Metaheuristic Algorithms for Convolution Neural Network

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).Comment: Article ID 1537325, 13 pages. Received 29 January 2016; Revised 15 April 2016; Accepted 10 May 2016. Academic Editor: Martin Hagan. in Hindawi Publishing. Computational Intelligence and Neuroscience Volume 2016 (2016

CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING

Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering’s based on Euclidian distance

CIELab Color Moments: Alternative Descriptors for LANDSAT Images Classification System

This study compares the image classification system based on normalized difference vegetation index (NDVI) and Latent Dirichlet Allocation (LDA) using CIELab color moments as image descriptors.  It was implemented for LANDSAT images classification by evaluating the accuracy values of classification systems. The aim of this study is to evaluate whether the CIELab color moments can be used as an alternatif descriptor replacing NDVI when it is implemented using LDA-based classification model.  The result shows that the LDA-based image classification system using CIELab color moments provides better performance accuracy than the NDVI-based image classification system, i.e 87.43% and 86.25% for LDA-based and NDVI-based respectively.  Therefore, we conclude that the CIELab color moments which are implemented under the LDA-based image classification system can be assigned as alternative image descriptors for the remote sensing image classification systems with the limited data availability, especially when the data only available in true color composite images.This study compares the image classification system based on normalized difference vegetation index (NDVI) and Latent Dirichlet Allocation (LDA) using CIELab color moments as image descriptors.  It was implemented for LANDSAT images classification by evaluating the accuracy values of classification systems. The aim of this study is to evaluate whether the CIELab color moments can be used as an alternatif descriptor replacing NDVI when it is implemented using LDA-based classification model.  The result shows that the LDA-based image classification system using CIELab color moments provides better performance accuracy than the NDVI-based image classification system, i.e 87.43% and 86.25% for LDA-based and NDVI-based respectively.  Therefore, we conclude that the CIELab color moments which are implemented under the LDA-based image classification system can be assigned as alternative image descriptors for the remote sensing image classification systems with the limited data availability, especially when the data only available in true color composite images

Combining Deep Belief Networks and Bidirectional Long Short-Term Memory

This paper proposes a new combination of Deep Belief Networks (DBN) and Bidirectional Long Short-Term Memory (Bi-LSTM) for Sleep Stage Classification. Tests were performed using sleep stages of 25 patients with sleep disorders. The recording comes from electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG) represented in signal form. All three of these signals processed and extracted to produce 28 features. The next stage, DBN Bi-LSTM is applied. The analysis of this combination compared with the DBN, DBN HMM (Hidden Markov Models), and Bi-LSTM. The results obtained that DBN Bi-LSTM is the best based on precision, recall, and F1 score

Psikologi Agama

buku ini membahas tentang fungsi agama dalam kehidupan sehari har

EKSTRAKSI FITUR FRAKTAL DAN MORFOLOGI SINYAL ELEKTROKARDIOGRAM DAN PEMANFAATANNYA DALAM KLASIFIKASI DEEP SLEEP

Detak jantung manusia dapat memberikan informasi yang berguna tentang aktivitas yang terjadi di dalam tubuh. Salah satu informasi yang dapat diperoleh dari rekaman detak jantung atau elektrokardiogram adalah tingkat keterlelapan tidur seseorang (sleep stages). Dari sinyal elektrokardiogram seseorang, tingkat keterlelapan tidurnya dapat dikenali dengan terlebih dahulu mengekstrak fitur yang merepresentasikan sinyal elektrokardiogram tersebut secara keseluruhan. Ekstraksi dilakukan agar dimensi data dapat tereduksi sehingga proses klasifikasi dapat lebih mudah dilakukan. Penelitian ini melakukan ekstraksi fitur fraktal dan morfologi dari sinyal elektrokardiogram yang diperoleh dari PhysioNet. Sebelum melakukan ekstraksi fitur morfologi dari sinyal elektrokardiogram, terlebih dahulu dilakukan “Wavelet Denoising†untuk menghilangkan noise yang terdapat pada sinyal. Human heart rate can provide useful information about the activities that occur in the body. One of information which may be obtained from recording the heart rate or electrocardiogram is commonly called a person's level of deep sleep (sleep stages). From a person's electrocardiogram signal, the level of deep sleep recognizable by extracting features that represent the electrocardiogram signal as a whole. Extraction is done so that the dimension of the data can be reduced so that the classification process can be more easily done. This study aims to extract fractal features and morphology of the electrocardiogram signal obtained from PhysioNet. Prior to the extraction of morphological features of the electrocardiogram signal, first performed “Wavelet Denoising†to remove the noise contained in the signal

Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera

The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86% -97% recognition rate on a variety of poses and variations in angles between 0 °- 22.5 °. The variation of illuminance measured using a lux meter can result in 90% -100% recognition rate for the category of at least dim lighting levels (above 10 lux)

Batik Classification using Deep Convolutional Network Transfer Learning

Batik fabric is one of the most profound cultural heritage in Indonesia. Hence, continuous research on understanding it is necessary to preserve it. Despite of being one of the most common research task, Batik’s pattern automatic classification still requires some improvement especially in regards to invariance dilemma. Convolutional neural network (ConvNet) is one of deep learning architecture which able to learn data representation by combining local receptive inputs, weight sharing and convolutions in order to solve invariance dilemma in image classification. Using dataset of 2,092 Batik patches (5 classes), the experiments show that the proposed model, which used deep ConvNet VGG16 as feature extractor (transfer learning), achieves slightly better average of 89 ± 7% accuracy than SIFT and SURF-based that achieve 88 ± 10% and 88 ± 8% respectively. Despite of that, SIFT reaches around 5% better accuracy in rotated and scaled dataset

A non-negative matrix factorization based clustering to identify potential tuna fishing zones

Many nonnegative matrix factorization based clusterings are employed in discovering pattern and knowledge. Considering the sparseness nature of our data set about the daily tuna fishing data, we attempted to utilize a clustering approach, which is based on non-negative matrix factorization. Adding sparseness constraint and assigning good initial value in the modified NMF method, a proposed algorithm Direct-NMFSC yielded better result cluster compared to other methods which are also utilizing sparse constraint to their approaches, SNMF and NMFSC. The result of this study shows that Direct-NMFSC has 5.376 times of iteration number less than NMFSC in average with 531.97 as the CH index result. The determination of potential fishing zones is one of the essential efforts in the potential fishing zone mapping system for tuna fishing. By means of this novel data-driven study to construct the information and to identify the potential tuna fishing zones is done. We also showed that utilizing the Direct-NMFSC can spot and identify the potential tuna fishing zones presented in red cluster that covers both the spatial and temporal information