Data Mining - Concepts and Techniques.

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Texture features for image classification and retrieval.

A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA-ANN model for the prediction of time series data. Many of the hybrid ARIMA-ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a moving-average filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA-ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA-ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy.

A moving-average filter based hybrid ARIMA-ANN model for forecasting time series data

Image analysis and machine learning for detecting malaria

Most of the application domain suffers from not having sufficient labeled data whereas unlabeled data is available cheaply. To get labeled instances, it is very difficult because experienced domain experts are required to label the unlabeled data patterns. Semi-supervised learning addresses this problem and act as a half way between supervised and unsupervised learning. This paper addresses few techniques of Semi-supervised learning (SSL) such as self-training, co-training, multi-view learning, TSVMs methods. Traditionally SSL is classified in to Semi-supervised Classification and Semi-supervised Clustering which achieves better accuracy than traditional supervised and unsupervised learning techniques. The paper also addresses the issue of scalability and applications of Semi-supervised learning.

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Semi-supervised learning: a brief review

k-means clustering method is an iterative partition-based method which for finite data-sets converges to a solution in a finite time. The running time of this method grows linearly with respect to the size of the data-set. Many variants have been proposed to speed-up the conventional k-means clustering method. In this paper, we propose a prototype-based hybrid approach to speed-up the k-means clustering method. The proposed method, first partitions the data-set into small clusters (grouplets), which are of varying sizes. Each grouplet is represented by a prototype. Later, the set of prototypes is partitioned into k clusters using the modified k-means method. The modified k-means clustering method is similar to the conventional k-means method but it avoids empty clusters (the clusters to which no pattern is assigned) in the iterative process. In each cluster of prototypes, each prototype is replaced by its corresponding set of patterns (which formed the grouplet) to derive a partition of the data-set. Since this partition of the data-set can deviate from the partition obtained using the conventional k-means method over the entire data-set, a correcting step is proposed. Both theoretically and experimentally, the conventional k-means method and the proposed hybrid method (augmented with the correcting step) are shown to yield the same result (provided, the initial k seed points are same). But, the proposed method is much faster than the conventional one. Experimentally, the proposed method is compared with the conventional method and the other recent methods that are proposed to speed-up the k-means method.

A hybrid approach to speed-up the k-means clustering method

Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging

With the advent of smart city and its implications, different components of the smart city such as traffic, pollution monitoring, infotainment and others need to be considered as well for enabling the complete smartness. One of the major components of the smart city is regulation of traffic and its standards for smooth vehicular movement and it depends on the road conditions and environment. Road conditions provide valuable information for developing assistive systems for the vehicles and to be provided and trained with different road conditions for better accuracy and finally enabling the smartness around the system. With the increasing vehicular population around the globe and in developing countries like India, assistive systems based on Internet of Things (IoT) play a major role in developing smart systems for the vehicles. Once the population of the vehicles increases, assistive systems need a smart mechanism for parking as well. The proposed Internet of things (IoT) based system provides the user with different conditions of the road and a smart parking solution.

B. Eswara Reddy

Papers

A moving-average filter based hybrid ARIMA-ANN model for forecasting time series data

Semi-supervised learning: a brief review

A hybrid approach to speed-up the k-means clustering method

Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging

Smart Vehicular System based on the Internet of Things