Artificial Intelligence
Contains 80 questions of LeetCode weekly and bi-weekly contests released after March 2024.
Each question contains an average of 644 test cases, as well as programming solutions in Python language collected from the official LeetCode website. The input fields of the data set contain function headers and natural language descriptions, which are mainly used to evaluate the ability of large models to solve programming problems according to requirements.
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34 Views
Heart Rate Variability (HRV) parameters provide valuable insights into the autonomic nervous system’s regulation of the cardiovascular system in response to various physiological conditions, particularly during dynamic tasks. This dataset consists of ECG recordings obtained during the execution of three dynamic tasks based on the three-dimensional movement of the upper limb. The data is available in both .XLSX and .CSV formats, containing 166 rows and 21 columns, all of which correspond to HRV parameters such as RR intervals, heart rate, and frequency-domain measures.
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344 Views
This dataset can be used for vulnerability detection. This repository is devised to explain vulEmbedding,
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First "altKlasörTaraTahmin.R" file is for searching code files to generate suitable numeric matrix,
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createKeywordMatrix.R is for generating keyword matrix, thereby checking vulnerabilities,
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sphericalLabeling.R is for generating spherical labeling.
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Final you can run deepnetVersion2.R to produce vulnerability prediction.
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100 Views
This dataset comprises 2,052 .jpeg image samples from 74 students, offering a comprehensive portrayal of student life. Capturing academic, extracurricular, and social dimensions, it provides insights into diverse learning environments, activities, and interactions. From classrooms to sports fields, cultural events to social gatherings, the dataset encapsulates the multifaceted nature of student experiences. Researchers can utilize these images to explore educational dynamics, analyze social behaviors, and develop algorithms for image recognition and analysis.
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456 Views
This study includes three commercially available 3D printers for soft material printing based on the Material Extrusion (MEX) AM process. The samples are 3D printed for six different AM process parameters obtained by varying layer height and nozzle speed. The novelty part of the methodology is incorporating an AI-based image segmentation step in the decision-making stage that uses quality inspected training data from the Non-Destructive Testing (NDT) method.The performance of the trained AI model is compared with the two software tools based on the classical thresholding method.
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170 Views
In order to enable an efficient and reliable integration of photovoltaic power plants into power
grids in sub-Saharan Africa in general and Chad in particular, which is committed to massively exploiting
solar energy to address its energy deficit, this work aims to forecast hourly temperature and global horizontal
irradiation (GHI). In this study, we employ ensemble methods represented by random forest (RF) and extreme
boosting gradient (XGB) model. Their performances are then compared with the support vector regression
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38 Views
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.
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171 Views
Abstract—Sparse Mobile CrowdSensing is an efficient data collection paradigm that recruits participants to gather data from partial spatiotemporal regions and leverages inherent correlations among these data to infer the remaining uncollected data. However, enabling accurate inference requires participants to upload sensitive spatiotemporal information, which poses significant privacy leakage risks. Traditional methods address these risks by obfuscating the uploaded spatial data, but this often compromises inference accuracy.
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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.
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193 Views
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.
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40 Views