In an age dominated by the rapid advancement of digital transactions, safeguarding sensitive financial information in cloud computing, particularly credit card data, has become increasingly crucial. Traditional security measures in financial cloud computing have shown weaknesses in ensuring the confidentiality, privacy and integrity of data, leaving unencrypted information vulnerable to unauthorized access and exposure. This paper introduces an innovative method called Tokenized Elliptic-Homomorphic Cryptography (TE-HC) which improves the financial information security in cloud computing. Additionally, Artificial Intelligence (AI) is integrated to detect whether the data is normal or potentially intrusive in financial information. Consisting of merchant and tokenization modules along with a token vault, the system enables secure transmission and validation of credit card details. Implemented results demonstrate its effectiveness, speed, while comparative analyses showcase its superior performance in credit card security. Overall, TE-HC offers enhanced security, efficiency and user experience, presenting a compelling solution to the critical challenges of protecting credit card data in the digital age.

An essential component of the Indian economy is agriculture. Precision farming, another call for smart farming, is a novel method of farming that incorporates modern innovations to improve production, sustainability, and performance over traditional techniques. These involve strong data security measurements, the reliance on high-quality sensor data, and possible integration issues with current farming systems. This study proposed a methodology for smart farming using IoT sensor statistics incorporated with a unique Dynamic Artificial Fish Swarm Driven XGBoost (DAFSXGBoost) model. Initially, research was conducted on a Kaggle dataset. Substantially, the "Crop recommender CSV" file, containing vital IoT sensor records, which include temperature, humidity, rainfall, soil potential of Hydrogen (pH), and nutrient ratios, changed into utilized. Data cleaning procedures were implemented to rectify inaccuracies, incompleteness, and redundancy. The dataset has been split into subsets for testing and training to improve the model. The proposed DAFSXGBoost approach, carried out with the use of MATLAB, is designed to enhance predictive accuracy while maintaining low latency and efficient hardware usage. The findings demonstrated that the proposed DAFS-XGBoost method achieves effective recall, accuracy, precision, f1-score, training time, and MSE rates as 93.76%, 96.73%, 94.61%, 90.12, 0.0006, and 0.19. The framework’s potential to combine outstanding performance and a hardware-friendly design to promote the longterm viability of smart farming is confirmed by simulations.

Financial cloud computing is based on the virtualization technology paradigm, which has recently gained prominence in the information technology (IT) industry. The resource allocation in the existing system is not guaranteed, and convergence problems occasionally cause the process to move slowly. Consequently, there has been a notable decline in the overall efficacy of financial cloud computing. In this work, new methods are presented to improve the financial cloud. The three main stages of the proposed system are resource allocation, load balancing and cost-effective virtual machine (VM) migration. To enhance load balancing, this study employs the Enhanced Weighted Round Robin Algorithm (EWRR) technique. Load balancing is achieved by shifting workloads from overloaded to underloaded nodes. The Differential Evaluation-based Bat Algorithm (DEBA) efficiently chooses more optimal resources to accomplish the optimal resource allocation in the financial cloud. Resolute Support Vector Machine (RSVM) technique is utilized to Provide Cost-effective VM migration. The simulation's findings show that the recommended DEBA-EWRR algorithm performs better than existing techniques due to advancements in throughput, Makes pan, Fitness score and resource utilization.

The mobile banking system allows consumers to access financial services through mobile phones. It includes services like money transfers, account balance checks and bill payments. Robust data security methods, including encryption and multi-factor authentication, secure user information while preserving the confidentiality and integrity of financial transactions, therefore increasing confidence in the system. In this research, we intend to develop an innovative novel machine learning (ML)-based approach for securing cloud data in mobile banking systems. The cloud computing risk management paradigm enhances financial security. To improve consumer services and banking experiences while maintaining information security, this study explains how mobile cloud architecture might be implemented. This article addressed several difficulties associated with mobile banking, including processing speed and storage capacity, by concentrating on cloud-based risk management for banking systems. We proposed versatile Adaptive Boosting (V-AdaBoost) to improve effectiveness in learning by selecting characteristics and training bank individuals. We implemented our suggested approach in Python software. The performance assessment step employs various metrics to assess the efficacy of the suggested prediction model. We performed a comparative study with other existing methods and the results demonstrate that the suggested model performed better than other conventional methods for securing cloud data in mobile banking systems.

The process of safely granting people or organizations access to networks, systems, or resources via authentication and authorization is known as an ID management system. Ensuring safe access control involves a range of procedures, including identity verification, authorization, and authentication. ID Management System may have several disadvantages, such as high setup costs, possible incompatibilities with current infrastructure, and the demand for maintenance and upgrades to handle changing security risks and legal obligations. Research proposed a novel technique discriminative fragile support vector machine (DFSVM) to secure the user ID in cloud security. The ID management data is gathered. Here, research utilize the tokenization for data pre-processing, and Term frequency Inverse document frequency (TF-IDF) is employed for feature extraction to extract the pre-processed data. To reduce the risk of identity theft, illegal access, and data breaches, the system incorporates extensive authentication, authorization, and access control measures. The system reduces the potential of illegal access and risk from insiders by integrating role-based authorizations and selective access controls, which provide users with fine-grained control over highly confidential financial information. Performance evaluates thorough security assessments of the financial sector by using our proposed method. The parameter metrics used in this study are F1-score (96%), precision (97%), RMAE (22.24), MAE (5.8), and recall (96.51%). The financial industry greatly benefits by using DFSVM for an ID Management System, which improves user authentication and data integrity to efficiently minimize cyber risks and maintain regulatory compliance.