Journal of Electronic & Information Systems

Abstract: Traditional collaborative signature schemes face significant challenges in resisting quantum computing attacks, securing private keys in distributed architectures, and balancing operational efficiency, which are critical requirements for modern electronic and information systems like IoT, blockchain, and federated learning. This paper proposes P-CSNKS, a novel post-quotum collaborative signature scheme featuring a non-linear private key splitting technique.... More

Abstract: A simulation-based and deterministic approach was employed to assess the health-related fitness of upper primary school students through a Fuzzy Inference System (FIS) implemented in MATLAB. Standardized physical assessments were used to gather fitness data, which were then systematically categorized by gender and grade. While statistical metrics such as mean and standard deviation were extracted,... More

Abstract: An advanced low-power True Single Phase Clock (TSPC) flip-flop design leveraging a synergistic integration of three power-saving techniques: auto-gated clock gating, power gating, and redundant-transition suppression. The proposed architecture targets both dynamic and leakage power reduction in sequential circuits without sacrificing speed or timing integrity. Auto-gated clock gating dynamically disables the clock signal when input... More

Abstract: Magnetometers are widely used spacecraft attitude sensors due to their numerous advantages. Typically, fully observing a spacecraft’s attitude requires the use of at least two distinct sensor types. Thus, relying exclusively on a magnetometer introduces major challenges for estimation algorithms. The problem of spacecraft attitude estimation based on magnetometer measurements is generally nonlinear. Cubature Kalman... More

Abstract: In this work, we introduce m-FDPC, a mass-based variant of the Fast Density Peak Clustering (FDPC) algorithm, aimed at improving both performance and ease of use in unsupervised learning tasks. Traditional FDPC relies on Euclidean distance and requires careful parameter tuning and data normalization, which can significantly affect clustering outcomes—especially for heterogeneous or high-dimensional datasets.... More

Abstract: The recent surge in edge computing and wireless connectivity has accelerated the adoption of Federated Learning (FL), a paradigm that enables privacy-preserving distributed intelligence across resource-constrained devices. However, implementing FL in practical wireless edge networks introduces significant challenges, particularly excessive energy consumption and communication overhead. This survey provides a system-level exploration of energy-efficient FL strategies,... More

Abstract: This paper proposes a deterministic annealing neural network algorithm to address critical resource partitioning challenges in coal mining, such as equipment scheduling, safety zone division, and logistics optimization. By integrating a novel square-root barrier function within a temperature-controlled annealing framework, this algorithm transforms the NP-hard minimum bisection problem into a tractable convex optimization problem with... More

Abstract: Digital device usage now transcends age and demographic boundaries, having become commonplace among children from all racial and ethnic groups. The rapid proliferation of touch screen use among infants (6–24 months) has outpaced evidence-based design standards, creating an urgent need for developmentally-grounded interfaces. In this paper, BabySens-an ethical Infant-Centered HCI Design (ICHD) framework that integrates... More

Abstract: The increasing sophistication and scale of malicious network activities demand a fundamental shift from traditional signature-based intrusion detection systems toward adaptive, data-driven security architectures. Machine learning (ML) provides an effective paradigm for addressing this challenge by identifying intricate and non-linear patterns associated with cyber threats within complex, high-dimensional network data. This study presents a... More

Abstract: The sentiment is seen as valuable information that can represent people's opinions, and its analysis is regarded as an essential component of decision-making processes. Social media has led to an exponential increase in the volume of shared textual content, and natural language processing as a potential area provides a variety of cutting-edge, deep learning-based models... More