Optimization of extraction process of compound Clematidis Radix spray by support vector machine

Sentiment analysis is one of the popular techniques gaining attention in recent times. Nowadays, people gain information on reviews of users regarding public transportation, movies, hotel reservation, etc., by utilizing the resources available, as they meet their needs. Hence, sentiment classification is an essential process employed to determine the positive and negative responses. This paper presents an approach for sentiment classification of train reviews using MapReduce model with the proposed Kernel Optimized-Support Vector Machine (KO-SVM) classifier. The MapReduce framework handles big data using a mapper, which performs feature extraction and reducer that classifies the review based on KO-SVM classification. The feature extraction process utilizes features that are classification-specific and SentiWordNet-based. KO-SVM adopts SVM for the classification, where the exponential kernel is replaced by an optimized kernel, finding the weights using a novel optimizer, Self-adaptive Lion Algorithm (SLA). In a comparative analysis, the performance of KO-SVM classifier is compared with SentiWordNet, NB, NN, and LSVM, using the evaluation metrics, specificity, sensitivity, and accuracy, with train review and movie review database. The proposed KO-SVM classifier could attain maximum sensitivity of 93.46% and 91.249% specificity of 74.485% and 70.018%; and accuracy of 84.341% and 79.611% respectively, for train review and movie review databases.

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Personality Analysis through Handwriting Detection Using Android Based Mobile Device

Journal of Information Technology and Computer Science ◽

10.25126/jitecs.20172237 ◽

2018 ◽

Vol 2 (2) ◽

Cited By ~ 2

Author(s):

Waskitha Wijaya ◽

Herman Tolle ◽

Fitri Utaminingrum3

Keyword(s):

Support Vector Machine ◽

Personality Traits ◽

Mobile Device ◽

Extraction Process ◽

Support Vector ◽

Machine Method ◽

Support Vector Machine Method

Graphology is one of the psychology disciplines which aims to study the personality traits of individuals through interpretation of handwriting. We can get information of one’s personality through graphology. In addition, by using android based mobile device, graphology analysis could show one’s personality faster. This study was conducted by taking 42 samples of handwriting from different backgrounds. The feature used in this study was handwriting margin. Besides, Support Vector Machine method was employed to classify the result feature from extraction process. The result of this study showed the accurate average of the application reached 82.738%.

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Classification of color features in butterflies using the Support Vector Machine (SVM)

IJCONSIST JOURNALS ◽

10.33005/ijconsist.v2i02.50 ◽

2021 ◽

Vol 2 (02) ◽

pp. 83-87

Author(s):

Dhian Satria Yudha Kartika ◽

Hendra Maulana

Keyword(s):

Support Vector Machine ◽

Pieris Rapae ◽

Color Space ◽

Danaus Plexippus ◽

Research Process ◽

Extraction Process ◽

Junonia Coenia ◽

Support Vector ◽

Color Quantization ◽

Color Features

Research in digital images is expanding widely and includes several sectors. One sector currently being carried out research is in insects; specifically, butterflies are used as a dataset. A total of 890 types of butterflies divided into ten classes were used as a dataset and classified based on color. Ten types of butterflies include Danaus plexippus, Heliconius charitonius, Heliconius erato, Junonia coenia, Lycaena phlaeas, Nymphalis antiopa, Papilio cresphontes, Pieris rapae, Vanessa atalanta, Vanessa cardui. The process of extracting color features on butterfly wings uses the RGB method to become HSV color space with color quantization (CQ). The purpose of adding CQ is that the computation process is carried out faster without reducing the image's information. In the color feature extraction process, the image is converted into 3-pixel sizes and normalized. The process of normalizing the dataset has the aim that the value ranges in the dataset have the same value. The 890 butterfly dataset was classified using the Support Vector Machine (SVM) method. Based on this research process, the accuracy of the 256x160 pixel size is 72%, the 420x315 pixel is 75%, and the 768x576 pixel is 75%. The test results on a system with a 768x576 pixel get the highest results with a precision value of 74.6%, a recall of 72%, and an f-measure of 73.2% Keywords—image processing; classification; butterflies; color features; features extraction

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Translation of Sign Language for Deaf and Dumb People

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e6555.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 5592-5595

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Sign Language ◽

Extraction Process ◽

Support Vector ◽

Hand Gestures ◽

Sign Languages ◽

Normal People

Deaf-mute people can communicate with normal people with help of sign languages. Our project objective is to analyse and translate the sign language that is hand gestures into text and voice. For this process, RealTimeImage made by deafmute peopleiscapturedanditisgivenasinput to the pre-processor. Then, feature extraction process by using otsu’s algorithm and classification by using SVM(support Vector Machine) can be done. After the text for corresponding sign has been produced. The obtained text is converted into voice with use of MATLAB function. Thus hand gestures made by deaf-mute people has been analysed and translated into text and voice for better communication.

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Rice Grain Classification using Multi-class Support Vector Machine (SVM)

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v8.i3.pp215-220 ◽

2019 ◽

Vol 8 (3) ◽

pp. 215

Author(s):

Shafaf Ibrahim ◽

Nurul Amirah Zulkifli ◽

Nurbaity Sabri ◽

Anis Amilah Shari ◽

Mohd Rahmat Mohd Noordin

Keyword(s):

Support Vector Machine ◽

Brown Rice ◽

Major Axis ◽

Extraction Process ◽

Processing Technique ◽

Image Processing Technique ◽

Support Vector ◽

Rice Grain ◽

Axis Length

<span>Presently, the demands for rice are increasing. This will affects the need for producing and sorting rice grain in faster and exceed the normal requirement. However, the manual rice classification using naked eyes are not very accurate and only professionals are able to do it. Machine learning is found to be a suitable technique for rice classification in producing an accurate result and faster solution. Thus, a study on the classification of rice grain using an image processing technique is presented. The rice grain image went through the pre-processing process which includes the grayscale and binary conversion, and segmentation before the feature extraction process. Four attributes of shape descriptor which are area, perimeter, major axis length, and minor axis length and three attributes of color descriptor which are hue, saturation and value were extracted from each rice grain image. In another note, a Multi-class Support Vector Machine (SVM) is used to classify the three types of rice grain which are basmathi, ponni and brown rice. The performance of the proposed study is evaluated to 90 testing images which returned 92.22% of classification accuracy. The study is expected to assist the Agrotechnology industry in automatic classification of rice grain in the future.</span>

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Support vector machine? – not only for MRI-images

10.1055/s-0039-3403050 ◽

2020 ◽

Author(s):

V Vasilevska ◽

K Schlaaf ◽

H Dobrowolny ◽

G Meyer-Lotz ◽

HG Bernstein ◽

...

Keyword(s):

Support Vector Machine ◽

Support Vector

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

2019 ◽

Vol 15 (2) ◽

pp. 275-280

Author(s):

Agus Setiyono ◽

Hilman F Pardede

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Spam Detection ◽

Support Vector Machine Algorithm ◽

Data Mining Techniques ◽

To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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