Behera, Sandip Ranjan
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Solana blockchain technology: a review Mishra, Debani Prasad; Behera, Sandip Ranjan; Behera, Subhashis Satyabrata; Patro, Aditya Ranjan; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp197-205

Abstract

The introduction of a review article on the Solana blockchain is critical to setting the stage for the arguments and evidence to follow. This paragraph will provide context to the reader by discussing the current state of blockchain technology and introducing Solana as a potential solution. Blockchain technology has the potential for countless applications, ranging from financial transactions to secure data storage. However, existing blockchain systems suffer from scalability issues, were confirmation times and network congestion limit transaction volumes. This review paper on the Solana blockchain is valuable for those seeking an in-depth understanding of the design and efficacy. Given the increasing number of blockchain technologies available in the market, potential adopters face the challenge of selecting the most suitable blockchain network for their specific use case. A well-constructed review provides necessary information on the functioning of the technology, including its strengths and limitations. It also enables readers to compare various blockchain technologies and judge their suitability for their specific needs. Therefore, reviews like this one play a crucial role in helping to advance blockchain technology by driving the adoption of superior blockchain networks.
Metaheuristic algorithms for parameter estimation of DC servo motors with quantized sensor measurements Mishra, Debani Prasad; Behera, Sandip Ranjan; Dash, Arul Kumar; Ojha, Prajna Jeet; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i1.pp101-108

Abstract

Manufacturing, aviation, and robotics have increased servo motor use due to their precision, reliability, and adaptability in various applications. This study compares three metaheuristic techniques for servo motor model parameter estimation with sensor measurement quantization, focusing on their accuracy and efficiency. Armature resistance, back electromotive force (EMF) constant, torque constant, coil inductance, friction coefficient, and rotor-load inertia are crucial to servo motor behavior prediction, significantly impacting overall system performance. Each approach was rigorously tested and analyzed to evaluate its effectiveness in predicting servo motor characteristics. The results revealed that particle swarm optimization and the firefly algorithm delivered comparable performance, particularly excelling in scenarios where sensor measurement quantization introduced noise or imprecision in the data. These methods demonstrated strong resilience and accuracy under such challenging conditions. In contrast, the genetic algorithm did not perform as well, falling short when compared to the other two techniques in handling noisy or imprecise data, indicating its relative inefficiency in such environments. These findings give servo motor designers and engineers across industries a powerful tool for performance prediction.
Enhancing logo security: VGG19, autoencoder, and sequential fusion for fake logo detection Mishra, Debani Prasad; Ojha, Prajna Jeet; Dash, Arul Kumar; Sethy, Sai Kanha; Behera, Sandip Ranjan; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp506-515

Abstract

This paper deals with a way of detecting fake logos through the integration of visual geometry group-19 (VGG19), an autoencoder, and a sequential model. The approach consists of applying the method to a variety of datasets that have gone through resizing and augmentation, using VGG19 for extracting features effectively and autoencoder for abstracting them in a subtle manner. The combination of these elements in a sequential model account for the improved performance levels as far as accuracy, precision, recall, and F1-score are concerned when compared to existing approaches. This article assesses the strengths and limitations of the method and its adapted comprehension of brand identity symbols. Comparative analysis of these competing approaches reveals the benefits resulting from such fusion. To sum up, this paper is not only a major contribution to the domain of counterfeit logo detection but also suggests prospects for enhancing brand security in the digital world.