LWIR and MWIR images dimension reduction and anomaly detection with locally linear embedding

Aydoğdu, Ayşe Sıddıka; Hatipoglu, Poyraz Umut; Ozparlak, Levent; Yüksel, Seniha Esen

doi:10.1109/siu.2015.7129954

Cited by 3 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a dimensionality reduction algorithm, PCA is a data preprocessing method that allows shifting of m-dimensional X data to n-dimensional Y data with minimum loss. 20,21 The main goal in this method is to determine the projection vectors which are in the direction of the greatest variance. The obtained projection vectors are represented by unique values in a different space.…”

Section: Methodsmentioning

confidence: 99%

“…linear discriminant analysis, locally linear embedding, locality preserving projections, locality sensitive discriminant analysis, neighborhood preserving embedding). 20,22…”

Section: Methodsmentioning

confidence: 99%

“…The obtained projection vectors are represented by unique values in a different space. 20 A vector x and a vector y are specified on the same plane. The projection of the vector x on the vector y (defined as f) is calculated according to equation (1) f…”

Section: Pcamentioning

confidence: 99%

See 2 more Smart Citations

Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm

Ballı

Sağbaş

Peker

2018

Measurement and Control

View full text Add to dashboard Cite

Background: Detecting of human movements is an important task in various areas such as healthcare, fitness and eldercare. It is now possible to achieve this aim using mobile applications. These applications provide users, doctors and related persons a better understanding about daily physical activities. It can also lead to various useful habits by following the activities of the users in their daily life. In addition, dangerous actions such as the fall of elderly people or young children are identified and necessary precautions are taken as soon as possible. Classification of human motions with motion sensor data is among the current topics of study. Smart watches have these sensors built-in. Thus, it is possible to follow the activities of a user carrying only a smart watch. Methods: The purpose of this work is to detect human movements using smart watch sensor data and machine learning methods. The data are obtained from the accelerometer, gyroscope, step counter and heart rate sensors of the smart watch. The obtained data have been divided into 2 s windows and a data set containing 500 patterns for each class has been created for each class. Results and Discussion: After the features were determined, the data set to which the principal component analysis has been applied was classified by random forest, support vector machine, C4.5 and k-nearest neighbor methods, and their performances were compared. The most successful result was obtained from the random forest method.

show abstract

Section: Methodsmentioning

confidence: 99%

“…linear discriminant analysis, locally linear embedding, locality preserving projections, locality sensitive discriminant analysis, neighborhood preserving embedding). 20,22…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm

Ballı

Sağbaş

Peker

2018

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…One of the algorithms used to reduce the dimension of the feature vector and extract its principal features is PCA. The PCA is the algorithm that transforms a d-dimensional X image to an n-dimensional Y image with minimal loss [19].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Multi-channel capsule network ensemble for plant disease detection

Peker

2021

SN Appl. Sci.

View full text Add to dashboard Cite

This study presents a new deep learning approach based on capsule networks and ensemble learning for the detection of plant diseases. The developed method is called as multi-channel capsule network ensemble. The main innovation behind the proposed method is the use of multi-channel capsule networks, individually trained on images applied different preprocessing techniques and then combined together. In this way, the final ensemble can better detect plant diseases by making use of different attributes of the data. Our experiments carried out using a well-known data set and various state-of-the-art classification methods demonstrate that our classification approach can provide competitive advantages in terms of classification accuracy. Article Highlights An ensemble of capsule networks has been developed for the automated detection of plant diseases with high accuracies. Better accuracy has been achieved with ensemble learning compared to a single model With the proposed method, better results have been obtained compared to state-of-the-art classification methods in the literature

show abstract

Classification of histopathological gastric images using a new method

Korkmaz

2021

Neural Comput & Applic

View full text Add to dashboard Cite

LWIR and MWIR images dimension reduction and anomaly detection with locally linear embedding

Cited by 3 publications

References 7 publications

Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm

Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm

Multi-channel capsule network ensemble for plant disease detection

Classification of histopathological gastric images using a new method

Contact Info

Product

Resources

About