Artificial Intelligence; Big Data; Dataset; Machine Learning; dataset

This dataset comprises comprehensive information on chemical compounds sourced from the PubChem database, including detailed descriptions for each compound. Each entry in the dataset includes unique PubChem Compound Identifiers (CIDs), molecular structures, physicochemical properties, biological activities, and associated descriptive metadata. The dataset is designed to support research in drug discovery, chemical informatics, and other fields requiring extensive chemical compound information.

These datasets were developed through a collaboration between Zhejiang Sci-Tech University and Hangzhou Zhiyi Technology Co., Ltd., encompassing a four-year span from January 2019 to October 2023.

In our ever-expanding world of advanced satellite and communications systems, there's a growing challenge for passive radiometer sensors used in the Earth observation like 5G. These passive sensors are challenged by risks from radio frequency interference (RFI) caused by anthropogenic signals. To address this, we urgently need effective methods to quantify the impacts of 5G on Earth observing radiometers. Unfortunately, the lack of substantial datasets in the radio frequency (RF) domain, especially for active/passive coexistence, hinders progress.

The Garbage Image Dataset consists of images of garbage items collected from nearby localities using smartphones. The dataset is categorized into five different classes. Each category represents a specific type of garbage item commonly found in everyday waste. The purpose of the Garbage Image Dataset is to provide a collection of labelled images of garbage items from different categories. The dataset can be used to train and evaluate deep learning models for garbage classification tasks.

This is a test dataset for comparison with the latest multi-objective evolutionary algorithms. We have split the experiment into two groups in high and low dimensions respectively, and the experimental results are outstanding. We used IGD as the performance metric, and the data in parentheses are the std of 20 independent repetitions of the experiment and were analyzed for significance.

As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices remains a continuing challenge.The deployment of IoT applications makes protection more challenging with the increased attack surfaces as well as the vulnerable and resource-constrained devices. Anomaly detection is a crucial procedure in protecting IoT. A promising way to perform anomaly detection on IoT is through the use of machine learning algorithms. There is a lack in the literature to identify the optimal (with regard to both effectiveness and efficiency) anomaly detection models for IoT.

In this short communication we present the results taken from the real-time weather dataset of Tabouk City, Saudi Arabia. In the results we have applied machine learning techniques to predict the future air temperature of the region. This dataset's results have informed in the creation of determinants driving agricultural and urban expansion contribute to the analysis of the main causes of land use change.

In this short communication we present the results taken from the real-time weather dataset of Tabouk City, Saudi Arabia. In the results we have applied machine learning techniques to predict the future air temperature of the region. This dataset's results have informed in the creation of determinants driving agricultural and urban expansion contribute to the analysis of the main causes of land use change.

Please cite the following paper when using this dataset:

N. Thakur, S. Cui, K. A. Patel, N. Azizi, V. Knieling, C. Han, A. Poon, and R. Shah, “Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior,” Journal of Computation, Vol. 11, Issue. 11, Article. 234, Nov. 2023, DOI: http://dx.doi.org/10.3390/computation11110234

Abstract

Please cite the following paper when using this dataset:

N. Thakur, K. A. Patel, I. Hall, Y. N. Duggal, and S. Cui, “A Dataset of Search Interests related to Disease X originating from different Geographic Regions”, Preprints 2023, 2023081701, DOI: https://doi.org/10.20944/preprints202308.1701.v1

Abstract: