Computational Intelligence
With the advent of huge data availability and cheap processing power to derive insights from data ,applications of Big Data and Machine Learning are gaining popularity in every industry. Now, Predicting about future is no more an alternate but a necessity to increase efficiency with accurate output. Forecasting for a shorter duration (Nowcasting) fits perfectly in this space to estimate the final production for better planning and control.
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This is a model of artificial intelligence architecture developed using the
technique of targeting and assembling various components (as needed) together
to get artificial intelligence with characteristics similar to human intelligence.
This model represents the general form, the strategy, and the mechanism of
manufacturing an artificial intelligence device and the fact that based on what
structure it can turn into artificial intelligence. One of the important features of
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This dataset is for validate and evaluate English-Bangla machine translation systems.
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This dataset contains 70,861 English-Bangla sentence pairs and more than 0.8 million tokens in each side.
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A 128-dimensional vector for one document in text format, where each dimension is represented as a single precision floating-point number。
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The database contains PROCON metrics values extracted from more than 30400 source code files (with 14950 bug reports) of GitHub repository. Various Machine earning (ML) models trained using PROCON metrics outperform the ones trained using OO metrics of PROMISE repository.
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This dataset is a highly versatile and precisely annotated large-scale dataset of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users.
The dataset comprises 7 months of measurements, collected from all sensors of 4 smartphones carried at typical body locations, including the images of a body-worn camera, while 3 participants used 8 different modes of transportation in the southeast of the United Kingdom, including in London.
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To investigate the generalization performance of the evolved scheduling policies(SPs), which are generated by the hyper-heuristic coevolution, the evolutionary SPs extracted from the aggerate Pareto front were applied to 64 testing scenarios to compare with the combinations of 320 existing man-made SPs which include 32 job sequencing rules and 10 machine assignment rules. This dataset provides the simulation performance of the evolved SPs and the 320 existing man-made SPs on the multi-objective dynamic flexible job shop scheduling problem.
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The experimental dataset involves the interval values for the bed slope (m/m) measured with a theodolite, the river flow rate (m^3/s), and the average surface velocity (m/s) from the Ter River (Spain).
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There are no explicit labelled errors in FB15K. Considering the experience that most errors in real-world KG derive from the misunderstanding between similar entities, we consider the methods described in paper "DoesWilliamShakespeareREALLYWrite Hamlet? Knowledge Representation Learning with Confidence" to generate fake triples as negative examples automatically with less human annotation. Three kinds of fake triples may be constructed for each true triple: one by replacing head entity, one by replacing relationship, and one by replacing tail entity.
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