Blockchain Papers

Nataliya Bilous, Danylo Ostapchenko, Iryna Ahekian, Marcus Frohme

Remote tele-rehabilitation requires objective pain assessment, but existing approaches fail in two distinct ways. Self-report scales such as the Visual Analog Scale and the Numeric Pain Rating Scale are easy to falsify, opening a special case of the Oracle problem in blockchain-based insurance. Cloud-based computer vision handles falsification but transmits raw biometric video off the patient’s device, violating privacy requirements. A decentralized Edge AI-Oracle architecture is proposed that combines MediaPipe Face Mesh landmark extraction with a recurrent classifier mapping Action-Unit feature sequences to a learned pain score aligned with the Prkachin and Solomon Pain Intensity scale. The recurrent cell is selected empirically across short-context (T = 2) and long-context (T = 120 frames at 24 fps) regimes, with a two-layer Long Short-Term Memory (LSTM) network adopted for deployment. Inference and Elliptic Curve Digital Signature Algorithm (ECDSA) signing run inside an ARM TrustZone Trusted Execution Environment (TEE). Biometric logs are stored off-chain on the InterPlanetary File System (IPFS). Smart contracts anchor results on-chain and open a 24 h optimistic verification window for an off-chain Watchtower auditor. On SynPAIN the LSTM reaches F1 = 0.683 on T = 120 video (leave-one-stratum-out), with a directional but non-significant advantage over Gated Recurrent Unit (GRU) (Wilcoxon p = 0.167). Cross-dataset validation on BioVid Heat Pain Database Part A (87 subjects, 174 paired observations, leave-one-subject-out) yields F1 = 0.519 for LSTM and 0.499 for GRU (Wilcoxon p = 0.549). A processor-only TEE surrogate benchmark estimates 1.96 ms (FP32) and 0.45 ms (INT8) inference latency at T = 120 with a 0.34 MB footprint and 707 µs ECDSA signing latency, leaving the INT8 inference latency more than an order of magnitude below the 33 ms per-frame budget. The dual-layer storage reduces gas costs by a factor of 23.4 (160,261 vs. 3,744,872 gas), corresponding to an illustrative mainnet cost of approximately 0.53 USD per submission at 1 gwei, rising to roughly 16 USD at a busier 30 gwei, and falling to approximately 0.005 USD on Arbitrum One (April 2026 reference parameters), so that continuous monitoring is economically practical on Layer-2. An adaptive-adversary analysis of the Watchtower shows that gross score tampering is detected at every usable operating threshold, whereas a rational adversary who inflates by less than the dispute threshold, or who shapes the injected score to fall just inside it, evades detection. Because the false-positive rate reaches zero only for δ≳0.15, the protocol bounds rather than eliminates patient-side fraud and motivates a zero-knowledge proof-of-inference successor. The framework is architecturally and economically feasible as a cryptographically verifiable, privacy-preserving tele-rehabilitation substrate aligned with General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) requirements through the Zero-Video Transmission principle, while remaining economically viable under post-Dencun mainnet and Layer-2 conditions. Recognition accuracy on real-world data and robustness to small-magnitude tampering remain limitations that the interchangeable recognition and audit components must improve before clinical deployment.

sarah Osama anis, Mohammed Mabrouk Morsey, Mostafa Aref

In the rapidly evolving landscape of cryptocurrency, gaining a deep understanding of public sentiment has become increasingly essential, especially given the significant impact of social media platforms on market perceptions and trends. This paper introduces a sophisticated sentiment classification model that utilizes a Bi-LSTM architecture to analyse over one million tweets related to Bitcoin. By integrating Explainable AI techniques, particularly LIME (Local Interpretable Model-agnostic Explanations) framework, our model not only achieves an impressive test accuracy of 98% but also offers valuable insights into its decision-making process, making the results more interpretable for users Our findings highlight robust performance metrics across precision, recall, and F1-scores, which collectively underscore the model's reliability and effectiveness in real-world applications. Furthermore, we delve into the opaque nature of the Bi-LSTM model through the application of LIME, which sheds light on how particular words and phrases have a strong impact on sentiment predictions. This research equips future investigations with conceptual frameworks and analytical tools that can be customized to study a broader range of cryptocurrencies. Through this work, we aim to foster a more nuanced comprehension of how public sentiment shapes market behaviour and decision-making in the digital currency space.

Lourdusamy Selvam, S. Abarna, R. Santhana Krishnan, Azarudeen K · 6 authors

Ensuring vaccine integrity requires rigorous cold-chain management, as even minor temperature deviations can compromise safety, cause financial losses, and endanger public health. Conventional monitoring systems, dependent on centralized databases and manual oversight, remain prone to delays, inaccuracies, and tampering. To address these limitations, we propose an integrated framework that combines deep learning and blockchain for secure, predictive, and real-time cold-chain monitoring. A distributed network of IoT sensors captures temperature, humidity, vibration, and GPS data at one-minute intervals. Long Short-Term Memory (LSTM) networks forecast short-term temperature trends, Autoencoders (AE) detect anomalies, and one-dimensional Convolutional Neural Networks (1D CNN) classify shipment states as Safe, At Risk, or Spoiled. A decision engine fuses outputs from these models into a unified risk score, enabling timely and data-driven interventions. Critical events and metadata are immutably recorded on the Ethereum blockchain, while raw sensor data is stored off-chain using IPFS to ensure auditability and efficiency. The system also supports automated alerts, real-time monitoring through Grafana dashboards, and adaptive model retraining for continuous improvement. Evaluation covers forecasting accuracy, anomaly detection precision, classification performance, and blockchain efficiency. By uniting predictive analytics with tamper-proof logging, the proposed framework reduces vaccine spoilage, strengthens supply chain resilience, and offers a scalable solution for broader temperature-sensitive logistics applications.

Ernest Ntizikira, Lei Wang, Jenhui Chen, Kiran Saleem

No abstract is available for this record.

Ahmed M. Alwakeel, Mohammed M. Alwakeel, Mohammed M. Alwakeel, Syed Rameem Zahra · 10 authors

Cities have undergone numerous permanent transformations at times of severe disruption. The Lisbon earthquake of 1755, for example, sparked the development of seismic construction rules. In 1848, when cholera spread through London, the first health law in the United Kingdom was passed. The Chicago fire of 1871 led to stricter building rules, which led to taller skyscrapers that were less likely to catch fire. Along similar lines, the COVID-19 epidemic may have a lasting effect, having pushed the global shift towards greener, more digital, and more inclusive cities. The pandemic highlighted the significance of smart/remote healthcare. Specifically, the elderly delayed seeking medical help for fear of contracting the infection. As a result, remote medical services were seen as a key way to keep healthcare services running smoothly. When it comes to both human and environmental health, cities play a critical role. By concentrating people and resources in a single location, the urban environment generates both health risks and opportunities to improve health. In this manuscript, we have identified the most common mental disorders and their prevalence rates in cities. We have also identified the factors that contribute to the development of mental health issues in urban spaces. Through careful analysis, we have found that multimodal feature fusion is the best method for measuring and analysing multiple signal types in real time. However, when utilizing multimodal signals, the most important issue is how we might combine them; this is an area of burgeoning research interest. To this end, we have highlighted ways to combine multimodal features for detecting and predicting mental issues such as anxiety, mood state recognition, suicidal tendencies, and substance abuse.

Pian Qi, Diletta Chiaro, Fabio Giampaolo, Francesco Piccialli

No abstract is available for this record.

RICARDO CARREÑO AGUILERA, ADAN ACOSTA BANDA, Miguel Patiño-Ortiz, Julián Patiño-Ortiz

This paper proposes an innovative method to take advantage of Blockchain Convolutional Neural Networks (BCNNs) in Emotion Recognition (ER). Based on Artificial Intelligence, this proposal uses audio-visual emotion patterns to determine psychiatric profiles to attend to the most urgent as a priority. BCNN architectures were used to identify emergency patterns. The results indicate that the proposed method is adequate for classifying and identifying audio-visual patterns using Deep Learning (DL) with Boltzmann’s restricted machines. It is concluded that it is sufficient to consider the audio-visible critical features from the patient’s face and voice for the proposed model to recognize a psychiatric services emergency for immediate action: the emergency with no control and the Emergency under control. User personal dynamic profiles are stored in the blockchain ecosystem since they are deemed sensitive data. System security is provided by blockchain and authentication uses non-fungible tokens (NFT) technology.

Muhammad Zahid Khan, Muhammad Zahid Khan, Muhammad Zahid Khan, Muhammad Usman Khan · 6 authors

Studies have been actively conducted on analyzing the driver's behavior inside the vehicle premises. Moreover, the transmission of the tempered proof multimedia content is also a major point of interest for the research community. At present, most of the techniques for detecting the distracted behavior of the driver is based on the detection of different face attributes like eyes and head posture etc, by using the traditional hand crafted features. In this paper we propose the deep learning based algorithm using the Convolution Neural Network. The proposed algorithm is independent of feature extraction of the specific parts, instead, it automatically picks the best features specific to the problem. We have utilized the State Form Distracted Driver Detection dataset to train our proposed algorithm. Furthermore, this paper also proposes a secure and tempered proof multimedia transaction. Original video data may be edited and fabricated with the false information. Multimedia blockchain can be helpful in tackling this problem. We have used Secure Hashing Algorithm (SHA‐256) algorithm for extracting the hashes of multimedia content. By utilizing the blockchain, we safely transmit the tempered proof video data coming from inside the vehicle, automatically detecting abnormal activities with our deep learning based algorithm. So, this paper combines the deep learning algorithms with blockchain techniques which is novel in research. Comparison between the results of proposed algorithm with the current state of the art work shows that proposed algorithm outperforms by achieving 86.02% accuracy on the test data.