scholarly journals Recognition of The Baby Footprint Characteristics Using Wavelet Method and K-Nearest Neighbor (K-NN)

2021 ◽  
Vol 12 (1) ◽  
pp. 41
Author(s):  
I Made Aris Satia Widiatmika ◽  
I Nyoman Piarsa ◽  
Arida Ferti Syafiandini
Keyword(s):  
Nearest Neighbor ◽  
Individual Recognition ◽  
Computation Time ◽  
K Nearest Neighbor ◽  
Birth Process ◽  
Security Systems ◽  
Wavelet Method ◽  
Biometric Technology ◽  
Test Computation ◽  
Object Feature

Individual recognition using biometric technology can be utilized in creating security systems that are important in modern life. The individuals recognition in hospitals generally done by conventional system so it makes more time in taking identity. A newborn baby will proceed an identity tagging after birth process is complete. This identity using a bracelet filled with names and ink stamps on paper that will be prone to damage or crime. The solution is to store the baby's identity data digitally and carry out the baby's identification process. This system can increase safety and efficiency in storing a baby's footprint image. The implementation of baby's footprint image identification starting from the acquisition of baby's footprint image, preprocessing such as selecting ROI size baby's footprint object, feature extraction using wavelet method and classification process using K-Nearest Neighbor (K-NN) method because this method has been widely used in several studies of biometric identification systems. The test data came from 30 classes with 180 images test right and left baby's footprint. The identification results using 200x500 size ROI with level 4 wavelet decomposition get recognition results with an accuracy of 99.30%, 90.17% precision, and 89.44% recall with a test computation time of 8.0370 seconds.  

Improving the Performance of kNN in the MapReduce Framework Using Locality Sensitive Hashing

2019 ◽  
Vol 10 (4) ◽  
pp. 1-16
Author(s):  
Sikha Bagui ◽  
Arup Kumar Mondal ◽  
Subhash Bagui
Keyword(s):  
Nearest Neighbor ◽  
Parallel Implementation ◽  
Block Size ◽  
Computation Time ◽  
Locality Sensitive Hashing ◽  
K Nearest Neighbor ◽  
Mapreduce Framework ◽  
Data Set ◽  
Data Object ◽  
Very Large Datasets

In this work the authors present a parallel k nearest neighbor (kNN) algorithm using locality sensitive hashing to preprocess the data before it is classified using kNN in Hadoop's MapReduce framework. This is compared with the sequential (conventional) implementation. Using locality sensitive hashing's similarity measure with kNN, the iterative procedure to classify a data object is performed within a hash bucket rather than the whole data set, greatly reducing the computation time needed for classification. Several experiments were run that showed that the parallel implementation performed better than the sequential implementation on very large datasets. The study also experimented with a few map and reduce side optimization features for the parallel implementation and presented some optimum map and reduce side parameters. Among the map side parameters, the block size and input split size were varied, and among the reduce side parameters, the number of planes were varied, and their effects were studied.

scholarly journals Automatic amyotrophic lateral sclerosis detection using tunable Q-factor wavelet transform

2020 ◽  
Vol 9 (4) ◽  
pp. 744
Author(s):  
Abdelouahad Achmamad ◽  
Abdelali Belkhou ◽  
Atman Jbari
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Neuromuscular Disorders ◽  
Computation Time ◽  
Support Vector ◽  
Q Factor ◽  
K Nearest Neighbor ◽  
Emg Signal ◽  
Lateral Sclerosis

Early diagnosis of amyotrophic lateral sclerosis (ALS) based on electromyography (EMG) is crucial. The processing of a non-stationary EMG signal requires powerful multi-resolution methods. Our study analyzes and classifies the EMG signals. In the present work, we introduce a novel flexible method for classification of EMG signals using tunable Q-factor wavelet transform (TQWT). Different sub-bands generated by the TQWT technique were served to extract useful information related to energy and then the calculated features were selected using a filter selection (FS) method. The effectiveness of the feature selection step resulted not only in the improvement of classification performance but also in reducing the computation time of the classification algorithm. The selected feature subsets were used as inputs to multiple classifier algorithms, namely, k-nearest neighbor (k-NN), least squares support vector machine (LS-SVM) and random forest (RF) for automated diagnosis. The experimental results show better classification measures with k-NN classifier compared with LS-SVM and RF. The robustness of the classification task was tested using a ten-fold cross-validation method. The outcomes of our proposed approach can be exploited to aid clinicians in neuromuscular disorders detection.

scholarly journals Performance Analysis of Supervised Learning Models for Product Title Classification

2019 ◽  
Vol 8 (3) ◽  
pp. 228 ◽  
Author(s):  
Norsyela Muhammad Noor Mathivanan ◽  
Nor Azura Md.Ghani ◽  
Roziah Mohd Janor
Keyword(s):  
Supervised Learning ◽  
Nearest Neighbor ◽  
Computation Time ◽  
Classification Problem ◽  
Short Description ◽  
Support Vector ◽  
Data Sets ◽  
Learning Models ◽  
K Nearest Neighbor ◽  
Online Business

<p>Online business development through e-commerce platforms is a phenomenon which change the world of promoting and selling products in this 21<sup>st</sup> century. Product title classification is an important task in assisting retailers and sellers to list a product in a suitable category. Product title classification is apart of text classification problem but the properties of product title are different from general document. This study aims to evaluate the performance of five different supervised learning models on data sets consist of e-commerce product titles with a very short description and they are incomplete sentences. The supervised learning models involve in the study are Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM) and Random Forest. The results show KNN model is the best model with the highest accuracy and fastest computation time to classify the data used in the study. Hence, KNN model is a good approach in classifying e-commerce products.</p>

scholarly journals HIGH ACCURACY RECOGNITION BIO METRICS BASED ON FINGER VEIN SCREENING SENSOR

2020 ◽  
Vol 3 (2) ◽  
pp. 35-46
Author(s):  
Shereen S. Jumaa ◽  
Khamis A. Zidan
Keyword(s):  
Nearest Neighbor ◽  
Low Cost ◽  
Computation Time ◽  
Recognition System ◽  
K Nearest Neighbor ◽  
Finger Print ◽  
Finger Vein ◽  
Vein Recognition ◽  
Histogram Of Gradients ◽  
Micro Lens

One of the safest biometrics of today is finger vein- but this technic  arises with some specific challenges, the most common  one being that the vein pattern is hard to extract because finger vein images are always low in quality, significantly  hampered the feature extraction and classification stages. Professional  algorithms want to be considered with the conventional hardware for capturing finger-vein images is  by using red Surface Mounted Diode (SMD) leds for this aim. For capturing images, Canon 750D camera with micro lens is used. For high quality images the integrated micro lens  is used, and with some adjustments it can also obtain finger print. Features extraction was used by a combination of Hierarchical Centroid and Histogram of Gradients. Results were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results displayed improvement as compared to three latest benchmarks in this field that used 6-fold validation and SDUMLA-HMT. The work novelty is owing to the hardware design of the sensor within the finger-vein recognition system to obtain, simultaneously, highly secured recognition with low computation time ,finger vein and finger print at low cost, unlimited users for one device and open source.

scholarly journals Indoor Localization Method Based on Regional Division with IFCM

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 559 ◽  
Author(s):  
Junhuai Li ◽  
Xixi Gao ◽  
Zhiyong Hu ◽  
Huaijun Wang ◽  
Ting Cao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
Pearson Correlation ◽  
Computation Time ◽  
Access Point ◽  
Target Point ◽  
Localization Algorithm ◽  
K Nearest Neighbor ◽  
Localization Method ◽  
Regional Division

With the development of wireless technology, indoor localization has gained wide attention. The fingerprint localization method is proposed in this paper, which is divided into two phases: offline training and online positioning. In offline training phase, the Improved Fuzzy C-means (IFCM) algorithm is proposed for regional division. The Between-Within Proportion (BWP) index is selected to divide fingerprint database, which can ensure the result of regional division consistent with the building plane structure. Moreover, the Agglomerative Nesting (AGNES) algorithm is applied to accomplish Access Point (AP) optimization. In the online positioning phase, sub-region selection is performed by nearest neighbor algorithm, then the Weighted K-nearest Neighbor (WKNN) algorithm based on Pearson Correlation Coefficient (PCC) is utilized to locate the target point. After the evaluation on the effect of regional division and AP optimization of location precision and time, the experiments show that the average positioning error is 2.53 m and the average computation time of the localization algorithm based on PCC reduced by 94.13%.

Weighted K-nearest Neighbor Fast Localization Algorithm Based on RSSI for Wireless Sensor Systems

2020 ◽  
Vol 13 (2) ◽  
pp. 295-301
Author(s):  
Lu Bai ◽  
Chenglie Du ◽  
Jinchao Chen
Keyword(s):  
Nearest Neighbor ◽  
Simulation Analysis ◽  
Computation Time ◽  
Wireless Sensor ◽  
Sensor Systems ◽  
Localization Algorithm ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Wireless Positioning ◽  
Computationally Intensive

Background: Wireless positioning is one of the most important technologies for realtime applications in wireless sensor systems. This paper mainly studies the indoor wireless positioning algorithm of robots. Methods: The application of the K-nearest neighbor algorithm in Wi-Fi positioning is studied by analyzing the Wi-Fi fingerprint location algorithm based on Received Signal Strength Indication (RSSI) and K-Nearest Neighbor (KNN) algorithm in Wi-Fi positioning. The KNN algorithm is computationally intensive and time-consuming. Results: In order to improve the positioning efficiency, improve the positioning accuracy and reduce the computation time, a fast weighted K-neighbor correlation algorithm based on RSSI is proposed based on the K-Means algorithm. Thereby achieving the purpose of reducing the calculation time, quickly estimating the position distance, and improving the positioning accuracy. Conclusion: Simulation analysis shows that the algorithm can effectively shorten the positioning time and improve the positioning efficiency in robot Wi-Fi positioning.

scholarly journals Comparison of short-term electrical load forecasting methods for different building types

2021 ◽  
Vol 4 (S3) ◽  
Author(s):  
Arne Groß ◽  
Antonia Lenders ◽  
Friedhelm Schwenker ◽  
Daniel A. Braun ◽  
David Fischer
Keyword(s):  
Nearest Neighbor ◽  
Forecast Error ◽  
Load Forecasting ◽  
Computation Time ◽  
Energy System ◽  
Electrical Load ◽  
K Nearest Neighbor ◽  
To Come ◽  
The Individual ◽  
The Cost

AbstractThe transformation of the energy system towards volatile renewable generation, increases the need to manage decentralized flexibilities more efficiently. For this, precise forecasting of uncontrollable electrical load is key. Although there is an abundance of studies presenting innovative individual methods for load forecasting, comprehensive comparisons of popular methods are hard to come across.In this paper, eight methods for day-ahead forecasts of supermarket, school and residential electrical load on the level of individual buildings are compared. The compared algorithms came from machine learning and statistics and a median ensemble combining the individual forecasts was used.In our examination, nearly all the studied methods improved forecasting accuracy compared to the naïve seasonal benchmark approach. The forecast error could be reduced by up to 35% compared to the benchmark. From the individual methods, the neural networks achieved the best results for the school and supermarket buildings, whereas the k-nearest-neighbor regression had the lowest forecasting error for households. The median ensemble narrowly yielded a lower forecast error than all individual methods for the residential and school category and was only outperformed by a neural network for the supermarket data. However, this slight increase in performance came at the cost of a significantly increased computation time. Overall, identifying a single best method remains a challenge specific to the forecasting task.

scholarly journals Improving Classification Accuracy Using Clustering Technique

2018 ◽  
Vol 7 (3) ◽  
pp. 465-470
Author(s):  
Norsyela Muhammad Noor Mathivanan ◽  
Nor Azura Md.Ghani ◽  
Roziah Mohd Janor
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Computation Time ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
Real World Data ◽  
Data Set ◽  
Clustering Technique ◽  
Clustering And Classification ◽  
Correct Category

Product classification is the key issue in e-commerce domains. Many products are released to the market rapidly and to select the correct category in taxonomy for each product has become a challenging task. The application of classification model is useful to precisely classify the products. The study proposed a method to apply clustering prior to classification. This study has used a large-scale real-world data set to identify the efficiency of clustering technique to improve the classification model. The conventional text classification procedures are used in the study such as preprocessing, feature extraction and feature selection before applying the clustering technique. Results show that clustering technique improves the accuracy of the classification model. The best classification model for all three approaches which are classification model only, classification with hierarchical clustering and classification with K-means clustering is K-Nearest Neighbor (KNN) model. Even though the accuracy of the KNN models are the same across different approaches but the KNN model with K-means clustering had the shortest time of execution. Hence, applying K-means clustering prior to KNN model helps in reducing the computation time.

scholarly journals Klasifikasi Multi Label pada Hadis Bukhari Terjemahan Bahasa Indonesia Menggunakan Mutual Information dan k-Nearest Neighbor

2020 ◽  
Vol 9 (3) ◽  
pp. 357-364
Author(s):  
Afrian Hanafi ◽  
Adiwijaya Adiwijaya ◽  
Widi Astuti
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Text Classification ◽  
Classification System ◽  
Nearest Neighbor ◽  
Feature Selection Method ◽  
Computation Time ◽  
Computational Time ◽  
Correct Prediction ◽  
K Nearest Neighbor

Hadith is the second source of law for Muslims after the Qur'an which comes from various forms of the words, actions and stipulations of the Prophet Muhammad or referred to as his sunnah. In order to make it easier for Muslims to apply the teachings of the hadiths, a classification system is needed that can categorize a hadith into a class or a combination of two of the three classes which called a multi-label classification. In building a text classification system, there are various classification techniques, one of which is k-Nearest Neighbor (KNN). KNN is a simple and effective classification method for text classification, but has a weakness in processing data with high vector dimensions so that the computation time is higher and the efficiency of text classification is very low. Mutual Information (MI) is used as a feature selection method to reduce vector dimensions because it has the ability to show how strong a feature is in making a correct prediction of a class. In this study Problem Transformation Method with the Binary Relevance (BR) approach is used so that the multi label classification process can be accomplished. The optimum results obtained in this study shows the value of hamming loss is 0.0886 or about 91.14% of data were correctly classified and computational time for 595 seconds by using MI as a feature selection, but without stemming.

scholarly journals Pengenalan Pola Garis Telapak Tangan Menggunakan Metode Fuzzy K-Nearest Neighbor

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Nurul Fajriani
Keyword(s):  
Nearest Neighbor ◽  
Individual Recognition ◽  
Good Condition ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Line Drawings ◽  
Average Accuracy ◽  
Access Rights ◽  
Palm Of The Hand ◽  
Gain Access

Abstrak Pengenalan telapak tangan merupakan sistem biometrik yang digunakan untuk pengenalan individu pada penggunaan proses autentikasi atau password untuk mendapatkan hak akses. Ini karena telapak tangan memiliki karakteristik unik, dan cenderung stabil. Selain itu, pengenalan telapak tangan tidak mengganggu kenyamanan seseorang saat pengambilan citra. Namun hingga kini masih ada kendala pada sistem pengenalan telapak tangan. Seperti gambar garis telapak yang tidak dalam kondisi baik untuk dikenali, karena  diambil dengan menggunakan kamera biasa. Oleh karena itu,. Untuk mendapatkan pengenalan pola telapak tangan yang baik, penelitian ini menggunakan ekstraksi fitur morfologi dan pengenalan pola garis telapak tangan dengan metode Fuzzy K-Nearest Neighbor (FKNN). Berdasarkan hasil percobaan yang dilakukan, penggunaan metode Fuzzy K-Nearest Neighbor (FKNN) dalam pengenalan pola garis telapak tangan diperoleh nilai akurasi tertinggi sebesar 93%, dan untuk nilai akurasi rata-rata sebesar 82,6%. Kata kunci: Fuzzy K-Nearest Neighbor (FKNN), Pengenalan Telapak Tangan  Abstract The introduction of the palms is a biometric system used for individual recognition on the use of the authentication process or password to gain access rights. This is because the palms have unique characteristics, and tend to be stable. In addition, the introduction of the palm does not interfere with one's comfort when taking the image. But until now there are still obstacles in the system of recognition of the palm of the hand. Such as palm line drawings that are not in good condition to be recognized, as taken with ordinary camera. Therefore,. To obtain a good palm line image, this study used morphological feature extraction and palm pattern recognition using the Fuzzy K-Nearest Neighbor (FKNN) method. Based on the experimental results, the use of Fuzzy K-Nearest Neighbor (FKNN) method in recognition of palm pattern pattern obtained the highest accuracy value of 93%, and for the average accuracy value of 82.6%.

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