Computational Intelligence

Social Media Big Dataset for Research, Analytics, Prediction, and Understanding the Global Climate Change Trends is focused on understanding the climate science, trends, and public awareness of climate change. The use of dataset for analytics of climate change trends greatly helps in researching and comprehending global climate change trends.

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3490 Views

The Numerical Latin Letters (DNLL) dataset consists of Latin numeric letters organized into 26 distinct letter classes, corresponding to the Latin alphabet. Each class within this dataset encompasses multiple letter forms, resulting in a diverse and extensive collection. These letters vary in color, size, writing style, thickness, background, orientation, luminosity, and other attributes, making the dataset highly comprehensive and rich.

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488 Views

The instantaneous state (situation) of the game was constituted by four values: the cart position, the cart speed, the pole angle to the vertical axis, and the pole angular velocity.

For each action taken by the human player in the game, a tuple containing the four values representing the current game situation, along with the action and reward obtained (utility), is recorded as a situation-decision-utility (SDU) tuple.

3 types of actions have been recorded: Move left (-1), move rght (1) and no action (0).

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14 Views

The dataset aims to facilitate research in the optimization of the carbon footprint of recipes. Consisting of 30 Excel files processed through various Python scripts and Jupyter notebooks, the dataset serves as a versatile resource for both performance analysis and environmental impact assessment. The unique attribute of this dataset lies in its ability to calculate representative values of carbon footprint optimization through multiple algorithmic implementations.

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226 Views

RITA (Resource for Italian Tests Assessment), is a new NLP dataset of academic exam texts written in Italian by second-language learners for obtaining the CEFR certification of proficiency level.
RITA dataset is available for automatic processing in CSV and XML format, under an agreement of citation.

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396 Views

# MAHN for disease-metabolite associations prediction

 

## Dependecies

- Python 3.9

- pytorch 1.12.1

- dgl 1.1.1

- numpy 1.22.4

- pandas 1.4.4

 

## Dataset

disease-metabolite associations:association_DME.xlsx

disease-microbe associations:association_DMI.xlsx

microbe-metabolite associations:association_MIME.xlsx

disease semantic networks based on metapath DMED and DMID:A_DMED.xlsx and A_DMID.xlsx

metabolite semantic networks based on metapath MEDME and MEMIME: A_MEDME.xlsx and A_MEMIME.xlsx 

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12 Views

Please cite the following paper when using this dataset:

N. Thakur, K. A. Patel, I. Hall, Y. N. Duggal, and S. Cui, “A Dataset of Search Interests related to Disease X originating from different Geographic Regions”, Preprints 2023, 2023081701, DOI: https://doi.org/10.20944/preprints202308.1701.v1

Abstract:

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688 Views

The dataset that we published in this data repository can be used to build neural networks-based inverse kinematics for NAO robot arms. This dataset is named ARKOMA. ARKOMA is an acronym for ARif eKO MAuridhi, all of whom are the creators of this dataset. This dataset contains input-output data pairs. In this dataset, the input data is the end-effector position and orientation, while the output data is a set of joint angular positions. For further applications, this dataset was split into the training dataset, validation dataset, and testing dataset.

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586 Views

Most plant diseases have observable symptoms, and the widely used approach to detect plant leaf disease is by visually examining the affected plant leaves. A model which might carry out the feature extraction without any errors will process the classification task successfully.  The technology currently faces certain limitations such as a large parameter count, slow detection speed, and inadequate performance in detecting small dense spots. These factors restrict the practical applications of the technology in the field of agriculture.

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792 Views

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