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.
This dataset comprises over 38,000 seed inputs generated from a range of Large Language Models (LLMs), including ChatGPT-3.5, ChatGPT-4, Claude-Opus, Claude-Instant, and Gemini Pro 1.0, specifically designed for the application in fuzzing Python functions. These seeds were produced as part of a study evaluating the utility of LLMs in automating the creation of effective fuzzing inputs, a method crucial for uncovering software defects in the Python programming environment where traditional methods show limitations.
This dataset includes environmental perception, vehicle motion status, and battery consumption during vehicle operation. The dataset is collected by human operators during the safe and smooth operation of vehicles. By dividing the hazardous areas in the current environment of the vehicle and processing them, a radar map of the hazardous areas is generated; The vehicle motion status section contains the position information (x, y, z) and kinematic information (v, w, a, alpha, beta, gamma) of the vehicle.
This study used boots, aircraft, cells, pliers, and guitars from 2D shapes included in the literature as data sets to test modeling success. These 2D shapes, which are mostly not publicly available data sets, form the target curves of IP. In this study, hand drawings with a curved structure were used in modeling, where the success of fitting precision could be better determined.
