The effect of electronic word of mouth communication on purchase intention moderate by trust: a case online consumer of Bahawalpur Pakistan

The aim of this study is concerned with improving the previous research finding complete filling the research gaps and introducing the e-WOM on purchase intention and brand trust as a moderator between the e-WOM, and purchase intention an online user in Bahawalpur city Pakistan, therefore this study was a focus at linking the research gap of previous literature of past study based on individual awareness from the real-life experience. we collected data from the online user of the Bahawalpur Pakistan. In this study convenience sampling has been used to collect data and instruments of this study adopted from the previous study. The quantitative research methodology used to collect data, survey method was used to assemble data for this study, 300 questionnaire were distributed in Bahawalpur City due to the ease, reliability, and simplicity, effective recovery rate of 67% as a result 202 valid response was obtained for the effect of e-WOM on purchase intention and moderator analysis has been performed. Hypotheses of this research are analyzed by using Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The result of this research is e-WOM significantly positive effect on purchase intention and moderator role of trust significantly affects the relationship between e-WOM, and purchase intention. The addition of brand trust in the model has contributed to the explanatory power, some studied was conduct on brand trust as a moderator and this study has contributed to the literature in this favor. significantly this study focused on current marketing research. Unlike past studies focused on western context, this study has extended the regional literature on e-WOM, and purchase intention to be intergrading in Bahawalpur Pakistan context. Lastly, future studies are recommended to examine the effect of trust in other countries allow for the comparison of the findings

A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network

This study proposes a method based on Dempster-Shafer theory (DST) and fuzzy neural network (FNN) to improve the reliability of recognizing fatigue driving. This method measures driving states using multifeature fusion. First, FNN is introduced to obtain the basic probability assignment (BPA) of each piece of evidence given the lack of a general solution to the definition of BPA function. Second, a modified algorithm that revises conflict evidence is proposed to reduce unreasonable fusion results when unreliable information exists. Finally, the recognition result is given according to the combination of revised evidence based on Dempster’s rule. Experiment results demonstrate that the recognition method proposed in this paper can obtain reasonable results with the combination of information given by multiple features. The proposed method can also effectively and accurately describe driving states

Driver monitoring system based on eye tracking

Dissertação de mestrado integrado em Engenharia Electrónica Industrial e ComputadoresRecent statistics indicate that driver drowsiness is one of the major causes of road accidents and deaths behind the wheel. This reveals the need of reliable systems capable of predict when drivers are in this state and warn them in order to avoid crashes with other vehicles or stationary objects. Therefore, the purpose of this dissertation is to develop a driver’s monitoring system based on eye tracking that will be able to detect driver’s drowsiness level and actuate accordingly. The alert to the driver may vary from a message on the cluster to a vibration on the seat. The proposed algorithm to estimate driver’s state only requires one variable: eyelid opening. Through this variable the algorithm computes several eye parameters used to decide if the driver is drowsy or not, namely: PERCLOS, blink frequency and blink duration. Eyelid opening is obtained over a software and hardware platform called SmartEye Pro. This eye tracking system uses infrared cameras and computer vision software to gather eye’s state information. Additionally, since this dissertation is part of the project "INNOVATIVE CAR HMI", from Bosch and University of Minho partnership, the driver monitoring system will be integrated in the Bosch DSM (Driver Simulator Mockup).Estatísticas recentes indicam que a sonolência do condutor é uma das principais causas de acidentes e mortes nas estradas. Isto revela a necessidade de sistemas fiáveis capazes de prever quando um condutor está sonolento e avisá-lo, de modo a evitar colisões com outros veículos ou objetos estacionários. Portanto, o propósito desta dissertação é desenvolver um sistema de monitorização do condutor baseado em eye tracking que será capaz de detetar o nível de sonolência do condutor e atuar em conformidade. O alerta para o condutor pode variar entre uma mensagem no painel de instrumentos ou uma vibração no assento. O algoritmo proposto para estimar o estado do condutor apenas requer a aquisição de uma variável: abertura da pálpebra. Através desta variável, o algoritmo computa alguns parâmetros utilizados para verificar se o condutor está sonolento ou não, nomeadamente: PERCLOS, frequência do pestanejar e duração do pestanejar. A abertura da pálpebra é obtida através de uma plataforma de hardware e software chamada SmartEye Pro. Esta plataforma de eye tracking utiliza câmaras infravermelho e software de visão por computador para obter informação sobre o estado dos olhos. Adicionalmente, uma vez que esta dissertação está inserida projeto: "INNOVATIVE CAR HMI", da parceria entre a Bosch e a Universidade do Minho, o sistema desenvolvido será futuramente integrado no Bosch DSM (Driver Simulator Mockup)

Analysis of yawning behaviour in spontaneous expressions of drowsy drivers

Driver fatigue is one of the main causes of road accidents. It is essential to develop a reliable driver drowsiness detection system which can alert drivers without disturbing them and is robust to environmental changes. This paper explores yawning behaviour as a sign of drowsiness in spontaneous expressions of drowsy drivers in simulated driving scenarios. We analyse a labelled dataset of videos of sleep-deprived versus alert drivers and demonstrate the correlation between hand-over-face touches, face occlusions and yawning. We propose that face touches can be used as a novel cue in automated drowsiness detection alongside yawning and eye behaviour. Moreover, we present an automatic approach to detect yawning based on extracting geometric and appearance features of both mouth and eye regions. Our approach successfully detects both hand-covered and uncovered yawns with an accuracy of 95%. Ultimately, our goal is to use these results in designing a hybrid drowsiness-detection system

Intelligent and secure real-time auto-stop car system using deep-learning models

In this study, we introduce an innovative auto-stop car system empowered by deep learning technology, specifically employing two Convolutional Neural Networks (CNNs) for face recognition and travel drowsiness detection. Implemented on a Raspberry Pi 4, our system is designed to cater exclusively to certified drivers, ensuring enhanced safety through intelligent features. The face recognition CNN model accurately identifies authorized drivers, employing deep learning techniques to verify their identity before granting access to vehicle functions. This first model demonstrates a remarkable accuracy rate of 99.1%, surpassing existing solutions in secure driver authentication. Simultaneously, our second CNN focuses on real-time detecting+ of driver drowsiness, monitoring eye movements, and utilizing a touch sensor on the steering wheel. Upon detecting signs of drowsiness, the system issues an immediate alert through a speaker, initiating an emergency park and sending a distress message via Global Positioning System (GPS). The successful implementation of our proposed system on the Raspberry Pi 4, integrated with a real-time monitoring camera, attains an impressive accuracy of 99.1% for both deep learning models. This performance surpasses current industry benchmarks, showcasing the efficacy and reliability of our solution. Our auto-stop car system advances user convenience and establishes unparalleled safety standards, marking a significant stride in autonomous vehicle technology

Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking

It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video and image processing techniques to detect the drivers’ eye condition. Four features of the eye were extracted to determine the condition of drowsiness, the percentage of eyelid closure (PERCLOS), blink frequency, maximum closure duration of the eyes, and eye aspect ratio (ARE). These parameters were then integrated into an ANN and combined with the proposed method (gray wolf optimizer with ANN [GWOANN]) for drowsiness classification. The accuracy of these models was calculated, and the results demonstrate that the proposed method is the best. An Adadelta optimizer with 3 and 4 hidden layer networks of (13, 9, 7, and 5) and (200, 150, 100, 50, and 25) neurons was utilized. The GWOANN technique had 91.18% and 97.06% accuracy, whereas the ANN model had 82.35% and 86.76%

Fatigue and drowsiness detection using a support vector machine for traffic accident reduction

Fatigue and drowsiness are major contributors to road safety issues, causing slower reactions, poor decision-making, and increased accidents. Support vector machine (SVM) can improve road safety by analyzing complex data sets and patterns related to driver behavior. When using features extracted from electrooculography signals to determine driver fatigue, SVM demonstrated high classification accuracy. This shows that it could be a useful tool in real-time fatigue detection systems. SVM's successful application in traffic accident reduction demonstrates its potential for improving road safety through predictive modeling and early warning systems. Integrating SVM algorithms into traffic accident prediction models enables the analysis of a wide range of factors, including road conditions, driver behavior, and vehicle characteristics, in order to identify potential risk factors and take proactive measures to avoid accidents. Studies have shown that SVM-based systems can predict accidents with high accuracy, resulting in timely interventions and, ultimately, fewer road fatalities and injuries. In conclusion, using SVM to detect driver fatigue and drowsiness is critical for increasing road safety. Future research should focus on improving the system's accuracy and real-time capabilities, incorporating advanced machine learning algorithms, and developing adaptive SVM models that constantly learn and update their parameters based on real-time data