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As increase in the internet services and usage with open access to sensitive data, necessity of security to these systems had become a need of the hour. Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more crucial issue. To detect the attacks hitting the network it is very obligatory to properly monitor the flow...
Software fault prediction improves software qualityand testing efficiency by early identification of faults. Classification models using code attributes are constructed and used for prediction. This paper is a study of software fault prediction using Multi-Layered Perceptron, Bayesian Network and Naive Bayes classifier and their comparison by showing predictive and comprehensible performance. A framework...
In this paper, a change detection method for remotely sensed satellite images based on a decision theoretic method is proposed. The proposed method works in two stages. In the first stage, a difference image was computed using change vector analysis (CVA) technique. For multispectral images, CVA technique works well as it uses all the bands of two multidate satellite images to compute the difference...
Data Mining is the process of discovering interesting patterns and knowledge from large amounts of data. One of the most important techniques of Data Mining is classification which is used for prediction purposes. In this paper, we present a novel classifier for classification in the field of Medical Data Mining. The idea is to apply the Adaptive Classifier on the sample medical dataset, and compare...
Data mining is now one of the most active field of research. Extracting those nuggets of information is becoming crucial and one of its important technique is classification. It helps to group the data in some predefined classes. Various techniques for classification exists which classifies the data using different algorithms. Each algorithm has its own area of best and worst performance. This paper...
Machine Learning has found to be playing a significant role in solving issue of demand forecasting in supply chain management, where many traditional methods result in substandard accuracies. There is a high demand of robust computational systems for predicting the trends of demands for the purpose of Inventory Management in supply chain management of an organization. Every organization has Terabytes...
This paper describes our efforts to apply various advanced supervised machine learning and natural language processing techniques, including Binomial Logistic Regression, Support Vector Machines, Neural Networks, Ensemble Techniques, and Latent Dirichlet Allocation (LDA), to the problem of detecting fraud in financial reporting documents available from the United States’ Security and Exchange Commission...
Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed...
Classification algorithms are very important for several fields such as data mining, machine learning, pattern recognition, and other data analysis applications. This work presents the weighted nearest neighbors and fuzzy k-nearest neighbors algorithms to classify chosen medical datasets. This involves several distance functions to calculate the difference between any two instances. Classification...
In big data universities, an understanding of how the individual learning style and preferences interacts with the instructional medium presented is needed. In this study we examined the VARK learning style inventory using the variable-centered, person-centered and social approaches. We worked on a big “data set” which encompasses two data sources the first was LMS while the second was social media...
In regression analysis, outliers in the data can induce a bias in the learned function, resulting in larger errors. In this paper we derive an empirically estimable bound on the regression error based on a Euclidean minimum spanning tree generated from the data. Using this bound as motivation, we propose an iterative approach to remove data with noisy responses from the training set. We evaluate the...
The successful and widespread deployment of biometric systems brings on a new challenge: the spoofing, which involves presenting an artificial or fake biometric trait to the biometric systems so that unauthorized users can gain access to places and/or information. We propose a fingerprint spoof detection method that uses a combination of information available from pores, statistical features and fingerprint...
The online retail industry is one of the world's largest and fastest growing industries having huge amount of online sales data. This sales data includes information about customer buying history, goods or services offered for the customers. Hidden relationships in sales data can be discovered from the application of data mining techniques. Data mining is an inter disciplinary promising field that...
Intrusion Detection System (IDS) is used to preserve the data integrity and confidentiality from attacks. In order to identify the type of attack in IDS, different methodologies like various data mining techniques exist. But some are very time consuming and laborious. Therefore we have proposed the usage of SVM (Support Vector Machine) for classification of attack from large amount of raw intrusion...
Human-Computer Interaction gets more natural when the machine can detect human emotions faster and accurate. A lot of research is being carried out in the field of affective computing in order to improve the accuracy with speed. Bio-inspired algorithms for feature extraction and classification stages, has improved accuracy and speed further. In this paper, we propose a hybrid algorithm using CSO (Cat...
Search engines are usually used for exploring the net and finding required information. When search results are shown usually 10 links are included in the first page. It must be notices how many percent of achieved results are related to our request. Unfortunately some of advertisement websites utilize phony techniques to attract users so that they could obtain their personal goals (such as increase...
Recovery of shredded documents helps in security informatics, forensic and investigation science. Shredded document reconstruction requires much time and human effort. Hence, there is a great need to enhance its performance due to the high growth of critical cases requiring fast shredded document reconstruction. In this paper, we focus particularly on the most influential sub-problem which is enhancing...
Threats to computer networks are numerous and potentially devastating. Intrusion detection techniques provide protection to our data and track unauthorized access. Many algorithms and techniques have been proposed to improve the accuracy and minimize the false positive rate of the intrusion detection system (IDS). Statistical techniques, evolutionary techniques, and data mining techniques have also...
In this paper, an EOG based assistive system for typing text using a virtual keyboard is presented. An indigenously developed acquisition system based on arduino interfaced ADS1299 with a wearable dry electrode mask is used to record and process EOG signals. An accuracy of 100% and an average speed of 1 char/12 sec was achieved by an untrained person in online implementation of this system. This can...
Credit risk analysis is to determine if a customer is likely to default on the financial obligation. In this paper, we will introduce sparse non-negative matrix factorization method to discovery the lower dimensional space for reducing the data dimensionality, which will contribute to good performance and fast computation in the credit risk classification performed by support vector machine. We test...
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