• Decision Support System enhances player decision-making in the dynamic Metaverse. • Blockchain and NFTs can be used for accurate asset representation in the Metaverse. • Genetic algorithms optimize character traits under constraints in the Metaverse. • Genetic algorithms dominate over greedy in repeated games simulation. The Metaverse, a virtual world enabling user interaction with digital environments and assets, increasingly utilizes blockchain and Non-Fungible Tokens (NFTs) to represent unique characters and items. Representing and valuing individual digital characters is crucial in the Metaverse as it establishes ownership, identity, and status within these virtual worlds. This research investigates decision-making within collaborative online games, where players face the challenge of optimizing their character's value through strategic NFT acquisition. This challenge is analogous to the Knapsack Problem, aiming to maximize the value of items selected within a limited capacity, just as players seek to maximize character rarity within budget constraints. We propose a Decision Support System (DSS) employing genetic algorithms to assist players in tackling this complex optimization problem. A simulation framework, based on the "SunflowerLand" Metaverse, demonstrates that players using the DSS achieve significantly higher character rarity, highlighting the potential of this approach to enhance player experience.
As immersive technology becomes more streamlined and accessible to larger audiences, it is imperative for advertisers and businesses to understand and engage in new spaces where prospective consumers are gathered. The introduction of the metaverse, a virtual hub of experiences, commerce, and impressions, for consumers through revolutionary peripherals such as virtual and augmented reality represents a shift in how online users and brands are positioned within the digital space. This thesis analyses the industry space and how brands, researchers, and consumers are utilizing revolutionary technologies to reconceptualize the idea of commerce, interactivity, and self-expression. This report and research have largely been conducted by interviewing industry experts across the globe and attending Web3 conferences, including DCentral 2022 in Miami, Florida, and serves as a detailed guide for marketers and brands to the utility of the metaverse found in the decentralized layout, incorporation of digital assets and product authenticity, as well as the increased opportunities found in innovative marketing research and development.
The accelerating convergence of Cloud computing and the Internet of Things (IoT) has revolutionized data-driven services, yet it has also introduced a significant trust deficit in highly regulated sectors such as healthcare and finance. Traditional architectures, characterized by static security protocols and reactive monitoring, are increasingly inadequate for protecting sensitive medical records and financial assets against sophisticated cyber-threats and operational anomalies. This review article proposes a "Cognitive Cloud–IoT Architecture" that integrates human-like reasoning, self-learning, and context-aware decision-making into the data exchange process. We evaluate a multi-layered framework comprising an intelligent perception layer, a cognitive middleware reasoning engine, and a secure cloud core designed to establish objective trust through continuous verification. The study analyzes key mechanisms for trustworthy exchange, including Zero-Knowledge Proofs (ZKP), blockchain-enabled immutable ledgers, and privacy-preserving federated learning. In the healthcare domain, we examine the application of "cognitive patients" through remote monitoring systems that differentiate between sensor noise and clinical emergencies. In the financial sector, we explore the "cognitive ledger" for autonomous fraud forensics and secure cross-border settlements. Furthermore, the article addresses critical strategic challenges, such as the computational overhead of running cognitive models on edge devices and the legal necessity of algorithmic explainability. By synthesizing future trends, including quantum-safe hybridization and sovereign cognitive clouds, this research provides a comprehensive roadmap for developing resilient, intelligent ecosystems. Ultimately, we demonstrate that cognitive architecture is the essential bridge to an "invisible intelligence" that ensures the integrity of human life and global financial stability in an increasingly connected world.
Christy Dwita Mariana, Zaäfri A. Husodo, Irwan Adi Ekaputra, Mochammad Fahlevi
Metaverse, a virtual space created as a digital version of the real world, is currently under development. Over the past two years, there has been a significant rise in scientific research on the metaverse. However, prior research necessitates a methodical examination of the literature on developing the metaverse environment, particularly with regard to payment ecology. To bridge this gap, we reviewed 40 journal articles related to the metaverse digital payment ecosystem and offer recommendations for future research. We refer to the Scientific Procedures and Rationales for Systematic Literature Reviews” (“SPAR-4-SLR”) procedure for carrying out a thorough review of the literature. Bibliometric analysis was also performed for the thematic observations. The results reveal five clusters in the Metaverse digital payment ecosystem: (1) metaverse reality, (2) technology, (3) data analytics, (4) blockchain, (5) non-fungible tokens (NFTs), and other tokens. Metaverse digital payment ecosystem research is still somewhat scattered and has no significant theme. Furthermore, this study provides a path for future research on the stability of the payment ecosystem and the metaverse regulatory framework.
Since Satoshi Nakamoto first proposed the idea of bitcoin in 2009, the cryptocurrency and prediction methods for it have grown and changed exceptionally quickly. The Patterned Dataset Model was a valuable tool in earlier studies to explain how changes in the price of Bitcoin affect the movements of other cryptocurrencies in a digital trading market. Three different kinds of datasets are generated by this model: patterned datasets under full conditions, patterned datasets under dropping prices (Crash), and patterned datasets under rising prices (Moon). The K-means approach was then used to cluster these three datasets. Specifically, each dataset was split into two clusters, and the clustering score was determined by utilizing eight unique clustering metrics. Consequently, the best clustering score was found in the patterned dataset in the crash situation. Additionally, from 2022 to 2024, the raw data from this crash-condition-patterned dataset is used to determine the possibility of reaching maximum profit and return on investment (ROI) daily and monthly. According to the calculation results, the range computed over the course of a whole month (30 to 31 days) is significantly larger than the daily range (24 hours multiplied by one month), which represents the most significant profit and ROI attained before the emergence of the first diamond crash level. This research also covers the application of a deep learning model to forecast patterned datasets for crash scenarios that may occur many days in advance. The ConvLSTM2D Model performs better in predicting pattern dataset values for the subsequent crash scenario, according to the hyperparameter comparison between the Gated Recurrent Unit (GRU) Model and the 2D Convolutional Long Short-Term Memory Model.
Medina Ayta Mohammed, Carmen De‐Pablos‐Heredero, José Luis Montes Botella
Blockchain technology and its business applications have attracted considerable scholarly interest, leading to a surge in academic studies. While this wealth of research is beneficial, it also poses challenges in identifying the most relevant publications. Despite the availability of survey articles, research on this topic remains fragmented and concentrates on specific industrial sectors. This review addresses this gap by providing a detailed literature analysis, highlighting key themes, recent advancements, the benefits of blockchain adoption for businesses, and associated challenges. This study employs a multi-method literature review approach called bibliometric systematic literature review (B-SLR), combining bibliometric analysis with systematic literature review (SLR) techniques. This review critically examines studies of blockchain adoption in modern business from 2017 to 2023. Our findings reveal a decline in academic publications on blockchain for businesses since 2023, along with a shift in core themes from traditional supply chains to exploring blockchain’s role in environmentally sustainable supply chains, such as reverse logistics, green supply chains, and the circular economy. Additionally, there is an emerging focus on the role of blockchain in virtual environments, such as the metaverse and digital twins. Drawing from our analysis, we also present a theoretical framework and highlight ten crucial areas for future research.
This paper uses data from January 2018 to early May 2024 as a sample to study the impact of macroeconomic factors on cryptocurrency returns. The empirical study finds that the price index of means of production significantly negatively impacts bitcoin returns. The US dollar exchange rate also significantly negatively impacts bitcoin returns, while Treasury yields have a positive effect.
Blockchain technology has rapidly emerged with a multitude of applications, among which smart contracts have garnered considerable attention. Smart contracts represent a promising solution for streamlining trade and business transactions between untrusted parties without intermediaries. These self-executing pieces of code automatically execute predefined actions when specific conditions are met. Despite the growing enthusiasm for blockchain and smart contracts, researchers believe this powerful combination has not yet reached its full potential. Hence, a systematic study is conducted to explore the various facets of blockchain-based smart contracts comprehensively. The research follows the PRISMA framework and employs two primary approaches: bibliometric analysis and systematic literature review. The process was initiated by formulating targeted search queries within the Scopus database, identifying a total of 1,949 publications spanning from January 2019 to August 2023. Subsequently, a bibliometric analysis was conducted on these publications using VOSviewer and Biblioshiny. Further, the full text of these publications was meticulously screened to isolate those with a significant focus on smart contracts. This led to the identification of 48 publications, each offering unique insights into various smart contract applications. Upon further examination, it was observed that the majority of these publications held rankings within the China Computer Federation, which refers to the qualitative research work in this domain. The study concludes with the current state of blockchain-based smart contracts, their platforms, applications, and challenges and reveals the substantial potential in handling tasks with predefined conditions and security requirements.
Monitoring a company's efficiency is one of its primary responsibilities. There are many approaches in our contemporary society that either use IT or the conventional technique. Methods for measuring efficiency fall into three primary categories: parametric, nonparametric, and ratio indicators. We prioritize a firm's inputs and outputs when choosing metrics to measure efficiency. Establishing objectives and goals in entrepreneurship necessitates a thorough comprehension, appreciation, and knowledge of sustainability, and assessing the economic growth quality of a corporation is an essential task for theoretical and empirical sustainability assessment. When measuring the efficiency of entrepreneurship in terms of achieving desired values of macroeconomic indicators (e.g., sustainable economic growth objectives), data envelopment analysis (DEA), a widely used technique in efficiency analysis, has taken into account the economic, environmental, and social impact of entrepreneurship as the three dimensions of sustainability. The objective of this paper is to test the influence of FinTech/Blockchain adoption on corporate ESG and DEI performances using a novel DEA approach for sustainable development assessment. It highlights the significance of using a scalable technique for ESG efficiency study and gives scholars a more thorough viewpoint on the subject. In a collection of 50 enterprises, a DEA model was utilized for the analysis. For sustainable performance assessment using the proposed DEA technique, we defined as inputs six financial metrics, and as outputs 11 ESG/Blockchain adoption, and four DEI quality metrics to measure the firm’s efficiency. The annual business data was gathered between 2017 and 2023. In all country situations we discovered that, when DEI initiatives mediate, there is a strong correlation between ESG corporate performance and the quality of economic growth (particularly in the innovation and integrity blockchain adoption performance success metrics). Our study provides additional in-depth details on the FinTech/blockchain adoption environment in comparison to the findings of previous researchers. The sustainable entrepreneurship performance (a latent variable regarded as a dependent target factor) is calculated using eight (8) factors as observed variables, which is the first to consider the dynamics of ESG/BCA and DEI quality metrics as DEA outputs. The study also examines the mediating role of DEI corporate initiatives. By conducting an empirical investigation, the suggested scalable framework makes it evident which company is more efficient in moving toward sustainability, assisting corporate management in increasing the effectiveness of economic growth.
Rahul Diliprao Tamhane, Aman Grewal, Ishan Sandhu, Prof. Vivek Rastogi · 6 authors
The integration of Artificial Intelligence (AI) and blockchain technologies into Customer Relationship Management (CRM) systems has the potential to revolutionize how organizations engage with their customers, streamline business processes, and ensure data integrity. This review synthesizes recent literature and market reports to highlight the emerging trends, benefits, and challenges of using AI and blockchain in CRM. We explore the capabilities of AI-driven data analytics, predictive modeling, and personalized customer engagement features, as well as the trust and transparency offered by distributed ledger technology. Furthermore, we identify technical, regulatory, and organizational hurdles that must be overcome for successful adoption. The review concludes by suggesting avenues for future research, including the development of standardized protocols, improved interoperability, and ethical frameworks for responsible AI and blockchain use in CRM ecosystems.
Muhammed Zakir Hossain, Fatema Tuj Johora, Mamunur R. Raja, Latul Hasan
This research paper uses qualitative analysis to examine the profound influence of artificial intelligence (AI) and blockchain technologies on accounting practices. The study utilizes case studies and semi-structured interviews with industry experts to identify central themes, including efficiency and automation, accuracy and data integrity, fraud detection and security, professional roles and skills, and ethical and regulatory considerations. The results demonstrate that AI increases efficiency by automating repetitive tasks and enhancing fraud detection, while blockchain guarantees the precision and reliability of financial records. Nevertheless, incorporating these technologies into existing systems poses difficulties, including technical obstacles, adherence to regulatory requirements, and ethical considerations such as safeguarding data privacy and addressing algorithmic bias. Due to these findings, accounting professionals must acquire new skills in data analytics and technology management. It is recommended that educators integrate artificial intelligence (AI) and blockchain into accounting curricula. At the same time, policymakers are advised to establish well-defined regulatory frameworks to facilitate the adoption of these technologies. The study also identifies areas for future investigation, such as the enduring effects of AI and blockchain on accounting methods, the factors that influence user adoption, and the creation of efficient regulatory structures. The research thoroughly analyzes how AI and blockchain are transforming the accounting profession, providing insights into the opportunities and challenges they bring.
This study aims to investigate the impact of the digital economy on consumer preferences in the Metaverse era, focusing on the role of Non-Fungible Tokens (NFTs) and tokenization in changing purchase intentions. Using a mixed-methods approach, this study combines quantitative and qualitative analysis to gain a comprehensive understanding of the key determinants of consumer purchase intentions in a rapidly evolving digital context. The results of the quantitative analysis show that perceived value, trust in blockchain technology, and user experience significantly influence consumer purchase intentions towards NFTs. Qualitative analysis through in-depth interviews reveals that scarcity, digital ownership, and community involvement are the main factors driving NFT adoption, while lack of technological understanding and regulatory uncertainty are the main barriers. This study also introduces novelty through the use of machine learning-based sentiment analysis and social network analysis to explore the role of social interactions in the formation of purchase intentions. These findings make important contributions to the literature on the digital economy by offering new insights into consumer dynamics in the Metaverse and highlighting the need for marketing strategies that focus on creating community value and immersive user experiences. Practical implications of this study include the need for consumer education and clear regulations to increase NFT adoption, as well as marketing strategies that leverage the emotional value and scarcity of digital assets. This research contributes to a better understanding of consumer behavior in the digital age and offers recommendations for further research in this area.
Abstract Background/Objectives Blockchain technology is revolutionizing various industries by enhancing security, transparency, and efficiency. In healthcare, its small-scale application demonstrates the potential to transform healthcare. This study explores how cryptocurrency and blockchain technology enhance healthcare financial stability, secure medical information exchange, increase payment recovery and interoperability. Methods Guided by the Diffusion of Innovations (DOI) Theory, this study reviews 34 articles on blockchain and cryptocurrency adoption in healthcare. We performed a thematic analysis focusing on empirical data, theoretical analyses, and existing implementations. Data were categorized by DOI elements: relative advantage, compatibility, complexity, trialability, observability, and social system. The findings were synthesized into a detailed framework for adopting these technologies in healthcare. Results Our analysis shows that cryptocurrency offers a secure, decentralized payment platform, eliminating the need for third-party intermediaries. Blockchain adoption enhances payment and patient record protection. Cryptographic methods ensure data integrity, transparency, and security, preventing modifications once recorded. Altering data requires consensus from the network majority, protecting healthcare providers from ransomware attacks. Conclusions Evidence suggests that cryptocurrency and blockchain will disrupt healthcare payments, data management, and accommodate interoperability within the next decade. These technologies promise improved security, improved public health research, and increased trust in the healthcare system. Key messages • Our research will be assessing the challenges of integrating digital coins and blockchain technology into the healthcare system. • Examine the potential benefits and risks of implementing blockchain technology in healthcare settings.
The cryptocurrency market, known for its volatility and rapid growth, is influenced by various actors, including investors, speculators, and volunteers. Investors and speculators often seek short-term profits, sometimes disregarding the long-term fundamentals of projects, leading to significant market instability and reputational damage. Conversely, volunteers contribute consistently to the development, governance, and sustainability of crypto projects, focusing on long-term value creation. This paper examines the contrasting roles of these groups and provides real-world examples where speculators and investors have caused harm to the market, while volunteers have played a vital role in building and preserving value.
Current autonomous building research primarily focuses on energy efficiency and automation. While traditional artificial intelligence has advanced autonomous building research, it often relies on predefined rules and struggles to adapt to complex, evolving building operations. Moreover, the centralized organizational structures of facilities management hinder transparency in decision-making, limiting true building autonomy. Research on decentralized governance and adaptive building infrastructure, which could overcome these challenges, remains relatively unexplored. This paper addresses these limitations by introducing a novel Decentralized Autonomous Building Cyber-Physical System framework that integrates Decentralized Autonomous Organizations, Large Language Models, and digital twins to create a smart, self-managed, operational, and financially autonomous building infrastructure. This study develops a full-stack decentralized application to facilitate decentralized governance of building infrastructure. An LLM-based artificial intelligence assistant is developed to provide intuitive human-building interaction for blockchain and building operation management-related tasks and enable autonomous building operation. Six real-world scenarios were tested to evaluate the autonomous building system's workability, including building revenue and expense management, AI-assisted facility control, and autonomous adjustment of building systems. Results indicate that the prototype successfully executes these operations, confirming the framework's suitability for developing building infrastructure with decentralized governance and autonomous operation.
The vision of Web3 is to improve user control over data and assets, but one challenge that complicates this vision is the prevalence of non-transparent, scam-prone applications and vulnerable smart contracts that put Web3 users at risk.While code audits are one solution to this problem, the lack of smart contracts source code on many blockchain platforms, such as Sui, hinders the ease of auditing.A promising approach to this issue is the use of a decompiler to reverse-engineer smart contract bytecode.However, existing decompilers for Sui produce code that is difficult to understand and cannot be directly recompiled.To address this, we developed the SuiGPT Move AI Decompiler (MAD), a Large Language Model (LLM)-powered web application that decompiles smart contract bytecodes on Sui into logically correct, human-readable, and recompilable source code with prompt engineering.Our evaluation shows that MAD's output successfully passes original unit tests and achieves a 73.33% recompilation success rate on real-world smart contracts.Additionally, newer models tend to deliver improved performance, suggesting that MAD's approach will become increasingly effective as LLMs continue to advance.In a user study involving 12 developers, we found that MAD significantly reduced the auditing workload compared to using traditional decompilers.Participants found MAD's outputs comparable to the original source code, improving accessibility for understanding and auditing non-open-source smart contracts.Through qualitative interviews with these developers and Web3 projects, we further discussed the strengths and concerns of MAD.MAD has practical implications for blockchain smart contract transparency, auditing, and education.It empowers users to easily and independently review and audit non-open-source smart contracts, fostering accountability and decentralization.Moreover, MAD's methodology could potentially extend to other smart contract languages, like Solidity, further enhancing Web3 transparency.
Jayesh Rane, Ömer Kaya, Suraj Kumar Mallick, Nitin Liladhar Rane
This systematic literature review aims to discuss how digital transformation and digitalization have influenced businesses and management by synthesizing the latest research trends and findings. The digital transformation, defined as the integration of digital technologies in all business areas, has reshaped conventional business models, operational processes, and value propositions. On the other side, digitalization is a subcategory of digital transformation, referring to a process for the conversion of information from an analogue into a digital format for the automation and optimization of business processes. The review brings out that digital transformation is no longer a technological pursuit but a strategic compulsion impacting organizational culture, leadership, and customer engagement. It is found from emerging trends that only those businesses which are using sophisticated technologies such as Artificial Intelligence, Blockchain, Big Data Analytics, Cloud Computing, and Internet of Things are now gaining competitive advantage through resilience, innovation, and customer-centricity. This research calls for a holistic approach to the integration of technology into the strategic vision with organizational change management for successful digital transformation.
Purpose : The current study examined the influence of neurotransmitters on cryptocurrency investment choices. Moreover, the research assessed the mediator risk tolerance (RT) and moderator investment experience on the connection between neurotransmitters and investment choices.Research Design/Methodology : The analysis of the data involved 504 responses from individuals in India’s Western and Northern regions who either invested in cryptocurrencies or had knowledge of such investments. The proposed theoretical model of cryptocurrency investment choices was examined in the study using “variance-based partial least square structural equation modeling†(PLS-SEM).Findings : The outcomes of this research specified that neurotransmitters play a substantial role in cryptocurrency investment choices, and they had a substantial impact on making investment choices. It was also noted that a significant moderator between neurotransmitters and Bitcoin investment decisions is RT. However, it was determined that investment experience had no moderating effect.Practical Implications : This study revealed that, in order to make better-informed investment decisions, businesses, governments, and investors should consider the impact of neurotransmitters.Value/Originality : The study was innovative since it is one of the first to examine how neurotransmitters, together with mediator RT and moderator investment experience, affected Bitcoin investment decisions. Additionally, the conceptual framework could be very helpful to cryptocurrency portfolio managers and investors in understanding how the brain functions during the decision-making process. They would then be better equipped to allocate their assets with knowledge and efficiency.
Integration of Internet of Things (IoT) and blockchain combined with the power of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are transforming the sphere of smart industries, propagating a new era of boosted productivity, information assurance, and data-influenced deliberation. Our research looks into how these cutting-edge technologies flow together to enable smart industry breakthroughs. This offers conductive connectiveness and communication capabilities between devices and can create large pools of data, that are essential for making more informed decisions and finally, operating more sustainably. This data is then scaled and processed by the AI ML and DL algorithm to get the predictive insights; process optimization and to improve on automation. The security and immutability of data are critical in an IoT network, and this is something that blockchain technology excels at and ensures data exchanged within these networks is safe and unalterable. Thanks to recent developments in AI, ML, and DL, they can now better meet the challenges of industrial applications well beyond predictive maintenance and supply chain optimization and extend into real-time monitoring and autonomous operations. The perspective taken in this research is instead one of a practical, real-world implementations, illustrating some of the advantages as well as challenges when integrating these technologies. The results point to the enormous transformative capability of this integration and suggest a level of efficiency, security and innovation not seen before that will redefine intelligent industries today and possibly more importantly tomorrow, in effect defining the fourth industrial revolution and beyond.
This article presents a Blockchain-based solution for the management of multipolicies in insurance companies, introducing a standardized policy model to facilitate streamlined operations and enhance collaboration between entities. The model ensures uniform policy management, providing scalability and flexibility to adapt to new market demands. The solution leverages Merkle trees for secure data management, with each policy represented by an independent Merkle tree, enabling updates and additions without altering existing policies. The architecture, implemented on a private Ethereum network using Hyperledger Besu and Tessera, ensures secure and transparent transactions, robust dispute resolution, and fraud prevention mechanisms. The validation phase demonstrated the model’s efficiency in reducing data redundancy and ensuring the consistency and integrity of policy information. Additionally, the system’s technical management has been simplified, operational redundancies have been eliminated, and privacy is enhanced.
George Lăzăroiu, Tom Gedeon, Elżbieta Rogalska, Katarína Valášková · 17 authors
Research background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and labor-intensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes. Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions. Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization. Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms. Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies.
Nida Shakeel, Prof. Shiva Prakash, Shagufta Shakeel
In traditional healthcare sceneries, real-time patient record monitoring and analyzing information for the prompt diagnosis of chronic illnesses under specific health conditions is an essential procedure. Failure to diagnose chronic diseases promptly can lead to severe consequences, including patient mortality. Regarding disease diagnosis and treatment, wearable device physiological data can be analyzed by artificial intelligence (AI) to produce intelligent recommendations. Contemporary medical and healthcare frameworks leverage Internet of Things (IoT) technologies, utilizing autonomous sensors to monitor and assess patients' health conditions while recommending suitable interventions. This paper introduces an innovative hybrid approach that integrates IoMT and Telemedicine to facilitate the early detection and ongoing monitoring of three distinct chronic diseases, including asthma, brain tumors, and Alzheimer’s. Furthermore, blockchain can enhance healthcare services by enabling decentralized data exchange, safeguarding user privacy, empowering data, and guaranteeing the dependability of data administration. Blockchain, wearable, and AI integration may improve current chronic illness management paradigms by moving away from hospital-centered care and toward patient-centered care. In this study, we further investigate the use of these integrated technologies in the management of chronic diseases and theoretically provide a patient-centric technical framework based on blockchain, artificial intelligence, and wearable technology.
The management of user data in the cloud is easily poised to become a giant issue for any business, with so much digital information floating around these days that open it up as an easy target when companies aren’t vigilant. In this paper, a novel cloud-based service is proposed that employs various advanced NLP and encryption methods with the use of blockchain. These techniques are combined to provide a solid solution to secure fundamental data such as credit card numbers, passports or any government identity cards etc. Using a hybrid NLP model, integrating Transformer Models and Named Entity Recognition (NER), to automatically catagorzing data as critical vs non-critical. Only the most important data is encrypted by a user’s cryptographic wallet before being divided into multiple chunks and stored on an exclusive cloud cluster; metadata then takes turns managing securely through blockchain to provide traceable means of retaining integrity. Smart contracts provide strict access control measures and change the cryptographic nonce if need be to prevent illegal entrance into a specific zone, thus create security. This all-inclusive strategy maintains well-known high security standards for protecting the confidentiality, availability and integrity of your sensitive data on a global scale—delivering you simple yet scalable secure world-class cloud-based data management. A proposed framework developed to fulfil the security requirements for current cloud services, which is a beneficial contribution in context of data protection and cloud Security.
This paper explores the volatility spillover effects between the cryptocurrency market and the Pakistan Stock Exchange (PSX). Utilising data from January 1, 2019, to April 5, 2024, sourced from Investing and Yahoo Finance, the study employs the Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) BEKK model to assess the dynamic interactions between these markets. Stationarity tests confirmed the non-stationarity of time series data at their levels, which became stationary after first differencing, ensuring robust econometric analysis. The results indicate significant volatility spillovers from major cryptocurrencies, such as Bitcoin and Ethereum, to the PSX, highlighting a solid interconnectedness between these markets. This suggests that digital asset volatility significantly influences traditional financial systems. The study concludes that integrating cryptocurrencies into global financial markets introduces risks and opportunities for investors and policymakers. The findings underscore the need for market participants to account for these volatility interactions in their risk management strategies. Additionally, policymakers must consider these interlinkages to maintain financial stability. This research contributes to the literature on financial market volatility by emphasising the importance of understanding the impact of emerging digital currencies on traditional stock markets.