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Transformation of the initial feature in NSL-KDD dataset based on principal component analysis (PCA), generates the new features in smaller dimension. In that dimension, network scanning (Ra-Probe) has a characteristic sign of the average value that is different from the normal activity. The selection used the characteristics of these factors result in two-dimensional subset of the 75% rate reduction...
Anomalous traffic detection on internet is a major issue of security as per the growth of smart devices and this technology. Several attacks are affecting the systems and deteriorate its computing performance. Intrusion detection system is one of the techniques, which helps to determine the system security, by alarming when intrusion is detected. In this paper performance of NSL-KDD dataset is evaluated...
Software project managers need information such as cumulative number of failures present in a software after testing a certain period of time to determine release time of software. In this paper, an artificial neural network (ANN) based model which uses a new network architecture is proposed to predict cumulative number of failures in software. An extra layer is added between input layer and hidden...
The dynamic spectrum access method based on human ear perception can keep the quality of analog signal in HD Radio (Hybrid Digital Radio) stable, but it takes too much time to meet the real-time requirement of Radio. This paper proposes a dynamic spectrum access method in HD Radio based on BP NN (Back Propagation Neural Network). In the method, the data of dynamic spectrum access method based on human...
ECG refers to non-invasive bioelectrical recording of the heart. Under the clinical settings, the ECG is interpreted by cardiologists via conventional inspection techniques. The methods however are exposed to visual error which leads to inaccurate diagnosis of the heart condition. Hence, as an attempt towards an automated diagnostic system, the paper elaborates on arrhythmia modelling based on ECG...
Extreme Learning Machine (ELM) is a special single-hidden-layer feedforward neural networks with very fast learning speed and has attracted significant research attentions in recent years. The salient feature of ELM is that the input parameters can be randomly generated instead of being exhaustively tuned, and thus saving a great deal of computational expenses. However, the architecture of ELM has...
Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing, document authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and Artificial Neural Network Based Off-line Signature Verification and Forgery Detection System...
This paper presents the application of FATHOM, a computerised non-verbal comprehension detection system, to distinguish participant comprehension levels in an interactive tutorial. FATHOM detects high and low levels of human comprehension by concurrently tracking multiple non-verbal behaviours using artificial neural networks. Presently, human comprehension is predominantly monitored from written...
Machine learning (ML) techniques such as artificial neural network (ANN) and support vector machine (SVM) have been increasingly used to predict harmful algal blooms (HABs). In this paper, we use the biweekly data in Tolo Harbour, Hong Kong, and choose several machine learning methods to develop prediction models of algal blooms. Three different kinds of models are designed based on back-propagation...
This paper proposes ANN based method for fingerprint ROI (Region of Interest) segmentation. Proposed ANNs where trained with 10000 samples extracted from 20 fingerprint images (in grey-scale and binary modes). The experimental results, including three statistical performance indicators, shows very good performance of the proposed method on a test database of 200 fingerprint images.
Solar radiation is a source of alternative energy that is very influential on the photovoltaic performance in generating energy. The need for solar radiation estimation has become a significant feature in the design of photovoltaic (PV) systems. Recently, the most popular method used to estimate solar radiation is artificial neural network (ANN). However, a new approach, called the extreme learning...
The problem of intrusion is gradually becoming nightmare for several organizations. To protect the valuable data of their clients, organizations implement security systems to detect and prevent security breaches. But since the intruders are using sophisticated techniques to penetrate the systems, even the highly reputed secured systems have become vulnerable now. To deal with the current scenario,...
Blast-induced ground motion is analyzed by means of two prediction methods. First conventional approach assumes several types of nonlinear dependence of peak particle velocity on scaled distance from the explosion charge, while the second technique implements a feed-forward three-layer back-propagation neural network with three nodes in input layer (total charge, maximum charge per delay and distance...
This paper describes an implementation of speech recognition that recognizes and suppresses ten (10) defined profane and vulgar Filipino words. The adapted speech recognition architecture was that of the Oregon Graduate Institute's (OGI) Center for Spoken Language and Learning (CSLU). It utilizes a hybrid Hidden Markov Model/ Artificial Neural Network (HMM/ANN) keyword spotting framework. The feature...
In recent years, progress in the field of artificial neural networks provides a very important tool for complex problems in pattern recognition, data mining and medical diagnosis. The training algorithms of neural networks play an important role for adjustment the network parameters. Different algorithms have been presented for training neural networks; the most common one is the use of gradient descent...
This paper proposes a novel approach for automatic estimation of four important traits of speakers, namely age, height, weight and smoking habit, from speech signals. In this method, each utterance is modeled using the i-vector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor Analysis (NFA) framework which is based on a...
Link prediction problem may be applied in many different domains. In the network, nodes represent authors and a connection between them indicates they coauthored a paper. This paper describes an improved method for predicting possibility of a link between authors. The algorithm is implemented in C# programming language and testing has been performed on Intel Core2Duo processor with 4 GB RAM and 2...
Software Testing is a very important phase in the cycle of software development. It is the only phase which ensures the reliability on the software. Generally 40–50% of the software development cost is spent on this phase. Though many automatic testing tools are present, but still most research is required in this field to reduce cost and time allotted for this phase. Test Oracle is a process which...
Software Testing has been a costly process used in industry for purpose of verification of software. It consumes nearly thirty to fifty percent of the entire software development cost. Test oracle is considered as an important component of the testing process, which ensures about the correctness of software behavior. In search based test generations, specification based test generations or in intelligent...
Hashtags are useful for categorizing and discovering content and conversations in online social networks. However, assigning hashtags requires additional user effort, hampering their widespread adoption. Therefore, in this paper, we introduce a novel approach for hashtag recommendation, targeting English language tweets on Twitter. First, we make use of a skip-gram model to learn distributed word...
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