Artificial Intelligence

Image representation of Malware-benign dataset. The Dataset were compiled from various sources malware repositories:  The Malware-Repo, TheZoo,Malware Bazar, Malware Database, TekDefense. Meanwhile benign samples were sourced from system application of Microsoft 10 and 11, as well as open source software repository such as Sourceforge, PortableFreeware, CNET, FileForum. The samples were validated by scanning them using Virustotal Malware scanning services. The Samples were pre-processed by transforming the malware binary into grayscale images following rules from Nataraj (2011).

With authorization from relevant departments in the hospital, we are uploading some research data.  This dataset is used for research on indoor navigation systems in hospitals. The data includes panoramic photos (jpg) of the hospital, 3D modeling maps (png), as well as surveillance videos (ts) of indoor staircases, intersections, and other areas within the hospital. All data has been anonymized, and faces in the monitoring videos are all wearing masks to protect personal privacy.

Any damage that affects the normal functioning of the lungs is termed as a lung disease,

which can prove fatal if not detected early. To address this challenge, two innovative techniques proposed

for the lung disease classification, supporting medical professionals to diagnose and provides preventive

measures at an early stage. The proposed Model 1 integrates a custom MobileNetV2L2 architecture, that

builds upon the MobileNetV2 framework through fine-tuning and customization. This model incorporates a

Wi-Fi FTM RSSI Localization dataset

Wi-Fi Fine Time Measurement for positioning / Indoor Localization in 3 different locations and using 8 different APs
 
Custom APs using ESP32C3 and Raw FTM is measured in nanoseconds
 
Data is only measured at the Router Side
 
Data is not measured at client side
 
Has 4 datasets inside the zip folder with over 100,000 data points
 
Contains processed Wi-Fi FTM packets from various routers in: