Browsing by Author "Hassanein, Ahmed"

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    Design and analysis of a new brake-by-wire system using machine learning
    (Institute of Advanced Engineering and Science (IAES), 2023-03) Hassanein, Ahmed; Dawod, Nourhan; Hassan, Nouran
    One of the main aims of the recent research on brake-by-wire systems is to decrease mechanical components. In this paper, we propose replacing the brake pedal with a driving wheel that is fully covered by pressure braking batch sensors. The new mechanism for braking translates pressure exerted through the driver’s hands on the driving wheel to a corresponding electrical signal. A proposed design for the pressure braking batch (PBB) is made out of a mesh of conducting threads separated by a resistive sheet. To the best of our knowledge, this idea has not been raised before in other research papers. Different people have different muscle strengths and so the problem of identifying the intention of the user when pressing the PBB is tackled. For this aim, a new dataset of its kind is created by several volunteers. From each volunteer, age, gender, body mass index (BMI), and maximum pressure exerted on the driving wheel are collected. Using Weka software, the detection accuracy is calculated for a new volunteer to know the intention of his/her pressure on PBB. Among the three algorithms tried, the regression tree gives the best results in predicting the class of the pressure exerted by the volunteers
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    Understanding the link between customer feedback metrics and firm performance
    (Elsevier Ltd., 2023-03) Agag, Gomaa; Durrani, Baseer Ali; Shehawy, Yasser Moustafa; Alharthi, Majed; Alamoudi, Hawazen; El-Halaby, Sherif; Hassanein, Ahmed; Abdelmoety, Ziad H.
    Practitioners utilise customer feedback metrics (CFM’s) to monitor business performance. However, the influence of CFM’s on firm performance has been ignored. Thus, this paper aims to examine the effects of CFM’s on firm performance. Our study collected data about CFM’s, marketing efforts, and financial performance over the period 2005–2020 from American Customer Satisfaction Index. The present study used a multiple regression panel analysis to investigate the influence of different CFMs (i.e., SAT, Top-2-Box, NPS proportion, NPS value, and CES) on firm performance (i.e., gross margin, sales growth, and Tobin’s Q), moderating by operating environment factors (i.e., munificence, power, and dynamism). Our results revealed that Top-2-box is the best predictor CFM’s to compare firms in online booking, hotels, and online shopping industries, while consumer satisfaction is the best predictor for electronic and fixed telecom industries. CES is the best CFM’s to compare companies in res- taurants industries. Moreover, NPS is the best metric to compare different companies in holiday parks industries. The results provide considerable managerial implications for effective use of resources regarding investing in most suitable CFM’s to enhance firm performance.