An optimization of the K-Nearest Neighbor using Dynamic Time Warping as a measurement similarity for facial expressions recognition

2018 ◽  
Author(s):  
Anas Abouyahya ◽  
Sanaa El Fkihi
Keyword(s):  
Facial Expressions ◽  
Dynamic Time Warping ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Time Warping ◽  
Dynamic Time ◽  
Facial Expressions Recognition

Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors

2017 ◽  
Vol 8 (3) ◽  
pp. 22-37 ◽  
Author(s):  
Aya Hamdy Ali ◽  
Ayman Atia ◽  
Mostafa-Sami M. Mostafa
Keyword(s):  
Dynamic Time Warping ◽  
Traffic Accidents ◽  
Nearest Neighbor ◽  
Road Traffic ◽  
K Nearest Neighbor ◽  
Time Warping ◽  
Driving Behaviors ◽  
Smartphone Sensors ◽  
Dynamic Time ◽  
Total Accuracy

Road traffic accidents are caused 1.25 million deaths per year worldwide. To improve road safety and reducing road accidents, a recognition method for driving events is introduced in this paper. The proposed method detected and classified both driving behaviors and road anomalies patterns based on smartphone sensors (accelerometer and gyroscope). k-Nearest Neighbor and Dynamic Time Warping algorithms were utilized for method evaluation. Experiments were conducted to evaluate k-nearest neighbor and dynamic time warping algorithms accuracy for road anomalies and driving behaviors detection, moreover, driving behaviors classification. Evaluation results showed that k-nearest neighbor algorithm detected road anomalies and driving behaviors with total accuracy 98.67%. Dynamic time warping algorithm classified (normal and abnormal) driving behaviors with total accuracy 96.75%.

Recognizing Driving Behavior and Road Anomaly Using Smartphone Sensors

2019 ◽  
pp. 651-667
Author(s):  
Aya Hamdy Ali ◽  
Ayman Atia ◽  
Mostafa-Sami M. Mostafa
Keyword(s):  
Dynamic Time Warping ◽  
Nearest Neighbor ◽  
Road Traffic ◽  
K Nearest Neighbor ◽  
Time Warping ◽  
Driving Behaviors ◽  
Smartphone Sensors ◽  
Classification Evaluation ◽  
Dynamic Time ◽  
Total Accuracy

Road traffic accidents are caused 1.25 million deaths per year worldwide. To improve road safety and reducing road accidents, a recognition method for driving events is introduced in this paper. The proposed method detected and classified both driving behaviors and road anomalies patterns based on smartphone sensors (accelerometer and gyroscope). k-Nearest Neighbor and Dynamic Time Warping algorithms were utilized for method evaluation. Experiments were conducted to evaluate k-nearest neighbor and dynamic time warping algorithms accuracy for road anomalies and driving behaviors detection, moreover, driving behaviors classification. Evaluation results showed that k-nearest neighbor algorithm detected road anomalies and driving behaviors with total accuracy 98.67%. Dynamic time warping algorithm classified (normal and abnormal) driving behaviors with total accuracy 96.75%.

scholarly journals User Identification Based on the Dynamic Features Extracted from Handwriting on Touchscreen Devices

2020 ◽  
Vol 14 (11) ◽  
pp. 126
Author(s):  
Suleyman Al-Showarah ◽  
Wael Alzyadat ◽  
Aysh Alhroob ◽  
Hisham Al-Assam
Keyword(s):  
Nearest Neighbor ◽  
Movement Time ◽  
Identification Accuracy ◽  
Dynamic Features ◽  
K Nearest Neighbor ◽  
Time Warping ◽  
User Identification ◽  
Dynamic Time ◽  
Neighbor Classifier ◽  
Finger Pressure

<p><strong>Abstract</strong> - <strong>This research presents a methodology for user identification using ten English words written by a finger on smartphone and mini-tablet. This research considers three features, namely Signature Precision (SP), Finger Pressure (FP), and Movement Time (MT) that were extracted from each of ten English words using dynamic time warping. The features are then used individually and combined for the purpose of user identification based on the Euclidean distance and the k-nearest neighbor classifier. We concluded that the best identification accuracy results from the combinations of (SP and FP) features with an average accuracies of 74.55% and 69% were achieved on small smartphone and Mini-tablet respectively using a dataset of 42 users.</strong></p>

scholarly journals Synchronized Time Profile Similarity in Applications to Nearest Neighbor Classification

2020 ◽  
Author(s):  
Qimin Liu
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Nearest Neighbor ◽  
Time Series Data ◽  
Time Profile ◽  
Series Data ◽  
Time Warping ◽  
Nearest Neighbor Classification ◽  
Dynamic Time ◽  
Neighbor Classification

One of the existing approaches to time series classification exploits the time profiles using the original data with synchronization instead of model-implied data. Synchronization aligns inter-individual data from different time points to account for potential phase offsets and nonstationarity in the data. Such synchronization has been applied in psychology: For example, coordinated motion between two individuals exchanging information was used as a predictor and outcome of psychological processes. Synchronization also affords better classification outcomes, as discussed in the data mining community, through aligning the data to reveal the maximally shared profile underlying two compared data sequences. For inter-individual comparison of univariate time series data, existing similarity indices include Euclidean distances and squared correlations. For synchronization, we introduce dynamic time warping and window-crossed lagging. The current study compares the Euclidean distance and the squared correlation before and after synchronization using window-crossed lagging and dynamic time warping in applications to one-nearest-neighbor classification tasks. Discussion, limitations, and future directions are provided.

Haar Wavelets for Online-Game Player Classification with Dynamic Time Warping

2008 ◽  
Vol 12 (2) ◽  
pp. 150-155 ◽  
Author(s):  
Ruck Thawonmas ◽  
◽  
Keita Iizuka
Keyword(s):  
Dynamic Time Warping ◽  
Nearest Neighbor ◽  
Haar Wavelet ◽  
Online Game ◽  
Time Warping ◽  
Haar Wavelet Transform ◽  
Nearest Neighbor Classifier ◽  
Action Sequences ◽  
Dynamic Time ◽  
Neighbor Classifier

Online game players’ action sequences, while important to understand their behavior, usually contain noise and/or redundancy, making them unnecessarily long. To acquire briefer sequences representative of players’ features, we apply the Haar wavelet transform to action sequences and reconstruct them from selected wavelet coefficients. Results indicate that this approach is effective in classification when thek-nearest neighbor classifier is used to classify players based on dynamic time warping distances between reconstructed sequences.

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