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Design and implementation of a decentralized trustless data standardization framework for blockchain interoperability using smart contracts

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Alexandria engineering Journal 2025年 129卷 168-191页

作者： Abdulrahman Alzahrani Amin Noaman Ahmed A.A. Gad-Elrab Fathy Eassa Maher Khemakhem Faisal Albalwy Hosam Aljihani Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Cybersecurity College of Computer Science and Engineering Taibah University Madinah 42353 Saudi Arabia Department of Computer Science College of Computer Science and Engineering Taibah University Madinah 42353 Saudi Arabia

Interoperability among heterogeneous blockchain platforms remains a significant challenge in decentralized ecosystems, especially with the growing adoption of blockchain across various industries. This research introduces the Data Standardization Module (DSM). This framework facilitates seamless communication between platforms like Ethereum and Hyperledger Fabric using a unified schema for data transformation and exchange. DSM leverages Concise Binary Object Representation (CBOR) encoding, which reduces data size by 60 % and achieves a compression ratio (CR) of 2.5. This outperforms conventional JSON-based methods, typically achieving only 20–30 % compression with lower CR values. The system ensures data security and integrity through end-to-end encryption, access control, and validation mechanisms. Performance evaluations show that DSM supports an average throughput of 250 transactions per second (TPS). In comparison, interoperability frameworks like Cosmos and Polkadot typically achieve 100–150 TPS, while early implementations of Hyperledger Fabric reported TPS as low as 300. However, optimized settings of Hyperledger Fabric now achieve over 100,000 TPS. DSM strikes a balance by providing high throughput with minimal resource overhead, making it suitable for real-world applications. This work advances blockchain interoperability by offering a lightweight, secure, and scalable framework, ideal for high-frequency use cases in healthcare, finance, and supply chains.

关键词： Blockchain interoperability Decentralized systems Data standardization module CBOR compression SHA-256 hashing

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Multi-Task Driven Multi-Level Dynamical Fusion for Single-Cell Multi-Omics Cell Type Annotation

Multi-Task Driven Multi-Level Dynamical Fusion for Single-Ce...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Jiawei Chen, Shizhan Han, Zongbo Li, Wei Tang, Jijun Guo, Fei Tianjin University College of Intelligence and Computing Tianjin300072 China Nankai University College of Artificial Intelligence Tianjin518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Central South University School of Computer Science and Engineering Changsha410083 China

ISBN: (纸本)9798350386226

The emergence of single-cell multi-omics sequencing technology has enabled the simultaneous profiling of diverse omics data within individual cells. It offers a more comprehensive perspective on cellular phenotypes and heterogeneity. However, single-cell multi-omics data are inherently high-dimensional and heterogeneous. Due to technical limitations and scarce starting materials, the data are often affected by noise and dropout effects. To address these challenges, we propose a novel multitask driven multi-level dynamical fusion algorithm for single-cell multi-omics cell type annotation, named scMMDyn. Our approach incorporates reconstruction and classification auxiliary tasks to guide the training of trustworthy modules at both the feature and modality levels. It executes dynamical fusion during these stages and finally achieves cross-modality fusion via an attention mechanism. This method effectively mitigates data quality issues through reconstruction tasks and feature-level dynamical fusion while providing interpretability at both feature and modality levels. Experimental results across diverse single-cell multi-omics datasets show that our method surpasses existing approaches in cell type annotation. © 2024 IEEE.

关键词： cell type annotation multi-task mutli-level dynamical fusion single-cell multi-omics trustworthy

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The Design Innovation Space of Intelligent Personal Assistants (IPAs) in Healthy Buildings in the Era of Artificial Intelligence 26th

The Design Innovation Space of Intelligent Personal Assistan...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Liu, Yuqi Zhang, Jiawen Zeng, Yibei Liu, Yunlu Shidujamam, Mohammad Academy of Arts and Design Tsinghua University Beijing China Department of Service Design School of Design Royal College of Art London United Kingdom Fine Arts College Shanghai Normal University Shanghai China Faculty of Science Engineering and Computing Kingston University London United Kingdom Department of Computer Science and Engineering Independent University Dhaka Bangladesh

ISBN: (纸本)9783031768088

The implementation of Intelligent Personal Assistants (IPA) in healthy buildings cannot just elevate the buildings, intelligence level, but also boost their interactivity and interaction, enhance the quality of life for residents, and establish tailored health management approaches. Given China’s status as the world’s most populous country and a significant consumer market for emerging smart personal assistant products, this study focuses on the Chinese market. Utilizing a sample of 665 user data, it defines the demand, function, and design domains of IPA products in healthy buildings. Furthermore, it establishes a diversified design innovation space model for IPAs in healthy buildings amidst the artificial intelligence era. Initially, the study quantitatively assesses the impact of demographic variables on the acceptance of IPAs, offering a preliminary clarification and definition of the user profile of Chinese consumers. Additionally, by analyzing user needs and preferences, it explores the specific factors that guide users, purchasing decisions and categorizes these factors into four levels based on their significance. In terms of functional domains, this study has comprehensively gathered and categorized 30 service functions pertinent to Intelligent Personal Assistants (IPA), encompassing both current and anticipated functionalities. Utilizing questionnaire responses and statistical analysis, the study ranked the significance and precedence of these service functions, while employing principal component analysis to categorize user needs for smart speakers into seven distinct dimensions. Additionally, focusing on design domains, this research summarizes the diverse and innovative spatial models for intelligent personal assistants within architectural spaces, drawing insights from the functional domain analysis. This comprehensive study serves as a crucial foundation for the innovative design of IPA products in healthy buildings, while also facilitating the expans

关键词： Architectural design

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ITNR: Inversion Transformer-based Neural Ranking for cancer drug recommendations

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computers in Biology and Medicine 2024年 172卷 108312-108312页

作者： Sotudian, Shahabeddin Paschalidis, Ioannis Ch. Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA United States Department of Biomedical Engineering and Faculty of Computing and Data Sciences Boston University BostonMA United States

Personalized drug response prediction is an approach for tailoring effective therapeutic strategies for patients based on their tumors’ genomic characterization. While machine learning methods are widely employed in the literature, they often struggle to capture drug-cell line relations across various cell lines. In addressing this challenge, our study introduces a novel listwise Learning-to-Rank (LTR) model named Inversion Transformer-based Neural Ranking (ITNR). ITNR utilizes genomic features and a transformer architecture to decipher functional relationships and construct models that can predict patient-specific drug responses. Our experiments were conducted on three major drug response data sets, showing that ITNR reliably and consistently outperforms state-of-the-art LTR models. © 2024 Elsevier Ltd

关键词： Cell culture

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SAGPCN: Self-Attention Graph Pooling Convolutional Network for Battery State of Health Estimation 3

SAGPCN: Self-Attention Graph Pooling Convolutional Network f...

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3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Zhang, Jiusi Tian, Jilun Jiang, Yuchen Luo, Hao Huang, Cong-Sheng Chow, Mo-Yuen Yin, Shen Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States Norwegian University of Science and Technology Faculty of Engineering Trondheim7034 Norway

ISBN: (纸本)9798350324754

Lithium-ion batteries are widely applied in sustainable energy conversion system. Consequently, it is of great research significance to accurately estimate the state of health (SOH) of batteries. To effectively model the input features at the spatial level, this article proposes a self-attention graph pooling convolutional network (SAGPCN) to estimate the SOH. The advantages of SAGPCN proposed in this paper can be reflected as follows: (1) The SAGPCN can consider node characteristics and graph topology, which focuses the attention on key parts of the graph. (2) The SAGPCN designs a self-attention mechanism to reserve significant nodes and delete secondary nodes, so as to optimize the network structure. A real-world dataset is adopted to evaluate the proposed battery SOH estimation approach in this paper. Experimental results represent that the estimation performance of the proposed SAGPCN is better than some data-driven SOH prediction approaches. © 2023 IEEE.

关键词： Lithium-ion batteries

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SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain Management 4

SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain ...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Amarasinghe, A.A.B.G. Gamagedara, G.M.H.S. Pitigala, K.K. Subawickrama, H.D.A.E. Abeywardena, Kavinga Yapa Yapa, Kanishka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application. © 2022 IEEE.

关键词： Smart contract

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Optimizing Patient Satisfaction Surveys and Feedback with Cloud computing and NLP Techniques 8

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

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Automated heart disease prediction using improved explainable learning-based technique

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Neural computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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PV Grid Forecasting Analysis using Artificial Neural Network

PV Grid Forecasting Analysis using Artificial Neural Network

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International Middle-East Power System Conference, MEPCON

作者： Mohamed R. Aboelmagd Ali Selim Mamdouh Abdel-Akher Department of Electrical Engineering Faculty of Engineering Aswan University Aswan Egypt Department of Control and Computer Engineering Dep. Almaaqal University Basra Iraq

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

In addition to its use in building and agriculture, global solar irradiance is one of the most critical aspects in designing and considering any solar station's volume. Because the Iraqi metrological organization and seismology lack exact information regarding irradiance, this project aims to use historical global data, develop numerical analysis using artificial neural networks, and forecast hourly irradiance. The test is run over Basra to determine references to their nearest places. The learning technique in this study is a forward neural network (FNN) and time series neural network. Their comparison depends on seven input variables: temperature, wind speed, wind direction, sunshine duration, and date. One of the most essential integrated and impactful events for managing the system of new and renewable energy sources in the power grid is accurate solar energy forecasting. The study offers a valuable method for forecasting solar energy generation using neural networks. It is difficult to apply neural networks to anticipate PV power generation because of the unpredictability of variations in the weather; unlike traditional mathematical models, which struggle to handle complicated nonlinear interactions and provide poor predictions, neural network approaches achieve superior prediction performance. The suggested forecasting model based on neural networks provides a practical means of raising the accuracy of PV generation estimation

关键词： Renewable energy sources Accuracy Wind speed Time series analysis Production Artificial neural networks Power system stability Mathematical models Forecasting Wind forecasting

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Organising unstructured data using Double Net Self-Organising Map (DNSOM) model

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Soft computing 2025年第7期29卷 3533-3554页

作者： You, Cheng Chun Lim, Seng Poh Lee, Chen Kang Tan, Joi San Lim, Seng Chee Department of Computer Science Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Jalan University Bandar Barat Perak Kampar31900 Malaysia Department of Computing Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Jalan Sungai Long Bandar Sungai Long Cheras Selangor Kajang43000 Malaysia School of Computing and Informatics ViTrox College 746 Persiaran Cassia Selatan 3 Batu Kawan Industrial Park Penang Bandar Cassia14110 Malaysia

Surface reconstruction is the process of representing surfaces using the data obtained from scanning devices. When the data obtained is unstructured during data collection, it can cause problems in presenting the surface due to the lack of connectivity information for the data. It shows that data collection is a crucial task in surface reconstruction. Previous works have proved that Self-Organising Map (SOM) models can be used to organise unstructured data. However, the 2-D SOM model will generate a surface with holes for the closed surfaces. Besides, the 3-D SOM model will generate an incorrect surface due to the connectivity of the internal neurons. In addition, the Cube Kohonen SOM (CKSOM) model is limited to the same grid size. Hence, a SOM model known as Double Net SOM (DNSOM) is proposed via the merging of two 2-D SOMs to overcome the issues. Three data sets (cube, sphere, and talus bone) with different grid sizes are applied to test the models. When the grid size of all the models increases, the surface becomes smoother. The DNSOM contains a lower quantisation error than the 2-D SOM and the lowest topographic error among the other SOM models. It performs faster than the 3-D SOM and CKSOM. It presents the correct closed surface with a smaller number of neurons and different grid sizes. Microsoft Visual Studio 2022 with the C++ programming language is used to develop the models, while GNUPlot is used to visualise the results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Visualization

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