Md. Nahidul Islam Opu, Md Shahidul Islam, Sara Rouhani, Shaiful Chowdhury
Blockchain-based software systems are increasingly deployed across diverse domains, yet a systematic understanding of their development challenges remains limited. This paper presents a large-scale empirical study of 497,742 issues mined from 1,209 open-source blockchain projects hosted on GitHub. Employing BERTopic, a transformer-based topic modeling technique, we identify 49 distinct issue topics and organize them hierarchically into 11 major subcategories. Our analysis reveals that both general software development issues and blockchain-specific concerns are nearly equally represented, with Wallet Management and UI Enhancement emerging as the most prominent topics. We further examine the temporal evolution of issue categories and resolution times, finding that Wallet issues not only dominate in frequency but also exhibit the longest resolution time. Conversely, Mechanisms issues are resolved significantly faster. Issue frequency surged after 2016 with the rise of Ethereum and decentralized applications, but started declining after 2022. These findings enhance our understanding of blockchain software maintenance, informing the development of specialized tools and practices to improve robustness and maintainability.
This paper examines the challenges hindering rooftop solar (RTS) deployment in India and explores how a U.S.-India partnership can unlock its full potential. RTS, a decentralized and scalable renewable energy solution, is critical to India’s renewable energy goals, including its target of 40 GW of RTS capacity under the National Solar Mission by 2022 which remains unmet with only 10.9 GW installed as of 2023. Key barriers include high upfront costs, limited financing options, policy fragmentation, and resistance from distribution companies (DISCOMs). The paper identifies actionable solutions, leveraging U.S. expertise in innovative financing models, net metering policies, and grid modernization. Collaborative initiatives, such as USAID-backed loan guarantees and green bonds, could alleviate financing gaps, while technical assistance from the U.S. can support DISCOMs in integrating distributed solar. The paper also emphasizes the need for performance-based incentives, consumer awareness programs, and community solar models to accelerate adoption. A successful U.S.-India collaboration can address systemic challenges, enhance RTS scalability, and contribute to global climate goals. By leveraging shared expertise and resources, this partnership offers a pathway to strengthen India’s renewable energy transition while fostering bilateral cooperation in clean energy development.
Jonas Ballweg, Amir Kafshdar Goharshady, Zhaorun Lin
We consider the classical problem of running a decentralized and trustless auction, using a smart contract, on a programmable block-chain such as Ethereum. In our setting, there are n bidders who have paid a deposit to join the protocol. Each bidder i can make a bid 1 ≤ bi ≤ m and our goal is to find the highest bid (maxi bi) and its corresponding bidder (argmaxi bi) in a publicly-verifiable manner. Each bidder must be unaware of others' bids when making their own and should not be able to change their bid after having committed to it. Additionally, and most importantly, we aim to provide privacy to the losing bidders, ensuring that their bids remain undisclosed. This is particularly crucial in use-cases with repeated auctions in which knowledge of the bids in the previous auctions can affect the bidders' strategies. Formally, the information gained by any observer, whether a participant in the protocol or not, should precisely consist of the winning bid and its bidder and nothing more. We present a novel yet simple protocol for private sealed-bid auctions on the blockchain. Our protocol is decentralized and trustless. It is also both time- and gas-efficient. Our approach takes O(log m) time and costs O(log m) units of gas for each bidder. It also guarantees observational determinism with respect to all losing bids.
Weiming Xie, Zhaomin Yao, Xiaozhou Bai, Lang Mai · 10 authors
Nowadays, the global digital transformation of healthcare is advancing rapidly with the help of technologies such as electronic medical records, telemedicine, and mobile medical applications. However, there are still challenges in EMR interoperability, security, and data exchange. To address these existing limitations, This study proposes a voice electronic medical record system driven by artificial intelligence and blockchain, which is designed to improve clinical records and nursing coordination. This system adopts a dedicated deep learning architecture. It transcribe the conversations between doctors and patients into text, and then uses natural language processing to extract the relevant medical information. At the same time, it also provides diagnostic prompts, which can reduce the risk of misdiagnosis. Doctors can view and edit these summaries generated by artificial intelligence. Then safely record them on the decentralized crypto blockchain ledger. With federated learning, the model can be continuously improved in multiple centers without infringing on data privacy. This solution integrates automatic speech recognition, distributed ledger technology, and collaborative deep learning, aiming to enhance the EMR efficiency, security, data integrity, and care continuity of medical institutions. The combination of blockchain technology and artificial intelligence technology holds great potential. It can transform fragmented health data into portable and interoperable records under patient control, thus bringing strategic advantages to the health system that is undergoing a comprehensive digital transformation.
Bu çalışmada ortaya ilk çıkarılan on kripto para getiri ve işlem hacimleri ile birlikte varil başına Batı Teksas (WTI) ham petrol getirileri arasındaki ilişki test edilmiştir. Analiz için 29 Nisan 2013 – 04 Ağustos 2024 arası günlük veriler kullanılmıştır. Çalışmada ampirik olarak Granger ve Toda Yamamoto Nedensellik Analizi' nden yararlanılmıştır. Her iki analize göre WTI ile Bitcoin (BTC) arasında negatif tek yönlü ilişkiye rastlanmıştır. Granger nedensellik analizine göre WTI ile Ethereum (ETH) arasında, Toda Yamamoto nedensellik analizine göre ise WTI ile Filecoin (FIL) getirisi arasında negatif çift yönlü bir nedensellik ilişkisi olduğu sonucuna ulaşılmıştır. Elde edilen bulgular enerji fiyatlarında yaşanan dalgalanmaların küresel finansal istikrara etkilerini ortaya koymuştur. Enerji piyasalarındaki sürdürülebilirlik hedefleri ile blok zinciri teknolojisinin çevresel etkilerini en aza indirgemek için uluslararası regülasyonların geliştirilmesi ve bütüncül politikalar oluşturulması gerekmektedir. Bu öneriler kripto para birimlerinin, enerji piyasalarından kaynaklanan volatiliteye karşı daha dayanıklı hale getirilmesi için stratejik bir yol haritası sunmaktadır.
This study investigates the application of blockchain technology in cross-regional agricultural product traceability platforms to address challenges such as information silos, data tampering risks, and inefficient regulatory coordination in traditional systems. Using blockchain's distributed ledger, smart contracts, and consensus algorithms, a framework is proposed to boost transparency, collaboration, and data security. Pilot case analysis in East China shows production data completeness improved from 72.4% to 98.6%, logistics real-time update rate reached 95.3%, cross-regional collaboration time reduced by 81.3%, data tampering incidents dropped by 96.7%, and quality safety incidents fell by 77.9%. The integration of cross-chain technology, multi-center collaboration mechanisms, and privacy protection techniques further ensures end-to-end traceability and stakeholder trust. Results highlight blockchain's potential to foster a secure, transparent, and efficient agricultural supply chain for cross-regional circulation.
Abstract This thesis explores the growing influence of cryptocurrencies on global financial markets and traditional banking institutions. As decentralized digital assets challenge the centralization and regulatory mechanisms of conventional finance, the research analyzes shifts in investment behaviors, market volatility, regulatory responses, and the evolving role of banks. The study employs a mixed-methods approach, combining quantitative data analysis and qualitative interviews to understand the transformative potential of cryptocurrencies. Key findings suggest cryptocurrencies are reshaping financial intermediation, promoting financial inclusion, but also introducing systemic risks due to their volatility and lack of regulatory oversight.
The rise of cryptocurrencies over the past decade has transformed the global financial landscape, introducing new paradigms in investment, value storage, and monetary exchange. This study investigates the role of cryptocurrencies—specifically Bitcoin (BTC) and Ethereum (ETH)—as alternative investment assets within modern portfolio frameworks. As digital currencies continue to gain legitimacy and acceptance among retail and institutional investors, it becomes imperative to examine their financial performance, volatility characteristics, and correlation with conventional asset classes such as equities, bonds, and commodities. This research adopts a hybrid methodological approach, combining rigorous quantitative analysis with qualitative review. Using historical market data from 2015 to 2024, it evaluates key performance indicators such as average returns, standard deviation, Sharpe and Sortino ratios, Value at Risk (VaR), and maximum drawdown. It further explores the utility of cryptocurrencies in enhancing portfolio efficiency through diversification benefits, while also considering risk mitigation through dynamic asset allocation and rebalancing. The study extends beyond price metrics to include macroeconomic factors, such as inflation trends and monetary policy shifts, which influence crypto markets. It also addresses behavioral finance phenomena—including herd behavior, market sentiment, and media impact—that contribute to the observed volatility and price surges. The emergence of decentralized finance (DeFi), stablecoins, and central bank digital currencies (CBDCs) are also discussed to contextualize the evolving ecosystem and its implications for future investment strategies. Key findings indicate that while cryptocurrencies have historically outperformed traditional assets in terms of absolute returns, they also exhibit significantly higher volatility and downside risk. Despite these risks, their low to moderate correlation with conventional financial instruments enhances their value as diversification tools in modern portfolios. However, the study cautions that this benefit may diminish during times of extreme market stress when cross-asset correlations tend to rise. Moreover, the research highlights critical regulatory, technological, and environmental challenges associated with crypto adoption, including inconsistent global regulations, concerns over energy-intensive proof-of-work systems, and vulnerabilities in smart contracts. These factors underscore the need for robust governance frameworks and investor education to support sustainable growth in the digital asset market. In conclusion, the paper asserts that cryptocurrencies can serve as high-risk, high-reward components of a diversified portfolio, particularly for investors with higher risk tolerance and a long-term investment horizon. The future integration of cryptocurrencies into mainstream finance will depend largely on regulatory clarity, technological innovation, and the maturation of supporting infrastructure such as custody services, derivative markets, and institutional-grade investment vehicles
The subject article deals with the impact of cryptocurrencies on the future of financial and banking stability, the SWIFT system and the economy, as well as the attitudes of countries and some international institutions towards them. In today's world, we are witnessing the rapid development and digital technological revolution in all economic, financial and banking streams. This has led to the emergence of new monetary instruments that are compatible with the development of society and the spread of the Internet. Many financial, commercial and economic crises, epidemics and other factors contributed to the introduction of cryptocurrencies. The introduction of cryptocurrencies brought a series of challenges and risks associated with their use, such as money laundering, terrorist financing and trade in illegal goods, as well as challenges related to legal and regulatory issues that harm the economy, financial and banking stability in many countries. There are two research questions addressed in this paper: 1) Do virtual currencies have the same standards as money in its current form? 2) What are the implications of using virtual currencies? The scientific objective of this research is to describe and scientifically explain the main components of virtual currencies as a form of monetary policy and identify the challenges they face. The social goal of the research is to present future prospects for their use to the scientific and social public based on the views of countries, international organizations and central banks on the issuance of virtual currencies and to point out possible obstacles in their proper application. Almost all basic methods of knowledge and research were used in the preparation of the article. Of the general scientific methods, the statistical and comparative method was applied, and of the data collection methods exclusively the method of content analysis of the documents listed in the list of used literature.
Ahmed Abubakar Aliyu, Mohammed Ibrahim, Sa’adatu Abdulkadir
Traditional Intrusion Detection Systems (IDSs) face significant challenges in keeping pace with the rapidly evolving landscape of cyber threats, primarily due to limitations in continuous learning and the accuracy of data classification and analysis. This often results in delayed detection and leaves networks susceptible to severe attacks. This paper introduces an innovative IDS empowered by blockchain technology to mitigate these shortcomings, leveraging continuous learning and self‑adaptive neural networks. The proposed system adopts a proactive approach by continuously assimilating intrusion logs, utilizing a Long Short‑Term Memory (LSTM) core to discern patterns and enhance its real‑time threat detection capabilities, removing a major bottleneck in traditional IDS models by eliminating the need for manual tagging. To further strengthen the security measures, self‑updating neural networks are embedded in each block of the blockchain, forming a decentralized “brain” that evolves defences against even the most sophisticated adversaries. These networks are securely housed in Trusted Execution Environments (TEEs) to maintain operational integrity, enabling tamper‑proof operation and effective threat detection. Real‑world evaluations conducted on the Binance Smart Chain and Ethereum Classic datasets demonstrate the system’s superior performance. With an impressive accuracy rate of 98.50% and a minimal false positive rate of 1.50%, the model demonstrates a remarkable ability to distinguish legitimate network activity from malicious intrusions.
The primary aim is to systematically review scholarly literature to understand the technological, psychological, ethical, and regulatory dynamics of integrating blockchain and AI in U.S. financial markets. A systematic review methodology was employed, analyzing 45 peer-reviewed articles across disciplines. The data were thematically synthesized using qualitative coding based on research methods, theories, and findings. The integration of blockchain and AI enhances financial transparency, operational efficiency, and autonomous decision-making. However, behavioral and emotional factors such as emotional intelligence and risk perception strongly influence adoption. Moreover, ethical concerns, regulatory fragmentation, and lack of transparency in AI algorithms pose barriers to public trust and systemic resilience. While blockchain-AI integration shows transformative potential, its success depends on addressing ethical and regulatory gaps and aligning technologies with human behavior and cognition. Future research and policy should prioritize ethical frameworks, explainable AI, behavioral modeling, global regulatory coherence, and digital literacy initiatives..
NFTree creates incentives to protect ecosystems of high ecological value and strengthens territorial resilience by integrating companies, communities, and key stakeholders under an innovative financing model. Through decentralized community management structures and natural asset tokenization (a process that digitally converts ecological value into verifiable assets), it turns ecosystem stewards into direct protagonists, ensuring transparency, traceability, and placing both people and ecosystems at the core of sustainable development. This publication provides a clear replication guide to consolidate a sustainable financing system that ensures long-term conservation. Based on Fundación Futuro's experience in the Andean Chocó and other successful conservation models, it offers a strategic framework to integrate stakeholders, implement digital tools, and develop governance and financing mechanisms that make ecological and social regeneration viable. The goal is to share experience and inspire replication in other contexts and regions. The publication shares key lessons and insights from Fundación Futuro and Grupo Futuro's journey, aiming to inspire adaptation and replication in diverse contexts and regions.
Dnyaneshwar Jivanrao Ghode, Vinod Yadav, Rakesh Jain, Gunjan Soni
Industries aims to have a paradigm shift in supply chains (SC) to provide transparency in the shared information for the economic and social benefits of the stakeholders in an SC. The revolution of Blockchain Technology (BT) allows all the parties in the network to share secured data among themselves. This paper aims to develop a framework to integrate an SC with BT for the exchange of physical products and secured information among the stakeholders. The framework has been implemented by developing a generic SC with BT using Python 3.8.1. The framework comprises a blockchain-based distributed ledger that shares transaction information among manufacturers, distributors, retailers, and customers. For each transaction, a hash code was generated using the SHA-256 algorithm, and the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm was used to verify the transactions. The quantity and rate of products have been checked through a smart contract. The influencing factors are inter-organizational trust, regulatory governance, data transparency, data immutability, interoperability, product type, social influence, and behavioural intention. This framework provides transparency in transactions between SC stakeholders and the provenance of products throughout the SC.
Bitcoin has attained increasing recognition and interest from individuals and corporations, with more than $1 billion market capitalization. Twitter users’ sentiment on the topic is a major factor that influences volatility of Bitcoin’s price. Compared to other financial markets, there are a limited number of studies that discuss the price fluctuation prediction of Bitcoin using Twitter sentiment. A dataset with 16 million tweets from August 2018 to October 2019 was utilized for finding the correlation between the daily close price of Bitcoin and Twitter sentiment. This dataset was pre-processed by following steps such as removing null, duplicate and non-English tweets. The sentiment analysis was carried out using VADER sentiment analyzer. This research utilized hyperparameter optimization and improved two deep learning models (with Long Short-Term Memory and Convolutional Neural Network architectures), for the tasks of direction and magnitude prediction with accuracies of 82.35% and 72.06%, respectively on test datasets. With hyperparameter optimization this research addresses a gap in the existing research of this research area, which was not utilizing hyperparameter optimization to improve deep learning models.
José Antonio, Stephanie Wang, Frederic Ivan L. Ting, Jeremiah R Vallente · 6 authors
Hemophilia is a rare, chronic bleeding disorder with high treatment costs that pose significant economic challenges, particularly in many countries of Southeast Asia (SEA), where disease burden is disproportionately high. In this commentary, we explore the drivers of financial toxicity (FT) in hemophilia care across SEA. We identify key contributors including inadequate health insurance coverage, reliance on unstable humanitarian aid, geographic disparities in care access, and cultural factors that place financial and emotional strain on families. Despite some policy efforts-such as universal health coverage and national cancer control plans-systemic gaps in financing, infrastructure, and provider distribution persist. We propose actionable steps using the social ecological model of health to reduce FT at the individual, interpersonal, community, and policy levels, including expanded insurance coverage, decentralized care, telehealth access, financial navigation services, and public education. Addressing the multifactorial nature of FT is essential to improving quality of life and equity in care for patients with hemophilia in SEA.
Penelitian ini bertujuan untuk merancang dan membangun aplikasi pengelolaan sertifikat digital berbasis Non-Fungible Token (NFT) pada jaringan blockchain Solana. Aplikasi ini memungkinkan pengguna untuk membuat, memverifikasi, dan mentransfer sertifikat dalam bentuk NFT yang aman dan terdesentralisasi. Pengujian dilakukan dalam dua aspek utama: fungsional dan performa. Hasil pengujian fungsional menunjukkan tingkat keberhasilan 100%, yang mengindikasikan bahwa seluruh fitur utama aplikasi berjalan sesuai ekspektasi. Dari sisi performa, pembuatan NFT membutuhkan waktu rata-rata 29 detik dengan biaya 0.00008 SOL, verifikasi NFT berlangsung sangat cepat dalam 1.5 detik tanpa biaya, dan transfer NFT memakan waktu 19 detik dengan biaya yang sama seperti pembuatan. Hasil ini menunjukkan bahwa aplikasi mampu menjalankan fungsinya secara efisien dan ekonomis di atas jaringan Solana. Penelitian ini memberikan kontribusi dalam pemanfaatan teknologi blockchain untuk pengelolaan dokumen digital yang transparan dan aman.
Against the backdrop of the Industry 5.0 transformation, traditional centralized Manufacturing Execution Systems (MES) struggle to meet the complex demands of flexible and small-batch production. This paper proposes a four-level distributed architecture oriented toward human-centric manufacturing, encompassing Manufacturing Units (UMS), Production Lines (PL-MES), Workshops (MOM), and Factories (MOM), supporting dynamic reconfiguration and resource optimization from the unit level to cross-factory operations. Building on this, the study designs a smart factory operating system architecture based on ‘cloud-edge-end’ collaboration, achieving vertical integration of equipment, systems, and business processes through an industrial internet platform. Leveraging intelligent connectivity gateways and modular microservices, the architecture constructs a decentralized decision-making chain. Innovatively integrating multi-agent systems and federated learning, the architecture significantly enhances the autonomous decision-making capabilities of manufacturing units and cross-level collaboration efficiency. Additionally, the system supports rapid deployment for small and medium-sized enterprises through low-code toolchains, lowering technical barriers. This research provides a theoretical framework and technical pathway for human-machine collaborative manufacturing in the Industry 5.0 era, driving the paradigm shift of manufacturing systems from rigid control to ecological self-organization, laying a solid foundation for the sustainable development of future smart factories
Miguel Guinalíu, Francisco Liébana‐Cabanillas, Pau Jordán
In recent years, technological advancements have significantly transformed the financial and business sectors. These innovations have introduced new payment methods, reshaping consumer perceptions of money and influencing their payment behaviors. Among these innovations, cryptocurrencies—particularly Bitcoin—have emerged as novel alternatives with strong growth potential in retail and consumer services. This study investigates the factors influencing Bitcoin adoption by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The extension includes users’ predisposition to adopt new technologies, their risk acceptance, and the moderating role of familiarity. Data were gathered through an online survey from a validated sample of 515 active Bitcoin users, and the findings were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results show that performance expectancy, social influence, hedonic motivation, and risk predisposition significantly impact the intention to use Bitcoin, with familiarity as a partial moderator. The study offers practical insights for companies and retailers integrating cryptocurrency payments.
P Siddharth Krishna, Mohamed Mustaf Ahmed, Mohammed Abdul Wasey, Mrs. Hyma Birudaraju
Abstract—Ensuring compliance with regulatory requirements through efficient Know Your Customer (KYC) processes continues to pose a major challenge for financial institutions globally. Traditional KYC procedures are often inefficient, repetitive, and exposed to significant security risks such as identity fraud, data breaches, and inconsistent verification protocols. As consumer expectations for smooth digital banking interactions rise, current systems frequently fail to balance both security and operational efficiency. This paper introduces a blockchain-driven solution built on the Ethereum platform aimed at enhancing the security and effectiveness of KYC operations. By leveraging blockchain’s decentralized structure, customer data is stored on a distributed ledger, which guarantees data integrity, transparency, and resistance to tampering. The proposed model also grants individuals greater control over their personal information, allowing them to selectively share data with financial institutions, thereby minimizing redundancy and strengthening privacy. Smart contracts are integrated into the system to automate the KYC verification steps, ensuring swift processing aligned with regulatory standards. Additionally, a central banking body is entrusted with managing a complete directory of all involved financial entities and monitoring their compliance with the rules enforced within the blockchain ecosystem.
This study compares two approaches for measuring Conditional Value-atRisk (CoVaR), emphasizing the role of high-frequency intraday data in assessing systemic risk within financial systems. The first approach, AB CoVaR, estimates the risk of an asset Y conditional on another asset X being exactly at its Value-at-Risk (VaR) threshold. In contrast, the GE CoVaR refines this measure by capturing the risk of Y when X exceeds its VaR threshold, thereby accounting for more extreme scenarios and larger potential losses. To estimate these CoVaR measures, we employ high-frequency data sampled at five-minute intervals from major cryptocurrencies, including Bitcoin, Ethereum, Ripple, Solana, and Binance Coin. The results indicate that the GE CoVaR approach systematically yields higher risk estimates and exhibits superior predictive performance when applied to intraday data. Moreover, the analysis reveals strong interconnectedness among cryptocurrency returns. Bitcoin and Ethereum emerge as the primary sources of systemic risk, whereas Solana and Binance Coin are the most heavily affected assets. These findings underscore the granular risk dynamics captured through intraday analysis.
Title: Systemic Risk Analysis of Synthetic Asset Issuance Protocols in Decentralized Finance (DeFi) Networks Based on Network Analysis and On-Chain Data DBA Candidate, University of Tehran Abstract: This study aims to analyze the systemic risk of synthetic asset issuance protocols within Iranian Decentralized Finance (DeFi) networks. To this end, a network analysis and on-chain data approach was employed to investigate financial interdependencies among key domestic cryptocurrency platforms, including Ramzinex, Nobitex, Bit24, Phintra, and IranExChain. Real-world data from 382,747 transactions spanning from March to December 2024 (Farvardin to Azar 1403 SH) were extracted and modeled as a cross-platform transaction matrix. Network analysis results indicate that the structure of Iran’s decentralized financial ecosystem is scale-free and heterogeneous, and liquidity concentration in two primary nodes (Ramzinex = 0.62, Nobitex = 0.51) significantly increases the probability of financial contagion among protocols. The network propagation model, with transmission coefficients β=0.16\beta = 0.16β=0.16 and γ=0.09\gamma = 0.09γ=0.09, demonstrates that under a scenario of a 30% drop in collateral value, the ratio of infected nodes to the entire network increases. The composite Network Stability Index (NSI), valued at 0.74 under normal conditions and 0.46 under severe shocks, confirms the transition of the system into a fragility phase. Sensitivity and bootstrap analyses recorded an error of 0.038, validating the robustness of the model. Furthermore, results show that the introduction of the digital Rial (Rial Digital Currency) in June 2024 led to a 12% reduction in average systemic risk and enhanced cross-platform stability. Based on the findings, three strategies are proposed to mitigate systemic risk in Iran’s DeFi ecosystem: improving on-chain data transparency, regulating Rial-based collateralization policies, and designing an early warning system based on the SIR model. By combining local on-chain data with international network metrics, this study presents the first comprehensive analytical framework for assessing financial resilience in Iranian synthetic asset protocols. Keywords: Systemic Risk, Synthetic Assets, Decentralized Finance (DeFi), Network Analysis, On-Chain Data, Stability Index, Rial Digital Currency, SIR Model.
E Srija, G S V Mithelesh, G. R. Reddy, Mohamad Irfan
The increasing reliance on the internet has escalated the frequency and sophistication of cyber threats, making timely identification and mitigation essential. This research presents an AI-powered framework for cyber threat detection and profiling using Natural Language Processing (NLP) and Machine Learning (ML) techniques. By utilizing Twitter as an Open Source Intelligence (OSINT) platform, the system collects real-time threat intelligence, classifies threats, and maps them to the MITRE ATT&CK framework to provide actionable insights. Key processes include data preprocessing, feature extraction using advanced NLP models, and threat profiling to assess intent, origins, and potential impacts. The automated approach reduces analyst workload, enhances accuracy, and accelerates response times, addressing the limitations of manual threat analysis and noisy data sources. This framework aims to advance proactive cybersecurity by delivering real-time, context-aware threat intelligence. Keywords: Blockchain, Non-Fungible Tokens(NFTS), InterPlanetary File System(IPFS), Social Networking, Online Social Networks, Decentralization, Data Ownership, Content Monetization, Reputation Sytsem, NFT
Prince Jebedass Isaac Chandran, Hana Ahmed Khalil, PK Hashir, S Veerasingam
Aquaculture is vital for global food security, yet traditional methods often struggle with inefficiencies, disease outbreaks, and environmental concerns. This review explores how an integrated framework of Internet of Things (IoT), Artificial Intelligence (AI), and blockchain technology can transform aquaculture into a more efficient, sustainable, and intelligent industry. IoT enhances real-time monitoring and precision feeding, AI optimizes disease detection and resource management, and blockchain ensures transparency and traceability across the supply chain. This novel approach not only mitigates existing challenges but also fosters predictive analytics, automation, and data-driven decision-making. Although high costs and technical constraints pose challenges, adopting emerging technologies like 5 G, edge computing, and decentralized ledgers can accelerate industry-wide adoption and enhance resilience and scalability. Future innovations in AI-driven aquaculture must focus on adaptive machine learning models and cross-disciplinary collaborations to ensure resilience and scalability in the face of global demands.