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Investigation of the Surrogate Model in an ANN-Meanline Hybrid Model for Radial Turbine Performance Prediction

International Journal of Gas Turbine, Propulsion and Power Systems 2024年第2期15卷 9-17页

作者： Ren, Pangbo Stuart, Charles Zhang, Mimi Inomata, Ryosuke Nakamura, Kazuaki Morita, Isao Spence, Stephen School of Engineering Trinity College Dublin The University of Dublin Ireland I-Form Advanced Manufacturing Research Centre Science Foundation Ireland Ireland IHI Corporation Yokohama Japan

The ability to efficiently optimise the charging system as part of the complete powertrain for a given duty is attracting significant research interest. A hybrid meanline model integrating Artificial Neural Networks as surrogate models for loss and blockage prediction has shown great potential in wide-range radial turbine performance prediction, demonstrating enhanced accuracy compared to traditional approaches. However, the configuration of surrogate models employed in the hybrid meanline modelling approach has not been studied thoroughly considering the wide range of geometrical variables and the dimensionality of the problem. This paper presents an investigation into a hybrid meanline model with regard to the choice of the surrogate model algorithm and the corresponding impact of the training database size. By optimizing the surrogate model hyperparameters via Bayesian Optimization, the effect of the hyperparameters on the performance of the surrogate models has been isolated. It was found that the mean square error level of the surrogate model was reduced significantly by 29% when the training database size increased from 60% to 100%. The two surrogate models investigated were ANN and SVR. Both were sensitive to changes in the size of the training data set. The overall results showed that ANN performed better in this turbomachinery loss modelling application. Copyright ©2024 Pangbo Ren, Charles Stuart, Mimi Zhang, Ryosuke Inomata, Kazuaki Nakamura, Isao Morita and Stephen Spence.

关键词： Mean square error

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Block Chain Based Electronic Vaccination Record Storing System 8

Block Chain Based Electronic Vaccination Record Storing Syst...

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8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Shaikh, Ruwaida Nazim Jadhav, Chaitali Gangadhar Bhogawade, Vedant Rajaram Narang, Geetika Gangadhar, Aditi Manoj Computer Engineering Savitribai Phule Pune University Pune India Trinity College of Engineering and Research Dept. Computer Engineering Pune India

ISBN: (纸本)9781665408165

In this paper, a technique has been proposed in order to maintain the immutability of the vaccination records and providing provisional access to one's record and validate the certificate which is generated when one's records are entered on the blockchain network via smart contract which will be later stored on the IPFS where the key roles of the system is to maintain the integrity of the data by keeping it safe from malicious invaders who try to tamper with the records. And once the certificates are generated, they can be further validated so forgery of records can be prevented. Every certificate will consist of a uniquely generated hash value key which will be further used to validate the legitimacy i.e., the authenticity of the certificate through a portal. The purpose of this project is to come up with a safe and secure environment. It is based on smart contracts built over a blockchain network stored on IPFS. The paper will further give detailed explanation of all the components that will be used also the data flow as well and design and development of the project. © 2022 IEEE.

关键词： Blockchain

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Prediction of Insurance Claims for Health Sector using Machine Learning Techniques 3

Prediction of Insurance Claims for Health Sector using Machi...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pragatheeswari, E. Kavitha, M.N. Saranya, S.S. Dhanushree, D. Jasmitha, S. Abiradhie, P.V. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science and Design Kongu Engineering College Perundurai Erode India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. Health insurance has become a vital aspect of people's lives. In this massive community, to access healthcare services such as insurance policies, LIC, ICICI, HDFC ERGO, Star Healthcare are benefits for claiming an amount for their medical expenses. The dataset encompasses three categories, such as generalized data, hospitalized data and claim data. Each record in the dataset represents an individual’s health insurance charges along with corresponding demographic and health-related characteristics. In this paper, Machine Learning techniques are used to group the claim amount that has been adopted by the individuals based on the hospitalized expenditure and insurance charges paid by the individuals. In the health sector, machine learning techniques are used to forecast insurance claims by building predictive models from historical data on claims, patient demographics, treatment types, and results. By predicting future claims based on trends seen in the data, these models help insurance firms better manage risk and allocate resources. In case of a fresher to this concept, it feels challenging to understand whereas generating as a pattern can be more flexible to acquire knowledge about how much amount has been claimed. © 2025 IEEE.

关键词： Electronic health record

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A Novel Intelligent Task Offloading Scheme for Multicontroller Environment in Software Defined Internet of Vehicles

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IEEE Internet of Things Journal 2025年第12期12卷 20018-20029页

作者： Hussain, Mir Wajahat Sangaiah, Arun Kumar Reddy, K. Hemant Kumar Roy, Diptendu Sinha Alenazi, Mohammed J. F. Javvaji, Pavan Kumar Alliance University Department of Computer Science and Engineering Alliance College of Engineering and Design Anekal562106 India National Yunlin University of Science and Technology International Graduate Institute of Artificial Intelligence Yunlin Douliu64002 Taiwan Sunway University School of Engineering and Technology Subang Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Mohali140413 India VIT-AP University School of Computer Science and Engineering Amaravati522237 India National Institute of Technology Department of Computer Science and Engineering Shillong793003 India King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing (CC) platform meets these stringent computation requirements, but it has a significant latency that fog computing (FC) effectively handles. Software defined network (SDN) has become the de facto standard for next-generation networking due to its unique and flexible features, which handle the network prerequisites in an agile manner. Task offloading (TO) is a crucial issue in software defined-IoV (SD-IoV), particularly when the vehicle’s resources are insufficient. Fog nodes schedule offloaded tasks;however, neglecting computational and network details during task scheduling can lead to longer completion times, thereby restricting the functionality of SD-IoV due to its time-sensitive nature. Therefore, this article introduces two proactive intelligent TO (ITO’ and ITO) schemes. These schemes schedule tasks onto fog nodes with enhanced computational capabilities, dynamically enable the network through the ovs-ofctl SDN utility, and consider vehicle mobility during task scheduling. Experiments with Mininet show that the proposed (ITO’ and ITO) schemes improve performance in terms of CPU availability, bandwidth, and throughput by 5.2 times, 50% and (30.7–13.9)% more than the existing scheme. They also reduce average packet loss, delay, round trip time (RTT), and offloaded node time selection by (45.35–21.3)%, (50–33.3)%, (53.9–9.9)% and (43.2–26.06)% which demonstrates the efficacy of the proposed scheme. © 2014 IEEE.

关键词： Fog computing

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High-Speed Visible Light Communication Using Phenothiazine/Dimesitylborane Derivatives as Color Conversion Materials in Semipolar Micro-LED-Based White-Light Systems

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ACS Photonics 2024年第2期11卷 489-497页

作者： Sadhu, Annada Sankar Chen, Li-Yin Pai, Yi-Hua Hsieh, Chung-An Lin, Hao-Wu Chow, Chi-Wai Kuo, Hao-Chung Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu30013 Taiwan

Visible light communication (VLC) has emerged as a cutting-edge high-speed communication technology, poised to meet the surging capacity demands of 6G networks. Micro-light-emitting diodes (μLEDs) are considered as the light sources for achieving high-speed VLC, distinguished by their remarkable modulation bandwidths. However, achieving broadband white light emission hinges on the utilization of color-conversion materials with wide emission spectra. The transmission speed of the white-light system is inherently constrained by the characteristics of these color-conversion materials. In this work, we demonstrate CC-MP7 and CC-MP8, two derivatives of phenothiazine/dimesitylborane, as color conversion materials in a semipolar (20-21) micro-LED-based white-light system for high-speed VLC. The color conversion layers possess wide emission spectra, enabling them to achieve excellent color rendering performance when combined with blue micro-LEDs. CCMP7 and CC-MP8 demonstrate rapid photoluminescence decay characteristics, thereby enhancing the modulation bandwidth of the color-conversion layer in the white-light system. The resulting bandwidths achieved by CC-MP7 and CC-MP8 are 210 and 240 MHz, respectively, which represents an approximately 45-fold increase compared to ordinary phosphors. By combining semipolar (20-21) micro-LEDs with CC-MP7 and CC-MP8, the resulting white-light systems exhibit correlated color temperatures of 6860 and 7500 K, CIE coordinates of (0.3009, 0.3577) and (0.2958, 0.3129), and color-rendering indexes of 80 and 85, respectively. Furthermore, both systems offer high bandwidths of 1063 and 1084 MHz with the data rates of 1.72 Gbps and 1.74 Gbps using non-return-to-zero on-off keying (NRZ-OOK) format, respectively, indicating the significant potential of CC-MP7 and CC-MP8 for practical applications in VLC. © 2024 The Authors.

关键词： Bandwidth

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Detection of Manhole and Sewage Overflow Using computer Vision 8

Detection of Manhole and Sewage Overflow Using Computer Visi...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kesharwani, Manika Beli, Shivabasamma Nasreen, Azra Ramakanth Kumar, P. Sreelakshmi, K. Shankar, Shri T. Rv College of Engineering® Karnataka Bengaluru India Rv College of Engineering® Karnataka Bengaluru India Rv College of Engineering® Center of Computer Vision Research Karnataka Bengaluru India

ISBN: (纸本)9798331505462

Maintenance and management of urban sewage systems are critical for public health and environmental sustainability. Manholes play a crucial role in these systems, providing access points for inspection, maintenance, and environmental protection. Neglected or improperly closed manholes pose significant risks, including accidents, property damage, and environmental pollution. Sewage overflow adds further complications, leading to health hazards and infrastructure damage. This paper presents a deep learning based model for manhole and sewage overflow detection. Utilizing OpenCV for image processing and DarkNet for model integration, proposed system accurately identifies open/closed manholes, and sewage overflows with high precision rates of 97%, 99.87%, and 96.27%, respectively. By automating the maintenance, these solutions contribute to the building of smart cities that enhances safety, reduces operational costs, and safeguards the environment. © 2024 IEEE.

关键词： Health hazards

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Decentralized File Sharing

Decentralized File Sharing

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2023 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2023

作者： Vaidya, Chandu Takalkar, Kunal Ghosekar, Anurag Nimgade, Susmit Ghode, Vivek Rajiv Gandhi College of Engineering and Research Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350398748

The popularity of peer-to-peer architecture has expanded during some recent years. This is due to the fact in comparison to conventional systems, Peer-to-peer systems offer better performance, scalability, availability, security, and anonymity. Most applications and services rely on central authorities for transfer of files. This introduces a single point of failure to the system. The relevant authority should be depended on to have facts saved via way of means of the utility to be had at any given time. More importantly, the privacy of the consumer relies upon the provider company's functionality to hold the facts safe. A decentralized system could be the technique to remove the dependency on a central authority. Moreover, because of the speedy boom of cellular tool utilization, the provision of decentralization should now no longer be confined best to desktop computers. This survey research examines the architecture of such systems using various peer-to-peer architectures. In this work, we aim at studying a decentralized file-sharing system to make it more secure than in previous works. This can be achieved by implementing a P2P protocol with authentication. We validate the outcomes via way of means of deploying and measuring the accuracy and security of file transfer in multiple devices. © 2023 IEEE.

关键词： Authentication

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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4-D Structured Tensor Decomposition-Based Channel Estimation for RIS-Aided mmWave MIMO-NOMA System in Internet of Vehicles

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IEEE Internet of Things Journal 2025年第12期12卷 22298-22310页

作者： Cai, Wanyuan Li, Youming Wu, Yonghong Sheng, Menglei Qi, Qinke Guo, Qiang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China China Research Institute of Radio Wave Propagation Fourth Department Qingdao266107 China Harbin Engineering University College of Information and Communication Engineering Harbin150000 China

Severe Doppler shift and the obstruction of the Line-of-Sight (LoS) path significantly decrease the communication performance. This article considers a downlink channel estimation problem for reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple-input-multiple-output nonorthogonal multiple access (MIMO-NOMA) system in Internet of Vehicles (IoV) with high-mobility scenarios. By introducing the concept of aggregated slot and half slot, a 5G subframe partitioning scheme without changing the standard 5G frame structure is first proposed to facilitate the formulation of the received signal. Then, the received signal is modeled as a quadrilinear tensor, meeting with a canonical polyadic decomposition (CPD) form, which separates Angle of Arrival (AoA), Angle of Departure (AoD), time delay, and Doppler shift into four corresponding factor matrices and avoids parameters coupling. Subsequently, by leveraging the Vandermonde structure of the factor matrix and the low-rank property of the mmWave channel, we design a four-dimension (4-D) structured tensor decomposition-based method to decompose the tensor into four factor matrices in a closed-form solution, which can avoid initialization and iteration. Accordingly, the channel parameters can be extracted by a simple correlation-based estimator. After obtaining the channel parameters, we construct a least squares problem to obtain the channel path gain, which can avoid solving the scaling matrix. Finally, numerical experiments are conducted to confirm the effectiveness of the proposed algorithm, in which the Cramér-Rao bound (CRB) results for channel parameters are derived as the benchmark. © 2025 IEEE.

关键词： Least squares approximations

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