با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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German solar power generation data mining and prediction with transparent open box learning network integrating weather, environmental and market variables
استخراج و پیش بینی داده های تولید انرژی خورشیدی آلمان با شبکه یادگیری جعبه باز شفاف متغیرهای آب و هوا ، محیط زیست و بازار-2019 A compiled dataset of hourly-averaged solar power generation (MW) for Germany in 2016 integrates eight
influencing weather, environmental and market price variables (8784 data records including 5012 non-zero
generation periods). It provides valuable insight to solar power variations over the course of one year. The
dataset is evaluated with the transparent open box (TOB) learning network for data mining and prediction
purposes. This algorithm provides accurate and repeatable MW predictions and enables detailed evaluation of
the key influencing variables on each hourly data record. TOB Stage 1 applies a data matching routine driven by
the squared errors between the independent variables. TOB Stage 2 applies a customized memetic firefly optimizer
to minimize the root mean squared error (RMSE) for MW predictions over subsets and/or the full dataset.
TOB achieves high prediction accuracy (average of five cases RMSE=1044.4 MW; R2=0.975) using tuning
subsets of only ∼303 data records (∼6% of the full dataset). The dataset displays some significant MW prediction
outliers that are readily identified and explained individually by the TOB algorithm’s data mining
capabilities. A slightly filtered dataset (4918 data records excluding 94 outlier data records) improves MW
prediction accuracy (average of five cases RMSE=936.1 MW; R2=0.980). Whereas the prediction outliers are
readily segregated as a separate subset for more detailed evaluation. The TOB algorithm’s combined machinelearning
and data mining capabilities provide valuable insight to the dataset and the influences of its independent
variables. The algorithm couples high-prediction accuracy and detailed evaluation of long-term and
short-term time series data and for spatial scales varying from country level to individual solar farms. Keywords: German country-wide solar power generation | Machine learning transparency | Integrating environmental and market | variables | Data mining outlier analysis | Data filtering |
مقاله انگلیسی |
2 |
A secure kNN query processing algorithm using homomorphic encryption on outsourced database
یک الگوریتم پردازش پرس و جو kNN امن با استفاده از رمزگذاری همگن در پایگاه داده برون سپاری شده-2017 With the adoption of cloud computing, database outsourcing has emerged as a new platform.
Due to the serious privacy concerns associated with cloud computing, databases must be
encrypted before being outsourced to the cloud. Therefore, various k-nearest neighbor (kNN)
query processing techniques have been proposed for encrypted databases. However, existing
schemes are either insecure or inefficient. In this paper, we propose a new secure kNN query
processing algorithm. Our algorithm guarantees the confidentiality of both encrypted data and
users’ query records. To achieve a high level of query processing efficiency, we also devise an
encrypted index search scheme that performs data filtering without revealing data access
patterns. A performance analysis shows that the proposed scheme outperforms the existing
scheme in terms of query processing costs while preserving data privacy.
Keywords: Database outsourcing | Database encryption | Encrypted index structure | Data Privacy | KNN query processing |
مقاله انگلیسی |
3 |
A social cognitive heuristic for adaptive data dissemination in mobile Opportunistic Networks
اکتشافی شناختی اجتماعی برای انتشار اطلاعات سازگار در شبکه های اپورتونیستی سیار-2017 It is commonly agreed that data (and data-centric services) will be one of the cornerstones of Future
Internet systems. In this context, mobile Opportunistic Networks (OppNets) are one of the key paradigms to
efficiently support, in a self-organising and decentralised manner, the growth of data generated by localized
interactions between users mobile devices, and between them and nearby smart devices such as IoT nodes.
In OppNets scenarios, the spontaneous collaboration among mobile devices is exploited to disseminate data
toward interested users. However, the limited resources and knowledge available at each node, and the vast
amount of data available in the network, make it difficult to devise efficient schemes to accomplish this task.
Recent solutions propose to equip each device with data filtering methods derived from human information
processing schemes, known as Cognitive Heuristics. They are very effective methods used by human brains
to quickly drop useless information and keep only the most relevant information. Althought cognitive-based
OppNet solutions proved to be efficient (with limited overheads), they can become less effective when facing
dynamic scenarios or situations where nodes cannot fully collaborate with each other, as we show in this
paper. One of the reasons is that the solutions proposed so far do not take take into account the social
structure of the environment where the nodes are moving in. In order to be more effective, the selection
of information performed by each node should take into consideration not only the relevance of content for
the local device, but also for other devices will encounter in the future due to mobility. To this end, in
this paper we propose a social-based data dissemination scheme, based on a cognitive heuristic, known as
the Social Circle Heuristic. This heuristic is an evaluation method that exploits the structure of the social
environment to make inferences about the relevance of discovered information. We show how the Social
Circle Heuristic, coupled with a cognitive-based community detection scheme, can be exploited to design an
effective data dissemination algorithm for OppNets. We provide a detailed analysis of the performance of
the proposed solution via simulation.
Keywords: Opportunistic Networks | Cognitive Heuristics | Data Dissemination | Social | Self-Organising |
مقاله انگلیسی |