A Melanoma Skin Cancer Detection Using Machine Learning Technique: Support Vector Machine

<p>Information extraction from data is one of the key necessities for data analysis. Unsupervised nature of data leads to complex computational methods for analysis. This paper presents a density based spatial clustering technique integrated with one-class Support Vector Machine (SVM), a machine learning technique for noise reduction, a modified variant of DBSCAN called Noise Reduced DBSCAN (NRDBSCAN). Analysis of DBSCAN exhibits its major requirement of accurate thresholds, absence of which yields suboptimal results. However, identifying accurate threshold settings is unattainable. Noise is one of the major side-effects of the threshold gap. The proposed work reduces noise by integrating a machine learning classifier into the operation structure of DBSCAN. The Experimental results indicate high homogeneity levels in the clustering process.</p>

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Kriging Empirical Mode Decomposition via support vector machine learning technique for autonomous operation diagnosing of CHP in microgrid

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2018.09.028 ◽

2018 ◽

Vol 145 ◽

pp. 58-70 ◽

Cited By ~ 18

Author(s):

Zehui Shao ◽

Karzan Wakil ◽

Muhammet Usak ◽

Mohammad Amin Heidari ◽

Bo Wang ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Empirical Mode Decomposition ◽

Support Vector ◽

Machine Learning Technique ◽

Autonomous Operation ◽

Mode Decomposition ◽

Learning Technique

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Application of Support Vector Machine in Designing Theo Jansen Linkages

Applied Sciences ◽

10.3390/app9030371 ◽

2019 ◽

Vol 9 (3) ◽

pp. 371 ◽

Cited By ~ 1

Author(s):

Min-Chan Hwang ◽

Chiou-Jye Huang ◽

Feifei Liu

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Circular Orbit ◽

Energy Efficient ◽

Support Vector ◽

Legged Robot ◽

Machine Learning Technique ◽

Learning Technique ◽

Four Bar Linkage ◽

Lateral Axis

Theo Jansen linkage is an appealing mechanism to implement a bio-inspired motion for a legged robot. The oval orbit that is generated by the Theo Jansen linkage, possessing a transversal axis longer than a lateral axis, achieves energy efficient walking comparing to the circular orbit that is generated by the four-bar linkage. However, the ensemble of its links can produce different patterns of orbits other than oval orbits, some of which are not qualified to be the foot trajectories. It is vital to give a guideline, to which one can refer, to ensure the design of a Theo Jansen leg always possessing its eligibility. In this paper, the machine learning technique, called SVM (Support Vector Machine) along with machine vision serving as a classifier to distinguish desired trajectories from undesired ones, is employed and two databases gathering all eligible data concerned with properties of orbits and dimensions of Theo Jansen linkages are established. Based upon SVM to delimit the eligible designs, one can seek the improvement of a Theo Jansen linkage by resizing its links without rendering an ineligible design. The ensemble dimensions of Theo Jansen linkage can be determined by searching the orbits in compliance with the specification of obliqueness and slenderness from the database of properties and using their correspondent identity numbers to list all candidates of TJLs from the database of dimensions. With the aid of this proposed method, the TJLs have been successfully designed and implemented on a legged robot.

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Aplikasi Mobile Deteksi Dini Kanker Kulit Berdasarkan Image Processing

Jurnal Litbang Edusaintech ◽

10.51402/jle.v2i2.44 ◽

2021 ◽

Vol 2 (2) ◽

pp. 100-106

Author(s):

Fina Royana ◽

Puput Yuniar Maulida ◽

Rully Nurul Hasanah ◽

Sondari Setia Rahayu ◽

Rasim Rasim

Keyword(s):

Machine Learning ◽

Skin Cancer ◽

Cancer Detection ◽

Future Health ◽

Skin Cancers ◽

Threatening Condition ◽

Life Threatening ◽

Management Techniques ◽

Skin Cancer Detection ◽

Melanoma Skin

Currently, between 2 and 3 million non-melanoma skin cancers and 132,000 melanoma skin cancers occur globally each year (WHO, 2017). Skin cancer is one type of cancer that can cause death for many people. Because of this, an application is needed to easily detect skin cancer early that the cancer can be handled with more quickly. Besides, consultations with dermatologists have better prognosis (Avilés-Izquierdo et. al., 2016). Due to that, we built an early skin cancer detection application with dermatologist consultation. Our application helps to diagnose skin cancer before it grows into a life-threatening condition and is crucial to preserving lifestyle, future health, and aesthetics. Besides, thanks to online doctor consultations we have, however, getting diagnosed, prescribed and treated for your issues without spending time travelling to and from the doctors and waiting in queues can be just as effective. We used three management techniques such as machine learning to create data pipelines, build a model, and convert the model to TensorFlow lite with post-training quantization. Android to deploy the TensorFlow lite model and create the application. The application has a real-time connection using firebase. Moreover, cloud to create a simple database for doctor and diagnosis services on firebase.

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