Recent technologies and innovations have encouraged users to adopt cloud-based architectures. 1,2 This has reduced IT barriers and provided new capabilities of dynamic provisioning, monitoring and managing resources by providing immediate access to resources, enabling easy scaling up of services and implementation of new classes of existing applications. However, sharing the same pool when requesting services involves the risk of data breaches, account compromises, injection vulnerabilities, abusive use of features such as the use of trial periods and distributed denial of service (DDoS) attacks. 3,4 As a result, many customers rank cloud security as a major challenge that threatens their work and reduces their trust in cloud service providers. Cloud-based architectures have reduced IT barriers and provided new capabilities of dynamic provisioning, monitoring and managing resources by providing immediate access to resources, enabling the easy scaling up of services. However, sharing the same pool when requesting services involves the risk of data breaches, account compromises, injection vulnerabilities and distributed denial of service (DDoS) attacks. As a result, many customers rank cloud security as a major challenge that threatens their work and reduces their trust in cloud service providers. Amar Meryem and Bouabid EL Ouahidi propose an architecture that eradicates malicious behaviours by detecting known attacks using log files; blocks suspicious behaviours in real time; secures sensitive data; and establishes better adaptations of security measures by dynamically updating security rules.
As credit card becomes the most popular payment mode particularly in the online sector, the fraudulent activities using credit card payment technologies are rapidly increasing as a result. For this end, it is obligatory for financial institutions to continuously improve their fraud detection systems to reduce huge losses. The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data, using attention mechanism and LSTM deep recurrent neural networks. The proposed model, compared to previous studies, considers the sequential nature of transactional data and allows the classifier to identify the most important transactions in the input sequence that predict at higher accuracy fraudulent transactions. Precisely, the robustness of our model is built by combining the strength of three sub-methods; the uniform manifold approximation and projection (UMAP) for selecting the most useful predictive features, the Long Short Term Memory (LSTM) networks for incorporating transaction sequences and the attention mechanism to enhance LSTM performances. The experimentations of our model give strong results in terms of efficiency and effectiveness.
The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.
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