Co-training with a Single Natural Feature Set Applied to Email Classification

2005 ◽  
Author(s):  
J. Chan ◽  
I. Koprinska ◽  
J. Poon
Keyword(s):  
Natural Feature ◽  
Email Classification

Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification

2020 ◽  
Vol 4 (2) ◽  
pp. 377-383
Author(s):  
Eko Laksono ◽  
Achmad Basuki ◽  
Fitra Bachtiar
Keyword(s):  
Frequency Distribution ◽  
Confusion Matrix ◽  
High Accuracy ◽  
K Value ◽  
A Value ◽  
Optimal Value ◽  
Classification Evaluation ◽  
Email Spam ◽  
Email Classification

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

Liverworts of the Uzon Volcano caldera (Kronotsky Nature Reserve, Kamchatka)

2011 ◽  
Vol 45 ◽  
pp. 386-393
Author(s):  
A. D. Potemkin ◽  
E. Yu. Kuzmina ◽  
T. I. Koroteeva (Nyushko)
Keyword(s):  
Species Composition ◽  
Nature Reserve ◽  
Uzon Caldera ◽  
Red Data Book ◽  
Natural Feature ◽  
Data Book ◽  
First Time ◽  
Kronotsky Nature Reserve

Species composition of liverworts of unique natural feature of Kamchatka — Uzon Volcano caldera is listed. It includes 38 species. 29 of them are found for the first time for the Uzon caldera. Marsupella funckii, Nardia assamica, N. unispiralis included in Red Data Book of Kamchatka (2007).

Medical students' memory for faces: Comparing the influences of a non-natural feature and emotion

2021 ◽  
Vol 429 ◽  
pp. 118520
Author(s):  
Vinicius Figueiredo ◽  
Monaí Oliveira ◽  
Mayara Nunes ◽  
Pietro De Aguiar ◽  
Bianca Andrade ◽  
...  
Keyword(s):  
Medical Students ◽  
Natural Feature

scholarly journals Efficient email classification approach based on semantic methods

2018 ◽  
Vol 9 (4) ◽  
pp. 3259-3269 ◽  
Author(s):  
Eman M. Bahgat ◽  
Sherine Rady ◽  
Walaa Gad ◽  
Ibrahim F. Moawad
Keyword(s):  
Classification Approach ◽  
Email Classification

scholarly journals Natural Feature Detection on Mobile Phones with 3D FAST

2010 ◽  
Vol 9 (4) ◽  
pp. 29-34 ◽  
Author(s):  
Achim Weimert ◽  
Xueting Tan ◽  
Xubo Yang
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Hessian Matrix ◽  
Detection Algorithm ◽  
Scale Invariant ◽  
Natural Feature ◽  
Detection Approach ◽  
Scale Levels ◽  
Speed Up ◽  
Low Performance

In this paper, we present a novel feature detection approach designed for mobile devices, showing optimized solutions for both detection and description. It is based on FAST (Features from Accelerated Segment Test) and named 3D FAST. Being robust, scale-invariant and easy to compute, it is a candidate for augmented reality (AR) applications running on low performance platforms. Using simple calculations and machine learning, FAST is a feature detection algorithm known to be efficient but not very robust in addition to its lack of scale information. Our approach relies on gradient images calculated for different scale levels on which a modified9 FAST algorithm operates to obtain the values of the corner response function. We combine the detection with an adapted version of SURF (Speed Up Robust Features) descriptors, providing a system with all means to implement feature matching and object detection. Experimental evaluation on a Symbian OS device using a standard image set and comparison with SURF using Hessian matrix-based detector is included in this paper, showing improvements in speed (compared to SURF) and robustness (compared to FAST)

scholarly journals Augmented Reality Pengenalan Landmark Negara Asia Tenggara Menggunakan Algoritma FAST Corner dan Natural Feature Tracking

2021 ◽  
Vol 8 (3) ◽  
pp. 1012-1026
Author(s):  
Adam Raihan Fadhlurahman
Keyword(s):  
Augmented Reality ◽  
Feature Tracking ◽  
Corner Detection ◽  
Natural Feature

Pada saat ini diseluruh bagian dunia sedang terlanda sebuah virus yaitu COVID-19 yang membuat setiap orang tidak dapat berpergian ke luar negri dengan mudah. Namun di era sudah canggih ini semua informasi dapat disajikan secara digitalisasi mulai dalam bentuk tulisan maupun gambar dan dapat juga ditampilkan berbentuk objek 3 dimensi dibantu dengan Augmented Reality untuk menampilkannya. Peneleitian ini memanfaatkan teknologi Augmented Reality digunakan untuk alat pengenalan landmark dari negara Asia Tenggara agar tampilan objek akan menjadi lebih atraktif dan dapat mengetahui informasi tentang landmark dari setiap negara dengan mudah. Dalam penelitian ini aplikasi Augmented Reality dibuat menggunakan FAST Corner Detection (FCD) algoritma dan Natural Feature Tracking (NFT) untuk mendeteksi marker. Pengujian dari aplikasi ini dilakukan pada tiga perangkat smartphone android, pada sudut kemiringan 20° - 90° ketiga smartpone dapat mendeteksi marker menggunakan kameranya dan menampilkan objek 3 dimensi sesuai dengan landmark yang dipilih pada layar smartphone. Untuk pengukuran jarak maximal yang dapat dibaca oleh handphone rata-rata ±75cm dan jarak minimun pendeteksian yaitu ±10cm.

Sign in / Sign up

Export Citation Format

Share Document