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).

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The "MANUU: Handwritten Urdu OCR Dataset" is an extensive and meticulously curated collection to advance OCR (Optical Character Recognition) for handwritten Urdu letters, digits, and words. The compilation of the dataset has been conducted methodically, ensuring that it encompasses a wide variety of handwritten instances. This comprehensive collection enables the construction and assessment of strong models for Optical Character Recognition (OCR) systems specifically designed for the complexities of the Urdu script.

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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.

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This data was recorded for emg based force/Torque estimation. EMG and torque signals were collected during simultaneous, isometric, but continuously varying contractions, corresponding to two wrist DoF. The experiment was carried out in two trials with a 5-min rest in between. Each trial included six combinations of tasks, separated by 2 min of rest to minimize the effect of fatigue. The performed tasks were categorized into individual and combined (simultaneous) DoF to test the ability to estimate isolated torque and torque in two simultaneous DoF.

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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

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<div style="color: #d4d4d4; background-color: #1e1e1e; font-family: Consolas, 'Courier New', monospace; font-size: 18px; line-height: 24px; white-space: pre;">Weakly-supervised segmentation (WSS) has emerged as a solution to mitigate the conflict between annotation cost and model performance by adopting sparse annotation formats (e.g., point, scribble, block, etc.). Typical approaches attempt to exploit anatomy and topology priors to directly expand sparse annotations into pseudo-labels.

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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:   

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Wi-Fi BLE RSSI SQI Localization dataset

 
Wi-Fi BLE RSSI for positioning / Indoor Localization in 4 different locations and using 18 different APs

Data is only measured at the Router Side

Data is not measured at client side

Has 12 datasets inside the zip folder with over 1,000,000 data points

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This is the supplementary document for the review paper titled “Comprehensive and Data-Driven Literature Review of Supernumerary Robotic Limbs,” which presents a comprehensive and data-driven review that offers a quantitative analysis of Supernumerary Robotic Limbs (SRLs), covering application areas, structural designs, control strategies, embodiments, and their interconnections.

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The "Thaat and Raga Forest (TRF) Dataset" represents a significant advancement in computational musicology, focusing specifically on Indian Classical Music (ICM). While Western music has seen substantial attention in this field, ICM remains relatively underexplored. This manuscript presents the utilization of Deep Learning models to analyze ICM, with a primary focus on identifying Thaats and Ragas within musical compositions. Thaats and Ragas identification holds pivotal importance for various applications, including sentiment-based recommendation systems and music categorization.

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