Marwan Alshar'E

In the modern artificial intelligence-driven banking regime, the world is witnessing exponential growth of transactional and behavioural data, and therefore, for establishing and sustaining financial institutions, it is imperative that higher-order customer risk management and creditworthiness evaluation are done. This requires not only developing knowledge architecture but also establishing decision-aware threshold optimisation. This study proposes a unified model that combines customer segmentation with creditworthiness profiling based on multi-view clustering applied together with predictive risk scores. The proposed framework uses the gradient boosting decision tree (LightGBM) classifier to identify the level of customer credit risk using a combination of consolidated demographic, financial and behavioural variables. This algorithm is commonly used in financial risk analytics because it has demonstrated good performance in dealing with nonlinear relationship modelling and in dealing with disproportionate structured banking data. The empirical analysis is based on a large real-world dataset. It comprises 1,013,193 transactions, 2,000 customers and 6,146 card payments. It also captures detailed transactional, behavioural, card-level and demographic characteristics. The representations of the customers are built on three complementary views, namely (i) demographic and financial health (income, debt, age, credit score); (ii) card and credit utilisation; and (iii) transactional behaviour and expenditure dynamics. The multi-view embedding strategy and unsupervised clustering resulted in two separate groups of customers bordering the distinction between a high-value cohort and a higher-risk cohort. The accuracy of the baseline model is 86.5% and its ROC-AUC is 0.935, which highlights its discriminatory effectiveness. Further optimisation through decision-threshold optimisation, improves operational performance and eventually yields the highest F1-score of 0.843, balanced precision of 0.838 and recall of 0.848. This analysis also indicates that the model can be implemented on various risk considerations; it is scalable and precisely guides risk-conscious financial decision-making, reconciling more advanced machine learning technologies with viable banking and credit-management needs.

In modern high-volume payment systems, detecting fraud is still essentially confined by abhorrent class imbalance, changing transaction patterns, and lack of dependably labelled fraud occurrences. The current research questions the issue of whether self-supervised learning (SSL) can add to the extraction of the knowledge related to fraud in comparison with the capability of strong supervised baselines in the multi-channel payment setting. Using a real world banking dataset of over 13.3 million transactions in the 2010-2019 period, we perform an extensive analysis, including supervised machine learning, anomaly-based SSL, and methods of integrating knowledge into machine learning strategies. Gradient-boosting models are able to build a strong base (F1 = 0.86, ROC-auc = 0.99) that suggests that the trained model has a near-saturation discriminative ability that is solely based on tabular transaction characteristics. We show that naive, generic, SSL-based anomaly detectors lead to reduced precision, and task-adapted representations of supervised models, stacked with task-adapted representations, can increase fraud recall by up to 4.9 with a small F1 increase ( +0.6). However, with strict operationally imposed conditions of accuracy ≥ 0.90, the added benefits of the use of SSL are not experienced, highlighting inherent thresholds of representation based improvement. Such results enhance a knowledge based perspective of when self-supervised representations add value to the decision making and when supervised models have already acquired adequate information about fraud meaning thereby guiding the design of financial fraud knowledge-management models in a robust way.

Recent breakthroughs in cryptographic technology are being thoroughly scrutinized due to their emphasis on innovative approaches to design, implementation, and attacks. Lightweight cryptography (LWC) is a technological advancement that utilizes a cryptographic algorithm capable of being adjusted to function effectively in various constrained environments. This study provides an in-depth analysis of elliptic curve cryptography (ECC), which is a type of asymmetric cryptographic method known as LWC. This cryptographic approach operates over elliptic curves and has two applications: key exchange and digital signature authentication. Next, we will implement asymmetric cryptographic algorithms and evaluate their efficiency. Elliptic curve elgamal algorithms are implemented for encryption and decryption of data. Elliptic curve Diffie-Hellman key exchange is used for sharing keys. Experimental results have shown that ECC needs small size keys to provide similar security. ECC takes less time in key generation, encryption and decryption of plain text. Time taken by ECC to generate a 2,048 bit long key is 1,653 milliseconds in comparison to 4,258 millisecond taken by Rivest-Shamir-Adleman (RSA) technique.

In this paper, the researchers studied the effects of different activation functions in hidden layers and how they impact the overfitting or underfitting of the model in the multiclass prediction of chronic diseases. This paper also evaluated the effects of varying the number of layers, and hyperparameters and its impact on the accuracy of the model and its generalization capabilities. It was found that exponential linear unit (ELU) does not have a significant advantage over rectified linear unit (ReLU) when used as an activation function in the hidden layer. Additionally, the performance of softmax function, when used in the output layer, is the same as a classic sigmoid output activation function. In terms of the ability of the model to generalize, the researchers achieved a classification accuracy of 100% when the trained model was used to predict unseen data. Through this research, the researchers should be able to assist medical professionals and practitioners in Oman in the validation and diagnosis of chronic diseases in clinics and hospitals.

Blockchain, a breakthrough technology, offers solutions to several healthcare challenges, includes Managing and sharing data securely, Making different systems work together seamlessly, Increasing transparency in the supply chain, Giving patients more control over their health information, and Adhering to regulations and guidelines. This paper offers an in-depth study of current literature on blockchain in healthcare. It covers areas that have been investigated, studied and implemented by previous research, provides valuable insights, and introduces practical suggestions. Research findings indicate that blockchain has the capacity to improve data security, privacy, and immutability. It can also facilitate effortless data sharing among different parties involved, enhance transparency in pharmaceutical supply chains, empower patients in managing their health information, and address regulatory concerns. The results of this study recommends Healthcare organizations to consider implementing secure transaction protocols to ensure the confidentiality and integrity of their data. Also to explore the use of Hyperledger Fabric architecture, a blockchain framework specifically designed for enterprise applications, Implementation of Secure Transaction Protocols, Utilization of Blockchain for Data Sharing, Adoption of Lightweight Consensus Protocols. By leveraging blockchain technology, healthcare businesses may securely share data with other trusted parties, improving interoperability and data exchange. Additionally, adopting lightweight consensus methods can help streamline decision-making processes within the organization. Future research shall involve the integration of blockchain technology with artificial intelligence and data analytics, the establishment of decentralized parallel healthcare organizations, the integration of blockchain with cloud computing, and the resolution of regulatory and standards difficulties

KSII Transactions on Internet and Information Systems TIIS Paper Details

With the current continuous increase in the usage of communication technologies and data transmission amount, the available web services and mobile applications are used to access and complete several online activities such as online education, e-commerce, and online financial transactions. The needs for a strong and secure access control authentication solution of users’ data and the available online resources are crucial. Authentication can play a major rule in protection by implementing a solution, which allows users to prove their identities and provide access permission to all legitimate users only. This paper proposes two-factor authentication scheme based on the usage of a modified copy of the Schnorr Signcryption algorithm. The proposed scheme achieves the target goal of this paper, which provides the users with an efficient solution that can be implemented in any authentication solution as the second factor, with no special requirement and can be used to authenticate the users and control recourses access permission. Moreover, Java-based application is developed in order to examine the proposed scheme in a client/server architecture where it is found that the solution can successfully authenticate authorised users and reject any unauthorised access request with an adequate security protection level and acceptable performance.

Plans for preventing and mitigating vulnerabilities in computer networks are known as cybersecurity frameworks. With the assistance of the Cybersecurity Framework, organisations are able to reduce their vulnerability to cyber attacks and improve the strength of their defences. The cybersecurity framework has made the decision to take part in trials so that it may improve its ability to handle long-term security frameworks. This gives rise to concerns about cost and time rather than safety at this point in time because of the situation that is occurring. A firm may utilise the similarities that are shared by many cyber security frameworks as a reference to assist it in selecting the framework that is the most suitable for the needs that are unique to the organisation. According to the findings of this study, the risk maturity level, cost, and certification are the three most significant components of CSF implementation for firms.

The purpose of this study is to look into the components that affect the behavioral intention of Jordanians to use Virtual Reality Technology in the Learning Environment (VRTLE). For the educational and entertainment industries, virtual reality presents both obstacles and opportunities. The goal of this study is to determine the factors that influence VRTLE acceptance and to propose how such technology may be integrated into the educational setting of students and universities. A total of 60 students from private universities took part in the research. Among the participants, 63.3 were females and 36.7 were males. The modified UTAUT model used in this study only looked at the effects of four independent variables and two external factors, namely, acceptability (ACC) and usability (USA). Acceptability and usability have a substantial impact on student acceptance of VRTLE, according to the findings. Furthermore, as moderators, there is a major effect on both Awareness and Experience. This paper contributes significantly to the UTUAT model. It also tackles educational concerns about the transition to a virtual reality learning environment. As a result, it would be useful to look into other factors as well. As a result, a comprehensive empirical study should be designed to allow for the assessment of the effects of other variables as well.