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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Li, Xiaomeng Zhao, Chengli Huang, Qiangjuan Wang, Xiaojie Yi, Dongyun Natl Univ Def Technol Coll Sci Changsha Hunan Peoples R China

ISBN: (纸本)9781538621653

With the arrival of the big data era, data is playing a prominent role increasingly. At present, data is stored in a variety of ways, which relational data is one of the most important. This paper aims to combine the method of data mining and complex network to analyze and utilize relational data, and a method of constructing feature network is proposed to discover some interesting patterns hidden in the massive relational data. First, a method is introduced to transform relational data to complex networks, in which the features of data is defined as nodes of networks and the correlation of two features is taken as edge weight of networks. Second, some measures of feature networks is calculated to find some data patterns. Finally, the above method is applied in two medical data sets, and the analyzed result shows that different kind of data (healthy people and patients) is significantly different in their topology of feature networks. The method is expected to provide an efficient way to discover data patterns and classifying data.

关键词： Complex Network data Mining Feature Network data patterns Regression Analysis

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data-Pattern-Based Predictive On-Chip Power Meter in DNN Accelerator

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2024年第12期43卷 4753-4766页

作者： Peng, Jian Liang, Tingyuan Jiang, Jingbo Zhang, Yipu Lin, Zhe Xie, Zhiyao Zhang, Wei Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Sun Yat Sen Univ Sch Integrated Circuit Guangzhou 510275 Peoples R China

Advanced power management techniques, such as voltage drop mitigation and fast power management, can greatly enhance energy efficiency in contemporary hardware design. Nevertheless, the implementation of these innovative techniques necessitates accurate and fine-grained power modeling, as well as timely responses for effective coordination with the power management unit. Additionally, existing performance-counter-based and RTL-based on-chip power meters have difficulty in providing sufficient response time for fast power and voltage management scenarios. In this article, we propose PROPHET, a data-pattern-based power modeling method for multiply-accumulate-based (MACC) deep neural network (DNN) accelerators. Our proposed power model extracts the predefined data patterns during memory access and then a pretrained power model can predict the dynamic power of the DNN accelerators. Thus, PROPHET can predict dynamic power and provide sufficient responding time for power management units. In the experiments, we evaluate our predictive power model in four DNN accelerators with different dataflows and data types. In power model training and verification, our proposed data-patterns-based power model can realize the 2-cycle temporal resolution with R-2 > 0.9 , normalized mean absolute error <7%, and the area and power overhead lower than 4.5%.

关键词： Power system management Energy efficiency System-on-chip Meters data models Arrays Voltage measurement data patterns deep neural network (DNN) accelerator on-chip power meter (OPM) power modeling power prediction

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Semantic Interpretation of data patterns by Using the Structural Approach

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SCIENTIFIC AND TECHNICAL INFORMATION PROCESSING 2014年第5期41卷 283-292页

作者： Khoroshevsky, V. F. Russian Acad Sci Dorodnicyn Comp Ctr Moscow Russia Natl Res Univ Ctr Informat & Analyt Syst Higher Sch Econ Inst Stat Studies & Econ Knowledge Moscow Russia Moscow Inst Phys & Technol Moscow Russia

This paper discusses a structured approach to image processing and semantic interpretation of data patterns. A brief overview of the state of research on a structured approach to pattern recognition and scene analysis is presented. The proposed structural approach is described in the context of discussions on image processing of data patterns, as well as semantic interpretation of data patterns obtained by using classical methods for statistical processing of indicators related to science, education, and innovation activity of the regions in the Russian Federation.

关键词： data patterns structured approach semantic interpretation of data patterns

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An exploratory approach to data driven knowledge creation

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JOURNAL OF BIG data 2023年第1期10卷 29页

作者： Thanos, Costantino Meghini, Carlo Bartalesi, Valentina Coro, Gianpaolo CNR Ist Sci & Tecnol Informaz A Faedo ISTI Pisa Italy

This paper describes a new approach to knowledge creation that is instrumental for the emerging paradigm of data-intensive science. The proposed approach enables the acquisition of new insights from the data by exploiting existing relationships between diverse types of datasets acquired through various modalities. The value of data consistently improves when it can be linked to other data because linking multiple types of datasets allows creating novel data patterns within a scientific data space. These patterns enable the exploratory data analysis, an analysis strategy that emphasizes incremental and adaptive access to the datasets constituting a scientific data space while maintaining an open mind to alternative possibilities of data interconnectivity. A technology, the Linked Open data (LOD), was developed to enable the linking of datasets. We argue that the LOD technology presents several limitations that prevent the full exploitation of this technology to acquire new insights. In this paper, we outline a new approach that enables researchers to dynamically create data patterns in a research data space by exploiting explicit and implicit/hidden relationships between distributed research datasets. This dynamic creation of data patterns enables the exploratory data analysis strategy.

关键词： data exploration data relationships data patterns data analyzer data publication

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Bitcoin Price Prognosis Using Machine Learning 9

Bitcoin Price Prognosis Using Machine Learning

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Begum, A. Benazir Bhuvanesh, R. Babu, R.R.M. Mathesh Velammal College of Engineering and Technology Department of Cse Madurai India

ISBN: (纸本)9798350377972

This research explores the application of machine learning techniques to predict Bitcoin price dynamics. Bitcoin, as a decentralized digital currency, has garnered significant attention due to its high volatility and potential for rapid value fluctuations. This study aims to develop a machine learning model that analyzes historical Bitcoin price data to identify patterns and predict future price trends. While precise short-term predictions may be challenging, the model can provide valuable insights into overall price trends and inform investment strategies. This research contributes to a better understanding of Bitcoin price dynamics and has the potential to assist investors in making more informed decisions. © 2024 IEEE.

关键词： Bitcoin Price Prediction Cryptocurrency Dynamics data Analysis data patterns Digital Currency Machine Learning Predictive Algorithms Price Trends

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Outlier Elimination and Reliability Assessment for Peak and Declining Time Series datasets 23

Outlier Elimination and Reliability Assessment for Peak and ...

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23rd IEEE International Conference on data Mining (IEEE ICDM)

作者： Yoon, Jungeun Moon, Aekyeung Son, Seung Woo ETRI Daejeon South Korea Univ Massachusetts Lowell Lowell MA USA

ISBN: (纸本)9798350381641

Environmental information such as climate and soil has been influencing every aspect of time-sensitive agriculture. With substantial advances in emerging and enabling technologies of IoT sensors, a vast amount of IoT-based environmental data allows preparation for the adverse impacts by providing helpful information for predictive and precise services. However, data acquired by IoT sensors can be corrupted by external environmental factors, which can negatively affect the integrity of data interpretation. To address this issue, our previous study proposed outlier detection techniques using transform-based sparse profiles. However, it would lose its worth without an evaluation methodology for data reliability after probing datasets by outlier detection. Therefore, this study proposes a process for preprocessing defective soil moisture sensor data through outlier detection methods and evaluating the fidelity of the data. To do this, we use the results of statistical evaluation experiments to determine whether the data outliers have been well eliminated, that is, the fidelity of the data. Experiments using real-world soil moisture datasets show that the transform-based OD (outlier detection) method outperforms other statistical outlier detection methods, suggesting that the preprocessed data can improve the integrity of IoT datasets.

关键词： IoT data analytics outliers outlier detection time-series data data patterns

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Statistical modeling and inference in the era of data Science and Graphical Causal modeling

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JOURNAL OF ECONOMIC SURVEYS 2022年第5期36卷 1251-1287页

作者： Spanos, Aris Virginia Polytech Inst & State Univ Dept Econ Blacksburg VA 24061 USA

The paper discusses four paradigm shifts in statistics since the 1920s with a view to compare their similarities and differences, and evaluate their effectiveness in giving rise to 'learning from data' about phenomena of interest. The first is Fisher's 1922 recasting of Karl Pearson's descriptive statistics into a model-based [M theta(x)] statistical induction that dominates current statistics (frequentist and Bayesian). A crucial departure was Fisher's replacing the curve-fitting perspective guided by goodness-of-fit measures with a model-based perspective guided by the statistical adequacy: the validity of the probabilistic assumptions comprising M theta(x). Statistical adequacy is pivotal in securing trustworthy evidence since it underwrites the reliability of inference. The second is the nonparametric turn in the 1970s aiming to broaden M theta(x) by replacing its distribution assumption with weaker mathematical conditions relating to the unknown density function underlying M theta(x). The third is a two-pronged development initiated in Artificial Intelligence (AI) in the 1990s that gave rise to data Science (DS) and Graphical Causal (GC) modeling. The primary objective of the paper is to compare and evaluate the other competing approaches with a refined/enhanced version of Fisher's model-based approach in terms of their effectiveness in giving rise to genuine "learning from data;" excellent goodness-of-fit/prediction is neither necessary nor sufficient for statistical adequacy, or so it is argued.

关键词： Big data data patterns data Science functional analysis Graphical Causal modeling Machine Learning mathematical approximation model-based inference probably approximately correct (PAC) statistical adequacy Statistical Learning Theory trustworthy evidence

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Reducing SRAM Reading Power With Column data Segment and Weights Correlation Enhancement for CNN Processing

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2021年第11期40卷 2237-2250页

作者： Xu, Han Li, Ziwei Li, Ziru Fan, Deliang Qiao, Fei Wei, Qi Luo, Li Liu, Xinjun Yang, Huazhong Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ BNRist Beijing 100084 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Arizona State Univ Dept Elect Comp & Energy Engn Tempe AZ 85287 USA Tsinghua Univ Dept Precis Instrument Beijing 100084 Peoples R China Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China

Convolutional neural network (CNN) has been widely deployed in various processors for intelligent visual signal processing. However, the large amount of activations and weights in CNN causes huge power consumption on SRAM access. data-adaptive SRAM design is a widely studied method to reduce SRAM reading power based on the utilization of data patterns, while current designs only exploit data patterns in a coarse granularity, and have no advantages when faced with randomly distributed weight data. In this article, we propose a hardware-software co-design scheme to reduce SRAM reading power for CNN processing. First, we propose a reconfigurable data-adaptive SRAM architecture with column data segmentation (CDS-RSRAM) to utilize data patterns. data in one column is partitioned into several segments, and finer-grained data patterns are exploited within each segment for further reading power reduction. Then, a novel training method-minimum segmented neighbor difference (miniSND)-is proposed for enhancing the correlation of weights. MiniSND improves the similarity of weights without classification accuracy degradation, thus weights could benefit from CDS-RSRAM and be read out with less power consumption. Simulation results demonstrate that the co-design scheme saves up to 66%(8b)/89%(2b) power consumption compared with 8T SRAM.

关键词： Random access memory Correlation Program processors System-on-chip Training Arrays Writing Convolutional neural network (CNN) processor correlation enhancement data patterns low power SRAM

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Optimization assisted frequent pattern mining for data replication in cloud: Combining sealion and grey wolf algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2023年 176卷

作者： Rambabu, D. Govardhan, A. Sreenidhi Inst Sci & Technol Hyderabad India Jawaharlal Nehru Technol Univ Hyderabad 500085 India

It is critical in cloud computing to have excellent data accessibility and system performance. To improve system availability, commonly used data should be duplicated to many places, allowing users to access it from a nearby site. Deciding on a sensible number and location for replicas is a difficult problem in cloud computing. Therefore, a novel data Replication system based on data mining techniques is being proposed in this research work. The data replication is done here by locating commonly utilized data patterns in a node's massive database. This will be accomplished using an optimization-assisted frequent pattern mining approach, with a novel hybrid algorithm performing the best threshold selection. The proposed hybrid algorithm referred to as Greywolves Updated Exploration and Exploitation with Sealion Behaviour (GUEES), hybrids the concept of Sealion Optimization Model (SLnO) and Grey wolf optimization (GWO) algorithms. Apart from this, the mining will be carried out under the defining dual constraints such as (i) Prioritization and (ii) Cost. The prioritization falls under two cases: queuing both high and low-priority data, and the cost relies on the evaluation of storage demand. The highpriority queues are optimized with the GUEES model. Finally, a comparative validation is carried out to validate the efficiency of the adopted model. Accordingly, when the number of requests=1000, the network usage of the proposed model is 35.07%, 34.9%, 30.5%, 29.23%, 24.57%, 16.8%, and 16.85% higher than the existing methods like SMO, LA, ROA, GWO, SLnO, PSO, HCS, respectively.

关键词： data replication data patterns Optimal frequent pattern mining approach Dual-constraints GUEES

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Knowledge Base Model for Choking Detection in College Tennis as a Component of an Expert System

Knowledge Base Model for Choking Detection in College Tennis...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Vancurik, Stepan Callahan, Dale W. Univ Alabama Birmingham Elect & Comp Engn Dept Birmingham AL 35294 USA

ISBN: (纸本)9781728185293

Expert system is a tool to transfer human expertise into specific domains by simulating human intelligence, behavior, and thinking. Choking under pressure in sports is characterized as suboptimal performance in certain actions. Expert systems have found their applications in various domains including sports and athletics. However, the concept of choking under pressure is yet to be tackled by the utilization of the expert system model. Therefore, this paper proposes an approach for choking detection in college tennis by designing an essential component of an expert system, knowledge base. The knowledge basemodel is formed based upon the expertise of tennis coaches with American college coaching experience. The model is designed for three detection levels: general, semi-personalized, and personalized. The detection of choking momentsis done from racquet swing speed measurements collected by a swing speed sensor. Study data were collected with eight participants completing a total of 32 tennis sessions. Results suggest practicality of the proposed model and feasibility of choking detection in college tennis from swing speed measurements by showing that choking detection can reach 68% accuracy while keeping the error rate at 15%. The results reveal individual variations of choking patterns among college tennis players as individually oriented detection techniques return superior detection performance. Future plans for the research are to collect measurements with additional participants and to combine the gathered data with physiological parameter measurements such as heartrate and with other sport specific information such as footwork performance.

关键词： expert system knowledge base choking under pressure detection model data patterns data collection swing speed sensor college tennis

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