Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques. The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

The field of finance makes extensive use of real-time prediction of stock price tools, which are instruments that are put to use in the process of creating predictions. In this article, we attempt to predict the price of Bitcoin in a manner that is both accurate and reliable. Deep learning models, as opposed to more traditional methods, are used to manage enormous volumes of data and to generate predictions. The purpose of this research is to develop a method for predicting stock prices using the Hybrid Convolutional Recurrent Model (HCRM) architecture. This model architecture integrates the advantages of two separate deep learning models: The 1-Dimensional-Convolusional Neural Network (1D-CNN) and the Long-Short Term Memory (LSTM). The 1D-CNN is responsible for the feature extraction, while the LSTM is in charge of the temporal regression. The developed 1D-CNN-LSTM model has an outstanding performance in predicting stock values.

Text classification is a fundamental task in Natural Language Processing (NLP) with a wide range of applications such as sentiment analysis, document classification and content recommendation. Traditional approaches like Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF) relied on feature engineering but lacked contextual understanding. Deep learning came into the picture for text classification with transformer models such as Bidirectional Encoder Representations from Transformers (BERT), which could understand contextual words bidirectionally. In this article, we utilize a pre-trained BERT model fine-tuned on the Reuters-21578 dataset to classify news articles. We aim to measure the performance of transfer learning against common machine learning models and non-fine-tuned BERT. The fine-tuned model achieves 91.77% accuracy, which significantly outperforms the non-fine-tuned BERT, and performs better than classical classifiers such as NB, SVM and RF. The results show that fine-tuning allows BERT to contextualize domain-specific intricacies, resulting in improved classification performance. We also address the computational trade-offs associated with transformer models, highlighting the need for optimal methods for deployment. Thus, this study further enables the use of fine-tuned BERT in automatic news classification and is of significant value for information retrieval and content personalization.

This paper presents an ensemble learning-based approach to improve sentiment classification accuracy in the IMDB movie reviews dataset. To this end, we tap three diversified models: Logistic Regression, Random Forest, and a Bidirectional Long Short-Term Memory neural network. Each one contributes its unique strengths to the ensemble, enhancing the overall performance. The text data has been processed using a statistical formula that converts the text document into a vector from the relevancy of the word with bigrams; data have been transformed to make it useful for Logistic Regression and Random Forest classifiers. The LSTM neural network is designed to capture sequential dependencies through an embedding layer followed by a bidirectional LSTM and dense layers with dropout regularization. The ensemble method then combines predictions of these models by majority voting, thus the interpretability and robustness of conventional classifiers are preserved, while advanced capabilities from neural networks are maintained. Our experiments prove that this ensemble approach does obtain an accuracy of 89.2% on the test dataset, which outperforms individual models. This study realizes some possible ways to combine traditional machine learning techniques with deep learning models in sentiment analysis tasks.