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.

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.

در این مقاله به بررسی رفتار ارتعاشی یک تیر دوسرگیردار متصل به یک جاذب غیرخطی ارتعاشاتی که تحت بار نوسانی با دامنه های تحریک مختلف قرار دارد، پرداخته شده است. این سیستم درواقع یک مدل ساده و محلی از سازه های دریایی تحت تحریک سیال خارجی را ارائه می-دهد. برای مدل سازی تیر از تئوری اویلر-برنولی و برای مدل سازی جاذب (چاه غیرخطی انرژی)، از فنر غیرخطی و میراگر خطی استفاده شده است. پاسخ سیستم به دو روش تحلیلی (روش متوسط گیری مختلط شونده) و عددی (روش رانگ-کوتای مرتبه چهارم)، به دست آمده است و بر این اساس محدوده مناسب پارامترهای سیستم و جاذب، برای کاهش بهینه ارتعاشات استخراج شده است. علاوه بر این اثرات میرایی سیستم، تاثیر مودهای بالاتر، محدوده های رخداد پاسخ شبه تناوبی و شرایط رخداد ناپایداری در پاسخ حالت ماندگار سیستم بررسی شده است. نتایج نشان دادند که آستانه های نیرویی برای پدیده هایی نظیر ناحیه منفصل فرکانسی و محدوده های انشعابات و پرش در سیستم با تغییر محل نصب جاذب به شدت تغییر خواهد کرد.