Non Supervised Classification Tools Adapted to Supervised Classification

1987 ◽  
pp. 57-62
Author(s):  
R. Fages ◽  
M. Terrenoire ◽  
D. Tounissoux ◽  
A. Zighed
Keyword(s):  
Supervised Classification

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>

Un-Supervised approach to backorder prediction using deep autoencoder

2019 ◽  
Vol 12 ◽  
Author(s):  
Gunjan Saraogi ◽  
Deepa Gupta ◽  
Lavanya Sharma ◽  
Ajay Rana
Keyword(s):  
Anomaly Detection ◽  
Predictive Model ◽  
Inventory Management ◽  
Supervised Classification ◽  
Operating Characteristic ◽  
Imbalanced Data ◽  
Bullwhip Effect ◽  
Detection Methods ◽  
Good Representation ◽  
Operational Management

Background: Backorders are an accepted abnormality affecting accumulation alternation and logistics, sales, chump service, and manufacturing, which generally leads to low sales and low chump satisfaction. A predictive archetypal can analyse which articles are best acceptable to acquaintance backorders giving the alignment advice and time to adjust, thereby demography accomplishes to aerate their profit. Objective: To address the issue of predicting backorders, this paper has proposed an un-supervised approach to backorder prediction using Deep Autoencoder. Method: In this paper, artificial intelligence paradigms are researched in order to introduce a predictive model for the present unbalanced data issues, where the number of products going on backorder is rare. Result: Un-supervised anomaly detection using deep auto encoders has shown better Area under the Receiver Operating Characteristic and precision-recall curves than supervised classification techniques employed with resampling techniques for imbalanced data problems. Conclusion: We demonstrated that Un-supervised anomaly detection methods specifically deep auto-encoders can be used to learn a good representation of the data. The method can be used as predictive model for inventory management and help to reduce bullwhip effect, raise customer satisfaction as well as improve operational management in the organization. This technology is expected to create the sentient supply chain of the future – able to feel, perceive and react to situations at an extraordinarily granular level

Semi-supervised Classification and Noise Detection

2009 ◽  
Author(s):  
Yunna Duan ◽  
Ying Gao ◽  
Xiaojuan Ren ◽  
Haoyuan Che ◽  
Keyang Yang
Keyword(s):  
Supervised Classification ◽  
Noise Detection

scholarly journals Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine

2021 ◽  
Vol 11 (9) ◽  
pp. 3867
Author(s):  
Zhewei Liu ◽  
Zijia Zhang ◽  
Yaoming Cai ◽  
Yilin Miao ◽  
Zhikun Chen
Keyword(s):  
Extreme Learning Machine ◽  
Supervised Classification ◽  
Structured Data ◽  
Model Complex ◽  
Euclidean Domain ◽  
Noise Data ◽  
Data Points ◽  
Learning Machine ◽  
Random Hypergraph ◽  
Special Case

Extreme Learning Machine (ELM) is characterized by simplicity, generalization ability, and computational efficiency. However, previous ELMs fail to consider the inherent high-order relationship among data points, resulting in being powerless on structured data and poor robustness on noise data. This paper presents a novel semi-supervised ELM, termed Hypergraph Convolutional ELM (HGCELM), based on using hypergraph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random hypergraph convolutional layer followed by a hypergraph convolutional regression layer, enabling it to model complex intraclass variations. We show that the traditional ELM is a special case of the HGCELM model in the regular Euclidean domain. Extensive experimental results show that HGCELM remarkably outperforms eight competitive methods on 26 classification benchmarks.

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