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.

Internet entrepreneurs Methods for identification and use of knowledge, skills and insights related to their business, have major impacts on creation and effective management of such businesses. Due to the diversity of Learning Methods used by internet entrepreneurs, this article intended to identify and prioritize them. In order to do so, semi-structured interviews were conducted with 10 successful internet entrepreneurs and by means of coding method, conventional Learning Methods for internet entrepreneurs were identified. Then, by distribution and using Friedman analysis of 376 questionnaires, superior Learning Methods were prioritized. Findings of this study indicated four major sources of Learning as follows: Learning from teaching – publications, Learning from work – task, Learning from social-human interaction, and Learning from imitation – benchmarking. Furthermore, Learning Methods prioritization results showed that observation and imitation of best websites, reading websites content, personal thoughts and initiatives of problem solutions are three main Methods of Learning for Iranian entrepreneurs.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine Learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using deep machine Learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase Learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some state-of-the-art works.

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.

Introduction: Learning is one of the important psychological processes in which permanent changes in the behavior are brought about.Literature review: there are different kinds of Learning strategies which differ in degree of complexity. Simple Learning such as verbal information form the building blocks of complicated Learning such as problem-solving and cognitive strategies.Discussion: In nursing education, it is necessary to pay special attention to all kinds of Learning including attitude, intellectual skills, motor skills and cognitive strategies. Therefore, the nursing instructors and educators are required to become familiar with different kinds of Learning and the appropriate teaching method for each of them. In this article, a series of Learning strategies together with an appropriate method for its effective realization are presented.

One of the ways of Learning which is emphasized in Quran is Learning wisdom. What is Teaching wisdom and what is its role in education? What are the different forms of wisdom? How is it different form argumentation? What are its influential conditions? What are the ways to pose the issue? What are the tools for acquiring it? What are the probable effects on teaching wisdom? These questions are answered in this research paper using the documentary- interpretive methodology in Quran. The findings demonstrate that there are three kinds of wisdom, namely, theoretical, practical, and real; wisdom means certainty in knowledge or correct practice and based on rational foundations. The outcomes are as follows: guidance, thanksgiving, infallibility, which are achieved through self-purification, faith, devotion, silence, thinking and forgiveness.

Introduction With the advent of big data in the last two decades, to exploit and use this type of data, the need to integrate databases for building a stronger evidence base for policy and service development is felt more than ever. Therefore, familiarity with data linkage methodology as one of the data integration Methods and machine Learning Methods to facilitate the process of recording records is essential. Material and Methods The record linkage process has five major steps including data pre-processing, indexing, record pair comparison, classification and evaluation step. There are two key Methods (exact and probabilistic record linkage) for linking records. Exact linkage involves using a unique identifier that is present on both files to link records. In the presence of a unique identity number in a different data source, record linkage is easy to implement. Where a unique identifier is not available, or is not of sufficient quality, it is made by probabilistic record linkage which check the similarity of the features of each record that are common to both files to find records that are likely to belong to the same person. Classifying the compared record pairs based on their comparison vectors is a two-class (match or a non-match) or three-class (match, non-match or potential matches) classification task. In traditional data integration approaches, record pairs are classified into one of three classes, rather than only matches and non-matches and a manual clerical review is required to decide the final match status. Most research in record linkage in the past decade has concentrated on improving the classification accuracy of record pairs. Various machine Learning techniques have been investigated, both unsupervised and supervised. In this paper, in addition to introducing the record linkage process and some related Methods, machine Learning algorithms are used to increase the speed of database integration, reduce costs and improve record linkage performance. Most classification techniques such as support vector machine, decision tree and bagging method, classify each compared record pair individually and independently from all other record pairs. From the classification point of view, each compared record pair is represented by its comparison vector that contains the individual similarity values that were calculated in the comparison step. These comparison vectors correspond to the feature vectors that are employed to train a classification model, and to classify record pairs with unknown match status. Results and Discussion In this paper, two databases of the Statistical Center of Iran and the Social Security Organization are linked. Three classification techniques including support vector machine, decision tree and bagging method, were used for data integration. In addition, ROC curves were plotted to find the best method of classification. The results showed that the support vector machine and decision tree method performed better than the bagging method. Conclusion Statistical organizations are challenged by the need to integrate diverse sets of inconsistent data and produce stable outputs. Instead of making the best possible statistics from a single data source, finding the best combination of sources is necessary to deliver the indicators or statistics that most efficiently satisfy the users’,needs.