Finding an Optimal Parameter for Threshold Cryptography

2016 ◽  
pp. 223-232
Author(s):  
Weena Janratchakool ◽  
Sirapat Boonkrong ◽  
Sucha Smanchat
Keyword(s):  
Optimal Parameter ◽  
Threshold Cryptography

Optimal parameter area for steel corrugated web beams

2020 ◽  
Vol 0 (7) ◽  
pp. 45-56
Author(s):  
Lyudmila Lavrinenko ◽  
Danylo Oliinyk
Keyword(s):  
Optimal Parameter ◽  
Corrugated Web

Modified algorithm for fast bandwidth selection for kernel estimates of multidimensional probability densities

2020 ◽  
pp. 9-13
Author(s):  
A. V. Lapko ◽  
V. A. Lapko
Keyword(s):  
Probability Density ◽  
Optimal Parameter ◽  
Random Variables ◽  
Kernel Functions ◽  
Independent Random Variables ◽  
Functional Dependencies ◽  
Approximation Properties ◽  
Probability Density Estimation ◽  
Multidimensional Probability ◽  
Selection Of

An original technique has been justified for the fast bandwidths selection of kernel functions in a nonparametric estimate of the multidimensional probability density of the Rosenblatt–Parzen type. The proposed method makes it possible to significantly increase the computational efficiency of the optimization procedure for kernel probability density estimates in the conditions of large-volume statistical data in comparison with traditional approaches. The basis of the proposed approach is the analysis of the optimal parameter formula for the bandwidths of a multidimensional kernel probability density estimate. Dependencies between the nonlinear functional on the probability density and its derivatives up to the second order inclusive of the antikurtosis coefficients of random variables are found. The bandwidths for each random variable are represented as the product of an undefined parameter and their mean square deviation. The influence of the error in restoring the established functional dependencies on the approximation properties of the kernel probability density estimation is determined. The obtained results are implemented as a method of synthesis and analysis of a fast bandwidths selection of the kernel estimation of the two-dimensional probability density of independent random variables. This method uses data on the quantitative characteristics of a family of lognormal distribution laws.

Implementasi Metode Winter untuk Forecasting Pertumbuhan Jumlah Penduduk: Studi Kasus Wilayah Provinsi NTB

2020 ◽  
Vol 3 (1) ◽  
pp. 51-61
Author(s):  
Syaharuddin ◽  
Abdul Adhiim Rizky ◽  
Lutfi Jauhari ◽  
Siti Fatimah ◽  
Wahyu Ningsih ◽  
...  
Keyword(s):  
Time Series Data ◽  
Optimal Parameter ◽  
Series Data ◽  
Percentage Error ◽  
Average Error ◽  
Ratio Data ◽  
Female Data ◽  
Forecasting System ◽  
Conducting Research ◽  
And Gender

This research aims to analyse the acceleration of population growth based on gender in West Nusa Tenggara Province (NTB) using the Forecasting system by constructing the winter's method in the shape of the Multiple Forecasting System (G-MFS) based on Matlab by calculating the period indicator for accuracy to find time series data in the year 2020-2029. At the simulation stage, researchers used the population and gender ratio data in NTB Province in 2009-2019. The method used in conducting research is to use the winter's method. The evaluation of Forecasting results is done by calculating the average error value using the Mean Absolute Percentage Error (MAPE) method. From this study obtained the most optimal parameter value on male data namely ʌ, β and γ sequential values of 0.9, 0.5 and 0.9 while in female data, the value of ʌ, β and γ respectively, 0.2, 0.1 and 0.5. Then with the value of the parameter obtained MAPE value in male data of 1.7785% and in female data of 0.89034%.

scholarly journals A novel two-phase cycle algorithm for effective cyber intrusion detection in edge computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yiguang Gong ◽  
Yunping Liu ◽  
Chuanyang Yin
Keyword(s):  
Intrusion Detection ◽  
Optimal Parameter ◽  
Edge Computing ◽  
Cloud Services ◽  
Second Phase ◽  
Pareto Optimal ◽  
Two Phase ◽  
Multi Objective ◽  
Phase Cycle ◽  
Positive Rate

AbstractEdge computing extends traditional cloud services to the edge of the network, closer to users, and is suitable for network services with low latency requirements. With the rise of edge computing, its security issues have also received increasing attention. In this paper, a novel two-phase cycle algorithm is proposed for effective cyber intrusion detection in edge computing based on a multi-objective genetic algorithm (MOGA) and modified back-propagation neural network (MBPNN), namely TPC-MOGA-MBPNN. In the first phase, the MOGA is employed to build a multi-objective optimization model that tries to find the Pareto optimal parameter set for MBPNN. The Pareto optimal parameter set is applied for simultaneous minimization of the average false positive rate (Avg FPR), mean squared error (MSE) and negative average true positive rate (Avg TPR) in the dataset. In the second phase, some MBPNNs are created based on the parameter set obtained by MOGA and are trained to search for a more optimal parameter set locally. The parameter set obtained in the second phase is used as the input of the first phase, and the training process is repeated until the termination criteria are reached. A benchmark dataset, KDD cup 1999, is used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover a pool of MBPNN-based solutions. Combining these MBPNN solutions can significantly improve detection performance, and a GA is used to find the optimal MBPNN combination. The results show that the proposed approach achieves an accuracy of 98.81% and a detection rate of 98.23% and outperform most systems of previous works found in the literature. In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives.

scholarly journals Just-in-Time Adaptive Algorithm for Optimal Parameter Setting in 802.15.4 WSNs

2016 ◽  
Vol 10 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Simone Brienza ◽  
Manuel Roveri ◽  
Domenico De Guglielmo ◽  
Giuseppe Anastasi
Keyword(s):  
Adaptive Algorithm ◽  
Optimal Parameter ◽  
Just In Time ◽  
Parameter Setting

Ultrafast laser micro-nano structured superhydrophobic teflon surfaces for enhanced SERS detection via evaporation concentration

2020 ◽  
Vol 9 (1-2) ◽  
pp. 89-100 ◽  
Author(s):  
Xinyu Hu ◽  
Rui Pan ◽  
Mingyong Cai ◽  
Weijian Liu ◽  
Xiao Luo ◽  
...  
Keyword(s):  
Rhodamine 6G ◽  
Optimal Parameter ◽  
Solute Concentration ◽  
Processing Parameter ◽  
Detection Techniques ◽  
Peak Intensity ◽  
Target Analytes ◽  
Sers Detection ◽  
Scanning Path ◽  
Evaporation Concentration

AbstractEvaporation concentration of target analytes dissolved in a water droplet based on superhydrophobic surfaces could be able to break the limits for sensitive trace substance detection techniques (e.g. SERS) and it is promising in the fields such as food safety, eco-pollution, and bioscience. In the present study, polytetrafluoroethylene (PTFE) surfaces were processed by femtosecond laser and the corresponding processing parameter combinations were optimised to obtain surfaces with excellent superhydrophobicity. The optimal parameter combination is: laser power: 6.4 W; scanning spacing: 40 μm; scanning number: 1; and scanning path: 90 degree. For trapping and localising droplets, a tiny square area in the middle of the surface remained unprocessed for each sample. The evaporation and concentration processes of droplets on the optimised surfaces were performed and analyzed, respectively. It is shown that the droplets with targeted solute can successfully collect all solute into the designed trapping areas during evaporation process on our laser fabricated superhydrophobic surface, resulting in detection domains with high solute concentration for SERS characterisation. It is shown that the detected peak intensity of rhodamine 6G with a concentration of 10−6m in SERS characterisation can be obviously enhanced by one or two orders of magnitude on the laser fabricated surfaces compared with that of the unprocessed blank samples.

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