1,720,956 research outputs found
On the Development of Improved Mammogram Detection System using Machine Learning Approaches
No full textIn recent years, breast cancer has become one of the most prevalent causes of death among women. Once the malignant cells are developed in the breast, it spreads to different body organs very quickly. Detection at the early stages and diagnosis is the only way to prevent mortality. Mammography, a noninvasive, non-radioactive imaging technique, has been widely used in diagnosing breast tissue abnormalities. Manual diagnosis based on visual inspection of mammograms is time-consuming, inconvenient, and necessitates skilled supervision. Thus, automated detection using modern imaging, machine learning, and deep learning approaches has become vital for quick, reliable, and correct conclusions. In the last decade, the development of automated computer-aided diagnosis/detection (CAD) models has progressed remarkably. However, there is still an opportunity for improvement in terms of automation, usability, and accuracy. This dissertation is aimed at designing automated CAD frameworks that will help radiologists validate their clinical diagnoses. This research primarily proposes various feature extraction techniques and classifiers for detecting breast tumors in mammography images. The first contribution consists of three frameworks with various feature extraction techniques like discrete wavelet transform (DWT), lifting wavelet transform (LWT), and fast curvelet transforms (FCT). For all frameworks, a combined feature reduction technique such as principal component analysis (PCA) and linear discriminant analysis (LDA) has been employed for feature vector computation. Finally, a simple and flexible learning scheme called the extreme learning algorithm (ELM), back-propagation neural network, k-nearest neighbors, and support vector machine have been used separately to obtain the classification accuracy. This contribution describes an empirical analysis of ELM with other classifiers. In the second contribution, a set of innovative hybrid classification systems proposes to reduce the bottleneck caused by extreme learning machines and contemporary meta-heuristic optimization techniques to classify mammogram images. The optimization techniques have been utilized to obtain the hidden node parameters of the ELM. Here, the same feature reduction technique is used as in the previous contribution. Different hybrid classification systems have examined the three handcrafted feature extraction techniques: DWT, LWT, and FCT. The third contribution is about designing a framework based on non-handcrafted features. Here, deep learning algorithms are used to solve the challenge of manually selecting appropriate features for mammogram classification. The different deep CNN models such as VGG-16, ResNet-50, and Inception-V3 have been utilized for feature extraction, and the same feature reduction method is applied as previous frameworks. Finally, various hybrid classifiers are used for the classification task. The final contribution involves designing a customized CNN model for multiclass mammogram images. This model is designed to extract high-level features from mammogram images automatically. End-to-end learning is facilitated by the proposed deep architectures, which aid in generating promising results. To evaluate the efficiency of each suggested CAD framework, a significant number of experiments have been conducted individually utilizing binary and multiclass mammogram classification. Various performance measurements have been used to compare the suggested CAD frameworks with existing standard techniques. Experimental results demonstrate that the proposed methodologies are superior to existing binary and multiclass breast cancer detection models. The customized CNN model removes manual handcrafted feature extraction issues and avoids feature reduction tasks. As a result, the proposed CAD frameworks are faster and can be used as an enhanced tool by clinicians to validate their diagnoses
Wavelet Based Robust Watermarking Scheme
No full textDigital Watermarking is an established security modality with many application. Its performance is influenced by different intentional and unintentional attacks. This thesis focuses on removing these challenges by developing efficient embedding and extraction algorithm and enhance the security system against various attacks.
Digital watermarking is the process of embedding a watermark in a multimedia content like images, video, audio or any digital content for information hiding. With the help of digital watermarking technique, we can protect the multimedia content against different illegal actions. The watermark provides the authentication of the owner data. Here, the data hiding scheme hides the authorized logo with the original image. These techniques have been proposed for different applications like copyright protection, content authentication, ownership protection and tamper detection.
The motivation of this thesis is to increase the watermark strength and provide a better trade-off between imperceptibility and robustness of the watermarked image. For this purpose, we have discussed a robust watermarking wavelet technique called Lifting Wavelet Transform (LWT) in the frequency domain. Here low-pass sub-band is used for data hiding process. The coefficients of low-pass sub-band are randomly shuffled by using the secret key. By the help of block selection procedure, the low-pass sub-band is divided into small blocks. After that two maximum coefficients of each block quantizing to embedded binary watermark with the host image. During the extraction process, the reverse lifting technique is followed. As per the concerned security randomization of coefficients, blocks, secret key and lifting technique enhanced the security level of the system
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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