Data Mining - Concepts and Techniques.

http://www.airccse.org/journal/ijaia/papers/0711ijaia04.pdf

Improved Cuckoo Search Algorithm for Feed forward Neural Network Training

Stock-market prediction using machine-learning technique aims at developing effective and efficient models that can provide a better and higher rate of prediction accuracy. Numerous ensemble regressors and classifiers have been applied in stock market predictions, using different combination techniques. However, three precarious issues come in mind when constructing ensemble classifiers and regressors. The first concerns with the choice of base regressor or classifier technique adopted. The second concerns the combination techniques used to assemble multiple regressors or classifiers and the third concerns with the quantum of regressors or classifiers to be ensembled. Subsequently, the number of relevant studies scrutinising these previously mentioned concerns are limited. In this study, we performed an extensive comparative analysis of ensemble techniques such as boosting, bagging, blending and super learners (stacking). Using Decision Trees (DT), Support Vector Machine (SVM) and Neural Network (NN), we constructed twenty-five (25) different ensembled regressors and classifiers. We compared their execution times, accuracy, and error metrics over stock-data from Ghana Stock Exchange (GSE), Johannesburg Stock Exchange (JSE), Bombay Stock Exchange (BSE-SENSEX) and New York Stock Exchange (NYSE), from January 2012 to December 2018. The study outcome shows that stacking and blending ensemble techniques offer higher prediction accuracies (90–100%) and (85.7–100%) respectively, compared with that of bagging (53–97.78%) and boosting (52.7–96.32%). Furthermore, the root means square error (RMSE) recorded by stacking (0.0001–0.001) and blending (0.002–0.01) shows a better fit of ensemble classifiers and regressors based on these two techniques in market analyses compared with bagging (0.01–0.11) and boosting (0.01–0.443). Finally, the results undoubtedly suggest that an innovative study in the domain of stock market direction prediction ought to include ensemble techniques in their sets of algorithms.

/pdf/a-comprehensive-evaluation-of-ensemble-learning-for-stock-14pcf72a0y.pdf

A comprehensive evaluation of ensemble learning for stock-market prediction

This paper presents a new efficient embedding algorithm in the wavelet domain of digital images based on the diamond encoding (DE) scheme. Current discrete wavelet transform (DWT) steganography adds an unacceptable distortion to the images and is considered as an ineffective in terms of security. Applying the DE scheme to the current DWT steganographic methods solves the problems of these methods, and reduces the distortion added to the images, and thus improves the embedding efficiency. The proposed algorithm first converts the secret image into a sequence of base-5 digits. After that, the cover image is transformed into the DWT domain and segmented into 2 × 1 coefficient pairs. The DE scheme is used then to change at most one coefficient of each coefficient pair to embed the base-5 digits. Experimental results depict that the proposed algorithm is more efficient in embedding compared to other methods in terms of embedding payload and image quality. Moreover, the proposed algorithm is attacked by well-known steganalysis software. Results are showing that the proposed algorithm is secure against the powerful universal steganalyzer “ensemble classifier” and the histogram attack. The results also reveal that the proposed algorithm is robust against different image processing attacks such as compression, added noise, and cropping attacks.

Secure and imperceptible digital image steganographic algorithm based on diamond encoding in DWT domain

Display Omitted A CAD system for disease diagnosis using Differential Evolution with Global Information (DEGI) based Back Propagation (BP) is proposed.The CAD system is trained and tested using six benchmark dataset.DEGI overcomes the premature convergence due to stagnation.The CAD system can be used by junior clinicians for medical decision support. A Computer-Aided Diagnostic (CAD) system that uses Artificial Neural Network (ANN) trained by drawing in the relative advantages of Differential Evolution (DE), Particle Swarm Optimization (PSO) and gradient descent based backpropagation (BP) for classifying clinical datasets is proposed. The DE algorithm with a modified best mutation operation is used to enhance the search exploration of PSO. The ANN is trained using PSO and the global best value obtained is used as a seed by the BP. Local search is performed using BP, in which the weights of the Neural Network (NN) are adjusted to obtain an optimal set of NN weights. Three benchmark clinical datasets namely, Pima Indian Diabetes, Wisconsin Breast Cancer and Cleveland Heart Disease, obtained from the University of California Irvine (UCI) machine learning repository have been used. The performance of the trained neural network classifier proposed in this work is compared with the existing gradient descent backpropagation, differential evolution with backpropagation and particle swarm optimization with gradient descent backpropagation algorithms. The experimental results show that DEGI-BP provides 85.71% accuracy for diabetes, 98.52% for breast cancer and 86.66% for heart disease datasets. This CAD system can be used by junior clinicians as an aid for medical decision support.

Neural network classifier optimization using Differential Evolution with Global Information and Back Propagation algorithm for clinical datasets

Image steganography is the art of hiding information into a cover image. This paper presents a novel technique for Image steganography based on DWT, where DWT is used to transform original image (cover image) from spatial domain to frequency domain. Firstly two dimensional Discrete Wavelet Transform (2-D DWT) is performed on a gray level cover image of size M × N and Huffman encoding is performed on the secret messages/image before embedding. Then each bit of Huffman code of secret message/image is embedded in the high frequency coefficients resulted from Discrete Wavelet Transform. Image quality is to be improved by preserving the wavelet coefficients in the low frequency subband. The experimental results show that the algorithm has a high capacity and a good invisibility. Moreover PSNR of cover image with stego-image shows the better results in comparison with other existing steganography approaches. Furthermore, satisfactory security is maintained since the secret message/image cannot be extracted without knowing decoding rules and Huffman table.

A novel technique for image steganography based on Block-DCT and Huffman Encoding

A novel Neuro-fuzzy classification technique for data mining

In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the scenario. The major advantage of using GA in the discovery of frequent itemsets is that they perform global search and its time complexity is less compared to other algorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is to find all the frequent itemsets from given data sets using genetic algorithm.

/pdf/mining-frequent-itemsets-using-genetic-algorithm-6e2wvujjxn.pdf

Mining Frequent Itemsets Using Genetic Algorithm

In this paper, a highly compact ultra-wideband (UWB) monopole antenna with high gain is proposed. The UWB monopole antenna consists of U-shaped radiating patch. The ground plane comprises of edge-truncated partial rectangular metallic plane with a rectangular slot at the centre of the top edge. The proposed antenna having a very compact dimension of 16 mm × 22 mm × 1.6 mm is compatible and highly suitable for compact wireless systems. Further for enhancement of directivity of the UWB antenna, a patch type frequency selective surface (FSS) is placed below the antenna. The combined FSS-antenna structure operates with high gain (peak gain of 9.4 dBi) across the entire UWB range (3.1-18.6 GHz). The proposed design also offers an appreciably high front-to-back lobe ratio of ~10 dB across the entire UWB range. The simulated results are verified experimentally and the results are discussed in detail. The overall dimension of the proposed FSS integrated composite antenna is 52 mm × 62.5 mm × 23.2 mm, which is highly relevant for high-gain applications.

Debasree Sarkar

Papers

A novel technique for image steganography based on Block-DCT and Huffman Encoding

A novel Neuro-fuzzy classification technique for data mining

Mining Frequent Itemsets Using Genetic Algorithm

Mining Frequent Itemsets Using Genetic Algorithm

Compact ultra-wideband antenna: improvement of gain and FBR across the entire bandwidth using FSS