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 systematic review of physiological signals based driver drowsiness detection systems.

Driving a vehicle is a complex, multidimensional, and potentially risky activity demanding full mobilization and utilization of physiological and cognitive abilities. Drowsiness, often caused by stress, fatigue, and illness declines cognitive capabilities that affect drivers' capability and cause many accidents. Drowsiness-related road accidents are associated with trauma, physical injuries, and fatalities, and often accompany economic loss. Drowsy-related crashes are most common in young people and night shift workers. Real-time and accurate driver drowsiness detection is necessary to bring down the drowsy driving accident rate. Many researchers endeavored for systems to detect drowsiness using different features related to vehicles, and drivers' behavior, as well as, physiological measures. Keeping in view the rising trend in the use of physiological measures, this study presents a comprehensive and systematic review of the recent techniques to detect driver drowsiness using physiological signals. Different sensors augmented with machine learning are utilized which subsequently yield better results. These techniques are analyzed with respect to several aspects such as data collection sensor, environment consideration like controlled or dynamic, experimental set up like real traffic or driving simulators, etc. Similarly, by investigating the type of sensors involved in experiments, this study discusses the advantages and disadvantages of existing studies and points out the research gaps. Perceptions and conceptions are made to provide future research directions for drowsiness detection techniques based on physiological signals. [Abstract copyright: © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Drowsy Driver Detection System Using Eye Blink Patterns

International audienceThis paper presents an automatic drowsy driver monitoring and accident prevention system that is based on monitoring the changes in the eye blink duration. Our proposed method detects visual changes in eye locations using the proposed horizontal symmetry feature of the eyes. Our new method detects eye blinks via a standard webcam in real-time at 110fps for a 320×240 resolution. Experimental results in the JZU [3] eye-blink database showed that the proposed system detects eye blinks with a 94% accuracy with a 1% false positive rate

SleepyWheels: An Ensemble Model for Drowsiness Detection leading to Accident Prevention

Around 40 percent of accidents related to driving on highways in India occur due to the driver falling asleep behind the steering wheel. Several types of research are ongoing to detect driver drowsiness but they suffer from the complexity and cost of the models. In this paper, SleepyWheels a revolutionary method that uses a lightweight neural network in conjunction with facial landmark identification is proposed to identify driver fatigue in real time. SleepyWheels is successful in a wide range of test scenarios, including the lack of facial characteristics while covering the eye or mouth, the drivers varying skin tones, camera placements, and observational angles. It can work well when emulated to real time systems. SleepyWheels utilized EfficientNetV2 and a facial landmark detector for identifying drowsiness detection. The model is trained on a specially created dataset on driver sleepiness and it achieves an accuracy of 97 percent. The model is lightweight hence it can be further deployed as a mobile application for various platforms.Comment: 20 page

Eye-tracking assistive technologies for individuals with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research

PERCLOS: An Alertness Measure of the Past

The growing number of fatigue related accidents in recent years has become a serious concern. Accidents caused by fatigue, or more precisely impaired alertness, in transportation and in mining operations involving heavy equipment can lead to substantial damage and loss of life. Preventing such fatigue related accidents is universally desirable, but requires techniques for continuously estimating and predicting the operator’s alertness state. PERCLOS (percentage of eye closure) was introduced as an alertness measure. Some years later, it was claimed to be superior in fatigue detection to any other measure, including the general Eye-Tracking Signal (ETS) and even EEG recordings. This study will show that this is not the case. To put things into the prospective a fair and objective comparison between PERCLOS, the general ETS and EEG/EOG has to be established. To achieve this purpose, a protocol was established to investigate the fatigue detection capabilities of PERCLOS, ETS, and EEG/EOG in a simple two class discrimination analysis using an ensemble of Learning Vector Quantization (LVQ) networks as a classification tool. Karolinska Sleepiness Scale (KSS) and Variation of Lane Deviation (VLD) were used in order to obtain independent class labels, whereas KSS provided subjective alertness labels while VLD provided objective alertness labels. The general ETS and the fused EEG/EOG measures contain substantially greater amounts of fatigue information than the PERCLOS measures alone. These conclusions were found to be valid for all three commercially available infrared video camera systems that were utilized in the study. The data utilized in the discrimination analysis were obtained from 16 young volunteers who participated in overnight experiments in the real car driving simulation lab at the University of Schmalkalden