per1مدیریت فردا2228-60472228-60472026-042483articlesaid bakhshisaid_bakhshi@yahoo.com1mohammad javad jamshidimj.jamshidi@razi.ac.ir2mahdi hossein pourmahdi.hosseinpour65@gmail.com3<p>Electronic Human Resource Management (e-HRM) is one of the newest research fields in management science. The purpose of this study is to Investigate the Effect of Electronic Human Resource Management(e-HRM) on Entrapreneurship with the Mediating Role of Knowledge Sharing of Middle Managers. The present study is a quantitative research in terms of methodology and applied in terms of purpose and it is a descriptive-survey research in terms of data collection. The research population includes all middle managers working in governmental organizations in Kermanshah city, which all of them have been surveyed using the counting method and finally 278 questionnaires were completed and used as the basis of statistical analysis of this study. The data collection tool was standard questionnaires which their validity and reliability was reviewed and confirmed. The research data were analyzed by structural equation modeling using AMOS software. The results indicate that the effect of electronic human resource management on the two variables of managerial knowledge sharing and organizational entrepreneurship is significant. Knowledge sharing by managers also plays a mediating role in the impact of electronic human resource management on organizational entrepreneurship. Electronic Human Resource Management is the key to success in organizational entrepreneurship. This will facilitate the successful adoption of e-HRM. Since the expected benefits have a positive impact on the acceptance of e-HRM, government agencies need to make these opportunities a reality and maximize the use of e-HRM tools to achieve Entrapreneurship and sharing knowledge.</p>http://modiriyatfarda.ir/Article/53879E-HRM Knowledge Sharing Entrapreneurship Organization Middle Managersper1مدیریت فردا2228-60472228-60472026-042483135162articleFatemeh saramifatemeh.sarrami@ut.ac.ir1Hossein Mohammadi Dolat Abadihmohammadi@ut.ac.ir2Industrial engineering, College of Farabi, university of tehranuniversity of tehran<p>Objective: This study aims to investigate the capability of convolutional neural networks (CNNs) to identify and classify vehicle damage after accidents and to evaluate their potential application in the insurance industry. Necessity: Vehicle damage assessment in the insurance industry is generally time-consuming and requires expert inspection. The application of intelligent image-processing methods can facilitate preliminary damage assessment, reduce claim-processing time, and support the development of intelligent insurance systems. Methodology: The research data consists of real images of damaged vehicles belonging to third-party insurance customers during 2020 and 2021. From approximately 20,000 images, 4,100 were selected to train and evaluate seven deep learning architectures: AlexNet, VGG-19, ResNet-50, ResNet-101, EfficientNetB7, EfficientNetV2L, and MobileNetV2. The models were evaluated for classifying six types of vehicle damage: superficial damage, severe damage, side-mirror damage, windshield damage, tire damage, and vehicle-light damage. Findings: The results showed that model performance in terms of speed and accuracy was influenced by the structure and characteristics of the pre-trained architecture. The classification accuracy of the evaluated models ranged from 59% to 63%. The findings also demonstrated the capability of deep convolutional neural networks to extract visual features and distinguish different types of vehicle damage. Conclusion: The findings indicate that convolutional neural networks and transfer learning methods have promising potential for the identification and preliminary assessment of vehicle damage after accidents. These models can provide a foundation for developing intelligent damage assessment systems in the insurance industry, helping to reduce assessment time, accelerate claim-processing procedures, and improve the efficiency of insurance processes</p>http://modiriyatfarda.ir/Article/54810Deep Learning Image Processing Convolutional Neural Network Vehicle Damage Insurance Industry