Traffic data set

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48 Views

The fast development of urban advancement in the past decade requires reasonable and realistic solutions for transport, building infrastructure, natural conditions, and personal satisfaction in smart cities. This paper presents and explores predictive energy consumption models based on data-mining techniques for a smart small-scale steel industry in South Korea. Energy consumption data is collected using IoT based systems and used for prediction.

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518 Views

India is known for its highly disciplined foreign policies, strategic location, vibrant and massive Diaspora. India envisages enhancing its scope of cooperation, trade and widens its sphere of relations with the Pacific. As a result, the world is witnessing the rise of Indo-Pacific ties. Before the 1980’s the keystone of the universe was called the Atlantic, but now a radical shift to the east is noticed by the term “Indo-Pacific‟.

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289 Views

This dataset was extracted from Twitter using keywords related to Dilma Roussef and Aécio Neves, that were the candidates of the second round of the 2014 presidential election in Brazil. This dataset contains texts in Portuguese and the respective classification of sentiments resulting from the techniques described in the article published in the 2018 IEEE International Conference on Data Mining Workshops - ICDMW (https://ieeexplore.ieee.org/abstract/document/8637504). 

 

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The .zip file is divided into four .csv files with data organized in 11 columns named: date, amount of retweets, amount of favorites, tweet text, mentions, hashtags, id, permalink, a score of classification, label of sentiment.

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155 Views

Depths to the various subsurface anomalies have been the primary interest in all the applications of magnetic methods of geophysical prospection. Depths to the subsurface geologic features of interest are more valuable and superior to all other properties in any correct subsurface geologic structural interpretations.

Instructions: 

The Neural Network Pattern Recognition help to select the appropriate data sets, create and train the network, and evaluate its performance using the cross-entropy and convolution matrices in MATLAB with fusion python. The Neural Network utilizes a Two-Layer feed-forward network to solve the pattern recognition problem with a six inputs data (i.e., the SI values), a Hidden Layer and a SoftMax Output Layer Neurons. The method excellently classified vector attributes when sufficient neuron in the hidden layer is selected. In this study, a six inputs data from the various SI values obtained was used in one hundred, (100) hidden layers of the neutrons, and weights combined with a six layers output of neutrons, and weights to generate the six-final output that represent each of the SI values depths as shown.

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337 Views

Development of Industrial IoT System for Anomaly Detection in Smart Factory

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1901 Views

a research data about campaign participation in Surabaya City 2015

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119 Views