Machine Learning
This study introduces a novel soil texture dataset designed to overcome geographic constraints and improve the generalization of classification models. Using the USDA soil classification triangle as a framework, the dataset is systematically generated by combining pure sand, silt, and clay in varying proportions to create diverse soil texture classes. The soil mixtures are captured using a multispectral sensor with seven bands, ensuring a rich representation of spectral information.
- Categories:
9 Views
This study identifies representative sensors for monitoring fan performance by analyzing vibration data collected from piezoelectric sensors during various operational modes. The dataset, which includes measurements at a rate of 300 samples/sec from 10 sensors, covers six modes of operation: Maximum Speed, Maximum Speed with Oscillation, Minimum Speed, Minimum Speed with Oscillation, Minimum to Maximum Speed, and a comprehensive dataset combining all modes.
- Categories:
12 Views
Moving away from plain-text DNS communications,
users now have the option of using encrypted DNS protocols
for domain name resolutions. DNS-over-QUIC (DoQ) employs
QUIC—the latest transport protocol—for encrypted communi-
cations between users and their recursive DNS servers. QUIC is
also poised to become the foundation of our daily web browsing
experience by replacing TCP with HTTPP/3, the latest version
of the HTTP protocol.
Traditional TCP-based web browsing is vulnerable to website
- Categories:
7 Views
This dataset is collected at KAIST, Daejeon, and KAIST by ISILAB to research seamless indoor-outdoor detection. The collecting device is a Raspberry Pi 4B+ with touchscreen UI connected with a Pmod Nav module and a PmodGPS. This collection has a rough three-month time span, which mitigates the specific time-specific bias. Further, in the collection, we also swap the wiring to simulate the device bias. The dynamic calibration is not applied to the dataset; searchers may choose to apply the dataset or not.
- Categories:
5 Views
This dataset integrates three Publicly available sources of drug-target interaction data: the Human dataset, the Biosnap dataset, and the DrugBank dataset, combining them into a comprehensive resource for drug discovery and bioinformatics research. It includes a diverse set of human proteins identified as potential drug targets, along with a variety of corresponding drug molecules. Each drug-target pair is accompanied by interaction labels, indicating whether the drug interacts with the protein target.
- Categories:
4 Views
This dataset integrates three valuable sources of drug-target interaction data: the Human dataset, the Biosnap dataset, and the DrugBank dataset, combining them into a comprehensive resource for drug discovery and bioinformatics research. It includes a diverse set of human proteins identified as potential drug targets, along with a variety of corresponding drug molecules. Each drug-target pair is accompanied by interaction labels, indicating whether the drug interacts with the protein target.
- Categories:
2 Views
The Human dataset provides a comprehensive collection of drug-target interactions specific to human proteins, aimed at facilitating research in drug discovery and bioinformatics. This dataset includes a diverse range of human proteins as drug targets, along with associated drug molecules and their respective interaction labels. The data consists of molecular descriptors of drugs, protein sequences, and experimentally validated interactions sourced from various biological databases.
- Categories:
9 Views
The Human dataset provides a comprehensive collection of drug-target interactions specific to human proteins, aimed at facilitating research in drug discovery and bioinformatics. This dataset includes a diverse range of human proteins as drug targets, along with associated drug molecules and their respective interaction labels. The data consists of molecular descriptors of drugs, protein sequences, and experimentally validated interactions sourced from various biological databases.
- Categories:
3 Views
Create Graph：we collected asymmetric drug-drug interaction (DDI) entries from version 5.1.12 of DrugBank, released on March 14, 2024. After a thorough double-check, we removed drugs with incorrect SMILES strings or those that could not be represented by Morgan fingerprints . This filtering resulted in a dataset containing 1,752 drugs and 508,512 asymmetric interactions. Subsequently, we organized the DDI entries into a directed interaction network, where directed edges represent the asymmetric interactions between drugs.
- Categories:
9 Views
Training and testing the accuracy of machine learning or deep learning based on cybersecurity applications requires gathering and analyzing various sources of data including the Internet of Things (IoT), especially Industrial IoT (IIoT). Minimizing high-dimensional spaces and choosing significant features and assessments from various data sources remain significant challenges in the investigation of those data sources. The research study introduces an innovative IIoT system dataset called UKMNCT_IIoT_FDIA, that gathered network, operating system, and telemetry data.
- Categories:
69 Views