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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

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Sentimental Analysis Using Twitter Data Using LSTM, CNN and Extra Tree Classifier Methods 5

Sentimental Analysis Using Twitter Data Using LSTM, CNN and ...

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5th IEEE international conference on advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Kumar, Arvind Sharma, Devender Singh, Deepti Galgotias University School Of Computing Science And Engineering India India

ISBN: (纸本)9798350330861

Twitter can be said as a Microblogging. One of the most popular microblogging computer sites. Tweets of Twitter can be considered as emotion of users, this is the reason why it is the easiest ways of poignant study. Sentiment analysis can be stated as the process of analysing different opinions and classifying them in types. Opinions are broadly distributed in four groups: neutral, constructive, unhealthy, undesirable. Basically, information mining is used to find pertinent information from websites, particularly from social networking websites. Using an approach that combines several disciplines, such as text mining, human language processing, and device intelligence. This study mainly has highlights that how the tweets are stated emotionally, according to info congregated through Twitter. Earlier the Researchers used device mastering procedures that were already in place aimed at sentimentality assessment, unfortunately it came to know that the outcomes of these methods were not successful in providing superior sentiment category results. As a result, improving them finally lead to sentiment evaluation, where we tend to use collective mechanism knowledge methods to increase the effectiveness and quality of the projected strategy. Tentative fallouts proves that proposed technique offers higher type outcomes in terms of accurateness compared to character classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Two Birds One Stone: Dual-Role Path Based Subgraph Matching Using Partial Evaluation 25th

Two Birds One Stone: Dual-Role Path Based Subgraph Matching ...

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25th international conference on Web Information Systems Engineering, WISE 2024

作者： Li, Chengguo Wang, Xin Yin, Yongqi Wang, Hui College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (纸本)9789819605668

In recent years, considerable attention has been drawn to the development of algorithms for subgraph matching in distributed scenarios. Many distributed engines inherently support join-based methods, which can lead to numerous redundant intermediate results and redundant processing. In contrast, exploration-based algorithms, though effective at limiting such extraneous results, necessitate arbitrary access to data. This paper introduces an efficient subgraph matching algorithm that leverages dual-role paths, which is implemented within the framework of partial evaluation and unifies the strengths of binary join and preprocessing enumeration approaches. During the partial evaluation phase, we decompose queries into dual-role paths to enhance the parallel capabilities of the system. For assembly, dual-role paths are treated as vertices for preprocessing enumeration, which reduces invalid intermediate results and avoids a large communication cost. The proposed algorithm has been implemented in a state-of-the-art distributed graph database system, and the comprehensive testing indicates that our proposed algorithm has increased efficiency by up to three times based on the partial evaluation framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph algorithms

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Virtual complex impedance-based droop control for a parallel three phase inverters-based islanded microgrids

Virtual complex impedance-based droop control for a parallel...

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2024 international conference on advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Saoudi, Achraf Chebabhi, Ali Fellah, Mohammed Karim Abdelhalim, Kessal Benkhoris, Mohamed-Fouad Rekioua, Toufik University of M'sila Electrical Engineering Dept Algeria Djillali Liabes University of Sidi Bel-Abbes Iceps Laboratory Algeria University of Nantes Ireena Laboratory Saint-Nazaire France Algeria

ISBN: (纸本)9798350353754

This paper aims to improve the control performance of VSI-based distributed generation (DG) units in an islanded microgrid (IMG) under line impedance mismatches, focusing on power sharing, power decoupling, power oscillation reduction, and circulating current eliminations, all while utilizing a decentralized method. The traditional droop control method (TDCM), with well-established inner voltage and current controllers, can work effectively under ideal IMG. However, the existence of mismatches in line impedances tends to worsen its performance. In this context, a virtual complex impedance-based droop control method is proposed in this paper for three VSI-based DG units in an IMG. To examine and validate the performance of the proposed virtual complex impedance (VCI)based droop control method (DCM) used for controlling the three VSI-based DG units of the IMG, simulations using MATLAB/Simulink are performed. The results demonstrate that the proposed method can significantly enhance the control performance of the IMG. The system shares the active and reactive powers equally, with few oscillations. It also gets rid of the three DG units' reactive power errors and mitigates the circulating currents. © 2024 IEEE.

关键词： MATLAB

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On a General Method for Modeling the Controlled Dynamics of Manipulators with parallel Kinematics as Systems with Geometrical Constraints 10

On a General Method for Modeling the Controlled Dynamics of ...

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10th international conference on advances in Machinery, Material Science and Engineering Application, MMSE 2024

作者： Krasinskiy, Aleksandr Ya Dept. of Computer Modeling and Probability Theory Moscow Aviation Institute National Research UniversityInformation and Computing Center Rosbiotech Moscow Russia

ISBN: (纸本)9781643685465

An adequate mathematical model including all components of an automatic system is a prerequisite for the most cost-effective implementation of the device's potential capabilities. Such a strict nonlinear mathematical model must make it possibility to minimize the actuator drives number, reduce the measurement information amount (the number of measuring sensors) and analyze transient processes in actuator drives. The traditional application of Lagrange equations with constraint multipliers in dynamics models of non-free systems leads to an increase in their dimensionality. The variables of such models are all coordinates, all (including dependent) velocities and also constraint multipliers. The article develops the application of the theoretical mechanics rigorous methods non-free systems to minimize the dimensionality of nonlinear mathematical models of parallel manipulators as the systems with geometric constraints, taking into account the dynamics of actuator electric drives. The transition to equations in redundant coordinates, free from constraint multipliers, excludes from consideration not only multipliers, but also dependent velocities. The application of the proposed approach to modeling the stand Ball and Beam dynamics is considered. © 2024 The Authors.

关键词： Manipulators

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Improving parallel Exhaustive Subgroup Discovery with Early Search Space Pruning 31

Improving Parallel Exhaustive Subgroup Discovery with Early ...

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31st IEEE international conference on High Performance computing, Data, and Analytics Workshops, HiPCW 2024

作者： Jyoti Litoriya, Ashutosh Kailasam, Sriram School of Computing and Elect. Engg. Indian Institute of Technology Mandi India Dept. of Computer Sci. and Engg. and IT Jaypee University of Info. Tech. Solan India Genpact India Pvt. Ltd Bangalore India Dept. of Computer Sci. and Engg. National Institute of Technology Warangal India

ISBN: (纸本)9798331509118

In this digital era, vast amounts of data are generated from various applications, necessitating robust data mining algorithms to extract valuable insights. Subgroup discovery (SD) [1] is a widely used supervised descriptive data mining method that aims to find interesting subgroups of individuals (samples, objects) with high associations to a target property of interest. However, SD faces two major challenges. The first is the high computational expense of exploring the combinatorial search space of subgroups. Second, when testing multiple subgroups to identify a few significant associations, ensuring that each subgroup meets a quality threshold is not enough to control the occurrence of false positives. The latter issue has been addressed recently with permutation testing while for the former, exploring the search space of closed subgroups instead of all possible subgroups reduces the search space without loss of information. Pellegrina et al. [2] incorporated both these approaches and provided a sequential implementation TopKWY for discovering the top-k significant subgroups while controlling false discoveries. It is considered state-of-the-art. However, its huge computational requirement of assessing patterns against many permutations raises scalability concerns, while the specific optimizations in its algorithm for computing p-values do not generalize to other quality functions. Jyoti et al. [3] addressed both these concerns in their proposed framework ParaDiS. The algorithm of ParaDiS is parallel and distributed, employs efficient communication mechanisms for load sharing and generalizes to other quality functions. However, ParaDiS uses exhaustive search and therefore, suffers from performance bottlenecks. The existing heuristic techniques in SD neither control false discoveries nor consider the search space of closed subgroups. Further, these heuristics can miss potentially interesting subgroups w.r.t. quality. © 2024 IEEE

关键词： Economic and social effects

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SECO: Multi-Satellite Edge computing Enabled Wide-Area and Real-Time Earth Observation Missions

SECO: Multi-Satellite Edge Computing Enabled Wide-Area and R...

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IEEE conference on Computer Communications (IEEE INFOCOM)

作者： Zhai, Zhiwei Zeng, Liekang Ouyang, Tao Yu, Shuai Huang, Qianyi Chen, Xu Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350383515;9798350383508

Rapid advances in low Earth orbit (LEO) satellite technology and satellite edge computing (SEC) have facilitated a key role for LEO satellites in enhanced Earth observation missions (EOM). These missions (e.g., remote object detection) typically require multi-satellite cooperative observations of a large region of interest (RoI) area, as well as the observation image routing and computation processing, enabling accurate and real-time responsiveness. However, optimizing the resources of LEO satellite networks is nontrivial in the presence of its dynamic and heterogeneous properties. To this end, we propose SECO, a SEC-enabled framework that jointly optimizes multi-satellite observation scheduling, routing and computation node selection for enhanced EOM. Specifically, in the observation phase, we leverage the orbital motion and the rotatable onboard cameras of satellites, and propose a distributed game-based scheduling strategy to minimize the overall size of captured images while ensuring full (observation) coverage. In the sequent routing and computation phase, we first adopt image splitting technology to achieve parallel transmission and computation. Then, we propose an efficient iterative algorithm to jointly optimize image splitting, routing and computation node selection for each captured image. On this basis, we propose a theoretically guaranteed system-wide greedy-based strategy to reduce the total time cost (i.e., transmission, computation and queuing delay) over simultaneous processing for multiple images. Extensive experiments based on real-world datasets demonstrate that SECO can achieve up to a 60.7% reduction in overall time cost compared to baselines.

关键词： Satellite observatories

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Property Directed Reachability for Planning Revisited 20

Property Directed Reachability for Planning Revisited

引用

20th international conference on Principles of Knowledge Representation and Reasoning, KR 2023

作者： Clifton, Ava Gretton, Charles School of Computing The Australian National University Australia

ISBN: (纸本)9781956792027

Property Directed Reachability (PDR) is a relatively new SAT-based search paradigm for classical AI planning. Compared to earlier SAT-based paradigms, PDR proceeds without unrolling the system transition function, and therefore without having the underlying procedure reason about potentially computationally expensive multi-step formulae. By maintaining a queue of obligations-i.e., a state at a timestep-and knowledge about what is possible at each planning step, PDR iteratively evaluates whether an obligation can be progressed by one step towards the goal. We develop and evaluate two new distributed PDR algorithms for planning, and additionally implement serial and portfolio PDR algorithms for planning. We are the first to consider distributed PDR for planning and the first to consider PDR based on incremental SAT solving in that setting. Our first new algorithm, PS-PDR, evaluates many obligations independently in parallel using a pool of incremental SAT workers. PS-PDR is unique amongst distributed PDR algorithms in centrally maintaining a single queue of obligations, enabling an efficient focused search compared to a PDR portfolio. Our second new algorithm, PD-PDR, sequences subproblems according to the compositional structure of the concrete problem at hand. Subproblems are solved independently in parallel, with a concrete plan obtained by combining subproblem plans. Our experimental evaluation exhibits strong runtime gains for both new algorithms in both satisfiable and unsatisfiable planning benchmarks. © 2023 Proceedings of the international conference on Knowledge Representation and Reasoning. All rights reserved

关键词： Iterative methods

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Predictive Meteorology with PySpark: Analyzing and Forecasting Weather Data 5

Predictive Meteorology with PySpark: Analyzing and Forecasti...

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5th IEEE international conference on advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Chandra, Thota Bhuvana Chandra, Kona Mourya Sai Sai, Gurram Kumar Niketh, K Sai Manohar, Irigineni Nisha, K.S. Amrita Vishwa Vidyapeetham Amrita School Of Computing Department Of Computer Science And Engineering Amritapuri India

ISBN: (纸本)9798350330861

The spread of huge information across numerous realms, including social networks and financial markets, has led to the emergence of big data analytics as a means to gain useful understandings. This study explores using big data analytics in climate forecasting using instruments like Hadoop and Apache Spark. The challenges of predicting complex meteorological situations are talked about, emphasizing the likely benefits of advanced analytics. The paper stresses the importance of Hadoop's distributed data system(HDFS)and the MapReduce algorithm for productive data handling. Two separate implementations, MapReduce and Spark, are used to analyze data, showing their strengths in managing enormous datasets. Additionally, sequence analysis is exhibited using an Autoregressive Integrated Moving Averages (ARIMA) model to predict future temperatures. The experimental results reveal valuable discoveries, like ordinary temperature variations, extreme temperature stations, and the application of sequence analysis for temperature prediction. The paper ends by stressing the relevance of big data analytics, Hadoop, and Spark in improving weather prediction. This has wide implications for sectors reliant on precise meteorological insights. © 2023 IEEE.

关键词： MapReduce

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Prediction of Chronic Diseases Using parallel 1D-CNN Feature Extraction and SVM Classification Based on SMOTE 4th

Prediction of Chronic Diseases Using Parallel 1D-CNN Feature...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Mesbah, Fatma Zohra Tassadit Ait Abidine, M'hamed Bilal Fergani, Belkacem Univ Sci & Technol Houari Boumediene USTHB Lab Ingn Syst Intelligents & Commun LISIC Fac Elect Engn BP 32 El Alia Algiers 16111 Algeria

ISBN: (纸本)9783031821523;9783031821530

Due to their severity and prevalence, the prediction of chronic diseases (CDs) has become an important area of research, particularly with advances in deep learning. In this paper, we propose a new automated technique for detecting these diseases. A deep network architecture using a parallel unidimensional convolutional neural network (1D-PCNN) is employed to extract deep features. Subsequently, the Support Vector Machine (SVM) technique is applied for CD classification. The uniqueness of our framework lies in the design of the 1D-PCNN, which can learn the deep features of the input layer through parallel convolutional layers. As a result, the deep features of each parallel branch are simultaneously extracted before being combined in the fusion layer. Furthermore, in order to improve the efficiency of the proposed model, the Synthetic Minority Oversampling Technique (SMOTE) is used. This strategy manages class imbalance in CD databases. The suggested model is analysed against standard 1D-CNN, 1D-CNN model combined with conventional machine learning methods and other existing state-of-the-art models. The effectiveness of the suggested method was tested using two known databases the Pima Indian Diabetes Database (PIDD) and the Cleveland Heart Disease Database (CHDD). Results, from these databases indicate that the proposed approach yielded an accuracy rate of 83% and 88% an F score of 73% and 90% and an AUC of 80% and 87% correspondingly. Finally, after applying the SMOTE method, accuracy was improved to 86% and 92%, Fscore to 86% and 93%, and AUC to 86% and 92%, respectively, and outperforms other methods.

关键词： Chronic diseases Data classification SMOTE parallel unidimensional convolutional neural network (1D-PCNN) SVM

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Survival of Blockchain Process and Block Chain Technology

Survival of Blockchain Process and Block Chain Technology

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2022 IEEE international conference on advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Rohini, K.R. Rajakumar, P.S. Geetha, S. Dr. MG. R. Educational and Research Institute Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665495295

Due of transparency, decentralisation, and security qualities, Block chain is a cultural revalution which can be having significant process at current environment. For the process of its very first implementation of Crypto currencies, such as Bitcoin, Block chain has received a lot of attention. distributed ledger system is designed for evolve at a rapid pace, conduct, and exchange information in future. Educators', corporate executives and scholars have recently been started to examine various elements of Ethereum in modern innovation. Among previous Chain polls that focus on the technology's deployments, difficulties, features, and protection. Researchers further give a comparison of frameworks, a categorization for compromise methods, and an examination on potential threats and unique experiences employed in the Block chain to date. Ultimately, the study discusses critical new approaches, unique application scenarios, and areas for further research that researchers might investigate in addition to making additional progress in this area. © 2022 IEEE.

关键词： distributed ledger

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