scholarly journals NEURO-FUZZY SYSTEM FOR DETECTING PD PATIENTS BASED ON EUCLID DISTANCE, FFT, AND PCA

2020 ◽  
Vol 20 (09) ◽  
pp. 2040017
Author(s):  
SEOK-WOO JANG ◽  
SANG-HONG LEE
Keyword(s):  
Pressure Sensor ◽  
Euclidean Distance ◽  
Performance Enhancement ◽  
Principal Component ◽  
Sensor Data ◽  
Second Step ◽  
Neuro Fuzzy ◽  
Euclidean Distances ◽  
Left And Right ◽  
The Individual

This study proposes a method to distinguish between healthy people and Parkinson’s disease patients using sole pressure sensor data, neural network with weighted fuzzy membership (NEWFM), and preprocessing techniques. The preprocessing techniques include fast Fourier transform (FFT), Euclidean distance, and principal component analysis (PCA), to remove noise in the data for performance enhancement. To make the features usable as inputs for NEWFM, the Euclidean distances between the left and right sole pressure sensor data were used at the first step. In the second step, the frequency scales of the Euclidean distances extracted in the first step were divided into individual scales by the FFT using the Hamming method. In the final step, 1–15 dimensions were extracted as the features of NEWFM from the individual scales by the FFT extracted in the second step by the PCA. An accuracy of 75.90% was acquired from the eight dimensions as the inputs of NEWFM.

scholarly journals Determination of Whole-Cell Fatty Acid Profiles for the Characterization and Differentiation of Isolates of Rhizoctonia solani AG-4 And AG-7

Plant Disease ◽  
2000 ◽  
Vol 84 (7) ◽  
pp. 785-788 ◽  
Author(s):  
R. E. Baird ◽  
R. D. Gitaitis ◽  
D. E. Carling ◽  
S. M. Baird ◽  
P. J. Alt ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Rhizoctonia Solani ◽  
Euclidean Distance ◽  
Methyl Esters ◽  
Principal Component ◽  
Identification System ◽  
Microbial Identification ◽  
Euclidean Distances

Fatty acid methyl esters (FAMEs) of isolates of Rhizoctonia solani AG-4 and AG-7 were characterized by gas chromatography and analyzed with Microbial Identification System software. Palmitic, stearic, and oleic acids were common in all isolates from both anastomosis groups (AGs) and accounted for 95% of the C14 to C18 fatty acids present. Oleic acid, most common in both R. solani AG-4 and AG-7 isolates, accounted for the greatest percentages of total FAMEs. The presence, quantities, or absence of individual fatty acids could not be used for distinguishing AG-4 and AG-7 isolates. Anteisopentadecanoic and 9-heptadecanoic acids, however, were specific to all three AG-7 isolates from Japan but absent in other AG-7 isolates and all AG-4 isolates. Pentadecanoic acid occurred in only two of the R. solani AG-4 isolates, but was not found in any of the AG-7 isolates. The AG-4 isolates could be distinguished from AG-7 isolates when quantities of FAMEs and key FAME ratios were analyzed with cluster analysis and principle components were plotted. Isolates of AG-7 from Arkansas, Indiana, and Georgia appeared to be more closely related to each other than to AG-7 isolates from Japan and Mexico. These differences in FAMEs were sufficiently distinct that isolate geographical variability could be determined. A dendrogram analysis cluster constructed from the FAMEs data showed results similar to that of the principal component analysis. Euclidean distances of total AG-4 isolates were distinct from total AG-7 isolates. The Arkansas and Indiana AG-7 isolates had a similar Euclidean distance to each another but the percentages were different for the AG-7 isolates from Japan and Mexico. In conclusion, variability of the FAMEs identified in this study would not be suitable as the main diagnostic tool for distinguishing individual isolates of R. solaniAG-4 from AG-7.

scholarly journals THE IDENTIFICATION OF EAR PRINTS USING COMPLEX GABOR FILTERS

2012 ◽  
Vol 6 (1) ◽  
pp. 32
Author(s):  
Alexander A S Gunawan ◽  
Heni Kurniaty ◽  
Wikaria Gazali
Keyword(s):  
Euclidean Distance ◽  
Recognition Rate ◽  
Principal Component ◽  
Gabor Filters ◽  
Forensic Evidence ◽  
Behavioral Traits ◽  
Feature Based ◽  
Left And Right ◽  
Dimension Space ◽  
Principal Componentanalysis

Biometrics is a method used to recognize humans based on one or a few characteristicsphysical or behavioral traits that are unique such as DNA, face, fingerprints, gait, iris, palm, retina,signature and sound. Although the facts that ear prints are found in 15% of crime scenes, ear printsresearch has been very limited since the success of fingerprints modality. The advantage of the useof ear prints, as forensic evidence, are it relatively unchanged due to increased age and have fewervariations than faces with expression variation and orientation. In this research, complex Gaborfilters is used to extract the ear prints feature based on texture segmentation. Principal componentanalysis (PCA) is then used for dimensionality-reduction where variation in the dataset ispreserved. The classification is done in a lower dimension space defined by principal componentsbased on Euclidean distance. In experiments, it is used left and right ear prints of ten respondentsand in average, the successful recognition rate is 78%. Based on the experiment results, it isconcluded that ear prints is suitable as forensic evidence mainly when combined with otherbiometric modalities.Keywords: Biometrics; Ear prints; Complex Gabor filters; Principal component analysis;Euclidean distance

Enhancing WO3 gas sensor selectivity using a set of pollutant detection classifiers

Sensor Review ◽  
2018 ◽  
Vol 38 (1) ◽  
pp. 65-73 ◽  
Author(s):  
Rabeb Faleh ◽  
Sami Gomri ◽  
Mehdi Othman ◽  
Khalifa Aguir ◽  
Abdennaceur Kachouri
Keyword(s):  
Hybrid Approach ◽  
Principal Component ◽  
Sensor Data ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbors ◽  
Classification Rate ◽  
Content Type ◽  
Linear Svm ◽  
The Individual

Purpose In this paper, a novel hybrid approach aimed at solving the problem of cross-selectivity of gases in electronic nose (E-nose) using the combination classifiers of support vector machine (SVM) and k-nearest neighbors (KNN) methods was proposed. Design/methodology/approach First, three WO3 sensors E-nose system was used for data acquisition to detect three gases, namely, ozone, ethanol and acetone. Then, two transient parameters, derivate and integral, were extracted for each gas response. Next, the principal component analysis (PCA) was been applied to extract the most relevant sensor data and dimensionality reduction. The new coordinates calculated by PCA were used as inputs for classification by the SVM method. Finally, the classification achieved by the KNN method was carried out to calculate only the support vectors (SVs), not all the data. Findings This work has proved that the proposed fusion method led to the highest classification rate (100 per cent) compared to the accuracy of the individual classifiers: KNN, SVM-linear, SVM-RBF, SVM-polynomial that present, respectively, 89, 75.2, 80 and 79.9 per cent as classification rate. Originality/value The authors propose a fusion classifier approach to improve the classification rate. In this method, the extracted features are projected into the PCA subspace to reduce the dimensionality. Then, the obtained principal components are introduced to the SVM classifier and calculated SVs which will be used in the KNN method.

Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping

2014 ◽  
Vol 41 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Suyong Yeon ◽  
ChangHyun Jun ◽  
Hyunga Choi ◽  
Jaehyeon Kang ◽  
Youngmok Yun ◽  
...  
Keyword(s):  
Principal Component ◽  
Point Clouds ◽  
Previous Method ◽  
Divide And Conquer ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Content Type ◽  
Extraction Algorithm ◽  
Plane Extraction ◽  
Scan Data

Purpose – The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach – The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy. Findings – Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features). Originality/value – The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor noise and extracts more accurate plane parameters.

scholarly journals Bioactive Phenolic Metabolites from Adriatic Brown Algae Dictyota dichotoma and Padina pavonica (Dictyotaceae)

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1187
Author(s):  
Ivana Generalić Mekinić ◽  
Vida Šimat ◽  
Viktorija Botić ◽  
Anita Crnjac ◽  
Marina Smoljo ◽  
...  
Keyword(s):  
Total Phenolics ◽  
Antioxidant Activities ◽  
Principal Component ◽  
Ultrasound Assisted Extraction ◽  
Antioxidant Power ◽  
Dictyota Dichotoma ◽  
Assisted Extraction ◽  
Sample Data ◽  
Ferric Reducing Antioxidant Power ◽  
The Individual

In this study, the influences of temperature (20, 40 and 60 °C) and extraction solvents (water, ethanol) on the ultrasound-assisted extraction of phenolics from the Adriatic macroalgae Dictyota dichotoma and Padina pavonica were studied. The extracts were analysed for major phenolic sub-groups (total phenolics, flavonoids and tannins) using spectrometric methods, while the individual phenolics were detected by HPLC. The antioxidant activities were evaluated using three methods: Ferric Reducing/Antioxidant Power (FRAP), scavenging of the stabile 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical and Oxygen Radical Antioxidant Capacity (ORAC). The aim of the study was also to find the connection between the chemical composition of the extracts and their biological activity. Therefore, principal component analysis (PCA), which permits simple representation of different sample data and better visualisation of their correlations, was used. Higher extraction yields of the total phenolics, flavonoids and tannins were obtained using an alcoholic solvent, while a general conclusion about the applied temperature was not established. These extracts also showed good antioxidant activity, especially D. dichotoma extracts, with high reducing capacity (690–792 mM TE) and ORAC values (38.7–40.8 mM TE in 400-fold diluted extracts). The PCA pointed out the significant influence of flavonoids and tannins on the investigated properties. The results of this investigation could be interesting for future studies dealing with the application of these two algae in foods, cosmetics and pharmaceuticals.

Adaptive neuro-fuzzy fusion of multi-sensor data for monitoring of CNC machining

Sensor Review ◽  
2017 ◽  
Vol 37 (1) ◽  
pp. 78-81 ◽  
Author(s):  
Srdjan Jovic ◽  
Obrad Anicic ◽  
Milivoje Jovanovic
Keyword(s):  
Selection Process ◽  
Spindle Speed ◽  
Cnc Machining ◽  
Numerical Control ◽  
Periodic Component ◽  
Sensor Data ◽  
Depth Of Cut ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose Acoustic emission (AE) could be used for prevention and detection of tool errors in Computer Numerical Control (CNC) machining. The purpose of this study is to analyze the AE form of CNC machining operations. Design/methodology/approach Experimental measurements were performed with three sensors on the CNC lathe to collect the data of the CNC machining. Adaptive neuro-fuzzy inference system (ANFIS) was applied for the fusion from the sensors’ signals to determine the strength of the signal periodic component among the sensors. Findings There were three inputs, namely, spindle speed, feed rate and depth of cut. ANFIS was also used to determine the inputs’ influence on the prediction of strength of the signal periodic component. Variable selection process was used to select the most dominant factors which affect the prediction of strength of the signal periodic component. Originality/value Results were shown that the spindle speed has the most dominant effect on the strength of the signal periodic component.

scholarly journals Qualitative Analysis of Traditional Italian Dishes: FTIR Approach

2018 ◽  
Vol 10 (11) ◽  
pp. 4112 ◽  
Author(s):  
Alessandra Durazzo ◽  
Johannes Kiefer ◽  
Massimo Lucarini ◽  
Emanuela Camilli ◽  
Stefania Marconi ◽  
...  
Keyword(s):  
Qualitative Analysis ◽  
Ftir Spectroscopy ◽  
Functional Groups ◽  
Food Systems ◽  
Principal Component ◽  
Integrated Approach ◽  
Spectroscopic Characterization ◽  
Cooking Methods ◽  
Food Research ◽  
The Individual

Italian cuisine and its traditional recipes experience an ever-increasing popularity around the world. The “Integrated Approach” is the key to modern food research and the innovative challenge for analyzing and modeling agro-food systems in their totality. The present study aims at applying and evaluating Fourier Transformed Infrared (FTIR) spectroscopy for the analysis of complex food matrices and food preparations. Nine traditional Italian recipes, including First courses, One-dish meals, Side courses, and Desserts, were selected and experimentally prepared. Prior to their analysis via FTIR spectroscopy, the samples were homogenized and lyophilized. The IR spectroscopic characterization and the assignment of the main bands was carried out. Numerous peaks, which correspond to functional groups and modes of vibration of the individual components, were highlighted. The spectra are affected by both the preparation procedures, the cooking methods, and the cooking time. The qualitative analysis of the major functional groups can serve as a basis for a discrimination of the products and the investigation of fraud. For this purpose, the FTIR spectra were evaluated using Principal Component Analysis (PCA). Our results show how the utilization of vibrational spectroscopy combined with a well-established chemometric data analysis method represents a potentially powerful tool in research linked to the food sector and beyond. This study is a first step towards the development of new indicators of food quality.

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