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The paper presents the results of a comparative study performed between two computational intelligence techniques, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) applied to particulate matter (fraction PM2.5) air pollution forecasting. The experiments were realized on datasets from the Airbase databases with PM2.5 hourly measurements. The main statistical parameters...
Feature selection is an increasingly important part of machine learning. The purpose of feature selection is dimension reduction in a large multi-dimensional data set and it can be the key step of successful knowledge discovery in those problems where the number of features is large. This research area has huge practical significance because it accelerates decisions and improves performance. The requirements...
Intention recognition is an important topic in the field of Human Robot Interaction. If the robot is wanted to make counter movements just in time according to human's actions, a robotic system must recognize the intention of the human necessarily. In this paper, a method for a robotics system to estimate the human's intention is presented. In our method, the information is provided from the sensor...
Computers are man-made machines which work according to the given set of inputs and perform some operation on the input to generate a new set of outputs. They can be programmed to perform huge and complex task yet they lack imagination and ability to understand things. On the hand human brain is a machine which can learn new tasks by process of acquiring knowledge and understanding through thought,...
The ability of the machine to infer knowledge from the user documents can be tested based on its ability to answer the question asked. Conventional Artificial Neural Network (ANN) models for knowledge extraction only answer to the questions which are simple and objective as they don't analyze the questions and don't try to understand what really the content of document mean. The proposed question...
An approach for selection of features using principal component analysis technique to classify segmented (isolated) Kannada characters is presented in this paper. Artificial neural network is used as classifier. The ability of neural networks to learn by ordinary experience, as we do, and to take sensitive decisions give them the power to solve problems found intractable or difficult for traditional...
The introduction of game-based learning (GBL) into the pedagogical processes and curriculum design can increase student engagement in the learning process. There are a range of game based learning approaches available, but, so far, limited adoption of serious games has been recorded. The digital habits of learners should be studied carefully, to better understand the way current technology-savvy students...
Educational data mining concerns of developing methods to discover hidden patterns from educational data. The quality of data mining techniques depends on the collected data and features. In this paper, we proposed a new student performance model with a new category of features, which called behavioral features. This type of features is related to the learner interactivity with e-learning system....
We all know that the information passed through internet is in terms of packets. The alerts produced by all the existing intrusion detection systems are false alerts which can cause to decrease the efficiency and the accuracy is also low. The alerts generated by all the existing intrusion detection systems are isolated alerts and they will focuses on low-level attacks. So in this research paper diverse...
The concept behind this particular aspect lies on the fact to determine and customize the simplicity and the most basic scenario. The basicity lies on the fact that we have been using the concept of Data Mining and even the algorithms are included that merely includes the efficiency of NIDS that is Network Intrusion Detection System. We have seen a lot of aspects and different concepts being used...
Performance of any brain computer interface system depends upon features of electroencephalogram signals. Electroencephalogram signals undergo for unpredictable changes when vigilance state of human brain alters widely. This may cause adverse changes in extracted features and affect classification performance of brain computer interface system. To avoid miss-classification, brain computer interface...
In this paper, we propose a hybrid method for intrusion detection which is based on k-means, naive-bayes and back propagation neural network (KBB). Initially we apply k-means which is partition-based, unsupervised cluster analysis method. In the form of clusters, we attain the gathered data which can be easily processed and learned by any machine learning algorithm. These outcomes are provided to...
This paper focuses on the attempt to formulate the prescription prediction logic based on the medical data analysis towards the future computer-assisted-diagnosis for Kampo medicine. We constructed and evaluated prediction models for some frequently-used prescriptions using six kinds of machine learning algorithms including artificial neural network, multinomial logit, random forest, support vector...
Detection and isolation of link failures in directed networks of LTI systems have been the focus of our previous study. Our results relate the failure of links in the network to jump discontinuities in the derivatives of the output responses of the nodes and exploit this relation to propose failure detection and isolation (FDI) techniques, accordingly. In this work, we consider these results in the...
We describe a method for circuit synthesis that determines the parameter values by using a set of artificial neural networks (ANNs) that learn in sequence. Each ANN is optimized to output only one design parameter, and the latter constrains the learning/recall of its successor(s). Two competing ANN architectures are considered, the multilayer perceptron (MLP) and the radial basis functions (RBF) network,...
Expert short-term management of diabetes through good glycaemic control, is necessary to delay or even prevent serious degenerative complications developing in the long term, due to consistently high blood glucose levels (BGLs). Good glycaemic control may be achieved by predicting a future BGL based on past BGLs and past and anticipated diet, exercise schedule and insulin regime (the latter for insulin...
Biological systems have many key answers for our current limitations in scaling. Miniaturization and more speed were the driving forces for VLSI technology in past few decades. As we are reaching the dead end of Moore's law, now the paradigm has shifted towards intelligent machines. Many efforts are made to mimic the commonsense observed in animals. Automation and smart devices have taken a great...
This study aims at designing and developing an electronic system that depends on the Audio Fingerprint to access the learning management system (Blackboard) by students of King Khalid University, as a complement to the current system based on username and password only. [1]
In previous works of ours [1-3], we proposed a neural network-based face detection and facial expression analysis system, which was able to classify three expressions in frontal view face images. In the present work, we examine the possibility of classifying these expressions in side view face images. Specifically, we evaluate the extracted facial feature discrimination power of three image acquisition...
Data normalization for use in Artificial Neural Networks often requires extensive statistical analysis. This paper presents an initial investigation of a case study involving credit card fraud detection, where Cluster Analysis was applied to data normalization. Early results obtained from the use of Artificial Neural Networks and Cluster Analysis on fraud detection has shown that neuronal inputs can...
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