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SSRN 2024年

作者： Li, Yunfei Fu, Xiaodong Liu, Li Ding, Jiaman Peng, Wei Jia, Lianyin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Graduate School Yunnan University of Finance and Economics Kunming650221 China Yunnan Provincial Key Laboratory of Computer Technology Application Kunming University of Science and Technology Kunming650500 China

Local Differential Privacy (LDP) has garnered considerable attention in recent years because it does not rely on trusted third parties and has low interactivity and high operational efficiency. However, current LDP frequency estimation mechanisms aggregate data using different privacy budgets within the same data domain, overlooking the aggregation requirements across different data domains. This limits the potential for enhancing the data utility under fixed privacy budgets and meeting user preferences in multiple data domains and privacy budgets. To address this issue, we define a Multi-Domains Personalized Local Differential Privacy (MDPLDP) model that allows users to freely choose data domains and privacy budgets according to their privacy preferences. Furthermore, based on the MDPLDP model, two new frequency estimation mechanisms are proposed: MDPLDP-Generalized Randomized Response and MDPLDP-basic Randomized Aggregatable Privacy-Preserving Ordinal Response. These mechanisms support cross-domains data aggregation and optimize data utility by adjusting the data domains and increasing privacy budgets. Theoretical analysis reveals that these new mechanisms have lower estimation errors than the traditional LDP mechanisms. Experiments on real and synthetic datasets demonstrate that the proposed mechanisms effectively reduce estimation errors and enhance the utility of data-frequency estimation. © 2024, The Authors. All rights reserved.

关键词： Frequency estimation

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Model Predictive Control Method for Ten-Switch Three-Phase Three-Level Inverter 50

Model Predictive Control Method for Ten-Switch Three-Phase T...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Guler, Naki Fesli, Ugur Komurcugil, Hasan Bayhan, Sertac Technology Faculty Gazi University Ankara Turkey Renewable Energy and Smart Grids Application and Research Center Gazi University Ankara Turkey Electronic and Automation Department Technical Sciences Vocational School Gazi University Ankara Turkey Department of Computer Engineering Eastern Mediterranean University Famagusta Via Mersin 10 Turkey Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781665464543

This paper proposes a multi-objective model predictive control (MPC) for a grid-connected ten-switch three-phase three-level inverter. The main objective of the proposed MPC method is to control the grid current. In addition, the voltage balancing of the DC capacitors in the ten-switch three-level inverter is essential to generate voltage levels properly. Therefore, two objectives need to be achieved simultaneously. The multi-objective cost function feature of the MPC method is used to achieve these objectives. The simulation studies are performed using MATLAB/Simulink environments to investigate the effectiveness of the proposed MPC method. The results show that the proposed control method exhibits accurate performance under steady-state conditions and fast dynamic responses under transient conditions. Moreover, the grid current spectrums show that the total harmonic distortion is 2.21% in normal operation, and it is under the desired limits, even with a 50% parameter mismatch in filter inductance. © 2024 IEEE.

关键词： Model predictive control

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Revolutionizing Healthcare Record Management: Secure Documentation Storage and Access through Advanced Blockchain Solutions

arXiv

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arXiv 2025年

作者： Brijwani, Geeta N. Ajmire, Prafulla E. Junaid, Mohammad Atique Mohammad Charasia, Suhashini Awadhesh Bhende, Deepali O. Computer Science Dept. Sant Gadge Baba Amravati University Amravati India Department of Computer Science Faculty of Engineering & Technology Sant Gadge Baba Amravati University India Department of Computer Science Rashtrasant Tukadoji Maharaj Nagpur University Nagpur India Department of Master of Computer Application Rashtrasant Tukadoji Maharaj Nagpur University Nagpur India

Integrating blockchain technology into healthcare systems presents a transformative approach to documenting, storing, and accessing electronic health records (EHRs). This research introduces a novel blockchain-based EHR system designed to significantly enhance security, scalability, and accessibility compared to existing solutions. Current systems primarily utilize SHA-256 for security and either IPFS or centralized storage, which, while effective, have limitations in providing comprehensive data integrity and security. The proposed system leverages a hybrid security algorithm combining Argon2 and AES and integrates a hybrid storage and consensus mechanism utilizing IPFS and PBFT. This multifaceted approach ensures robust encryption, efficient consensus, and high fault tolerance. Furthermore, the system incorporates Multi-Factor Authentication (MFA) to safeguard against unauthorized access. It utilizes advanced blockchain tools like MetaMask, Ganache, and Truffle to facilitate seamless interaction with the decentralized network. Simulation results demonstrate that this system offers superior protection against data breaches and enhances operational efficiency. Specifically, the proposed hybrid model substantially improves data integrity, consensus efficiency, fault tolerance, data availability, latency, bandwidth utilization, throughput, memory usage, and CPU usage across various healthcare applications. To validate the performance and security of the proposed system, comprehensive analyses were conducted using real-world healthcare scenarios. The findings highlight the significant advantages of the blockchain-based EHR system, emphasizing its potential to revolutionize healthcare data management by ensuring secure, reliable, and efficient handling of sensitive medical information. © 2025, CC0.

关键词： Electronic health record

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Correction: Structural and shielding effectiveness properties of (NiFe2O4/chopped strands) composites for 6.5–18 GHz applications

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Journal of Materials Science: Materials in Electronics 2024年第30期35卷 1-2页

作者： Sahin, Ethem İlhan Ibrahim, Jamal-Eldin F. M. Emek, Mehriban Sinsoysal, Bahaddin Amanzholova, Alina Advanced Technology Research and Application Center Adana Alparslan Türkes ̧ Science and Technology University Adana Turkey Doctoral School of Material Science and Technology Institute of Ceramic and Polymer Engineering Faculty of Material Science and Engineering University of Miskolc Miskolc Hungary Department of Physics Golbasi Vocational School Computer Technologies Adiyaman University Adıyaman Turkey Department of Mathematics and Computer Engineering Faculty of Engineering İstanbul Gedik University Istanbul Turkey Department of Mathematics Faculty of Science Khoja Akhmet Yassawi International Kazakh-Turkish University Turkistan Kazakhstan

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Construction Equipment Performance with Cloud Data Analysis for Predictive Maintenance 6

Construction Equipment Performance with Cloud Data Analysis ...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Seeni, Senthil Kumar Maheswari, P. Konidhala, Jothibabu Suguna, M. Raja Ishwarya, M.V. Srinivasan, S. Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chennai India University of Technology and Applied Sciences- Al Musannah Department of Electronics and Communication Engineering South Al Batina Musanna Oman Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381689

The research uses Internet of Things technology for predictive maintenance in the construction sector. The system addresses incorporating Internet of Things sensors into construction machinery to enable real-time monitoring of operating parameters. The study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. The abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. The study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. This validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry. © 2024 IEEE.

关键词： Predictive maintenance

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Adaptive Ensemble Model for Ultra-short-term Wind Power Forecasting 11

Adaptive Ensemble Model for Ultra-short-term Wind Power Fore...

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11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Li, Yunlong Jin, Huaiping Jin, Huaikang Cao, Yundong Kunming University of Science and Technology Faculty of Information Engineering and Automation Department of Automation Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Huaneng Renewables Co. Ltd. Yunnan Branch Kunming650000 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Stable and reliable wind power forecasting is of great significance for grid dispatching. However, wind power has strong nonlinear characteristics. Its amount of historical data is very large, and traditional machine learning methods cannot fit the nonlinear relationship well. Furthermore, owing to the seasonality and periodicity of the wind power, the performance of the offline model inevitably deteriorates. To address these problems, this study proposes an adaptive ensemble model for ultra-short-term wind power forecasting. First, a long short-term memory (LSTM) network is established with historical wind power data for an entire year to ensure a strong generalization performance. Second, local weighted partial least squares (LWPLS) were used to obtain LSTM network prediction errors in real time by adaptive modeling and further improving its prediction accuracy. In addition, just-in-time learning (JITL) is used to ensure the adaptability of forecasting using LWPLS. Finally, to fully integrate the prediction advantages of different prediction models, a random forest (RF) was used to transform the determination of the ensemble weight into a three-classification problem. The adaptive fusion of the three different forecasting mechanisms is realized by weighting through the posterior probability of classification. Finally, the effectiveness and superiority of the proposed method are verified in a practical case. © 2022 IEEE.

关键词： Error correction

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A big data smart agricultural system: recommending optimum fertilisers for crops

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International Journal of Information technology (Singapore) 2023年第1期15卷 249-265页

作者： Ngo, Vuong M. Duong, Thuy-Van T. Nguyen, Tat-Bao-Thien Dang, Cach N. Conlan, Owen School of Computer Science Technological University Dublin Dublin Ireland Ho Chi Minh City Open University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Science and Technology Application for Sustainable Development Research Group Ho Chi Minh City University of Transport Ho Chi Minh City Viet Nam School of Computer Science Trinity College Dublin Dublin Ireland

Nutrients are important to promote plant growth and nutrient deficiency is the primary factor limiting crop production. However, excess fertilisers can also have a negative impact on crop quality and yield, cause an increase in pollution and decrease producer profit. Hence, determining the suitable quantities of fertiliser for every crop is very useful. Currently, the agricultural systems with internet of things make very large data volumes. Exploiting agricultural Big Data will help to extract valuable information. However, designing and implementing a large scale agricultural data warehouse are very challenging. The data warehouse is a key module to build a smart crop system to make proficient agronomy recommendations. In our paper, an electronic agricultural record (EAR) is proposed to integrate many separate datasets into a unified dataset. Then, to store and manage the agricultural Big Data, we built an agricultural data warehouse based on Hive and Elasticsearch. Finally, we applied some statistical methods based on our data warehouse to extract fertiliser information such as a case study. These statistical methods propose the recommended quantities of fertiliser components across a wide range of environmental and crop management conditions, such as nitrogen (N), phosphorus (P) and potassium (K) for the top ten most popular crops in EU. © 2022, The Author(s).

关键词： Crop yield Data warehouse Electronic agricultural record Nutrient

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Leveraging AI to Unlock the Potential of Data Science

Leveraging AI to Unlock the Potential of Data Science

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International Conference on Computing and Networking technology (ICCNT)

作者： Rakesh Kumar Yadav Gadug Sudhamsu Manish Kumar Goyal S. Yuvaraj Leena A. Deshpande Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaining knowledge of algorithms, and modern computing architectures, AI is allowing researchers to quick and as it should be discover insights from huge and complicated datasets. Furthermore, AI tools are offering corporations with the ability to make higher and more knowledgeable choices from the evaluation of hundreds of thousands of statistics points. The skills of information technology powered by means of AI are revolutionizing industries which includes healthcare, finance, and manufacturing-producing savings, uncovering efficiencies, and identifying new opportunities. As AI-driven records technology maintains to strengthen, organizations that capitalize on its ability stand to gain precious insights and unlock new degrees of overall performance.

关键词： Industries Finance Organizations Medical services computer architecture Data science Artificial intelligence Information technology

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Optimized Defogging Algorithm for Dark Channel Based on Multi-Scale Convolutional Neural Network

Optimized Defogging Algorithm for Dark Channel Based on Mult...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Zhaochuan Wei Rongtian Yang Somsack Inthasone Jundao Pan Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin China Computer Science Department Faculty of Natural Nayional University of Laos Vientiane Capital Lao PDR Chinese Academy of Sciences Institute of Space and Astronautical Information Innovation Beijing China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

To address the issue of significant discrepancies between the estimated transmission in traditional single-image dehazing algorithms and the actual scenario, a multi-scale convolutional neural network-based dehazing algorithm optimized by dark channel prior is proposed. The algorithm utilizes guided filtering, which preserves edge smoothness, to filter the feature transmission map extracted by the convolutional neural network, making the processed transmission closer to the actual transmission of the input hazy image. Subsequently, the dark channel prior theory is employed to obtain the global atmospheric light value of the hazy image, and finally, The atmospheric scattering model serves as a crucial tool when engaging in the operation of image dehazing, effectively enhancing the clarity of images affected by haze. The outcomes of the experiments indicate that this algorithm surpasses the latest approaches in both qualitative and quantitative evaluations. The algorithm put forward shows remarkable enhancements in peak signal - to - noise ratio and structural similarity, leading to significantlyenhanced dehazing performance.

关键词： Filtering Atmospheric modeling Noise Channel estimation Scattering Estimation Filtering algorithms Feature extraction Filtering theory Convolutional neural networks

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A Programmed Cardiac Arrhythmia Analysis System by Adaptive Cardiac Outlining

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SN computer Science 2023年第5期4卷 548页

作者： Pachar, Sunita Singh, Alka Agrawal, Vani Cotrina-Aliaga, Juan Carlos Arias-Gonzáles, José Luis Tripathi, Durga Prasad IBM Department GLA University Mathura India School of Computer Application Noida Institute of Engineering and Technology Greater Noida India Department of CSA ITM University Gwalior Gwalior India Faculty of Human Medicine Chincha Peru Pontificia Universidad Católica del Perú San Miguel Peru Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522002 India

For inter- and intra-patient variation scenarios, high-classification accuracy is still an active topic of research in automated arrhythmia-diagnosis systems. However, there are significant flaws with this strategy. Because of this, the building of a “patient adjustable” classifier is necessary. A mathematical strategy based on altering window length in accordance with the distance from the adjacent R peak is used to reduce unwanted noise. The electrocardiogram's most prominent waveform is the QRS beat (ECG). Automated ECG analysis relies heavily on the detection of QRS beats. We suggested a method that is typically used for intrusion detection in networks to detect QRS beats in ECG signals. Use this network intrusion detection method for the detection of worms in networking applications, as well as for determining the most common strings. The proposed structure calculates various features, such as QR level, RS level, QR slope, and RS slope. The suggested algorithm's classification efficiency is much higher than that of existing methods. The proposed approach can also be used to detect patient-specific cardiac fluctuation. We have proposed a method to classify normal, right bundle branch block, left bundle branch block, atrial premature contractions, and ventricle premature contractions. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CVD ECG QRS SVM WT

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