Artificial Intelligence

The images containing honey bees were extracted from the video recorded  in the Botanic Garden of the University of Ljubljana, where a beehive with a colony of the Carnolian Grey, the native Slovene species, is placed. We set the camera above the beehive entrance and recorded the honey bees on the shelf in front of the entrance and the honey bees entering and exiting the hive. With such a setup, we ensured a non-invasive recording of the honey bees in their natural environment. The dataset contains 65 images of size 2688 x 1504 pixels.

Simulation code for the paper:  "AoI Minimization in Energy Harvesting and Spectrum Sharing Enabled 6G Networks" 

we use a neural network to approximate the Q-value function. The state is given as the input and the Q-value of all possible actions is generated as the output.

This dataset consists of temporal and temperature drift characteristics of Si3N4-gate iSFET andsupplementary files

UCSA-20 contains 9,601 user reviews, of which 4,843 are positive and the remaining are negative

<p>The dataset comprises 2035 images from 14 different software architectural patterns (100+ images each), viz., Broker, Client Server, Microkernel, Repository, Publisher-Subscriber, Peer-to-Peer, Event Bus, Model View Controller, REST, Layered, Presentation Abstraction Controller, Microservices, and Space-based patterns.</p>

Dataset including over 40,000 generated images of malicious binaries for malware classification in machine learning as outlined in NARAD - A Novel Auto-learn Real-time Fuzzy Machine Learning Anomaly Detection and Classification System.

The simulation code for the paper:

"AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning"

The overall architecture of the proposed MARL framework is shown in the figure.

Modified MADDPG: This algorithm trains two critics (different from legacy MADDPG) with the following functionalities: