Emmanuel Asiamah is a graduate researcher at the Distributed IoT-Based Platforms, Privacy, and Edge-Intelligence Research (DIPPER) Lab, KNUST. His research f...
Emmanuel Asiamah is a graduate researcher at the Distributed IoT-Based Platforms, Privacy, and Edge-Intelligence Research (DIPPER) Lab, KNUST. His research focuses on leveraging artificial intelligence to address societal challenges, with expertise in computer vision, reinforcement learning, and resource-efficient algorithms. Currently, He is working on adversarial robustness of AI models, focusing on mitigating hallucinations in generative systems and improving their reliability in critical applications. Additionally, his research explores blockchain and IoT integration, specifically investigating scalable solutions for secure data management and decentralized applications in IoT ecosystems.
Theresa-Samuelle Adjaidoo, Griffith Selorm Klogo
Efficient blockchain querying is crucial for unlocking the full potential of blockchain technology in diverse applications and enabling it to compete with traditional databases in data management. This paper comprehensively analyzes existing literature and presents a taxonomy of state-of-the-art tec
Prince Odame, Mohammed Al-Khalidi, Griffith Selorm Klogo
With its promise of transparency, security, and decentralization, blockchain technology faces significant challenges related to data storage and query efficiency. Current indexing methods, which often rely on structures like Merkle trees and Patricia tries, contribute to excessive storage overhead a