Hierarchical clustering using transitive closure and semi-supervised classification based on fuzzy rough approximation

2012 ◽  
Author(s):  
Sadaaki Miyamoto ◽  
Satoshi Takumi
Keyword(s):  
Hierarchical Clustering ◽  
Supervised Classification ◽  
Transitive Closure ◽  
Rough Approximation

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

PEMANFAATAN DATA PENGINDERAAN JAUH DAN TEKNOLOGI SIG UNTUK MENGIDENTIFIKASI BUILT UP AREA DAN KAITANNYA DENGAN RTRW DI KABUPATEN KENDAL

2019 ◽  
Vol 3 ◽  
pp. 521
Author(s):  
Mailendra Mailendra
Keyword(s):  
Supervised Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm

Integrasi data penginderaan jauh dengan sistem informasi geografis telah banyak dikembangkan, dan salah satunya dalam melihat perkembangan lahan terbangun. Tujuan penelitian ini adalah untuk melihat perkembangan lahan terbangun dan kesesuaiannya dengan Rencana Pola Ruang Kabupaten Kendal. Kemudian metode yang digunakan yaitu metode supervised classification dengan memanfaatkan data citra landsat 5 TM dan landsat 8 OLI yang selanjutnya dihitung luas dari masing lahan terbangun berdasarkan data temporal tahun 1990, tahun 2015 dan tahun 2017. Setelah diketahui luas lahan terbangun selanjutnya dioverlay dengan peta rencana pola ruang Kabupaten Kendal untuk melihat sesuai atau tidaknya penempatan lahan terbangun tersebut. Adapun hasil penelitiannya yaitu setiap tahunnya lahan terbangun terus meningkat di Kabupaten Kendal, terjadi peningkatan yang cukup signifikan dalam dua tahun terakhir yaitu tahun 2015 hingga tahun 2017. Selanjutnya diperkirakan 88 % lahan terbangun tersebut telah sesuai dengan RTRW karena sudah berada pada kawasan budidaya.

PEMETAAN HABITAT BENTIK INTERTIDAL: Informasi Awal Perencanaan Pengelolaan Pesisir Laut Secara Spasial di Bulak Setra dan Batu Karas, Kabupaten Pangandaran

2018 ◽  
Vol 2 ◽  
pp. 105
Author(s):  
Rendra Pranata
Keyword(s):  
Dissolved Oxygen ◽  
Supervised Classification ◽  
Landsat 7 ◽  
Avicennia Alba

<p>Ekosistem pesisir Kabupaten Pangandaran memiliki biodiversitas yang cukup tinggi, namun pasca-tsunami tahun 2006 terjadi penurunan kerapatan ekosistem mangrove akibat rusaknya daerah pesisir dan wilayah permukiman sepanjang 28 km. Penelitian ini bertujuan untuk mengetahui kondisi habitat bentik di kawasan intertidal seperti mangrove dan makrozoobentos, serta mengukur parameter kualitas air. Metode yang digunakan yaitu interpretasi citra Landsat 7 tahun 2017 dengan melakukan <em>masking</em> dan <em>supervised classification</em> untuk mengetahui daerah tutupan mangrove di Bulak Setra dan Batu Karas, kemudian dilakukan identifikasi mangrove dengan transek kuadran 10x10 meter sepanjang 50 meter ke arah laut pada 7 plot di Bulak Setra dan 14 plot di Batu Karas untuk validasi data citra satelit. Selain itu juga dilakukan pengukuran parameter kualitas air serta identifikasi makrozoobentos. Hasil penelitian menunjukkan bahwa mangrove di Bulak Setra didominasi oleh <em>Scyphiphora hydrophyllacea</em> dari 8 spesies lain yang ditemukan dengan Indeks Nilai Penting (INP) 94,41%, sedangkan di Batu Karas didominasi oleh <em>Avicennia alba</em> dari 8 spesies lain yang ditemukan dengan INP 157%. Nilai rata-rata parameter kualitas air di Bulak Setra dan Batu Karas berturut-turut yaitu suhu 30<sup>o</sup>C dan 29,41<sup>o</sup>C, salinitas 5,56 psu dan 27,23 psu, pH 7,48 dan 6,86 serta konsentrasi <em>Dissolved Oxygen</em> (DO) 5,2 dan 6,5 mg/L. Makrozoobentos didominasi oleh kelas <em>G</em><em>astropoda</em>. Faktor sosial ekonomi masyarakat juga disajikan sebagai informasi sumber daya manusia yang akan berperan menjadi komponen pembangunan pengelolaan pesisir. Diharapkan penelitian ini dapat menjadi informasi awal dalam pengelolaan perencanaan wilayah pesisir di Bulak Setra dan Batu Karas.<strong></strong></p><p><strong>Kata kunci</strong>: bentik, intertidal, mangrove</p>

Effectiveness of Supervised Classification Models for Hate Speech on Twitter

2020 ◽  
Author(s):  
Kunal Srivastava ◽  
Ryan Tabrizi ◽  
Ayaan Rahim ◽  
Lauryn Nakamitsu
Keyword(s):  
Supervised Classification ◽  
Hate Speech ◽  
The Internet ◽  
Classification Models ◽  
Personal Attack ◽  
Political Beliefs ◽  
The Many ◽  
Primary Medium ◽  
Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

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