cognitively normal subjects and individuals with MCI. The study employs a publicly available neuroimaging dataset consisting of structural brain scans and associated clinical information. Experimental results demonstrate that the proposed framework achieves strong classification performance while maintaining low computational complexity. The model achieves 88.2% subject-wise test accuracy and 0.90 cross-validation accuracy, outperforming commonly used architectures such as VGG16 (78.1%) and ResNet50 (53.7%). These findings indicate that lightweight neural networks combined with region-based anatomical analysis can effectively support automated screening of MCI. The proposed approach has potential implications for scalable clinical decision support systems and may assist neurologists in early diagnosis, timely intervention, and improved cognitive healthcare management. Future research may explore multimodal data integration and longitudinal clinical validation to further enhance diagnostic reliability.
Co-Authors AP, Suma Arun, Vanishri D, Rajeshwari Dhananjaya, Shashank Kumar MV, Likith M, Nandini B R, Lokesha H S, Lavanya M Thammaiah, Ananthapadmanabha