hadi saboohi

 One of the main challenges in machine learning is the need for extensive training data, which increases the cost and time required for data preparation. In some cases, there may be limited data available, or privacy concerns may arise. Generalized zero-shot learning (GZSL) offers a suitable option in such situations, enabling the prediction of both seen and unseen classes. Although various methods have been proposed in this field, accurately recognizing seen and unseen classes still presents multiple challenges. This paper introduces a hybrid generalized zero-shot learning (H-GZSL) approach, which improved classification accuracy in GZSL. The proposed method simultaneously utilizes two classification techniques, enhancing classification accuracy for both seen and unseen classes. To address different scenarios and resolve conflicts in classification, a correction mechanism is employed. This mechanism compares the predicted classes from the two methods and assigns the instance to the most appropriate one. The proposed method was evaluated on the AWA2, SUN, aPY, CUB, and FLOW datasets. The evaluation separately measured the performance on seen and unseen classes, and the harmonic mean of classification accuracy across these classes was calculated. Compared to previous methods, the proposed approach achieved an improvement in the harmonic mean accuracy on the CUB and SUN datasets.

Text analysis has been one of the issues in recent research to identify users' sentiments. Most studies have identified sentiments' positive and negative polarity in Persian, and limited research has been done on analyzing emotions in Persian sentences by covering the primary emotional states. In this study, first, a dataset of emotional sentences was prepared to label six basic emotional states, JAMFA. This dataset contains 2350 sentences and (31222 words). This paper presents two models, efficient BERT-BiLSTM(EBB) and XLM-R Catboost(XLM-RC), that enhance the performance of the Persian text emotion classification. This study has the advantages of human intelligence methods and statistical approaches to achieve better accuracy in sentence labeling. The evaluation indicates the accuracy of labeling is 92%, and the reliability of the dataset based on the type of emotions is 88%. The results show that the models at best achieved 86\% accuracy in basic emotion classification and an 81% F-score in binary classification.

Sybil attacks are increasingly growing and expanding in social networks. A malicious user with a fake identity, known as a Sybil attack, can create a large number of fake accounts to generate spam, impersonate other users, commit fraud, and gain access to many legitimate users' information. For security reasons, such fake accounts should be identified and disabled. Various identification methods have been proposed to deal with fake accounts. However, most of these methods detect fake accounts using social structure graphs, which leads to poor performance, or use machine learning methods, which have low accuracy for identifying Sybil attacks. In this paper, a hybrid clustering method called CRNM is proposed. The proposed method is based on clustering, so that by combining different community detection methods; A new community detection method is presented. The combination of these methods has led to higher accuracy, more reliable results and more stability. The CRNM method has been evaluated on datasets collected from Twitter, Reddit, Instagram and Facebook. Unlike other machine learning-based approaches, the proposed method focuses on different levels of user profile features. The evaluation results have shown that the CRNM method detects Sybil nodes with an accuracy of 85.13%.