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Impact of metaheuristic iteration on artificial neural network structure in medical data
Get PDFShaker, Khalid/0000-0001-9108-5553; Salman, Ihsan/0000-0002-0974-4271Medical data classification is an important factor in improving diagnosis and treatment and can assist physicians in making decisions about serious diseases by collecting symptoms and medical analyses. In this work, hybrid classification optimization methods such as Genetic Algorithm (GA), Particle Swam Optimization (PSO), and Fireworks Algorithm (FWA), are proposed for enhancing the classification accuracy of the Artificial Neural Network (ANN). The enhancement process is tested through two experiments. First, the proposed algorithms are applied on five benchmark medical data sets from the repository of the University of California in Irvine (UCI). The model with the best results is then used in the second experiment, which focuses on tuning the parameters of the selected algorithm by choosing a different number of iterations in ANNs with different numbers of hidden layers. Enhanced ANN with the three optimization algorithms are tested on biological gene sequence big dataset obtained from The Cancer Genome Atlas (TCGA) repository. GA and FWA are statistically significant but PSO was statistically not, and GA overcame PSO and FWA in performance. The methodology is successful and registers improvements in every step, as significant results are obtained
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
Tıbbi veri sınıflandırması için yapay sinir ağını geliştirmek için meta-heuristik algoritmalar
No full textDoktoraBilgisayar donanım teknolojilerinin muazzam büyümesi ve büyük miktarlardaki veri karmaşıklığını çözme yetenekleri, araştırmacıları karmaşık veri madenciliği zorluklarını ve problemlerini aşmaya yöneltmiştir. Tıbbi veri seti sınıflandırması, yapay zeka ve veri madenciliği alanındaki araştırmaların karşılaştığı en önemli ve karmaşık problemlerden birini temsil etmektedir. Farklı hastalıklar ve birden fazla test kullanarak çeşitli teşhis yolları, çok miktarda karmaşık tıbbi veri üretmiştir. Üstelik, klinik merkezler ve hastanelerdeki ve diğer sağlık kurumlarındaki hasta kayıtlarının sayısı, doktorların ve terapistlerin, hastaların kritik koşullarda olup olmadığına veya uzak takiplere ihtiyaç duyulup duyulmadığına bakılmaksızın vakaları araştırmasına yardımcı olmak için gelişmiş ve doğru tıbbi madencilik uygulamalarına ihtiyaç duymaktadır. Bu tez, tıbbi veri madenciliğinin üst üste binen alanları için bir sınıflandırma modelinin doğruluğunu arttırmak için yapay sinir ağı (YSA) ve metaheuristik algoritmaların melezleştirilmesine odaklanmaktadır. Tıbbi tanılarla ilişkili temel problemler, son derece doğru sınıflandırma modellerinin tanımlanmasını içerir. Bu tezin katkıları, ilgili literatürde vurgulanan iki önemli sınıflandırma problemi veya konu etrafında döner. İlk strateji için, YSA yapısı ve optimize edilmiş algoritma arasındaki ilişki kurulmuştur. İkinci strateji için, çeşitlendirme ve yoğunlaşma arasındaki geçiş, optimal küresel çözüm arayışının bir parçası olarak incelenmiştir. Birinci bölümde bir arka plan girişini tartıştık ve ikinci bölümde problem üzerine uygulanan yaklaşımlar hakkında bir literatür taraması yaptık. Bu tezin üçüncü bölümünde, metaheüristik yinelemenin YSA yapısına etkisi tartışılmaktadır. Metaheuristik algoritma yinelemesinin YSA yapısı üzerindeki etkisini iyileştirmek suretiyle önerilen çalışmanın yeniliği gösterilmiştir. YSA, üç farklı metaheuristik algoritma (parçacık sürüsü optimizasyonu PSO, genetik algoritma GA ve havai fişek algoritması FW) kullanılarak geliştirilmiştir. Önerilen modeller beş standart tıbbi kriter ve bir büyük veri tıbbi veri kümesi üzerinde test edilmiştir. Önerilen çalışma başarıyla uygulandı ve dikkate değer sonuçlar elde edildi. Ayrıca, serbest-öğle yemeği teoremi NFLT çalışmanın bağlamında doğrulanır, yani, tüm sorun alanları için hiçbir algoritma evrensel değildir. Bu tezin ileriki bölümü, tıbbi veri sınıflandırmasının en iyi doğruluğunu temsil eden en uygun küresel çözümü elde ederken, keşif ve sömürü arasındaki geçişi araştırmaktadır. Gizli katmanların sayısı ve her katmandaki nöronların sayısı hem ANN öğrenimini etkileyebilir. Böylece, bu tezde kullanılan YSA, yüksek doğrulukta sonuçlara ulaşabilen karmaşık bir yapının seçilmesini içerir; Sonuç olarak, küresel optimum için arama yaparken metaheuristik algoritma verimliliği garanti edilebilir. Diferansiyel evrim algoritması DE ve benzetimli tavlama SA olarak adlandırılan iki meta-yandaş algoritma, problem alan adı için yeni ve geliştirilmiş bir algoritma DESA formüle etmek üzere birleştirilmiştir. Bununla birlikte, son derece hassas iki algoritmanın seçilmesi zorunlu değildir; bunun yerine, kolaylık sağlamak için ampirik testler yapılabilir. Önerilen yöntemin orijinalliği, küresel çözümler ve yoğun olarak kullanılan yerel çözümler için geniş bir alanı araştırmak için arama ve sömürü arasında denge sağlamak üzere, evrimsel metaheuristik algoritma olarak DE ve yörünge algoritması olarak SA'yı birleştirmektedir. DESA yöntemi GA ve DE olan tow evrimsel ile ve SA ve Tabu TS olan iki yörüngeyle karşılaştırıldı. Önerilen yöntem DESA başarıyla uygulandı ve daha iyi sonuçlar elde edildi.The tremendous growth of computer hardware technologies and their abilities to solve huge amounts of complex of data has motivated researchers to overcome complicated data mining challenges and problems. Medical dataset classification represents one of the most crucial and complicated problems faced by researches in the field of artificial intelligence and data mining. The different diseases and the various ways of diagnosis by using multiple testing have produced large amounts of complex medical data. Moreover, the huge number of patient records in clinical centers and hospitals and other health institutions has generated the need for advanced and accurate medical mining applications to help doctors and therapists investigate cases regardless whether patients are in critical conditions or require remote follow-ups. This thesis focuses on the hybridization of the artificial neural network (ANN) and metaheuristic algorithms to enhance the accuracy of a classification model for the overlapping fields of medical data mining. The key problems associated with medical diagnoses involve the identification of highly accurate classification models. The contributions of this thesis revolve around the two important classification problems or issues highlighted in the related literature. For the first strategy, the relation between the ANN structure and the optimized algorithm is established. For the second strategy, the tradeoff between diversification and intensification is investigated as part of the search for the optimal global solution. In the first chapter we discuss a background introduction and in the second chapter a literature survey about the approaches applied on the problem. The third chapter of this thesis discusses the effect of metaheuristic iteration on ANN structure. The novelty of the proposed work shown through improving the impact of metaheuristic algorithm iteration on ANN structure. ANN is enhanced using separate three metaheuristic algorithms (particle swarm optimization PSO, genetic algorithm GA, and fireworks algorithm FW). The proposed models are tested on five standard medical benchmarks and one big-data medical dataset. The proposed study is successfully implemented, and remarkable results are obtained. Furthermore, the no-free-lunch theorem (NFLT) is verified in the study's context, that is, no algorithm is universal for all problem domains. The forth chapter of this thesis investigates the tradeoff between exploration and exploitation when obtaining the optimal global solution which represent best accuracy of medical data classification. The number of hidden layers and the number of neurons in each layer can both affect ANN learning. Thus, the ANN used in this thesis involves the selection of a complex structure that can achieve highly accurate results; consequently, metaheuristic algorithm efficiency can be guaranteed when searching for the global optimum. Two metaheuristic algorithms named differential evolution algorithm DE and simulated annealing SA are combined to formulate a new and improved algorithm DESA for considered problem domain. However, selecting the highly accurate two algorithms is not mandatory; instead, empirical tests can be performed for convenience. Originality of proposed method is combining between DE as evolutionary metaheuristic algorithm and SA as trajectory algorithm to provide balance between exploration and exploitation to explore search space widely for global solutions and intensively exploited local solutions. DESA method compared with tow evolutionary which are GA and DE, and with two trajectory which are SA and Tabu search TS. Proposed method DESA is implemented successfully, and better results obtained
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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