The RT-IoT2022, a proprietary dataset derived from a real-time IoT infrastructure, is introduced as a comprehensive resource integrating a diverse range of IoT devices and sophisticated network attack methodologies. This dataset encompasses both normal and adversarial network behaviours, providing a general representation of real-world scenarios. Incorporating data from IoT devices such as ThingSpeak-LED, Wipro-Bulb, and MQTT-Temp, as well as simulated attack scenarios involving Brute-Force SSH attacks, DDoS attacks using Hping and Slowloris, and Nmap patterns, RT-IoT2022 offers a detailed perspective on the complex nature of network traffic. The bidirectional attributes of network traffic are meticulously captured using the Zeek network monitoring tool and the Flowmeter plugin. Researchers can leverage the RT-IoT2022 dataset to advance the capabilities of Intrusion Detection Systems (IDS), fostering the development of robust and adaptive security solutions for real-time IoT networks.
This repository holds the data from a cohort of pediatric patients with suspected appendicitis admitted with abdominal pain to Children’s Hospital St. Hedwig in Regensburg, Germany, between 2016 and 2021. Each patient has (potentially multiple) ultrasound (US) images, aka views, tabular data comprising laboratory, physical examination, scoring results and ultrasonographic findings extracted manually by the experts, and three target variables, namely, diagnosis, management and severity.
This is a subset of the NPHA dataset filtered down to develop and validate machine learning algorithms for predicting the number of doctors a survey respondent sees in a year. This dataset’s records represent seniors who responded to the NPHA survey.
The Infrared Thermography Temperature Dataset contains temperatures read from various locations of inferred images about patients, with the addition of oral temperatures measured for each individual. The 33 features consist of gender, age, ethnicity, ambiant temperature, humidity, distance, and other temperature readings from the thermal images. The dataset is intended to be used in a regression task to predict the oral temperature using the environment information as well as the thermal image readings.
This dataset has 17 classes. Data are divided in three partition train, val and test. The classes are 0 : Beet Armyworm 1 : Black Hairy 2 : Cutworm 3 : Field Cricket 4 : Jute Aphid 5 : Jute Hairy 6 : Jute Red Mite 7 : Jute Semilooper 8 : Jute Stem Girdler 9 : Jute Stem Weevil 10 : Leaf Beetle 11 : Mealybug 12 : Pod Borer 13 : Scopula Emissaria 14 : Termite 15 : Termite odontotermes (Rambur) 16 : Yellow Mite
This data set contains 13 clinicopathologic features aiming to predict recurrence of well differentiated thyroid cancer. The data set was collected in duration of 15 years and each patient was followed for at least 10 years.
Soybean cultivation is one of the most important because it is used in several segments of the food industry. The evaluation of soybean cultivars subject to different planting and harvesting characteristics is an ongoing field of research. We present a dataset obtained from forty soybean cultivars planted in subsequent seasons. The experiment used randomized blocks, arranged in a split-plot scheme, with four replications. The following variables were collected: plant height, insertion of the first pod, number of stems, number of legumes per plant, number of grains per pod, thousand seed weight, and grain yield, resulting in 320 data samples. The dataset presented can be used by researchers from different fields of activity.
The "Recipe Reviews and User Feedback Dataset" is a comprehensive repository of data encompassing various aspects of recipe reviews and user interactions. It includes essential information such as the recipe name, its ranking on the top 100 recipes list, a unique recipe code, and user details like user ID, user name, and an internal user reputation score. Each review comment is uniquely identified with a comment ID and comes with additional attributes, including the creation timestamp, reply count, and the number of up-votes and down-votes received. Users' sentiment towards recipes is quantified on a 1 to 5 star rating scale, with a score of 0 denoting an absence of rating. This dataset is a valuable resource for researchers and data scientists, facilitating endeavors in sentiment analysis, user behavior analysis, recipe recommendation systems, and more. It offers a window into the dynamics of recipe reviews and user feedback within the culinary website domain.
An image classification dataset of waste items across 9 major material types, collected within an authentic landfill environment.
In this work, I have developed an Offline Handwritten Text Recognition (HTR) model architecture based on Neural Networks that can be taught to recognise whole pages of handwritten Bangla (Bengali) text without image segmentation. Bengali being a resource-constrained Indic language, there is a lack of proper annotated dataset consisting scanned images of Bangla handwritten scripts. In this work, I have introduced a new dataset, `Bongabdo', which consists of full-page handwritten scripts collected from a wide variety of contributors of various age groups, occupation and gender. Further, recently proposed State-of-the-art Image-to-Sequence architecture with different settings of hyperparameters have been applied on these images and they have been evaluated in terms of Character Error Rate (CER), Word Error Rate (WER) and Sequence Error Rate (SER) to finally come up with a comparative study.
0 to 10 of 664