Rim Romdhane1, Zeineb Ghrib1 and Rakia Jaziri2, 1Deavoteam Research and Innovation, France, 2Paris 8 University, France
Anomaly detection is the process of identifying unexpected items or events in data sets, which differ from the norm. Anomaly detection is an active research field which attracts the attention of many business and research actors. Typically, anomalous data can be connected to some kind of problem or rare event such as bank fraud, medical problems, cloud monitoring or network intrusions detection, etc. Dealing with effective anomaly detection for complex and high-dimensional time series data remains a challenging task. In this work, We propose an approach based on an LSTM Autoencoder trained on normal records to learn efficient normal sequence representations combined with a supervised classifier to detect abnormal data. Experimental results show that the encoding step based on LSTM pretrained encoder alows to get efficient representation of data that we can accurately detect abnormal records. In fact, the encoded representation reduces significantly the correlations between normal and abnormal records and allows us to have an efficient latent data representation that separates consistently the two classes. The proposed approach was compared with state-of-the art approaches ,,, and outperform them by reducing significantly the classification error.
Anomaly detection · LSTM Autoencoder · Supervised Classification · Latent Representation.
Panaree Chaipayom1, Assc Prof.Somying Thainimit2, Dr.Duangrat Gansawat3 and Prof.Hirohiko Kaneko4, 1Department of Electrical Engineering, Kasetsart University, Bangkok, Thailand, 2Department of Electrical Engineering, Kasetsart University, Bangkok, Thailand, 3National Electronics and Computer Technology Center, Pathum Thani, Thailand and 4Department of Information and Communications Engineering, Tokyo Institute of Technology, Yokohama, Japan
Glaucoma is the second most common cause of blindness. It is caused by high intraocular pressure within the eye, result in an injury to the optic nerve. Fundus images are widely used to detect diagnosis of glaucoma. Therefore, image processing technology is used in various systems that aid in screening as glaucoma screening system can be used to screen for glaucoma. The use of glaucoma screening system can help solve cost issues and the workload of healthcare professionals. This study proposes a method for identifying glaucoma from fundus images by using a fusion three feature to find glaucoma’s significant by using wavelet decomposition and texture such as Discrete Wavelet Transform (DWT), Principal Components Analysis (PCA), and Local Binary Patterns (LBP). Support vector machine (SVM) is used to classify high accuracy at 95% by using tenfold cross-validation with HRF Database and using fusion three feature as DWT, PCA, and LBP.
Glaucoma, Fundus Image, Data Mining, Feature Extraction, Feature Ranking, Classification.
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