This study applied parallel processing of incoming packets to reduce processing time. Experimental results show that proposed mechanisms enhanced network performance, allowing efficient resource use, reducing network latency, and ensuring stable packet transmission to fit service requirements.
We present DataPoll, an “end-to-end” Big Data analysis tool designed to simplify the process and enhance accessibility for scientists across disciplines. DataPoll introduces innovative features and techniques for analyzing and interpreting digital data. Its capabilities and effectiveness are demonstrated through a case study on multi-source data from the Ukrainian-Russian conflict.
This research introduces a system that integrates machine learning with blockchain technology, ensuring data transparency, security, and immutability while enhancing predictive accuracy. Demonstrated with real-world health insurance data, this hybrid approach significantly improves fraud detection accuracy and efficiency. Advanced machine learning algorithms provide insights into patterns and anomalies, enabling proactive fraud prevention. The solution is scalable and adaptable to other sectors prone to fraud. The use of Hyperledger blockchain ensures robust data integrity and security, addressing challenges related to data tampering and unauthorized access. These contributions collectively advance fraud detection and prevention in the health insurance industry.
Japanese Keigo known as honorific, is a way to reflect social status, intimacy, and the relationships among speakers, listeners, and other participants in a conversation. It is a very special and important language phenomenon that conveys respect and politeness based on the social status of the speaker and listener and their relationships. Unlike many other languages, Japanese has various forms of honorific expressions, and these honorific forms change depending on social group relations and occasion fields.
This study randomly experiments different combinations of the multilayer perceptron’s hyperparameters, to find those that best improve our model’s performance.
Unmanned aerial vehicles (UAVs) are being successfully used in a variety of applications, including agriculture, search and rescue operations, surveillance systems, and mission-critical services, thanks to some technological and practical advantages, such as high mobility, the ability to extend wireless coverage areas, or the capacity to reach locations inaccessible to humans. In contrast, attacks against drones, as opposed to traditional cyberattacks, typically happen as a result of serious design flaws and a lack of wireless security protection methods. The study examines lightweight asymmetric cryptographic algorithms for secure Internet of Drones (IoD) communication, addressing cybersecurity
challenges within this emerging technology. It evaluates RSA, ElGamal, DiffieHellman, and Elliptic Curve Cryptography (ECC), focusing on their suitability for IoD through comparative analysis on calculation time, memory usage, key size, and security. The goal is to contribute to developing robust, efficient, and secure communication protocols for IoD, promoting growth while mitigating risks. This research is pivotal for the advancement of IoD security, exploring the application of these cryptographic techniques to ensure secure, efficient operations within the IoD framework.
This study uses image enhancement methods to analyze liver nodule progression and radiological features in liver cancer development. A detection strategy has been developed from CT image characteristics for early identification of liver cancer. The study also explores how the size of nodules influences detection accuracy and classification between benign and malignant types, which is vital for refining detection algorithms and improving diagnostic precision.
This work presents a novel knowledge distillation framework that utilizes multiple intermediated assistant models of varying sizes and architectures to facilitate knowledge transfer from a teacher (source) model to a student (target) model.
Health scientists argue that understanding the local leads to effective interventions
Despite these benefits, most researchers keep foregoing this valuable geo-resource
Specialized training and lack of no-code software are barriers for geocontextalizing
We address this issue by offering a user-friendly software to understand the local
GeoStoryTelling handles multi-media inputs to provide context/nuance in HTML outputs
We offer access to a freeware and data that illustrates the process to conduct the analyses proposed
Deniable authentication protocol enable a recipient to verify the authenticity of a received message while keeping the sender’s identity hidden from any third parties. Compared to interactive protocol, non-interactive approaches offer improved efficiency by reducing authentication overhead. This advantage has led to the proposal of numerous non-interactive deniable authentication protocols. Hence, an idea come up to develop a deniable authentication protocol that is non-interactive, secure and efficient. This paper introduces the development of deniable authentication protocol based on Chebyshev Polynomial over GF(q). An enhancement rooted in the Chebyshev Polynomial over finite fields is proposed, since Chebyshev Polynomial over GF(q) provides security of the algorithm rely on the difficulty of computing discrete logarithms over finite fields together with fast execution time. Concurrently, the proposed protocol exhibits characteristics of completeness, deniability, resistance to forgery, impersonation and man-in-the-middle attacks that has been proved.