Guoyao Wu, Fan Pan, Minyu Luo, Zhiqiang Lan · 5 authors
Conventional service evaluation systems are increasingly plagued by data opacity, susceptibility to tampering, and delayed feedback loops, which erode stakeholder trust and hinder effective quality governance. To address these critical challenges, this study proposes and empirically validates a blockchain-enabled framework for trusted closed-loop management of the entire service evaluation process. The proposed architecture synergizes distributed ledger technology, autonomous smart contracts, and a dynamic Bayesian trust scoring model to achieve real-time data verification, automated corrective feedback, and adaptive trust computation. We analyzed a comprehensive dataset of 1,200 service interactions across the hospitality, healthcare, and e-commerce sectors, characterized by customer satisfaction scores ranging from 5.1 to 9.8, reliability indices between 0.72 and 0.96, and normalized positive interaction frequencies from 0.42 to 0.89. Empirical results demonstrate that the integration of the blockchain framework significantly elevated mean trust scores from 0.71 (± 0.12) to 0.88 (± 0.09), representing a statistically significant 23.7% improvement. Furthermore, the system reduced the variance in satisfaction ratings by 0.48 and lowered overall service discrepancy rates by up to 15.4%. Sector-specific dynamic weight adjustments yielded optimized outcomes, including a 7.4% increase in reliability for healthcare and a 6.3% improvement in consistency for hospitality. Comparative analysis reveals that while conventional digital evaluation systems typically achieve only 5–12% performance gains, our blockchain-based approach substantially enhances trust, accuracy, and process transparency. Crucially, the closed-loop mechanism facilitated timely interventions, reducing critical service deviations by 17.5% in healthcare and 15.4% in e-commerce. These findings offer robust theoretical validation and practical guidelines for deploying transparent, accountable, and adaptive service evaluation ecosystems in diverse industrial contexts.
Electronic evidence of forensic medical images plays a key role in forensic identification. The existing deposit technology is difficult to cope with the dual challenges of image format change and AI forgery, and the fusion mechanism of digital watermarking and blockchain has the problems of robustness and traceability accuracy imbalance. This article proposes a dynamic trusted certificate storage system that integrates deep learning perceptual hash and alliance chain. A semantic hash generation network based on multi-scale frequency domain features is designed, and a lightweight intelligent contract architecture optimized by SM2/SM3 algorithm of state secrets is established. The full link traceability is realized by combining adaptive frequency domain and time domain nested watermarking algorithms. Experiments show that under the attacks of Gaussian noise, JPEG compression and geometric deformation, the Hamming distance of the hash is stable within 3 bits, which is better than the mutation of more than 30 bits in the traditional cryptographic hash. When the rotation is 10, the false recognition rate is less than 1%, and the sample collision probability is maintained at a very low order of magnitude; When the watermark embedding strength increases, the PSNR remains above 45 dB, and the normalized cross-correlation coefficient is higher than 0.92 under the condition of JPEG compression quality of 70. In the alliance chain scenario, the consensus delay of 30 nodes is 180 ms, and the delay rises to 320ms after the expansion of 100 nodes, and the system throughput is not significantly attenuated. In this study, the synergy between robustness, transparency and traceability efficiency is optimized, which can provide a reference technical scheme for judicial acceptance of forensic electronic evidence chain.
This study investigates the theoretical foundations, practical applications, and optimization strategies of financial big data analysis and network security optimization in support of Sustainable Development Goals (SDGs). A comprehensive framework is developed to integrate sustainable financial management, environmental cost-benefit analysis, socially responsible investment decision-making, and sustainable supply chain management. The study further proposes a network security optimization architecture incorporating multi-level data encryption, access control, real-time threat monitoring, intelligent defense mechanisms, and blockchain-based data protection. The proposed framework is particularly applicable to communication-intensive environments, including wireless communication infrastructures and antenna-supported information transmission networks, where secure and reliable financial data exchange is essential. Experimental analyses demonstrate that the integration of financial big data technologies and network security mechanisms enhances data protection, operational efficiency, and sustainable decision-making capabilities. The results provide a practical reference for secure financial data governance and sustainable development in complex digital and communication-oriented systems.
R. Priyadharsini, Ravikanth Reddy Vadamala, R. Raajalakshmi, K. Raghav Prasad · 5 authors
The rapid transformation of global business environments driven by digitalization, technological advancement, changing consumer expectations, and competitive market dynamics has significantly altered traditional marketing practices and strategic business operations. Organizations operating in highly dynamic economic ecosystems are increasingly recognizing that conventional marketing frameworks alone are insufficient to sustain long-term growth, customer engagement, and market relevance. In this context, innovation-driven marketing models have emerged as a critical strategic approach that integrates creativity, data intelligence, technological innovation, customer-centric design, and adaptive business strategies to enhance organizational competitiveness and sustainable value creation. This research examines the growing significance of innovation-driven marketing models and their influence on consumer behavior, brand positioning, digital engagement, operational efficiency, and business sustainability across modern industries. The study explores how emerging technologies such as artificial intelligence, machine learning, big data analytics, blockchain, cloud computing, augmented reality, and social media ecosystems are transforming traditional marketing processes into highly personalized, predictive, and experience-oriented systems capable of responding to rapidly evolving market demands. The research further investigates how innovation-oriented marketing strategies support product differentiation, dynamic pricing, omnichannel communication, customer relationship management, and real-time market responsiveness in both online and offline commercial environments. Particular emphasis is placed on the role of innovation in enhancing customer engagement through interactive digital platforms, data-driven personalization, automated communication systems, influencer-based branding strategies, and experiential marketing campaigns. The study also evaluates how organizations leverage innovative business models to improve customer retention, market expansion, and strategic decision-making while simultaneously addressing challenges related to market uncertainty, consumer trust, technological adaptation, and ethical data utilization. A comparative assessment of traditional marketing approaches and innovation-driven marketing frameworks demonstrates that organizations adopting innovation-centric strategies experience stronger consumer loyalty, improved operational agility, enhanced brand visibility, and higher adaptability to changing economic conditions. Additionally, the research highlights the growing importance of sustainability-oriented marketing innovation, where businesses integrate environmental responsibility, social value creation, and ethical consumer engagement into their branding and communication practices. The findings indicate that innovation-driven marketing models not only contribute to commercial profitability but also strengthen organizational resilience and long-term strategic sustainability in highly competitive global markets. The study concludes that future business success increasingly depends on the ability of organizations to continuously innovate their marketing structures, technological capabilities, and customer engagement mechanisms in alignment with digital transformation and evolving consumer expectations. Therefore, innovation-driven marketing represents a transformative strategic paradigm capable of reshaping modern business ecosystems through intelligent, adaptive, and customer-focused value creation models.
B Santhosh Kumar, P. Penchala Prasad, M. Raghavendra Reddy
Abstract Alzheimer’s disease is a neurodegenerative disorder that affects millions of individuals worldwide, making early diagnosis through Magnetic Resonance Imaging a significant clinical necessity. Existing medical image analysis techniques often suffer from limitations associated with inadequate preprocessing, reduced sensitivity to subtle abnormalities in the hippocampus and cortex, poor generalization across heterogeneous MRI acquisition systems, and insufficient mechanisms for secure medical data management. To address these challenges, this research proposes an integrated framework combining the Internet of Medical Things (IoMT), Artificial Intelligence, and blockchain technology for secure and efficient Alzheimer’s disease monitoring. The proposed framework employs Feature Pooling VGG16 (FPVGG16) for discriminative feature extraction, while feature selection is optimized using the Wave Search Binary Waterwheel Plant Optimization algorithm. Subsequently, a feature-selective Coordinated Xception-based Convolutional Spatial Network (CXCSN) is utilized for accurate disease classification. Blockchain technology is incorporated to provide secure, tamper-resistant, and privacy-preserving management of patient information and MRI records. Experimental evaluations conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets validate the effectiveness of the proposed framework, achieving accuracies of 99.31% and 99.21%, precisions of 99.28% and 99.25%, and recalls of 99.18% and 99.14 %, respectively. The results indicate that the proposed framework provides an effective solution for secure, reliable, and highly accurate Alzheimer’s disease diagnosis and monitoring.
Under the dual carbon targets, China's energy companies are speeding up their green transformation, but they usually encounter some common obstacles including lack of capital, weak technical assistance and an incomplete risk control system. The combination of digital technology and financial services provides new approaches to solve these problems. According to the specific characteristics of the transformation of energy enterprises, this research examines the mechanisms of digital finance from two aspects – financing enhancement and technological enhancement. It is found that methods such as digital green loans, bonds and equity financing can efficiently relieve the financial pressure of enterprises, while technologies like big data, blockchain and artificial intelligence can greatly improve the accuracy of emission reduction and the efficiency of energy operation. Furthermore, the enhancing effects have regional differences and threshold characteristics. Thus, countermeasures are put forward from four fields: improving service provision, deepening technological integration, setting up a risk management system and improving policy regulation, which offer guidance for the actual transformation of energy enterprises and the development of relevant policies.
The high aggregation of user relationship and behavioral data in social networks continues to aggravate privacy leaks. How to strike a balance between privacy protection and data availability has become a research hotspot. To collaboratively optimize user information security and community structure identification, this study proposes a social network privacy protection model that integrates differential privacy technology and community discovery algorithms. First, a differential privacy noise injection mechanism is constructed to perturb node data and combine it with blockchain storage to ensure that the data cannot be tampered with. Then, a community division strategy based on information entropy and mutual information is introduced to achieve high-precision community identification through modularity optimization. The accuracy of the proposed model reached 98.1% when the data set size was 800, which was about 3.4% and 9% higher than that of other models, respectively. The root mean square error was 8.2, which was about 20% lower than that of the traditional model. The convergence speed was increased to 380 iterations, which was about 15% faster than that of the comparison algorithm. The privacy protection strength and scalability scores reached 9.3 and 9.5, respectively. The simulation test results showed that, under different data types, the accuracy of the model grew from 0.87 to 0.98, and the F1 value grew from 0.84 to 0.95. The integration of differential privacy and community discovery effectively improves the privacy protection strength and structural analysis accuracy of social networks, providing a highly feasible solution for multi-scenario social data security analysis.
Ensuring transparency and traceability in horticultural supply chains is difficult due to complex logistics, seasonal variability, and multiple intermediaries.We propose a framework that couples b-local irregular vertex coloring (b-LIVC) with a blockchain architecture to enable end-to-end verification of production and trade.On the Jember Regency subdistrict graph, we compute the b-local irregular chromatic number and obtain χ b-lis (J) = 6, yielding six planting color classes that schedule sowing and harvests to distribute output across the year.The local irregularity induces distinct neighborhood weights, which we use as cryptographic features for unique, verifiable batch identifiers.We implement the pipeline on a public blockchain: harvest lots are tokenized as video NFTs with QR links to a verification page and on-chain records.The integration of discrete mathematics and distributed ledgers provides auditable provenance and transaction history, practical scheduling that reduces harvest clustering, and a low-overhead mechanism for farmer-level transparency.
This study aims to provide a comprehensive science mapping and bibliometric analysis of the FinTech landscape within Islamic banking and finance. It deciphers the intellectual structure and thematic evolution of the field during the transformative window from 2017 to early 2026. Methodology: Utilizing the Scopus database, a dataset of 725 scholarly documents was extracted and analyzed. The research employs a multi-tool approach, integrating R-Bibliometrix (Biblioshiny) for longitudinal performance analysis and VOSviewer for visualizing keyword co-occurrence and institutional collaboration networks. The PRISMA 2020 protocol was followed to ensure methodological transparency. Findings. The results reveal an exponential surge in scientific production, characterized by an impressive annual growth rate of 28.42%. Malaysia and Indonesia emerge as the primary global knowledge hubs, with the International Islamic University Malaysia leading institutional contributions. The analysis identifies three core intellectual clusters: (1) Blockchain and Cryptocurrencies, (2) AI and Regulatory Compliance, and (3) Financial Inclusion and Institutional Stability. Thematic evolution indicates a strategic shift from basic FinTech adoption toward advanced applications in Artificial Intelligence, Ethical Technology, and the Sustainable Development Goals (SDGs). Originality,