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International Conference on Contemporary Computing and Informatics (IC3I)

作者： V Asha S Sreekanth Ginni Nijhawan K. Varada Rajkumar Monica Garg Ali Abduhussien Hameed Department of Computer Applications New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This research introduces an approach named “Cloud-Driven Personalized Experience Enhancement” to satisfy the escalating demand for enhanced digital connectivity. Via cutting-edge cloud services and applications, the CPExE framework provides a groundbreaking solution for digital platforms. The framework is built upon three core components: Together with the Quantum-Inspired data Analytics and Evaluation (QIDAE) and Personalized Content Recommendations (PRA), Dynamic Resource Allocation and Adaptation (DRAA) completes the framework. By leveraging quantum-inspired approaches, QIDAE analyzes vast datasets with outstanding effectiveness and exactness improving processes for data handling and personalization. This strategy allows for swift detection of patterns while making certain that data-driven understandings are both on time and precise. By changing cloud resources according to user requests the DRAA module guarantees uninterrupted access to computing power and data storage. By managing resources effectively in real-time it decreases latency and improves service speeds. By using complex algorithms to assess user habits and choices the PRA system boosts the digital experience. PRA uses both content-focused filtering and collaborative screening strategies to provide users with engaging content that matches their unique preferences. By applying sophisticated analytics and resource management methods with content personalization CPExE boosts the user experience and develops cloud technology efficiency. This framework signifies an important progress toward developing dynamic and tailored online environments.

关键词： Heating systems Histograms data analysis Heuristic algorithms Scalability Memory Dynamic scheduling Real-time systems User experience Resource management

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Attention-Enhanced Neural Network Models for Turbulence Simulation

arXiv

引用

arXiv 2021年

作者： Yuan, Zelong Wang, Jianchun Peng, Wenhui Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Guangzhou511458 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Shenzhen518055 China Department of Computer Engineering Polytechnique Montreal H3T1J4 Canada

Deep neural network models have shown a great potential in accelerating the simulation of fluid dynamic systems. Once trained, these models can make inference within seconds, thus can be extremely efficient. However, it becomes more difficult for neural networks to make accurate predictions when the flow becomes more chaotic and turbulent at higher Reynolds numbers. One of the most important reasons is that, existing models lack the mechanism to handle the unique characteristic of high-Reynolds-number turbulent flow: multi-scale flow structures are non-uniformly distributed and strongly nonequilibrium. In this work, we address this issue with the concept of visual attention: intuitively, we expect the attention module to capture the nonequilibrium of turbulence by automatically adjusting weights on different regions. We compare the model performance against a state-of-the-art neural network model as baseline, the Fourier Neural Operator (FNO), on two-dimensional (2D) turbulence prediction task. Numerical experiments show that the attention-enhanced neural network model outperforms existing state-of-the-art baselines, and can accurately reconstruct a variety of statistics and instantaneous spatial structures of turbulence at high Reynolds numbers flow. Furthermore, the attention mechanism provides 40% error reduction with 1% increase of parameters, at the same level of computational cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Turbulence

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm

An Enhanced Traffic Incident Detection using Factor Analysis...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. Rajesh Kanna S. Vanithamani P. Karunakaran P. Pandiaraja N. Tamilarasi P. Nithin Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Master of Computer Applications M. Kumarasamy College of Engineering Karur Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Nehru Institute of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector Machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks.

关键词： Measurement Support vector machines Logistic regression Machine learning algorithms Decision making Transportation Receivers data models Classification algorithms Random forests

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A survey of hybrid human-artificial intelligence for social computing

arXiv

引用

arXiv 2021年

作者： Wang, Wenxi Ning, Huansheng Shi, Feifei Dhelim, Sahraoui Zhang, Weishan Chen, Liming School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083 China Department of Software Engineering China University of Petroleum Qingdao266580 China School of Computing Ulster University United Kingdom

Along with the development of modern computing technology and social sciences, both theoretical research and practical applications of social computing have been continuously extended. In particular with the boom of artificial intelligence (AI), social computing is significantly influenced by AI. However, the conventional technologies of AI have drawbacks in dealing with more complicated and dynamic problems. Such deficiency can be rectified by hybrid human-artificial intelligence (H-AI) which integrates both human intelligence and AI into one unity, forming a new enhanced intelligence. H-AI in dealing with social problems shows the advantages that AI can’t surpass. This paper firstly introduces the concepts of H-AI. AI is the intelligence in the transition stage of H-AI, so the latest research progresses of AI in social computing are reviewed. Secondly, it summarizes typical challenges faced by AI in social computing, and makes it possible to introduce H-AI to solve these challenges. Finally, the paper proposes a holistic framework of social computing combining with H-AI, which consists of four layers: object layer, base layer, analysis layer, and application layer. It represents HAI has significant advantages over AI in solving social problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Social sciences computing

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Machine learning-driven conservative-to-primitive conversion in hybrid piecewise polytropic and tabulated equations of state

arXiv

引用

arXiv 2024年

作者： Kacmaz, Semih Haas, Roland Huerta, E.A. Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States

We present a novel machine learning (ML) method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditional root-finding techniques are computationally expensive, particularly for large-scale relativistic hydrodynamics simulations. To address this, we employ feedforward neural networks (NNC2PS and NNC2PL), trained in PyTorch and optimized for GPU inference using NVIDIA TensorRT, achieving significant speedups with minimal accuracy loss. The NNC2PS model achieves L1 and L∞ errors of 4.54×10−7 and 3.44×10−6, respectively, while the NNC2PL model exhibits even lower error values. TensorRT optimization with mixed-precision deployment substantially accelerates performance compared to traditional root-finding methods. Specifically, the mixed-precision TensorRT engine for NNC2PS achieves inference speeds approximately 400 times faster than a traditional single-threaded CPU implementation for a dataset size of 1,000,000 points. Ideal parallelization across an entire compute node in the Delta supercomputer (Dual AMD 64 core 2.45 GHz Milan processors;and 8 NVIDIA A100 GPUs with 40 GB HBM2 RAM and NVLink) predicts a 25-fold speedup for TensorRT over an optimally-parallelized numerical method when processing 8 million data points. Moreover, the ML method exhibits sub-linear scaling with increasing dataset sizes. We release the scientific software developed, enabling further validation and extension of our findings. This work underscores the potential of ML, combined with GPU optimization and model quantization, to accelerate conservative-to-primitive inversion in relativistic hydrodynamics simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphics processing unit

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APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Le...

引用

IEEE International Conference on e-science and Grid Computing

作者： Zilinghan Li Shilan He Pranshu Chaturvedi Trung-Hieu Hoang Minseok Ryu E. A. Huerta Volodymyr Kindratenko Jordan Fuhrman Maryellen Giger Ryan Chard Kibaek Kim Ravi Madduri Data Science and Learning Division Argonne National Laboratory Lemont IL USA Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL USA National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Urbana IL USA Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA School of Computing and Augmented Intelligence Arizona State University Tempe AZ USA Department of Physics University of Illinois at Urbana-Champaign Urbana IL USA Department of Computer Science The University of Chicago Chicago IL USA Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA University of Chicago Consortium for Advanced Science and Engineering Chicago IL USA

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://***

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Exploring K-Means Meta-Heuristic Techniques For Prediction of Anomalies In IoT-Enabled Industrial Systems

Exploring K-Means Meta-Heuristic Techniques For Prediction o...

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International Conference on Computing and Networking Technology (ICCNT)

作者： T. Gnanasekaran S Girinath K Venkatesh N. Valarmathi Sunil Kumar Bandili S Balasubramani Department of ECE R.M.K. Engineering College Kavaraipettai Tamilnadu India Department of Computer Applications Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati AP India Department of Computer Science and Engineering (Data Science) Srinivasa Ramanujan Institute of Technology Anantapur Aandra Pradesh India Department of Information Technology M.Kumarasamy College of Engineering Thalavapalayam Karur Inda Department of Computer and Information Sciences School for Professional Studies Saint Louis University St. Louis MO USA Department of ECE R.M.D. Engineering College Tamilnadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Due to the broad implementation of the Internet of Things (IoT) in industrial systems, the introduction of Industry 4.0 resulted in a significant increase in operational efficiency. However, it also resulted in the introduction of new challenges when it came to identifying abnormalities. This paper presents a new method for boosting the flexibility and precision of anomaly detection models by integrating the best aspects of K-Means clustering with meta-heuristic approaches. The utilisation of K-Means meta-heuristic techniques is the driving force behind this research, which is being driven by the growing demand for reliable anomaly prediction in industrial systems that are enabled by the IoT. Aiming to discover methods that can improve the reliability of anomaly detection algorithms is the objective of this research. K-Means clustering is systematically integrated with metaheuristic techniques in this study, which allows for the optimisation of the clustering algorithm for the purpose of detecting abnormalities in industrial data that is enabled by the IoT. We use real-world datasets from a wide range of businesses in order to measure the effectiveness of the proposed strategy in comparison to more traditional ways. The results demonstrate that the K-Means meta-heuristic strategy greatly enhances the accuracy of anomaly detection in comparison to the approaches that are considered conventional. Recall, false positive rates, and precision are all areas in which it excels beyond its capabilities. The results indicate that this unique approach has the potential to be useful when applied to the challenges of anomaly detection in industrial systems that are enabled by the IoT.

关键词： Accuracy Metaheuristics Force Clustering algorithms Prediction algorithms Fourth Industrial Revolution Reliability Internet of Things Anomaly detection Testing

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Grey Wolf Optimizer with Deep Learning based Short Term Traffic Forecasting in Smart City Environment

Grey Wolf Optimizer with Deep Learning based Short Term Traf...

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International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： R Jegadeesan E Vijayakrishna Rapaka K. Himabindu Nihar Ranjan Behera Arvind Kumar Shukla Arvind Kumar Dangi Jyothishmathi Institute of Technology and Science Karimnagar India Data Science Indian Institute of Technology Madras Chennai Tamilnadu India Department of CSA KL University Guntur Andhrapradesh India Swiss School of Business and Management Geneva Av. des Morgines 12 1213 Petit-Lancy Switzerland Department of Computer Applications IFTM University Moradabad Uttar Pradesh India MRIIRS Faridabad Haryana

Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that aids in minimizing traffic congestion and improving traffic quality. ITS provides real-time analysis and very effective traffic management by utilizing big data and communication technology. Traffic Flow Prediction (TFP) becomes a dynamic component in smart city management and was utilized for predicting the future traffic conditions on transportation networks relevant to past data. Machine Learning (ML) and Neural Network (NN) techniques can be broadly used in resolving real-time problems as these techniques are capable of managing adaptive data for some time. Deep Learning (DL) is a sub-divison of ML methods which earns effective performance on prediction and data classification tasks. This article designs a Grey Wolf optimizer with Deep Learning Based Short Term Traffic Forecasting (GWODL-STTF) in smart city environment. The presented GWODL-STTF technique concentrates on the prediction of traffic flow in smart cities. The presented GWODL-STTF technique involves two major processes. At the initial stage, the GWODL-STTF technique employed gated recurrent unit-neural network (GRU-NN) model to forecast traffic flow. Next, in the second stage, the GWODLSTTF technique makes use of GWO algorithm as a hyperparameter optimizer. The simulation values of the GWODL-STTF method can be tested under several metrics and the outcomes show the significant performance of the GWODLSTTF method over recent approaches with minimum MSE of 105.627.

关键词： Deep learning Measurement Smart cities Transportation Predictive models Prediction algorithms Real-time systems

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An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

引用

Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

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A MULTIVARIATE POLYA TREE MODEL FOR META-ANALYSIS WITH EVENT TIME DISTRIBUTIONS

arXiv

引用

arXiv 2023年

作者： Poli, Giovanni Fountzilas, Elena Tsimeridou, Apostolia-Maria Müller, Peter Department of Statistics Computer Science Applications G. Parenti Università degli Studi di Firenze Florence Italy Department of Medical Oncology St Luke’s Clinic Thessaloniki Greece and European University Cyprus Limassol Cyprus Department of Investigational Cancer Therapeutics The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Statistics and Data Science University of Texas at Austin AustinTX United States

We develop a non-parametric Bayesian prior for a family of random probability measures by extending the Polya tree (PT) prior to a joint prior for a set of probability measures G1, . . ., Gn, suitable for meta-analysis with event time outcomes. In the application to meta-analysis, Gi is the event time distribution specific to study i. The proposed model defines a regression on study-specific covariates by introducing increased correlation for any pair of studies with similar characteristics. The desired multivariate PT model is constructed by introducing a hierarchical prior on the conditional splitting probabilities in the PT construction for each of the Gi. The hierarchical prior replaces the independent beta priors for the splitting probability in the PT construction with a Gaussian process prior for corresponding (logit) splitting probabilities across all studies. The Gaussian process is indexed by study-specific covariates, introducing the desired dependence with increased correlation for similar studies. The main feature of the proposed construction is (conditionally) conjugate posterior updating with commonly reported inference summaries for event time data. The construction is motivated by a meta-analysis over cancer immunotherapy studies. © 2023, CC BY-NC-ND.

关键词： Bayesian networks

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