In today’s fast changing digital world, the need for secure, transparent, and reliable financial transactions is more important than ever especially in areas where fraud, delays, and unauthorized access are common concerns. Traditional payment systems often depend on centralized middlemen, which can lead to slow processing, high fees, and risks of data tampering or cyberattacks. This work introduces an automated payment processing system powered by blockchain technology, designed to make digital transactions faster, safer, and more trustworthy without relying on third parties. The motivation for this system came from real-world frustrations with issues like payment fraud, slow transactions, and the lack of visibility in how money moves within traditional financial systems. To build this system effectively, the Structured Systems Analysis and Design Methodology (SSADM) was adopted. This method provides a clear, step-bystep approach for understanding problems and creating effective systems. With blockchain at its core, the system will support real-time transaction validation, ensure that data can’t be altered, reduce costs, and remove central points of failure. Overall, it aims to build user confidence and create a more resilient payment infrastructure. By solving key problems found in conventional systems, this project hopes to contribute to the next generation of secure, scalable, and efficient financial technologies for businesses and organizations.
It has been set out to explore how the digital revolution and the rise of Fintech are fundamentally changing the way global trade routes are managed. The goal is to see if these new tools could fix the old headaches of international trade—think sky-high costs, shadowy processes, and constant security worries—and replace them with supply chains that actually work better, stay safe, and respect the planet. Instead of just looking at numbers, we took a deep dive into qualitative insights by combing through academic papers, latest industry trends, and real-world case studies. It is paid close attention to the heavy hitters: blockchain, smart contracts, digital payments, and AI-powered logistics. To make it practical, we looked at how these technologies are performing in the real world across vital trade links like the Black Sea, the Middle Corridor, and the New Silk Road. The result of the paper is to going digital makes everything smoother. It cuts down waiting times, handles boring paperwork automatically, and finally lets everyone see what’s happening in the supply chain in real-time. It was also found that Fintech is a game-changer for smaller businesses (SMEs) and developing areas, giving them a seat at the global trade table for the first time. That said, it’s not all smooth sailing; we still have to deal with patchy internet, messy regulations, cyber threats, and a serious lack of people who know how to run these systems. Digital tools and Fintech aren't just minor upgrades; they are revolutionary for trade management. But, to make it work, governments and private companies need to start rowing in the same direction. We need smart investments in better internet for everyone, global rules that actually match up, tighter security, and training programs that prepare people for the jobs of tomorrow. We wrap up the paper with a roadmap for leaders and businesses to help them make this transition without getting left behind.
In the current days with the growth of communication systems, the Internet of Things (IoT) has become a famous mechanism that allows large systems to be allowed with connectivity with heterogeneous frameworks. Nevertheless, it exists with technical complexity in the existing networks to manage certain massive systems in an effective way. Nowadays, the Software Defined Network (SDN) method with its elasticity and agility has been integrated with IoT to face the powerful flexibility and scale demands and create a novel IoT framework. Effective routing models with high security and low latency are needed, as the SDN-IoT architecture’s size is enhanced. However, the existing SDN routing models are still suspicious of flow control’s dynamic change, more importantly when the network is under threat. The IoT systems are normally performed in unattended and hostile environments. In addition, the routing in the present IoT framework becomes ineffective because of the existence of unauthenticated and malicious nodes, insecure routing, minimum network lifespan, and so on. In order to manage these problems, this work designs an effective SDN routing strategy-enabled IoT system with a blockchain mechanism to prevent malicious threats during data transmission. The deep learning strategy is supportive for recognizing suspicious IoT devices based on each node’s energy features. This article performs two significant tasks including the identification of malicious nodes and the selection of the optimal path. At first, the data of the IoT node is stored in the blockchain since the nodes in the IoT have a constrained lifetime. In addition, the nodes are validated to verify the authentication by applying a smart contract. The Cascaded Dilated Recurrent Neural Network (CD-RNN) is employed for recognizing the malicious and trusted nodes of the network. After recognizing the malicious node, the selection of the optimal route is carried out. In this, the routes are chosen optimally by the Transitive Phase of Pelican Optimization (TPPO). Lastly, the estimation is conducted by considering some factors including security, Packet Delivery Ratio (PDR), delay, and throughput. Hence, the suggested system offers better functionality than the previous approaches. The suggested scheme presents a hybrid mechanism, which combines a CD-RNN-based malicious node detection with TPPO-based routing optimization and blockchain-based trust management that guarantee a high level of security and performance in SDN-IoT settings.
Live-streaming (LS) e-commerce has become a key sales channel linking manufacturers with end markets, yet live-streaming supply chains (LSS) still suffer from information asymmetry and a lack of credibility in the disclosures. Although AI and blockchain offer potential for real-time, traceable, and interactive information sharing, their impact on disclosure strategies remains underexplored in the field of production operations. This study develops game models for a manufacturer and a live-streaming enterprise (LSE), incorporating rational, risk-averse behaviours under both traditional and AI-blockchain disclosure mechanisms. Through model analysis, optimal strategies from the production stage to the sales stage have been identified. Results reveal three disclosure phases – full disclosure, partial disclosure by LSE only, and partial disclosure by both – while LSE consistently exhibits stronger disclosure willingness. The trust-amplifying effect generated by AI-blockchain technology is non-linear. Adoption of AI-blockchain enhances disclosure and profits when costs are below critical thresholds, whereas traditional strategies offer greater operational robustness under high uncertainty or low willingness. Findings highlight how digital technologies reshape incentives and pricing within SCs, providing practical guidance on information disclosure strategies, technology investment decisions and the improvement of SC operational performance.
Haitham A. Mahmoud, Ahmed Soliman, Mohammed El-Meligy, Azhar Imran · 5 authors
Abstract Modern digital ecosystems rely mostly on blockchain technology, such as decentralized and immutable ledger systems. This technology avails guarantees of secure transaction and data administration in keeping with the privacy of consumers. Thus, the blockchain systems often suffer in resource-constrained environments to experience considerable computational overhead along with low scalability and issues in handling real-time data. To overcome these restrictions, this research incorporates federated learning, decentralized storage using IPFS, and lightweight cryptographic methods to deliver secure, scalable, and real-time analytics in the IoT system. This research has proposed a novel framework based on blockchain, privacy-preserving techniques, and predictive maintenance models to address some of the security, scalability, and reliability challenges observed in IoT ecosystems. The framework guarantees secure data management, efficient real-time analytics, and robust anomaly detection by using the most advanced technologies such as federated learning, decentralized storage, and lightweight cryptographic methods. The suggested technique exceeds traditional methods by means of accuracy and error reduction with the astonishingly low FPV value of 0.005954% and FNR value of 0.000274% while giving extraordinary performance metrics that reach 99.88% accuracy, 99.89% precision, 99.97% recall, and 99.93% F1-score. This solution establishes secure, scalable, and tamper-proof infrastructure for all the applications from industrial automation, healthcare to vehicular networks, hence enabling smart and sustainable IoT governance for these applications.