Computational Intelligence

SSADMO algorithm is proposed to study the optimal spatial trajectories of sea buckthorn fruit vibration separation. In this study, kinematics interpolation analysis of manipulator trajectories was carried out to compare the trajectories of cubic polynomials and quintic polynomial interpolation, and to compare the trajectories and attitudes of the manipulators under the condition of limited operating range and speed, in this paper, we propose a new SSADMO algorithm which combines the advantages of SSA and DMO optimization algorithms in the 3-5-3 polynomial interpolation.

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.

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.

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).

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.

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.

# 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

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:

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.