IEEE

Forest-Type Change Detection by Synergizing Multi-Source Remote Sensing Data in a Deep Learning Model FCCDNet

Abstract: This study introduces an end-to-end deep learning model (FCCDNet) for forest-type change detection using bi-temporal Sentinel-1 (S1) or Sentinel-2 (S2) images. FCCDNet consists of a parallel ...
IEEE

A Systematic Review on Detection, Repair, and Explanation of Vulnerabilities in Source Code Using Large Language Models

Abstract: Software vulnerabilities pose critical risks to the security and reliability of modern systems, requiring effective detection, repair, and explanation techniques. Large Language Models (LLMs ...
IEEE

Nonlinear UIO-Based Robust Detection of False-Data Injection Attacks in Cascaded Voltage-Source Inverters

Abstract: Voltage-source inverter (VSI) systems with LCL filters are vital for renewable-energy integration but remain susceptible to stealthy false-data injection (FDI) attacks that can destabilise ...
IEEE

Position-Aware Indoor Human Activity Recognition and Fall Detection

Abstract: With increasing life expectancy, particularly in developed nations, the proportion of elderly individuals is rising rapidly, necessitating advanced systems for continuous monitoring and ...
IEEE

Comprehensive Benchmarking of Attention Enhanced CNNs and Transfer Learning Models for Real Time Driver Drowsiness Detection

Abstract: Driver drowsiness is a leading cause of traffic accidents worldwide, highlighting the urgent need for accurate and real-time detection systems to enhance road safety. Recent advances in deep ...
IEEE

Optimized Model Selection for Embedded Drowsiness Detection: A Comparative Study of MobileNetV2 and EfficientNet-B0 on ESP32-CAM

Abstract: Efficient driver drowsiness detection is a critical safety application, as drowsy driving is a leading cause of road accidents. Lightweight CNNs such as MobileNetV2 have been specifically ...
IEEE

Enhancing Driver Drowsiness Detection Using OBD-II and MFCC Features: A Transformer Based Approach

Abstract: Driver drowsiness is a critical factor contributing to road accidents, demanding the development of accurate and responsive detection systems for intelligent transportation. This paper ...
IEEE

Multimodal Drive Drowsiness Detection Using Deep Learning and Eye-Tracking Technology Should not be Used

Abstract: An alarming cause of road accidents is driver drowsiness. It leads to severe consequences on an individual and public level. This paper discusses a multimodal driver drowsiness detection ...
The New York Times

Mayor Mamdani and the Taxi Driver

Richard Chow befriended Zohran Mamdani five years ago and has driven him to City Hall and Gracie Mansion. By Winnie Hu When Zohran Mamdani took the stage at his election night party last year, he ...
IEEE

EEG-Based Driver Drowsiness Detection: A Data-driven Approach for Enhancing Road Safety

Abstract: Driver drowsiness is one of the main causes of road accidents, which is why early detection is critical. This research focuses on enhancing road safety by developing a datadriven approach ...
IEEE

Driver Drowsiness Detection Using Machine Learning and Computer Vision Techniques

Abstract: Driver drowsiness is a significant cause of road accidents, making early detection essential for improving traffic safety. This paper proposes a vision-based software system for detecting ...
IEEE

Driver Drowsiness Detection and Alert System

Abstract: Drowsy driving is one of the major causes of road vehicle accidents, leading to serious injuries and fatalities. This paper introduces a novel Driver Drowsiness Detection System with ...