500 research outputs found
Sort by
Applying the Computational Intelligence Paradigm to Nuclear Power Plant Operation: A Review (1990-2015)
No full textIn the guise of artificial neural networks (ANNs), genetic/evolutionary computation algorithms (GAs/ECAs), fuzzy logic (FL) inference systems (FLIS) and their variants as well as combinations, the computational intelligence (CI) paradigm has been applied to nuclear energy (NE) since the late 1980s as a set of efficient and accurate, non-parametric, robust-to-noise as well as to-missing-information, non-invasive on-line tools for monitoring, predicting and overall controlling nuclear (power) plant (N(P)P) operation. Since then, the resulting CI-based implementations have afforded increasingly reliable as well as robust performance, demonstrating their potential as either stand-alone tools, or - whenever more advantageous - combined with each other as well as with traditional signal processing techniques. The present review is focused upon the application of CI methodologies to the - generally acknowledged as - key-issues of N(P)P operation, namely: control, diagnostics and fault detection, monitoring, N(P)P operations, proliferation and resistance applications, sensor and component reliability, spectroscopy, fusion supporting operations, as these have been reported in the relevant primary literature for the period 1990-2015. At one end, 1990 constitutes the beginning of the actual implementation of innovative, and – at the same time – robust as well as practical, directly implementable in H/W, CI-based solutions/tools which have proved to be significantly superior to the traditional as well as the artificial-intelligence-(AI)derived methodologies in terms of operation efficiency as well as robustness-to-noise and/or otherwise distorted/missing information. At the other end, 2015 marks a paradigm shift in terms of the emergent (and, swiftly, ubiquitous) use of deep neural networks (DNNs) over existing ANN architectures and FL problem representations, thus dovetailing the increasing requirements of the era of complex - as well as Big - Data and forever changing the means of ANN/neuro-fuzzy construction and application/performance. By exposing the prevalent CI-based tools for each key-issue of N(P)P operation, overall as well as over time for the given 1990-2015 period, the applicability and optimal use of CI tools to NE problems is revealed, thus providing the necessary know-how concerning crucial decisions that need to be made for the increasingly efficient as well as safe exploitation of NE
Media Selection in Knowledge Transfer: A Decision Model
No full textManaging knowledge transfer is a complex issue: when firms attempt to accelerate knowledge transfer at a rational cost, there is often a byproduct of knowledge spillover which harms the firms' competitive advantages. As the channel of knowledge transfer, media play a vital role for the success of knowledge transfer. However, the extant literature offers neither comprehensive framework nor a decision method to guide firms' media selection in knowledge transfer. This article develops a framework of media selection from the perspectives of performance, cost, and risk of knowledge spillover, and proposes a decision model of media selection in knowledge transfer based on analytic hierarchy process (AHP). Finally, this article applies the model to a case study to verify its effectiveness in practice. The framework is helpful to guide firms' media selection, and the decision model is valuable to facilitate firms' media selection in big knowledge transfer projects
Social Media Big Data Analytics for Demand Forecasting: Development and Case Implementation of an Innovative Framework
No full textSocial media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain
Hadoop Paradigm for Satellite Environmental Big Data Processing
No full textThe important growth of industrial, transport, and agriculture activities, has not led only to the air quality and climate changes issues, but also to the increase of the potential natural disasters. The emission of harmful gases, particularly: the Vertical Column Density (VCD) of CO, SO2 and NOx, is one of the major factors causing the aforementioned environmental problems. Our research aims to contribute finding solution to this hazardous phenomenon, by using remote sensing (RS) techniques to monitor air quality which may help decision makers. However, RS data is not easy to manage, because of their huge amount, high complexity, variety, and velocity, Thus, our manuscript explains the different aspects of the used satellite data. Furthermore, this article has proven that RS data could be regarded as big data. Accordingly, we have adopted the Hadoop big data architecture and explained how to process efficiently RS environmental data
Analysis of Speeches by the Former President of the US, Barack Hussein Obama, Regarding the Middle East and Northern Africa
No full textThe main intention in this article is to critically analyze the ex-president Barack Obama's speeches regarding the Middle East and (North) Africa and see how US-America, Middle East, and Africa are framed in political ideologies. Data is collected from the four speeches delivered by the ex-president of the USA in different places and settings. The data is analyzed using critical discourse analysis (CDA). The findings revealed that political ideology sleeplessly aspires to safeguard the interests of America and her “true” allies to sustain their world power and to suppress the “others” in the counterfeit names of tolerance, engagement, aid and support, democracy and freedom, knowledge-driven economy, peace and security, etc., that targets the younger generation. Contemporary pretexts and extensions have been done with discourse manipulations and real-life interventions
The Challenges of Azerbaijani Transliteration on the Multilingual Internet
No full textSeveral scripts have been adopted so far in Azerbaijan in different periods. Literary manuscripts and political documents developed in each of the adopted scripts are of great importance and have to be transliterated for the next generation. However, adoption of different scripts and their frequent changes lead to the emergence and dissemination of various transliteration versions on the web. This article touches upon the challenges of Azerbaijani-English transliteration process in real life and online. In this regard, the significance and dominating status of the English language on the Internet and throughout the globe is explored. Adoption of a unique transliteration standard for Azerbaijani language may contribute to the solution to this problem. The standards for the transliteration of the Azerbaijani language with others are analyzed, and the inevitability of a new approach to Azerbaijani-English transliteration is emphasized. Moreover, the article underlines future contribution of the proposed transliteration system for machine translation system of Azerbaijani-English language pair
SeFra: A Secure Framework to Manage eHealth Records Using Blockchain Technology
No full textElectronic health information is an efficient technique for providing health care services to society. Patient health information is stored in the cloud, to allow access of eHealth information from anywhere, and at any time, but the technical problems are security, privacy, etc. Sharing the medical data in a trustless environment is overcome by the proposed framework SeFra. The proposed work provides a secure framework to manage the eHealth record by using blockchain (SeFra). For authentication purposes, a temporal shadow is used and the integrity of health records is ensured by blockchain technology
Distributional Semantic Model Based on Convolutional Neural Network for Arabic Textual Similarity
No full textThe problem addressed is to develop a model that can reliably identify whether a previously unseen document pair is paraphrased or not. Its detection in Arabic documents is a challenge because of its variability in features and the lack of publicly available corpora. Faced with these problems, the authors propose a semantic approach. At the feature extraction level, the authors use global vectors representation combining global co-occurrence counting and a contextual skip gram model. At the paraphrase identification level, the authors apply a convolutional neural network model to learn more contextual and semantic information between documents. For experiments, the authors use Open Source Arabic Corpora as a source corpus. Then the authors collect different datasets to create a vocabulary model. For the paraphrased corpus construction, the authors replace each word from the source corpus by its most similar one which has the same grammatical class applying the word2vec algorithm and the part-of-speech annotation. Experiments show that the model achieves promising results in terms of precision and recall compared to existing approaches in the literature
A Novel Video Forgery Detection Model Based on Triangular Polarity Feature Classification
No full textVideo forgery has been increasing over the years due to the wide accessibility of sophisticated video editing software. A highly accurate and automated video forgery detection system will therefore be vitally important in ensuring the authenticity of forensic video evidences. This article proposes a novel Triangular Polarity Feature Classification (TPFC) video forgery detection framework for video frame insertion and deletion forgeries. The TPFC framework has high precision and recall rates with a simple and threshold-less algorithm designed for real-world applications. System robustness evaluations based on cross validation and different database recording conditions were also performed and validated. Evaluation on the performance of the TPFC framework demonstrated the efficacy of the proposed framework by achieving a recall rate of up to 98.26% and precision rate of up to 95.76%, as well as high localization accuracy on detected forged videos. The TPFC framework is further demonstrated to be capable of outperforming other modern video forgery detection techniques available today
Reading Promoters' Training: New Service of Public Library - A Case Study of Pudong Library of China
No full textIn order to make reading promotion more professional and qualified, some libraries and associations in China are training reading promoters. The Pudong Library of Shanghai is a typical case. This article summarizes the theory and the practice of reading promoter training, and discusses the operation offered by Pudong Library. It also concludes the achievements, innovations and the characteristics of the training, and further provides the developmental directions of the training. This article aims to introduce a new service that was undertook by the Pudong Library, and considers how to make this practice more effective and well-developed