Machine Learning

Public safety is seriously threatened by road accidents, which are a major global concern in urban settings. The capital of Bangladesh, Dhaka City, stands out among these locations as a perfect illustration of the complicated difficulties confronted by highly populated cities in ensuring road safety. In this paper, we have used time-series analysis to model the temporal patterns and trends in accident occurrence and machine learning algorithms to identify accident hotspots and comprehend the causes of traffic accidents.


The uploaded project is the code and dataset for Charging Efficiency Optimization Based on Swarm Reinforcement Learning under Dynamic Energy Consumption for WRSN. The details of each document in the uploaded project are as follows. Document data: The data file contains network data and simulation data. Document iostream: The iostream file contains the program for reading data and writing data. Document main: The main file contains the main program that executes the simulation. Document network: The network file


The Colour-Rendered Bosphorus Projections (CRBP) Face Dataset represents an innovative advancement in facial recognition and computer vision technologies. This dataset uniquely combines the precision of 3D face modelling with the detailed visual cues of 2D imagery, creating a multifaceted resource for various research activities. Derived from the acclaimed Bosphorus 3D Face Database, the CRBP dataset introduces colour-rendered projections to enrich the original dataset.


This work presents a new labeled dataset of videos with native and professional interpreters articulating words and expressions in Libras (Brazilian Sign Language). We used a methodology based on related studies, the support of the team of articulators, and the existing datasets in the literature.


This dataset is designed for the purpose of curve fitting, a key process in the reconstruction of implicit curves. It encompasses a collection of point cloud data that has been sampled directly from curves, as well as the code necessary to generate point cloud data from these curves.


X-CANIDS Dataset (In-Vehicle Signal Dataset)

In March 2024, one of our recent research "X-CANIDS: Signal-Aware Explainable Intrusion Detection System for Controller Area Network-Based In-Vehicle Network" was published in IEEE Transactions on Vehicular Technology. Here we publish the dataset used in the article. We hope our dataset facilitates further research using deserialized signals as well as raw CAN messages.

Real-world data collection. Our benign driving dataset is unique in that it has been collected from real-world environments.


Inertial sensors are widely used in a variety of applications. A common task is orientation estimation. To tackle such a task, attitude and heading reference system algorithms are applied. Relying on the gyroscope readings, the accelerometer measurements are used to update the attitude angles, and magnetometer measurements are utilized to update the heading angle. In indoor environments, magnetometers suffer from interference that degrades their performance.


Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences. The monitoring and classification of these equipment anomalies can be performed using fault detection and classification (FDC). However, class imbalance in semiconductor process data poses a significant obstacle to the introduction of FDC into semiconductor equipment. Overfitting can occur in machine learning due to the diversity and imbalance of datasets for normal and abnormal.


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.


Anomaly detection plays a crucial role in various domains, including but not limited to cybersecurity, space science, finance, and healthcare. However, the lack of standardized benchmark datasets hinders the comparative evaluation of anomaly detection algorithms. In this work, we address this gap by presenting a curated collection of preprocessed datasets for spacecraft anomalies sourced from multiple sources. These datasets cover a diverse range of anomalies and real-world scenarios for the spacecrafts.