ABSTRACT Class imbalance is a critical challenge in big data analytics, often leading to biased predictive models. This imbalance can lead to biased models that perform well on the majority class but poorly on the minority class. Many machine learning models tend to be biased towards the majority class because they aim to minimise overall error, often leading to poor performance on the minority class. This paper presents the balanced ensemble neural network, a novel solution to effectively address class imbalance in big data. Balanced ensemble neural network combines the robust capabilities of neural networks with the power of ensemble learning, incorporating class balancing strategies to ensure fair representation of minority classes. The methodology involves integrating multiple neural networks, each trained on balanced subsets of data using techniques like Synthetic Minority Over‐sampling Technique and Random Undersampling. This integration aims to leverage the strengths of individual networks while reducing their inherent biases. Our extensive experiments across various datasets reveal that BENN achieves an AUC‐ROC score of 0.94, surpassing other models such as random forest (0.88), support vector (0.84) and single neural net (0.80). It was also observed that BENN's performance is better compared to traditional neural network models and standard ensemble methods in key metrics like accuracy, precision, recall, F1‐score and AUC‐ROC. The results specifically highlight BENN's effectiveness in accurately classifying instances of minority classes, a notable challenge in many existing models. These findings underscore BENN's potential as a substantial advancement in handling class imbalance within big data environments, offering a promising direction for future research and application in machine learning.

The emergence of the cloud platform with substantial resources to offer on-demand instigated the researchers to migrate the scientific workflows to the cloud environment. The scheduling of workflows with diverse QoS parameters is not a trivial task, but an NP-Complete problem. Several heuristics for QoS constrained workflows have been investigated. However, most of them focus only on time and cost and do not guarantee high resource utilization. The scheduling of the workflow tasks over the minimum cloud resources under the defined time limit is a grave concern. In this article, an algorithm named MFGA (Modified Fuzzy Adaptive Genetic Algorithm) has been formulated to minimize the makespan and improve resource utilization under both deadline and budget constraints. A fuzzy logic controller has also been devised to control the crossover and mutation rates that prevent MFGA from getting stuck in a local optimum. MFGA has a novel crossover technique that adds the fittest solutions in the population. Additionally, a new mutation technique has also been introduced, which minimizes the makespan and increases the reusability of the resources. The simulation experiments with the real workflows show that the proposed MFGA outperforms other state-of-the-art algorithms.

The keystone of information security has been access control. Very often, User data is misused and users are oblivious to the use of their data by unauthorized parties. Current strategies to provide storage for confidential data and subsequent authentication involve relying on a trusted third party for the same, which could be victims of Denial of Service (DoS) attacks or technical failures. This paper examines a strategy where the underlying framework for providing Access Control is the blockchain, hence decentralizing the mechanism of providing access control. Further in this paper, we demonstrate and model the User Data access on the Ethereum framework. Personal Information of the user by a website or an application is retrieved on a need-to-know basis from the off-blockchain, as determined by the user, the true owner of the data. Personal data is highly protected and the different permissions to different websites or applications are determined by the Smart Contract.

Cloud computing is being a potential role in providing services for utilizing a huge data in various application, as it is ubiquitous. In emerging growth of Cloud services been focused on security issues and optimal data storage used by consumers. Eventually, the Cloud storage is the best way to keep essential business data secure and accessible. Along with that, there are few important feature been considered. i.e( file versioning, automatic sync,collaboration tools, security File Encryption). In our research article, the framework is designed for real-time Healthcare business application to be achieved all the essential features with Inter-Cloud data storage.To do additionally, has been implemented and tested by an efficient CP-ABE (Cipher Policy-Attribute Based Encryption) algorithm for secure data transmission. Outcomes were powerful in a such way that can be promised in a designed framework developed in Python 3 in Charm-Cryptography.