Abstract Platform‐as‐a‐Service (PaaS) is one of the cloud computing services which will be offered to the clients through the virtualization platform. The problem is constructed in the PaaS platform effectively excluding internet service suppliers which do not meet highly secure standards, and so security issues can be a significant a hurdle to online computation. To address the aforementioned issues, the suggested method primarily concentrated proceeding the safety of PaaS infrastructure by using a novel fuzzy based access control technique, in which the information source is realized by using CDO security feature. To further enhance the security, security using trust based signature has been used in which the class and packet filter for each specified authorization were mapped to our class by modifying the security feature code. This public key will be utilized for authenticate that transaction relevant signature if the identity provider (IdP) has provided public safety data about this same two major components represents this little token on whose trusted trademark components are built put, that is, the entire item or even the claims contained. The suggested technique outperforms other current methods in terms of several performance parameters, with a high throughput of 14, CPU utilization of 12.5, and a low execution time of 5.2 s. The proposed ‘PaaS platform security enhancement with the use of fuzzy and trust‐based signatures’ provides high throughput and low execution time. This research work proposes a novel approach to enhancing the security of PaaS infrastructure, which is offered to clients through the virtualization platform in cloud computing. The current problem with PaaS is that it excludes internet service providers that do not meet high‐security standards, which poses a significant challenge to online computation. The suggested approach focuses on improving the safety of the PaaS platform by employing a fuzzy‐based access control technique, using the connectionless data objects security feature as the information source. To further strengthen security, the approach utilizes a trust‐based signature technique where the class and packet filter for each authorization are mapped to the class of the proposed method by modifying the security feature code. The proposed public key is then used to authenticate the transaction relevant signature, provided the IdP has given public safety data about these two components of the token on which Trusted Trademark components are built, that is, the entire item or even the claims contained within it. The proposed approach outperforms current methods in terms of several performance parameters, such as a high throughput of 14, a high CPU utilization of 12.5, and a low execution time of 5.2 s. In summary, the proposed PaaS platform security enhancement using fuzzy and trust‐based signatures provides an effective way to achieve high throughput and low execution time while ensuring platform security.

Stock market forecasting remains a difficult problem in the economics industry due to its incredible stochastic nature. The creation of such an expert system aids investors in making investment decisions about a certain company. Due to the complexity of the stock market, using a single data source is insufficient to accurately reflect all of the variables that influence stock fluctuations. However, predicting stock market movement is a challenging undertaking that requires extensive data analysis, particularly from a big data perspective. In order to address these problems and produce a feasible solution, appropriate statistical models and artificially intelligent algorithms are needed. This paper aims to propose a novel stock market prediction by the following four stages; they are, preprocessing, feature extraction, improved feature level fusion and prediction. The input data is first put through a preparation step in which stock, news, and Twitter data (related to the COVID-19 epidemic) are processed. Under the big data perspective, the input data is taken into account. These pre-processed data are then put through the feature extraction, The improved aspect-based lexicon generation, PMI, and n-gram-based features in this case are derived from the news and Twitter data, while technical indicator-based features are derived from the stock data. The improved feature-level fusion phase is then applied to the extracted features. The ensemble classifiers, which include DBN, CNN, and DRN, were proposed during the prediction phase. Additionally, a SI-MRFO model is suggested to enhance the efficiency of the prediction model by adjusting the best classifier weights. Finally, SI-MRFO model’s effectiveness compared to the existing models with regard to MAE, MAPE, MSE and MSLE. The SI-MRFO accomplished the minimal MAE rate for the 90th learning percentage is approximately 0.015 while other models acquire maximum ratings.

Summary The Internet of Drones (IoD) is a layered network or platform that controls and coordinates the drones. The unmanned aerial vehicles also called as drones are objects that utilized in Internet of Things (IoT) smart devices. The security is the major problem in the architecture for IoD. Hence, a multilevel authentication method is devised in this article to enable secure communication in IoD. The analysis of security is depending on four stages, such as pre‐deployment, user registration, login phase, and authentication phase. The drones are registered in the IoD infrastructure in the pre‐deployment phase. Then, the users registration with the drone is done in user registration phase, which is executed to register the users with the drone for accessing the information in real‐time via the user. Then, the user communicates with the server is done in the login phase, and at last, the authentication of drones, servers, and the users are done in authentication phase to offer secured communication. The devised approach attains the maximum performance with a delay, throughput, and packet loss of 0.099 s, 62.855 bps, and 2.52012 s, respectively.

Abstract Breast cancer is a very dangerous disease that mainly affects women. It is a deadliest disease that highly affects the women's life. Therefore, it is necessary to predict and classify this deadly disease for early diagnosis. There exist numerous data mining techniques for early prediction and classification of this disease. The big data based analytical model provides the better solution for storing, manipulating, and analyzing a great number of mammographic images. In this article, a new improved fractional rough fuzzy K‐means clustering strategy is considered for disease prediction. Then, a new Tunicate Swarm Algorithm (TSA) is introduced to optimize the weight parameters. TSA is a bio‐inspired metaheuristic optimization approach. Finally, the labeled ensemble classifier (LEC) is utilized for classifying the stages of breast cancer as malignant and benign. Here, the data is randomly generated from breast cancer Wisconsin dataset (diagnosis) obtainable on UCI machine learning repository. The proposed strategy is compared with different existing strategies, like Logistic Regression Classifier, Random Forest Classifier. From the analysis, it is observed that the proposed big data based analytical model using LEC provides 99.3% accuracy that is very high when compared to the accuracy of existing approaches.