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Visualization of binary string convergence by Sammon mapping
No full textUnderstanding the evolution of a complex genetic algorithm is a non-trivial problem, however, genetic-algorithm visualization is in its infancy. This paper reviews some of the current approaches and presents a new visualization approach based on Sammon mapping. Sammon mapping is a nonlinear mapping of a set of vectors in p-dimensional space to a set in r-dimensional space, where r < p. The mapping attempts to preserve in r-space the Euclidean inter-vector distances present in p-space. We demonstrate that a Sammon mapping to 2-space of binary chromosomes present in a higher-dimensional allele space during the execution of a genetic algorithm can indicate the presence of multiple solutions. Shortfalls of this approach are discussed along with possible solutions
Rev. Jospeh P. Sammon and altar boys observing Candlemas
No full textPhotograph shows ceremony inside church.''Sunday marks the end of the Christmas religious season and the observance of Candlemas, or the Blessing of the Candles. The Rev. Joseph P. Sammon, OMI, of St. Mary's church, is shown blessing candles with the assistance of Altar Boys Ronald Duman, right, and Harry Nass. The observance is followed Monday by the Feast of the Purification.'
Die tracheoösophageale Ersatzstimme: grafische Darstellung mithilfe der Sammon-Transformation
No full textObjektiv-apparative Stimmbewertungen werden heute meist auf der Basis gehaltener Vokale durchgeführt, aus denen Parameter berechnet werden, die zur grafischen Darstellung einer Pathologie dienen. In dieser Studie, einem Teilprojekt eines von der Deutschen Krebshilfe geförderten Forschungsvorhabens zur tracheoösophagealen Ersatzstimme TE Laryngektomierter, ging es um ein neues Verfahren. Für die Analyse werden die internen akustischen Parameter eines bereits für Marktzwecke professionalisierten automatischen Spracherkennungssystems an den jeweiligen Sprecher adaptiert. Die Abweichung von den ursprünglichen Werten dient als Messgröße für die Abweichung von der Normalstimme. Mithilfe der sog. Sammon-Transformation wird die große Zahl der Parameter bei minimalem Informationsverlust in eine zweidimensionale, grafische Darstellung gebracht. Verschiedene Gruppen von Sprechern (18 Sprecher mit TE, 18 chronisch heisere Sprecher, 18 alte und 16 junge Normalsprecher) wurden miteinander verglichen. Die Ergebnisse zeigen nicht nur eine grafische Trennung zwischen pathologischen und Normalstimmen, sondern auch von unterschiedlichen pathologischen Stimmen.Ebenso wurden junge und alte, männliche und weibliche Sprecher voneinander getrennt. Die Projektion eines neuen Sprechers in eine bestehende Grafik erlaubt eine Aussage über dessen Pathologie im Vergleich zum vorhandenen Sprecherkollektiv
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe 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
Iterative Reflect Perceptual Sammon and Machine Learning-Based Bagging Classification for Efficient Tumor Detection
No full textA tumor is an abnormal development of cells in the human body. A tumor develops when cells divide without any control. Tumors change their size from a small to large lump. Tumors appear anywhere in the body. The early stage of diagnosis is an essential one in disease treatment. Many researchers carried out their research on different tumor detection methods. However, the tumor detection accuracy level was not improved and tumor detection time consumption not minimized. In order to address these problems, an Iterative Reflect Perceptual Sammon Bagging Classification (IRPS-BAC) Method is introduced. The aim is to accurately detect brain tumors as early as possible and make the method suitable for real-time applications. The IRPS-BAC Method comprises two processes, namely, feature selection and classification using the iterative reflect perceptual sammon feature selection process and bagging classification process. In the IRPS-BAC Method, an input of medical data are gathered from the Epileptic Seizure Recognition Data Set and Cervical Cancer Risk Classification database. After that, iterative reflect perceptual sammon feature selection process is carried out to select the relevant features. Iterative reflect perceptual divergence computes the variation between two features. After that, sammon mapping projects the similar and dissimilar features into feature space. By this manner, the relevant features get selected using the IRPS-BAC Method. With the help of selected relevant features, bagging classification process is carried out. In bagging classification process, internal node processes the selected features and leaf node to make the tumor decision as normal or cancerous one based on information gain. This, in turn, helps to reduce the time complexity and error rate. The performance of the proposed IRPS-BAC Method is determined by two benchmark datasets through comparing the parameter such as tumor detection time, tumor detection accuracy and error rate with the existing approaches. In the Epileptic Seizure Recognition Data Set, the proposed IRPS-BAC Method improves tumor detection accuracy by 16%, with minimum time period and the error rate of 41 ms and 58% for tumor detection as compared to existing methods. By using Cervical Cancer Risk Classification, the proposed IRPS-BAC Method exhibited higher classification performance measures, including accuracy (14%), time (46 ms), and error rate (61%), than the current conventional approaches
Lancaster Stem Sammon Projective Feature Selection based Stochastic eXtreme Gradient Boost Clustering for Web Page Ranking
No full textWeb content mining retrieves the information from web in more structured forms. The page rank plays an essential part in web content mining process. Whenever user searches for any information on web, the relevant information is shown at top of list through page ranking. Many existing page ranking algorithms were developed and failed to rank the web pages in accurate manner through minimum time feeding. In direction to address the above mentioned issues, Lancaster Stem Sammon Projective Feature Selection based Stochastic eXtreme Gradient Boost Clustering (LSSPFS-SXGBC) Approach is introduced for page ranking based on user query. LSSPFS-SXGBC Approach has three processes for performing efficient web page ranking, namely preprocessing, feature selection and clustering. LSSPFS-SXGBC Approach in account of the numeral of operator request by way of an input. Lancaster Stemming Preprocessed Analysis is carried out in LSSPFS-SXGBC Approach for removing the noisy data from the input query. It eradicates the stem words, stop words and incomplete data for minimizing the time and space consumption. Sammon Projective Feature Selection Process is carried out in LSSPFS-SXGBC Approach to select the relevant features (i.e., keywords) based on user needs for efficient page ranking. Sammon Projection maps the high-dimensional space to lower dimensionality space to preserve the inter-point distance structure. After feature selection, Stochastic eXtreme Gradient Boost Page Rank Clustering process is carried out to cluster the similar keyword web pages based on their rank. Gradient Boost Page Rank Cluster is an ensemble of several weak clusters (i.e., X-means cluster). X-means cluster partitions the web pages into ‘x’ numeral of clusters where each reflection goes towards the cluster through adjacent mean value. For every weak cluster, selected features are considered as the training samples. Subsequently, all weak clusters are joined to form the strong cluster for attaining the webpage ranking results. By this way, an efficient page ranking is carried out through higher accurateness and minimum time consumption. The practical validation is carried out in LSSPFS-SXGBC Approach on factors such ranking accurateness, false positive rate, ranking time and space complexity with respect to numeral of user query
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe 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
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