Fateme Daneshfar

Sentiment analysis is an essential task in numerous domains, necessitating effective dimensionality reduction and feature extraction techniques. This study introduces MultiObjective Manifold Representation for Opinion Mining (MOMR). This novel approach combines deep global and local manifold feature extraction to reduce dimensions while capturing intricate data patterns efficiently. Additionally, incorporating a self-attention mechanism further enhances MOMR's capability to focus on relevant parts of the text, resulting in improved performance in sentiment analysis tasks. MOMR was evaluated against established techniques such as Long Short-Term Memory (LSTM), Naive Bayes (NB), Support Vector Machines (SVM), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), as well as recent state-of-the-art models across multiple datasets including IMDB, Fake News, Twitter, and Yelp. Therefore, our comparative analysis underscores MOMR's efficacy in sentiment analysis tasks across diverse datasets, highlighting its potential and applicability in real-world sentiment analysis applications. On the IMDB dataset, MOMR achieved an accuracy of 99.7% and an F1 score of 99.6%, outperforming other methods such as LSTM, NB, SMSR, and various SVM and CNN models. For the Twitter dataset, MOMR attained an accuracy of 88.0% and an F1 score of 88.0%, surpassing other models, including LSTM, CNN, BiLSTM, Bi-GRU, NB, and RNN. In the Fake News dataset, MOMR demonstrated superior performance with an accuracy of 97.0% and an F1 score of 97.6%, compared to techniques like RF, RNN, BiLSTM+CNN, and NB. For the Yelp dataset, MOMR achieved an accuracy of 80.0% and an F1 score of 80.0%, proving its effectiveness alongside other models such as Bidirectional Encoder Representations from Transformers (BERT), aspect-sentence graph convolutional neural network (ASGCN), Multi-layer Neural Network, LSTM, and bidirectional recurrent convolutional neural network attention (BRCAN).

Nonnegative Matrix Factorization (NMF), as a group representation learning model, produces part-based representation with interpretable features and can be applied to various problems, such as text clustering. The findings indicate that the NMF model with Kullback-Leibler divergence (NMFk), and NMF with β divergence (β-NMF) exhibits promising performance in the task of data clustering. However, existing NMFbased data clustering methods are defined within a latent decoder model, lacking a verification mechanism. Recently, self-representation techniques have been applied to a wide range of tasks, empowering models to autonomously learn and verify representations that faithfully reflect the intricacies and nuances inherent in their input data. In this research, we propose two self-representation factorization models for data clustering that incorporates semantic information and grapg regularization into its learning process, respectively. The Semantic-aware Encoder-Decoder NMF model based on Kullback-Liebler divergence (SEDNMFk), and Encoder-Decoder NMF with β divergence integrates encoder and decoder factorizations into a unified cost function that mutually verify and refine each other, resulting in the formation of more distinct clusters. We present an efficient and effective optimization algorithms based on multiplicative update rules to solve the two proposed unified model. The experimental results on the ten well-known datasets show that the proposed models outperforms other state-of-the-art data clustering methods.

Colorectal cancer (CRC), accounting for 10% of global cancer cases and being the third most prevalent type, is expected to see a significant increase in the coming years. This surge underscores the need for precise diagnostics. Effective treatment relies on accurate histopathological analysis of hematoxylin and eosin (H&E) stained biopsies, which is critical for recommending minimally invasive treatments. However, manual evaluations of these biopsies are labor-intensive and error-prone due to staining variations and inconsistencies, complicating the tasks of pathologists. To address these challenges, advanced automated image analysis, including deep learning with convolutional neural networks (CNNs) and machine learning (ML) techniques, has significantly enhanced computer-aided diagnosis systems. Consequently, this paper proposes a composite model that combines deep learning and machine learning to improve colorectal cancer diagnosis accuracy. Specifically, the model aims to increase diagnostic precision, reduce complexity and computing demands, and effectively prevent overfitting for reliable performance. Therefore, the proposed cascaded design includes feature extraction using MobileNetV2 and DenseNet121 via transfer learning (TL), data distribution balancing in the Extended Bioimaging Histopathological Image Segmentation (EBHI-Seg) dataset using the Synthetic Minority Over-sampling Technique (SMOTE), key feature selection using a Chisquare test, classification by machine learning algorithms, and improving classification accuracy through hyperparameter tuning. Finally, the results evaluated on the available EBHI-Seg dataset achieve 97.28% accuracy, 97.29% precision, 97.27% recall, 96.27% F1- score, and 99.4% area under the curve (AUC), demonstrating that the suggested model is superior to other methods already in use.

Breast cancer represents one of the leading cancer diagnoses in women around the world. Early detection and accurate classification of breast cancer from medical images are crucial, as they enable timely treatment, which can significantly improve patient outcomes. Ultrasound imaging is a popular diagnostic method in radiology for evaluating breast health. Over the past ten years, deep learning approaches, especially Convolutional Neural Networks (CNNs), have been used to develop comprehensive systems for recognizing image patterns. More recently, the Vision Transformer (ViT) has gained attention as a novel deep learning architecture, largely because of its self-attention mechanisms, which have greatly improved the field of image processing. These models have exhibited strong performance across a wide range of image-related applications. Computer-Aided Diagnosis (CAD) systems in medical field have increasingly adopted deep learning methodologies, recognized for their superior ability to extract essential features from medical images. This study proposes a hybrid deep learning approach that integrates CNNs with ViTs to enhance breast cancer diagnosis in ultrasound images. This method capitalizes on the beneficial attributes of CNNs and ViTs to boost the accuracy of breast cancer diagnosis. By combining the powerful local feature extraction ability of CNNs with ViTs focus on long-range dependencies and global features, the hybrid network, integrating multiple vision architectures, optimizes the utilization of information, enabling a more thorough and nuanced interpretation of medical imaging data. The methodology was assessed using two publicly accessible datasets, revealing superior performance compared to current state-of-the-art techniques. This indicates that our method has the potential to generalize across various datasets. The high accuracy achieved by this hybrid deep learning model suggests that it can play a significant role in improving breast cancer diagnosis.

Our brain is capable of describing and categorizing the images that appear before us. But how can a computer process an image and identify it with an appropriate and accurate description? This seemed unattainable a few years ago, but with the advancement of machine vision algorithms and deep learning, as well as the availability of datasets and suitable artificial intelligence models, creating an appropriate description generator for an image has become easier. Image captioning is also a growing industry worldwide. The process of generating image captions involves converting images into a series of words using a series of pixels. Image captioning can be seen as an end-to-end challenge in the form of a sequence-to-sequence challenge. To achieve this goal, it is necessary to process both words and images. In this thesis, first, an explanation of image captioning and its applications in various fields is presented, and then, the evolutionary course of image captioning methods is examined. Various methods that have been proposed over time for image captioning have been comprehensively reviewed. This coherent classification helps us to gain a deeper understanding of the techniques and methods available in image captioning. Also, recent articles in the field of image captioning have been reviewed in this thesis. Based on the results obtained from the review of recent articles, the necessity of continuing research in the field of image captioning has been emphasized. These researches can lead to significant improvements in existing methods for image captioning and also the discovery of newer and more advanced methods. In this thesis, an encoder-decoder method based on attention has been used. Unlike previous methods where attention was only applied to one of the sections, the attention mechanism has been applied to both the image and the text. This is a new idea in this field, and the final caption is generated word by word. The FLICKR8K dataset has been used, and the evaluation metrics used are BLEU (1,2,3,4), ROUGE, and METEOR. The results are 51, 49, 48, 44, 52, and 37.5 respectively. These results indicate an improvement over previous method.

Today, with the ever-increasing amount of information and the wide range of topics, classifying texts is one of the challenges of artificial intelligence. Text classification is a branch of natural language processing in which texts are placed into categories or groups. Classification of texts is one of the things that has received attention recently and has many applications, among the most important of them are document classification, information retrieval, question and answer, polarization measurement, etc. Kurdish language is one of the Indo-Iranian branches of the Indo-European languages spoken by more than 30 million people in Western Asia, mainly in Iraq, Turkey, Iran, Syria, Armenia and Azerbaijan. Kurdish language has various dialects and has its own grammar system and rich vocabulary. Most text classification systems can be summarized into four steps: feature extraction, dimensionality reduction, classifier selection, and evaluation. At first, feature extraction from a text (using word coding) is done in different ways. Since most of the extracted features are redundant and irrelevant, they can cause errors in the classifier. Then the selection of more important features is considered as a fundamental problem in text classification. Selecting important features from all features plays a significant role in increasing the efficiency of classification accuracy. At this stage, we are trying to select the best features using machine learning methods, which is done on the Kurdish text data set. Among the methods of machine learning in optimization problems is the use of meta-heuristic algorithms. Many meta-heuristic algorithms have been introduced to date, each inspired by nature. These algorithms make few assumptions about a problem or can search very large spaces of candidate solutions. The laying hen algorithm is one of the best meta-heuristic algorithms in solving optimization problems in continuous space. Using the meta-heuristic algorithm of the laying hen, the features extracted from the text are selected in such a way as to increase the accuracy of the classifier. For this purpose, first, an advanced version of this algorithm is presented in the discrete space, and then all feature selection states are placed in the sample space. By traversing the sample space and evaluating the states point by point, the algorithm moves from point to point. The main challenge is choosing a good starting point and choosing the right range of change for each point. In this research, we have achieved one of the best methods to improve feature selection in the task of text classification, which is a new method. On the other hand, by implementing this method on the Kurdish language (which is considered as one of the few languages in natural language processing), we have enriched our research. The results of this research on a small scale (due to the lack of processing resources) show a one percent improvement in the accuracy of the classifier, which shows the efficiency of the presented approach and opens a new door for dear researchers.

In this dissertation, intelligent controllers are used, in the structure of which, control performance standards are used in order to follow these standards in addition to proper load-frequency control. The results showed that by applying performance standards in the controller structure, the controller performance improved in meeting control objectives such as reduced settling time and overshoot. In this dissertation, in addition to the use of classical algorithms, a controller based on multi-agent systems was used, taking into account performance standards to both reduce the exhaustion of Governor equipment and follow NERC performance standards to increase reliability. The results show that controllers that follow NERC standards perform better and their frequency response improves.