Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Eligibility for a credit card is determined by several factors, including credit score, income, employment status, existing debt and credit history. This research investigates the use of advanced computational techniques to predict credit card eligibility. Specifically, the study employs Decision Tree Classifier (DTC) and Random Forest Classifier (RFC) models, enhanced by the Fitness Dependent Optimiser (FDO) and Chita Optimisation Algorithm (COA), to improve prediction accuracy. The hybrid models were tested and compared based on their ability to predict acceptance or rejection of credit card applications. Results indicate that the RFCO model excels under rejection conditions, achieving the highest accuracy and a recall value of 0.9582. The RFFD model follows closely behind the RFCO model, achieving a recall value of 0.9504. Additionally, the DTFD model outperforms the DTCO model, with recall values of 0.9426 and 0.9399, respectively. The standalone DTC and RFC models achieved recall values of 0.9347 and 0.9321.

This study presented a new integrated forecasting model for improving the accuracy of stock price predictions for the Hang Seng Index. Using the ensemble learning theory, this research constructs the core modelling engine based on Adaptive Boosting (AdaBoost) is fine-tuned using three metaheuristic optimisation approaches: Particle Swarm Optimisation, Antlion Optimisation and Biogeography-Based Optimisation. These algorithms are based on nature search for optimising AdaBoost hyperparameters such as the number of estimators, learning rate and complexity of the base learner to improve generalisation and minimise prediction errors. The BBO-AdaBoost was the most superior one. A data set including daily HSI stock prices (open, high, low, close) and volume were obtained from 2015 to 2023. The data were min-max normalised and were split into 80:20 for training and testing. The results show that the BBO-AdaBoost achieved the best result with the least MAPE of 2.29%, outperforming both the stand-alone AdaBoost and other hybrid models.

A Distributed Denial of Service (DDoS) attack is intended to hinder network assets, making them unavailable to approved users. Attacks have become increasingly common, posing significant threats to internet users. DDoS attacks seek, in most cases, to temporarily or even irreparably cripple a target's web presence. In contrast, cloud infrastructure continues to develop, with container technology allowing for efficient use of resources and expandable service delivery. The current work proposes a new scheme for DDoS detection using deep learning in conjunction with a math model for countermeasures. In its proposed algorithm, artificial intelligence methodologies such as Deep Convolutional Neural Networks (DCNNs) and an autoencoder have been added to enhance accuracy in detection. With use of existing databases, performance in relation to complex detection tools is similar, yet processing time is significantly reduced. It is argued that such an approach is specifically suitable for operational environments with restricted capabilities.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

The properties of the blockchain are not suitable for the storage of sensitive privacy data, and the excellent characteristics of the blockchain will be seriously affected by the existence of massive amounts of data on the chain.To address the above issues, propose a Loosely Coupled Local Differential Privacy Blockchain Federated Learning method (LL-BCFL).First of all, a client selection mechanism is put forward to ensure honesty and positivity in joining the training client and the correct effectiveness of the final global model aggregation.Secondly, use federated learning to alleviate the 'data silos' phenomenon and achieve joint training of big data stored in distributed multi-parties.Additionally, a differential privacy method is introduced to act on federated learning networks to avoid inference attacks.Finally, the MNIST dataset was used to confirm the availability of the LL-BCFL method on balanced and unbalanced datasets.