Short-term traffic volume forecasting : a k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm

To enhance the performance of the k-nearest neighbors approach in forecasting short-term traffic volume, this paper proposed and tested a two-step approach with the ability of forecasting multiple steps. In selecting k-nearest neighbors, a time constraint window is introduced, and then local minima of the distances between the state vectors are ranked to avoid overlappings among candidates. Moreover, to control extreme values’ undesirable impact, a novel algorithm with attractive analytical features is developed based on the principle component. The enhanced KNN method has been evaluated using the field data, and our comparison analysis shows that it outperformed the competing algorithms in most cases.

Setting water quality objectives for the health of freshwater fish

An important responsibility of the Environment Protection Authority, Victoria, is to set objectives for levels of environmental contaminants. To support the development of environmental objectives for water quality, a need has been identified to understand the dual impacts of concentration and duration of a contaminant on biota in freshwater streams. For suspended solids contamination, information reported by Newcombe and Jensen [ North American Journal of Fisheries Management , 16(4):693--727, 1996] study of freshwater fish and the daily suspended solids data from the United States Geological Survey stream monitoring network is utilised. The study group was requested to examine both the utility of the Newcombe and Jensen and the USA data, as well as the formulation of a procedure for use by the Environment Protection Authority Victoria that takes concentration and duration of harmful episodes into account when assessing water quality. The extent to which the impact of a toxic event on fish health could be modelled deterministically was also considered. It was found that concentration and exposure duration were the main compounding factors on the severity of effects of suspended solids on freshwater fish. A protocol for assessing the cumulative effect on fish health and a simple deterministic model, based on the biology of gill harm and recovery, was proposed. References D. W. T. Au, C. A. Pollino, R. S. S Wu, P. K. S. Shin, S. T. F. Lau, and J. Y. M. Tang. Chronic effects of suspended solids on gill structure, osmoregulation, growth, and triiodothyronine in juvenile green grouper epinephelus coioides . Marine Ecology Press Series , 266:255--264, 2004. J.C. Bezdek, S.K. Chuah, and D. Leep. Generalized k-nearest neighbor rules. Fuzzy Sets and Systems , 18:237--26, 1986. E. T. Champagne, K. L. Bett-Garber, A. M. McClung, and C. Bergman. {Sensory characteristics of diverse rice cultivars as influenced by genetic and environmental factors}. Cereal Chem. , {81}:{237--243}, {2004}. S. G. Cheung and P. K. S. Shin. Size effects of suspended particles on gill damage in green-lipped mussel perna viridis. Marine Pollution Bulletin , 51(8--12):801--810, 2005. D. H. Evans. The fish gill: site of action and model for toxic effects of environmental pollutants. Environmental Health Perspectives , 71:44--58, 1987. G. C. Grigg. The failure of oxygen transport in a fish at low levels of ambient oxygen. Comp. Biochem. Physiol. , 29:1253--1257, 1969. G. Holmes, A. Donkin, and I.H. Witten. {Weka: A machine learning workbench}. In Proceedings of the Second Australia and New Zealand Conference on Intelligent Information Systems , volume {24}, pages {357--361}, {Brisbane, Australia}, {1994}. {IEEE Computer Society}. D. D. Macdonald and C. P. Newcombe. Utility of the stress index for predicting suspended sediment effects: response to comments. North American Journal of Fisheries Management , 13:873--876, 1993. C. P. Newcombe. Suspended sediment in aquatic ecosystems: ill effects as a function of concentration and duration of exposure. Technical report, British Columbia Ministry of Environment, Lands and Parks, Habitat Protection branch, Victoria, 1994. C. P. Newcombe and J. O. T. Jensen. Channel suspended sediment and fisheries: A synthesis for quantitative assessment of risk and impact. North American Journal of Fisheries Management , 16(4):693--727, 1996. C. P. Newcombe and D. D. Macdonald. Effects of suspended sediments on aquatic ecosystems. North American Journal of Fisheries Management , 11(1):72--82, 1991. K. Schmidt-Nielsen. Scaling. Why is animal size so important? Cambridge University Press, NY, 1984. J. S. Schwartz, A. Simon, and L. Klimetz. Use of fish functional traits to associate in-stream suspended sediment transport metrics with biological impairment. Environmental Monitoring and Assessment , 179(1--4):347--369, 2011. E. Al Shaw and J. S. Richardson. Direct and indirect effects of sediment pulse duration on stream invertebrate assemb ages and rainbow trout ( Oncorhynchus mykiss ) growth and survival. Canadian Journal of Fish and Aquatic Science , 58:2213--2221, 2001. P. Tiwari and H. Hasegawa. {Demand for housing in Tokyo: A discrete choice analysis}. Regional Studies , {38}:{27--42}, {2004}. Y. Tramblay, A. Saint-Hilaire, T. B. M. J. Ouarda, F. Moatar, and B Hecht. Estimation of local extreme suspended sediment concentrations in california rivers. Science of the Total Environment , 408:4221--

Interacting motile agents : taking a mean-field approach beyond monomers and nearest-neighbor steps

We consider a discrete agent-based model on a one-dimensional lattice, where each agent occupies L sites and attempts movements over a distance of d lattice sites. Agents obey a strict simple exclusion rule. A discrete-time master equation is derived using a mean-field approximation and careful probability arguments. In the continuum limit, nonlinear diffusion equations that describe the average agent occupancy are obtained. Averaged discrete simulation data are generated and shown to compare very well with the solution to the derived nonlinear diffusion equations. This framework allows us to approach a lattice-free result using all the advantages of lattice methods. Since different cell types have different shapes and speeds of movement, this work offers insight into population-level behavior of collective cellular motion.

Selecting, optimizing and fusing ‘salient’ Gabor features for facial expression recognition

This paper describes a novel framework for facial expression recognition from still images by selecting, optimizing and fusing ‘salient’ Gabor feature layers to recognize six universal facial expressions using the K nearest neighbor classifier. The recognition comparisons with all layer approach using JAFFE and Cohn-Kanade (CK) databases confirm that using ‘salient’ Gabor feature layers with optimized sizes can achieve better recognition performance and dramatically reduce computational time. Moreover, comparisons with the state of the art performances demonstrate the effectiveness of our approach.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

A high performance liquid chromatography and electrospray ionization mass spectrometry method for the analysis of the natural medicine, Forsythia suspensa

Samples of Forsythia suspensa from raw (Laoqiao) and ripe (Qingqiao) fruit were analyzed with the use of HPLC-DAD and the EIS-MS techniques. Seventeen peaks were detected, and of these, twelve were identified. Most were related to the glucopyranoside molecular fragment. Samples collected from three geographical areas (Shanxi, Henan and Shandong Provinces), were discriminated with the use of hierarchical clustering analysis (HCA), discriminant analysis (DA), and principal component analysis (PCA) models, but only PCA was able to provide further information about the relationships between objects and loadings; eight peaks were related to the provinces of sample origin. The supervised classification models-K-nearest neighbor (KNN), least squares support vector machines (LS-SVM), and counter propagation artificial neural network (CP-ANN) methods, indicated successful classification but KNN produced 100% classification rate. Thus, the fruit were discriminated on the basis of their places of origin.

Combining HPLC-DAD and ICP-MS data for improved analysis of complex samples: Classification of the root samples from Cortex moutan

A combined data matrix consisting of high performance liquid chromatography–diode array detector (HPLC–DAD) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements of samples from the plant roots of the Cortex moutan (CM), produced much better classification and prediction results in comparison with those obtained from either of the individual data sets. The HPLC peaks (organic components) of the CM samples, and the ICP-MS measurements (trace metal elements) were investigated with the use of principal component analysis (PCA) and the linear discriminant analysis (LDA) methods of data analysis; essentially, qualitative results suggested that discrimination of the CM samples from three different provinces was possible with the combined matrix producing best results. Another three methods, K-nearest neighbor (KNN), back-propagation artificial neural network (BP-ANN) and least squares support vector machines (LS-SVM) were applied for the classification and prediction of the samples. Again, the combined data matrix analyzed by the KNN method produced best results (100% correct; prediction set data). Additionally, multiple linear regression (MLR) was utilized to explore any relationship between the organic constituents and the metal elements of the CM samples; the extracted linear regression equations showed that the essential metals as well as some metallic pollutants were related to the organic compounds on the basis of their concentrations

Image processing and classification procedure for the analysis of Australian frog vocalisations

Frog species have been declining worldwide at unprecedented rates in the past decades. There are many reasons for this decline including pollution, habitat loss, and invasive species [1]. To preserve, protect, and restore frog biodiversity, it is important to monitor and assess frog species. In this paper, a novel method using image processing techniques for analyzing Australian frog vocalisations is proposed. An FFT is applied to audio data to produce a spectrogram. Then, acoustic events are detected and isolated into corresponding segments through image processing techniques applied to the spectrogram. For each segment, spectral peak tracks are extracted with selected seeds and a region growing technique is utilised to obtain the contour of each frog vocalisation. Based on spectral peak tracks and the contour of each frog vocalisation, six feature sets are extracted. Principal component analysis reduces each feature set down to six principal components which are tested for classification performance with a k-nearest neighbor classifier. This experiment tests the proposed method of classification on fourteen frog species which are geographically well distributed throughout Queensland, Australia. The experimental results show that the best average classification accuracy for the fourteen frog species can be up to 87%.

Acoustic classification of Australian anurans using syllable features

Acoustic classification of anurans (frogs) has received increasing attention for its promising application in biological and environment studies. In this study, a novel feature extraction method for frog call classification is presented based on the analysis of spectrograms. The frog calls are first automatically segmented into syllables. Then, spectral peak tracks are extracted to separate desired signal (frog calls) from background noise. The spectral peak tracks are used to extract various syllable features, including: syllable duration, dominant frequency, oscillation rate, frequency modulation, and energy modulation. Finally, a k-nearest neighbor classifier is used for classifying frog calls based on the results of principal component analysis. The experiment results show that syllable features can achieve an average classification accuracy of 90.5% which outperforms Mel-frequency cepstral coefficients features (79.0%).

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

Static analysis of executables for collaborative malware detection on Android

Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone malwares are currently limited to signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new and unknown malwares creating a window of opportunity for attackers. As smartphones become host for sensitive data and applications, extended malware detection mechanisms are necessary complying with the corresponding resource constraints. The contribution of this paper is twofold. First, we perform static analysis on the executables to extract their function calls in Android environment using the command readelf. Function call lists are compared with malware executables for classifying them with PART, Prism and Nearest Neighbor Algorithms. Second, we present a collaborative malware detection approach to extend these results. Corresponding simulation results are presented.

Fast exact nearest neighbour matching in high dimensions using d-D Sort

Data structures such as k-D trees and hierarchical k-means trees perform very well in approximate k nearest neighbour matching, but are only marginally more effective than linear search when performing exact matching in high-dimensional image descriptor data. This paper presents several improvements to linear search that allows it to outperform existing methods and recommends two approaches to exact matching. The first method reduces the number of operations by evaluating the distance measure in order of significance of the query dimensions and terminating when the partial distance exceeds the search threshold. This method does not require preprocessing and significantly outperforms existing methods. The second method improves query speed further by presorting the data using a data structure called d-D sort. The order information is used as a priority queue to reduce the time taken to find the exact match and to restrict the range of data searched. Construction of the d-D sort structure is very simple to implement, does not require any parameter tuning, and requires significantly less time than the best-performing tree structure, and data can be added to the structure relatively efficiently.

Gold nanoresistors with near-constant resistivity in the cryogenic-to-room temperature range

Using a multiple plasma deposition-annealing (MDA) technique, we have fabricated an Au nanoisland-based thin film nanoresistor with a very low temperature coefficient of electrical resistivity in a cryogenic-to-room temperature range of 10 to 300 K. The nanoislanded gold film was deposited on a SiO2/Si wafer (500 nm SiO2 thickness) between two 300 nm thick Au electrodes which were separated by 100 m. A sophisticated selection of the thickness of the nanoislanded gold film, the annealing temperature, as well as the number of deposition/annealing cycles resulted in the fabrication of a nanoresistor with a temperature coefficient of electrical resistivity of 2.1 × 10-3 K-1 and the resistivity deviation not exceeding 2% in a cryogenic-to-room temperature range. We have found that the constant resistivity regime of the nanoisland-based thin film nanoresistor corresponds to a minimized nanoisland activation energy (approximately 0.3 meV). This energy can be minimized by reducing the nearest neighbor distance and increasing the size of the Au nanoislands in the optimized nanoresistor structure. It is shown that the constant resistivity nanoresistor operates in the regime where the thermally activated electron tunneling is compensated by the negative temperature dependence of the metallic-type conductivity of nanoislands. Our results are relevant to the development of commercially viable methods of nanoresistor production for various nanoelectronics-based devices. The proposed MDA technique also provides the opportunity to fabricate large arrays of metallic nanoparticles with controllable size, shapes and inter-nanoparticle gaps.

A quantification of the interaction and spatial ordering in nano-arrays

This paper reports on the use of a local order measure to quantify the spatial ordering of a quantum dot array (QDA). By means of electron ground state energy analysis in a quantum dot pair, it is demonstrated that the length scale required for such a measure to characterize the opto-electronic properties of a QDA is of the order of a few QD radii. Therefore, as local order is the primary factor that affects the opto-electronic properties of an array of quantum dots of homogeneous size, this order was quantified through using the standard deviation of the nearest neighbor distances of the quantum dot ensemble. The local order measure is successfully applied to quantify spatial order in a range of experimentally synthesized and numerically generated arrays of nanoparticles. This measure is not limited to QDAs and has wide ranging applications in characterizing order in dense arrays of nanostructures.