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Employees are the most important assets of the organization and the performance of the organization depends on their performance. Achieving high performance of the workforce requires the identification of employee communication mechanisms, which is pursued in the framework of the concept of employee relationship management (ERM). Employee relationship management is a strategic tool and a kind of human resource management process that focuses on the continuity and strengthening of relationships between organizations and employees by relying on improving relationships and creating shared perspectives. This study aims to develop a practical framework for employee relationship management in the organization and is analytical-descriptive. Data collection was based on interviews with 14 senior and middle managers of Tondgooyan Oil Refining Company. Based on the data analysis, the obtained codes were classified into six main activity groups including Knowledge Management, Relationship Management, Employee Assistance Program Management, Employee Development Program Management, Employee Cognitive Program Management and Employee Involvement and finally each subgroup of activities is assigned to key and non-ley employees.

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Cancer is one of the leading causes of death worldwide. Breast cancer is the most common cancer among women and causes a high number of annual deaths. The most reliable method for successful cancer management is accurate and early diagnosis. On the other hand, the lack of timely diagnosis leads to the spread of cancer in the body, making it difficult to treat and control. The gold standard method for breast cancer diagnosis is biopsy. Usually, visual inspection and manual assesement are used to diagnose cancer, where the pathologist examines the histopathology slides under a microscope which is error-prone and time- consuming procedure and requires years of expertise. Therefore, computer-aided diagnosis is essential to help physicians improve the efficiency of interpreting medical images. In this study, we use deep learning models, especially convolutional neural networks (CNNs) to detect whether or not histopathological images are cancerous. The AUC, Precision and F1-score obtained using the pre-trained Incetion-V3 deep neural network are 98.36%, 95.28% and,97.25% respectively, and the same parameters for the pre-trained ResNet-18 deep neural network are equal to 97.90 %, 97.46% and 98.22%. The presented models are able to provide reliable diagnosis results for different morphologies of breast tissues.