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Compositional design and phase formation capability of high-entropy rare-earth disilicates from machine learning and decision fusion

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npj Computational Materials 2024年第1期10卷 2257-2271页

作者： Yun Fan Yuelei Bai Qian Li Zhiyao Lu Dong Chen Yuchen Liu Wenxian Li Bin Liu National Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structure Harbin Institute of TechnologyHarbin 150080China School of Materials Science and Engineering Shanghai UniversityShanghai 200444China National Engineering Research Center for Magnesium Alloy Chongqing UniversityChongqing 400044China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150080China UNSW Materials&Manufacturing Futures Institute School of Materials Science and EngineeringUniversity of New South WalesSydney NSW 2052Australia Institute of Coating Technology for Hydrogen Gas Turbines Liaoning Academy of MaterialsShenyang 110004China

A key strategy for designing environmental barrier coatings is to incorporate multiple rare-earth(RE)components intoβ-andγ-RE_(2)Si_(2)O_(7)to achieve multifunctional performance ***,the polymorphic phase presents s... 详细信息

关键词： rare earth silicate

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Machine Utilization Prediction in Cloud Using Informer Model 4

Machine Utilization Prediction in Cloud Using Informer Model

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Naik, Archana Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529635

This study investigates CPU utilization prediction in cloud environments using the Informer model, a deep learning framework optimized for long-sequence forecasting. By leveraging its ability to capture complex temporal patterns, the Informer model is benchmarked against GRU, LSTM, Prophet, and XGBoost across diverse CPU utilization datasets. Performance is evaluated using RMSE, MSE, MAE, and MAPE metrics. Experimental results demonstrate that the Informer model significantly outperforms the comparison models, excelling in capturing both gradual trends and abrupt fluctuations. Specifically, the Informer achieved a 47.51% reduction in RMSE over GRU, 49.90% over LSTM, 67.36% over Prophet, and 50.46% over XGBoost. These findings underscore the Informer model's superior adaptability and precision, making it a robust solution for dynamic resource utilization prediction in cloud environments. Its predictive accuracy is pivotal for enhancing workload management and optimizing resource allocation. © 2024 IEEE.

关键词： Prediction models

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Assessing Document Clustering Algorithms for Effective Information Retrieval 8

Assessing Document Clustering Algorithms for Effective Infor...

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

作者： Patel, Vaishali Hiran, Dilendra Dangarwala, Kruti Paher University Department of Computer Engineering Rajasthan Udaipur India Institute of Technology Department of Computer Science and Engineering Svm Bharuch India

ISBN: (纸本)9798350376425

An exponential growth of document data on the web, Effective information retrieval is a challenging task which fulfills user's query. Cluster based information retrieval as well as Pattern mining is the most popular solution to retrieve relevant Information queries are based on a user's query on the web. Document clustering is one of the most effective techniques for information retrieval. And many solutions have been proposed for effective and quality. Information retrieval from a set of documents for a specific field of research. K-Means is one of the widely used document clustering algorithm for information retrieval from sets of documents on web. However, it has major challenging issues which affect on The performance of information retrieval. This work discusses the importance of document clustering for information retrieval, motivation for document clustering and categories of document clustering algorithms. This work also presents steps of proposed work, implementation of traditional clustering algorithms, Result analysis and discussion of variants of K-Means, Mini-BatchkMeans, Spectral using term frequency and dimension Reduction measures on 20 NewsGroups document dataset. It concludes the importance of cluster based information retrieval from a large set of documents on the web, various research progresses, major challenges and future research work direction for effective Information retrieval. © 2024 IEEE.

关键词： Structured Query Language

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Advanced Double Input Layered Neural Network for medical diagnosis by using cloud computing technology 1

Advanced Double Input Layered Neural Network for medical dia...

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1st IEEE International Conference on Emerging research in Computational science, ICERCS 2023

作者： Gowri, S. Jabez, J. Nirmalrani, V. Srinivasulu, Senduru Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Advanced Double Input Layered Neural Network has the potential to revolutionize medical diagnostics by solving pressing problems. It improves diagnostic precision by providing a single, unified platform for the examination of both organized and unstructured medical data. It provides real-time decision assistance and data management by utilizing the scalable, secure, and efficient data processing capabilities made possible by cloud computing. Improved patient care, more effective therapy, and higher-quality healthcare are some areas where Advanced Double Input Layered Neural Network can make a difference. Rapid, precise, and secure data analysis;the administration of multiple data sources;and realtime decision support are a few of the difficulties inherent in medical diagnosis. This research proposes an Advanced Double Input Layered Neural Network (ADILNN) with a double input layered neural community, enabling it to examine prepared and unstructured scientific data. This novel technique improves the community's mastering functionality from various data kinds. By centralizing statistics garage, processing, and analysis on the cloud, computing sources may be extra reliably accessed whilst wanted. The network's diagnostic precision and flexibility are each advanced through way of tool gaining knowledge of strategies (MLM). Because of its adaptability, ADILNN can be utilized in diverse medical fields, which include radiology, pathology, cardiology, and genetics. It permits examine genomic information, making recovery alternatives, and analyzing x-ray photos. The technique has numerous ability makes use of in healthcare, improving prognosis accuracy in diverse settings. Simulation analysis is used to gauge ADILNN's capacity by way of gauging its diagnostic accuracy, processing speed, scalability, and records protection. These research validate ADILNN's potential to improve clinical analysis, streamline facts management, and guarantee healthcare records's safe and effectiv

关键词： Diagnosis

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Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application with Blockchain-Based Authentication

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1370-1379页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Illinois Institute of Technology Information Technology and Management ChicagoIL60616 United States

The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block. © 1975-2011 IEEE.

关键词： Authentication

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Analyzing the Prediction Accuracy of Corn Leaf Diseases Using A Pre-Trained Network Model

Analyzing the Prediction Accuracy of Corn Leaf Diseases Usin...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Ashwini, C. Sellam, V. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396485

During corn's exploration and manufacturing phases, farmers have a complicated issue in accurately diagnosing corn crop infections. To solve this issue, this work provides a method for specific position three prevalent diseases of corn leaves: grey spot, leaf blight, and rusty depending on grouping and an upgraded deep learning method. Deep learning advancements paved the path for enhancing prediction accuracy as its significance is adopted over various research fields. Disease prediction and classification is time-consuming due to the lesser foreground and background intensity information. To identify three illnesses, first cluster reference images using the clustering technique, then input them into the enhanced deep network. The influences of various 'k' values on corn diagnostic techniques are investigated in this research. The trial findings show that the approach has the most significant identification impact on observations with the analytical prediction of corn disease, rust, and grey spot disease. Here, VGG-16 and ResNet18 likewise produce the best diagnostic findings, with an average diagnostic accuracy, respectively. The technique presented in this study has a diagnostic performance of 95% for the three corn diseases. It has a more substantial diagnostic impact than another four techniques and can be used to safeguard crops in the agricultural area. The simulation is done in the MATLAB 2020a environment, and various performance metrics like accuracy, precision, F1-score, recall and some other statistical measures are evaluated and compared with existing approaches. © 2023 IEEE.

关键词： Forecasting

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Design and Implementation of Students Grievance and Database 2

Design and Implementation of Students Grievance and Database

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Kuhan, Sakthi Grace, L.K.Joshila Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665456302

Tracking student data and complaints is critical to track student performance in the classroom even while studying. This research study aims to address the grievances of the students and presents the website as a portal built using Javascript, HTML, Python, and MySQL, where students may file concerns, and the department handling the case is notified. The student has complete access to the complaint's history and may see if it has been examined, investigated, sent, rejected, or handled. The proposed technique is totally transparent, and if a complaint remains unanswered for several days, the system will automatically transmit it to the person specified further up the hierarchy. Any correctional system should be concerned about language quality since it may be used to spread incorrect information by making comments about people's gender, color, or religion. Employing the state-of-the-art technologies like deep learning and machine learning helps to detect hate speech. After training 11,325 tweets and analyzing the outcomes using evaluation metrics including F1 score, recall, and precision. Bi-LSTM, LSTM, and SVM models are utilized. By reaching the metrics values of 0.884, 0.84, and 0.86, the LSTM model outscores the other models. © 2023 IEEE.

关键词： Students

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Fuzzy Logic Based Deep Learning Approach (FRNN) for Autism Spectrum Disorder Detection

Fuzzy Logic Based Deep Learning Approach (FRNN) for Autism S...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Kavitha Nair, R. Ranjana, P. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350398465

Autism Spectrum Disorder (ASD) is regarded as a spectrum of developmental problems with neurological roots that affect communication, social interaction, and behavior. As it has a detrimental effect on both psychological and physical health, the anxiety experienced by the average individual concerns the physician, especially for those with ASD. researchers are working very hard to understand the genetic underpinnings of autism and use the genetic information to choose logical targets for efficient therapy. International consortiums are using linkage analysis to pinpoint chromosomes and their correlation with ASD in order to pinpoint potential genetic illnesses. This study applies a recurrent neural network with fuzzy logic and provides an empirical assessment of how well it can diagnose autism using data from an MRI scan of the brain. To evaluate their performances, precision, sensitivity, specificity, and classification accuracy metrics are used. The experimental results show that FRNN, a deep learning-based classifier, produces the best outcome. © 2023 IEEE.

关键词： Recurrent neural networks

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Navigating Data Scarcity in Multivariate Time Series Forecasting: A Hybrid Model Perspective

Navigating Data Scarcity in Multivariate Time Series Forecas...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Saravana, M.K. Roopa, M.S. Arunalatha, J.S. Venugopal, K.R. University of Visvesvaraya College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364866

Data scarcity in Multivariate Time series (MTS) analysis (MTS) poses significant challenges across various fields, including scientific research, business, healthcare, and technology development. Factors such as, high costs of data collection, sensor failures, and the complexity of maintaining synchronized multivariable datasets limits the data availability for MTS analysis. The paper presents an innovative approach for energy consumption forecasting in the context of limited historical data by integrating Graph Neural Networks (GNNs) with Long Short-Term Memory (LSTM) networks. It begins with thorough data preprocessing, enhancing the dataset with temporal features. Sophisticated GNN model is fine-tuned using hyperparameter optimization, bolstered by K-Fold cross-validation for robustness. Synthetic data generated from the trained GNN model enriches the training set. Subsequently, an LSTM network captures temporal dependencies, trained with early stopping techniques to prevent overfitting. Evaluation encompasses metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 score. Visual inspection of real versus forecasted data validates the model's performance. By seamlessly integrating data science and machine learning, this approach advances energy consumption forecasting for sustainable energy management. © 2024 IEEE.

关键词： Graph neural networks

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A Dataset Development for Fake News Detection in Low-Resource Romanized Manipuri 3

A Dataset Development for Fake News Detection in Low-Resourc...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Devi, Takhellambam Babylon Jamatia, Anupam Rudrapal, Dwijen Chakma, Kunal National Institute of Technology Agartala Computer Science & Engineering Department Agartala India

ISBN: (纸本)9798331523893

In today's digital landscape, the rapid spread of fake news presents significant challenges, particularly in multilingual nations like India. Manipuri, the primary language of Manipur in Northeast India, has seen a surge in online usage and digital adaptation, making it easier for Manipuri-speaking communities to engage with each other. However, despite this growth, there is a lack of resources for identifying fake news in Manipuri, especially in its Romanized form. This study focuses on developing a dataset to detect fake news in Romanized Manipuri, especially in the sports domain. Since there were no existing datasets for this language, the dataset was compiled by collecting articles from credible Manipuri news sources, transliterating them with contributors, and labelling each as either 'fake' or 'real' to aid in effective detection. Every step, from collecting data to labelling it, was done carefully by hand to ensure accuracy. Statistical tests were also performed to find important language patterns that help distinguish between real and fake news. Our findings show the unique language features of Romanized Manipuri and provide a solid foundation for future work in developing machine-learning models for detecting misinformation. By developing this dataset, we aim to improve the availability of reliable information for Manipuri-speaking communities. © 2025 IEEE.

关键词： Transfer learning

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