scholarly journals Synovial Sarcoma Classification Technique Using Support Vector Machine and Structure Features

2022 ◽  
Vol 32 (2) ◽  
pp. 1241-1259
Author(s):  
P. Arunachalam ◽  
N. Janakiraman ◽  
Arun Kumar Sivaraman ◽  
A. Balasundaram ◽  
Rajiv Vincent ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Synovial Sarcoma ◽  
Support Vector ◽  
Classification Technique

scholarly journals Hydrometeor classification from two-dimensional video disdrometer data

2014 ◽  
Vol 7 (9) ◽  
pp. 2869-2882 ◽  
Author(s):  
J. Grazioli ◽  
D. Tuia ◽  
S. Monhart ◽  
M. Schneebeli ◽  
T. Raupach ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Visual Inspection ◽  
Particle Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Time Intervals ◽  
Classification Technique ◽  
Statistical Behavior ◽  
Over Time

Abstract. The first hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data is presented. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using the statistical behavior of a set of particle descriptors as input, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over 60 s precipitation time steps labeled by visual inspection. In this way, eight dominant hydrometeor classes can be discriminated. The algorithm achieved high classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.

SUPPORT VECTOR MACHINES FOR THAI PHONEME RECOGNITION

2001 ◽  
Vol 09 (06) ◽  
pp. 803-813 ◽  
Author(s):  
NUTTAKORN THUBTHONG ◽  
BOONSERM KIJSIRIKUL
Keyword(s):  
Support Vector Machine ◽  
Dimensional Space ◽  
High Dimensional ◽  
Support Vector ◽  
Phoneme Recognition ◽  
Classification Technique ◽  
Separating Hyperplane ◽  
Vector Machines ◽  
Tone Recognition ◽  
Vowel Recognition

The Support Vector Machine (SVM) has recently been introduced as a new pattern classification technique. It learns the boundary regions between samples belonging to two classes by mapping the input samples into a high dimensional space, and seeking a separating hyperplane in this space. This paper describes an application of SVMs to two phoneme recognition problems: 5 Thai tones, and 12 Thai vowels spoken in isolation. The best results on tone recognition are 96.09% and 90.57% for the inside test and outside test, respectively, and on vowel recognition are 95.51% and 87.08% for the inside test and outside test, respectively.

scholarly journals Alzheimer Disease Classification using Machine Learning

2020 ◽  
Vol 9 (6) ◽  
pp. 6-9
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Developed Countries ◽  
Disease Classification ◽  
Support Vector ◽  
Data Set ◽  
Classification Technique ◽  
Routine Tasks ◽  
Memory Power ◽  
To Come

Alzheimer's disease is the most popular and persuading dementia that affects our memory power, reasoning and deportment. Symptoms rise up slowly and worsen with time, becoming an obstacle in doing our routine tasks. Alzheimer is not conventional wedge of aging. The substantial and known risk factor is up surging age. The prevalence of AD is depicted to be around 5% after an age of 65 years and took a leap of 30% for people of 85 years old in developed countries [1]. In this project we proposed a detection and classification technique using Random Forest(RF) and Support Vector Machine(SVM) algorithms on the oasis longitudinal data set and compare their respective accuracies to come to a conclusion that which algorithm best suits for this detection and classification. paper Setup must be in A4 size with Margin: Top 0.7”,

scholarly journals Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM)

2021 ◽  
Vol 8 (1) ◽  
pp. 147
Author(s):  
Primandani Arsi ◽  
Retno Waluyo
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Public Discourse ◽  
Capital City ◽  
Support Vector ◽  
The Public ◽  
Machine Learning Methods ◽  
Classification Technique

<p class="Abstrak">Dewasa ini, media sosial berkembang pesat di internet, salah satu yang banyak digemari adalah Twitter. Berbagai topik ramai diperbincangkan di Twitter mulai dari ekonomi, politik, sosial, budaya, hukum dan lain-lain. Salah satu topik yang ramai diperbincangkan di Twitter adalah terkait isu pemindahan ibu kota Indonesia. Namun dibalik hal tersebut terdapat kontroversi dari  pihak yang merasa  pro dan kontra, masing-masing memiiki sudut pandang yang berbeda.  Hal ini menyebabkan munculnya fenomena perdebatan khususnya di Twitter yang sebenarnya menunjukkan perhatian kolektif mengenai wacana publik tersebut. Analisis sentimen adalah proses mengekstraksi, memahami dan mengolah data berupa teks yang tidak terstruktur secara otomatis guna mendapatkan informasi sentimen yang terdapat pada sebuah kalimat pendapat atau opini. Dalam penerapan analisis sentimen menggunakan metode <em>machine learning</em> terdapat beberapa metode yang sering digunakan. Dalam penelitian ini diusulkan metode <em>Support Vector Machine</em> (SVM) untuk diterapkan pada <em>tweets</em> topik pemindahan ibu kota Indonesia untuk tujuan klasifikasi kelas sentimen pada media sosial <em>twitter</em>. Teknis klasifikasi  dilakukan dengan cara mengklasifikasikan menjadi 2 kelas yakni positif dan negatif. Berdasarkan hasil pengujian yang dilakukan terhadap <em>tweets</em> sentimen pemindahan ibu kota dari media sosial twitter sebanyak 1.236 <em>tweets</em> (404 positif dan 832 negatif) menggunakan SVM diperoleh akurasi =96,68%, <em>precision=</em>95.82%, <em>recall</em>=94.04% dan AUC = 0,979.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em><em>Today, social media is growing fast on the internet<span lang="EN-GB">.</span><span lang="EN-GB">On</span>e of the most popular<span lang="EN-GB"> social media</span> is Twitter. Many topics are discussed on Twitter such as economic, politic, socia<span lang="EN-GB">l</span>, cultur<span lang="EN-GB">e</span>, <span lang="EN-GB">and l</span>aw<span lang="EN-GB">.</span> One of the hot topics discussed on Twitter is the issue of relocating Indonesia's capital city. However<span lang="EN-GB">, </span>there is controversy from supporters and opponents<span lang="EN-GB">. They</span> have different views. <span lang="EN-GB">This issue leads to</span> a phenomenon of debate on Twitter <span lang="EN-GB">that </span>actually show<span lang="EN-GB">s a </span>collective concern about the public discourse. Sentiment analysis is a process of extracting, understand<span lang="EN-GB">ing </span>and process<span lang="EN-GB">ing</span> unstructured data to get sentiment information which is<span lang="EN-GB"> found</span> in an opinion sentence. Application of sentiment analysis using machine learning methods<span lang="EN-GB"> shows that</span> there are several methods that are often used. In this study, the Support Vector Machine (SVM) method is proposed to be applied to tweets on the topic of relocating Indonesia's capital city for sentiment classification on social media twitter. The classification technique is carried out into 2 classes, namely positive and negative. Based on testing on the sentiment of relocating Indonesia's capital city from social media twitter from 1,116 tweets (404 positive and 832 negative) using SVM obtained accuracy = 96.68%, precision = 95.82%, recall = 94.04% and AUC = 0.979.</em></em></p>

Leveraging Incrementally Enriched Domain Knowledge to Enhance Service Categorization

2012 ◽  
Vol 9 (3) ◽  
pp. 43-66 ◽  
Author(s):  
Jia Zhang ◽  
Jian Wang ◽  
Patrick Hung ◽  
Zheng Li ◽  
Neng Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Structural Feature ◽  
Text Classification ◽  
Domain Knowledge ◽  
Support Vector ◽  
Iterative Approach ◽  
Classification Technique ◽  
Unique Structural Feature ◽  
Service Oriented ◽  
Open Service

This paper reports the authors’ study over an open service and mashup repository, ProgrammableWeb, which groups stored services into predefined categories. Leveraging such a unique structural feature and hidden domain knowledge of the service repository, they extend the Support Vector Machine (SVM)-based text classification technique to enhance service-oriented categorization. An iterative approach is presented to automatically verify and adjust service categorization, which will incrementally enrich domain ontology and in turn enhance the accuracy of service categorization.

Automatic User Domain Classification Based on Support Vector Machine (SVM)

2019 ◽  
Vol 16 (8) ◽  
pp. 3327-3331
Author(s):  
Mercy Paul Selvan ◽  
A. Viji Amutha Mary ◽  
S. Jancy
Keyword(s):  
Support Vector Machine ◽  
Specific Area ◽  
Support Vector ◽  
Svm Classifier ◽  
Keyword Extraction ◽  
Publish Or Perish ◽  
Search Results ◽  
Classification Technique ◽  
Two Phases ◽  
Training Standard

Finding domain of a research paper and a researcher is a crucial task and would be highly appreciable in order to provide personalized search results to the user. An automatic user domain classification technique based on SVM has been proposed in this paper in order to determine the domain of a user based on her publications. In this technique, for a given user, his specific area of domain is determined by classifying the keywords from his publication works. It consists of two phases: keyword extraction and domain classification. In keyword extraction phase, the list of publications corresponding to a user mail id is retrieved by using publish or perish tool. From each of the published papers, the keywords are extracted. In domain classification, SVM classifier is applied to determine the domain of the user. This is performed by training standard keywords from each domain into the SVM classifier. If a user belongs to more than one domain, then the primary domain with more publications will be considered.

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