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نتیجه جستجو - محاسبات نرم

تعداد مقالات یافته شده: 7
ردیف عنوان نوع
1 A Soft Computing Approach for group decision making: A supply chain management application
یک رویکرد نرم افزار محاسباتی برای تصمیم گیری گروهی: یک برنامه مدیریت زنجیره تأمین-2020
This paper presents a novel Soft Computing Approach called ‘‘Neuro-Fuzzy Analytical Network Process (NFANP)’’ for the group decision-making problems based on the conventional Analytic Network Process (ANP) method. The proposed approach deals with the interval values of judgments in a fuzzy environment using mobile, not fixed, trapezoidal and triangular membership functions, as well as the interval numerical ratio defined by alpha-cuts and the decision maker’s confidence levels. The consistency problem of the fuzzy reciprocal matrices is addressed in the proposed paper by allowing a certain tolerance deviation to be less than 0.20. Furthermore, trained Artificial Neural Networks (ANNs) are included in the proposed approach to reduce the large number of computations of the arithmetic operations required to correlate decision factors with the alternatives. In the proposed implementation, the selection problem is defined into three main decision groups: Supplier Characteristics, On-Going Performance, and Project Management Capabilities. The supplier alternatives are classified by the decision makers corresponding to company size, quality system implementation, and cost management. The application of the proposed approach shows a great accuracy in the final utility values and a significant reduction in the calculation requirements.
Keywords: Soft Computing | Neuro-Fuzzy Analytic Network Process | (NFANP) | Fuzzy judgments | Group decision-making | Supply chain management | ANNs
مقاله انگلیسی
2 Soft computing approaches to homogenized properties of inclusion-modified concrete mixtures: Application to XLPE-modified concrete
روش محاسبات نرم به خصوصیات همگن مخلوط بتن اصلاح شده شامل: کاربرد بتن اصلاح شده XLPE-2020
This work focuses on determining the homogenized elastic properties of concrete mixtures containing randomly oriented chopped inclusions using soft computing techniques, to identify optimal inclusion ratios. It also addresses the efficient resolution of the inverse problem, to identify the elastic properties of the constituents from those of the mix. A solution manifold is constructed using computationally efficient 2D axially-symmetrical finite element models of a typical concrete cylinder, at the mesoscale, with randomly generated structures of round aggregates and flat inclusions. To overcome the prohibitive cost of solving the reverse component property identification problem, more efficient analytical and machine learning approaches are proposed and tested for their ability to learn the numerical manifold. The best model is used to determine the elastic properties of the constituents of a set of real cross-linked polyethylene (XLPE) modified concrete mixtures, from their observed homogenized behavior. A maximum XLPE inclusion ratio of about 230 kg/m3 is also determined to maintain an apparent stiffness consistent with structural applications
Keywords: Inclusion-modified concrete | Plastic-waste recycling | Homogenization | Big data analysis | Machine learning | Manifold technique | Property identification
مقاله انگلیسی
3 A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier
استخراج ویژگی منحصر به فرد با استفاده از MRDWT برای طبقه بندی خودکارضربان قلب غیر طبیعی از داده های بزرگ ECG با چند لایه طبقه بندی احتمالی شبکه عصبی-2018
This paper employs a novel adaptive feature extraction techniques of electrocardiogram (ECG) signal for detection of cardiac arrhythmias using multiresolution discrete wavelet transform from ECG big data. In this paper, five types ECG arrhythmias including normal beats have been classified. The MIT-BIH database of 48 patient records is utilized for detection and analysis of cardiac arrhythmias. Proposed feature extraction utilizes Daubechies as wavelet function and extracts 21 feature points which include the QRS complex of ECG signal. The Multilayered Probabilistic Neural Network (MPNN) classifier is pro posed as the best-suited classifier for the proposed feature. Total 1700 ECG betas were tested using MPNN classifier and compared with other three classifiers Back Propagation (BPNN), Multilayered Perceptron (MLP) and Support Vector Machine (SVM). The system efficiency and performance have been evaluated using seven types of evaluation criteria: precision (PR), F-Score, positive predictivity (PP), sensitivity (SE), classification error rate (CER) and specificity (SP). The overall system accuracy, using MPNN technique utilizing the proposed feature, obtained is 99.53% whereas using BPNN, MLP and SVM provide 97.94%, 98.53%, and 99%. The processing time using MPNN classifier is only 3 s which show that the proposed techniques not only very accurate and efficient but also very quick.
Keywords: Signal processing ، Artificial intelligence ، Pattern recognition ، Soft computing ، Wavelet transform
مقاله انگلیسی
4 A soft computing approach to big data summarization
رویکرد محاسباتی نرم برای خلاصه سازی داده های بزرگ-2018
The added value of a dataset lies in the knowledge a domain expert can extract from it. Considering the continuously increasing volume and velocity of these datasets, efficient tools have to be defined to generate meaningful, condensed and human-interpretable representations of big datasets. In the proposed approach, soft computing techniques are used to define an interface between the numerical and categorical space of data definition and the linguistic space of human reasoning. Based on the expert’s own vocabu lary about the data, a personal summary composed of linguistic terms is efficiently generated and graphically displayed as a term cloud offering a synthetic view of the data properties. Using dedicated indexing strategies linking data and their subjective linguis tic rewritings, exploration functionalities are provided on top of the summary to let the user browse the data. Experimentations confirm that the space change operates in linear time wrt. the size of the dataset making the approach tractable on large scale data.
Keywords: Data personalisation; Linguistic summaries; Soft computing; Knowledge extraction; Visualization; Specificity measure
مقاله انگلیسی
5 مدل رگرسیون فازی جدید مبتنی بر شبکه عصبی فازی و مدیریت بوسیله ی این مدل
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 21
در این مقاله یک روش ترکیبی جدید مبتنی بر شبکه عصبی فازی با بازه ‏زمانی برای تقریب مدل های رگرسیون فازی با بازه زمانی ارائه شده است. در این مقاله بدنبال گسترش تحقیقاتی مدل های رگرسیون فازی هستیم. آموزش شبکه عصبی فازی با بازه ‏زمانی (IVFNN) را می توان با داده های فازی با بازه زمانی و حلقه¬ای انجام داد. شبکه عصبی ، بخشی از یک میدان بزرگ به نام محاسبات عصبی یا محاسبات نرم محسوب می شود. علاوه بر این، برای یافتن پارامترهای تقریبی، الگوریتم ساده¬ای از تابع هزینه شبکه عصبی فازی ارائه شد. در نهایت، به شرح رویکردمان با نمونه های عددی پرداخته و این روش را با روش های موجود مقایسه کردیم.
کلید واژه ها: شبکه های عصبی فازی با بازه ‏زمانی | مدل رگرسیون فازی با بازه ‏زمانی | شبکه عصبی Feedforward | الگوریتم یادگیری
مقاله ترجمه شده
6 Big data analytics by automated generation of fuzzy rules for Network Forensics Readiness
تجزیه و تحلیل داده های بزرگ با تولید خودکار قوانین فازی برای آمادگی شبکه های جنایی-2017
Analysis of large-scale traffic dumps in Network Forensics can be a complex and non-trivial problem. This is an important step in collecting evidences and making threat intelligence to foresee new ille gal activities. Machine Learning comes into help to automatically support decision of forensics expert. Furthermore, application in live systems may bring additional obstacles related to forensics readiness and knowledge discovery. We believe that it can be mitigated by means of Neuro-Fuzzy, a fusion of human-understandable model and automated data analytic. This method includes optimal unsupervised grouping of samples with so-called Self-Organizing Features Map and fuzzy rules tuning by Artificial Neural Network. In this work we propose improvements of the methods that makes it possible to extract fewer fuzzy rules in a faster manner. The new method has two advantages in comparison to existing. First, we improve the estimation of fuzzy patches. Second, parameterization that represents the data by incorporating additional ellipse compactness information. By using ellipse rotation and flattering infor mation, the membership functions can be derived. To even further enhance the generalization of the method, the bootstrap aggregation was tested during the grouping phase. Finally, the method has been assessed on the intrusion detection dataset with a five millions samples with classification accuracy 94% using only 12 rules.
Keywords:Big data|Soft Computing|Neuro-Fuzzy|Intrusion detection|Self-organizing feature maps|Computational forensics
مقاله انگلیسی
7 Big data analytics by automated generation of fuzzy rules for Network Forensics Readiness
تجزیه و تحلیل داده های بزرگ توسط نسل خودکار از قوانین فازی برای شبکه پزشکی قانونی-2017
Analysis of large-scale traffic dumps in Network Forensics can be a complex and non-trivial problem. This is an important step in collecting evidences and making threat intelligence to foresee new ille gal activities. Machine Learning comes into help to automatically support decision of forensics expert. Furthermore, application in live systems may bring additional obstacles related to forensics readiness and knowledge discovery. We believe that it can be mitigated by means of Neuro-Fuzzy, a fusion of human-understandable model and automated data analytic. This method includes optimal unsupervised grouping of samples with so-called Self-Organizing Features Map and fuzzy rules tuning by Artificial Neural Network. In this work we propose improvements of the methods that makes it possible to extract fewer fuzzy rules in a faster manner. The new method has two advantages in comparison to existing. First, we improve the estimation of fuzzy patches. Second, parameterization that represents the data by incorporating additional ellipse compactness information. By using ellipse rotation and flattering infor mation, the membership functions can be derived. To even further enhance the generalization of the method, the bootstrap aggregation was tested during the grouping phase. Finally, the method has been assessed on the intrusion detection dataset with a five millions samples with classification accuracy 94% using only 12 rules.
Keywords:Big data|Soft Computing|Neuro-Fuzzy|Intrusion detection|Self-organizing feature maps|Computational forensics
مقاله انگلیسی
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