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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

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Impact of Input data Preparation on Multi-criteria Decision Analysis Results 23rd

Impact of Input Data Preparation on Multi-criteria Decision ...

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23rd international conference on Computational Science, ICCS 2023

作者： Wątróbski, Jaroslaw Bączkiewicz, Aleksandra Król, Robert Institute of Management University of Szczecin ul. Cukrowa 8 Szczecin71-004 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland

ISBN: (纸本)9783031360237

Multi-criteria decision analysis (MCDA) methods support stakeholders in solving decision-making problems in an environment that simultaneously considers multiple criteria whose objectives are often con flicting. These methods allow the application of numerical weights rep resenting the relevance of criteria and, based on the provided decision matrices with performances of alternatives, calculate their scores based on which rankings are created. MCDA methods differ in their algorithms and can calculate the scores of alternatives given constructed reference solutions or focus on finding compromise solutions. An essential initial step in many MCDA methods is the normalization procedure of the input decision matrix, which can be performed using various techniques. The possibility of using different normalization techniques implies getting different results. Also, the imprecision of the data pro vided by decision-makers can affect the results of MCDA procedures. This paper investigates the effect of normalizations other than the default on the variability of the results of three MCDA methods: Additive Ratio Assessment (ARAS), Combined Compromise Solution (CoCoSo), and Technique for Order of Preference by Similarity to Ideal Solution (TOP SIS). The research demonstrated that the normalization type’s impact is noticeable and differs depending on the explored MCDA method. The results of the investigation highlight the importance of benchmarking different methods and techniques in order to select the method that gives solutions most robust to the application of different computing methods supporting MCDA procedures and input data imprecision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Input output programs

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Comparison and Analysis of Various Machine Learning algorithms for Disease Prediction

Comparison and Analysis of Various Machine Learning Algorith...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Sriya Kanamarlapudi Venkata Santhosh Yakkala Badisa Gayathri Krishna Vamsi Nusimala S.S Aravinth Srithar S Department of Computer science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

People nowadays suffer from a variety of diseases because of environmental factors and their lifestyle choices. As a result, disease prediction at an earlier stage becomes a critical task. Machine learning is increasingly being used in medical diagnosis. Using machine learning in disease prediction would make it easier for doctors to treat patients. However, supervised machine learning (ML) algorithms have shown significant promise in outperforming standard systems for disease diagnosis and assisting medical experts in the early detection of high-risk diseases. Having these prediction systems works as an additional hand for the doctors in order to identify the disease more accurately in less time. The challenges involved in this research are the accuracy issues that is the predicted disease might be differing every single time even though the input symptoms given are the same. Also, another challenge is that the model could not handle big data properly. To overcome these challenges, we have used different algorithms and obtained the result for every algorithm individually. The algorithm with highest accuracy gives the output for that user and is the best algorithm for the problem statement.

关键词： Machine learning algorithms Machine learning Predictive models Prediction algorithms data models Medical diagnosis Task analysis

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Consensus algorithms for Opt-in/Opt-out and proximity marketing context

Consensus algorithms for Opt-in/Opt-out and proximity market...

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international conference on computing, Networking and Communications (ICNC)

作者： Fatima Chahal Hacene Fouchal Dominique Gaiti LIST3N University of Technology of Troyes Troyes France CReSTIC Université de Reims Champagne-Ardenne Reims France

data security is today regarded as one of the most difficult duties that IT professionals must fight for. And today, consumers are more concerned about their data, and they begin to wonder why the rules that attempted to protect their data, particularly in the proximity marketing arena, have failed. Users’ information is shared without their awareness among market participants. Many systems have emerged through regulation to safeguard users and the market, the most well-known of which is Opt-in/Opt-out, which asks users for permission to use and share personal information with a third party. In addition, they have the opportunity to revoke their permission at any moment. In practice, however, this approach is ineffectual because opting out does not prohibit corporations from exploiting the data. In this work, we propose a novel invention that combines Opt-in/Opt-out with consensus algorithms in a decentralized way to provide a more secure environment for the user, giving the user the primary control to grant the company access to his data or a real withdrawal consent at any time.

关键词： Technological innovation Backtracking data security Process control Consensus algorithm Companies Regulation

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Exploring data as Quantum Bits: Analysis of Quantum Image Encoding Patterns for NISQ era

Exploring Data as Quantum Bits: Analysis of Quantum Image En...

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Quantum Technologies, Communications, computing, Hardware and Embedded Systems Security (iQ-CCHESS), international conference on

作者： Parvathy Gopakumar G Rubell Marion Lincy Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam PalaKottayam Kerala India

The generall efficiency of a computational system, be it simple or complex, presentation of the data plays a crucial role in getting the most out of it. Classical information theory, quantum mechanics, and computer science collectively influence Quantum Information in comprehending how information can be encoded, transmitted, and modified using quantum systems. Quantum mechanics allows quantum information to incorporate a variety of properties, such as quantum encoding of classical information, storing coherent details in quantum superpositions, quantum computations and entanglement, which occurs when two or more qubits become highly correlated and share an unbreakable quantum state. There are numerous barriers and constraints in the broad field of quantum computation, ranging from dealing with Decoherence to ensuring error correction. Moreover, the qubit, a fundamental computational unit, is challenging to create, manipulate, and store. To ensure the quality of the Quantum algorithms, the area of quantum data encoding has been thoroughly researched and explored as relevant results can be obtained by adequate encoding. In this paper, we reviewed the terminology and physical modeling of qubits, as well as the encoding approaches used to transform data from classical to quantum models. Furthermore, different encoding schemes for Quantum Image processing were explored to determine the efficiency of quantum pixel representation(QPIXL) framework for effective application in NISQ era.

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Designing Transformer Networks for Sparse Recovery of Sequential data Using Deep Unfolding

Designing Transformer Networks for Sparse Recovery of Sequen...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Brent De Weerdt Yonina C. Eldar Nikos Deligiannis ETRO Department Vrije Universiteit Brussel (VUB) Brussels Belgium imec Leuven Belgium Department of Math and Computer Science Weizmann Institute of Science Rehovot Israel

Deep unfolding models are designed by unrolling an optimization algorithm into a deep learning network. These models have shown faster convergence and higher performance compared to the original optimization algorithms. Additionally, by incorporating domain knowledge from the optimization algorithm, they need much less training data to learn efficient representations. Current deep unfolding networks for sequential sparse recovery consist of recurrent neural networks (RNNs), which leverage the similarity between consecutive signals. We redesign the optimization problem to use correlations across the whole sequence, which unfolds into a Transformer architecture. Our model is used for the task of video frame reconstruction from low-dimensional measurements and is shown to outperform state-of-the-art deep unfolding RNN and Transformer models, as well as a traditional Vision Transformer on several video datasets.

关键词： Recurrent neural networks Correlation Signal processing algorithms Training data Signal processing Transformers Sensors

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SMVAR: A Novel RNN Accelerator Based on Non-blocking data Distribution Structure

SMVAR: A Novel RNN Accelerator Based on Non-blocking Data Di...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Jinwei Xu Jingfei Jiang Shiyao Xu Lei Gao Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha Hunan China

Recurrent neural networks (RNNs) have become common models in the field of artificial intelligence to process temporal sequence task, such as speech recognition, text analysis, natural language processing, etc. To speedup RNNs inference, previous research proposed model sparse pruning techniques. However, the pruning rate of existing sparse pruning algorithms will be affected by pruning granularity and hardware friendliness. In order to approximate nonstructured pruning algorithm, this paper proposes Large Region Balanced Sparse (LRBS) pruning method, which does not limit sub-matrix shape and effectively improves pruning rate. Furthermore, we propose Sparse Matrix Vector Multiplication Accelerator for RNNs (SMVAR), which adopt non-blocking data distribution structure to solve the problem of efficient execution of large region irreg-ular matrix multiplication. To further improve the accelerator performance, SMVAR fine-grained adjusts the pipeline between macro-operations to reduce the idle of compute components. In addition, according to the coarse-grained block characteristics of LRBS algorithm, we develop the coarse-grained parallelism of accelerator with multiply compute units(CUs) structure. Experiments show that the pruning rate of our proposed LRBS is 1.25x-2.5x higher than that of the existing pruning algorithms. Compared with the existing work, the execution efficiency is improved by more than 2.02x-35.9x in the same application scenario.

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Federated Learning Framework for Blockchain based on Second-Order Precision

Federated Learning Framework for Blockchain based on Second-...

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international conference on Big data and Smart computing (BIGCOMP)

作者： Wenjie Song Tongjiang Yan College of Science China University of Petroleum Qingdao China

FL has been gaining attention due to its ability to protect data privacy. By exchanging encrypted information between data providers, FL allows collaboration on global models without sharing local data. As many participants are involved, a centralized framework is not suitable. To decentralize FL, we propose BDFL, a novel architecture integrating FL into the blockchain consensus process. It uses a second-order algorithm to improve model convergence and reduce communication consumption, with the added benefit of handling data heterogeneity in Vertical FL. Blockchain is used to replace centralized parties, aggregating information and enabling traceability of malicious parties. Experimental results show that BDFL is faster and more communication-efficient than traditional algorithms, and robust to malicious nodes.

关键词： data privacy Costs Federated learning Computational modeling Collaboration Computer architecture Big data

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A Study of Race Momentum Based on Machine Learning Methods and Pearson's Correlation Coefficient

A Study of Race Momentum Based on Machine Learning Methods a...

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data Analytics, computing and Artificial Intelligence (ICDACAI), international conference on

作者： Ruofei Ma Mingjie Hu Xiaoyang Wang Beihang University Beijing China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

In this study, we developed two models to deeply investigate and predict momentum effects in tennis matches. First, a momentum assessment system model was constructed to categorise momentum effects into past momentum, serving situations, psychological and physical factors, and to identify the effects through data processing and correlation analysis, defining the momentum function and applying it to the 2023 Wimbledon tournament; second, we carried out hypothesis testing to demonstrate the significant effects of momentum on match progression and player scoring. Additionally, we developed the Match Swing Prediction Model, which uses a machine learning approach to predict lead and swing points in a match, providing practical advice to players. By exploring the model's extension to women's tennis and table tennis matches, we found that the momentum effect for women's matches was slightly less significant than for men's matches, but that the model performed well in predicting women's matches. These findings provide useful insights into understanding and predicting momentum changes in tennis matches and provide practical references for players and coaches.

关键词： Analytical models Buildings Psychology Machine learning Predictive models Prediction algorithms data models Real-time systems Sports Testing

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Interpretable Multi-Scale Neural Network for Granger Causality Discovery

Interpretable Multi-Scale Neural Network for Granger Causali...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chenchen Fan Yixin Wang Yahong Zhang Wenli Ouyang AI Lab Lenovo Research Beijing P. R. China

We propose a novel multi-scale neural network for Granger causality discovery (MSNGC) in multivariate time series. Compared with existing counterparts, our model avoids the explicit data segmentation between series and between time lags for the first time. By extracting diverse causal information from the data with different delay ranges and then integrating them effectively via the learned attention weights, it can capture the complete causal relationships between all the series and provide accurate weighted adjacency matrix estimation. Thus, further interpretable inference can be better supported. Specifically, we propose a consistency-based thresholding algorithm for binary causal structure inference, an effect sign detection method to distinguish the positive causal effects from the negative ones, and a self-adaptive lag discovery algorithm to identify the lagged time points. Experiments on multiple benchmarks demonstrate that our model significantly outperforms the state-of-the-art methods.

关键词： Neural networks Time series analysis Signal processing algorithms Estimation Signal processing Inference algorithms data models

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Utilization of data Mining Classification Technique to Predict the Food Security Status of Wheat 4th

Utilization of Data Mining Classification Technique to Predi...

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4th international conference on Soft computing and its Engineering Applications (icSoftComp)

作者： Ali, Mohamed M. Reda Hazman, Maryam Khafagy, Mohamed H. Thabet, Mostafa Fayoum Univ Fac Comp & Artificial Intelligence Al Fayyum Egypt ARC Climate Change Informat Ctr Renewable Energy & Expert Syst CCICREES Giza Egypt

ISBN: (纸本)9783031276088;9783031276095

Egypt faceswheat insecurity due to the limited cropped area of agricultural lands and the limited horizontal expansion disproportionate to the population increase. The issue of food security, crop consumption rates, and self-sufficiency is considered one of the most important problems facing countries that seek to improve sustainable agriculture and economic development to eliminate poverty or hunger. This research aims to use data mining classification techniques and decision tree algorithms to predict the food security status of strategic agricultural crops (e.g., wheat) as an Agro intelligence technique. Also, the outputs and extracted information from the prediction process will help decision-makers to take an appropriate decision to improve the self-sufficiency rate of wheat, especially in epidemic crises and hard times such asCOVID-19, political, and economic disturbances. On the other hand, the research investigates the patterns of wheat production and consumption for the Egyptian population from 2005 to 2020. This research presents a methodology to predict the food security status of strategic agricultural crops through the case study of wheat in Egypt. The proposed model predicts the food security status of wheat with an accuracy of 92.3% to determine the self-sufficiency ratio of wheat in Egypt during the years from 2015 to 2020. Also, it identifies the factors affecting the food security status of wheat in Egypt, their impact on determining and improving the food security state and its rate of self-sufficiency.

关键词： data Mining (DM) Food Balance of Wheat dataset (FBWD) Food Security Status of Wheat (FSSW) Agro Intelligent Decision Support System (AIDSS)

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