Comparison of Candidate Itemset Generation and Non Candidate Itemset Generation Algorithms in Mining Frequent Patterns

Association rule mining is one of the important techniques of data mining used for exploring fruitful patterns from huge collection of data. Generally, the finding of frequent itemsets is the most significant step in association rules mining, and most of the research will be centered on it. Numerous algorithms have been discovered to find effective frequent itemsets. This paper compares the frequent pattern mining algorithms that use candidate itemset generation and the algorithms without candidate itemset generation. In order to have on field simulation for comparison, a case study algorithm from both types was chosen such as ECLAT and FP-growth algorithms. Equivalence class clustering and bottom up lattice traversal (ECLAT) algorithm accommodates ?Depth First Search? approach and requires the generation of candidate itemset. The FP-growth algorithm follows the ?Divide and Conquer? method and does not require candidate itemset generation. In this paper, the benchmark databases considered for comparison are Breast Cancer, Customer Data, and German Data etc. The performances of both the algorithms have been experimentally evaluated in terms of runtime and memory usage. From the result it is analyzed that the FP-tree algorithm is more advantageous as it does away with the need of generation of candidate patterns

Clustering of Images from Social Media Websites using Combination of Histogram and SIFT Features

In recent years, the rapid growth of high dimensional datasets has created an emergent need to extract the knowledge. With the tremendous growth of social network, there has been a development in the amount of new data that is being created every minute on the networking sites. This work presents an efficient analysis of SIFT and color histogram features with spectral clustering algorithm. In this work the images from social media websites are downloaded. The downloaded images are stored in the database. The proposed feature extraction technique is based on combination of both SIFT descriptor and color Histogram to increase the efficiency. The extracted features are then clustered using spectral clustering algorithm. The spectral clustering method is a clustering area which achieves the clustering goal in high dimension by allowing clusters to be formed with their own correlated dimension

8-Meth­oxy-3-(4-methyl­benzyl­idene)-6-(prop-1-en­yl)chroman-4-one

In the title compound, C21H20O3, the tolyl ring makes a dihedral angle of 31.11 (6)° with the benzene ring of the chromanone unit. The pyrone ring adopts a half-chair conformation. The mol­ecular structure is stabilized by a weak intra­molecular C—H⋯O inter­action and the crystal packing is stabilized by weak inter­molecular C—H⋯O inter­actions and a C—H⋯π inter­action

An efficient image retrieval scheme for colour enhancement of embedded and distributed surveillance images

From the past few years, the size of the data grows exponentially with respect to volume, velocity, and dimensionality due to wide spread use of embedded and distributed surveillance cameras for security reasons. In this paper, we have proposed an integrated approach for biometric-based image retrieval and processing which addresses the two issues. The first issue is related to the poor visibility of the images produced by the embedded and distributed surveillance cameras, and the second issue is concerned with the effective image retrieval based on the user query. This paper addresses the first issue by proposing an integrated image enhancement approach based on contrast enhancement and colour balancing methods. The contrast enhancement method is used to improve the contrast, while the colour balancing method helps to achieve a balanced colour. Importantly, in the colour balancing method, a new process for colour cast adjustment is introduced which relies on statistical calculation. It adjusts the colour cast and maintains the luminance of the image. The integrated image enhancement approach is applied to the enhancement of low quality images produced by surveillance cameras. The paper addresses the second issue relating to image retrieval by proposing a content-based image retrieval approach. The approach is based on the three features extraction methods namely colour, texture and shape. Colour histogram is used to extract the colour features of an image. Gabor filter is used to extract the texture features and the moment invariant is used to extract the shape features of an image. The use of these three algorithms ensures that the proposed image retrieval approach produces results which are highly relevant to the content of an image query, by taking into account the three distinct features of the image and the similarity metrics based on Euclidean measure. In order to retrieve the most relevant images, the proposed approach also employs a set of fuzzy heuristics to improve the quality of the results further. The result

Perception-based fuzzy partitions for visual texture modelling

Visual textures in images are usually described by humans using linguistic terms related to their perceptual properties, like “very coarse”, “low directional”, or “high contrasted”. Computational models with the ability of providing a perceptual texture characterization on the basis of these terms can be very useful in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we address the problem of simulating the human perception of texture, obtaining linguistic labels to describe it in natural language. For this modeling, fuzzy partitions defined on the domain of some of the most representative measures of each property are employed. In order to define the fuzzy partitions, the number of linguistic labels and the parameters of the membership functions are calculated taking into account the relationship between the computational values given by the measures and the human perception of the corresponding property. The performance of each fuzzy partition is analyzed and tested using the human assessments, and a ranking of measures is obtained according to their ability to represent the perception of the property, allowing to identify the most suitable measure

Intelligent bite marking analysis and classification using deep convolutional neural network based Xception model

Bite mark analysis and classification play a vital role in forensics. The recent advances in computer vision and deep learning models paves a way for the design of automated bite mark detection and classification process. This article focuses on the design of intelligent bite marking analysis and classification using deep convolutional neural network based Xception model. The major goal of the proposed model is to determine the appropriate class labels for the bite marked images. The proposed model initially intends to pre-process the bite marked images in different ways such as hair removal, median filtering based noise removal, and adaptive histogram based contrast enhancement. Besides, Chan Vese Segmentation approach is applied for segmenting the bite marked images. The data augmentation process is performed for increasing the count of images. In addition, Xception model is employed for the extraction of features. Finally, two machine learning (ML) classifications such as support vector machine (SVM) and logistic regression (LR) models are employed for image classification. For demonstrating the enhanced performance of the presented models, a set of simulations were carried out on their own dataset and the results ensured the betterment of the proposed model over the other existing models

Ionospheric disturbances triggered by the 25 April, 2015 M7.8 Gorkha earthquake, Nepal: Constraints from GPS TEC measurements

The ionosphere response to the April 11, 2015 (Mw 7.8) Gorkha earthquake, occurring in the Himalayan arc, is analysed using GPS Total Electron Content (TEC) measurements, from GPS sites in Nepal and India, situated both close to and far from the epicentre. In the near field, the Coseismic Ionospheric disturbance (CID) following the earthquake rupture propagation arrive east of the epicentre, within 5–7 min with a propagation velocity of 980 m/s, equal to the speed of the shock acoustic waves at the ionospheric heights, and on to the west with a reduced speed of 650 m/s, within 8–11 min, after the earthquake occurrence. The phenomenon of CID splitting into two modes, east and west of the epicentre is observed. In the far-field region, up to epicentral distances of 2200 km, Rayleigh wave induced ionospheric disturbance are recorded with a propagation speed of 2.6 km/s. Higher TEC amplitude of 0.2–1.5 TECU is observed east of the epicentre compared to the west with 0.1–0.3 TECU. The characteristics of this dip-slip earthquake are well projected in the TEC waveforms. The ambient magnetic field in the mid-latitudes prohibited the propagation of ionospheric disturbance in the northward direction. In the present study the observed primary CID is essentially in congruence with the rupture propagation of the earthquake in E-SE direction