PENGENALAN WAJAH MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK-RESTRICTED BOLTZMANN MACHINE BERBASIS PRINCIPAL COMPONENT ANALYSIS

Teknologi pengenalan wajah berpotensi untuk diterapkan pada berbagai bidang dalam kehidupan sehari-hari. Penelitian ini melakukan pengembangan teknologi pengenalan wajah dengan mengusulkan metode Convolutional Neural Network-Restricted Boltzmann Machine (CNN-RBM) berbasis Principal Component Analysis (PCA) menggunakan set data Labeled Faces in the Wild (LFW). CNN-RBM berbasis PCA memanfaatkan PCA sebagai pereduksi dimensi pada input, kemudian menggunakan CNN sebagai ekstraksi fitur, dan menggunakan RBM pada tahap klasifikasi wajah. Hasil eksperimen membuktikan bahwa CNN-RBM berbasis PCA mampu mengungguli baseline dengan peningkatan akurasi sebesar 1,6%. Face recognition technology can be applied in various fields of in everyday life. This research develops face recognition technology using Convolutional Neural Network-Restricted Boltzmann Machine (CNN-RBM) based on Principal Component Analysis (PCA) using labeled Faces in the Wild (LFW) set data. PCN-based CNN-RBM uses PCA as a dimension reduction in input, then uses CNN as a feature extraction, and uses RBM in face classification. The experimental results prove that PCN-based CNN-RBM was able to outperform the baseline with 1,6% accuracy improvement

Manipulation Detection in Satellite Images Using Deep Belief Networks

Satellite images are more accessible with the increase of commercial satellites being orbited. These images are used in a wide range of applications including agricultural management, meteorological prediction, damage assessment from natural disasters, and cartography. Image manipulation tools including both manual editing tools and automated techniques can be easily used to tamper and modify satellite imagery. One type of manipulation that we examine in this paper is the splice attack where a region from one image (or the same image) is inserted (spliced) into an image. In this paper, we present a one-class detection method based on deep belief networks (DBN) for splicing detection and localization without using any prior knowledge of the manipulations. We evaluate the performance of our approach and show that it provides good detection and localization accuracies in small forgeries compared to other approaches

A survey of machine learning methods applied to anomaly detection on drinking-water quality data

Abstract: Traditional machine learning (ML) techniques such as support vector machine, logistic regression, and artificial neural network have been applied most frequently in water quality anomaly detection tasks. This paper presents a review of progress and advances made in detecting anomalies in water quality data using ML techniques. The review encompasses both traditional ML and deep learning (DL) approaches. Our findings indicate that: 1) Generally, DL approaches outperform traditional ML techniques in terms of feature learning accuracy and fewer false positive rates. However, is difficult to make a fair comparison between studies because of different datasets, models and parameters employed. 2) We notice that despite advances made and the advantages of the extreme learning machine (ELM), application of ELM is sparsely exploited in this domain. This study also proposes a hybrid DL-ELM framework as a possible solution that could be investigated further and used to detect anomalies in water quality data