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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction 17

Weakly-Supervised Photo-realistic Texture Generation for 3D ...

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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Yin, Xiangnan Huang, Di Fu, Zehua Wang, Yunhong Chen, Liming Department of Mathematics and Informatics Ecole Centrale de Lyon Lyon69134 France School of Computer Science and Engineering Beihang University Beijing100191 China France

ISBN: (纸本)9798350345445

Although much progress has been made recently in 3D face reconstruction, most previous work has been devoted to predicting accurate and fine-grained 3D shapes. In contrast, relatively little work has focused on generating high-fidelity face textures. Compared with the prosperity of photo-realistic 2D face image generation, high-fidelity 3D face texture generation has yet to be studied. In this paper, we propose a novel UV map generation model that predicts the UV map from a single face image. The model consists of a UV sampler and a UV generator. By selectively sampling the input face image's pixels and adjusting their relative locations, the UV sampler generates an incomplete UV map that could faithfully reconstruct the original face. Missing textures in the incomplete UV map are further full-filled by the UV generator. The training is based on pseudo ground truth blended by the 3DMM texture and the input face texture, thus weakly supervised. To deal with the artifacts in the imperfect pseudo UV map, multiple UV map and face image discriminators are leveraged. © 2023 IEEE.

关键词： Textures

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Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model

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Chinese Journal of Electronics 2023年第1期32卷 64-75页

作者： TANG Minli XIE Shaomin LIU Xiangrong Department of Computer Science and Technology School of Informatics Xiamen University School of Big Data Engineering KaiLi University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University

Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu＿C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A,a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiN et-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.

关键词： Training Speech recognition Artificial neural networks Feature extraction Stability analysis Character recognition Machine translation

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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-depth examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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Recurrent Optical Spectrum Slicing Receiver for Power Fading Mitigation in Highly Dispersive Links using Programmable Photonics

Recurrent Optical Spectrum Slicing Receiver for Power Fading...

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2024 IEEE Photonics Conference, IPC 2024

作者： Sozos, K. Da Ros, F. Yankov, Metodi Deligiannidis, Stavros Sarantoglou, G. Mesaritakis, C. Bogris, A. University of West Attica Department of Informatics and Computer Engineering Aghiou Spiridonos Egaleo Athens12243 Greece Technical University of Denmark Department of Electrical and Photonics Engineering Ørsteds Plads Kgs. LyngbyDK-2800 Denmark University of the Aegean Department of Information and Communication Systems Engineering Palama 2 Karlovassi83200 Greece

We experimentally validate our works in recurrent optical spectrum slicers for dispersion compensation in IM/DD links, exploiting programmable photonics. We equalize 50 km of 64 Gb/s PAM-4 transmission in C-band, with... 详细信息

ISBN: (纸本)9798350361957

关键词： Optical links

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Data Analytics for Medical Record Data of Covid-19 Patient with Descriptive Predictive Mining 23

Data Analytics for Medical Record Data of Covid-19 Patient w...

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23rd International Electronics Symposium, IES 2021

作者： Hanafiah, Alvintha Maharani Barakbah, Ali Ridho Karlita, Tita Muliawati, Tri Hadiah Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9781665443463

Currently, the number of COVID-19 patients in Indonesia has not shown a significant decline. One of the reasons is the difficulty of analyzing medical record data of COVID-19 patients. The analysis becomes difficult because of the large volume of data and the heterogeneous characteristics of the content. This is what makes medical personnel take a long time in the process of analyzing data on COVID-19 patients, and causes the slow handling of cases in Indonesia. Therefore, this study proposes a new approach to analyze and classify medical record data of COVID-19 patients using a data analytics approach with descriptive and predictive mining methods. Where descriptive method is used to grouping patient's disease risk categorization and predictive method is used to classify positive/negative patients. The input data used for the analysis process was sourced from the results of the RT-PCR test of COVID-19 patients. Before being analyzed, the data is normalized first, and the features are extracted. Next, the descriptive mining process was carried out using the Hierarchical Clustering Complete Linkage method and predictive mining using the Decision Tree and K-Nearest Neighbor (KNN) methods. If so, then the results of the process are tested using the confusion matrix and error ratio methods. From the whole process, it can be concluded that the error ratio of this approach is 8% for predictive mining and 21% for descriptive mining. And the features found at the end of the process were 19 features, including Leukocytes, Eosinophils, Red blood cells, Patient age quantile, etc. © 2021 IEEE.

关键词： COVID-19

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Real-Time Safety Helmet Detection in Construction Site Using Deep Learning

Real-Time Safety Helmet Detection in Construction Site Using...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Arna Fariza Tita Karlita Muhammad Iqbal Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Construction is a high-risk industry where workers are frequently exposed to potential injuries, particularly head injuries. Safety helmets serve as a critical defense, yet many workers neglect their use due to low safety awareness, increasing their vulnerability. This study proposes a real-time safety helmet detection system using an edge computing device, the NVIDIA Jetson Nano, to enable immediate data processing. The system employs YOLOv5 object detection models to classify individuals as wearing or not wearing safety helmets. A comparative evaluation between YOLOv5-small and YOLOv5-nano models was conducted, demonstrating that YOLOv5-nano outperforms YOLOv5-small in terms of real-case video analysis and testing dataset performance. This approach highlights the potential of lightweight deep-learning models for enhancing safety compliance in construction environments.

关键词： YOLO Deep learning Computational modeling Streaming media Helmets Real-time systems Safety Injuries Edge computing Testing

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Test-Driven Simulation of Robots Controlled by Enzymatic Numerical P Systems Models 15th

Test-Driven Simulation of Robots Controlled by Enzymatic N...

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15th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2023

作者： Bobe, Radu Traian Gheorghe, Marian Ipate, Florentin Niculescu, Ionuţ Mihai Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest Str Academiei 14 Bucharest010014 Romania Faculty of Engineering and Informatics University of Bradford West Yorkshire BradfordBD7 1DP United Kingdom

ISBN: (纸本)9783031575228

The simulation of robots behavior and the use of robust models are very important for building controllers. Testing is an important aspect in this process. In this paper, a test-driven approach for designing robot controllers based on enzymatic numerical P systems models is introduced. Four such models are defined and tested using three distinct scenarios. The paper reveals an effective way of using modelling, simulation and testing in a coherent way. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： Controllers

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Enhancing Cryptocurrency Value Prediction: A Comparative Study of Novel Random Forest and K-Nearest Neighbor Algorithms for Improved Accuracy 5

Enhancing Cryptocurrency Value Prediction: A Comparative Stu...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kiruthiga, S. Balamanigandan, R. Mahaveerakannan, R. Jenifer, A. Mary Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science Engineering Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Saveetha University Department of Spatial Informatics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350379945

The effectiveness of the Novel Random Forest (RF) Algorithm for predicting cryptocurrency prices was evaluated and compared to the K-Nearest Neighbor (KNN) Algorithm. Machine learning methods were used to develop the two algorithms, and a pretest power analysis was conducted using two groups with the iteration of 10 at 85% of G-power and the setup parameters are alpha = 0.05 and beta = 0.85. Hence the P value is less than 0.005 (P © 2024 IEEE.

关键词： k-nearest neighbors

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(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Quality Control

(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Qual...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Fotios Kalioras Gabriel Filios Stylianos Karatzas Sotiris Nikoletseas Department of Computer Engineering and Informatics University of Patras Greece Department of Computer Engineering and Informatics University of Patras Computer Technology Institute and Press “Diophantus” Greece Department of Civil Engineering University of Patras Greece

Indoor air quality (IAQ) is an important yet often overlooked aspect of public health, with poor IAQ contributing to a significant number of diverse health problems worldwide. Existing air quality standards have failed to fully address the problem, however emerging technologies in the field of IoT have demonstrated promise in effectively addressing this issue. This concept paper advocates a hybrid hierarchy system for IAQ monitoring that incorporates both offline and online connectivity. The system utilizes sensing elements that can perform a wide range of functions, including autocalibration, pollution event detection, and more, autonomously from the network. By combining a gateway device, the system's capabilities are enhanced, providing increased data granularity, additional calibration and sensing options as well as connection with Building Management Systems (BMS). Furthermore, by connecting the gateway to the cloud, the system can perform more advanced data analysis and machine learning, providing users with insights into not only air quality but also general environmental quality. Key features of the envisioned system include the integration of subjective perception of pollution via crowdsourcing, a TinyML technology at the edge, digital twins, and an optimised redundancy of sensors that can alleviate poor accuracy and drift as well as capture the spatial dispersion of pollution.

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