1,720,954 research outputs found
A New Alternative for Feature Selection in Coronary Artery Disease Detection
No full textoronary artery disease (CAD) is a major global health issue. Early detection plays a crucial role in reducing risk and improving patient outcomes. This study proposes a novel, efficient approach to CAD diagnosis by integrating a histogram-based feature selection method with a specially designed long short-term memory (LSTM) classifier. The method is evaluated on two benchmark datasets: Z-Alizadeh Sani and Cleveland. Imbalanced class distribution, a common challenge in medical datasets, is addressed using the synthetic minority over-sampling technique (SMOTE). The proposed feature selection technique offers a fast and simple alternative to traditional optimization methods like particle swarm optimization (PSO), teaching-learning-based optimization (TLBO), and the whale optimization algorithm (WOA), which typically require extensive parameter tuning and longer processing times. The histogram-based method selects features based on their distribution similarity to a Gaussian profile, aiming to enhance classification performance and computational efficiency. The selected features are then classified using a custom-designed LSTM architecture optimized through Grid Search and validated via k-fold cross-validation (k-fold). The effectiveness of the proposed method is demonstrated by comparing it with other feature selection approaches using metrics such as accuracy, precision, sensitivity, and the F1-score (f-score). Experimental results show that the histogram-based method significantly improves classification accuracy and reduces computational time. This approach offers a promising, low-cost, and scalable solution for CAD diagnosis, especially in resource-constrained settings, and provides valuable contributions to the field of medical data analysis
Enhancing early breast cancer diagnosis with MRMR and GRU-based model
Get PDFMeme kanseri, dünya genelinde kadınlarda en sık görülen kanser türlerinden biridir ve bu hastalıkta erken teşhis hayat kurtarıcı olabilir. Bu çalışma, Wisconsin Meme Kanseri Teşhisi (WMKT) veri setine odaklanarak meme kanseri teşhisi için doğru ve güvenilir bir model geliştirme amacı gütmektedir. Çalışmada, ilk aşamada Minimum Artıklık Maksimum Alaka Düzeyi (MAMA) yöntemi kullanılarak özellik seçimi yapılmıştır. Yöntem, veri madenciliği ve özellik seçimi alanında etkili bir araç olarak kullanılmaktadır. MAMA ile özelliklerin önem sıralaması yapılarak, sadece anlamlı olanlar kullanılmıştır. Özellik seçimi, modelin karmaşıklığını azaltırken performansı artırır. Daha sonra, MAMA ile seçilen bu özellikler, meme kanseri sınıflandırması için oluşturulan Kapılı Tekrarlayan Birim (KTB) tabanlı bir sinir ağı modeli ile sınıflandırılmaktadır. KTB, tek boyutlu özellik serilerini işleme yeteneğine sahiptir ve karmaşık sınıflandırma problemlerinde etkili sonuçlar verir. Sonuçlar, bu yenilikçi yaklaşımın meme kanseri teşhisinde oldukça başarılı olduğunu göstermektedir. Yapılan değerlendirmelerde doğruluk metriği için %98.28, kesinlik metriği için %98.59, duyarlık metriği için %98.59, özgüllük metriği için %97.67 ve F-puanı metriği için %98.59 değerleri elde edilmiştir. Sonuçlar yöntemin klinik uygulamalarda uzmanlara yardımcı olabileceğini ortaya koymaktadır. Önerilen yaklaşımın toplumun her kesimi için erişilebilirlik, basit sistemlerde bile hızlı ve yüksek doğrulukla çalışabilmek gibi önemli avantajları olduğu sonuçlardan anlaşılmaktadır.Breast cancer is one of the most common cancers in women worldwide and early detection can be life-saving. This study aims to develop an accurate and reliable model for breast cancer diagnosis by focusing on the Wisconsin Breast Cancer Diagnosis (WDBC) dataset. In the first stage, feature selection was performed using the Minimum Redundancy Maximum Relevance (MRMR) method. The method is used as an effective tool in the field of data mining and feature selection. With MRMR, the importance of the features is ranked and only the significant ones are used. Feature selection improves performance while reducing the complexity of the model. Then, these features selected by MRMR are classified by a Gated Recurrent Unit (GRU) based neural network model for breast cancer classification. The GRU is capable of handling one-dimensional feature series and gives effective results in complex classification problems. The results show that this innovative approach is highly successful in breast cancer diagnosis. In the evaluations, 98.28% for accuracy metric, 98.59% for precision metric, 98.59% for sensitivity metric, 97.67% for specificity metric and 98.59% for f-score metric were obtained. The results show that the method can help specialists in clinical practice. It is understood from the results that the proposed approach has important advantages such as accessibility for all segments of the society, fast and high accuracy even in simple systems
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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