Euclidian Distance

Top PDF Euclidian Distance:

Auto Halal Detection Products Based on Euclidian Distance  and Cosine Similarity

Auto Halal Detection Products Based on Euclidian Distance and Cosine Similarity

Abstract— Although Indonesia is the world the world's most populous Muslim-majority country, the number of halal-certified products in Indonesia is only 20% of the products on the Indonesian market. Halal certification is voluntary as such there are many food products which are halal but are not certified as halal. In principle, these food products may have similar halal ingredients with halal-certified products. In this study, we build a system that can compare products that have not been certified halal with halal certified products based on its ingredients. The food products are collected from Open Food Facts, Institute For Foods, Drugs, And Cosmetics Indonesian Council Of Ulama (LPPOM MUI) and our halal system. As of this paper writing, the halal-certified products are obtained from LPPOM MUI. The system uses the Euclidean Distance and Cosine Similarity that generate top-5 similar products. Those two similarity calculations are based on Term Frequency-Inverse Entity Frequency weighting function. The weighting function calculates the frequency of a term on a product name and ingredients. If a similarity value of a product with no halal certification and a halal-certified product is higher than 75%, then the former could be indicated as a halal product. In the end, the system can give a recommendation of unknown products from a related pool of halal-certified products based on similarity of product composition. Cosine similarity accuracy is higher than Euclidean Distance and MoreLikeThis accuracy. Cosine similarity gets the highest precision because the cosine similarity is based on the vector angle of the term in a product.
Show more

6 Read more

Text Extraction in Images Using DWT, Gradient Method And SVM Classifier

Text Extraction in Images Using DWT, Gradient Method And SVM Classifier

The confident region which we get may contain non-text regions. So to eliminate non-text region, Firstly we apply Euclidian distance measure to measure the color space values then we perform filtration with Otsu method [12] to obtain text clusters .This text clusters are being segmented from non- text by using the set value of global threshold. Then image is fed to SVM to get the classification of text and image more accurately.

5 Read more

DCT-DST Plane sectorization of Row wise Transformed color Images in CBIR

DCT-DST Plane sectorization of Row wise Transformed color Images in CBIR

The Figure 7 – Figure 10 depicts the Overall Average Precision and Recall cross over point performance of sectorization of Row wise DCT/DST transformed image in 4, 8, 12 and 16 sectors with augmentation of average value of zeroeth component of DCT and the average magnitude of last component of DST transformed image. Sum of absolute Difference and Euclidian distance are used as similarity measures. The comparison bar chart of cross over points of overall average of precision and recall for 4, 8, 12 and 16 sectors of DCT-DST Plane sectorization w.r.t. both similarity measuring parameters shown in the Figure10. The observation is that the overall retrieval performance of the proposed method with Euclidian distance as similarity measure gives on average cross over point of precision and recall to be 48% while it is 49% in the case of sum of absolute difference.
Show more

11 Read more

Proposing an Analysis System to Monitoring Weightlifting Based on Training (Snatch and Clean and Jerk)

Abdul Monem S. Rahma Maisa'a Abid Ali K.*

Proposing an Analysis System to Monitoring Weightlifting Based on Training (Snatch and Clean and Jerk) Abdul Monem S. Rahma Maisa'a Abid Ali K.*

Analysis system of sports players is very important for individuals in weightlifting. Assessment of player and strength is important for the performance of weightlifting. This paper proposes an analytical method for weightlifters with check-by-frame video. This analysis system can compute the major steps of seven positions in both snatch and clean and jerk methods in frame-video weightlifting monitoring of movements. Each user can compute the major steps of the seven positions of Hu moments among two frames in the video during training, and the Euclidian distance can be computed for the Hu moment values and lifting moment values in the snatch and clean and jerk methods during training. The outcome of the proposed system shows on efficient, accurate results in monitoring movement analysis in weightlifting for playersduring training in this area.
Show more

10 Read more

Centroid Based Clustering Algorithms- A Clarion          Study

Centroid Based Clustering Algorithms- A Clarion Study

K-means algorithm can be treated as two-phase approach where in the first phase; k centroids are being identified depending on the k value that has been chosen in general the distance measure is being calculated using Euclidian distance metric. The second phase involves in determining the new centroids for which the dissimilarity measures are very less. The process loops in finding new centroids until the property of convergence is met [12].

5 Read more

Error Reduction Approach for Performance Improvement in CSK Systems

Error Reduction Approach for Performance Improvement in CSK Systems

improvement of 3dB and 6dB, respectively. The width of the improvement of the performance in 4-CSK is wider than the 8-CSK. As shown in Fig. 2, the distance between symbols in 4-CSK is wide compared to 8-CSK. So, the probability of symbol error occurs is low. The proposed errors reduction approach dose not directly determine the symbols from CIE1931 color space, but applies the Euclidian distance in order to color correct in the RGB three-dimensional space to determine the received symbols close to the transmitted symbols. Then, in order to convert the xy coordinate, it is possible to reduce error.
Show more

7 Read more

Vol 10, No 3 (2018)

Vol 10, No 3 (2018)

Abstract: In Wireless Sensor Network, the sensor nodes are deployed using random or deterministic deployment methods. M any applications prefer random deployment for deploying the sensor nodes. Random deployment is the main cause of redundancy. Detection and elimination of redundant sensor nodes while preserving coverage is very important issue after the sensor nodes are deployed randomly in the region of interest. The redundancy elimination with coverage preserving algorithm is proposed in this paper and the results are presented. The proposed algorithm determines redundant sensor nodes and also the sensor nodes which provide the least coverage of region of interest. If two sensor nodes cover same area or if the Euclidian distance between two nodes is less than 25% of sensing range of a sensor node, the sensor which is not located at optimal position will be deactivated, so that, it reduces the number of optimal nodes required to cover complete region of interest. This in turn increases the lifetime of the network. The simulation results illustrate that the proposed algorithm preserves 100% coverage or region of interest by removing redundant nodes and also the nodes which provide the least coverage of region of interest. It also reduces the number of optimal nodes required to provide 100% coverage of region of interest.
Show more

8 Read more

Comparative Study on Fingerprint Recognition System

Comparative Study on Fingerprint Recognition System

Biometric system is used for security purpose and Fingerprint Recognition is one of the most simplest and successful biometric method for authentication. Various researchers gave different conclusion based on their research. Results of minutiae matching technique calculated by counting number of minutiae points has not been satisfactory as two different persons can have same number of minutiae points. Euclidian distance approach gives good result but the results are still not satisfactory for poor quality image.

6 Read more

A Hybrid Approach to Recognize Facial Image using Feature Extraction Method

A Hybrid Approach to Recognize Facial Image using Feature Extraction Method

computational complexity and recognition performance. Face recognition systems are built on the idea that each person has a particular face structure, and using the facial symmetry, computerized face-matching is possible. The work on face recognition has begun in the 1960’s, the results of which are being used for security in various institutions and firms throughout the world. The images must be processed correctly for computer based face recognition. The face and its structural properties should be identified carefully, and the resulting image must be converted to two dimensional digital data. An efficient algorithm and a database which consists of face images are needed to solve the face recognition problem. In this paper, Eigenfaces method is used for face recognition. In the recognition process, an eigenface is formed for the given face image, and the Euclidian distances between this eigenface and the previously stored eigenfaces are calculated. The eigenface with the smallest Euclidian distance is the one the person resembles the most. Simulation results are shown. In this category a single feature vector that represents the whole face image is used as input to a classifier. Several classifiers have been proposed in the literature e.g. minimum distance classification in the eigenspace , Fisher's discriminant analysis [13], and neural networks [6]. Global techniques work well for classifying frontal views of faces. However, they are not robust against pose changes since global features are highly sensitive to translation and rotation of the face. To avoid this problem an alignment stage can be added before classifying the face. Aligning an input face image with a reference face image requires computing correspondences between the two face images. The correspondences are usually determined for a small number of prominent points in the face like the centre of the eye, the nostrils, or the corners of the mouth. Based on these correspondences the input face image can be warped to a reference face image. In [I21 an affine transformation is computed to perform the warping. Active shape models are used in [10] to align input faces with model faces. A semi-automatic alignment step in combination with SVM classification was proposed in [9]. The face recognition system is similar to other biometric systems. The idea behind the face recognition system is the fact that each individual has a unique face. Similar to the fingerprint, the face of an individual has many
Show more

6 Read more

New Performance Estimation Formula for Evolutionary Testing of Switch-Case Constructs

New Performance Estimation Formula for Evolutionary Testing of Switch-Case Constructs

3.4. Fitness calculation based on the CMCFG approach and Euclidian distance. In the previous section the formula proposed by Korel [10] was used for calculating the branch distance. The fitness function used for evaluating each test data was composed of the approximation level and the normalized branch distance. Another new fitness calculation approach based on CMCFG representation of the switch-case construct and the Euclidian distance is proposed hereinafter. It uses for evaluating each test data the CMCFG model for representing the entire switch-case construct in combination with the fitness function formula presented in 3.5: F (test data) = Approx level + X Euclidian distance (3.5) The Euclidian distance formula is the most commonly used formula for calculating distances. It examines the square root differences between coordinates of a pair of objects. Considering two points in an n-dimensional search space, the Euclidian distance formula is shown in 3.6:
Show more

12 Read more

The accuracy of conformation of a generic surface mesh for the analysis of facial soft tissue changes

The accuracy of conformation of a generic surface mesh for the analysis of facial soft tissue changes

statistically different to the actual changes (p < 0.05). For all mandibular movements the mean Euclidian distance difference for the chin region was 0.5mm (±0.5mm) (95% CI -1.2mm to 1.0mm), for upper lip region was 0.3mm (±0.3mm) (95% CI -0.3mm to 0.5mm) and for the cheek regions were 0.6mm (±0.5mm) (95% CI -0.5mm to 0.4mm). For the x, y and z directions the majority of differences in the mean absolute distances were less than 1.0mm except in the x-direction for the left and right cheek regions which were associated with an error of over 2.0mm and the chin region during right mandibular displacement (1.2mm). The upper lip region was associated with less error compared to the peripheral regions i.e. cheeks and chin. This might be due to the lower number of landmarks used during initial alignment during con- formation and lack of distinguished surface topography upon which the elastic deformation relied. Given the lack of lateral anatomical landmarks in the region of the chin which could be used during the conformation process there will always probably be a higher magnitude of error.
Show more

14 Read more

Content Based Image Retrieval using HSV and Hadamard DWT

Content Based Image Retrieval using HSV and Hadamard DWT

International Journal for Research in Applied Science & Engineering Technology IJRASET the query image and the feature vectors stored in the dataset using Euclidian distance.. Sort the i[r]

10 Read more

Analysis and Classification of Internet activity Logs Based on Patterns of Traffic Rates  

Analysis and Classification of Internet activity Logs Based on Patterns of Traffic Rates  

In very first section results would be based upon the classification accuracy: From the Table 1, it can be inferred that while comparing the k-mean algorithm using manhattan and euclidian distances So, Bayes network was used to data classification. Correctly classification instances were higher in k-mean using Euclidian (98.622%) than k-mean using Manhattan distances (97.633%). In second section results would be based upon the prediction accuracy: Similarly the average magnitude of error measure by mean absolute error (MAE) was higher in k-mean using manhattan distance (0.217) as compared to Euclidian distance. The accuracy of both distances was again calculated while using the criteria of root mean square error (RMSE), while inferred that RMSE of (.265) as compared to k-mean (.2771). From the above discussion it can be concluded that k-mean using Euclidian distance is accurate and measure in data classification. Next step is to pre-process data against null values followed by normalization and prioritize dataset attribute according to expert knowledge [1].
Show more

6 Read more

Quantifying spatial accessibility in public health practice and research: an application to on-premise alcohol outlets, United States, 2013

Quantifying spatial accessibility in public health practice and research: an application to on-premise alcohol outlets, United States, 2013

In the United States, the census block is the basic unit of census geographic hierarchy (https ://www.censu s.gov/ geo/refer ence/hiera rchy.html). We used census block- level population as our demand area population. By using the population at finest level of census geography that is available in the United States, we minimized the geo- graphic aggregation error for population data described as Source A type of error in Hillsman and Rhoda [4]. Spa- tial access metrics based on higher levels of census geog- raphy than census blocks (e.g. census tract, ZIP Code, or county), even by using population weighted centroids to estimate demand population locations, could still intro- duce substantial spatial aggregation errors or Source A type errors. In addition, the census block-based spatial access metrics provide the flexibility to aggregate the metrics to any high-level geography that could be linked to geocoded individual or aggregated health outcomes. For example, we could easily aggregate census block- based spatial access metrics to higher-level census geo- graphic units, such as census tract, county, ZIP Code, school district, congressional district, or other adminis- trative or customized geographic units, such as hospital service areas, hospital referral regions, and primary care service area. However, there were no studies that quan- tify spatial accessibility based on different census block- level distance measures (Euclidian distance, driving distance and driving time) and evaluate their correlations between population-weighted spatial accessibility index and population-weighted distance metrics.
Show more

13 Read more

Title: Classification of Motion and Speech Based Brain EEG Signals using Bilayer Bayesian Classifier with Association Rule (BBC-AR)

Title: Classification of Motion and Speech Based Brain EEG Signals using Bilayer Bayesian Classifier with Association Rule (BBC-AR)

[10] StaffordMichahial, R.RanjithKumar,Hemath Kumar P,Puneeth Kumar A, Speech synthesizer And Feature Extraction Using DWT With classification By Euclidian Distance and neural network of EEG signals, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 1, Issue 4, June 2012 (Journal)

6 Read more

An Improved Amplitude Comparison Based Direction of Arrival Estimation

An Improved Amplitude Comparison Based Direction of Arrival Estimation

Many clustering methods use distance measures to determine the similarity or dissimilarity between any pair of objects. It is useful to denote the distance between two data a and b as: d(a,b). A valid distance measure should be symmetric and obtains its minimum value (usually zero) in case of identical vectors. Common measures are Euclidian distance which is the most popular, Mahalanobis distance, Manhattan distance etc...[10].

7 Read more

GIS Based Public Services Analysis Based on Municipal Election Areas: A Methodological Approach for the City of Makkah, Saudi Arabia

GIS Based Public Services Analysis Based on Municipal Election Areas: A Methodological Approach for the City of Makkah, Saudi Arabia

The spatial fairness of public services is a major aspect in offering a healthy and cheerful living environment in a city. The Geographic Information System (GIS) technology has been applied to investigate the spatial distribution of some selected public services over the municipal election areas within Makkah city, Saudi Arabia. These services include education, health, security, religious, commercial, and sports services. The attained results show that except for reli- gious services, other public services are not quantitatively proportional with the population variations in Makkah. It has been found that the third election area posses almost one third of five public services and two thirds of the sport services. That might be attributed to the fact that the third election area possesses almost a quarter of the city population. But, although the first election area has a closer percentage of the total population, it does not include a comparable percent- age of public services. On a spatial basis, results of GIS spatial analysis (particularly the Euclidian distance, the mean distance band to a neighbor, the standard distance, the directional distribution ellipse, and the average nearest neighbor ratio tools) have concluded that there is inequity in the services distribution over municipal election areas in Makkah city. Consequently, it is recommended that local planners and decision makers should take the obtained results into consideration to achieve fair and better distribution of public services in the city.
Show more

18 Read more

Automatic object recognition based on 
		Euclidean distance restricted auto encoder

Automatic object recognition based on Euclidean distance restricted auto encoder

In this paper, combine a Euclidian distance restricted auto encoder foreground extraction techniques to develop a real-time object detection algorithm. The pixels in the initial frames are used to model a zero mean unit variance Gaussian distribution. The new pixels are tested against these models and classified as foreground and background based on their variance ranges. The extracted foreground is then recognized with help of a multilayered auto encoder which gives better efficiency with lesser training data because of the Euclidian distance based restriction and dropout step to avoid over fitting. The efficiency of the method and the neural network are tabulated.
Show more

5 Read more

Comparison between Different ESI Methods on Refractory Epilepsy Patients Shows a High Sensitivity for Bayesian Model Averaging

Comparison between Different ESI Methods on Refractory Epilepsy Patients Shows a High Sensitivity for Bayesian Model Averaging

We found that the application of ESI for reconstructing IED activities is sensitivity for EEG acquired by few numbers of electrodes (<63). Although our methodological conditions were not the same of other studies re- ported in literature, the methods BMA and ED presented high sensitivities for temporal lobe epilepsy patients (86%). The BMA method also obtained the lowest mean CL-MM distance and the third lowest CL-BM, but presented solutions restricted to few structures and more representative than those obtained for the other meth- ods. Finally, we believe that the BMA method needs to be explored in wider sets of data, including more extra- temporal lobes patients and also EEG data acquired with different number of electrodes.
Show more

14 Read more

What performance analysts need to know about research trends in association football (2012 2016): A systematic review

What performance analysts need to know about research trends in association football (2012 2016): A systematic review

Based on the assumption that exposure to altitude and environmental heat stress has a detrimental impact on exercise performance, some researchers have investigated their effects on football players [23, 24]. Nassis [23] conducted a study that examined effects of altitude on soccer performance during the 2010 World Cup in South Africa. The study verified a 3.1% decrease in the total distance covered by teams during matches played at 1200-1400m and 1401-1753m, compared with sea level. Through the analysis of environmental heat stress in the 2014 FIFA World Cup (Brazil), Nassis et al. [24] concluded that there was no difference in playing time (average of both teams), total distance traveled (m/min/player), number of goals scored and number of cards issued by referees, compared to matches played under different environmental stress categories (e.g., the risk of heat stress at 50% relative humidity is ‘high’ for wet-bulb globe temperature (WBGT) 28–33°C, ‘moderate’ for WBGT 24–28°C and ‘low’ for WBGT <24°C.). High intensity activity was lower under high compared to low environmental stress, and the rate of successfully completed passes was greater in the former compared to the latter.
Show more

83 Read more

Show all 10000 documents...