KDA based WKNN-SVM Method for Activity Recognition System from Smartphone Data

This article describes a new scheme for a physical activity recognition process based on carried smartphone embedded sensors, such as accelerometer and gyroscope. For this purpose, the WKNN-SVM algorithm has been proposed to predict physical activities such as Walking, Standing or Sitting. It combines Weighted K-Nearest Neighbours (WKNN) and Support Vector Machines (SVM). The signals generated from the sensors are processed and then reduced using the Kernel Discriminant Analysis (KDA) by selecting the best discriminating components of the data. We performed different tests on four public datasets where the participants performed different activities carrying a smartphone. We demonstrated through several experiments that KDA/WKNN-SVM algorithm can improve the overall recognition performances, and has a higher recognition rate than the baseline methods using the machine learning and deep learning algorithms.

Remote hyperspectral sensing for the early detection of Melanaphis sacchari Zehntner, 1897 (Hemiptera: Aphididae) infestations in sorghum leaves

This paper aims to study the reflectance signature information of infested and non-infested sorghum leaves (Sorghum vulgare L.) by sugarcane aphid (Melanaphis sacchari) to discriminate infested sorghum. The study treatments were 0 (0 aphids/leaf), 1 (1-20 aphids/leaf), 2 (21-50 aphids/leaf), 3 (> = 51 aphids/leaf), 4 (> = 51 aphids/leaf + visible damage), 5 (abiotic stress) and 6 (> = 51 aphids/leaf + abiotic stress). An Ocean OpticsTM HR4000 spectrometer was used. The multifactor ANOVA and Kruskal-Wallis tests at 95% confidence indicated that the reflectance at 402.95, 528.43, 658.36, 788.13, and 965.14 nm wavelengths have significant differences between treatments and with the control. Also Kernel Discriminant analysis was carried out and the combination of the wavelengths centered at 788.17 and 965.14 nm allows 70 % of correct classification of treatments. The results indicate that it is possible to detect M. sacchari infested sorghum by using the spectral information of some specific wavelengths. This study may enable the research of an aerial sensor to make recommendation maps of application pesticides.

A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis

Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). AD considered as an irreversible neurodegenerative disorder with progressive memory impairment moreover cognitive functions, while MCI would be considered as a transitional phase amongst age-related cognitive weakening. Numerous machine learning approaches have been examined, aiming at AD computer-aided diagnosis through employing MR image analysis. Conversely, MR brain image changes could be caused by different effects such as aging and dementia. It is still a challenging difficulty to extract the relevant imaging features and classify the subjects of different groups. This paper would propose an automatic classification technique based on feature decomposition and kernel discriminant analysis (KDA) for classifications of progressive MCI (pMCI) vs. normal control (NC), AD vs. NC, and pMCI vs. stable MCI (sMCI). Feature decomposition would be based on dictionary learning, which is used for separation of class-specific components from the non-class-specific components in the features, while KDA would be applied for mapping original nonlinearly separable feature space to the separable features that are linear. The proposed technique would be evaluated by employing T1-weighted MR brain images from 830 subjects comprising 198 AD patients, 167 pMCI, 236 sMCI, and 229 NC from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset. Experimental results demonstrate that classification accuracy (ACC) of 90.41%, 84.29%, and 65.94% can be achieved for classification of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, indicating the promising performance of the proposed method.