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1
Cooperative multi agents for intelligent intrusion detection and prevention systems / Shahaboddin Shamshirband
Published 2014“…We investigate the detection capability based on the fuzzy Q-learning (FQL) algorithm and evaluate it using distribute denial of service attacks (DDoS). …”
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2
Fair bandwidth distribution marking and scheduling algorithm in network traffic classification
Published 2019“…Therefore, in order to improve bandwidth fairness along with efficient optimization to alleviate this problem, we propose two marker algorithms; a Double Modified Double Improved time sliding window Three Colour Marker (M2I2TSWTCM) algorithm, which makes a new value of that depends on the logarithm peak information rate (PIR) and which is added to the adaptive factor that exists in the previous algorithm. …”
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3
Dynamic replication aware load blanced scheduling in distributed environment / Said Bakhshad
Published 2018“…Grid computing is an effective distributed and adaptable processing network that manages a huge number of data applications. …”
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4
Genetic algorithm based adaptive offloading for improving IoT device communication efficiency
Published 2019“…This manuscript introduces genetic algorithm based adaptive offloading (GA-OA) for effective traffic handling in IoT-infrastructure-cloud environment. …”
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5
REGION-BASED ADAPTIVE DISTRIBUTED VIDEO CODING CODEC
Published 2011“…Although the performance evaluations show rate-penalty but it is acceptable considering the simplicity of the proposed algorithm. …”
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6
Improving resource management with multi-instance broker scheduling algorithm in hierarchical grid computing
Published 2016“…Multi-Instance Broker Scheduling Algorithm (MiBSA) has been proposed as a new scheduling algorithm to get rid of the drawback from the iHLBA algorithm. …”
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7
Adaptive and optimized radio resource allocation algorithms for OFDMA based networks
Published 2015“…Thus, in this thesis, an adaptive and optimized radio resource allocation algorithm (AORAA) with cross-layer mechanism is proposed. …”
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8
Sensorless Adaptive Fuzzy Logic Control Of Permanent Magnet Synchronous Motor
Published 2008“…The first motor is rated at 50W and the second is rated at 380W. Both experimental and simulation results obtained from the HMRASC and the position angle estimation algorithms showed superior results compared to other methods presented in the literature.…”
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9
Automatic control of flotation process using computer vision
Published 2015“…A marker based watershed algorithm had been adopted and improved so as to prevent the over segmentation of big bubbles and able to adapt itself with different scenario of froth images. …”
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10
Current Developments In Lte/Lte-Advanced : Adaptive-Proportional Fair Scheduling In Lte
Published 2015“…The most challenge of a PFS problem is the lack of analytic expression. Though the PFS algorithm has been a research focus for some time, the results are mainly obtained from computer simulations. …”
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Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization
Published 2024thesis::doctoral thesis -
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Skin Colour Detection Based On An Adaptive Multi-Thresholding Technique
Published 2007“…The experiment shows that the suggested algorithm achieves a noticeable performance improvement and offers a robust solution for skin detection under varying illumination. …”
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13
Efficient task scheduling strategies using symbiotic organisms search algorithm for cloud computing environment
Published 2022“…However, the efficiency of the clouds drops as the size of the search space gets larger, like in the case of most metaheuristic optimisation algorithms. Furthermore, existing tasks scheduling solutions suffer from local optima entrapment due to inadequate diversification of their local search space, a high degree of imbalance because of their static control parameters cannot maintain a balance between local and global search space and scalability issue as a result of static benefit factors. …”
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14
Performance Enhancement Of Ultra-Wideband Power Control Using Ranging And Narrowband Interference Mitigation
Published 2008“…In this thesis we propose a maximum likelihood algorithm enhanced with synchronization scheme to estimate the time delay of direct-path signal in NLOS multi-path fading environment and mean acquisition time. …”
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15
Accelerating learning performance of back propagation algorithm by using adaptive gain together with adaptive momentum and adaptive learning rate on classification problems
Published 2011“…Over the years, many improvements and modifications of the back propagation learning algorithm have been reported. In this research, we propose a new modified back propagation learning algorithm by introducing adaptive gain together with adaptive momentum and adaptive learning rate into weight update process. …”
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16
Variational Bayesian inference for exponentiated Weibull right censored survival data
Published 2023“…The exponential, Weibull, log-logistic and lognormal distributions represent the class of light and heavy-tailed distributions that are often used in modelling time-to-event data. …”
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17
Exploring dynamic self-adaptive populations in differential evolution
Published 2006“…In this paper, we present a first attempt at self-adapting the population size parameter in addition to self-adapting crossover and mutation rates. …”
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18
The effect of adaptive parameters on the performance of back propagation
Published 2012“…Thus, this research proposed a new method known as Back Propagation Gradient Descent with Adaptive Gain, Adaptive Momentum and Adaptive Learning Rate (BPGD-AGAMAL) which modifies the existing Back Propagation Gradient Descent algorithm by adaptively changing the gain, momentum coefficient and learning rate. …”
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19
Optimize single line to ground fault detection in distribution grid power system using artificial bee colony
Published 2023“…HV from capacitive currents (IC) damages cable insulation and PS appliances. …”
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Optimising neural network training efficiency through spectral parameter-based multiple adaptive learning rates
Published 2024“…The process of training neural networks heavily involves solving optimization problems. Most optimization algorithms use a !xed learning rate or a simpli!ed adaptive updating scheme in every iteration. …”
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