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.
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.
Short Summary - Current Internet protocols move, encrypt, authenticate, delegate, and record data—but they never answer one question: was this specific machine-generated act authorised to become real? This article proposes an execution-finality layer between computation and consequence for AI, cloud, telecom, payments, and critical infrastructure. The internet solved transport, secrecy, identity, delegation, and record-keeping. TCP/IP moves the data. TLS and HTTPS protect the channel and authenticate the endpoint. OAuth delegates access. EMV validates the payment credential. Distributed ledgers order and record the event. Every one of these remains essential. None of them answers the question that now matters most: Was the specific act represented by this data authorised to become externally effective? A packet can be delivered perfectly. A channel can be encrypted flawlessly. An endpoint can be genuine. A token can be valid. A cryptogram can verify. A transaction can be recorded. And still — none of that proves that an AI-generated command, a data export, a telecom transmission, a payment, an infrastructure change, a satellite instruction, a database write, or a physical actuation was ever authorised to cross from computation into consequence. WE BUILT OUR SAFEGUARDS FOR HUMAN TIME. MACHINES NO LONGER RUN ON IT. Earlier digital systems lived inside human reaction time. A suspicious payment could be reviewed. A wrongful disclosure could be investigated. Access could be revoked. A harmful output could be pulled down. AI-native infrastructure does not grant that luxury. A modern AI system can call tools, invoke APIs, export files, initiate payments, rewrite databases, reconfigure networks, drive machines, issue telecom commands, and trigger downstream workflows in milliseconds. By the time a log is read, the data has left the jurisdiction. The payment has settled. The command has executed. The infrastructure state has already changed. So the real problem is no longer detection. The real problem is this: Can the system stop the act from becoming effective before validation is complete? Post-event logging is evidence. Evidence is not prevention. THE LAYER THAT WAS NEVER BUILT The disclosed architecture introduces an execution-finality layer between computation and consequence. It replaces nothing. TCP/IP, TLS, HTTPS, OAuth, EMV, identity systems, policy engines, and ledgers all continue to do exactly what they do today. It adds the one technical condition none of them supply: A computational result does not become externally effective merely because a machine generated, signed, routed, or prepared it. An AI model, telecom function, cloud workload, payment system, satellite controller, application, or autonomous device may generate a proposed operation. The architecture treats that operation as a Candidate Act, held in a non-effective state. A Candidate Act may be an AI output, packet, tensor, API call, payment instruction, file export, storage write, model-memory update, telecom transmission, rendering event, actuator command, or any other consequential operation. Before that act can become real, a protected hardware or cryptographically isolated domain validates the required conditions — which may include authority, purpose, consent, jurisdiction, destination, revocation status, policy epoch, runtime integrity, freshness, quota, protected state, and the identity of the intended effectuation boundary. Only on success is protected evidence committed and a narrowly scoped, non-bearer capability released — bound to that particular act, scope, protected state, evidence, destination, and applicable Finality Sink. THE FINALITY SINK: WHERE COMPUTATION BECOMES CONSEQUENCE The Finality Sink is the precise point at which an act would first become externally effective — a model-output emitter, API dispatcher, telecom gateway, radio chain, SmartNIC, DPU, payment terminal, ledger bridge, memory controller, storage writer, renderer, satellite-command interface, or physical actuator. The Finality Sink verifies the capability before permitting release. Verification fails → the act remains non-effective. Verification succeeds → the capability is consumed before or atomically with effectuation, reducing replay, substitution, duplicate execution, and cross-sink misuse. WHY THIS IS NOT "BETTER SECURITY" Conventional systems place checks around an execution path. The application, model server, network function, or payment system typically retains the technical ability to complete the act anyway. This architecture removes that ability. The ordinary compute environment may calculate or prepare the act — but it does not independently hold the final authority to make the act effective. Authority is separated from computation, and verified again at the consequence boundary. Stated in one line each: Layer Question it answers TCP/IP How is information transported? TLS / HTTPS Is the channel protected? OAuth Who may delegate access? EMV Is the payment credential valid? Ledgers What happened, and in what order? Execution Finality May this specific act become real? The contribution is not another policy engine, authentication scheme, audit system, or cryptographic token. It is a structural dependency: protected validation becomes a technical precondition of effectuation. ONE GAP. EVERY INDUSTRY. The computation-to-consequence gap is not an AI problem. It is an infrastructure problem that appears wherever machines act faster than institutions can respond. Artificial intelligence — model outputs, tool calls, agent actions, code execution, data exports, memory writes, retrieval operations, autonomous workflows. Telecommunications and 5G/6G — packet forwarding, network slicing, roaming, radio emission, gateway egress, satellite communications, non-terrestrial networks, machine-to-machine commands. Cloud and data-centre infrastructure — CPUs, GPUs, AI accelerators, memory controllers, DMA engines, SmartNICs, DPUs, storage controllers, accelerator-interconnect boundaries. Financial systems — payment finality, account transfers, settlement, digital assets, CBDCs, ledger commitments, trading instructions. And beyond — data sovereignty, cross-border data use, industrial control, robotics, vehicles, healthcare infrastructure, energy systems, digital twins, content publication, cybersecurity response, critical infrastructure. Critically, the architecture supports jurisdictional and enterprise control without blanket data localisation and without duplicating national infrastructure. Computation may remain distributed and interoperable; only the authority to produce an external consequence stays protected. 8,598 PAGES. YOU ONLY NEED THREE STEPS. Readers are not expected to work through the specification sequentially. 1. Start with the short invention summary.It covers the Candidate Act, non-effective state, Protected Enforcement Domain, validation evidence, scoped capability, Finality Sink, the difference from conventional systems, the novelty position, and industrial applicability. 2. Download the navigation file.It explains the common inventive concept and routes you to the industry-specific embodiments relevant to AI, telecom, satellites, payments, cloud infrastructure, or cybersecurity. The industry mapping sits at approximately pages 57–61 of the main disclosure. 3. Download the main specification — and go straight to your embodiment.The length reflects the number of implementation environments, effectuation boundaries, hardware arrangements, failure states, and anti-bypass variants. It is not one example repeated 8,598 times. THE ONE SENTENCE THAT HOLDS THROUGHOUT A machine may compute, prepare, or propose an act — but computation alone does not create the authority to make that act externally effective
Artificial Intelligence (AI) has transformed the fintech services and created a robust business canvas elevating various personalized and automated services with lightning speed satisfying ever changing needs of a person and market. AI triggered financial models and Chatbots are changing the investment environment in India. The quantum computing enabled with the AI is able to design tailor made risk management models in the financial services front. On the other hand, AI is empowering the fintech models face the challenges of frauds and cybercrimes. Automated documents and know your customer verifications, e signatures, faster clearings are some of the inventions in the Fintech arena supported by AI driven technologies. Credit score calculations and data maintenance of customers, identification of risks in finance and credit portfolios are other tools minimizing the frauds in lending portfolios of the banking and non-banking institutions. Market, investors behaviours, funds-flow trends’ analysis are other important operational efficiency tools that the bankers enjoying. The decentralized platforms, mechanized and smarter modes of financial services designed by the technology are contributing to the growth of the financial services including the insurance, capital markets. Banking services blessed with technological inventions are transforming the traditional banking into new-age businesses by reducing the operational cost and minimized operational time. The digital payments, real time credit of cheques, UPI payments are contributing for secured transactions, faster mode of authentications, encryptions and many more. The dependence on natural human resources is becoming less even after with expanding base of customers and variety of financial services. Anytime, anywhere banking models, digital platforms, and digital channels dedicated for the financial transactions are other contributions of the technological developments. The Fintech is witnessing a remarkable transformation with the induction of AI and other technologies into designing, operational and distributive models of financial products. Inventions in this field of financial sector are continuous. Emerging computing technologies are adding value to fintech facilitating faster developments in the services sector and contributing the growth of the economy.
Open access
FinTech, Crowdfunding, Digital Finance
Innovations and Analysis in Business and Education
Abstract Artificial Intelligence (AI) has revolutionized supply chain management by improving decision-making, sustainability, and operational efficiency. Startups in sustainable agriculture are depending more and more on AI-powered technology to boost traceability throughout the agricultural value chain, optimize output, cut waste, and enhance logistics. Businesses have been prompted to include intelligent supply chain systems that reduce environmental impacts while guaranteeing product quality and transparency due to the increased consumer demand for environmentally friendly products. By analyzing recent research, identifying AI applications, talking about implementation issues, and putting forth a conceptual framework for sustainable AI-driven supply chains, this paper investigates the role of AI in supply chain management for eco-friendly products and sustainable agriculture startups. Using a methodical approach to literature research, the study synthesizes information from international organizations, industry publications, and peer-reviewed journals. Demand forecasting, precision agriculture, inventory optimization, cold-chain monitoring, transportation efficiency, blockchain-enabled traceability, and circular economy practices are all greatly improved by AI, according to the results. But obstacles including high implementation costs, inadequate digital infrastructure, cybersecurity issues, and a lack of skilled workers continue to pose serious problems for companies. In order to promote social responsibility, economic viability, and environmental sustainability, the paper suggests an integrated AI-enabled sustainable supply chain framework. Keywords: Artificial Intelligence, Sustainable Agriculture, Supply Chain Management, Eco-Friendly Products, Agriculture Startups, Green Supply Chain, Machine Learning, Blockchain.
Web3, the World Wide Web's third generation, is full of decentralization and blockchain technology. Artificial intelligence, otherwise known as AI, has the power to transform society. Put them together, and the world as it's currently known will be technologically revolutionized. Full article: https://davidohnstad.net/ai-and-web3-products/
This study investigates the theoretical foundations, practical applications, and optimization strategies of financial big data analysis and network security optimization in support of Sustainable Development Goals (SDGs). A comprehensive framework is developed to integrate sustainable financial management, environmental cost-benefit analysis, socially responsible investment decision-making, and sustainable supply chain management. The study further proposes a network security optimization architecture incorporating multi-level data encryption, access control, real-time threat monitoring, intelligent defense mechanisms, and blockchain-based data protection. The proposed framework is particularly applicable to communication-intensive environments, including wireless communication infrastructures and antenna-supported information transmission networks, where secure and reliable financial data exchange is essential. Experimental analyses demonstrate that the integration of financial big data technologies and network security mechanisms enhances data protection, operational efficiency, and sustainable decision-making capabilities. The results provide a practical reference for secure financial data governance and sustainable development in complex digital and communication-oriented systems.
Abstract This study explores the role of Artificial Intelligence (AI) in transforming agricultural supply chain management in Bangladesh through a systematic comparative analysis of existing literature, institutional reports, and global case studies. AI technologies including predictive analytics, machine learning, blockchain, and precision agriculture are examined for their potential to address longstanding inefficiencies in Bangladesh’s agri-supply chain. The study finds that AI-driven demand forecasting models using LSTM and ARIMA achieved 89–92% crop yield prediction accuracy, representing a 37% improvement over traditional methods. Smart warehousing systems reduced operational costs by 25% and increased order processing speed by 40%, while blockchain integration cut payment cycles from 15 days to 2.3 days and increased smallholder farmer incomes by 22–25%. Precision agriculture technologies achieved 25% yield growth with 15–20% water savings and 30% fertilizer efficiency gains. Despite these promising outcomes, Bangladesh’s AI adoption rate remains at only 18%, significantly behind India (35%) and Vietnam (28%), primarily due to insufficient infrastructure, lack of digital literacy, and high implementation costs. The study proposes targeted policy interventions including IoT subsidies, farmer training programs, and public-private partnerships to enable inclusive and sustainable AI integration across Bangladesh’s agricultural sector.
Abstract The exponential growth of IoT networks has made the supply of trustworthy digital IDs more important than ever. Limitations in scalability, transparency, and resilience to single points of failure are some of the inherent issues with modern centralised identity management systems. These problems are exacerbated in distributed IoT systems since there is no central authority to rely on for communication and trust establishment among the many devices and various parties involved. By introducing SecureChain-ID, a system that uses blockchain technology to provide distributed identity issuance, authentication, and lifecycle management, this article aims to solve the restrictions that now exist. For resource-constrained Internet of Things (IoT) devices, the system's use of elliptic curve cryptography (ECC) provides digital signatures that are both lightweight and efficient. Credentials may be verified with zero-knowledge proofs (ZKPs) as they do not divulge any personally identifiable information. The blockchain's smart contracts streamline the authentication and registration processes, enabling the widespread agreement on the smooth addition of new administrators and devices. The persistent documentation of all identity-related transactions ensures auditability and safeguards against manipulation. This includes updates, revocations, and access events. Unlike conventional approaches, SecureChain-ID establishes network-wide accountability and paves the way for decentralized governance to be aligned with administrative control, enabling flexible permissioning. Many Internet of Things (IoT) systems can benefit greatly from the proposed approach due to its emphasis on data security, low trust assumptions, and interoperability. This research successfully bridges the conceptual gaps between blockchain, identity, and Internet of Things (IoT) systems by introducing an efficient design that improves future cyber-physical systems' secure identification infrastructure.