Implementasi dan Analisis Algoritma Fast Condensed Nearest Neighbor Pada Proses Klasifikasi Data

ABSTRAKSI: Klasifikasi adalah salah satu fungsionalitas dalam data mining yang sering digunakan untuk menemukan suatu set rule yang menjelaskan atau membedakan kelas data. Salah satu algoritma yang sering digunakan untuk proses klasifikasi adalah algoritma K Nearest Neighbors (KNN). Namun karena kekurangan yang dimiliki oleh KNN dalam memproses large dataset maka diajukan satu algoritma perbaikan yaitu Condensed Nearest Neighbors (CNN). Sayangnya, CNN masih memiliki kekurangan yaitu CNN bersifat order dependent. Untuk menangani kekurangan yang dimiliki oleh CNN maka diajukan algoritma Fast Condensed Nearest Neighbors (FCNN) oleh Fabrizio Angiulli. Tugas akhir ini mengimplementasikan dan menganalisis algoritma Fast Condensed Nearest Neighbors (FCNN). Tugas akhir ini menganalisis akurasi yang dimiliki FCNN dalam mengklasifikasikan suatu data dan menganalisis karakteristik dari algoritma FCNN. Parameter acuan yang digunakan adalah nilai akurasi, jumlah subset yang terbentuk, jumlah iterasi, dan learning time. FCNN sendiri adalah algoritma yang bersifat order independent yang memungkinkan subset yang dihasilkannya selalu berisi sampel yang sama. Dengan sifatnya yang order independent, FCNN memiliki akurasi yang cenderung konstan untuk suatu data jika dibandingkan dengan CNN.Kata Kunci : klasifikasi, algoritma Fast Condensed Nearest Neighbors, order independentABSTRACT: Classification is one of the functionality in data mining which is used to find a set rule that can determined a class in dataset. One of the algorithms that usually used for classification process is K-Nearest Neighbor algorithm. In order to reduce the lack from KNN, there have been proposed an algorithm called Condensed Nearest Neighbors (CNN) . Unfortunately, CNN characteristic is order dependent. For handling the lack in CNN, Fabrizio Angiulli proposed an algorithm called Fast Condensed Nearest Neighbors (FCNN). In this Final Project the implementation and the analysis of Fast Condensed Nearest Neighbors (FCNN) algorithm is performed. The analysis is carried out to the accuracy that result from the implementation of FCNN and compared with the accuracy result from CNN. Comparison Parameter such as accuracy, subset size, iteration, and learning time is used in the analysis. In the other hand, FCNN it self is an order independent algorithm that can compute a training set consistent subset. This characteristic makes FCNN have a relative stable accuracy compared with CNN.Keyword: classification, Fast Condensed Nearest Neighbors algorithm, order independen

Atrial arrhythmogenicity of KCNJ2 mutations in short QT syndrome: Insights from virtual human atria

Gain-of-function mutations in KCNJ2-encoded Kir2.1 channels underlie variant 3 (SQT3) of the short QT syndrome, which is associated with atrial fibrillation (AF). Using biophysically-detailed human atria computer models, this study investigated the mechanistic link between SQT3 mutations and atrial arrhythmogenesis, and potential ion channel targets for treatment of SQT3. A contemporary model of the human atrial action potential (AP) was modified to recapitulate functional changes in IK1 due to heterozygous and homozygous forms of the D172N and E299V Kir2.1 mutations. Wild-type (WT) and mutant formulations were incorporated into multi-scale homogeneous and heterogeneous tissue models. Effects of mutations on AP duration (APD), conduction velocity (CV), effective refractory period (ERP), tissue excitation threshold and their rate-dependence, as well as the wavelength of re-entry (WL) were quantified. The D172N and E299V Kir2.1 mutations produced distinct effects on IK1 and APD shortening. Both mutations decreased WL for re-entry through a reduction in ERP and CV. Stability of re-entrant excitation waves in 2D and 3D tissue models was mediated by changes to tissue excitability and dispersion of APD in mutation conditions. Combined block of IK1 and IKr was effective in terminating re-entry associated with heterozygous D172N conditions, whereas IKr block alone may be a safer alternative for the E299V mutation. Combined inhibition of IKr and IKur produced a synergistic anti-arrhythmic effect in both forms of SQT3. In conclusion, this study provides mechanistic insights into atrial proarrhythmia with SQT3 Kir2.1 mutations and highlights possible pharmacological strategies for management of SQT3-linked AF

Diversity, distribution and conservation of the terrestrial reptiles of Oman (Sauropsida, Squamata)

All authors: Salvador Carranza , Meritxell Xipell, Pedro Tarroso, Andrew Gardner, Edwin Nicholas Arnold, Michael D. Robinson, Marc Simó-Riudalbas, Raquel Vasconcelos, Philip de Pous, Fèlix Amat, Jiří Šmíd, Roberto Sindaco, Margarita Metallinou †, Johannes Els, Juan Manuel Pleguezuelos, Luis Machado, David Donaire, Gabriel Martínez, Joan Garcia-Porta, Tomáš Mazuch, Thomas Wilms, Jürgen Gebhart, Javier Aznar, Javier Gallego, Bernd-Michael Zwanzig, Daniel Fernández-Guiberteau, Theodore Papenfuss, Saleh Al Saadi, Ali Alghafri, Sultan Khalifa, Hamed Al Farqani, Salim Bait Bilal, Iman Sulaiman Alazri, Aziza Saud Al Adhoobi, Zeyana Salim Al Omairi, Mohammed Al Shariani, Ali Al Kiyumi, Thuraya Al Sariri, Ahmed Said Al Shukaili, Suleiman Nasser Al Akhzami.In the present work, we use an exceptional database including 5,359 records of 101 species of Oman’s terrestrial reptiles together with spatial tools to infer the spatial patterns of species richness and endemicity, to infer the habitat preference of each species and to better define conservation priorities, with especial focus on the effectiveness of the protected areas in preserving this unique arid fauna. Our results indicate that the sampling effort is not only remarkable from a taxonomic point of view, with multiple observations for most species, but also for the spatial coverage achieved. The observations are distributed almost continuously across the two-dimensional climatic space of Oman defined by the mean annual temperature and the total annual precipitation and across the Principal Component Analysis (PCA) of the multivariate climatic space and are well represented within 17 out of the 20 climatic clusters grouping 10% of the explained climatic variance defined by PC1 and PC2. Species richness is highest in the Hajar and Dhofar Mountains, two of the most biodiverse areas of the Arabian Peninsula, and endemic species richness is greatest in the Jebel Akhdar, the highest part of the Hajar Mountains. Oman’s 22 protected areas cover only 3.91% of the country, including within their limits 63.37% of terrestrial reptiles and 50% of all endemics. Our analyses show that large areas of the climatic space of Oman lie outside protected areas and that seven of the 20 climatic clusters are not protected at all. The results of the gap analysis indicate that most of the species are below the conservation target of 17% or even the less restrictive 12% of their total area within a protected area in order to be considered adequately protected. Therefore, an evaluation of the coverage of the current network of protected areas and the identification of priority protected areas for reptiles using reserve design algorithms are urgently needed. Our study also shows that more than half of the species are still pending of a definitive evaluation by the International Union for Conservation of Nature (IUCN).This work was funded by grants CGL2012-36970, CGL2015-70390-P from the Ministerio de Economía y Competitividad, Spain (cofunded by FEDER) to SC, the project Field study for the conservation of reptiles in Oman, Ministry of Environment and Climate Affairs, Oman (Ref: 22412027) to SC and grant 2014-SGR-1532 from the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya to SC. MSR is funded by a FPI grant from the Ministerio de Economía y Competitividad, Spain (BES-2013-064248); RV, PT and LM were funded by Fundação para a Ciência e Tecnologia (FCT) through post-doc grants (SFRH/BPD/79913/2011) to RV, (SFRH/BPD/93473/2013) to PT and PhD grant (SFRH/BD/89820/2012) to LM, financed by Programa Operacional Potencial Humano (POPH) – Quadro de Referência Estrategico Nacional (QREN) from the European Social Fund and Portuguese Ministerio da Educação e Ciência