7 research outputs found
Score Fusion Using Hybrid Bacterial Foraging Optimization And Particle Swarm Optimization (Bfo-Pso) For Hand-Based Multimodal Biometrics
In recent times of biometric authentication, the influence of swarm intelligence algorithms role-played in enhancing the performance accuracy to a greater extent. Most researches related to Swarm Intelligence (SI) algorithms have done on the particular, due to the need to integrate more than one SI algorithm for better results. Therefore, this research is focused on the hand-based multimodal biometric score fusion which incorporates the scores of hand-based multimodalities and the optimal weights using Hybrid Bacterial Foraging - Particle Swarm Optimization (HBF-PSO) algorithm
A collision aware priority level medium access control protocol for underwater acoustic sensor networks
The Underwater Acoustic Sensor Network (UASN) plays a significant role in many application areas like surveillance, security, commercial and industrial applications. In UASN routing, propagation delay and collision are perennial problems due to data transfers from various sensor nodes to the Sink Node (SN) at the same time. In this paper, we propose a Collision Aware Priority Level mechanism based on Medium Access
Control protocol (CAPL-MAC) for transferring data from the Sensor Head (SH) to the SN. In the proposed protocol, we use Parallel Competition Scheme (PCS) for high channel utilization
and energy saving of battery. In each Competition Cycle (CC), the data packet produced by each SH in a different time slot can join in CC for data packet transmission in parallel with high channel
utilization. In CAPL-MAC, each SH is assigned with a different Priority Level Number (PLN) during every CC. Instead of broadcasting, each SH sends its respective PLN to each SH with the help of the nearest SH to save battery energy. Based on the
highest PLN, each SH communicates with SN without collision, and it will also reduce propagation delay as well as improve timing efficiency. Finally, Quality of Service is also improved. We adopt the single-layer approach with the handshaking protocol for communication. We carried out the simulation utilizing Aqua-Sim Network Simulator 2. The simulation results showed that the proposed CAPL-MAC protocol achieved the
earlier stated performance rather than by existing protocols such as Competitive Transmission-MAC and Channel Aware Aloh
HYBRID IMPROVED BACTERIAL SWARM OPTIMIZATION ALGORITHM FOR HAND-BASED MULTIMODAL BIOMETRIC AUTHENTICATION SYSTEM
This paper proposes a Hybrid Improved Bacterial Swarm (HIBS) optimization algorithm for the minimization of Equal Error Rate (EER) as a performance measure in a hand-based multimodal biometric authentication system. The hybridization of the algorithm was conducted by incorporating Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm to mitigate weaknesses in slow and premature convergence. In the proposed HIBS algorithm, the slow convergence of BFO algorithm was mitigated by using the random walk procedure of Firefly algorithm as an adaptive varying step size instead of using fixed step size. Concurrently, the local optima trap (i.e. premature convergence) of PSO algorithm was averted by using mutation operator. The HIBS algorithm was tested using benchmark functions and compared against classical BFO, PSO and other hybrid algorithms like Genetic Algorithm-Bacterial Foraging Optimization (GA-BFO), Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and other BFO-PSO algorithms to prove its exploration and exploitation ability. It was observed from the experimental results that the EER values, after the influence of the proposed HIBS algorithm, dropped to 0.0070% and 0.0049% from 1.56% and 0.86% for the right and left hand images of the Bosphorus database, respectively. The results indicated the ability of the proposed HIBS in optimization problem where it optimized relevant weights in an authentication system.
Hybrid Improved Bacterial Swarm Optimization Algorithm for Hand-Based Multimodal Biometric Authentication System
This paper proposes a Hybrid Improved Bacterial Swarm (HIBS) optimization algorithm for the minimization of Equal Error Rate (EER) as a performance measure in a hand-based multimodal biometric authentication system. The hybridization of the algorithm was conducted by incorporating Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm to mitigate weaknesses in slow and premature convergence. In the proposed HIBS algorithm, the slow convergence of BFO algorithm was mitigated by using the random walk procedure of Firefly algorithm as an adaptive varying step size instead of using fixed step size. Concurrently, the local optima trap (i.e., premature convergence) of PSO algorithm was averted by using mutation operator. The HIBS algorithm was tested using benchmark functions and compared against classical BFO, PSO and other hybrid algorithms like Genetic Algorithm-Bacterial Foraging Optimization (GA-BFO), Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and other BFO-PSO algorithms to prove its exploration and exploitation ability. It was observed from the experimental results that the EER values, after the influence of the proposed HIBS algorithm, dropped to 0.0070% and 0.0049% from 1.56% and 0.86% for the right- and left-hand images of the Bosphorus database, respectively. The results indicated the ability of the proposed HIBS in optimization problem where it optimized relevant weights in an authentication system
HYBRID IMPROVED BACTERIAL SWARM OPTIMIZATION ALGORITHM FOR HAND-BASED MULTIMODAL BIOMETRIC AUTHENTICATION SYSTEM
This paper proposes a Hybrid Improved Bacterial Swarm (HIBS) optimization algorithm for the minimization of Equal Error Rate (EER) as a performance measure in a hand-based multimodal biometric authentication system. The hybridization of the algorithm was conducted by incorporating Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm to mitigate weaknesses in slow and premature convergence. In the proposed HIBS algorithm, the slow convergence of BFO algorithm was mitigated by using the random walk procedure of Firefly algorithm as an adaptive varying step size instead of using fixed step size. Concurrently, the local optima trap (i.e. premature convergence) of PSO algorithm was averted by using mutation operator. The HIBS algorithm was tested using benchmark functions and compared against classical BFO, PSO and other hybrid algorithms like Genetic Algorithm-Bacterial Foraging Optimization (GA-BFO), Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and other BFO-PSO algorithms to prove its exploration and exploitation ability. It was observed from the experimental results that the EER values, after the influence of the proposed HIBS algorithm, dropped to 0.0070% and 0.0049% from 1.56% and 0.86% for the right and left hand images of the Bosphorus database, respectively. The results indicated the ability of the proposed HIBS in optimization problem where it optimized relevant weights in an authentication system.
Hybrid improved bacterial swarm optimization algorithm in hand-based multimodal biometric authentication system
This paper proposes a Hybrid Improved Bacterial Swarm
(HIBS) optimization algorithm for the minimization of Equal
Error Rate (EER) as a performance measure in a hand-based
multimodal biometric authentication system. The hybridization
of the algorithm was conducted by incorporating Bacterial
Foraging Optimization (BFO) and Particle Swarm Optimization
(PSO) algorithm to mitigate weaknesses in slow and premature
convergence. In the proposed HIBS algorithm, the slow
convergence of BFO algorithm was mitigated by using the random
walk procedure of Firefly algorithm as an adaptive varying step
size instead of using fixed step size. Concurrently, the local
optima trap (i.e. premature convergence) of PSO algorithm was
averted by using mutation operator. The HIBS algorithm was
tested using benchmark functions and compared against classical
BFO, PSO and other hybrid algorithms like Genetic Algorithm-
Bacterial Foraging Optimization (GA-BFO), Genetic Algorithm-
Particle Swarm Optimization (GA-PSO) and other BFO-PSO
algorithms to prove its exploration and exploitation ability. It
was observed from the experimental results that the EER values,
after the influence of the proposed HIBS algorithm, dropped to
0.0070% and 0.0049% from 1.56% and 0.86% for the right and
left hand images of the Bosphorus database, respectively. Th