ECG signal diagnosis using Discrete Wavelet Transform and K-Nearest Neighbor classifier.

2021 ◽  
Author(s):  
Youssef Toulni ◽  
Taoufiq Belhoussine Drissi ◽  
Benayad Nsiri
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier

ekspresi Mengidentifikasi Mood Mahasiswa Berdasarkan Ekspresi Wajah dengan Menggunakan Discrete Wavelet Transform dan Fuzzy K-Nearest Neighbor

2019 ◽  
Vol 11 (1) ◽  
pp. 34-38
Author(s):  
Nur Inzani Reski Amalia ◽  
Jayanti Yusmah Sari
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor

Mood adalah keadaan emosional yang bersifat sementara. Mood biasanya memiliki nilai kualitas positif atau negatif. Kecerdasan emosional memiliki peran lebih dari 80% dalam mencapai kesuksesan hidup dan menjadi salah satu faktor yang mempengaruhi daya tangkap mahasiswa dalam proses perkuliahan. Dengan mengetahui emosi-emosi mahasiswa, kita dapat membantu daya tangkap mahasiswa saat proses perkuliahan, serta dibutuhkannya sistem yang dapat mengidentifikasi emosi-emosi yang terbentuk saat perkuliahan berlangsung. Sistem ini dibangun menggunakan  Discrete Wavelet Transform yang mentransformasikan citra menjadi 4 sub-image. Citra hasil Discrete Wavelet Transform tampak kasar atau membentuk wajah yang dapat membedakan ekspresi mahasiswa. Hasil pengolahan citra Discrete Wavelet Transform di klasifikasikan dengan menggunakan Fuzzy K-nearest neighbor. Pengklasifikasian dibagi kedalam tiga ekspresi yaitu : Marah, Senang dan Sedih dengan akurasi 77,49%

scholarly journals EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm

2021 ◽  
Vol 17 (2) ◽  
pp. 38-45
Author(s):  
Samaa Abdulwahab ◽  
Hussain Khleaf ◽  
Manal Jassim
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Low Cost ◽  
Brain Diseases ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Brain Connection

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.

scholarly journals Stronger Automation for Flyspeck by Feature Weighting and Strategy Evolution

10.29007/5gzr ◽  
2018 ◽  
Author(s):  
Cezary Kaliszyk ◽  
Josef Urban
Keyword(s):  
Nearest Neighbor ◽  
Feature Weighting ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifier ◽  
Hol Light ◽  
Distance Weighted ◽  
Neighbor Classifier

Two complementary AI methods are used to improve the strength of the AI/ATP service for proving conjectures over the HOL Light and Flyspeck corpora. First, several schemes for frequency-based feature weighting are explored in combination with distance-weighted k-nearest-neighbor classifier. This results in 16% improvement (39.0% to 45.5% Flyspeck problems solved) of the overall strength of the service when using 14 CPUs and 30 seconds. The best premise-selection/ATP combination is improved from 24.2% to 31.4%, i.e. by 30%. A smaller improvement is obtained by evolving targetted E prover strategies on two particular premise selections, using the Blind Strategymaker (BliStr) system. This raises the performance of the best AI/ATP method from 31.4% to 34.9%, i.e. by 11%, and raises the current 14-CPU power of the service to 46.9%.

scholarly journals A patient distance metric for neurology

2020 ◽  
Author(s):  
Daniel B Hier ◽  
Jonathan Kopel ◽  
Steven U Brint ◽  
Donald C Wunsch II ◽  
Gayla R Olbricht ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Signs And Symptoms ◽  
Diagnostic Error ◽  
K Nearest Neighbor ◽  
Bipartite Matching ◽  
Nearest Neighbor Classifier ◽  
Neurological Signs ◽  
Neurological Patients ◽  
Machine Readable ◽  
Neighbor Classifier

Abstract Objective: Neurologists lack a metric for measuring the distance between neurological patients. When neurological signs and symptoms are represented as neurological concepts from a hierarchical ontology and neurological patients are represented as sets of concepts, distances between patients can be represented as inter-set distances.Methods:We converted the neurological signs and symptoms from 721 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated inter-concept distances based a hierarchical ontology and we calculated inter-patient distances by semantic weighted bipartite matching. We evaluated the accuracy of a k-nearest neighbor classifier to allocate patients into 40 diagnostic classes.Results:Within a given diagnosis, mean patient distance differed by diagnosis, suggesting that across diagnoses there are differences in how similar patients are to other patients with the same diagnosis. The mean distance from one diagnosis to another diagnosis differed by diagnosis, suggesting that diagnoses differ in their proximity to other diagnoses. Utilizing a k-nearest neighbor classifier and inter-patient distances, the risk of misclassification differed by diagnosis.Conclusion:If signs and symptoms are converted to machine-readable codes and patients are represented as sets of these codes, patient distances can be computed as an inter-set distance. These patient distances given insights into how homogeneous patients are within a diagnosis (stereotypy), the distance between different diagnoses (proximity), and the risk of diagnosis misclassification (diagnostic error).

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