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A large number of extreme floods were closely related to heavy precipitation which lasted for several days or weeks. Long-lead prediction of extreme precipitation, i.e., prediction of 6–15 days ahead of time, is important for understanding the prognostic forecasting potential of many natural disasters, such as floods. Yet, long-lead flood forecasting is a challenging task due to the cascaded uncertainty...
The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification...
Decision tree has become one of the most accepted tools for mining data streams after Hoeffding tree was anticipated in the literature. The most vital point of constructing the decision tree is to find out the best attribute to split the considered node. Numerous methods to resolve this problem were presented so far, however, there are some shortcomings such that they are either mathematically not...
In the recent years, the rapid advancement of computer networks has led to many security problems by malicious users to the modern computer systems. Hence, it is necessary to detect illegitimate users by monitoring the unusual user activities in the network. In this paper, we propose an Intrusion Detection System (IDS) which uses a genetic algorithm based feature selection approach and a Support vector...
Voting based Extreme learning machine was recently proposed to reduce the error due to variance in Extreme Learning Machine. This paper further refines the algorithm by using entropy based ensemble pruning. Results obtained shows significant improvement in performance along with reduction in computational and storage requirement.
KNN is amongst the simplest top ten classification algorithm of data mining. Being effective and efficient it has some drawbacks which cannot be overlooked. Moreover, real world data is fuzzy in nature. To overcome this drawback fuzzy KNN was introduced which was based on fuzzy membership. But, it had large time complexity as the membership is calculated at the classification period. To improve this,...
In this paper, an automated model selection approach guided by Cuckoo search is proposed for k-nearest neighbor (KNN) learning algorithm. The performance of KNN mostly depends on the value of k and the distance metric used. The values of these parameters are computed by optimizing an objective function designed for measuring the classification accuracy of KNN. Cuckoo search being an efficient optimization...
Online Social Networks (OSNs) are deemed to be the most sought-after societal tool used by the masses world over to communicate and transmit information. Our dependence on these platforms for seeking opinions, news, updates, etc. is increasing. While it is true that OSNs have become a new medium for dissemination of information, at the same time, they are also fast becoming a playground for the spread...
In literature, there are many supervised learning algorithms presented and applied in various problem domains. However, none of them could consistently perform well over all the datasets. This paper presents a novel approach for simultaneous selection of optimal feature subset and classifier for a given dataset. For large scale problems, this would require to search a huge solution space. Therefore,...
Recent developments in geotechnologies provide resources to propose innovative strategies for urban and environmental management, including remote sensing data and computational resources for processing them. With the main objective of identifying urban areas of illegal occupation, this work uses WorldView-2-sensor images and the InterIMAGE, an image interpretation software, based on knowledge, under...
The Hyperspectral image (HSI) contains several hundred bands of the same region called the Ground Truth (GT). The bands are taken in juxtaposed frequencies, but some of them are noisily measured or contain no information. For the classification, the selection of bands, affects significantly the results of classification, in fact, using a subset of relevant bands, these results can be better than those...
Alarm fatigue can cause many negative results. Regarding ECG signals, an algorithm to reduce alarm fatigue is described, regardless of whether or not VPBs are involved in the false alarms. ECG signals are divided into five signal quality index levels (SQI): 0∼4, where level 0 represents a noise free signal and level 4 indicates the worst signal quality. The key of the method is to judge the noise...
In this paper, we present a novel algorithm to evaluate the quality of ECG recordings. Our algorithm is designed to help clinicians in rapid selection of good quality ECG segments from long recordings collected by an ECG monitoring device such as a 12-lead bedside monitor. With some adjustments, we used the Computing in Cardiology Challenge 2011 database in order to compare the performance of our...
Changes in the network topology such as large-scale power outages or Internet worm attacks are events that may induce routing information updates. Border Gateway Protocol (BGP) is by Autonomous Systems (ASes) to address these changes. Network reachability information, contained in BGP update messages, is stored in the Routing Information Base (RIB). Recent BGP anomaly detection systems employ machine...
This study analyses the factors affecting students' academic performance that contributes to the prediction of their failure and dropout using educational data mining. This paper suggests the use of various data mining techniques to identify the weak students who are likely to perform poorly in their academics. WEKA, an open source tool for data mining was used to evaluate the attributes predicting...
The objective of the present work is to design a HADOOP based parallel Marathi content retrieval system using clustering technique to get the efficient and optimized result than existing systems. The system also focuses on providing the personalized documents in Marathi language to the end user based on their interests identified from the browsing history and using time session mechanism for re ranking...
Business and Research organizations are continuously generating huge amount of high dimensional data. They need to analyze this data in real-time with minimum cost. Data pre-processing techniques in combination with dimensionality reduction techniques are widely used by researchers to improve the quality of data and reduce the time, cost required to analyze the data. But standard methods are not available...
Recent emerging growth of data created so many challenges in data mining. Data mining is the process of extracting valid, previously known & comprehensive datasets for the future decision making. As the improved technology by World Wide Web the streaming data come into picture with its challenges. The data which change with time & update its value is known as streaming data. As the...
The diagnosis of the arythema disease is a real difficulty in dermatology. It causes redness induced in the lower level of the skin by hyperemia of the capillaries. It can harm several skin damages, inflammations. In this paper, we have put our efforts to design a diagnostic approach based on Support Vector Machine (SVM) with linear kernel by classifying the erythemato-squamous disease. SVM being...
Intrusion detection system is widely used to protect and reduce damage to information system. It protects virtual and physical computer networks against threats and vulnerabilities. Presently, machine learning techniques are widely extended to implement effective intrusion detection system. Neural network, statistical models, rule learning, and ensemble methods are some of the kinds of machine learning...
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