Blockchain Papers

Venice Lei De Luna Lucto

No abstract is available for this record.

Ibrahim Aqeel

No abstract is available for this record.

Cynthia Jayapal

No abstract is available for this record.

Risqy Pradana Putra, Fara Triadi -, Ahmad Rofiq Hakim

Perkembangan teknologi informasi mendorong penerapan sistem yang lebih efisien dan transparan dalam manajemen gudang. Penelitian ini bertujuan merancang sistem manajemen gudang berbasis QR Code dan Blockchain untuk meningkatkan akurasi, keamanan, dan efisiensi pelacakan barang. Sistem mengintegrasikan mikrokontroler ESP32, modul GM65 barcode scanner, printer thermal, dan UPS sebagai sumber daya mandiri. QR Code digunakan untuk identifikasi dan pelacakan barang secara real-time, sedangkan Blockchain memastikan data transaksi tersimpan secara aman, transparan, dan tidak dapat diubah. Penelitian menggunakan metode Waterfall yang meliputi analisis kebutuhan, perancangan, implementasi, dan pengujian sistem. Hasil pengujian menunjukkan bahwa sistem mampu melakukan pencatatan, pemindaian, dan pembaruan data stok secara real-time dengan tingkat akurasi yang tinggi. Sistem ini memberikan solusi yang efektif untuk meningkatkan efisiensi operasional, keamanan data, dan transparansi dalam pengelolaan gudang berbasis Internet of Things (IoT).

Liang Guo

Game-based teaching (GBT) has gained widespread adoption in modern education, yet teachers bear heavy burdens in designing GBT activities and interpreting student learning performance, while centralized educational data storage brings prominent security and credibility risks. To tackle the above bottlenecks, this paper proposes SmartTA, an integrated teaching assistant system combining GBT recommendation modules, automated machine learning (AutoML), and blockchain. Specifically, SmartTA supplies customized GBT cases and exam scoring suggestions for teachers, and leverages AutoML to automatically mine student learning behaviors with zero coding requirements. Three groups of experiments are conducted to validate the system: AutoML achieves a maximum prediction accuracy of 93% on six public educational datasets; the Hyperledger Fabric-based blockchain prototype enables data insertion with an average latency of approximately 2.2 seconds and query latency of approximately 150 ms; 20 frontline educational practitioners provide 85% positive user feedback. The experimental results suggest that SmartTA may help reduce teachers’ lesson preparation workload, support improved instructional quality, while enabling tamper-resistant data storage via blockchain. This study realizes the practical fusion of AutoML and blockchain for GBT scenarios, and establishes a novel, secure, data-driven teaching assistance paradigm that is accessible to non-technical educators.

X. L. Li, D. H. Chen, Y. F. Liu

In blockchain-enabled supply chain finance, traditional credit risk assessment models suffer from conflicts between data sharing and privacy protection, reliance on static evaluation methods, and limited data credibility. To overcome these challenges, this paper proposes a blockchain-based dynamic credit risk assessment model that integrates privacy computing and intelligent risk monitoring. First, blockchain’s immutability and traceability ensure the authenticity and transparency of supply chain transaction data, effectively mitigating information asymmetry and data tampering. Second, privacy-preserving technologies, including homomorphic encryption based on the Paillier algorithm and zk-SNARKs, enable secure data sharing and validity verification without exposing sensitive enterprise information, thereby improving assessment reliability. Third, a dynamic risk monitoring framework is constructed by combining smart contracts, long short-term memory (LSTM) networks, and an improved dynamic graph neural network (DGNN). LSTM models temporal risk evolution in transaction data, while DGNN captures risk propagation among upstream and downstream enterprises. Smart contracts synchronize transaction states in real time, allowing continuous updates of credit risk levels. The proposed secure information processing and dynamic graph modeling strategy also provides a valuable reference for trustworthy data interaction and intelligent decision-making in distributed electromagnetic sensing and communication networks, where reliable information propagation and adaptive resource management are essential. Experimental results based on a textile supply chain dataset show that the proposed model achieves approximately 94% credit assessment accuracy, outperforming traditional static models by 15%–20%, while maintaining excellent response speed and throughput for dynamic financial decision-making. The proposed framework provides a practical and secure solution for blockchain-based credit risk management and offers methodological insights for data-driven engineering systems requiring secure information fusion and dynamic network analysis.

Dušan Mitrović, Ivan Milenković, Miroslav Minović

The growing use of blockchain in e-commerce has produced hybrid environments in which private enterprise ledgers and public blockchain networks operate side by side. Consequently, efficient and secure interoperability between these networks has become increasingly important. This study presents a cross-chain interoperability framework that links a permissioned Hyperledger Fabric network with a public Ethereum network. The framework provides attestations of selected business events rather than moving assets. An interoperability smart contract on Fabric emits cross-chain events; an off-chain validator enforces uniqueness and replay protection; and a public verification contract on Ethereum records an immutable, publicly verifiable attestation of each event. The framework uses a two-of-three validator threshold to attest events, so safety holds as long as no more than one of the three validators is compromised. The prototype was evaluated by processing 21,000 events across sequential, concurrent, and peak-load workloads. On the local network, message validation averaged approximately 12 ms per event, and the interoperability layer added less than 200 ms of overhead per attestation. Sustained throughput ranged from 13.2 to 14.2 attestations per second, while the validator used approximately 16% mean CPU and less than 194 MiB of memory, with no sustained memory growth during the full experiment. On the Ethereum Sepolia public testnet, 55 transactions were confirmed with a 100% success rate and a mean confirmation time of 10,676.62 ms. Gas consumption stayed stable at about 51,743 gas per verification on the local network and about 189,092 gas on Sepolia, and the mean public testnet transaction cost was 0.000692 Sepolia ETH. Five adversarial tests were conducted, covering replay, forgery, malicious relayers, concurrent replay, and denial-of-service attacks. All five tests passed, including the rejection of 500 concurrent replay attempts with zero double registrations. The results show that the framework provides efficient, verifiable, and replay-resistant cross-chain interoperability suited to hybrid e-commerce ledgers.

Anshit Mittal, Rohit Jha, Jatin Sharma, Pawan Singh Mehra

No abstract is available for this record.

De-Graft Johnson Dei, Karim Awudu

The rapid expansion of institutional repositories (IRs) has heightened concerns about digital rights management (DRM), copyright protection, content authenticity, and long-term digital preservation, particularly in developing countries where institutional and technological capacities remain constrained. This study examines the feasibility of adopting blockchain technology as a DRM solution for Ghanaian institutional repositories and evaluates whether its application is transformative or largely aspirational. Guided by the Technology–Organization–Environment (TOE) framework and Diffusion of Innovations (DOI) theory, the study employed a sequential explanatory mixed-methods design that integrated quantitative survey data with qualitative interviews with ICT directors, repository managers, academic librarians, systems librarians, and faculty members from eight Ghanaian universities. The findings reveal low DRM maturity across institutional repositories. 40% of participating institutions lacked formal DRM mechanisms. Although awareness of blockchain technology was moderately high among respondents, substantial disparities existed across stakeholder groups, with ICT personnel demonstrating higher levels of understanding than faculty members and academic librarians. Institutional readiness for blockchain adoption remained generally poor, constrained by inadequate infrastructure, funding limitations, insufficient technical expertise, weak policy frameworks, and low organizational preparedness. Despite these limitations, stakeholders expressed strong support for blockchain’s potential to strengthen tamper-proof authorship verification, enhance content authenticity and integrity, improve transparency through immutable audit trails, and automate copyright management through smart contracts. The study further suggests that capacity building, phased implementation strategies, open-source platforms, interdisciplinary collaboration, and institutional policy alignment are critical pathways for integrating blockchain into institutional repositories. The study concludes that blockchain-enabled DRM in Ghanaian IRs is a promising, emerging innovation and that its successful implementation depends on sustained investment in digital infrastructure, institutional reforms, technical training, and supportive regulatory frameworks.

Mihai Duguleană, Gheorghe-Daniel Voinea

No abstract is available for this record.

Sangeetha Nagamani, Annamalai Selvarajan, Raghini Mohan, John Peter Vincent Paul · 6 authors

Secure and transparent water resource management is made possible by blockchain technology and its characteristics such as decentralisation, immutable records, and smart contracts. Tracking water consumption in real time is made possible by collaborating blockchain technology with Internet of Things–based sensors, authenticating data integrity and transparency in fetching records. Blockchain ensures the tamper-proof sustainability of water quality data for pollution prevention. It helps to secure real-time monitoring data permanently, including pH, turbidity, and pollutant levels, which enables regulators to address quality issues. The automation of water rights and allocation transactions in the water trade sector can be processed by blockchain-based smart contracts, which reduce transaction costs and administrative burdens. Water billing, permit issuing, and right transfers, which are part of water trading practices, are guaranteed by digital agreements. As a result, a peer-to-peer water auction can operate with reduced latency and robust fraud prevention. Hence, blockchain applications in water resource management remarkably enhance security, transparency, and efficiency for tracking, controlling pollution, and providing fair water trading through smart contracts.

Wiranto, Faisar Ananda, Heri Firmansyah

The rapid development of blockchain technology has introduced new forms of digital assets, including Non-Fungible Tokens (NFTs) and metaverse virtual land, creating legal uncertainty regarding their status as inheritable property under Indonesian Islamic Family Law. This study examines the legal status of these digital assets as inheritance objects, analyzes their distribution based on fiqh al-mawārīth and Indonesian positive law, and proposes a legal framework to strengthen legal certainty in digital inheritance. This research employs a normative legal method using statutory, conceptual, and Islamic jurisprudential approaches. Legal materials were analyzed through descriptive and deductive legal reasoning. The findings demonstrate that NFTs and metaverse virtual land satisfy the Islamic legal characteristics of māl because they possess lawful ownership, measurable economic value, legal control, and transferability, thereby qualifying as al-tirkah (inheritance estate). Their distribution should follow the principles of fiqh al-mawārīth while accommodating the technical characteristics of blockchain-based assets, particularly digital wallets and private-key access. The study also identifies a regulatory gap in Indonesian positive law concerning digital asset inheritance. Unlike previous studies that primarily discuss digital assets from commercial or general legal perspectives, this research develops an integrated framework combining Islamic inheritance law, Indonesian positive law, and digital estate planning to strengthen legal certainty, protect heirs' rights, and contribute to the development of Islamic Family Law in the digital era.

THOMAZ HENRIQUE VIARO BRIDI, Mayara Neves Pohlmann, Cecília Maria Villas Boas de Almeida, Rodrigo Franco Gonçalves

No abstract is available for this record.

Hebat Allah Adel, sayed abdelgaber, Wessam H. El-Behaidy

Ensuring transparency and security in digital recruitment systems remains a critical challenge. This study proposes BC-XAIA, a unified framework that integrates blockchain, smart contracts, explainable artificial intelligence (XAI), and agile methodology to enable consistent, secure, and traceable recruitment decision-making. Smart contracts, implemented in Solidity and deployed using the Remix Ethereum IDE, automate key processes such as identity verification, data access control, and behavior monitoring, reducing reliance on centralized intermediaries. To support intelligent decision-making, multiple machine learning models, including Random Forest, Logistic Regression, and Support Vector Machine (SVM), were trained and evaluated on a recruitment dataset, with Random Forest achieving the highest performance, reaching an accuracy of 93%. To enhance transparency, SHAP and LIME were employed to provide both global and local interpretability of model predictions. Furthermore, agile methodology is embedded to drive continuous adaptation, iterative development, and stakeholder feedback throughout the recruitment lifecycle. Unlike existing recruitment systems that treat blockchain, AI, and explainability separately, BC-XAIA unifies these technologies within an agile and decentralized architecture. Overall, BC-XAIA establishes a secure, transparent, and explainable decentralized recruitment ecosystem that enhances trust, fairness, and intelligent decision-making in next-generation HR systems.

Tsetsegjargal Ulambayar, Oyunbileg Pagjii, Oyuntsetseg Luvsandash, Gantulga Garamdorj · 5 authors

Abstract Machine learning models for audit anomaly detection are commonly evaluated using proprietary or synthetic datasets, with limited validation against official audit outcomes. This study proposes a four-layer ML framework designed to reduce audit detection risk and evaluates its performance on 3,329,189 general ledger transactions from three consecutive fiscal years (FY2023 to FY2025) of a Mongolian public sector energy utility. The dataset comprises 9,909 account rows and a cumulative debit flow of MNT 16.34 trillion. The proposed framework integrates unsupervised ensemble labeling through Isolation Forest, Z-score analysis, and debit-credit ratio screening, followed by supervised classification with Random Forest, Gradient Boosting, and Decision Tree models. An explainable AI layer maps SHAP feature attributions to specific ISA requirements. Against a simulated 20% MUS baseline, Random Forest achieves F1 = 0.966, AUC = 0.999, and Detection Risk = 2.01%, compared to MUS Detection Risk of 38.05% to 52.7%. McNemar’s test confirms statistically significant superiority (χ² = 1,666.63, p

Jaume Martin Bosch, Marco Combetto, Luca Tangi, A. Paula Rodriguez Müller

Introduction Blockchain technology (BCT) has been widely discussed as a potentially valuable technology for advancing sustainable development in the public sector. Its core features, including transparency, immutability and decentralisation, may contribute to more accountable, efficient and inclusive public services. However, limited empirical evidence exists on how BCT-based public sector initiatives align with the United Nations Sustainable Development Goals (SDGs). Methods This study examines 306 public sector BCT-based use cases across the EU, compiled by the Public Sector Tech Watch observatory. We apply a GPT-4o-based AI text classification pipeline to assess the degree of alignment between project descriptions and the 17 SDGs. The pipeline combines refined SDG descriptors, structured prompting and documented model parameters. Its outputs are benchmarked against a human-coded subset to assess validity. Results The results show strong alignment with SDG 9 (Industry, Innovation and Infrastructure) and SDG 17 (Partnerships for the Goals), followed by more moderate alignment with SDG 8 (Decent Work and Economic Growth). By contrast, goals such as SDG 2, SDG 6 and SDG 14 remain weakly represented. These findings provide an empirical overview of how BCT applications in EU public administrations are framed in relation to the SDGs. Discussion By highlighting patterns of alignment between BCT adoption and the SDGs, this study offers evidence to inform policymakers, practitioners and future research on sustainability-oriented public sector innovation. It also demonstrates the value of AI-assisted classification for mapping large corpora of digital government initiatives, while recognising that the results capture stated or perceived alignment rather than verified sustainability impacts.

Houda BenMabrouk, Safa Boukadida, Khaled Guesmi

Purpose The study investigates the effect of investor fear on cryptocurrency crash risk, with emphasis on overall market sentiment and COVID-19-related fear. It also evaluates the relative performance of Google search-based measures compared to the economic policy uncertainty (EPU) index and the volatility indexes (VIX) as benchmark indicators of uncertainty. Design/methodology/approach This study employs a quantitative empirical approach to examine the impact of investor fear on cryptocurrency price crash risk. Investor sentiment is proxied using the FEARS index derived from Google search volumes and the coronavirus fear index. Crash risk is measured using negative conditional skewness of weekly returns and down-to-up volatility. The analysis is based on weekly data for the top 10 cryptocurrencies from August 2010 to October 2021. Regression models are used to examine the relationship between investor fear and crash risk and to compare the explanatory power of Google-based fear indicators with traditional uncertainty measures. Findings The results show that investor fear significantly increases the risk, while COVID-19-related fear further intensifies this effect, highlighting the vulnerability of crypto markets during periods of heightened uncertainty. Moreover, Google-based fear indicators outperform the EPU index and the VIX in explaining and predicting crash risk. Overall, the findings suggest that investor attention and sentiment are more powerful drivers of cryptocurrency crash risk than traditional volatility-based measures. Originality/value This study links investor fear, including COVID-19 sentiment, to cryptocurrency crash risk and finds that Google-based fear indicators outperform traditional measures like the EPU index and the VIX in predicting market downturns.

Nidhi Maheshwari, Sanjeev Malhotra

Purpose This study aims to examine how digital cultural values, collaboration, innovation and customer-centricity enable successful technological adoption in the banking sector's digital transformation journey. It explores how emerging technologies such as artificial intelligence (AI), machine learning (ML), blockchain and metaverse-based interfaces are integrated to enhance customer experience and operational efficiency, with emphasis on the role of shared values in shaping strategy, leadership and organizational readiness. Design/methodology/approach A qualitative, case-based exploratory design is adopted. Data were collected through semi-structured interviews with senior managers across strategy, innovation, technology and customer experience functions. These were supplemented with secondary sources, including policy documents, digital strategy reports and industry analyses. Thematic analysis was used to identify cultural patterns and organizational factors influencing digital adoption in a regulated banking context. Findings The findings show that digital cultural values are critical enablers of successful technological adoption. Collaboration enhances cross-functional coordination and accelerates integration of emerging technologies. Innovation fosters experimentation and openness to AI, ML and immersive tools. Customer-centricity ensures that digital investments improve accessibility, transparency and service quality. Collectively, these values strengthen adaptability, operational efficiency and ecosystem integration, highlighting that cultural alignment is as important as technological capability in digital transformation. Originality/value The study positions digital cultural values as central enablers of technology adoption, extending digital transformation literature beyond technological capability perspectives. It contributes to theory by showing how shared values mediate the relationship between emerging technologies and service transformation in regulated banking environments. Practically, it offers guidance for building culturally aligned digital strategies that improve adoption, trust and customer experience.

Daniel Bilar

Legal systems governed by rule of law are, structurally, rule systems. Like any rule system, they contain gaps between specification and intent, concentrated in the deliberately under-specified provisions that legal philosophers call "open texture." Those gaps have always been exploitable, but exploitation was rate-limited by the cost of legal expertise and the size of the corpus to be searched. That rate-limit is now collapsing. This paper introduces the governance patch-gap: the ratio between the rate at which AI accelerates the discovery of exploitable legal ambiguities and the rate at which legislatures, courts, and treaty bodies can repair them. Using the Highly Optimized Tolerance (HOT) framework from complex-systems theory, we map legal systems onto designed artifacts whose optimization against anticipated disputes concentrates fragility at the boundaries of the specification. We define the patch-gap as a ratio of discovery rate to repair rate, identify a threat taxonomy (corporate optimizer, state actor, misaligned autonomous agent), distinguish exploit discovery from exploit execution as separate governance problems, and examine three defensive strategies and the structural limits that prevent any defense from closing the gap entirely. The paper closes with three falsifiable predictions for 2027 to 2028. TL;DR summaries (five audiences) For the SME (legal theory / AI safety / complexity). Legal systems are HOT artifacts: drafters optimize against anticipated disputes, so residual fragility concentrates in Hart's penumbra (open texture), not in the core. The paper's object is a rate ratio G = $R_d/R_p$ and a stock S with $dS/dt$ = $R_d − R_p$; G is a definition, not a fitted dynamical model. Regime labels (G ≈ 2, 10², 10³+) are heuristics. SocioHack is an unreplicated sandbox (κ = 0.55); A1/VERITE is 36 already-vulnerable contracts. Rice / FLP / attestation in §6.4 are analogical extensions, not a derivation that courts instantiate those models. The load-bearing claim that survives if SocioHack fails is the work-factor collapse in adjacent formal systems plus the discovery/execution split. For the practitioner (counsel / CISO / compliance). Treat "AI found a loophole" and "an agent filed on it" as different problems. Discovery is a tool-governance issue (access, disclosure, audit of comment corpora). Execution is an agency-and-liability issue (who is the principal; human-in-the-loop above a dollar / classification / cross-border threshold). Disclosure mandates reach corporate repeat players and miss unsupervised agents. Do not spend the policy budget on formalizing "reasonable" or "public interest"; Catala-class work shrinks the core, not the penumbra. Immediate moves: require AI-use disclosure in filings and litigation; log agent actions that change regulatory classification. For the lay person. Laws have always had gray zones on purpose; words like "reasonable" so judges can handle new cases. Finding those gray zones used to be slow and expensive (years of lawyers). AI can search the whole tax code and regulation pile cheaply and flag gaps nobody has noticed. Passing a fix still takes months to years. The paper names that mismatch the governance patch-gap: machines find holes faster than legislatures and courts can close them. The holes were always there. What changed is the cost to find them. For the decision-maker (executive / funder / board). This is not a model-refusal problem and will not be closed by a better system prompt or a voluntary commitment letter. The asset at risk is the stock of known-but-unpatched legal ambiguities, which grows whenever discovery outruns repair. Adjacent formal systems (smart-contract exploit agents at USD 0.01 – USD 3.59 / attempt; attacker break-even ~USD 6k vs defender ~USD 60k) already show the cost collapse. Do not wait for SocioHack to replicate before treating discovery-versus-execution as two budget lines. Near-term: rate-limit execution (human-in-the-loop, disclosure). Do not buy "formally verified law" as a complete close. For governance (legislatures / agencies / treaty bodies). Every new AI rule written in open-textured natural language is another search surface. The EU AI Act Art. 6 "significant risk to fundamental rights" is the same kind of term as "undue burden." Three defenses, all bounded: (1) AI red-team of draft text before enactment .. useful, not exhaustive; (2) formal methods core only; (3) rate-limits buy time, do not close G. Conflating corporate optimizers, state arbitrage, and unsupervised agents produces the wrong instrument. The paper's falsifiers are public: AI-authored substantive rulemaking comments by end-2027; an attributed in-production exploit by end-2027; two governments or the EU publishing legislative red-team reports by mid-2028. Non-claims. G is a definition, not a fitted dynamical model. Regime magnitudes are order-of-magnitude heuristics. The SocioHack result is an unreplicated preprint treated as suggestive. Rice / FLP / attestation are analogical extensions, not a formal derivation that legal institutions instantiate those models. v1.1. Adds §4.5, an illustrative software companion (concept 10.5281/zenodo.21918091): a toy that generates Rd; G and the stocks are outputs, not legal measurements. No figures in the PDF.

George Nana Agyekum Donkor, John Kwaku Darko Okrah, Dimy Doresca

This chapter discusses the overview of Small and Medium Enterprises (SME) development, conventional approaches to financing SMEs, challenges of SME financing, and the changing landscape of SME financing in Africa. SMEs generate about 90 per cent of economic activity in Africa, but their structure and informality exclude them from formal value chains, markets, and access to bank credit. They benefit from conventional finance like government credit programmes and factoring that complement formal finance like commercial bank credit, microfinance, venture capital (VC), and specialised stock markets. However, persistent challenges limit funding for small businesses because of structural and institutional barriers, information asymmetry and transparency gaps, low managerial capacity, high transaction and screening costs, and the government crowding-out effect. Innovative models like digital finance, impact investing, fintech, crowdfunding, cryptocurrency, blended finance, supply-chain finance (SCF), and development finance institutions (DFIs) are also helping SMEs bypass credit market shortfalls to access capital. This chapter provides relevant recommendations for governments, policymakers, and key stakeholders.

Marco Dautaj, Jinhua Xiao, Mónica Rossi, Satoru Goto · 5 authors

Industry 5.0 emphasises human-centric technologies (HCTs) as essential drivers of sustainable and resilient production. However, their specific contributions to Circular Economy (CE) strategies and the associated skill requirements are not well-defined. This paper investigates how HCTs support Circular Economy practices (CEPs) and which skills and competencies are needed for their effective implementation. A systematic literature review was conducted using Scopus and Web of Science, following established guidelines. The search employed a string that links Industry 5.0, human-centricity, and the 10 R framework of CE. After a multi-stage screening and snowballing process, 41 peer-reviewed contributions published between 2015 and 2025 were selected for analysis through a combination of bibliometric and qualitative content analysis. The review maps the main HCTs, such as AI, digital twin, XR, robotics, blockchain, and IoT, to CEPs and specific 10 R strategies. It identifies seven clusters of skills ranging from analytical and decision-making abilities to human-machine collaboration, CE-specific expertise, and green human resource management practices. A Sankey diagram visualises the primary linkages between technology and strategy. Then, the authors developed a framework (TSC framework) that links skill clusters, CE practices, and enabling technologies and validated it through an illustrative case study. Interpreting the findings through the Resource-Based View, the paper argues that value arises from socio-technical bundles that integrate technologies, circular practices, and human capabilities. The study concludes with implications for policymakers, educators, and practitioners and outlines potential avenues for future research on skills for human-centred circularity.

Murat Bayram, Florian Struck

No abstract is available for this record.

Jana Nemcova, Ľudomír Šlahor

No abstract is available for this record.

Narendra Kumar Dewangan, Padmavati Shrivastava

This chapter presents the concept of a Smart Rice Mill as an intelligent, connected, automated, and traceable rice-processing ecosystem. It integrates IoT sensors, computer vision, deep learning, Edge AI, cloud analytics, predictive maintenance, intelligent control, and blockchain to improve rice-processing operations. The chapter discusses automated grain inspection, variety classification, defect detection, broken-rice estimation, milling-quality prediction, machine monitoring, process optimization, and digital recording of batch history. It also examines implementation challenges involving legacy machinery, hardware and sensor reliability, cybersecurity, staff training, integration, and economic feasibility. The proposed future direction is a closed-loop Smart Rice Mill capable of sensing paddy and machine conditions, predicting quality, adjusting processing parameters, verifying output, and maintaining complete traceability.