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Cloud computing has gained huge attention over the past decades because of continuously increasing demands. There are several advantages to organizations moving toward cloud-based data storage solutions. These include simplified IT infrastructure and management, remote access from effectively anywhere in the world with a stable Internet connection and the cost efficiencies that cloud computing can bring. The associated security and privacy challenges in cloud require further exploration. Researchers from academia, industry, and standards organizations have provided potential solutions to these challenges in the previously published studies. The narrative review presented in this survey provides cloud security issues and requirements, identified threats, and known vulnerabilities. In fact, this work aims to analyze the different components of cloud computing as well as present security and privacy problems that these systems face. Moreover, this work presents new classification of recent security solutions that exist in this area. Additionally, this survey introduced various types of security threats which are threatening cloud computing services and also discussed open issues and propose future directions. This paper will focus and explore a detailed knowledge about the security challenges that are faced by cloud entities such as cloud service provider, the data owner, and cloud user.

This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Resonance Imaging (MRI) images. Using Fluid-Attenuated Inversion Recovery (FLAIR) abnormality segmentation masks and MRI scans from The Cancer Genome Atlas’s (TCGA’s) lower-grade glioma collection, our proposed approach uses a VGG19-based U-Net architecture with fixed pretrained weights. The experimental findings, which show an Area Under the Curve (AUC) of 0.9957, F1-Score of 0.9679, Dice Coefficient of 0.9679, Precision of 0.9541, Recall of 0.9821, and Intersection-over-Union (IoU) of 0.9378, show how effective the proposed framework is. According to these metrics, the VGG19-powered U-Net outperforms not only the conventional U-Net model but also other variants that were compared and used different pre-trained backbones in the U-Net encoder. Clinical trial registration Not applicable as this study utilized existing publicly available dataset and did not involve a clinical trial.

Cloud computing has been one of significant domains of processing service in social networks like the internet and local networks in recent years. One of the main problems in cloud computing is placing a virtual server onto physical servers. This problem will have a remarkable effect on energy consumption, because if a suitable placement is not chosen for it, a great amount of energy will be used to keep the physical servers on. This paper aims to optimize the use of energy in physical servers and in order to achieve it, the last placement in Virtual Machines (VMs) and Physical Machines (PMs) is considered. The proposed approach for allocating resources to VMs is the use of ant colony algorithm. This approach solves virtual machine placement problem and attempts to have the least effects on the environment and energy consumption.

Skin cancer is one of the most prevalent malignancies in humans and is generally diagnosed through visual means. Since it is essential to detect this type of cancer in its early phases, one of the challenging tasks in developing and designing digital medical systems is the development of an automated classification system for skin lesions. For the automated detection of melanoma, a serious form of skin cancer, using dermoscopic images, convolutional neural network (CNN) models are getting noticed more than ever before. This study presents a new model for the early detection of skin cancer on the basis of processing dermoscopic images. The model works based on a well-known CNN-based architecture called the VGG-16 network. The proposed framework employs an enhanced architecture of VGG-16 to develop a model, which contributes to the improvement of accuracy in skin cancer detection. To evaluate the proposed technique, we have conducted a comparative study between our method and a number of previously introduced techniques on the International Skin Image Collaboration dataset. According to the results, the proposed model outperforms the compared alternative techniques in terms of accuracy.

During recent decades, using credit cards represents a pivotal part of the financial lifeline. Credit cards and online payment gateways are vital elements in the world of world-wide-web. Given the fact that credit cards play an essential role in today's society, the misuse of these cards will lead to significant damages. One of the common ways to deal with these possible damages is using anomaly detection systems. These systems aim to take account of changes in customer and fraudsters’ behavior to detect anomaly patterns. In the current study, we present a model namely IF-GWO to learn fraudulent patterns through analyzing past transactions. The method employs a novel ensemble learning method using isolation forest (IF) and Grey Wolf Optimizer (GWO). The experimental results indicate the priority of our presented fraud-detection system based on a noticeable number of credit card account transactions. Compared to the conventional model used for anomaly detection, the proposed model can detect more fraud accounts with fewer false positives over comparative procedures. Based on a comparison with other models using the dataset contains 284,807 transactions that are made by European cardholders, the proposed model outperformed the other approaches and achieved the highest performance in terms of F-Measure (93.52%), Area under receiver operating characteristic curve (AUC) (94.17%), and G-means (94.10%).

Breast cancer is among the most common cancers women got, which can be effectively cured providing that it is diagnosed at the early stages. In the current study, we attempted to classify breast cancer into two groups of malignant and benign by proposing a new ensemble learning method using Multi-Verse Optimizer (MVO) and Gradient Boosting Decision Tree (GBDT). Moreover, the prediction rate of GBDT has been shown to be desirable, its efficiency and classification accuracy are significantly dependent on feature selection and parameter setting. Based on the MVO, we attempted to propose an efficient approach to optimize feature selection and GBDT’s parameters at the same time. In other words, the MVO algorithm is able to play the role of a tuner to set the GBDT’s main parameters and optimize feature selection results. To implement and test the proposed approach, standard criteria (i.e. accuracy, sensitivity, specificity, etc.) was used for performance evaluation. Also, the datasets of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer were considered for this purpose. Comparing the results of GBDT–MVO model with other proposed models demonstrated that this model is more precise and has considerably lower variance in the case of a breast cancer diagnosis.

The ability to predict multiaxial fatigue life of Al-Alloy 7075-T6 under complex loading conditions is critical to assessing its durability under complex loading conditions, particularly in aerospace, automotive, and structural applications. This paper presents a physical-informed neural network (PINN) model to predict the fatigue life of Al-Alloy 7075-T6 over a variety of multiaxial stresses. The model integrates the principles of the Geometric Multiaxial Fatigue Life (GMFL) approach, which is a novel fatigue life prediction approach to estimating fatigue life by combining multiple fatigue criteria. The proposed model aims to estimate fatigue damage accumulation by the GMFL method. The proposed GMFL-PINN combines this physics-based approach with data-driven neural networks. Experimental validation demonstrates that GMFL-PINN outperforms FS, Smith–Watson–Topper (SWT) and Li–Zhang (LZH) fatigue life prediction methods which provides a reliable and scalable solution for structural health assessment and fatigue analysis.