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ML Models and Big data Analytics to Predict the Cricket Match

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ML Models and Big Data Analytics to Predict the Cricket Matc...

Next Generation Communication & Information Processing (INCIP), International Conference on

作者： V. Asha S. Sreekanth Boddu Rajasekhar Ginni Nijhawan Tamam Ali Abd Ulabbas Abedi Ashish Parmar Department of Computer Applications New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India LPU Phagwara India Medical Technology The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

artificial intelligence (AI)and Machine learning have greatly improved the accuracy of cricket, one of the most popular, well-supported, and exhilarating sports of today. The quantity of matches played throughout time has led to an exponential growth in the data about cricket matches and individual players. Furthermore, big data analytics are in high demand, and this data has many potential applications. Some examples of its potential applications include team player selection and prediction utilizing machine learning models and significant data approaches. A machine learning linear reversion model was used to forecast the team's performance in the absence of massive data and the comprehensive Spark ML framework. Following the application of linear regression in Spark ML, the untried outcomes are evaluated using accuracy, mean square error (MSE) and mean absolute error (MAE) root mean square error (RMSE), which are reported as 95%, 30.2, 1350.34, and 28.2 correspondingly. More than that, may use this method for other kinds of sports.

关键词： Analytical models Accuracy Linear regression Urban areas Machine learning Predictive models Big data data models Sparks Sports

Semi-automatic staging area for high-quality structured data extraction from scientific literature

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arXiv 2023年

作者： Foppiano, Luca Mato, Tomoya Terashima, Kensei Suarez, Pedro Ortiz Tou, Taku Sakai, Chikako Wang, Wei-Sheng Amagasa, Toshiyuki Takano, Yoshihiko Ishii, Masashi Materials Modelling Group Data-driven Materials Research Field Centre for Basic Research on Materials NIMS Japan Knowledge and Data Engineering Centre for Computational Sciences University of Tsukuba Japan Frontier Superconducting Materials Group MANA NIMS Tsukuba Japan DFKI GmbH Germany

We propose a semi-automatic staging area for efficiently building an accurate database of experimental physical properties of superconductors from literature, called SuperCon2, to enrich the existing manually-built superconductor database SuperCon. Here we report our curation interface (SuperCon2 Interface) and a workflow managing the state transitions of each examined record, to validate the dataset of superconductors from PDF documents collected using Grobid-superconductors in a previous work [1]. This curation workflow allows both automatic and manual operations, the former contains "anomaly detection" that scans new data identifying outliers, and a "training data collector" mechanism that collects training data examples based on manual corrections. Such training data collection policy is effective in improving the machine-learning models with a reduced number of examples. For manual operations, the interface (SuperCon2 interface) is developed to increase efficiency during manual correction by providing a smart interface and an enhanced PDF document viewer. We show that our interface significantly improves the curation quality by boosting precision and recall as compared with the traditional "manual correction". Our semi-automatic approach would provide a solution for achieving a reliable database with text-data mining of scientific documents. © 2023, CC BY.

关键词： database systems

Dynamic Resource Allocation in Edge Computing via Deep Deterministic Policy Gradient Reinforcement Learning

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Dynamic Resource Allocation in Edge Computing via Deep Deter...

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Vidhya Gopal U Surendar A Bajulunisha M Tamilselvi V Sathiyapriya N Saranya Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering SRM institute of Science and Technology Chennai India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur India Department of Computer Science and Engineering Roever Engineering College Perambalur India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The difficulty with dynamic and heterogeneous natured edge computing environments is resource provisioning. Reinforcement Learning (RL) can be promising to solve the problems of resource allocation under conditions of dynamics and complexity. This research work presents a novel DDPG-based (Deep Deterministic Policy Gradient) approach for optimizing the dynamics of resource allocation in edge computing environments by exploiting the CRAWDAD KAIST–WIBRO dataset for the proposed model. This approach implements a neural network that learns the optimal policies of allocations with dynamism towards new changes in workloads and resource availabilities. In order to test the performance of this method, conducted several simulations over real-world workload and resource data, mapping the parameters like rewards and variation in episodes. The simulation results indicate that proposed approach performs superior compared to traditional heuristic-based approaches as well as other RL algorithms for resource usage, response time, and energy efficiency. This proposed approach permits optimized resource utilization along with quality of service, of paramount importance in executing complex and demanding applications at the edge computing environment.

关键词： Computational modeling Simulation Markov decision processes Neural networks Reinforcement learning Quality of service Dynamic scheduling Resource management Time factors Edge computing

SALI: A Scalable Adaptive Learned Index Framework based on Probability Models

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arXiv 2023年

作者： Ge, Jiake Zhang, Huanchen Shi, Boyu Luo, Yuanhui Guo, Yunda Chai, Yunpeng Chen, Yuxing Pan, Anqun Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China China China and Shanghai Qi Zhi Institute China Tsinghua University China Tencent Inc China

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is learned indexes, which use a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04× compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+. © 2023, CC BY-NC-ND.

关键词： Scalability

From knowledge Graph Development to Serving Industrial knowledge Automation:A Review 41

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From Knowledge Graph Development to Serving Industrial Knowl...

第41届中国控制会议

作者： Guangxuan Song Dongmei Fu Dawei Zhang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing Shunde Graduate School of University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology Beijing National Materials Corrosion and Protection Data Center University of Science and Technology Beijing

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

knowledge is essential for the management,scheduling,and operation of all industrial *** automation refers to the automation of knowledge-based work,which is a disruptive technology of the *** in the era of big data,KG inherits the good interpretability of traditional symbolism system approaches and becomes one of the feasible and important directions to realize industrial knowledge *** paper begins with the origin of KGs,introduces the characteristics of KGs,and reviews the state of the art of KG through the cases of KG constructed and applied in general and vertical *** paper also focuses on the research progress of KG applications in industry,by identifying the current challenges of KG in industrial applications,and highlighting the substantial potential of KG to serve knowledge automation.

关键词： knowledge knowledge Graph(KG) knowledge Automation Symbolism Industry

STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting

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STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lincan Li Kaixiang Yang Jichao Bi Fengji Luo Data and Knowledge Research Group University of New South Wales Sydney Australia School of Computer Science and Engineering South China University of Technology Guangzhou China Zhejiang Institute of Industry and Information Technology Hangzhou China The University of Sydney Sydney Australia

Efficiently capturing the complex spatiotemporal representations from large-scale traffic data with uneven data quality remains to be a challenging task. In considering of the dilemma, this work employs the advanced contrastive learning and proposes a novel Spatial-Temporal Synchronous Contextual Contrastive Learning (STS-CCL) model. First, we elaborate the basic and strong augmentation methods for spatiotemporal graph data. Second, we introduce a Spatial-Temporal Synchronous Contrastive Module (STS-CM) to simultaneously capture the decent spatial-temporal dependencies and realize graph-level contrasting. To further discriminate node individuals in negative filtering, a Semantic Contextual Contrastive method is designed based on semantic features and spatial heterogeneity, achieving node-level contrastive learning along with negative filtering. Finally, we present a hard mutual-view contrastive training scheme and extend the classic contrastive loss to an integrated objective function, yielding better performance. Extensive experiments and evaluations demonstrate that building a predictor upon STS-CCL contrastive learning model gains superior performance than existing traffic forecasting benchmarks. The proposed STS-CCL is highly suitable for large datasets with only a few labeled data and other spatiotemporal tasks with data scarcity issue.

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data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big data System, data Mining, and Closed-Loop Technologies

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arXiv 2024年

作者： Li, Lincan Shao, Wei Dong, Wei Tian, Yijun Yang, Kaixiang Zhang, Wenjie Data and Knowledge Research Group University of New South Wales Sydney Australia Data61 CSIRO ClaytonVIC Australia BYD Intelligent Driving R&D Centre Shenzhen China University of Notre Dame South Bend United States School of Computer Science & Engineering South China University of Technology Guangzhou China

The aspiration of the next generation’s autonomous driving (AD) technology relies on the dedicated integration and interaction among intelligent perception, prediction, planning, and low-level control. There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology. Recent advancement in AD simulation, closed-loop model training, and AD big data engine have gained some valuable experience. However, there is a lack of systematic knowledge and deep understanding regarding how to build efficient data-centric AD technology for AD algorithm self-evolution and better AD big data accumulation. To fill in the identified research gaps, this article will closely focus on reviewing the state-of-the-art data-driven autonomous driving technologies, with an emphasis on the comprehensive taxonomy of autonomous driving datasets characterized by milestone generations, key features, data acquisition settings, etc. Furthermore, we provide a systematic review of the existing benchmark closed-loop AD big data pipelines from the industrial frontier, including the procedure of closed-loop frameworks, key technologies, and empirical studies. Finally, the future directions, potential applications, limitations and concerns are discussed to arouse efforts from both academia and industry for promoting the further development of autonomous driving. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

Out-of-Distribution Generalization on Graphs via Progressive Inference

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arXiv 2025年

作者： Xu, Yiming Shi, Bin Peng, Zhen Liu, Huixiang Dong, Bo Chen, Chen School of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China School of Distance Education Xi’an Jiaotong University China University of Virginia CharlottesvilleVA United States

The development and evaluation of graph neural networks (GNNs) generally follow the independent and identically distributed (i.i.d.) assumption. Yet this assumption is often untenable in practice due to the uncontrollable data generation mechanism. In particular, when the data distribution shows a significant shift, most GNNs would fail to produce reliable predictions and may even make decisions randomly. One of the most promising solutions to improve the model generalization is to pick out causal invariant parts in the input graph. Nonetheless, we observe a significant distribution gap between the causal parts learned by existing methods and the ground-truth, leading to undesirable performance. In response to the above issues, this paper presents GPro, a model that learns graph causal invariance with progressive inference. Specifically, the complicated graph causal invariant learning is decomposed into multiple intermediate inference steps from easy to hard, and the perception of GPro is continuously strengthened through a progressive inference process to extract causal features that are stable to distribution shifts. We also enlarge the training distribution by creating counterfactual samples to enhance the capability of the GPro in capturing the causal invariant parts. Extensive experiments demonstrate that our proposed GPro outperforms the state-of-the-art methods by 4.91% on average. For datasets with more severe distribution shifts, the performance improvement can be up to 6.86%. 1 © 2025, CC BY.

关键词： Graph neural networks

Iterative Intraoperative Digital Tomosynthesis Image Reconstruction using a Prior as Initial Image 7

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Iterative Intraoperative Digital Tomosynthesis Image Reconst...

7th International Conference on Image Formation in X-Ray Computed Tomography

作者： Saad, Fatima Frysch, Robert Pfeiffer, Tim Saalfeld, Sylvia Schulz, Jessica Georgi, Jens-Christoph Nürnberger, Andreas Lauritsch, Guenter Rose, Georg Institute for Medical Engineering Otto-von-Guericke University Magdeburg Germany Forschungscampus STIMULATE Otto-von-Guericke University Magdeburg Germany Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany Siemens Healthcare GmbH Forchheim Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany

ISBN: (纸本)9781510656697

The efficacy of interventional treatments highly relies on an accurate identification of the target lesions and the interventional tools in the guidance images. Whereas X-ray radiography poses low doses to the patient, its weakness is in the superposition of the different image structures in a 2D image. Cone-beam computed tomography (CBCT) might look ideal providing exact 3D information, however this is at the cost of a higher radiation dose, longer imaging time, and more space requirements in the operating room. Introducing some depth information with relatively low dose, and requiring less space, digital tomosynthesis (DTS) is a potential candidate for guiding interventions. However, due to the few number of projections and to the limited angle acquisition, DTS has poor depth resolution. Since high quality patient-specific prior CT scans are usually performed prior to the intervention for diagnosis or to plan the intervention, and given that such images share a fair amount of information with the intraoperative DTS images, we propose in this work a prior-based iterative reconstruction framework to improve the intraoperative DTS image quality. The framework is based on registering the prior CT image to an intermediate low-quality intraoperative DTS image, then iteratively re-reconstructing the intraoperative DTS image using the coregistered prior CT as the starting image. We acquired prior CT and intraoperative CBCT data of a liver phantom and simulated some intraoperative DTS projection images using a spherical ellipse scan geometry. Our results show a great improvement in the DTS image quality with the proposed method and prove the importance of choosing a good starting point for the iterative DTS reconstruction. © 2022 SPIE.

关键词： Computerized tomography