A novel recognition system for human activity based on wavelet packet and support vector machine optimized by improved adaptive genetic algorithm

2014 ◽  
Vol 13 ◽  
pp. 211-220 ◽  
Author(s):  
Jin Jiang ◽  
Ting Jiang ◽  
Shijun Zhai
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Human Activity ◽  
Wavelet Packet ◽  
Recognition System ◽  
Support Vector ◽  
Adaptive Genetic Algorithm

scholarly journals An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification using Support Vector Machine Kernels

2021 ◽  
Vol 18 (17) ◽  
Author(s):  
Micheal Olaolu AROWOLO ◽  
Marion Olubunmi ADEBIYI ◽  
Chiebuka Timothy NNODIM ◽  
Sulaiman Olaniyi ABDULSALAM ◽  
Ayodele Ariyo ADEBIYI
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Malaria Vector ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Rna Seq ◽  
Gene Expressions

As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infected cells embrace an unpredictable and erratic life period. Millions of individual parasites have gene expressions. Ribonucleic acid sequencing (RNA-seq) is a popular transcriptional technique that has improved the detection of major genetic probes. The RNA-seq analysis generally requires computational improvements of machine learning techniques since it computes interpretations of gene expressions. For this study, an adaptive genetic algorithm (A-GA) with recursive feature elimination (RFE) (A-GA-RFE) feature selection algorithms was utilized to detect important information from a high-dimensional gene expression malaria vector RNA-seq dataset. Support Vector Machine (SVM) kernels were used as the classification algorithms to evaluate its predictive performances. The feasibility of this study was confirmed by using an RNA-seq dataset from the mosquito Anopheles gambiae. The technique results in related performance had 98.3 and 96.7 % accuracy rates, respectively. HIGHLIGHTS Dimensionality reduction method based of feature selection Classification using Support vector machine Classification of malaria vector dataset using an adaptive GA-RFE-SVM GRAPHICAL ABSTRACT

تطبيق تصنيف بيانات اللوكيميا باستخدام خوارزمية AGA-SVM Applying of Classification Leukemia's Dataset using AGA-SVM Algorithm

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
عمر صابر قاسم ◽  
محمد علي محمد
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Data Classification ◽  
Feature Space ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Features Selection ◽  
Svm Algorithm

تعد مسألة اختيار الميزات (Features selection) الضرورية في عملية تصنيف البيانات (Data Classification) من المسائل ذات الأهمية الكبيرة في تحديد كفاءة التقنية المستخدمة للتصنيف خصوصا عندما يكون حجم هذه البيانات كبيرا جدا مثل بيانات اللوكيميا (leukemia) المعتمدة على الجينات. اذ تم استخدام خوارزمية مقترحة(AGA_SVM) مهجنة بين الخوارزمية الجينية المعدلة (Adaptive Genetic Algorithm) مع تقنية الة المتجه الداعم (Support Vector Machine)، اذ تقوم الخوارزمية الجينية المعدلة بتحويل البيانات من فضاء الأنماط العالي البعد (High-D Patterns Space) إلى فضاء الخواص الواطئ (Low-D Feature Space) لأجل تحديد الميزات الضرورية واللازمة لعملية التصنيف والتي تتم من خلال تقنية الة المتجه الداعم. وتبين من خلال التطبيق على بيانات اللوكيميا ان نسبة التصنيف كانت (100%) لحالات التدريب والاختبار بالنسبة للطريقة المقترحة (AGA_SVM) مقارنة مع الطريقة الاعتيادية التي أخطأت في عدة حالات تصنيف، مما يدل على كفاءة الطريقة المقترحة مقارنة مع الطريقة الاعتيادية.

Classification of Surface EMGs Using Wavelet Packet Energy Analysis and a Genetic Algorithm-Based Support Vector Machine

2013 ◽  
Vol 45 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Y. Rong ◽  
D. Hao ◽  
X. Han ◽  
Y. Zhang ◽  
J. Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Energy Analysis ◽  
Wavelet Packet ◽  
Support Vector

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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