Face recognition from digital images is used for surveillance and authentication in cities, organizations, and personal devices. Internet of Things (IoT)-powered face recognition systems use multiple sensors and one or more servers to process data. All sensor data from initial methods was sent to the central server for processing, raising concerns about sensitive data disclosure. The main concern was that all data from all sectors that could contain confidential information was placed in a central server. Federated learning can solve this problem by using several local model training servers for each region and a central aggregation server to form a global model in IoT networks. This article presents a novel approach to optimize data transfer and convergence time in federated learning for a face recognition task using Non-dominated Sorting Genetic Algorithm II (NSGA II). The aim of the study is to balance the trade-off between training time and model accuracy in a federated learning environment. The results demonstrate the effectiveness of the proposed approach in reducing data transfer and convergence time, leading to improved performance in face recognition accuracy. This research provides insights for researchers and practitioners to enhance the efficiency of federated learning in real-world applications.

Today, teaching and learning is referred to as a regular process, according to which, in addition to strengthening students' skills and attitudes, it is necessary to use attractive and active methods, effective and efficient teaching. Success in this area requires pre-prepared facilities. Based on knowing the learners and planning to make better communication such as using appropriate beauty, articulation skills, good manners, not being strict and not giving hard homework, refreshing the class on the one hand and on the other hand, using attractive and active methods appropriate to the lesson, such as; Use planning and designing appropriate education, motivation, creating a favorable atmosphere and effective communication and responding to the special needs of students, using new technologies, strengthening inference skills, strengthening thinking skills, good listening, good reading, good question Brainstorming, exploratory method, problem method, units and games, etc. It has a performance appropriate to the lesson, including the Arabic lesson, which the author, after reviewing and briefly analyzing it, came to the conclusion that using the above methods, curious students can be eager to learn and it paved the way for sustainable learning, especially if creative and innovative methods and games are also used in the use of these methods.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

Since the measurement of soil moisture retention curve and its index points like field capacity and permanent wilting point are costly and time-consuming using direct methods, one of the quick and low costly estimation methods of these properties is estimating of those properties with respect to soil basic properties including particle size distribution and organic matter content as well as bulk density. In this study, index points of soil moisture retention curve such as field capacity, permanent wilting point and available water were estimated using MLP type neural network with three activate functions and regressions methodology with respect to soil basic properties. In order to conduct the aforementioned issue, 60 soil samples were collected from the case study, the region of Robatkarim of Karaj city, with an area of 24 ha. Then, the moisture content of the index points of soil moisture retention curve was measured by using the pressure plate. Further soil basic properties including the percentages of clay, sand, bulk density and organic matter as well as lime and the moisture content of the index points were measured using the aforementioned methodology. The results of the investigations showed that the artificial neural networks by sigmoid activate function a network with 6 neurons in the input layer and 10 neurons in the hidden layer as well as 3 neurons in the output layer with Performance indicators R2 and RMSE variations of 0.6197–0.8902 and 1.6846–2.0225, respectively was introduced as the efficient network which had the best performance in estimation of wanted outputs in comparison with another activate functions (method of artificial neural network) and regression method. Percentage of lime according to results of sensitivity analysis is considered as an effective factor in the range of soils texture tested.

The main purposes of this descriptive-exploratory research were to survey barriers and identify the best solutions for activating frontiersmen cooperatives in Kermanshah Province. The population included board of directors and managers of the cooperatives (N =115). Eighty-five members using simple random sampling method was chosen by the use of Krejcie & Morgan Table. The main tool for data collection was a questionnaire that its validity was conducted through experts from the cooperative and its reliability was using Cronbach's alpha coefficient. Factor analysis was used to elicit the barriers of cooperatives success. Based on results, these barriers are linked to the government, management of cooperatives, situations of education, socio – economic barrier and proficiency. Strategies such as providing suitable infrastructures, increase foreign exchange quota of frontiersmen and more freedom in the import and export are important for the activation of the cooperatives.