Perbandingan Local Binary Pattern untuk Klasifikasi Sel Darah Putih

2018 ◽  
Vol 9 (2) ◽  
pp. 118-121
Author(s):  
Felix Indra Kurniadi
Keyword(s):  
Support Vector Machine ◽  
Blood Cells ◽  
Local Binary Pattern ◽  
White Blood Cells ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Rotation Invariant ◽  
Blood Cancer ◽  
Index Terms ◽  
Uniform Local Binary Pattern

In recent year, a lot of researches try to overcome problem in recognition and classify white blood cells to help hematologists diagnose white blood cells disease such blood cancer, leukemia and AIDS. This paper compares several methods Local Binary Pattern such as Local Binary Pattern Uniform, Local Binary Pattern Rotation Invariant and Local Binary Pattern Rotation Invariant Uniform to classify five types of white blood cells using two classifier: Support Vector Machine and K-Nearest Neighbour. Index Terms—LBP, LBP-U, LBP-RI, LBP-RIU, white blood cells

scholarly journals Detection of Blood Cancer Cells Using Microscopic Images

2021 ◽  
Vol 9 (8) ◽  
pp. 995-1003
Author(s):  
Vidyashree M S
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cancer Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Blood Cancer ◽  
Acute Myeloid

Abstract: Blood Cancer cells forming a tissue is called lymphoma. Thus, disease decreases the cells to fight against the infection or cancer blood cells. Blood cancer is also categorized in too many types. The two main categories of blood cancer are Acute Lymphocytic Lymphoma and Acute Myeloid Lymphoma. In this project proposes a approach that robotic detects and segments the nucleolus from white blood cells in the microscopic Blood images. Here in this project, we have used the two Machine learning algorithms that are k-means algorithm, Support vector machine algorithm. K-mean algorithm is use for segmentation and clustering. Support vector machine algorithm is used for classification. Keywords: k-means, Support vector machine, Lymphoma, Acute Lymphocytic Lymphoma, Machine Learning

Perbandingan Distance Metric pada Nearest Neighbour untuk Klasifikasi Sel Darah Putih

2019 ◽  
Vol 11 (1) ◽  
pp. 16-19
Author(s):  
Felix Indra Kurniadi ◽  
Vinnia Kemala Putri
Keyword(s):  
Feature Extraction ◽  
Human Body ◽  
Blood Cells ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
White Blood Cells ◽  
Nearest Neighbour ◽  
Harmful Substance ◽  
Distance Metric ◽  
Minkowski Distance

White blood cells, have a function to protect human body from viruses, bacteria and any other harmful substance. In this research, Local Binary Pattern was proposed for feature extraction using Euclidean distance, Chebyshev distance and Minkowski distance as classifier.

scholarly journals Pengenalan Huruf Isyarat Tangan Menggunakan Ekstraksi Ciri Local Binary Pattern

2018 ◽  
Vol 3 (1) ◽  
pp. 75
Author(s):  
M. Adhi Satria ◽  
Kurniawan Nur Ramadhani ◽  
Anditya Arifianto
Keyword(s):  
Support Vector Machine ◽  
Local Binary Pattern ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Nearest Neighbour ◽  
P 16

<p>Pada penelitian ini dibangun sistem pengenalan huruf isyarat tangan menggunakan metode ekstraksi ciri Local Binary Patterns (LBP). Metode LBP memiliki kehandalan dalam melakukan analisis tekstur, mengatasi penskalaan dan citra yang kabur. Untuk algoritma klasifikasi, digunakan metode k-Nearest Neighbour (KNN) dan Support Vector Machine (SVM). Parameter LBP terbaik didapatkan untuk nilai R=10 dan P=16 menggunakan SVM dengan kernel Gaussian. Performansi terbaik dalam penelitian ini didapatkan untuk nilai F1-Score 99,84%.</p>

scholarly journals Pengenalan Wajah Manusia berbasis Algoritma Local Binary Pattern

2017 ◽  
Vol 17 (2) ◽  
pp. 29-38
Author(s):  
Ratih Purwati ◽  
Gunawan Ariyanto
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Cross Validation ◽  
Support Vector ◽  
Fold Cross Validation

Face Recognition merupakan teknologi komputer untuk mengidentifikasi wajah manusia melalui gambar digital yang tersimpan di database. Wajah manusia dapat berubah bentuk sesuai dengan ekspresi yang dimilikinya. Wajah manusia dapat berubah bentuk sesuai dengan eskpresi yang dimilikinya. Ekspresi wajah manusia memiliki kemiripan satu sama lain sehingga untuk mengenali suatu ekspresi adalah kepunyaan siapa akan sedikit sulit. Pengenalan wajah terus menjadi topik aktif di zaman sekarang pada penelitian bidang computer vision. Penggunaan wajah manusia sering kita jumpai pada fitur-fitur aplikasi media sosial seperti Snapchat, Snapgram dari Instagram dan banyak aplikasi sosial media lainnya yang menggunakan teknologi tersebut. Pada penelitian ini dilakukan analisa pengenalan ekpresi wajah manusia dengan pendekatan fitur alogaritma Local Binary Pattern dan mencari pengembangan alogaritma dasar Local Binary Pattern yang paling optimal dengan cara menggabungkan metode Hisogram Equalization, Support Vector Machine, dan K-fold cross validation sehingga dapat meningkatkan pengenalan gambar wajah manusia pada hasil yang terbaik. Penelitian ini menginput beberapa database wajah manusia seperti JAFFE yang merupakan gambar wajah manusia wanita jepang yang berjumlah 10 orang dengan 7 ekspresi emosional seperti marah, sedih, bahagia, jijik, kaget, takut dan netral ke dalam sistem. YALE yaitu merupakan gambar wajah manusia orang Amerika. Serta menggunakan dataset CALTECH yang merupakan gambar manusia yang terdiri dari 450 gambar dengan ukuran 896 x 592 piksel dan disimpan dalam format JPEG. Kemudian data tersebut di sesuaikan dengan bentuk tekstur wajah masing-masing. Dari hasil penggabungan ketiga metode diatas dan percobaan-percobaan yang sudah dilakukan, didapatkan hasil yang paling optimal dalam pengenalan wajah manusia yaitu menggunakan dataset JAFFE dengan resolusi 92 x 112 piksel dan dengan tingkat penggunaan processor yang tinggi dapat mempengaruhi waktu kecepatan komputasi dalam proses menjalankan sistem sehingga menghasilkan prediksi yang lebih tepat.

scholarly journals An Improved Dual Clustering Method for Classification of Microarray Image Segementation

2019 ◽  
Vol 8 (4) ◽  
pp. 2514-2519
Keyword(s):  
Support Vector Machine ◽  
Ct Scanning ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Resonance Imaging ◽  
Computerised Tomography ◽  
Medical Field ◽  
Microarray Image ◽  
Image Segementation

Microarray is a fast and rapid growing technology which plays dynamic role in the medical field. It is an advanced than MRI (Magnetic Resonance Imaging) and CT scanning (Computerised Tomography). The purpose of this work is to make fine perfection against the gene expression. In this study the two clustering are used which fuzzy c means and k means and also it classifies with better results. The microarray data base indicates the classification in support vector machine. Segmentation is most important step in microarray image. The classification in support vector machine is compared with other two classifiers which means the k nearest neighbour and with the Bayes classifiers.

Sentiment Analysis with Social Media Analytics, Methods, Process, and Applications

2017 ◽  
pp. 192-208
Author(s):  
Karteek Ramalinga Ponnuru ◽  
Rashik Gupta ◽  
Shrawan Kumar Trivedi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Social Media Analytics ◽  
Nearest Neighbour ◽  
Huge Amount ◽  
Word Clouds

Firms are turning their eye towards social media analytics to get to know what people are really talking about their firm or their product. With the huge amount of buzz being created online about anything and everything social media has become ‘the' platform of the day to understand what public on a whole are talking about a particular product and the process of converting all the talking into valuable information is called Sentiment Analysis. Sentiment Analysis is a process of identifying and categorizing a piece of text into positive or negative so as to understand the sentiment of the users. This chapter would take the reader through basic sentiment classifiers like building word clouds, commonality clouds, dendrograms and comparison clouds to advanced algorithms like K Nearest Neighbour, Naïve Biased Algorithm and Support Vector Machine.

Sign in / Sign up

Export Citation Format

Share Document