Classification of Encryption Algorithms using Fisher’s Discriminant Analysis

2016 ◽  
Vol 67 (1) ◽  
pp. 59 ◽  
Author(s):  
Prabhat Kumar Ray ◽  
Shrikant Ojha ◽  
Bimal Kumar Roy ◽  
Ayanendranath Basu
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Block Cipher ◽  
Good Classification ◽  
Finite Set ◽  
Encryption Algorithms ◽  
Fisher’S Discriminant Analysis

<p>Fisher’s Discriminant Analysis (FDA) is a method used in statistics and machine learning which can often lead to good classification between several populations by maximizing the separation between the populations. We will present some applications of FDA that discriminate between cipher texts in terms of a finite set of encryption algorithms. Specifically, we use ten algorithms, five each of stream and block cipher types. Our results display good classification with some of the features. In the present case we have little in terms of an existing standard; however, our limited study clearly shows that further exploration of this issue could be worthwhile.</p>

Tremor Identification Using Machine Learning in Parkinson's Disease

2019 ◽  
pp. 128-151
Author(s):  
Angana Saikia ◽  
Vinayak Majhi ◽  
Masaraf Hussain ◽  
Sudip Paul ◽  
Amitava Datta
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Discriminant Analysis ◽  
Learning Algorithms ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

Diagnostics of Mechanical Faults of Loudspeakers Using Principal Component Analysis and Fisher’s Discriminant Analysis

2011 ◽  
Author(s):  
Jaewon Choi ◽  
Michael D. Bryant
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Isolation ◽  
Model Based ◽  
Mechanical Faults ◽  
Fisher’S Discriminant Analysis ◽  
Baseline Estimation

This study illustrates a novel model based FDI method for the common mechanical faults arising during the manufacture of loudspeakers. To overcome the drawbacks of the conventional signal based approaches, the Bayesian classification of impulse responses based on a model based fault symptom database is proposed. The loudspeaker model is estimated via IRES and ARMA techniques. The fault symptom database is constructed with a novel nonlinear loudspeaker model. The performances of Principal Component Analysis (PCA) and Fisher’s Discriminant Analysis (FDA) are compared. The results show the effectiveness of the proposed method. It is also shown that the FDA based classifier performs better than PCA in terms of the accuracy and consistency of the healthy baseline estimation. However, the fault isolation is difficult due to the similarities of fault signatures.

Tremor Identification Using Machine Learning in Parkinson's Disease

2021 ◽  
pp. 341-365
Author(s):  
Angana Saikia ◽  
Vinayak Majhi ◽  
Masaraf Hussain ◽  
Sudip Paul ◽  
Amitava Datta
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Discriminant Analysis ◽  
Learning Algorithms ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

scholarly journals A Comparative Study of the Fisher’s Discriminant Analysis and Support Vector Machines

2017 ◽  
Vol 2 (8) ◽  
pp. 35
Author(s):  
Jude Chukwura Obi
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Comparative Study ◽  
Learning Community ◽  
Classification Problem ◽  
Support Vector ◽  
High Dimensions ◽  
Vector Machines ◽  
Fisher’S Discriminant Analysis

The relative merits of Fisher’s Discriminant Analysis (FDA) over Support Vector Machines or vice versa, will remain a bone of contention among statisticians and the machine learning community. This line of thought may be owed to the fact that FDA is due to Fishers R. A., a statistician, whereas SVM is a credit to Vanik and his team of the machine learning. In order to give a clearer picture on the strength and weakness of both classifiers, they are compared in terms of the different theories behind each one. We also look into the ways regularization is carried out by each classifier, and further examine how FDA and SVM respond to lineartransformations. We conclude with examination of the behaviour of FDA and SVM on data, given different scenarios, and in high dimensions too. In the end, we clearly draw out the differences and similarities between the two classifiers, and further highlight features that make each classifier ideal for a given classification problem.

scholarly journals ANALISIS TINGKAT KEBERHASILAN CRYOTERAPY MENGGUNAKAN NEURAL NETWORK

2019 ◽  
Vol 15 (2) ◽  
pp. 141-148
Author(s):  
Sri Rahayu ◽  
Fitra Septia Nugraha ◽  
Muhammad Ja’far Shidiq
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Side Effects ◽  
20Th Century ◽  
High Accuracy ◽  
Machine Learning Method ◽  
Learning Method ◽  
Good Classification ◽  
Auc Value

Human health is very important to always pay attention especially after someone has been declared suffering from an illness that can inhibit positive activities. One of the most feared diseases of the 20th century is cancer. This disease requires treatment that is quite expensive. Alternative treatments are cryotherapy or ice therapy. But cryotherapy also has side effects, it is necessary to do research on its success by taking into account certain conditions of the parameters. So the purpose of this study is to analyze the success of cryotherapy so that the dataset can be used as one of the benchmarks for the success of the cryotherapy tratment method. The method used in this study is the machine learning method of Neural Network with 500 training cycles, learning rate of 0,003 and momentum 0,9 which results in a good classification of obtaining quite high accuracy of 87,78% and AUC value of 0,955.

scholarly journals Multivariate and machine learning approaches for honey botanical origin authentication using near infrared spectroscopy

2019 ◽  
Vol 27 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Vittoria Bisutti ◽  
Roberta Merlanti ◽  
Lorenzo Serva ◽  
Lorena Lucatello ◽  
Massimo Mirisola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Canonical Discriminant Analysis ◽  
Learning Approaches ◽  
Botanical Origin ◽  
Infrared Spectral

In this work the feasibility of near infrared spectroscopy was evaluated combined with chemometric approaches, as a tool for the botanical origin prediction of 119 honey samples. Four varieties related to polyfloral, acacia, chestnut, and linden were first characterized by their physical–chemical parameters and then analyzed in triplicate using a near infrared spectrophotometer equipped with an optical path gold reflector. Three different classifiers were built on distinct multivariate and machine learning approaches for honey botanical classification. A partial least squares discriminant analysis was used as a first approach to build a predictive model for honey classification. Spectra pretreatments named autoscale, standard normal variate, detrending, first derivative, and smoothing were applied for the reduction of scattering related to the presence of particle size, like glucose crystals. The values of the descriptive statistics of the partial least squares discriminant analysis model allowed a sufficient floral group prediction for the acacia and polyfloral honeys but not in the cases of chestnut and linden. The second classifier, based on a support vector machine, allowed a better classification of acacia and polyfloral and also achieved the classification of chestnut. The linden samples instead remained unclassified. A further investigation, aimed to improve the botanical discrimination, exploited a feature selection algorithm named Boruta, which assigned a pool of 39 informative averaged near infrared spectral variables on which a canonical discriminant analysis was assessed. The canonical discriminant analysis accounted a better separation of samples according to the botanical origin than the partial least squares discriminant analysis. The approach used has permitted to achieve a complete authentication of the acacia honeys but not a precise segregation of polyfloral ones. The comparison between the variables important in projection and the Boruta pool showed that the informative wavelengths are partially shared especially in the middle and far band of the near infrared spectral range.

scholarly journals Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Yocanxóchitl Perfecto-Avalos ◽  
Alejandro Garcia-Gonzalez ◽  
Ana Hernandez-Reynoso ◽  
Gildardo Sánchez-Ante ◽  
Carlos Ortiz-Hidalgo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals A Single Standard to Determine Multi-Components Method Coupled with Chemometric Methods for the Quantification, Evaluation and Classification of Notopterygii Rhizoma et Radix from Different Regions

Molecules ◽  
2019 ◽  
Vol 24 (19) ◽  
pp. 3574 ◽  
Author(s):  
Xie-An Yu ◽  
Jin Li ◽  
John Teye Azietaku ◽  
Wei Liu ◽  
Jun He ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
High Performance ◽  
Internal Standard ◽  
Chemical Markers ◽  
Accurate Identification ◽  
Flight Mass Spectrometry ◽  
Single Standard ◽  
Components Method ◽  
Fisher’S Discriminant Analysis

An ultra-high-performance liquid chromatography-quadrupole/time of flight mass spectrometry is used to identify 33 compounds in Notopterygii rhizoma and radix, after which a single standard to determine multi-components method is established for the simultaneous determination of 19 compounds in Notopterygii rhizoma and radix using chlorogenic acid and notopterol as the internal standard. To screen the potential chemical markers among Notopterygii rhizoma and radix planted in its natural germination area and in others, the quantitative data of 19 compounds are analyzed via partial least-squares discriminant analysis (PLS–DA). Depending on the variable importance parameters (VIP) value of PLS–DA, six compounds are selected to be the potential chemical markers for the discrimination of Notopterygii rhizoma and radix planted in the different regions. Furthermore, the Fisher’s discriminant analysis is used to build the models that are used to classify Notopterygii rhizoma and radix from the different regions based on the six chemical markers. Experimental results indicate that Notopterygii rhizoma and radix planted in the Sichuan province are distinguished successfully from those in other regions, reaching a 96.0% accuracy rating. Therefore, a single standard to determine multi-components method combined with a chemometrics method, which contains the advantages such as simple, rapid, economical and accurate identification, offers a new perspective for the quantification, evaluation and classification of Notopterygii rhizoma and radix from the different regions.

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