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Penggabungan fitur dimensi fraktal dan lacunarity untuk klasifikasi daun
Get PDFTanaman memegang peranan penting dalam kehidupan manusia dan
makhluk hidup lainnya. Dengan semakin tingginya keanekaragaman spesies
tanaman di dunia, sulit untuk mengidentifikasi atau mengklasifikasi tanaman
secara manual melalui pengamatan langsung. Perkembangan penelitian di bidang
pengolahan citra digital telah membuka kesempatan luas bagi banyak peneliti di
berbagai bidang penelitian untuk mengklasifikasi tanaman secara cepat dan
otomatis. Daun merupakan bagian pada tanaman yang paling sering digunakan
dalam klasifikasi tanaman, baik secara manual maupun otomatis. Melalui
pengamatan pada daun, beberapa karakteristik bisa diperoleh; di antaranya adalah
bentuk pinggiran daun, bentuk urat daun serta tekstur daun.
Banyak objek-objek di alam memiliki sifat yang mirip fraktal, dimana
terdapat pola yang berulang pada skala tertentu, termasuk pada objek seperti daun.
Dimensi fraktal merupakan deskriptor fitur bentuk maupun tekstur yang telah
banyak diterapkan pada berbagai bidang penelitian karena mampu
mendeskripsikan kompleksitas sebuah objek dalam bentuk dimensi pecahan.
Sementara itu, lacunarity, merupakan deskriptor fitur tekstur yang mampu
menunjukkan seberapa heterogen suatu citra tekstur. Namun lacunarity belum
cukup dieksplorasi dalam banyak bidang penelitian dan belum ada penelitian
signifikan yang mencoba menggabungkan fitur dimensi fraktal dengan lacunarity
dalam penelitian yang berfokus pada klasifikasi citra digital daun.
Pada penelitian ini, diajukan penerapan konsep fraktal dalam
menyelesaikan masalah klasifikasi daun dengan berfokus pada penggabungan
fitur dimensi fraktal dan lacunarity. Ekstraksi fitur bentuk pinggiran dan tulang
daun dilakukan melalui perhitungan dimensi fraktal dengan menerapkan metode
box counting. Sedangkan fitur hasil perhitungan nilai lacunarity diperoleh melalui
proses ekstraksi fitur tekstur daun dengan menerapkan metode gliding box.
Menggunakan 626 dataset dari flavia, pengujian dilakukan dengan menganalisis
performa dari dimensi fraktal dan lacunarity ketika digunakan secara terpisah dan
ketika dikombinasikan satu sama lain dalam memperbaiki hasil klasifikasi daun
dari metode fraktal sebelumya, serta dengan mempertimbangkan parameter
ukuran kotak r yang paling optimal. Hasil uji coba dengan pengklasifikasi support
vector machine menunjukkan bahwa penggabungan fitur dimensi fraktal dan
lacunarity mampu meningkatkan akurasi klasifikasi hingga 93.92 %
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Plant plays an important role in the existence of all beings in the world.
With the high diversity in plant species, it is hard to classify plant manually only
by observing their properties. The development of study in digital image
processing opened a wide chance for many researches from various area of study
to quickly and automatically classify plant species. Plant leaf was the main
properties that commonly used in plant classification whether it is manually or
automatically. By looking at plant leaf, some unique characteristics can be
obtained; between them were leaf contour shape, leaf vein shape, and leaf surface
texture.
There are many natural objects and phenomenons that have characteristic
of fractals, like a pattern that repeated in a certain scale, including natural objects
like plant leaf. Fractal dimension was a widely known feature descriptor for shape
or texture that able to describe the complexity of an object in a form of fractional
dimension’s value. On the other hand, lacunarity is a feature descriptor that able
to describe the heterogeneity of a texture image. However, lacunarity was not
really exploited in many fields and there are no significant efforts that trying to
combine fractal dimension and lacunarity in the study of automatic plant leaf
classification.
In this study, a fractal concept and its performances in leaf classification
will be analyzed by using two fractal based feature: fractal dimension and
lacunarity. We focused on how to extract the two features and combine them for a
better classification result. A box counting approach is implemented to get the
fractal dimension feature vectors of leaf contour and vein, while an improved
gliding box algorithm is implemented to get the lacunarity feature vectors of leaf
texture. By combining this two feature, a feature vectors that highly represents the
unique feature of each leaf is then expected to be obtained. Using 626 leaf images
from flavia, experiment was conducted by separately or jointly analyzing the
performace of both fractal dimension feature vectors and lacunarity feature
vectors, while considering the optimal box size r. Using support vector machine
classifier, result shows that combination between fractal dimension and lacunarity
was able to increase the classification accuracy up to 93.92%
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
PEMANFAATAN ARDUINO DAN SENSOR KY-038 UNTUK MEMBEDAKAN SUARA MESIN CHAINSAW DAN MESIN LAIN DI AREA PEMBALAKAN LIAR
Get PDFIllegal logging is a significant environmental issue in Indonesia, particularly in the tropical forests of Southeast Sulawesi, which threatens biodiversity and contributes to global climate change. Manual monitoring of illegal activities in remote areas is often ineffective, necessitating innovative and real-time solutions for early detection. This study aims to develop an early detection system to distinguish the sound of chainsaws commonly used in illegal logging activities from other machine sounds such as RX King motorcycles and ketinting boats. The KY-038 sound sensor connected to an Arduino was used to capture environmental sounds, and the obtained data was classified using the K-Nearest Neighbors (KNN) algorithm. Experiments were conducted by collecting training data and testing the system with sound samples from each machine. The results showed that the developed sound detection system could classify the sounds of chainsaws, RX King motorcycles, and ketinting boats with good performance. With the optimal k value in KNN, the average classification accuracy reached 90%. This system can be used as an effective monitoring tool for the early detection of illegal logging activities, contributing to the conservation of tropical forests
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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