The Recognition Of Semaphore Letter Code Using Haar Wavelet And Euclidean Function

Putra, Leonardus Sandy Ade; Sumarno, Linggo; Gunawan, Vincentius Abdi

doi:10.1109/eecsi.2018.8752707

Cited by 3 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 1 Illustration of semaphore movement in the alphabet [4] Currently, the introduction of semaphore code letters mainly still relies on a simple method: direct observation by the human eye. Researchers argue that this observation is a traditional method that requires more time and energy without the assistance of technology in today's era.…”

Section: Semaphore Is a Means Of Long-distance Communication Using Se...mentioning

confidence: 99%

“…The recognition rate reached 95.4% by utilizing the decimation variation in the extraction feature. This study uses a maximum distance of 5 meters which can be used to recognize semaphore objects [4]. Tian et al [16] use semaphores in the robot's world to carry out the mirroring process of the semaphore movement by demonstrating to the imitating robot.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Semaphore letter code recognition system using wavelet method and back propagation neural network

2024

IJATEE

View full text Add to dashboard Cite

Section: Semaphore Is a Means Of Long-distance Communication Using Se...mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Semaphore letter code recognition system using wavelet method and back propagation neural network

2024

IJATEE

View full text Add to dashboard Cite

“…The next step is to cut the unused part outside the iris, leaving a part that is close to the iris. Then, The eye image will be resized so that the entire image has the same pixel size when it enters the extraction process [25]. Fig.…”

Section: B Pre-processingmentioning

confidence: 99%

Identification of Coronary Heart Disease through Iris using Gray Level Co-occurrence Matrix and Support Vector Machine Classification

Gunawan¹,

Putra²,

Imansyah³

et al. 2022

IJACSA

Self Cite

View full text Add to dashboard Cite

Now-a-days, coronary heart disease is one of the deadliest diseases in the world. An unfavorable lifestyle, lack of physical activity, and consuming tobacco are the causes of coronary heart disease aside from genetic inheritance. Sometimes the patient does not know whether he has abnormalities in heart function or not. Therefore, this study proposes a system that can detect heart abnormalities through the iris, known as the Iridology method. The system is designed automatically in the iris detection to the classification results. Feature extraction using five characteristics is applied to the Gray Level Cooccurrence Matrix (GLCM) method. The classification process uses the Support Vector Machine (SVM) with linear kernel variation, Polynomial, and Gaussian to obtain the best accuracy in the system. From the system simulation results, the use of the Gaussian kernel can be relied on in the classification of iris conditions with an accuracy rate of 91%, then the Polynomial kernel accuracy reaches 89%, and the linear kernel accuracy reaches 87%. This study has succeeded in detecting heart conditions through the iris by dividing the iris into normal iris and abnormal iris.

show abstract

Detection of Actinic Keratosis Skin Cancer Using Gray Level Co-occurrence Matrix Texture Extraction and Color Extraction With Support Vector Machine Classification

Putra,

Gunawan,

Saragih

2023

Teknik

View full text Add to dashboard Cite

Nowadays, humans tend to carry out activities during the day, both indoors and outdoors. Activities carried out outdoors cause human skin to often receive direct exposure to sunlight, which contains ultraviolet (UV) rays. Direct exposure to UV rays on the skin will harm the skin's health, which is the covering of the human body. Harmful effects on the skin usually include the skin becoming dark and dull, burns, and even causes cancer. One of the skin cancers that may appear on human skin is Actinic Keratosis (AK) cancer. AK cancer is a type of cancer that is classified as benign and can be cured with medical help. However, if this cancer is not caught early, it can become Squamous Cell Carcinoma (SCC), a type of malignant cancer. This research aims to design a system for identifying AK cancer types using color and texture feature extraction. RGB color feature extraction is obtained from image color segmentation and RGB values. The Gray Level Co-occurrence Matrix (GLCM) method is used to determine the texture of the skin cancer. Identification is carried out by a classification process using a Support Vector Machine (SVM), which can recognize the type of AK cancer. This research uses three classification methods: classification with color extraction, classification with texture extraction, and classification with color and texture extraction. Research shows that the highest level of accuracy in cancer recognition reaches 96% by combining color and texture extraction results as classification determinants. So, the system designed has succeeded in recognizing the type of AK cancer early on..

show abstract

The Recognition Of Semaphore Letter Code Using Haar Wavelet And Euclidean Function

Cited by 3 publications

References 4 publications

Semaphore letter code recognition system using wavelet method and back propagation neural network

Semaphore letter code recognition system using wavelet method and back propagation neural network

Identification of Coronary Heart Disease through Iris using Gray Level Co-occurrence Matrix and Support Vector Machine Classification

Detection of Actinic Keratosis Skin Cancer Using Gray Level Co-occurrence Matrix Texture Extraction and Color Extraction With Support Vector Machine Classification

Contact Info

Product

Resources

About