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This paper is aimed to model the electrical transmission grid of Germany accessing future of electricity generation targeting year 2023. For the need of alternating current, while shutting down nuclear power plants and reduction of emission of green house gases, renewable energy comes into picture. With the data from German Network Regulatory Body (BNA), the high voltage transmission grid of Germany...
In many real-world tasks a lot of unlabeled data are collected over time and, although they may be useful to improve the quality of classification models, they are usually ignored. Semi-supervised learning techniques combine unlabeled and labeled data to capture more useful information about a particular task. On the other hand, an incremental learning technique can incorporate new information to...
Data loss, i.e. the unauthorized/unwanted disclosure of data, is a major threat for modern organizations. Data Loss Protection (DLP) solutions in use nowadays, either employ patterns of known attacks (signature-based) or try to find deviations from normal behavior (anomaly-based). While signature-based solutions provide accurate identification of known attacks and, thus, are suitable for the prevention...
Human activity is almost always intentional. By understanding why user-generated events are happening and what purposes they serve, a system can offer a significantly improved and more engaging experience. Analyzing user actions such as clicks can reveal patterns and behaviors. However, understanding the goals behind these actions is a more challenging issue since goals cannot be easily captured....
In this study a model for data reliability in wireless sensor networks is proposed, in which machine learning methods are used. Proposed framework includes data modelling, missing data prediction, anomaly detection, data fusion and trust mechanism phases. Thus, temporal analysis is performed on the preprocessed sensor data and missing data are predicated. Then outliers on collected data are detected...
In this paper, we mainly study on n-gram models on text classification domain. In order to measure impact of n-gram models on the classification performance, we carry out Naïve Bayes classifier with various smoothing methods. Naïve Bayes classifier has generally used two main event models for text classification which are Bernoulli and multinomial models. Researchers usually address multinomial model...
In communications, the nodes with the ability of harvesting energy potentially can prolong the overall network lifetime. In this paper, we consider a single-user energy harvesting wireless communication system, in which arrival data and harvested energy curves are modeled as continuous functions. Our goal is to find an online algorithm for the throughput maximization problem, which only needs the...
Dynamic slicing and spectrum-based fault localization (SFL) are widely used as fault localization methods. While these methods are effective for localizing a faulty statement in a program, they have some practical drawbacks. One of the drawbacks with dynamic slicing is that if a program is large, the sliced program will also remain large in general. One of the drawbacks with SFL is that the suspiciousness...
Smartphone users' requirements for data plans from mobile network access services have become individualized. To cope with users' requirements, global mobile network operators have been replacing flat-rate data plans with tiered data plans in the mobile market. In addition, mobile virtual network operators who provide data plans to their own customers at retail prices have emerged in the mobile market...
Internet service providers (ISPs) have been facing heavy competition to attract more users in the mobile data market, along with growing operational costs. Most mobile data plans charge users a fixed fee for a monthly data quota, and any unused data at the end of each month will be wasted. In the beginning of 2015, both AT&T and T-Mobile reinstated rollover data plans with constrained eligibility...
The MoveUs project funded by the European Commission aims to foster sustainable eco-friendly mobility habits in cities. In this context predicting the traffic flow is useful for managers to optimize the configuration of the road network towards reducing the congestions and ultimately, the pollution. With the explosion of the so-called Big Data concept and its application to traffic data, a wide range...
Large enterprises require reliable and scalable network connectivity which relies heavily on correct network design for LAN and ample bandwidth on WAN. The latter is mostly affected by external market-defined prices, which, absent careful optimization and estimation, can result in unnecessary business expenses. This paper presents a framework for network capacity forecast of a large enterprise to...
This paper proposes a highly scalable framework that can be applied to detect network anomaly at per flow level by constructing a meta-model for a family of machine learning algorithms or statistical data models. The approach is scalable and attainable because raw data needs to be accessed only one time and it will be processed, computed and transformed into a meta-model matrix in a much smaller size...
In wireless sensor networks, one of the most important application is tracking diffusive and large-scale phenomena, called continuous object. A number of sources from the large size of a continuous object bring huge communication overhead that former studies have to deal with. Meanwhile, recent application scenarios, such as forest fire suppression by firefighters, require sink mobility support. Most...
For any large scale cloud applications coming around large geographic distribution, the large requirements have to be evaluated by the developers. Simulation procedures are very much useful for the researchers during their research process. Their own algorithms can be tested in a cloud environment using any type of simulators. Therefore, the common toolsets have to be used for evaluation and modeling...
While training a model with data from a dataset, we have to think of an ideal way to do so. The training should be done in such a way that while the model has enough instances to train on, they should not over-fit the model and at the same time, it must be considered that if there are not enough instances to train on, the model would not be trained properly and would give poor results when used for...
Integration of products in enterprises comes with hard challenges due to several factors such as products developed in house, off the shelf, developed over different time lines, available as services over internet, ever changing product APIs, disconnected data models among the products, extensions developed by partners and customers and many more. We propose Semantic Data Platform (SDP), built around...
Using various technologies in health services of Uttarakhand may allow the sharing of patient information among multiple sites. By using the concept of Cloud computing in healthcare services of Uttarakhand we could setup an effective model for delivering of health services. Role of data centre is very important while transforming health services in rural and remote Uttarakhand. Establishment of data...
As self-reporting of pain complaints is considered the most accurate assessment method, the evaluation of the adopted technology on it, raises several challenges. In addition, as pain could be assessed based on different dimensions such as pain intensity, symptoms of anxiety, catasthrophizing, and depression, then are promising new methods to evaluate the effect of the adoption of this technology...
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