Adaptive Online Sequential ELM for Concept Drift Tackling

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect underfitting condition.Comment: Hindawi Publishing. Computational Intelligence and Neuroscience Volume 2016 (2016), Article ID 8091267, 17 pages Received 29 January 2016, Accepted 17 May 2016. Special Issue on "Advances in Neural Networks and Hybrid-Metaheuristics: Theory, Algorithms, and Novel Engineering Applications". Academic Editor: Stefan Hauf

Kinerja Skema Pemberian Tanda Air Video Dijital Berbasis Dwt-svd Dengan Detektor Semi-blind

On the Performance of SVD-DWT Based Digital Video Watermarking Technique with Semi-Blind Detector.This paper presents a watermarking technique for digital video. The proposed scheme is developed based on the workof Ganic and Chan which took the virtue of SVD and DWT. While the previous works of Chan has the blind detectorproperty, our attempt is to develop a scheme with semi-blind detector, by using the merit of the DWT-SDV techniqueproposed by Ganic which was originally applied to still image. Overall, our experimental results show that our proposedscheme has a very good imperceptibility and is reasonably robust especially under several attacks such as compression,blurring, cropping, and sharpening

Peat moisture and water level relationship in a tropical peat swamp forest

Forest fire occurring in the tropical peat swamp forest has been a major concern and has been on the increase at an alarming rate during the past decades. This problem is further compounded by the fact that some of the affected areas have burned twice or more. If left unabated, peat areas that will be at risk to frequent fires will be on the increase. Peat soils when dry, posed a high risk of combustibility. It is therefore essential to understand the moisture characteristic of the peat soil in order to develop forest fire management programme. The objective of this study was to monitor peat moisture and water level relationship. A study has been conducted to investigate the temporal characteristics of the peat water level and to understand the relationship between water table and peat moisture. The study was conducted at Compartment 101, Raja Musa Forest Reserve, Selangor, Malaysia. This area was on fire in 1998, early June 1999 and 9 March 2000. A 180 m long transect starting from the edge of the canal into the forest was established. Twenty peizometers of 2 m length each, were installed along the established transect. Water table and peat samples were taken weekly beginning at 24 October to 20 December 2000. Peat soils were analyzed for soil moisture on oven-dry basis. The result showed that there was a systematic rise and fall of the water table. The maximum and minimum water table recorded were at 22.6 cm above ground and 31.5 cm below ground, respectively. In the forested area, results showed that the changes in water level had a smaller range (16.9 cm) compared to the open area (25.1 cm). Mean peat moisture sample at depths 0 cm (surface), 50 and 100 cm were 577,891 and 1070%, respectively. ANOVA analysis showed that lower depth has significantly higher moisture (at 95% confidence level) compared to higher layers. The study shows the temporal variations of water level in peat swamp forest. This variations can be used as a basis for early warning indicator of peat forest fire. © 2006 Asian Network for Scientific Information

Klasifikasi Beat Aritmia Pada Sinyal Ekg Menggunakan Fuzzy Wavelet Learning Vector Quantization

Pengenalan pola beat dalam analisa rekaman elektrokardiogram (EKG) menjadi bagian yang penting dalam deteksi penyakit jantung terutama aritmia. Banyak metode yang dikembangkan terkait dengan pengenalan pola beat, namun sebagian besar masih mengunakan algoritma klasifikasi klasik di mana masih belum mampu mengenali outlier klasifikasi. Fuzzy Learning Vector Quantization (FLVQ) merupakan salah satu algoritma yang mampu untuk mengenali outlier klasifikasi tetapi juga memiliki kelemahan untuk sistem uji yang bukan data berkelompok. Dalam tulisan ini peneliti mengusulkan Fuzzy Wavelet LearningVector Quantization (FWLVQ), yaitu modifikasi FLVQ sehingga mampu mengatasi data crisp maupun data fuzzy dan juga memodifikasi inferensi sistemnya sebagai perpaduan model fuzzy Takagi Sugeno Kang dengan wavelet. Sinyal EKG diperoleh dari database MIT-BIH. Sistem pengenalan pola beat secara keseluruhan terbagi atas dua bagian yaitu data pra proses dan klasifikasi. Hasil percobaan diperoleh bahwa FWLVQ memiliki akurasi sebesar 90.20% untuk data yang tidak mengandung outlier klasifikasi dan 87.19% untuk data yang melibatkan outlier klasifikasi dengan rasio data uji outlier klasifikasi dengan data non-outlier sebesar 1:1. The recognition of beat pattern in analysis of recording an electrocardiogram (ECG) becomes an important detection of heart disease, especially arrhythmias. Many methods are developed related to the recognition of beat patterns, but most still use the classical classification algorithms which are still not able to identify outlier classification. Fuzzy Learning Vector Quantization (FLVQ) is one of the algorithms that can identify outlier classification but also has a weakness for test systems that are not grouped data. In this paper we propose a Fuzzy Wavelet Quantization Learning Vector (FWLVQ), which is modified so as to overcome FLVQ crisp data and fuzzy data and also modify the inference system as a combination of Takagi Sugeno Kang fuzzy model with the wavelet. ECG signal obtained from the MIT-BIH database. Beat pattern recognition system as a whole is divided into two parts: data pre-processing and classification. The experimental results obtained that FWLVQ has an accuracy 90.20% for data that does not contain outlier classification and 87.19% for the classification of data involving outlier ratio outlier test data classification with non-outlier data of 1:1

Particle Filter with Binary Gaussian Weighting and Support Vector Machine for Human Pose Interpretation

Human pose interpretation using Particle filter with Binary Gaussian Weighting and Support Vector Machine isproposed. In the proposed system, Particle filter is used to track human object, then this human object is skeletonizedusing thinning algorithm and classified using Support Vector Machine. The classification is to identify human pose,whether a normal or abnormal behavior. Here Particle filter is modified through weight calculation using Gaussiandistribution to reduce the computational time. The modified particle filter consists of four main phases. First, particlesare generated to predict target’s location. Second, weight of certain particles is calculated and these particles are used tobuild Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, updateparticles based on each weight. The modified particle filter could reduce computational time of object tracking sincethis method does not have to calculate particle’s weight one by one. To calculate weight, the proposed method buildsGaussian distribution and calculates particle’s weight using this distribution. Through experiment using video datataken in front of cashier of convenient store, the proposed method reduced computational time in tracking process until68.34% in average compare to the conventional one, meanwhile the accuracy of tracking with this new method iscomparable with particle filter method i.e. 90.3%. Combination particle filter with binary Gaussian weighting andsupport vector machine is promising for advanced early crime scene investigation

Particle Filter with Gaussian Weighting for Human Tracking

Particle filter for object tracking could achieve high tracking accuracy. To track the object, this method generates a number of particles which is the representation of the candidate target object. The location of target object is determined by particles and each weight. The disadvantage of conventional particle filter is the computational time especially on the computation of particle’s weight. Particle filter with Gaussian weighting is proposed to accomplish the computational problem. There are two main stages in this method, i.e. prediction and update. The difference between the conventional particle filter and particle filter with Gaussian weighting is in the update Stage. In the conventional particle filter method, the weight is calculated in each particle, meanwhile in the proposed method, only certain particle’s weight is calculated, and the remain particle’s weight is calculated using the Gaussian weighting. Experiment is done using artificial dataset. The average accuracy is 80,862%. The high accuracy that is achieved by this method could use for the real-time system trackin

Development of a database-driven web-based library application (Table of contents and abstract only)

The internet has been the most popular ways for sharing information in recent years. As the number of publicly available site increase, the users demand for more dynamic content has also increased. This has lead to a proliferation of technologies available to serve up dynamic content. Currently the school library database system has been created as a MS Access 97 application, a stand-alone database which consists of a static site. MS Access provides an easy user interface, but it can only be used as a personal or single-user application and managing limited amount of data. The objective of this project is to develop and implement a database-driven web-based library system using MySQL (data management platform), PHF' (server-side scripting language) and Apache (web server). The advantage of implementing a database-driven web-based is that the unlimited access for looking up and searching for information in the database which also known as on-line search mechanism. An authenticated and a strict access control by password will apply to the administrative user in editing and maintenance operations. This system also implements a read-only, non-authentisized interface to serve other web user which should be available on a 24x7 basis. (Author's abstract

Dataset Suara dan Teks Berbahasa Indonesia Pada Rekaman Podcast dan Talk show

Salah satu faktor keberhasilan suatu model pembelajaran dalam machine learning atau deep learning adalah dataset yang digunakan. Pada tulisan ini menyajikan dataset suara dari rekaman podcast dan talk show beserta transkripsi berbahasa Indonesia. Dataset ini disajikan karena belum adanya ketersediaan dataset berbahasa Indonesia yang dapat diakses secara publik untuk digunakan pada pembelajaran model Text-to-Speech ataupun Audio Speech Recognition. Dataset terdiri dari 3270 rekaman yang diproses untuk mendapatkan transkripsi berupa teks atau kalimat berbahasa Indonesia. Dalam pembuatan dataset ini dilakukan beberapa tahapan seperti pra-pemrosesan, tahapan translasi, tahapan validasi pertama dan tahapan validasi kedua. Dataset dibuat dengan format yang mengikuti format dari dataset LJSpeech untuk memudahkan pemrosesan dataset ketika digunakan dalam suatu model sebagai input. Dataset ini diharapkan dapat membantu meningkatkan kualitas pembelajaran untuk pemrosesan Text-to-Speech seperti pada model Tacotron2 ataupun pada pemrosesan Audio Speech Recognition untuk bahasa Indonesia