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The general focus of this study is to design a multilevel deep learning model that provides big data analytics and emergency management knowledge. A big data covariance analysis approach has been used to find multilevel representations of data based on prior knowledge from large scale power systems. For purpose of meeting requirements of incremental knowledge discovery, an adaptive regression algorithm...
Sentiment analysis is a technology with great practical value, it can solve the phenomenon of network comment information disorderly to a certain extent, and accurate positioning of user information required. Currently for Chinese sentiment analysis research is relatively small, including a variety of supervised learning method of classification result and the text feature representation methods and...
Epigenetics is the study of heritable changesin gene expression that does not involve changes to theunderlying DNA sequence, i.e. a change in phenotype notinvolved by a change in genotype. At least three mainfactor seems responsible for epigenetic change including DNAmethylation, histone modification and non-coding RNA, eachone sharing having the same property to affect the dynamicof the chromatin...
With the recent advancement of multilayer convolutional neural networks (CNN), deep learning has achieved amazing success in many areas, especially in visual content understanding and classification. To improve the performance and energy-efficiency of the computation-demanding CNN, the FPGA-based acceleration emerges as one of the most attractive alternatives. In this paper we design and implement...
Convolutional neural networks (CNN) accelerators have been proposed as an efficient hardware solution for deep learning based applications, which are known to be both compute-and-memory intensive. Although the most advanced CNN accelerators can deliver high computational throughput, the performance is highly unstable. Once changed to accommodate a new network with different parameters like layers...
In this study, Kernel Principal Component Analysis is applied to understand and visualize non-linear variation patterns by inverse mapping the projected data from a high-dimensional feature space back to the original input space. Performance Evaluation of Random Forest on various data sets has been compared to understand accuracy and various statistical measures of interest.
Information needs of the users have grown exponentially with the advent of advancements in information and communication technology. The traditional ways of searching information from the online resources has been evolved and the tendency is geared more towards getting quality contents. In healthcare domain, the clinical researchers and physicians are even more interested to find quality information...
A new challenge for learning algorithms in cyber-physical network systems is the distributed solution of big-data classification problems, i.e., problems in which both the number of training samples and their dimension is high. Motivated by several problem set-ups in Machine Learning, in this paper we consider a special class of quadratic optimization problems involving a “large” number of input data,...
A concept of Four Properties (SiQi) of Chinese herbs is the important part of traditional Chinese medicine theory. The Chinese clinical medicine is a process of dialectical theory of governance of Chinese medicine prescriptions based these four properties. The Chinese medicine prescription uses a "Cold" and "Hot" model to judge the properties of Chinese herbs, and also judge the...
Haze and mist always affect the quality of vision. If an image is suffered from haze or mist, then the object is unclear and the image seems whiter than the original one. There are several haze removal algorithms that can reduce the effect of haze and mist. However, if an image is not suffered from the haze and mist, applying the haze removal algorithm may darken the image. Therefore, in computer...
This paper presents a new data classification approach which is based on the one hand on deep learning neural networks for effectively extracting well defined categorical information from data and on the other hand on an adaptable support vector machine, which appropriately represents existing related knowledge about user and context specific data. The proposed approach is implemented and successfully...
The work in this paper address the problem of removing non-uniform motion blur from a single image. The motion vector for an image patch is estimated by using a convolutional neural network (CNN). All the predicted motion vectors are combined to form a dense non-uniform motion estimation map. Furthermore, a second CNN is trained to perform deblurring given a blurry image patch and the estimated motion...
Kernel Methods have been successfully applied in different tasks and used on a variety of data sample sizes. Multiple Kernel Learning (MKL) and Multilayer Multiple Kernel Learning (MLMKL), as new families of kernel methods, consist of learning the optimal kernel from a set of predefined kernels by using an optimization algorithm. However, learning this optimal combination is considered to be an arduous...
This paper aims to highlight the performances and advantages of three improved and fast AI algorithms that are mainly used in classification problems suitable for various fields. The discussions regarding the benchmark results appeal to the Modified version of Radial Basis Function (RBF-M) mentioned in the paper as Fast Support Vector Classifier (FSVC) or Fast Support Vector Machine, Extreme Learning...
Deep learning is a multilayer neural network learning algorithm which emerged in recent years. It has brought a new wave to machine learning, and making artificial intelligence and human-computer interaction advance with big strides. We applied deep learning to handwritten character recognition, and explored the two mainstream algorithm of deep learning: the Convolutional Neural Network (CNN) and...
We propose a novel type of maxout that uses filters with kernels of multiple sizes for generating convolved maps. These filters extract the most effective features for recognition from many different variations of texture patterns. A convolved map is generated by convolution between feature maps and filters. If the size of filters is varied, the size of the convolved map will also vary; in which case,...
It is estimated that 80% of crashes and 65% of near collisions involved drivers inattentive to traffic for three seconds before the event. This paper develops an algorithm for extracting characteristics allowing the cell phones identification used during driving a vehicle. Experiments were performed on sets of images with 100 positive images (with phone) and the other 100 negative images (no phone),...
A recent trend for big data analytics is to provide heterogeneous architectures to allow support for hardware specialization. Considering the time dedicated to create such hardware implementations, an analysis that estimates how much benefit we gain in terms of speed and energy efficiency, through offloading various functions to hardware would be necessary. This work analyzes data mining and machine...
Jamu is an Indonesia herbal medicine made from natural materials such as roots, leaves, fruits, and animals. The purpose of this research is to develop a classification system for jamu efficacy based on the composition of plants using Support Vector Machine (SVM) and to implement the k-means clustering algorithm as a feature selection method. The result of this study was compared to the previous research...
The task of predicting the stature of human skeletal remains using bone measurements is an important one in bioarchaeology. Classical attempts to solve this problem mostly consist of linear regression formulas on various bone lengths. In order to improve these results, we propose using locally-weighted regression and radial basis function networks in order to fit the available data better, especially...
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