Artificial Intelligence

Data sources of MKG with structured medical knowledge database and unstructured scientific publications

The dataset aims to compile images of buildings with structural damage for analysis. The images can be classified by the severity of damage to building facades after seismic events using deep learning techniques, particularly pre-trained convolutional neural networks and transfer learning. The analysis can precisely identify structural damage levels, aiding in effective evaluation and response strategies.

The Defect Tracking dataset provides a comprehensive resource for software maintenance and defect prediction research. This dataset, downloaded from the Jira Spring website, includes detailed defect data from a variety of Spring application projects such as Spring Framework, Spring Boot, Spring Security, Spring Data, and others. It encompasses numerous attributes, including issue summaries, types, statuses, priorities, resolution details, and additional relevant information.

This is the MRI scan database used in the research work of classifying Meningioma Tumor in humans by using hybrid Ensemble Deep Learning Network  AlGoRes. It consist of two sets; one for training and another one for testing the Deep Learning Network  AlGoRes.

Training data set consist of 822 imagers with meningioma_tumor and 395 images without tumor.

Testing data set consist of 115 imagers with meningioma_tumor and 104 images without tumor.

This study utilizes the annual loan ledger data obtained from a commercial bank located in Jiangsu Province, China, which is called ChinaZJB. The ChinaZJB dataset consists of 1,329 valid samples of SMEs after merging the non-financial behavioral information and soft information on credit rating with the financial information, loan information, and non-financial basic information found in the annual loan ledger data.

This large dataset includes six small datasets, including two types, one contains the original node relationship information and node feature information, please use it through the common network construction methods; the other is the dataset which has been processed, including the direct edge information and node's association information, which can be used to construct the network directly through the network construction methods.

The LuFI-RiverSnap dataset includes close-range river scene images obtained from various devices, such as UAVs, surveillance cameras, smartphones, and handheld cameras, with sizes up to 4624 × 3468 pixels. Several social media images, which are typically volunteered geographic information (VGI), have also been incorporated into the dataset to create more diverse river landscapes from various locations and sources. 

Please see the following links: 

https://doi.org/10.1109/ACCESS.2024.3385425

We have developed three datasets, referred to as ER-C, Mito-C and Nucleus-C, respectively, for benchmarking robustness of DNN models against corruptions and adversarial attacks in semantic segmentation of fluorescence microscopy images. Degraded images in these three datasets are synthesized from raw images along with their manually annotated segmentation labels in the ER, Mito, and Nucleus datasets [1,2], respectively.

This dataset focuses on the redevelopment and psychometric evaluation of the Adversity Response Profile for Indian Higher Education Institution (ARP-IHEI) students, emphasizing its importance in understanding how individuals respond to adversity. The data were gathered from a sample of 122 second year students at school of computing, MIT Art, Design and Technology University students. Read_me file contains questionnaire.

The large and diverse access to data sources in healthcare has boosted the application of novel computer techniques that can extract meaningful information to improve patients' prognoses and other important medical uses. However, current systems require the professional to manually type the information, which increases the risk of transcription errors and cross-contamination. We propose an automated system that allows healthcare professionals to dictate clinical information that can be transcribed and analyzed.