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Due to the increased market competition increased data management and analysis has landed as in an era that requires further optimization data management and analysis. Big data technologies like apache HADOOP provide a frame work for parallel data processing and generation of analyzed results. MAPREDUCE method is used for analysis of data using various data analysis algorithms like clustering, fragmentation...
In this paper, we present a novel hardware implementation of a watermarking system applied on the digital image. The proposed hardware system is based on the DWT (Haar discrete wavelet transform) on the first level of the decomposition. The watermark is hidden in the LH0 (mean frequency sub-band) in goal to get a maximum of the compromise between the visibility and the robustness factors against several...
Software projects are getting more complex and thus it is very difficult for the companies to develop their projects alone. There are multiple heterogeneous systems which are different by multiple perspectives such as different users functionalities delivered different CASE tools, technology adopted for software development, and different platform used for deployment. Thus heterogeneous systems are...
The deployment of the large-scale video analysis center that provides comprehensive video applications is the developing trend of video surveillance systems. It integrates intelligent video information analysis, the mass video image content retrieval, and the associated data mining of structural video data. In this paper, we present the hierarchical architecture of the video analysis center by discussing...
Health sector in most countries including Zanzibar lacks the single repository that integrates health management data produced by multitude of sources. The study proves cost effective but successful implementation of Data Warehouse (DW) plays useful role to influence informed decision making in the health sector. Successful DW needs to include necessary features such as Online Analytic Process (OLAP)...
The Internet of Vehicles (IoV) has become an indispensable part of a smart city, which can provide useful traffic information to supervise and guide drivers in metropolis. However, as the traffic data scales to a great volume, it becomes difficult for conventional database management tools to meet the requirement of massive data analysis. HBase is an open source, non-relational, distributed database...
With no limit on time and location [1], the number of users attracted by massive open online course (MOOC) has increased rapidly, and many platforms have been built to provide a variety of courses. All of these trigger an explosive growth in data volume. As we known, people have met big data in many areas and proposed many techniques and methods to deal with them. However, people still have no sense...
In clustering data, there are two popular methods which are usually used: k-Means and Fuzzy C Means (FCM). Clustering process by these two methods, however, are sometimes influenced by the data suitable being used. This may affect the performance, for example: execution time, accuracy level. In order to overcome this problem, especially in a student evaluation system, we propose a feature extraction...
Analyzing and visualizing large datasets generated by real-time spatio-temporal activities (e.g. vehicle mobility or large crowd movement) are a very challenging task. Recursive delays both at middleware and front end applications limit the of usefulness of the real-time analysis. In this paper, we present a framework “Spatial-Crowd” that first handles spatial-temporal data acquisition and processing...
The emerging technology of Software–Defined Networking (SDN) affords a platform and architecture which is dynamic, manageable, cost-effective, and adaptable, making it ideal for many applications that are high-bandwidth and dynamic in nature. As this technology grows and matures, there is a need for cybersecurity applications to be designed, developed and evaluated. In this paper, we propose a development...
Encouraged by recent waves of successful applications of deep learning, some researchers have demonstrated the effectiveness of applying convolutional neural networks (CNN) to time series classification problems. However, CNN and other traditional methods require the input data to be of the same dimension which prevents its direct application on data of various lengths and multi-channel time series...
Every company stores more and more product data. Most of the data are not analyzed and possible findings cannot be used. But the utilization of existing knowledge can make the system development process more efficient. Therefore, this paper focuses on the data analysis of system architectures. It develops a concept to identify patterns between system architectures of different products in a database...
Micron's new Automata Processor (AP) architecture exploits the very high and natural level of parallelism found in DRAM technologies to achieve native-hardware implementation of non-deterministic finite automata (NFAs). The use of DRAM technology to implement the NFA states provides high capacity and therefore provide extraordinary parallelism for pattern recognition. In this paper, we give an overview...
In the paper, the deep evolving neural network and its learning algorithms (in batch and on-line mode) are proposed. The deep evolving neural network's architecture is developed based on GMDH approach (in J. Schmidhuber's opinion it is historically first system, which realizes deep learning ) and least squares support vector machines with fixed number of the synaptic weights, which provide high quality...
In this paper, a self-healing scheme in active distribution network (ADN) with inverter-based distributed generators (IBDGs) based on multi-agent and big data is proposed. The multi-agent system (MAS), big data storage and mining technology are used to accomplish fault discrimination, fault localization, isolation and service restoration. In this paper, the use of a new type of the relay which takes...
Clinical skills education is an essential component of the teaching plan in medical science courses, such as nursing education. Simulation-based learning is an effective teaching method in any practical or vocational-based training. The development of simulation-based teaching has been impacted by the integration of emerging technologies, such as Intelligent Tutoring Systems (ITSs), which results...
The literature about enterprise architecture (EA) measurement discusses a number of challenges. This systematic mapping survey explores the EA measurement research area to figure out how the research area is structured, look at gaps in EA measurement research, and recommend future improvements. Some of the key findings of this paper show that current research only address a limit perspective on the...
This paper proposes a place cell model allowing place recognition in the context of robot autonomous navigation. The robustness of this approach lies in the fact that even if one or several patterns characterizing the place are removed or not visible anymore, a place can still be recognized. The recognition process in this work is improved with respect to the state-of-the-art place cells approach...
Sentence classification, serving as the foundation of the subsequent text-based processing, continues attracting researchers attentions. Recently, with the great success of deep learning, convolutional neural network (CNN), a kind of common architecture of deep learning, has been widely used to this filed and achieved excellent performance. However, most CNN-based studies focus on using complex architectures...
The process of discovering interesting patterns in large, possibly huge, data sets is referred to as data mining, and can be performed in several flavours, known as “data mining functions.” Among these functions, outlier detection discovers observations which deviate substantially from the rest of the data, and has many important practical applications. Outlier detection in very large data sets is...
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