This dataset was created based on the paper 'Andras Hajdu, Gyorgy Terdik, Attila Tiba, and Henrietta Toman: A stochastic approach to handle knapsack problems in the creation of ensembles'.To summarize our experimental setup for UCI binary classification problems, we have considered base classifiers perceptron, decision tree, Levenberg-Marquardt feedforward neural network, random neural network, and discriminative restricted Boltzmann machine classifier for the 5 UCI datasets MAGIC Gamma Telescope, HIGGS, EEG EyeState, Musk (Version 2), and Spambase; datasets of large cardinalities were sele

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Characteristic impedance Result of Microstrip Transmission lines with 3D EM simulation tool

 

These data had been donated by Peyman Mahouti in 2019.

Donators note:

Please cite the following paper if you use this data set:

[1]      Mahouti P, Gunes F, Belen MA, Demirel S. Symbolic Regression for Derivation of an Accurate Analytical Formulation Using Big Data : An Application Example. ACES JOURNAL 2017; 32(5): 574-591.

 

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Supplementary data for the IEEE Access paper Applicability of Immersive Analytics in Mixed Reality: Usability Study

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To obtain the prices of parts from the manufacturing characteristics and other manufacturing processes, feature quantity expression is innovatively applied. By identifying manufacturing features and calculating the feature quantities, the feature quantities are described in the form of assignments as data. To obtain the prices of parts intelligently, the most widely used and mature deep-learning method is adopted to realize the accurate quotation of parts

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This dataset used in the experiment of paper "Bus Ridesharing Scheduling Problem". This is a real-world bus ridesharing scheduling problem of Chengdu city in China, which includes 10 depots, 2,000 trips.

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This is the dataset used in the experiment of paper "Bus Pooling: A Large-Scale Bus Ridesharing Service". The dataset contains 60,822,634 trajectory data from 11,922 Shanghai taxis from one day (Apr 1, 2018). The 100 groups of coordinate sets containing three coordinates as experimental samples are used to compare the effectiveness and efficiency of location-allocation algorithms.

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Bitcoin is a decentralized digital currency that has gained significant attention and growth in recent years. Unlike traditional currencies, Bitcoin does not rely on a centralized authority to control the supply, distribution, and verification of the validity of transactions. Instead, Bitcoin relies on a peer-to- peer network of volunteers to distribute pending transactions and confirmed blocks, verify transactions, and to collectively implement a replicated ledger that everyone agrees on. This peer-to-peer (P2P) network is at the heart of Bitcoin and many other blockchain technologies.

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This paper presents a fast and open source extension based on the NSGA-II code stored in the repository of the Kanpur Genetic Algorithms Laboratory (KanGAL) and the adjustment of the selection operator. It slightly modifies existing well-established genetic algorithms for many-objective optimization called the NSGA-III, the adaptive NSGA-III (A-NSGA-III), and the efficient adaptive NSGA-III,  (A$^2$-NSGA-III).

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A Hybrid Approach to Service Recommendation Based on Network Representation Learning

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