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Multi-level STT-RAM cell is able to boost the memory density at the expense of read/write reliability. However, the induced data integrity issue in STT-RAM memory can be effectively masked by a wide spectrum of applications with intrinsic forgiveness, which belong to the specific domain such as multimedia, synthesis and mining. In this work, we leverage the reconfigurable capability of MLC STT-RAM...
The presence of a large number of irrelevant features degrades the classifier accuracy, reduces the understanding of data, and increases the overall time needed for training and classification. Hence, Feature selection is a critical step in the machine learning process. The role of feature selection is to select a subset of size ‘d’ (d<n) from the given set of ‘n’ features that leads to the smallest...
Astrology has started around 4000 years back and has significantly developed over a period of time. Till date no unified rules or standards for astrological prediction exist in the world. Astrologers concentrate on providing quality services to persons rather than defining universal rules and standards for astrological prediction. Advances in artificial intelligence resulted in large number of applications...
Accurate and timely traffic classification is a key to providing Quality of Service (QoS), application-level visibility, and security monitoring for network operations and management. A class of traffic classification techniques have emerged that apply machine learning technology to predict the application class of a traffic flow based on the statistical properties of flow-features. In this paper,...
To alleviate the loads of tracking web log file by human effort, machine learning methods are now commonly used to analyze log data and to identify the pattern of malicious activities. Traditional kernel based techniques, like the neural network and the support vector machine (SVM), typically can deliver higher prediction accuracy. However, the user of a kernel based techniques normally cannot get...
Malware proliferation has become a serious threat to the Internet in recent years. Most of the current malware are subspecies of existing malware that have been automatically generated by illegal tools. To conduct an efficient analysis of malware, estimating their functions in advance is effective when we give priority to analyze. However, estimating malware functions has been difficult due to the...
With the advent of social media and e-commerce sites, people are posting their unilateral, possibly subjective views on different products and services. Sentiment classification is the process of determining whether a given text is expressing positive or negative sentiment towards an entity (product or service) or its attributes. In this regard, we employed text mining involving steps like text preprocessing,...
In the past years there are several machine learning techniques have been proposed to design precise classification systems for several medical issues. This paper compares and analyses breast cancer classifications with different machine learning algorithms using k-Fold Cross Validation (KCV) technique. Decision Tree, Naïve Bayes, Neural Network and Support Vector Machine algorithm with three different...
It has always been difficult to accurately estimate the moment of inertia of an object, e.g. an unmanned aerial vehicle (UAV). Whilst various offline estimation methods exist to allow accurate parametric estimation by minimizing an error cost function, they require large memory consumption, high computational effort, and a long convergence time. The initial estimate's accuracy is also vital in attaining...
Today in data mining research we are daily confronted with large amount of data. Most of the time, these data contain redundant and irrelevant data that it is important to extract before a learning task in order to get good accuracy. The fact that today's computers are more powerful does not solves the problems of this ever-growing data. It is therefore crucial to find techniques which allow handling...
This paper presents our recent work on human activity detection based on smart phone embedded sensors and learning algorithms. The proposed human activity detection system recognizes human activities including walking, running, and sitting. While walking and running can be recorded as daily fitness activities, falling will also be detected as anomalous situations and alerting messages can be sent...
In this paper authors investigate the problem of predicting the fuel consumption of a vessel in real scenario based on data measured by the onboard automation systems. The goal is achieved by exploiting three different approaches: White, Black and Gray Box Models. White Box Models (WBM) are based on the knowledge of the physical underling processes. Black Box Models (BBMs) build upon statistical inference...
People like listening music primarily due to the emotion it evokes. Any activity or work that a person performs also generates emotions. Considering the above two statements it can assume that people tend to associate music with certain activity if it induces emotions that are in sync with it. In today's world of infinite storage, the number of songs that a user has is ever increasing. With the increased...
Multi-population genetic algorithms have been used with success for several multi-objective optimization problems. In this paper, we present a new general multi-population genetic algorithm for evolving decision trees. It was designed to improve the possibility of evolving balanced decision trees, simultaneously optimized for the predictions of each class. Single-population genetic algorithms namely...
This paper presents an attempt to solve the challenging problem of Devanagari numeral and character recognition. It uses structural and geometric features to represent the Devanagari numerals and characters. Each image is zoned in 9 blocks and 8 structural features are extracted from each block. Similarly 9 global geometric features are extracted. These 81 features are used for representing the image...
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...
Recent studies have demonstrated the potential of unsupervised feature learning for sound classification. In this paper we further explore the application of the spherical k-means algorithm for feature learning from audio signals, here in the domain of urban sound classification. Spherical k-means is a relatively simple technique that has recently been shown to be competitive with other more complex...
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...
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...
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...
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