Mint-Verse is a next-generation NFT marketplace designed to provide users with a seamless and immersive experience in buying, selling, and creating non-fungible tokens. The platform leverages blockchain technology to ensure transparency, security, and authenticity of digital assets, making it a trusted and decentralized ecosystem for digital creators and collectors. It offers a range of advanced features, including a dynamic NFT slider with countdown timers, an interactive bidding system, and a secure transaction page where users can purchase NFTs and receive digital receipts. The platform supports multiple sign-up methods, allowing users to create accounts easily and manage their profiles efficiently. A dedicated wallet section enables users to track their transactions, view their NFT collections, and manage digital assets with ease. Mint-Verse also introduces Mint-gram, an Instagram-like feature where users can showcase their NFT collections, interact with others, and engage with the growing NFT community. The platform further enhances creative possibilities by providing an NFT generation tool, allowing users to mint their own NFTs with customized attributes, ensuring flexibility and creative freedom. User experience is a core focus of Mint-Verse, incorporating an intuitive and aesthetically pleasing interface with animated loaders, a dynamic mouse cursor, interactive buttons, and real-time updates for a smooth and engaging browsing experience. Additional features such as a like button for NFTs, a contact section with email functionality, and a logout option contribute to a seamless and user-friendly navigation system. Built using the MERN stack, including MongoDB, Express.js, React.js, and Node.js, Mint-Verse ensures high performance, scalability, and efficiency. By integrating cutting-edge blockchain solutions, the platform aims to bridge the gap between artists, collectors, and investors by providing a decentralized, feature-rich, and user-friendly NFT marketplace. Mint-Verse envisions a future-proof digital ecosystem that empowers users to securely trade, create, and collect NFTs while embracing the evolving Web3 landscape, setting new standards for innovation in the digital asset industry.
Decentralized trust frameworks represent a fundamental transformation in cross-enterprise integration, addressing longstanding challenges in business-to-business interactions. These frameworks leverage Web3 technologies, specifically, Distributed Ledger Technology, Decentralized Identifiers, and Verifiable Credentials to establish inherent trust between organizations without relying on centralized intermediaries. Through cryptographic verification mechanisms, organizations gain enhanced security, verifiable data provenance, reduced reconciliation overhead, and improved operational resilience. The architectural components include a decentralized identity layer providing 99.98% authentication accuracy, credential exchange mechanisms enabling selective disclosure with 99.87% privacy preservation, shared ledger infrastructure ensuring immutable audit trails, and enterprise integration components bridging with existing systems. Implementation patterns such as credential-based API authorization, event-triggered credential issuance, ledger-anchored business processes, and credential-based data exchange deliver substantial improvements in security posture and operational efficiency. Despite significant benefits including 87.3% security enhancement and 73.4% reduction in reconciliation efforts, adoption challenges remain around technical complexity, standards maturity, legacy system integration, and governance frameworks. By addressing these challenges through phased implementation focusing on high-value integration points, organizations can gradually transform their integration landscape toward more secure, transparent, and resilient models that fundamentally change how trust is established in digital business ecosystems.
Abstract— This study integrates blockchain technology and machine learning to enhance credit card fraud detection. Precise fraud prediction is performed using advanced algorithms such as Random Forest, Logistic Regression, XGBoost, and Bayesian models. Tools such as Ganache and MetaMask from Ethereum blockchain facilitate safe and transparent tracking of suspicious transactions. Decentralized and tamper-proof properties of blockchain add reliability, and machine learning adds precision and flexibility. The system is highly accurate and transparent and has the potential to be used to fight financial fraud. Keywords— Credit Card Fraud, Blockchain, Machine Learning, Ethereum, Web3, SMOTE, XGBoost, Streamlit
A S M Touhidul Hasan, Rakib Ul Haque, Larry Wigger, Anthony Vatterott
Counterfeit products cause financial losses for both the manufacturer and the enduser; e.g., fake foods and medicines pose significant risks to the public’s health. Moreover, it is challenging to ensure trust in a product’s supply chain, preventing counterfeit goods from being distributed throughout the network. However, fake product detection methods are expensive and need to be more scalable, whereas a unified traceability system for packaged products is not available. Therefore, this research proposes a product traceability system, named Trusted Traceability Service (TTS), using Blockchain and Self-Sovereign Identity (SSI). The TTS can be incorporated across diverse industries because of its generic and manageable four-layer product packaging strategy. Blockchain-enabled SSI empowers distributed nodes, to verify them without a centralized client–server authorization architecture. Moreover, due to its distributed nature, the proposed TTS framework is scalable and robust, with the use of web3.0 distributed application development. The adoption of Fantom, a public blockchain infrastructure, allows the proposed system to handle thousands of successful transactions more cost-effectively than the Ethereum network. The deployment of the proposed framework in both public and private blockchain networks demonstrated its superiority in execution time and number of successful transactions.
Web3 grant programs are evolving mechanisms aimed at supporting innovation within the blockchain ecosystem, yet little is known on about their effectiveness. This paper proposes the concept of maturity to fill this gap and introduces the Grant Maturity Framework (GMF), a mixed-methods model for evaluating the maturity of Web3 grant programs. The GMF provides a systematic approach to assessing the structure, governance, and impact of Web3 grants, applied here to four prominent Ethereum layer-two (L2) grant programs: Arbitrum, Optimism, Mantle, and Taiko. By evaluating these programs using the GMF, the study categorizes them into four maturity stages, ranging from experimental to advanced. The findings reveal that Arbitrum's Long-Term Incentive Pilot Program (LTIPP) and Optimism's Mission Rounds show higher maturity, while Mantle and Taiko are still in their early stages. The research concludes by discussing the user-centric development of a Web3 grant management platform aimed at improving the maturity and effectiveness of Web3 grant management processes based on the findings from the GMF. This work contributes to both practical and theoretical knowledge on Web3 grant program evaluation and tooling, providing a valuable resource for Web3 grant operators and stakeholders.
ImageChain is a Python application that combines image steganography, blockchain, and Interplanetary File System (IPFS) to create a secure, decentralized system for image metadata management. The system allows users to hide secret messages in images using steganography, keeping data confidential and authentic. The images are then stored on IPFS, which creates a distributed storage hash. IPFS hash and title, description metadata are stored in a local Ethereum blockchain through Solidity-written smart contracts. Updating and retrieving metadata, as well as transferring ownership among Ethereum addresses through Web3.py, are supported by the platform. Image processing is carried out through OpenCV. With these technologies together, transparency, traceability, and tamper-evident record-keeping across the life cycle of the digital image are provided. ImageChain illustrates the value of the integration of steganography, decentralized storage, and blockchain technology to create secure systems for use in digital rights management, forensic processing, and secure communication.
Blockchain technology now stands as a revolutionary power that changes multiple businesses and breaks down established centralized operations.The following research studies blockchain development as it transforms different business sectors through decentralized systems mechanisms.The analysis evaluates the distinct properties of blockchain technology, including distributed ledger systems and immutable design, as well as peer-to-peer architecture that resolves recurring challenges within data protection, privacy, and security domains.Financial institutions are set up to adopt new ways of doing business through blockchain networks, which provide superior models for transaction processes, asset control systems, and regulatory compliance functions.The paper explores extended industry transformations created by blockchain technology, which goes beyond finance into alternate sectors, including energy systems, as well as internet decentralization through Web3.The paper uses extensive research from academic publications and industry documents to clarify blockchain development alongside its substantial transformations for business operations and societal delivery.
Abstract Pixel-Web3 Wallet is a hierarchical deterministic (HD) wallet designed for secure and decentralized asset management across multiple blockchain networks, including Ethereum and Solana. Unlike traditional wallets that depend on browser extensions or centralized servers, Pixel offers a web-based solution with user-controlled security through locally stored seed phrases. This paper explores the wallet’s architecture, security framework, and innovative features, such as real-time balance updates and flexible recovery options. Additionally, the research evaluates the scalability of Pixel and its potential expansion to support more blockchain networks. By eliminating reliance on third-party services, Pixel enhances accessibility while maintaining strong security, making it a promising solution for blockchain enthusiasts, traders, and developers. Keywords: Blockchain, HD Wallet, Cryptocurrency,Web3,Ethereum,Solana, Security
The article argues for the sociocultural contextualization of Web3 affordances by examining play-to-earn gaming in the Philippines. It first outlines how socioeconomic factors promote blockchain technology and cryptocurrency. Against this background, and based on scholarship in cultural communication, anthropology, and critical platform studies, the article illustrates how sociocultural frames shape the interpretation and enactment of blockchain-based gameplay affordances. A Grounded Theory analysis of interviews and documents reveals that players identify persistent access and ownership as technical affordances, performing them through the cultural frame of cockfighting and its digital economy version, the side hustle. The study challenges universalist notions of Web3 adoption, highlighting how technical affordances both support and disrupt sociocultural and economic reproduction through narratives of family, competition, and inclusivity. The research calls for comparative studies on how platform corporations structure societies in emerging economies, how platforms exploit culture as use value, and how adopters strategically utilize Web3 technologies.
THE IMPACT OF ARTIFICIAL INTELLIGENCE ON CLINICAL PRACTICE IN DIABETES MANAGEMENT Muzammil Mohammed Shadan Institute of Medical Sciences, Hyderabad, Telangana, India Background: Artificial Intelligence (AI) is revolutionizing diabetes management by enhancing early diagnosis, optimizing treatment plans, and enabling real-time monitoring. AI-driven tools improve clinical decision-making, reduce complications, and enhance patient adherence, ultimately transforming diabetes care. Aims and Objective: This research aims to investigate the impact of Artificial Intelligence (AI) on diabetes clinical practice, particularly its role in facilitating early diagnosis, tailoring treatment, enabling real-time monitoring, and enhancing decision-making processes. The study intends to evaluate the statistical significance of AI-based interventions and their effectiveness in improving patient outcomes, alongside a comprehensive examination of modifications in clinical protocols and the quality of patient care. Methodology: A retrospective analysis was conducted using clinical data from multiple healthcare facilities that have adopted AI technologies in diabetes management. The review encompassed AI-enhanced glucose monitoring systems, predictive models for disease progression, and individualized treatment strategies. Data were gathered from clinical trials, patient records, and AI-supported decision-making tools over a five-year timeframe. Statistical evaluations were performed using multivariate regression to assess the impact of AI on lowering HbA1c levels, enhancing treatment adherence, and mitigating diabetesrelated complications. Results were analysed by comparing AI-assisted care with traditional treatment approaches. Results: The analysis revealed a statistically significant enhancement in patient outcomes associated with the integration of AI in diabetes management. Patients utilizing AI-based monitoring systems experienced an average HbA1c level reduction of 1.2%, with a p-value of less than 0.01, indicating statistical significance. AI-driven predictive models demonstrated an 85% accuracy rate in anticipating diabetes progression, facilitating timely interventions, and decreasing the incidence of severe complications, such as diabetic retinopathy, by 40%. Furthermore, AI personalized treatment algorithms resulted in a 25% increase in medication adherence, supported by an odds ratio of 2.5 (95% CI: 1.9–3.1), suggesting that patients engaged in AI-supported interventions were significantly more likely to adhere to prescribed treatment plans compared to those receiving traditional care. AI tools have significantly improved clinical decision-making processes. The integration of real-time data from AI-assisted glucose monitoring systems has enhanced clinician responsiveness to both hypo- and hyperglycaemic incidents, leading to a 30% reduction in emergency hospitalizations. Additionally, the implementation of AI-enabled decision support systems has allowed clinicians to identify optimal treatment plans more swiftly, decreasing the average time required to achieve glycaemic control by 20%. This not only accelerates patient recovery but also alleviates the strain on healthcare resources. The statistical analysis of these outcomes demonstrates a high level of accuracy in predicting and enhancing clinical results. Conclusion: This study’s findings indicate that AI has profoundly transformed diabetes clinical practice, resulting in significant improvements in patient outcomes, adherence to treatment, and overall healthcare efficiency. AI-driven tools provide a tailored approach to diabetes management, enabling early diagnosis, predictive modelling of disease progression, and continuous monitoring. These advancements not only improve the accuracy of clinical decision-making but also help mitigate long-term complications related to diabetes. As AI technology continues to advance, its influence on chronic disease management is expected to grow, offering even greater advantages for both patients and healthcare providers. Keywords: Artificial intelligence in diabetes, clinical decision support, predictive analytics INTEGRATING REST STRATEGIES AND AI-DRIVEN TOOLS TO ENHANCE DIABETOLOGIST PRODUCTIVITY AND WELLNESS Harsh Atul Hirani, Alok Modi1, Dr. Bharat Saboo2 Life Care Centre for Diabetes, Hyderabad, Telangana, 1Dr. Alok Modi’s Diabetes Center and Kevalya Hospital, Thane, Maharashtra, 2Prayas Diabetes Center, Indore, Madhya Pradesh, India Background: Diabetologists face significant challenges, including managing extensive clinical workloads, adapting to rapid advancements in diabetes care, and addressing the emotional toll of chronic disease management. Burnout among diabetologists negatively impacts their personal well-being and the quality of care delivered to patients. There is a pressing need to integrate wellness strategies with innovative technological solutions to support diabetologists in sustaining high performance and improving patient outcomes. Aim and Objective: To assess the effectiveness of combining diversified rest strategies with AI-driven tools in enhancing the productivity, wellness, and patient care outcomes of diabetologists. Methodology: This study reviewed wellness practices and their applicability to diabetologists, focusing on seven types of rest: physical, mental, emotional, spiritual, social, sensory, and creative. Tailored interventions were developed for each rest type, incorporating AI-driven tools such as Large Language Models (LLMs), wearable devices, and mindfulness applications. Key outcomes were analyzed in terms of their impact on burnout reduction, productivity improvement, and patient satisfaction. Methods and specific strategies for each type of rest are detailed in Figure 1.Figure 1: Methods of rest, which outlines various strategies for achieving different types of rest: physical, mental, emotional, spiritual, social, sensory, and creative, along with supportive technologies and practices for eachResults: The integration of rest strategies and AI tools produced notable benefits. Wearable devices optimized physical activity and sleep patterns, improving focus and reducing fatigue. LLMs alleviated cognitive overload, supporting decision-making and administrative efficiency. Emotional resilience improved through AI-based journaling and coaching, while mindfulness apps enhanced mental clarity and reduced stress. Networking platforms facilitated meaningful social connections, and brainstorming tools fostered innovation, aiding creative rest. These interventions collectively enhanced work-life balance, reduced burnout, and improved patient care outcomes. Conclusion: Integrating rest strategies with AI-driven tools offers a transformative approach to addressing burnout and enhancing productivity among diabetologists. By fostering sustainable well-being practices, this framework ensures improved physician wellness and better diabetes care delivery. Keywords: Artificial intelligence, burnout prevention, diabetologist wellness, rest strategies, technological solutions, work-life balance NOVEL MARKERS FOR EARLY ONSET OF DIABETES: EVALUATION OF GLYCAEMIC VARIABILITY IN TYPE- 2 DIABETES MELLITUS USING CONTINUOUS GLUCOSE MONITORING SYSTEM: A PILOT STUDY Ravi Kumar, Santosh Kumar Singh Department of Internal Medicine, Armed Forces Medical College, Pune, Maharashtra, India Background: Continuous glucose monitoring (CGM) systems allow us to perform real-time monitoring of blood glucose levels. It can be used for assessment of the Glycemic Variability (GV) in subjects who are at high risk for the development of Diabetes, which can be taken as an early marker of the onset of diabetes. Aimand Objective: To detect glycaemic variability as an early marker for derangement in blood sugar using the CGM system in medium-high-risk subjects for diabetes. To evaluate GV in individuals with medium-high risk groups for type-2 diabetes mellitus and to find the association of GV with beta-cell dysfunction and Insulin resistance. Methodology: The study was an analytic cross-sectional study, done in a tertiary care hospital in western Maharashtra. Since this was a pilot study a sample size of 60 was taken. All patients reporting to OPD who are in medium or high-risk groups for Diabetes as per the IDRS were interviewed, their anthropometric parameters, and basic laboratory parameters were taken and the CGM system (Abbot freestyle Libre) (> 3 days -14 days) were attached to access the parameters of GV. The GV parameters were calculated using the standard software, and HOMA-IR & HOMA-B were calculated using the standard formulas. The data tabulated in the Excel sheets were evaluated using the statistical methods and software (SPSS) to access the prevalence of GV using its parameters i.e. early dysglycemia in mediumhigh-risk individuals for diabetes. Results: The various parameters GV like mean, standard deviation, LI, J index, HBGI, CONGA, and MAGE, were found to be high in the study population and with a statistically significant correlation between the various parameters of glycemic variability [Scatter plot diagram of the various parameters of GV is as attached as Figure 1].Figure 1: Scatter plot diagram of the various parameters of GVConclusions: The present study has helped us in the early detection of diabetes in patients having moderate to high risk for diabetes and will adequate preventive and therapeutic interventions will be advised to the individuals for management and prevention of complications. Need for more similar studies in various patient subsets of healthy individuals, and patients with chronic diabetes. Keywords: CGM, glycaemic variability, type-2 diabetes mellitus RETROSPECTIVE STUDY CORRELATING SELF MONITORING BLOOD GLUCOSE (SMBG) VALUES WITH HBA1C Abhisekh Raha Lumding Divisional Railway Hospital, Indian Railway Health Services, Lumding, Assam, India Background: Self‐monitoring of blood glucose (SMBG) plays an important role in the management of Type I Diabetes. SMBG has many proven benefits in Type 1 diabetics such as, minimizing glucose variability, helping to predict severe hypoglycemia and aiding the achievement of hemoglobin A1c (HbA1c) targets. Aim and Objective: Correlating HbA1c with multiple SMBG values in persons with Type 1 DM. Methodology: Retrospective analysis of 10 persons’ SMBG values with Type I DM above the age of 18,who were measuring their pre‐meal and bedtime sugars regularly (more than 20 times per week) was done. SMBG values between 70 to 180mg/dl were considered Points In Range (PIR). The mean HbA1c values of these persons were observed and correlated with SMBG data. Results: The SMBG values were more than 70% in range in 2 out of 10 persons. The mean HbA1c of these persons was 6.9%. 4 persons with 65% to 70% PIR had mean HbA1c of 7.2%, 2 persons with 60 to 65% PIR had mean HbA1c of 7.5%, 1 person with 55 to 60% PIR had mean HbA1c of 8.1% and 1 persons with 45 to 50% PIR had mean HbA1c of 8.4%. Conclusions: PIR correlates well with HbA1c in persons with Type I DM who measure their blood glucose more than 3 times in a day. Keywords: HbA1C, point in range, SMBG USAGE OF TECHNOLOGY IN GLYCEMIC CONTROL FOR RURAL PEOPLE WITH DIABETES Abhisekh Raha Lumding Divisional Railway Hospital, Indian Railway Health Services, Lumding, Assam, India Background: In a technological triad model, a connection is established between doctor and two voluntary workers to monitor remotely the patient’s glycemic levels and educate the patients online via conference video calls following up and daily monitoring the regular treatment and diet intake. Aim and Objective: Here, we studied the role of teamwork in achieving glycemic control in rural patients using a cost-effective technological triad model. Methodology: 65 people with diabetes were selected based on inclusion criteria, out of which only 30 agreed to participate in the study. Inclusion criteria: Men and women aged between 30 to 60 years with type 2 diabetes mellitus (T2DM), having an uncontrolled fasting blood sugar (FBS), post prandial blood sugar (PPBS) and HbA1c between 7.1 - 10.5 %. Exclusion criteria: Type 1 diabetes mellitus (T1DM), pre-existing renal, hepatic, or cardiac disease, HbA1c > 10.5 %. Tie up was made with the two voluntary worker to monitor remotely the patient’s glycemic levels and educate the patients online via conference video call following up and daily monitoring the regular treatment and diet intake. A technological triad was established two voluntary workers and the treating doctor. Results: 30 patients were randomly divided into two groups, i.e., group A and B which comprised of 15 patients each. Parameters such as FBS, PPBS, HbA1c and lipid profile, complete blood count, complete urinary analysis, Liver function test, Serum Creatinine, BMI were collected on day 1 and follow up data which includes measurement of FBS, PPBS, HbA1c and lipid profile were collected at 3 months and at 6 months. Data obtained was measured with SPSS version 17 software. A 10 were on daily remotely with the help of worker for diet regular of a in 6 months the B were on day 1 and these patients of B were the of diet and these groups were on in the for There was statistically significant in the mean FBS, PPBS, HbA1c and lipid profile at the of the study. the follow up 3 months was observed that the mean FBS, and were significantly in the group A 6 months was observed that mean FBS, PPBS, and was significantly in group A There was statistically significant in the mean levels between Conclusion: technological has a of to is a that the of and the not only for the patient but also for the care A with a physician not achieve glycemic control as in group B compared to a approach and monitoring that the adherence to medication and diet as demonstrated in group Keywords: Health rural technology triad FOR AND USING AI Hospital, India Background: and age related in patients with diabetes, for multiple can in early The intelligence (AI) on the on can for and The of AI for in diabetes care Aim and Objective: To for and using the AI in patients with diabetes. Methodology: This was a study of patients with diabetes at a tertiary care hospital in India using the were analyzed by the for each Results: In this study were was present in in and or in Patients for at 1 or 2 was and disease was in Conclusion: in patients had and 1 in 2 for at 1 in diabetes care can significantly improve early detection and management of multiple This study the need for to while treating patients with diabetes. The AI offers an to this approach and Keywords: comprehensive diabetic retinopathy, AI OF CONTINUOUS GLUCOSE MONITORING (CGM) IN GLYCEMIC CONTROL OF DIABETES IN A STUDY IN THE OF Department of Medicine, Hospital, Hospital, India Background: Continuous glucose monitoring (CGM) systems have as a for managing diabetes, real-time glucose data and that better glycemic This study the effectiveness of CGM systems in improving glycemic control among patients with diabetes in care Aim and Objective: the effectiveness of CGM in improving glycemic control among diabetes patients in a care the impact of CGM on diabetes management and patient adherence to treatment plans in the of the role of CGM in reducing complications associated with diabetes, such as glycemic control and quality of To evaluate the of care to CGM into clinical and its on their Methodology: A was conducted patients with type 1 and type 2 diabetes care were randomly to a CGM group or a control group using traditional of blood glucose HbA1c levels, of and outcomes on quality of were conducted at and months. Results: Patients in the CGM group a significant reduction in HbA1c levels compared to the SMBG group reduction of The CGM group also experienced per compared to the SMBG group per Additionally, outcomes and improved quality of among CGM Conclusion: The study demonstrates that CGM systems significantly improve glycemic control and reduce in care CGM also greater with their diabetes management. These findings that CGM into care practices can enhance diabetes management and improve patient outcomes, a for implementation in care Keywords: Continuous glucose monitoring, diabetes management, glycemic patient care OF IN DIABETES IN 2 Diabetes India Background: The of solutions to reduce and to and personal as well as has in years The treatment and care of patients with diabetes and its and regular monitoring and by solutions to standard in and provide access to diabetes care. Aim and Objective: The of this study was to the and glycemic in patients who up as their treatment in diabetes care. Methodology: A retrospective analysis was done of the patients in Diabetes at diabetes care A and follow up was done for months along with through from to Patients were in person 6 months as per Parameters like in of patients their follow up and adherence and were Results: It was observed that out of reduction of A1c was in of patients Conclusion: to patient care through the glycemic control and overall well of the patients. Keywords: type 2 diabetics INTEGRATING TOOLS IN DIABETES THE IN CLINICAL Health India Background: Type Diabetes is a chronic in which personalized disease management and adherence to The Diabetes for the of Diabetes the of patient’s approach for management. a personalized interventions with Aim and Objective: To evaluate the impact of the in managing clinical outcomes for individuals with Methodology: This study data from 20 patients in personalized diet and plans, glucose and regular cognitive The in in Range Range Range fasting blood glucose and blood glucose was evaluated as outcomes and in lipid and as outcomes. Results: The mean age of patients in the study was with a of the mean HbA1c significantly from to with and of and of patients were in the which to 85% by the of the study. Patients who improved their a mean HbA1c reduction of Additionally, from to while at compared to Additionally, a reduction in and reduction in BMI was Conclusion: The significant impact on clinical outcomes, along with high patient adherence, as a therapeutic that and the standard of care for management. Keywords: Diabetes care, AND CLINICAL IMPACT OF THE ON AND GLYCEMIC CONTROL IN Health India Background: Type 2 Diabetes a significant and healthcare a focus on both glycemic control and management. like Continuous (CGM) with platforms increase patient by real-time glucose The Glycemic in Diabetes this approach by personalized patient care. Aim and Objective: To evaluate the effectiveness of the in facilitating improvements in quality of and Health Methodology: This study 20 patients with who were by the physician to be the were personalized plans, and cognitive with CGM The outcomes measured were improvements in evaluated through reduction in the of an increase in and parameters such as outcomes evaluated through in and hospital Results: This study demonstrated with 85% of patients reducing and an average reduction of These were in better glycemic with HbA1c reduction observed in of is by in management and sleep quality by and Insulin were reduced in of and the between was for 25% of patients who an HbA1c of more than Conclusion: The in improving glycemic and in offers a to enhance outcomes and patient care. Keywords: Diabetes, glycemic A RETROSPECTIVE STUDY OF OF IN AND USING TOOLS Hospital, India Background: is of the complications of Diabetes and a leading of and as disease which leading to and in of DM glycemic with associated are the is on and as and and based on of and disease for early detection using as AI is revolutionizing management this study to incidence in and identify healthcare and Aim and Objective: for early detection using as AI is revolutionizing management this study to incidence in and identify healthcare and Methodology: A Retrospective study was conducted with data of patients from and from All were this study through examination with the help of AI software. such as of diabetes were taken into and and were calculated between and Results: and were both out of and were both out of Patients with also had significantly disease and HbA1c values and a prevalence of such as Conclusion: This a of in population compared to to glycemic of diabetes and of The findings the need in managing complications AI tools and predictive analytics for early detection risk and By AI a role in healthcare Keywords: Artificial intelligence, diabetic retinopathy, AI AND - A TO DIABETES MANAGEMENT of for India Background: Diabetes and have levels with people with diabetes in and indicating this will to by of with many individuals of their The is with healthcare in and to by healthcare systems focus on care, a need for innovative preventive strategies. and AI-driven transformative tools for personalized interventions, enabling real-time monitoring and predictive for diabetes prevention and management. This study a that these technologies while incorporating a for are not Aim and Objective: this study is to focus on & the in this innovative Methodology: A of with diabetes or evaluated the of a Health & This to AI-driven for personalized and technology for real-time and levels were Results: of that adherence to through AI 85% in AI-driven to blood sugar levels and improve 70% found the of for the impact of on be to to their were not demonstrated a of healthy by and personalized Conclusion: this of we are to a of this approach on this and This approach has the to care for by combining with studies its through and its be on and for more and This approach has the to care for by combining with studies its through and its be on and for more Keywords: AI coaching, intelligence, CGM, diabetes, analysis, predictive care AI and Madhya Pradesh, India Background: The the effectiveness of personalized interventions in improving intelligence (AI) platforms like were used in for but their in their in care. To these we developed algorithms and for AI to which are to improve Aim and Objective: The of this study is to investigate the effectiveness of of AI in improving compared to by a Methodology: with type-2 diabetes from diabetes years and glycaemic from to were in the study. group comprised subjects age years and control group 15 subjects age was Serum and were compared at the and of study A the of control group for group was by a a software to and these to Results: and control group statistically significant in study parameters but group performed better as compared to control 1: parameters with intelligence parameters with by Intelligence with in improving in care Keywords: Artificial intelligence, diabetes, personalized AI-DRIVEN EVALUATION OF FOR DIABETES of for India Background: a role in managing diabetes by improving glycemic and the variability in and of their Artificial Intelligence evaluations an innovative approach to and This study AI algorithms to evaluate the of different in diabetes management, offering into glycemic outcomes and Aim and Objective: The of this study is to the effectiveness of various including the and in improving glycemic control and through AI-driven Methodology: AI-driven and of trials, and studies between and was The analysis measured the impact of on fasting glucose and outcomes. and were calculated using Results: HbA1c reduction of (95% CI: to and improved reduction of (95% CI: to with significant and HbA1c reduction of (95% CI: to with benefits. HbA1c reduction of (95% CI: to with reduction of and improvements in glycemic control and with better adherence Conclusion: AI-driven that the and provide the glycemic The analysis the of personalized interventions in diabetes management. research platforms and data to and This study the transformative role of AI in studies are required to AI-driven and their applicability technology with continuous glucose monitoring systems enhance personalized strategies for optimal outcomes. Keywords: AI diabetes management, patterns, glycemic personalized
Objective: This research aims to explore how elements of immersive brand experiences in the metaverse-including gamification, interactive narrative, social interaction, visual realism, and the use of blockchain technology and NFTs-contribute to emotional engagement, belonging, and consumer loyalty. Research Design & Methods: This study uses an exploratory qualitative approach with in-depth interview techniques with more than 20 participants who actively interact with brands on metaverse platforms such as Roblox, Decentraland, and The Sandbox. Data analysis was conducted through a thematic approach to identify patterns of consumer engagement and perception. Findings: The results show that immersive experiences that are participatory and personalized drive strong emotional attachment to brands. Gamification increases intrinsic motivation, interactive narratives deepen the emotional experience, and social interactions form a sense of community. Realistic visualizations increase trust, while NFT ownership provides a sense of exclusivity and control that strengthens consumer loyalty. Implications & Recommendations: Brands are advised to design holistic metaverse experiences, integrating game elements, stories, communities and blockchain-based digital assets to build more meaningful and sustainable relationships with consumers. Contribution & Value Added: This research makes a theoretical contribution to the study of digital marketing and brand engagement by highlighting the importance of a multidimensional approach based on immersive technology in building consumer loyalty in the Web3 era.
Wenjie Li, Graciela Corral de Zubielqui, Sally Rao Hill
This study explores the evolving intersection of branding and digital assets through the lens of non-fungible tokens (NFTs), focusing on their role in shaping dynamic brand experiences. We propose a typology framework that examines how NFTs contribute to brand experience design and provides their implications for brand-consumer relationships. The research analyses five distinct NFT functions—storytelling media, identity badges, product access pass, change medallion and gamification element—and connects these roles to five types of brand experience design: brand heritage, community, product orientation, collaboration, and gamification. The findings contribute to digital branding literature by advancing the understanding of the function of digital assets within the brand experience design. This study offers a structured understanding of the value of NFTs in digital brand building by providing the roles NFTs play in brand experience. It explores the dynamic potential of brands to integrate NFTs into their strategies in the evolving Web3 environment. Finally, the industry pattern identified in this study provides insights for scholars and practitioners seeking to utilise NFTs effectively.
Open access
Consumer Behavior in Brand Consumption and Identification
Alex Wong, Duncan McFarlane, Charlotte Ellarby, M.B. Lee · 5 authors
Twenty-five years ago, the specification of the Intelligent Product was established, envisaging real-time connectivity that not only enables products to gather accurate data about themselves but also allows them to assess and influence their own destiny. Early work by the Auto-ID project focused on creating a single, open-standard repository for storing and retrieving product information, laying a foundation for scalable connectivity. A decade later, the approach was revisited in light of low-cost RFID systems that promised a low-cost link between physical goods and networked information environments. Since then, advances in blockchain, Web3, and artificial intelligence have introduced unprecedented levels of resilience, consensus, and autonomy. By leveraging decentralised identity, blockchain-based product information and history, and intelligent AI-to-AI collaboration, this paper examines these developments and outlines a new specification for the Intelligent Product 3.0, illustrating how decentralised and AI-driven capabilities facilitate seamless interaction between physical AI and everyday products.
Hongzhou Chen, Chenyu Zhou, Abdulmotaleb El Saddik, Wei Cai
In the rapidly evolving Web3 world, non-fungible token (NFT) communities are reshaping the formation, distribution, and activation of social capital in ways distinct from traditional models. However, despite their growing impact on societal prosperity, a comprehensive understanding of social capital dynamics within Web3 NFT communities remains limited. This study explores the Mfers community, a key example within Web3 NFT ecosystems. By analyzing social media and blockchain data and using a Delphi method-based human-large language model (LLM) collaboration, we uncovered unique social capital patterns across six dimensions. Our findings highlight a compelling blend of decentralization, inclusion, trust, and empowerment but also raise critical questions about wealth inequality, content quality, and ethical challenges. Based on the findings, we discussed the uniqueness of social capital in Web3 NFT communities, the tension between technical and power decentralization, and the multidimensional nature of societal prosperity. We also suggested directions for future research on decentralized online communities in the CSCW field. This study provides a systematic perspective on social capital in Web3 NFT communities and introduces an innovative human-LLM collaborative analysis, offering insights into the design and governance of benign decentralized online communities.
The implementation of an Electronic Health Record (EHR) management system has proven to be an effective alternative to the traditional manual method of handling medical records. However, concerns about data privacy and security persist in the healthcare sector, particularly due to the outdated centralized database systems that have failed to meet modern expectations.To address this, a secured blockchain database management model for medical based organization is designed for healthcare organizations, emphasizing a patientcentered approach. This model efficiently handles and maintains individuals’ health records using blockchain technology. Developed based on object oriented analysis and design methodology, the system was built using Visual Studio Code (VS Code), PHP, the Ethereum network, Web3.js, and Ganache.Blockchain technology provides the foundation for decentralizing patient information and ensuring secured data storage. Smart contracts arecritical in protecting patient privacy and data integrity by facilitating secured transactions, each modification is verifiable and propagated across the network. To further enhance the model’s security and integrity, a cryptocurrency wallet such as MetaMask is integrated, offering a centralized yet secure repository for medical records that authorized users like patients and healthcare providers can access anytime.By providing a robust platform for securely managing data with customizable access permissions and enabling the safe exchange of patient records, this blockchainbased system surpasses legacy systems in efficiency, reliability, and accessibility in the healthcare domain
This article analyzes the possibilities of developing an adaptive learning model based on Web3 technologies (blockchain, smart contracts, tokenization, and decentralized applications) and implementing it in the higher education system. The study used mathematical modeling and systems analysis techniques to provide a learning experience tailored to the individual needs of students. The model provides for a correspondence between the level of knowledge of students and the complexity of educational materials, updating the level of knowledge based on a Bayesian model, storing assessment results on a blockchain network, and an incentive system through tokens. The experimental results showed that the Web3-based adaptive learning system increased the level of mastery by 18% and motivation indicators by 22%. Also, transparency in assessment and timely feedback mechanisms have built student confidence.
Since Diffie and Hellman's pioneering work on asymmetric cryptography in 1976, digital signature technology has evolved through three phases—theoretical foundation, standardization, and diversified innovation—emerging as a cornerstone of trust in digital societies. Theoretically, foundational frameworks were established by RSA, DSA, and Schnorr algorithms. Standardization efforts, including NIST DSS, ISO/IEC series, and national systems (e.g., China's SM2/SM9, Russia's GOST), fostered a multipolar ecosystem. Extended-attribution technologies (blind, group, and ring signatures) addressed privacy and scenario-specific demands. Current challenges, such as quantum computing threats and privacy-regulation trade-offs, drive advancements in post-quantum cryptography (lattice-based signatures, hash-based XMSS) and privacy-enhancing mechanisms (verifiably encrypted signatures, homomorphic signatures), guided by ISO/IEC redactable standards and NIST's post-quantum initiative. Moving forward, digital signatures will deepen capabilities in provable security, quantum resistance, and adaptive policy control, underpinning trust architectures for emerging ecosystems like Web3 and the metaverse.
Open access
Cryptography and Data Security
Digital and Cyber Forensics
Advanced Steganography and Watermarking Techniques
Explosion-proof apparatus is a must in hazardous areas especially in anindustrial setting where certification is required to meet certain safety levels.Conventional certification mechanisms tend to be slow, non-transparent and vulnerable to forgery of documents and delays, particularly in the context of cross border transactions.This article presents the architecture of a blockchain-based certification platform, which could contribute to transparency, traceability, and efficiency in the certification lifecycle of explosion-proof equipment.It includes Ethereum smart contracts, IPFS (InterPlanetary File System) to store the comprehensive test reports on a decentralized platform, and a role-based web application interface for different kinds of users such as manufacturers, testing labs, certification bodies, and field auditors.Smart contracts are responsible for generating, revoking and handling certificate access control, all certification metadata and file hashes are suitably safeguarded on the blockchain, allowing records to remain tamper-proof and verifiable.A working prototype was implemented in Goerli Ethereum testnet and developed as React application.js frontend, Web3.js, IPFS and Architecture for a Blockchain-based Certification Platform for Explosion-Proof Devices https://iaeme.com/Home/journal/IJCET499
Abstract—The integrity and transparency of voting systems are fundamental to the democratic process; however, traditional voting mechanisms often encounter issues such as fraud, manipulation, limited transparency, and centralized control. To address these challenges, this research proposes a decentralized voting system utilizing blockchain technology. The system leverages the Ethereum blockchain, smart contracts developed in Solidity, and a React.js-based frontend integrated with Web3.js and MetaMask to ensure secure voter authentication, transparent vote casting, and immutable vote recording. Voter and candidate registrations are managed through decentralized smart contracts, and all transactions are permanently stored on the blockchain, providing public verifiability while preserving voter anonymity. Development and testing were conducted in a simulated environment using Ganache and the Truffle Suite, allowing for extensive validation of system functionalities. Experimental results demonstrate enhanced security, real-time result computation, prevention of double voting, and elimination of any single point of failure. This decentralized architecture significantly improves trust, transparency, and security in electoral processes, offering a scalable and reliable model for the future of electronic voting systems. Index Terms— Blockchain, Decentralized Voting, Ethereum, Smart Contracts, Solidity, Web3.js, MetaMask, Ganache, Truffle Framework, E-voting Systems.
Abstract - In the rapidly advancing digital era, the requirement for secure, transparent, and reliable data-sharing mechanisms has become increasingly critical across various sectors. Traditional centralized data-sharing models suffer from inherent limitations, including vulnerability to data breaches, unauthorized access, single points of failure, and insufficient transparency in data access and audit trails. These challenges compromise not only the confidentiality and integrity of sensitive data but also erode stakeholder trust in digital systems. To overcome these issues, this paper presents BlockShare, a blockchain-powered, decentralized framework designed to facilitate secure, tamper-proof, and efficient data exchange. BlockShare leverages the foundational principles of blockchain technology—namely decentralization, immutability, and transparency—to enhance the robustness and reliability of data-sharing architectures. The proposed system eliminates central authority dependence by distributing data storage and control across a decentralized ledger, thereby minimizing potential attack vectors and ensuring continuous data availability. To regulate data access and maintain policy enforcement, smart contracts written in Solidity are integrated within the system. These smart contracts autonomously manage permissions and user authentication, ensuring that only verified and authorized parties can access specific datasets, with every action recorded immutably on the blockchain. Moreover, data confidentiality is preserved through the implementation of AES-256 encryption, a widely recognized standard for high-security data protection. Prior to storage, all data is encrypted and then uploaded to a decentralized file system, specifically the InterPlanetary File System (IPFS), which provides enhanced fault tolerance, redundancy, and distributed access. This dual-layered approach—combining blockchain for governance and IPFS for storage—ensures that data remains protected both in transit and at rest. By integrating smart contract-based automation, robust encryption protocols, and distributed storage solutions, BlockShare delivers a scalable and resilient infrastructure for data exchange. The system is particularly applicable in domains requiring stringent data protection and transparency, such as healthcare, finance, legal, and government sectors. Through this innovative approach, BlockShare aims to redefine trust in digital interactions and lay the groundwork for the next generation of secure data-sharing ecosystems. Keywords - Blockchain, Data Sharing, Decentralized Storage, Smart Contracts, Encryption, IPFS, Ethereum, Security, Data Privacy, AES-256, Web3, Authentication, DApp, Decentralization, Access Control.
MintMart is a decentralized Web3 platform designed for simplifying the process of buying, selling and creating NFTs (Non Fungible Tokens). Currently there are various existing platforms in the market which have features like transparent transactions, auctions in marketplace etc. However they lack some focus in areas of secure transactions, irregular platform fee and royalty compensation for owners which plays an important role in such platforms. The proposed system is designed with an objective to encounter such problems and make the use of NFT marketplace more seamless for the users. The proposed system uses blockchain technology to confront the existing issues. Royalty compensation distribution becomes easier using smart contracts and libraries like OpenZeppelin which helps in better integration of royalty features using its ERC-721 standard. Also MintMart offers fixed minim MintMart is a decentralized Web3 platform designed for simplifying the process of buying, selling and creating NFTs (Non Fungible Tokens). Currently there are various existing platforms in the market which have features like transparent transactions, auctions in marketplace etc. However they lack some focus in areas of secure transactions, irregular platform fee and royalty compensation for owners which plays an important role in such platforms. The proposed system is designed with an objective to encounter such problems and make the use of NFT marketplace more seamless for the users. The proposed system uses blockchain technology to confront the existing issues. Royalty compensation distribution becomes easier using smart contracts and libraries like OpenZeppelin which helps in better integration of royalty features using its ERC-721 standard. Also MintMart offers fixed minimal platform fee making it more accessible for users The proposed system can successfully support multiple digital formats like images, videos etc. and has also achieved cross chain compatibility allowing users to interact with various blockchain networks. MintMart plans to expand its features in the near future. These include personalized recommendations, live bidding for NFTs on the marketplace, etc., thus aiming to increase user engagement. The proposed system can successfully support multiple digital formats like images, videos, etc., and has also achieved cross-chain compatibility, allowing users to interact with various blockchain networks. MintMart plans to expand its features in the near future. These include personalized recommendations, live bidding for NFTs on the marketplace, etc., thus aiming to increase user engagement.
Abstract- The evolution of blockchain and Web3 technologies has paved the way for decentralized application platforms that enable transparent, tamper-proof transactions without relying on centralized servers. However, existing solutions such as Gitcoin and Giveth are either too complex or unsuitable for modular deployment in educational and lightweight environments. This paper proposes MetaSuite, a role-based, blockchain-driven Software-as-a-Service (SaaS) platform that enables users to create, transfer, and donate tokens while allowing an administrator to securely withdraw funds. Built entirely on the Ethereum blockchain using Solidity smart contracts, MetaSuite operates without a backend and integrates wallet-based authentication via MetaMask and Ethers.js. The platform ensures transparent fund management through on-chain event logging and role-based access controls. Performance evaluations on the Ethereum HoleskyTestnet demonstrate the system’s reliability, gas-efficiency, and real-time responsiveness. By eliminating backend dependencies and emphasizing traceability, MetaSuite serves as a minimalistic yet scalable Web3 solution suitable for academic, experimental, and small-scale real-world deployments. Keywords—Blockchain, Web3, Smart Contracts, Ethereum, MetaMask, Tokenization, SaaS, Ethers.js, Decentralized Applications.
Sarthak Nimje, Rushab Taneja, Om Baviskar, Rachana Patil
Educational institutions face significant challenges with event attendance verification, including manual document validation, fraud risks, and delayed approval processes. This study introduces ElizaEdu, a novel decentralized AI agent system utilizing Ethereum blockchain and ElizaOS to automate and secure attendance verification workflows for academic events. The proposed system integrates autonomous AI agents to handle document validation, approval processes, and ERP integration, while utilizing blockchain technology for immutable record-keeping. The system employs four specialized agents: RequestBot for initial verification, VerifyBot for teacher validation, ApproveBot for department head confirmation, and ERPBot for automatic attendance updates. Through a 3-month pilot implementation with 120 students and 15 faculty members, ElizaEdu demonstrated an 85% reduction in verification time, complete elimination of document fraud, and 84% decrease in administrative workload. The system achieves 97.3% accuracy in document validation and 100% data integrity through blockchain verification. This study presents the architecture, implementation details, and evaluation results, demonstrating ElizaEdu’s effectiveness in transforming attendance management in educational institutions.
With the rapid expansion of blockchain applications, smart contracts are becoming increasingly complex, making the automated detection of contract vulnerabilities more critical than ever. Large language models, due to their advanced code comprehensive ability, are considered to have the potential to undertake the task of automated software vulnerability discovery. Although there have been empirical studies on ChatGPT's automated discovery of contract vulnerabilities, the current empirical research has not addressed how well ChatGPT can detect logical vulnerabilities in smart contracts or whether ChatGPT's detection performance for logical vulnerabilities can be improved. To fill this gap, this study collected and organized seven types of logical vulnerability source codes from 6165 real smart contract audit reports and three datasets, such as Web3Bugs, and used this database to validate ChatGPT's detection capability for logical vulnerabilities. To improve ChatGPT's accuracy in detecting logical vulnerabilities, we fine-tuned ChatGPT with a dataset marked with a specific method, achieving an average accuracy rate of 95% for single vulnerability detection per training session. We improved the original marking method to increase further the number of vulnerabilities that a single model can detect. We used a specific completion marking format, ultimately enabling ChatGPT to detect various logical vulnerabilities. In terms of enhancing model scalability, we found a special training set marking method that allows for the addition of detectable vulnerability types through secondary training.