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.
Panagiotis Chatzigiannis, Ke Wang, Sunpreet S. Arora, Mohsen Minaei
Modern Web3 wallets offer hybrid recovery solutions that combine multiple key recovery methods to balance security, availability, and usability. These methods include secret sharing of wallet private keys, encrypted cloud storage, and smart contract-based advanced recovery functionalities. However, such combined approaches can introduce new attack vectors that are not present in standalone recovery solutions. In this work, we propose a formal security analysis frame-work for blockchain/Web3 wallet designs with key or asset recovery functionalities. To assess whether a wallet design is secure, our framework considers several factors, including user availability and responsiveness to malicious actions, co-custodianship with external parties, the total value of assets managed by the wallet, and the reputation of the entities chosen by the user to facilitate spending or recovery functionalities. Through probabilistic model checking, our framework identifies the conditions under which a wallet design remains secure. We also include two examples of Web3 wallet designs with composite recovery mechanisms (inspired by existing designs) to demonstrate the effectiveness of our framework.
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
Blockchain has revolutionized various industries by incorporating its powerful security and robust capabilities that are used to securely share asserts. Industries are changing and adapting themselves to utilize this new technology to yield a no middleman management system. Education is one such sector which has been influenced by a lot new technology. Blockchains are security systems that are being primarily used for its decentralization nature that makes a ledger-like system to store responses and maintain context by sharing it all over the network. In this paper Blockchain model integrated with RPA is used to secure & share student academic certificates, final year projects and their works which is aimed to make a trustful profile for the University or college to showcase student’s performance. This project is to create a secure digital platform that helps in sharing the academic and other authorized certificates in a private college network using blockchain that helps in issuing certificates and managing them in a distributed manner and RPA is integrated to automate repetitive processes like issuing and retrieving data. The paper explores the usage of the Blockchain system and the decentralized model method to solve the problem of unauthorized and fake profiles of academic certificates, student data and their work profile in their course period. This paper helps in bringing a web3 technology in the learning society and fixing outdated processes of handling the student data and their years of training certificates. This paper is all about a private blockchain network with Rpa for Education society to handle data securely.
Amit Pandey, Aastha Sawhney, Geeti Sharma, Divya Singh
Web3 and the Metaverse are transforming the world of online marketing by providing better support for customers and predictive features. It decentralizes data ownership, giving control back to the users, and provides marketers ok transparency and trust like never before. With immersive, interactive environments, the Metaverse allow brands to engage consumers in real-time, creating emotional bonds and personalized experiences. This data collection of customers and their behavioral patterns results in the advanced behavioral analytics and AI-based predictions of customer trends and buying behavior. Businesses are changing their customer engagement strategies through implementing NFTs, gamification and virtual storefronts, resulting in better brand loyalty and retention. As Web3 and the Metaverse mature, they reshape digital marketing into a much more user-centric, data-resilient and interactive ecosystem, while improving the quality of consumer care and the precision of demand forecasting.
Digital Marketing and Social Media
Consumer Behavior in Brand Consumption and Identification
Yeduguri Geethika, S. Arun Kumar, Neeli Hari Kiran, V Nallarasan
In an era of swift technological progress in healthcare, there is an urgent need for solutions that elevate data security, enhance transparency, and streamline patient management. This study proposes an innovative healthcare system underpinned by blockchain technology to guarantee robust data integrity, protect patient privacy, and secure medical transactions. The architecture employs the Flask framework for web development, PostgreSQL for database handling, and the Ethereum blockchain with Web3 integration for maintaining decentralized records. Among its core features are fortified user authentication, tamper-proof prescription storage, a comprehensive hospital search tool, and one-time password (OTP) verification. Leveraging the decentralized nature of blockchain, the platform effectively thwarts unauthorized modifications and fraudulent practices, thereby cultivating a climate of trust between patients and healthcare providers. The paper further details the system’s design, the methodology for implementation, and suggests avenues for future enhancements.
In recent years, Web3 somehow became a buzzword vaguely used by both disruptors and incumbents to describe their innovations.a This confusion can lead to misguided financial, political, and research-scientific choices. When wrongly approached, Web3’s misinterpretation could result in actions that are unethical or even illegal. Therefore, it is critical to develop a reviewed and comprehensive concept of Web3 to guide its development and implementation with coherence.<br/>This chapter attempts to provide that framework. While it does not claim to offer the only valid interpretation of Web3, it aims to shed new light on the concept and explore its broader implications through an interdisciplinary lens. The approach is informed by the author’s extensive background, which spans over two decades of academic research and industry practice in fields such as Information Theory, Communication Science, Anthropology and Sociology, Digital Economy and Finance, Blockchain Technologies and Foundational Knowledge, Artificial Intelligence (AI), and Quantum Computing.<br/>The chapter begins by reviewing current concepts and frameworks related to Web3, highlighting inconsistencies and gaps in the literature. Following this, it expands on the history of the Internet, tracing its origins back to the mid-20th century through the lens of Information Theory and Political Economy. This historical context allows for a clearer understanding of Web3’s place within the broader Internet evolution. By considering Web3 through the lens of Information Theory, the chapter argues that Web3 represents a decentralized infrastructure that enables direct P2P communication of information, which further encourages decentralized governance and the creation and ownership of digital assets. This decentralized infrastructure is what distinguishes Web3 from previous iterations of the Internet.<br/>Furthermore, this chapter incorporates the political economy of communication to examine how the ownership of Web3’s decentralized infrastructure could redistribute power in the digital economy. Historically, control over the Internet’s infrastructure has rested with either the state or corporate entities. Web3, however, shifts control to individual users, offering the potential for a more inclusive and democratic digital economy. This is also a nod to its human-centric focus in the age of AI. Taking into account new emerging technologies, such as AI and quantum computing, this chapter presents them as part of the Web3 Tech Stack and contrasts the decentralized Web3 technologies with the centralized Internet architecture for clarity. Importantly, the presented technologies are seen only as tools supporting the overall objectives of Web3.<br/>This chapter concludes by situating Web3 as a new means of production for the digital age—enabling direct P2P value creation and exchange, with the potential to foster a more equitable and sustainable stakeholder-driven capitalism. In doing so, Web3 offers an advanced form of direct democracy. As AI continues to evolve, Web3’s decentralized infrastructure may also represent humanity’s best hope for safeguarding personal autonomy and preventing the monopolization of powerful technologies.
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.
‘Can digital self definition help save the planet?’ Belk’s (2013: 492) provocative question argued the need for further research on his concept of dematerialisation, already posing concern over the impact and value of digital goods. Despite existing digitally, with emergent technologies offering digital tools for unbound fashion expression, there is still little research or evidence that fully answers Belk’s question. To date, digital fashion, avatars, and the metaverse have generated significant publicity within the fashion media and are often marketed as tools for innovation and sustainability. The dematerialisation of fashion could offer a new way of dressing with the potential for environmental, psychological, social, and cultural impact. However, despite the rapid arrival of Web3 technologies, there is little understanding of digital fashion as an end product within the current Web 2.0 discourse. A holistic and mediated approach viewed through the lens of sustainable development is required for fashion and technology sectors to responsibly collaborate and innovate while considering the future of digital fashion with a key stakeholder in mind— the user. Adopting the sustainable development goals as a framework for contextualisation, this research includes a critical review of existing literature on digital fashion, identity and sustainability, supplemented by established video game theory, and an account of the author’s own experience of being digitised. It further explores two novel studies focusing on prominent users of DF: fashion models (Study One: Digital Bodies) and fashion consumers (Study Two: Digital Dressing). Within Study Two, participants were required to create and dress their digital selves during observational interviews. While grounded in the present, participants speculate on plausible, near-future scenarios where creating and dressing digital bodies becomes an essential extension of self, bringing both opportunity and risk. Key findings suggest that digital fashion augments rather than replaces physical fashion, serving to foster authentic representation through bolder experimentation or enabling style expression via idealised versions of the ‘default’ self. A digital sustainability paradox is implied, whereby users of DF are torn between feeling inspired and inadequate, with the potential to affect offline consumer behaviours. The creation of digital bodies within the DF journey may make users susceptible to body dissonance, and there is a wider risk to digital well-being, which requires industry responsibility. Additionally, issues surrounding digital ethics and autonomy for future fashion stakeholders are highlighted as societal implications, based on participants’ moralistic views of the digital self. 2 Traversing both online and offline worlds, this research aims to reframe and remediate the relationship between fashion and identity for generations that will never know a world without technology. To stay on the path of sustainable development within a blurred digital/physical fashion landscape, this thesis provides practical tools for the fashion and technology sectors to embed responsible innovation practices, thereby contributing to the emerging field of DF and sustainable development.
Fashion and Cultural Textiles
Sharing Economy and Platforms
Consumer Behavior in Brand Consumption and Identification
MyBlock is a web3 application that allows its users to contribute money to contribute money to a common cause. The application allows users to create groups called campaigns and contribute finances to a common goal. MyBlock allows users to track the crowdfunding campaigns that they are in. MyBlock also allows its users to make and manage contributions to these campaigns. The application makes use of blockchain technology to ensure transparent and immutable record keeping. Users can create campaigns, and create, remove, and manage the tiers of contribution in the campaign . Users can also add other wallets and use them to create wallet-specific campaigns.
This chapter dives into the visions and the emergent imaginary of Web3. It shows how they operate as a response to a disillusionment with what the Web has become. Visioners of Web3 depict Web 2.0 as a corporate takeover of the Web and offer their vision of the Web as a way for users and creators to reclaim control and a fair share of the value they co-create. However, instead of resurrecting the idea of a post-ownership Commons unrestricted by the imperatives of commerce and property, Web3 seeks to erect a new kind of Commons that cannot be usurped and exploited by Big Tech. This requires a radical break with the economic innocence of the early Web. Instead of rejecting ownership and business it embraces them, with an eye to re-erecting the Web on the principles of tokenomics. Decentralized token ownership is meant to ensure more efficient and sustainable management of the shared Web infrastructures and resources while also ensuring genuine participation of Web users in the distribution of economic rewards and the governance of the Web Commons. This chapter concludes with a reflection on the (d)evolving imaginaries of the Web surveyed across this part of the book.
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 chapter turns to recent advances in sociology of singularities (Reckwitz 2020) to advance the study of digital sigularization in four key respects. First, by opening up singularization research to the realities of late-modern society, dubbed as society of singularities by Reckwitz. Second, to (re)consider the role of digitalization and technology in singularization. The sociology of singularities finds in digitalization one of the key structural drivers propelling society from industrial modernity dominated by the social logic of the general to society of singularities (the social logic of the particular/singular). Third, Reckwitz offers useful tools to explore the processes and practices of collective singularization. Lastly, his sociology of singularities provides a fresh vantage point for further reflecting on Web3 and the evolution of Web imaginaries covered in Part II of the book.
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