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Nowadays, big data analytics has gained more popularity than any other domain in the research world. Its uses in domains like cyber security, but also the security of data itself, represent great challenges for researchers. Neural Network approaches have been of interest in developing models and architectures for discovering patterns and malicious activity of the users. In certain cases, the right...
Traditionally, Quality of experience is mostly examined in a laboratory experiments to enable a fixed contextual factor. While the results present an estimated mean opinion score representing perceived QoE. It is imperative to estimate mean opinion score employing large data (big data) gathered from the mobile network comprising of different user's location and time for a specific service. Because...
The Big data challenge includes dealing with a big number of heterogeneous and multidimensional datasets of all possible sizes not only with data of big size. As a result a huge number of Machine Learning (ML) tasks, which must be solved dramatically exceeds the number of data scientists who can solve these tasks. Next many ML tasks require critical input from subject matter experts (SME) and end...
Data-driven agronomy is becoming popular by reducing the climate uncertainty in the era of agriculture big data. In this paper, many data mining approaches were used to extract embedded knowledge from climate variability and explore the relationship between NDVI data and maize yield. The results show that the Oct NDVI is important for Liaoning, while the Aug NDVI is important for Heilongjiang and...
The IPTV video evaluation model based on big data is a beneficial basis for IPTV video evaluation. With the new media, social network, Internet of things and cloud computing continuing to evolve, the video-related big data arises at the historic moment. IPTV has also become the choice of more and more users. And IPTV editors are troubled by how to choose the best video for IPTV users. In this paper,...
Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the others that are stuck behind the wheels. Early detection of an accident can save lives,...
Deep Neural Networks have become a state of the art approach in perception processing like speech recognition, image processing and natural language processing. Many state of the art benchmarks for these algorithms are using deep learning techniques. The deep neural networks in today's applications need to process very large amount of data. Different approaches have been proposed to solve scaling...
To address the high-dimensionality of big data, numerous iterative algorithms have been introduced including least absolute shrinkage selection operator (Lasso) and iteratively sure independent screening (ISIS). However, the iterative nature of these algorithms renders the computational cost of retraining the learning model impractical. We take advantage of this key observation to propose a novel...
Words and texts are particularly important big data sources for intelligent transportation systems. There is relevance between the traffic condition and the text content which people published in the internet within a period of time. In order to predict traffic condition by the text content we need to analysis these words and texts for all kinds of means. Many traditional researches on neuro linguistic...
In this paper, we propose a communication-efficient model of sparse bidirectional neural network to intelligently process distributed data. The basic idea of the proposal is a modified bidirectional communication between the core and the edge of Internet by model parameters. The formulation and the procedures of the proposal are investigated. In theory, we prove that the proposed neural network is...
Many industries are applying various methods for optimizing energy use across the manufacturing life cycle. These methods are either physics-based or data-driven. Manufacturing systems generate a vast amount of data from operations and in simulations. Advances in data collection systems and data analytics (DA) tools have enabled the development of predictive analytics for energy prediction. Many of...
Large data has been accumulating in all aspects of our lives for quite some time. Advances in sensor technology, the Internet, wireless communication, and inexpensive memory have all contributed to an explosion of “Big Data”. System of Systems (SoS) integrate independently operating, non-homogeneous systems to achieve a higher goal than the sum of the parts. Today's SoS are also contributing to the...
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