From Collatz Conjecture to chaos and hash function حسب Masrat Rasool (17807813)

"…The results obtained from the comparative analysis highlight the superiority of the proposed hash function over existing alternatives, validating its potential as a robust solution for various cryptographic applications.…"

Genetic and heuristic algorithms for regrouping service sites. (c2000) حسب Tabbara, Hiba

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Scatter search for protein structure prediction. (c2008) حسب Kehyayan, Christine Houry

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Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization حسب Abu Zitar, Raed

"…The goal is to select a set of sensors based on norms of weighted distances cost function. …"
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Multi-Cluster Jumping Particle Swarm Optimization for Fast Convergence حسب Atiq Ur Rehman (8843024)

"…Each cluster in the swarm has its own cluster best position which is the best position within a cluster and the global best position is located by clusters communication. …"

A New Hamiltonian Semi-Analytical Approach to Vibration Analysis of Piezoelectric Multi-Layered Plates حسب Andrianarison, O.

"…By performing a Legendre Transform, the classical Lagrangian functional is recast into a Hamiltonian one, so that the resulting variational formulation can be expressed in terms of the displacements and electric potential and their transverse stresses and electric displacement dual variables. …"
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The Use of Enumerative Techniques in Topological Optimization of Computer Networks Subject to Fault Tolerance and Reliability حسب Abd-El-barr, Mostafa

"…It is shown that improving the fault tolerance of a network can be achieved while optimizing its reliability however at the expense of a reasonable increase in the overall cost of the network while remaining within a maximum pre-specified cost constraint.…"
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Label dependency modeling in Multi-Label Naïve Bayes through input space expansion حسب PKA Chitra (21749216)

"…Our proposed methodology seeks to encapsulate and systematically represent label correlations within the learning framework. The innovation of improved multi-label Naïve Bayes (iMLNB) lies in its strategic expansion of the input space, which assimilates meta information derived from the label space, thereby engendering a composite input domain that encompasses both continuous and categorical variables. …"