Euclidean Distance Method Research Articles

Subject Review: Hand Vascular Pattern Technology

Vein recognition systems are a form of biometric recognition that can distinguish people according to their vascular structure. Identification from hand-wrist vein pattern is one of these systems. In this study, hand-wrist vein images taken from people using an infrared light source with a wavelength of 850 nm were segmented by passing through various image processing algorithms. Scale-independent feature transformation (SURF) method was used for key point extraction from segmented images. The features obtained by the SURF method are rotation, camera angle, ambient light intensity, etc. This method has been preferred because it is invariant against situations. In the identification process, the Euclidean distance method was used by making use of the extracted key points. The accuracy rate was determined as 97% as a result of the matching processes using the hand-wrist vein patterns in the database.

The impact of information and communication technology on financial inclusion-based on a global perspective

<abstract> <p>In order to test the effect of information and communication technology (ICT) level on financial inclusion, based on the sample data of countries around the world in 2011, 2014, and 2017, this paper firstly uses the coefficient of variation method and the Euclidean distance method to construct the ICT level index and the financial inclusion index, and then conducts empirical analysis through a linear regression model. Further, a mediating effect model is employed to explore the impact mechanism of the ICT level on financial inclusion. Finally, the impact heterogeneity is explored based on the internal and external characteristics of each country. The empirical results show that: first, the ICT level can effectively improve financial inclusion, but its effects on the width and the depth of financial inclusion are different; second, the ICT level improves a country's financial inclusion by promoting the improvement and development of the digital payment system; third, the impact of the ICT level on financial inclusion shows significant heterogeneity among different countries or regions with differences in banking structure, economic development level and international financial environment. Last but not least, the conclusions of this paper can help countries or regions to improve financial inclusion in a targeted manner through information and communication technology.</p> </abstract>

Phenotypic diversity and clustering of germplasm accessions of cashew for utilization and conservation

To promote the better utilization of genetic resources of cashew, 91 accessions maintained at Cashew Research Station, Madakkathara were assessed for 38 phenotypic traits. Frequencies of the phenotypic classes of each character were calculated. The Shannon diversity index (H ') was used to estimate the magnitude of diversity. Polymorphism was high for tree habit (0.96), tree height (0.89), branching pattern (0.89), colour (0.80), ridges (0.93), apex (0.95), cavity (0.90) and weight (0.84) of cashew apple, attachment of nut to apple (0.85), flanks of nut (0.95) and cotyledonary grooves (0.99). The cluster analysis using the squared Euclidean distance method classified the accessions into 13 clusters at a 55 per cent similarity level. More than 50 per cent of the accessions were high yielding with moderate quality kernels. Grouping of the genotypes into agronomic groups revealed the existence of highly prolific accessions with high quality kernels that can be exploited for crop improvement programmes. Key words: Cashew, Phenotypic diversity, Cluster analysis, Euclidean distance

Mathematical modeling and multivariate analysis applied earliest soybean harvest associated drying and storage conditions and influences on physicochemical grain quality

Anticipating the harvest period of soybean crops can impact on the post-harvest processes. This study aimed to evaluate early soybean harvest associated drying and storage conditions on the physicochemical soybean quality using of mathematical modeling and multivariate analysis. The soybeans were harvested with a moisture content of 18 and 23% (d.b.) and subjected to drying in a continuous dryer at 80, 100, and 120 °C. The drying kinetics and volumetric shrinkage modeling were evaluated. Posteriorly, the soybean was stored at different packages and temperatures for 8 months to evaluate the physicochemical properties. After standardizing the variables, the data were submitted to cluster analysis. For this, we use Euclidean distance and Ward's hierarchical method. Then defining the groups, we constructed a graph containing the dispersion of the values of the variables and their respective Pearson correlations for each group. The mathematical models proved suitable to describe the drying kinetics. Besides, the effective diffusivity obtained was 4.9 × 10–10 m2 s−1 promoting a volumetric shrinkage of the grains and influencing the reduction of physicochemical quality. It was observed that soybean harvested at 23% moisture, dried at 80 °C, and stored at a temperature below 23 °C maintained its oil content (25.89%), crude protein (35.69%), and lipid acidity (5.54 mL). In addition, it is to note that these correlations' magnitude was substantially more remarkable for the treatments allocated to the G2 group. Furthermore, the electrical conductivity was negatively correlated with all the physicochemical variables evaluated. Besides this, the correlation between crude protein and oil yield was positive and of high magnitude, regardless of the group formed. In conclusion, the early harvest of soybeans reduced losses in the field and increased the grain flow on the storage units. The low-temperature drying and the use of packaging technology close to environmental temperatures conserved the grain quality.

Optimization of Fuzzy Support Vector Machine (FSVM) Performance by Distance-Based Similarity Measure Classification

This research aims to determine the maximum or minimum value of a Fuzzy Support Vector Machine (FSVM) Algorithm using the optimization function. SVM is considered as an effective method of data classification, as opposed to FSVM, which is less effective on large and complex data because of its sensitivity to outliers and noise. One of the techniques used to overcome this inefficiency is fuzzy logic with its ability to select the right membership function, which significantly affects the effectiveness of the FSVM algorithm performance. This research was carried out using the Gaussian membership function and the Distance-Based Similarity Measurement consisting of the Euclidean, Manhattan, Chebyshev, and Minkowsky distance methods. Subsequently, the optimization of the FSVM classification process was determined using four proposed FSVM models and normal SVM as comparison references. The results showed that the method tends to eliminate the impact of noise and enhance classification accuracy effectively. FSVM provides the best and highest accuracy value of 94% at a penalty parameter value of 1000 using the Chebyshev distance matrix. Furthermore, the model proposed will be compared to the performance evaluation model in preliminary studies (Xiao Kang et al., 2018). The result further showed that using FSVM with Chebyshev distance matrix and a Gaussian membership function provides a better performance evaluation value. Doi: 10.28991/HIJ-2021-02-04-02 Full Text: PDF

Morphological and nutritional diversity among accessions of marvel grass (Dichanthium annulatum(Forssk.) Stapf) and development of a core collection

AbstractDichanthium annulatumis one of the dominant grasses of India, North Africa, Southeast Asia, China, Australia, Fiji, New Guinea, Cuba, Haiti and Puerto Rico. This drought-tolerant grass is an excellent fodder in mixed pastures. Developing varieties with improved quality and tolerance to various abiotic stresses is hampered due to its apomictic nature. Germplasm collection, characterization, genetic diversity analysis and core subset development followed by selection for desirable traits seems to be the most plausible breeding tool for developing new cultivars. In the present study, 498 genotypes collected from different agro-ecological zones in India were included. Genotypes were characterized for various metric and non-numeric traits; and the nutritional parameters. Agglomerative clustering analysis, using the Euclidean distance method, showed 14 distinct clusters. High variability was recorded for green forage yield, quantitative traits and nutritive quality parameters. A core subset of 50 accessions was identified, which captured most of the morphological and nutritional variability present in the total germplasm. Clustering of genotypes was observed to be related to the climatic conditions of the place of collection. High genetic variability observed for various morphological traits as well as forage yield indicated that these genotypes or subset of genotypes can be evaluated in different abiotic stress conditions such as salt, light and moisture stress for the identification of suitable varieties for the respective areas. Variability was attributed to inter-generic, inter-specific crossing together with the occasional presence of sexual plants in nature.

Convergence of daily mean coordinates of precise positioning methods

As part of the research of modern movements of the Earth’s crust, an analysis of 7 high-precision methods for calculating GNSS positions was carried out for the convergence of their daily mean coordinates. Based on Euclidean distances, regular and maximal discrepancies between coordinates of different methods are given. According to the coordinates in the ITRF, 5 methods are stood out with regular coordinate discrepancies <1 mm, and individual maximum discrepancies up to 30 mm. The other two methods have regular discrepancies in coordinates up to 2 cm, and the maximum differences reach 1 m. For a group of stations global coordinates transformation into a local reference frame leads to the effect of coordinate stabilization and increases their relative precision in the time series. As a result of such procedure, the level of maximum coordinate discrepancies between the methods decreased to 46%. Moreover, one of the methods of calculating coordinates has improved its convergence with the other methods by 80%. Based on the Euclidean distance method, the quality of the raw data for each station was evaluated. Thus, there is a group of 8 stations, for which the convergence of coordinates in different methods are approximately at the same level, and 2-3 times better than for the other 2 stations.

Resistance Evaluation of Some Rice Varieties by Feeding Activity of Brown Planthopper Population in Java

Brown Planthopper (BPH), Nilaparvata lugens Stal (Hemiptera: Delpachidae) is the important pest of rice. The infestation of BPH reduced the rice yield on rice producer countries in Asia. In Indonesia, the area of BPH infestation frequently fluctuated over the year. BPH directly caused hopper burn and also vector of rice grassy stunt virus (RGSV) and rice ragged stunt virus (RRSV). This study aimed to evaluate the resistance of some rice varieties to different BPH population in Java. Three BPH populations collected from Karawang (West Java), Pekalongan (Central Java) and Kendal (Central Java) were used in this study. The rice resistance was evaluated by conducting honeydew test. There were eight rice varieties tested i.e Sembada 168, Ciherang, Inpari 32, Inpari 33, IR 64, TN1, Ratu Heenati and PTB 33 with five replications. A number of 200 female adults (five females per plot) were starved for 2 hours before inserting to pot containing one seedling. The BPH adults were allowed to feed 40-day-old plants for 48 hours. The area of honeydew excreted by BPH was measured using software Image J. We used analysis of variance (ANOVA) and cluster analysis with ward method and similarity index of Euclidean distance to analyze the rice resistance to BPH. The study indicated three groups of rice resistance level. The variety of Sembada 168 and TN1 were susceptible to BPH. Variety of Ciherang, Inpari 32, Inpari 33 and IR 64 were moderately resistant to BPH. However, PTB 33 and Ratu Heenati were strongly resistant to BPH population of Java.

Teknologi Pengolahan Citra Digital Untuk Ekstraksi Ciri pada Citra Daun untuk Identifikasi Tumbuhan Obat

The leaf image identification process depends on the feature extraction results. Each medicinal plant has different shapes and patterns of leaf venation. But for one type of medicinal plants have the same pattern of venation shape and pattern even though the size is different. One of the methods for extraction of leaf image form characteristics is by fractal-based feature extraction. Through fractal can be calculated the value of leaf dimensions and searched parts of leaves that have similarities between one part with other parts. As for the method of extracting the characteristics of venation pattern using B-Spline method. Benefits of research conducted is to help people identifying the types of medicinal plants found, knowing the benefits and ways of brewing. While the research contribution is prototype software application based on information technology that can be used by the people through mobile phones for the identification of medicinal plants. To identify or match the results of feature extraction on the leaf found whether included in the medicinal plant, conducted by Euclidean Distance method. In the experiments we used 1100 data consist of 55 variety of medicinal plants for each 20 samples.The experimental result show that the accuracy of identification using of fractal and b-spline is 85.30%.

Local linear embedding algorithm of mutual neighborhood based on multi-information fusion metric

Local linear embedding (LLE) algorithm is an effective tool, which mines low-dimensional features in high-dimensional space. However, the local region and inner structure directly affect the performance of the LLE algorithm. To address this problem, the LLE algorithm of mutual neighborhood by employing multi-information fusion metric (MIFM-MNLLE) is proposed. First, the Euclidean distance and cosine similarity method are combined to evaluate the similary among samples, by which the accuracy of selected neighbors can be improved. Subsequently, the idea of mutual neighbor structure is utilized to construct the neighbor and the mutual neighbor graph of the sample to describe the internal structure of the data set. Finally, the coefficient of LLE is rectified according to the mutual neighborhood relationship between the sample and its neighbors, so as to extract significant features effectively. Extensive experimental results show that the proposed method has better performance compared with the existing methods on bearing datasets.

Land Degradation and Development Processes and Their Response to Climate Change and Human Activity in China from 1982 to 2015

Land degradation and development (LDD) has become an urgent global issue. Quick and accurate monitoring of LDD dynamics is key to the sustainability of land resources. By integrating normalized difference vegetation index (NDVI) and net primary productivity (NPP) based on the Euclidean distance method, a LDD index (LDDI) was introduced to detect LDD processes, and to explore its quantitative relationship with climate change and human activity in China from 1985 to 2015. Overall, China has experienced significant land development, about 45% of China’s mainland, during the study period. Climate change (temperature and precipitation) played limited roles in the affected LDD, while human activity was the dominant driving force. Specifically, LDD caused by human activity accounted for about 58% of the total, while LDD caused by climate change only accounted for 0.34% of the total area. Results from the present study can provide insight into LDD processes and their driving factors and promote land sustainability in China and around the world.

Feature extraction Hue, Saturation, Value (HSV) and Gray Level Cooccurrence Matrix (GLCM) for identification of woven fabric motifs in South Central Timor Regency

South Central Timor (TTS) is one of the districts that has a weaving culture and also produces woven cloth in East Nusa Tenggara. The many types of woven fabric from each TTS tribe makes outsiders and even native TTS people do not recognize the typical TTS woven fabric, therefore we need a system that can help facilitate the community in recognizing the type and motif of woven fabric. In this study, digital image processing is used to identify the type of woven fabric in the TTS district using the HSV color feature extraction method, and the GLCM texture feature, and to measure the similarity of woven fabric using the Euclidean distance method. The image data of the woven fabric used is the image of woven fabric from 3 tribes of TTS district, namely the Amanatun, Amanuban, and Mollo tribes. Identification of woven fabric motifs using the K-fold cross validation test with two stages, namely the training and testing stages. The results of testing variants using 10 fold get an accuracy rate for GLCM texture features of 55%, for HSV color features of 62.5% and a combination of color and texture features of 91.67%.

PENGENALAN JENIS MAKER DENGAN METODE COLOR HISTOGRAM DAN EUCLIDEAN DISTANCE

In the midst of the covid-19 virus pandemic the use of masks is increasing to prevent transmission, various types of masks are available among the public but not all types of masks are advised to avoid the transmission of the covid-19 virus. Among them the use of KN-95 masks is recommended to avoid transmission of the virus. This application program uses color histogram and euclidean distance methods to distinguish the types of medical and non-medical masks, the medical masks used in this application program are kn-95 masks that are white. Color histogram is a way to get color dissemination data in photos or images. While euclidean distance is used to get the distance difference between 2 points. A total of 97 photos of medical masks were used as training data, 59 photos of medical masks and 96 photos of non-medical masks were used as test data. The application program recognizes 38 medical masks and 49 non-medical masks, the success rate of this program for recognizing medical and non-medical masks is 58.27%.

Classification of chronic myeloid leukemia neutrophils by hyperspectral imaging using Euclidean and Mahalanobis distances

Chronic Myeloid Leukemia (CML) is a type of blood cancer which needs to be diagnosed in early stages to facilitate effective treatment. This necessitates quick, error free and automated diagnostic techniques. In this study, hyperspectral images have been analyzed using statistical distances to classify neutrophils from CML versus healthy blood samples. The statistical distances were used in multidimensional space offered by hyperspectral images. For computational efficiency, principal component analysis was used to achieve dimensionality reduction. The Euclidean distance method, and Mahalanobis distance method which compensates the variance of the target data distribution were used to classify CML neutrophils. The effectiveness of the proposed methods were tested and compared using experimental results. The Euclidean distance was found to be superior when it came to sensitivity in detecting CML neutrophils whereas the Mahalanobis distance was better at detecting healthy neutrophils and distinguishing CML neutrophils from healthy neutrophils.

Novel hybrid geometric data perturbation technique by means of sampling data intervals

Privacy preserving data mining has much importance in the data analysis because it maintains confidential or highly sensitive data in the purpose of data analysis. Many perturbation techniques are developed, but do not perform well. There are used for different purposes in various techniques. Though, the reconstruction of original data can be done from the samples of perturbed data. It does not always preserve the distance that exists in the original data samples. During reconstruction of the original data, some of the information is loosed which is again a complex and time consuming task for traditional data mining algorithm. Therefore, a novel hybrid data perturbation technique is introduced that overcomes the above addressed issues. The proposed paper includes a novel data perturbation technique to hybridize the traditional Geometric Data Perturbation Technique along with the Euclidean distance method to preserve privacy. It obtains the distorted or perturbed data which is sampled at different intervals depending on the levels of privacy. The proposed approach attempts to reduce the tradeoffs that persists between privacy levels, accuracy and information loss. The experimental results for this approach is implemented using real world bench mark datasets downloaded from UCI Repositories using three different data mining clustering algorithms. It is shown that clustering accuracy is similar and fairly high for the perturbed data obtained from the proposed approach when matched with the original data set clustering algorithms. The analysis were shown for different privacy levels on different data samples.

Hot spots of extreme precipitation change under 1.5 and 2 °C global warming scenarios

Climate change hot spots are the regions where the climate variables are particularly responsive to global warming. This study quantifies the hot spots of extreme precipitation based on the simultaneous changes in the intensity (ΔI), frequency (ΔF), and interannual variability (Δσ) of extreme precipitation index under the 1.5 and 2 °C warming levels with respect to the period of 1986–2015. To investigate whether the hot spots would change as extreme precipitation index alters, precipitation extremes are defined based on the 90th, 95th, 99th, and 99.9th percentiles over the distribution of all wet days during 1986–2005. Daily precipitation from 15 GCMs in the CMIP5 project and the corresponding statistically-downscaled 0.25 ° × 0.25 ° dataset (NEX-GDDP) are adopted and compared. A gauge-based global precipitation product is also used to validate the extreme precipitation statistics in both datasets. The result shows that the CMIP5 GCMs and the NEX-GDDP display similar spatial patterns for percentile thresholds, ΔI, ΔF, and Δσ at global scale. Then with utilizing the Standard Euclidean Distance method, four aggregated indices (R90, R95, R99, and R99.9) are constructed to represent the comprehensive changes in extreme precipitation indices in the 1.5 and 2 °C warming levels. It is found that Sahara emerges as a hot spot of extreme precipitation change in both warming levels for R90, R95, and R99. Model spread in R99.9 is very large indicating large uncertainty in CMIP5 GCMs in the tail of the precipitation distribution. Comparison of the results for the two warming levels indicates that a smaller warming of 0.5 °C would lead to a reduction in the magnitude of extreme precipitation by about 7%–8% over global land regions. In addition, the values of ΔI, ΔF, and Δσ of P90, P95, and P99 are comparable for both warming levels, while the interannual variability of P99.9 responds more obviously to global warming than the intensity and frequency.

Interpreting Benchmark Assessment of Emergency Fire Service using Geoinformation Technology

Urbanization is a probable global phenomenon through the expansion and densification of urban centres challenging the emergency fire service which is an essential public service for sustainable growth. Urban centres are experiencing an incremental rise in fire incidences vulnerably causing significant loss of life and property threatening sustainable development. Adequate provisions of fire service facilities are essential to ensure sustainability through life safety, property protection, continuity of operations, environmental protection, and heritage conservation. With the aim to assess sustainable urbanization with the adequacy of fire service provision, interpretations of benchmark assessment BMA are essential to identify the substantial service gaps for up-gradation in the fire service facility. The study assessed fire service availability, proximity, and service area gap using geoinformation technology adopting the GIS tool combined with statistical analysis contexting the case of the typical urban centre of Nagpur city. Response distance approach through Euclidean distance method is adopted along with the network analysis for the evaluation of the actual service area to assess spatial accessibility. Correlation analysis of response distance between the coverage area and the population size through periods fulfills the globally accepted response distance measure of 3 km. Normalization evaluation with ranking assessed the suitability of fire service provision within the city. The application of BMA methodology has the potential to estimate the deficit for the entire country at a fast pace with immediate implications, helping efficiently upgrading the NDMA requirement to build urban resilient infrastructure and in accord with Smart City Mission.

Comparison of segmentation and identification of swietenia mahagoni wood defects with augmentation images

The largest income for Southeast Asian countries comes from the export activities of wood production. The potential for timber exports in Indonesia continues to increase each year. This soaring potential needs to be continually improved by maintaining quality so that trust and good cooperation can continue to be established with partner countries. Wood quality is closely related to wood defects. The faster the detection of wood defects is, the faster the quality of the wood will be determined. The wood industry which is still manual is also very susceptible to human eye fatigue. Technology is currently developing rapidly to help human productive activities and image processing is a breakthrough to detect wood defects. This study aims to identify swietenia mahagoni wood defects using the euclidean distance method from the extraction of 6 texture and shape features GLCM (Gray Level Co-Occurance Method) including metric, eccentricity, contrast, correlation, energy, and homogeneity, which was previously segmented with the best segmentation from the comparison results of thresholding and k-means segmentation and produced an average accuracy of 95.33% with an F1 score value of 0.95. The dataset used is the primary dataset with a total of 54 images on 3 types of wood defects, namely growing skin defects on wood ends, rotten wood eye on the body, and healthy wood eye on the body. Cross validation is also applied to test the reliability of the proposed model. By using 3-fold cross validation, the optimal average accuracy is 88.90%. Validation with other similar datasets was also carried out by identifying potato leaf defects resulting in an average accuracy of 92.86% with the most optimal 3-fold cross validation value achieved an average accuracy of 83.33%. Image augmentation is also carried out in order to reproduce the image so that the reliability test of the proposed method can be carried out, namely by rotating the image 45 degrees,90 degrees,120 degrees,180 degrees which produces 84 images of augmentation, so that the total image is 138 images and gets an average accuracy from the image augmentation is 80%.

Parametric 3D modeling of young women's lower bodies based on shape classification

Three-dimensional (3D) human body modeling is an important research direction in the field of clothing virtual design. On the basis of 3D human body scanning, this paper studied a method to build a 3D parametric lower body model according to body classification. The research includes three main parts. (1) Anthropometry and body shape classification. We randomly selected 333 young women ages 18–25 years old in Northeast China as the experimental sample. Then we divided the lower body shape into three categories using principal component analysis and K-means clustering. (2) Determination of feature cross sections and points, and reconstruction of feature curves. According to the average values of each body type, we obtained the mean reference body by Euclidean distance method. We determined feature cross sections and points, and extracted the 3D coordinates of the feature points of the mean reference body to reconstruct the feature curves. (3) The surface lofting and establishment of parametric 3D lower body model. According to the shape characteristics of the lower body, we constructed the guiding lines for the crotch and lower limbs, and established parametric lower body models for three body types. Relevance to industry3D human modeling is an important part of garment industry digitization. This research provides an effective way to construct a parametric 3D lower body model. The method offers a reference for the parametric virtual human modeling and virtual fitting of trousers.

An Innovative Approach for Risk Characterization in Operations with Case Studies

Workplace accidents constitute a major problem worldwide and pose a threat to human life, the social economy, and the ecological environment. This study proposes a method of risk characterization to assess the risk of accidents. First, the concepts of likelihood, event intensity, and event exposure are proposed based on the energy transfer theory. Second, the factors influencing these concepts are identified through a statistical analysis of cases of accidents and used to develop the proposed method of risk evaluation. Third, a combination of Dempster–Shafer (DS) evidence theory, the analytic hierarchy process, and the Euclidean distance method is used to develop the AHP-DS and ED-DS evidence theory models. Finally, experimental scenarios involving a storage facility for tailings and a section of converter steelmaking are used to verify the effectiveness of the proposed method.