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%.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

Gas Turbine Fault Classification Using Probability Density Estimation

2014 ◽  
Author(s):  
Igor Loboda
Keyword(s):  
Probability Density ◽  
Gas Turbine ◽  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Testing Procedure ◽  
Fault Classification ◽  
Confidence Probability ◽  
Probability Density Estimation ◽  
K Nearest Neighbor ◽  
Average Accuracy

Diagnostics is an important aspect of a condition based maintenance program. To develop an effective gas turbine monitoring system in short time, the recommendations on how to optimally design every system algorithm are required. This paper deals with choosing a proper fault classification technique for gas turbine monitoring systems. To classify gas path faults, different artificial neural networks are typically employed. Among them the Multilayer Perceptron (MLP) is the mostly used. Some comparative studies referred to in the introduction show that the MLP and some other techniques yield practically the same classification accuracy on average for all faults. That is why in addition to the average accuracy, more criteria to choose the best technique are required. Since techniques like Probabilistic Neural Network (PNN), Parzen Window (PW) and k-Nearest Neighbor (K-NN) provide a confidence probability for every diagnostic decision, the presence of this important property can be such a criterion. The confidence probability in these techniques is computed through estimating a probability density for patterns of each concerned fault class. The present study compares all mentioned techniques and their variations using as criteria both the average accuracy and availability of the confidence probability. To compute them for each technique, a special testing procedure simulates numerous diagnosis cycles corresponding to different fault classes and fault severities. In addition to the criteria themselves, criteria imprecision due to a finite number of the diagnosis cycles is computed and involved into selecting the best technique.

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.  

IMPLEMENTATION OF GAIN RATIO AND K-NEAREST NEIGHBOR FOR CLASSIFICATION OF STUDENT PERFORMANCE

2020 ◽  
Vol 16 (1) ◽  
pp. 19-24
Author(s):  
Tyas Setiyorini ◽  
Rizky Tri Asmono
Keyword(s):  
Student Performance ◽  
Nearest Neighbor ◽  
Educational Data Mining ◽  
K Nearest Neighbor ◽  
High Dimensions ◽  
Gain Ratio ◽  
Average Accuracy ◽  
Predicting Student Performance ◽  
Work Done

Predicting student performance is very useful in analyzing weak students and providing support to students who face difficulties. However, the work done by educators has not been effective enough in identifying factors that affect student performance. The main predictor factor is an informative student academic score, but that alone is not good enough in predicting student performance. Educators utilize Educational Data Mining (EDM) to predict student performance. KK-Nearest Neighbor is often used in classifying student performance because of its simplicity, but the K-Nearest Neighbor has a weakness in terms of the high dimensional features. To overcome these weaknesses, a Gain Ratio is used to reduce the high dimension of features. The experiment has been carried out 10 times with the value of k is 1 to 10 using the student performance dataset. The results of these experiments are obtained an average accuracy of 74.068 with the K-Nearest Neighbor and obtained an average accuracy of 75.105 with the Gain Ratio and K-Nearest Neighbor. The experimental results show that Gain Ratio is able to reduce the high dimensions of features that are a weakness of K-Nearest Neighbor, so the implementation of Gain Ratio and K-Nearest Neighbor can increase the accuracy of the classification of student performance compared to using the K-Nearest Neighbor alone.

Identification of Authenticity and Nominal Value of Indonesia Banknotes Using Fuzzy KNearest Neighbor Method

2019 ◽  
Vol 6 (1) ◽  
pp. 32-37
Author(s):  
Ricky Ramadhan ◽  
Jayanti Yusmah Sari ◽  
Ika Purwanti Ningrum
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Threshold Value ◽  
Segmentation Method ◽  
K Nearest Neighbor ◽  
Real Money ◽  
The Public ◽  
Average Accuracy ◽  
Segmentation Methods ◽  
The Government

The existence of counterfeit money is often troubling the public. The solution given by the government to be careful of counterfeit money is by means of 3D (seen, touched and looked at). However, this step has not been perfectly able to distinguish real money and fake money. So there is a need for a system to help detect the authenticity of money. Therefore, in this study a system was designed that can detect the authenticity of rupiah and its nominal value. For data acquisition, this system uses detection boxes, ultraviolet lights and smartphone cameras. As for feature extraction, this system uses segmentation methods. The segmentation method based on the threshold value is used to obtain an invisible ink pattern which is a characteristic of real money along with the nominal value of the money. The feature is then used in the stage of detection of money authenticity using FKNN (Fuzzy K-Nearest Neighbor) method. From 24 test data, obtained an average accuracy of 96%. This shows that the system built can detect the authenticity and nominal value of the rupiah well.

Building a predictive model to assist in the diagnosis of cervical cancer

2021 ◽  
Author(s):  
Emmanuel Kwateng Drokow ◽  
Adu Asare Baffour ◽  
Clement Yaw Effah ◽  
Clement Agboyibor ◽  
Gloria Selorm Akpabla ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Nearest Neighbor ◽  
Learning Models ◽  
K Nearest Neighbor ◽  
Effective Technique ◽  
Ensemble Technique ◽  
Cervical Cancer Risk ◽  
Average Accuracy ◽  
The World ◽  
Machine Learning Models

Aim: Cervical cancer is still one of the most common gynecologic cancers in the world. Since cervical cancer is a potentially preventive cancer, earlier detection is the most effective technique for decreasing the worldwide incidence of the illness. Materials and methods: This research presents a novel ensemble technique for predicting cervical cancer risk. Specifically, the authors introduce a voting classifier that aggregates prediction probabilities from multiple machine-learning models: logistic regression, K-nearest neighbor, decision tree, XGBoost and multilayer perceptron. Results: The average accuracy, precision, recall and f1-score of the voting classifier were 96.6, 97.4, 95.9 and 96.6, respectively. Furthermore, the voting algorithm gains average high values for all evaluation metrics (accuracy, precision, recall and f1-score). The f1-score of the algorithm is 96%, which demonstrates the robustness of the model. Conclusion: The findings suggest that the probability of having cervical cancer can be accurately predicted utilizing the voting technique.

scholarly journals SARM: Salah Activities Recognition Model Based on Smartphone

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 881
Author(s):  
Nafees Ahmad ◽  
Lansheng Han ◽  
Khalid Iqbal ◽  
Rashid Ahmad ◽  
Muhammad Adil Abid ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Major Effect ◽  
Sensor Data ◽  
Data Sets ◽  
Mobile Sensor ◽  
K Nearest Neighbor ◽  
Recognition Model ◽  
Average Accuracy ◽  
Smartphone Sensors ◽  
Model Features

Alzheimer’s is a chronic neurodegenerative disease that frequently occurs in many people today. It has a major effect on the routine activities of affected people. Previous advancement in smartphone sensors technology enables us to help people suffering from Alzheimer’s. For people in the Muslim community, where it is mandatory to offer prayers five times a day, it may mean that they are struggling in their daily life prayers due to Alzheimer’s or lack of concentration. To deal with such a problem, automated mobile sensor-based activity recognition applications can be supportive to design accurate and precise solutions with an objective to direct the Namazi (worshipper). In this paper, a Salah activities recognition model (SARM) using a mobile sensor is proposed with the aim to recognize specific activities, such as Al-Qayam (standing), Ruku (standing to bowing), and Sujud (standing to prostration). This model entails the collection of data, selection and placement of sensor, data preprocessing, segmentation, feature extraction, and classification. The proposed model will provide a stepping edge to develop an application for observing prayer. For these activities’ recognition, data sets were collected from ten subjects, and six different features sets were used to get improved results. Extensive experiments were performed to test and validate the model features to train random forest (RF), K-nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT). The predicted average accuracy of RF, KNN, NB, and DT was 97%, 94%, 71.6%, and 95% respectively.

scholarly journals PERANCANGAN SISTEM KLASIFIKASI UDANG BERACUN PADA JENIS UDANG TENGGEK MENGGUNAKAN METODE K-NEAREST NEIGHBOR (K-NN).

2020 ◽  
Vol 6 (1) ◽  
pp. 1-7
Author(s):  
Lisa Afrinanda ◽  
Ilyas Ilyas
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Nearest Neighbor ◽  
The Body ◽  
K Nearest Neighbor ◽  
Class A ◽  
The People ◽  
Average Accuracy ◽  
Class B

Shrimp is one of the seafood which is nutrient-rich needed by the body. However, due to the frequent case of the infected Tenggek-shrimp appeared, it makes people beware to consume it. The classification of Tenggek-shrimp by using image processing of the computer be able to classify the types of shrimp whether poisonous or not. The data mining techniques can be used to classify shrimp based on RGB colors (red, green, blue) and texture (energy, contrast, correlation, homogeneity). The class of Tenggek-shrimp is divided into two, The fresh Tenggek-shrimps that are caught naturally (Class A) and the poisoned Tenggek-shrimps that are caught by using the poison (Class B). The method used in this study is K-Nearest Neighbor (K-NN). This classification system is expected to help the people in selecting good and safe Tenggek-shrimp for consumption. Based on the evaluation results using the holdout method, obtained an average accuracy of 63% with an accuracy of identification of toxic tenggek shrimp of 71.66%, and the accuracy of identification of natural fresh shrimp is about 60%.

Prediction of Succinic Acid Extraction Efficiency in the Emulsion Liquid Membrane by using Machine Learning Techniques

2021 ◽  
Vol 43 (2) ◽  
pp. 104-104
Author(s):  
Sevda G l Sevda G l ◽  
Aynur Manzak and G k en etinel Aynur Manzak and G k en etinel
Keyword(s):  
Succinic Acid ◽  
Nearest Neighbor ◽  
Liquid Membrane ◽  
Solute Concentration ◽  
Machine Learning Techniques ◽  
Acid Extraction ◽  
K Nearest Neighbor ◽  
Emulsion Liquid Membrane ◽  
Average Accuracy ◽  
Artificial Neural

This research aims to predict succinic acid concentration in the external phase during the emulsion liquid membrane process by using artificial neural networks along with a popular alternative method: k-nearest neighbor technique. The solute concentration values can be predicted by the proposed method without performing a great number of emulsion liquid membrane experiments. Several computer simulations were performed to demonstrate the success of the system. Simulation results showed that the estimated solute concentration values are very close to the achieved experimental results. The optimal conditions for emulsion liquid membrane were found to be: solvent kerosene, TOPO concentration (1%w/w), Amberlite LA-2 concentration (4%w/w), surfactant concentration (5%w/w), Na2CO3 concentration (5%w/v), modifier (decanol) concentration (2%w/w), mixing speed 300 rpm. The average accuracy percentages achieved by artificial neural network and k-nearest neighbor approaches were 88.75and#177;1.94% and 90.2and#177;1.2%, respectively.

scholarly journals Identification of Urban Functional Areas by Coupling Satellite Images and Taxi GPS Trajectories

2020 ◽  
Vol 12 (15) ◽  
pp. 2449 ◽  
Author(s):  
Zhen Qian ◽  
Xintao Liu ◽  
Fei Tao ◽  
Tong Zhou
Keyword(s):  
Satellite Images ◽  
Nearest Neighbor ◽  
City Management ◽  
City Development ◽  
K Nearest Neighbor ◽  
Sustainable City ◽  
Time Frequency ◽  
Spatiotemporal Information ◽  
Average Accuracy ◽  
Gps Trajectories

Urban functional area (UFA) recognition is one of the most important strategies for achieving sustainable city development. As remote-sensing and social-sensing data sources have increasingly become available, UFA recognition has received a significant amount of attention. Research on UFA recognition that uses a single dataset suffers from a low update frequency or low spatial resolution, while data fusion-based methods are limited in efficiency and accuracy. This paper proposes an integrated model to identify UFA using satellite images and taxi global positioning system (GPS) trajectories in four steps. First, blocks were generated as spatial units in the study area, and the spatiotemporal information entropy of the taxi GPS trajectory (STET) for each block was calculated. Second, a 24-hour time-frequency series was formed based on the pick-up and drop-off points extracted from taxi trajectories and used as the interpretation indicator of the blocks. The K-Means++ and k-Nearest Neighbor (kNN) algorithm were used to identify their social functions. Third, a multilabel classification method based on the residual neural network (MLC-ResNets) and “You Only Look Once” (YOLO) target detection algorithms were used to identify the features of the typical and atypical spatial textures, respectively, of the satellite images in the blocks. The confidence scores of the features of the blocks were categorized by the decision tree algorithm. Fourth, to find the best way to integrate the two sub-models for UFA identification, the 10-fold cross-validation method based on stratified random sampling was applied to determine the most optimal STET thresholds. The results showed that the average accuracy reached 82.0%, with an average kappa of 73.5%—significant improvements over most existing studies. This paper provides new insights into how the advantages of satellite images and taxi trajectories in UFA identification can be fully exploited to support sustainable city management.

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