Artificial Intelligence

Lung cancer is a common and severe lung disease that poses a significant threat to health. However, early detection of lung cancer in patients significantly increases their chances of successful treatment. Therefore, leveraging and developing deep learning, which has demonstrated exceptional performance in the medical field, for lung cancer diagnosis is a matter of urgency.Recently, deep learning has started to make its mark in various fields, especially in the medical field where many success stories have emerged.

The Clarkson University Affective Data Set (CUADS) is a multi-modal affective dataset designed to assist in machine learning model development for automated emotion recognition. CUADS provides electrocardiogram, photoplethysmogram, and galvanic skin response data from 38 participants, captured under controlled conditions using Shimmer3 ECG and GSR sensors. ECG, GSR and PPG signals were recorded while each participant viewed and rated 20 affective movie clips. CUADS also provides big five personality traits for each participant.

This dataset is a dedicated dataset built by standard datasets such as YAGO3-10, NELL-995, and WN18RR for detecting conflicts at path granularities in knowledge graphs. Each data set contains an error triple with a path of the format "entity relation entity relation... label", a label of 1 indicates that the path contains no erroneous triples, and a label of -1 indicates that the path contains erroneous triples.

This dataset comprises 33,800 images of underwater signals captured in aquatic environments. Each signal is presented against three types of backgrounds: pool, marine, and plain white. Additionally, the dataset includes three water tones: clear, blue, and green. A total of 12 different signals are included, each available in all six possible background-tone combinations.

will to do

The ABERDEEN Face Database is a well-known dataset in the field of computer vision and pattern recognition, specifically designed for research into face detection, recognition, and related tasks. Compiled by researchers at the University of Aberdeen in Scotland, this database provides a valuable resource for scientists and engineers working on facial analysis algorithms.

The Essays-Big5 and Kaggle-MBTI datasets are valuable resources for personality research, combining diverse textual data with psychological labels. The Essays-Big5 dataset includes over 2,000 personal essays annotated with Big Five personality traits, enabling the exploration of linguistic patterns correlated with personality dimensions, with data split stratified by personality trait distributions to ensure balanced representation.

We collected patient-doctor interaction data from the Haodf online consultation platform on the six common diseases, categorized by different risk levels. Low-risk diseases include Common Cold (Cold) and Pneumonia (Pneu.), medium-risk diseases include Diabetes (Diab.) and Depression (Depr.), and high-risk diseases include Coronary Heart Disease (CHD) and Lung Cancer (Lung.). We only use publicly accessible data, with all patients and doctors remaining anonymous, ensuring effective protection of their privacy.

This dataset provides the foundational resources for evaluating and optimizing Formula L , a novel mathematical framework for semantic-driven task allocation in multi-agent systems (MAS) powered by large language models (LLM). The dataset includes Python code and both empirical and synthetic data, specifically designed to validate the effectiveness of Formula L in improving task distribution, contextual relevance, and dynamic adaptation within MAS.

The dataset comprises: