درخت حوزه‌های تخصصی

One of the new concepts in the field of human resources management is organizational intelligence, which is increasing in importance day by day. The main goal of the present study was to identify the factors affecting the organizational intelligence of secondary school principals. The current research method was qualitative-based theory and applied research in terms of its purpose. The participants in the research were principals and vice-principals of schools in Lorestan province. Totally 20 interviews were conducted with them using the purposeful sampling method until reaching theoretical saturation. The research tool was semi-structured interviews. Open, axial and selective coding was used to analyze the data. Also, for the validity of the findings, in addition to reviewing the interview process by researchers, internal participants and external auditors were used, and for reliability, evaluators outside the research were used, and the agreement coefficient was 0.80. The data analysis led to the formation of 9 core categories, based on the paradigm model of Straves and Corbin, "empowerment" and "elements of a smart organization (including justice-oriented components, organizational transparency and research-oriented components)" as causal conditions, "organizational culture" and "leadership" "cultural" as background conditions, "organizational global horizon" as intervening conditions, "financial, psychological and environmental horizon" and "talent management" as strategies/strategies and "productivity" and "career longevity" were also consequences of organizational intelligence. Based on the current findings, the education system should pay attention to the empowerment of managers, the elements of intelligent organization and the organizational culture of managers as factors that shape and accelerate organizational intelligence.

Predicting the development of cancer has always been a serious challenge for scientists and medical professionals. The prompt identification and prognosis of a disease is greatly aided by early-stage detection. Researchers have proposed a number of different strategies for early cancer detection. The purpose of this research is to use meta-learning techniques and several different kinds of convolutional-neural-networks(CNN) to create a model that can accurately and quickly categorize breast cancer(BC). There are many different kinds of breast lesions represented in the Breast Ultrasound Images (BUSI) dataset. It is essential for the early diagnosis and treatment of BC to determine if these tumors are benign or malignant. Several cutting-edge methods were included in this study to create the proposed model. These methods included meta-learning ensemble methodology, transfer-learning, and data-augmentation. With the help of meta-learning, the model will be able to swiftly learn from novel data sets. The feature extraction capability of the model can be improved with the help of pre-trained models through a process called transfer learning. In order to have a larger and more varied dataset, we will use data augmentation techniques to produce new training images. The classification accuracy of the model can be enhanced by using meta-ensemble learning techniques to aggregate the results of several CNNs. Ensemble-learning(EL) will be utilized to aggregate the results of various CNN, and a meta-learning strategy will be applied to optimize the learning process. The evaluation results further demonstrate the model's efficacy and precision. Finally, the suggested model's accuracy, precision, recall, and F1-score will be contrasted to those of conventional methods and other current systems.