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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

共 1361 条记录，以下是1261-1270 订阅

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A Ranking of Software Reliability Evaluation Based on Intuitionistic Fuzzy Aggregation Technique 21

A Ranking of Software Reliability Evaluation Based on Intuit...

引用

Proceedings of the 2021 4th international conference on Algorithms, computing and Artificial intelligence

作者： Linxin Chen Riqing Chen Jian Lin Digital Fujian Institute of the Big Data for Agriculture and Forestry Fujian Agriculture and Forestry University China

ISBN: (纸本)9781450385053

During the software life cycle, software reliability is limited by various uncertain factors, and relevant factors need to be aggregated for reliability evaluation. The influence of these factors on reliability is often determined by engineering experience and subjective judgment, which inevitably leads to subjectivity and vague, and fuzzy set theory is an effective method to deal with uncertain information. The research in this paper aims to help software development organizations complete the software engineering evaluations that are the efficient measures of software reliability. The metrics value of each evaluation criterion was elicited through expert opinion and an aggregation technology is then developed for transforming attribute values into intuitionistic fuzzy numbers (IFNs) to obtain the collective evaluation of each alternative. Next, according to the intuitionistic fuzzy weighted averaging operator, the collective attribute values are aggregated to overall evaluations of alternatives. Finally, the ranking functions are applied to indicate the score degree and the rank of each alternative. A practical example is provided for applying software project reliability evaluation to demonstrate the practicality and effectiveness of the proposed method.

关键词： Aggregation technology Intuitionistic fuzzy sets Multi-attribute decision-making Ranking functions Software reliability

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Machine Learning Approaches for Predicting Tree Growth Trends Based on Basal Area Increment 18th

Machine Learning Approaches for Predicting Tree Growth Tren...

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18th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2023

作者： Casas-Gómez, Pablo Martínez-Álvarez, Francisco Troncoso, Alicia Linares, Juan Carlos Physical Chemical and Natural Systems Department Faculty of Experimental Sciences Universidad Pablo de Olavide Seville41013 Spain Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (纸本)9783031425288

This paper explores the potential of machine learning in predicting Basal Area Increment (BAI) for the species Abies spectabilis, a commonly used metric for measuring tree growth. Machine learning algorithms are used to analyze environmental factors, biotic responses, growth, and their interactions to obtain accurate predictions of BAI under different climatic scenarios. The study aims to identify vulnerable tree at risk of dieback due to changes in climate or other environmental factors. The methodology includes data acquisition, preprocessing, feature selection, model development, and evaluation. The results reported in the study show M5’ performs better in predicting BAI than other machine learning models. The study’s findings can help in forest management and conservation decisions, such as selective harvesting, reforestation, carbon sequestration, and biodiversity conservation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Anchoring AI/Machine Learning on the African Technological Innovation and Investment Table 21

Anchoring AI/Machine Learning on the African Technological I...

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Proceedings of the 3rd Africa-Asia Dialogue Network (AADN) international conference on Advances in Business Management and Electronic Commerce Research

作者： Gabriel Kabanda Zimbabwe Academy of Sciences University of Zimbabwe Zimbabwe

ISBN: (纸本)9781450390415

Sustainable development begins with education primarily through lifelong open and distance learning (ODL). Africa faces an economic and sustainable development problem characterized by relatively low GDP per capita and limited capacity for industrialization and modernization which has not been adequately resolved by the numerous economic frameworks used in the past. A Pragmatism paradigm intricately related to the Mixed Methods approach was used in this research premised on an endogenous growth model supported by knowledge generation strategies and anchored on AI/ML. The research proved that the resultant E-Business and E-learning supply chain gives a huge Cash Return on Investment in the higher education sector and other sectors of the economy. Technological innovation and investment anchored on the AI/Machine Learning paradigms and which takes advantage of the advances in technology (Cloud computing, AI/ Machine Learning, Cybersecurity, big data Analytics, etc.) will create wealth and establish an innovation-led knowledge economy through breaking silos, synergizing and creating smart partnerships in the National Science, Technology and Innovation System (NSTIS). The phenomenal growth in the use of internet-based technologies has subjected organizations to cyberattacks, and so there is a need to revamp cyberdefense strategies for most organizations and take advantage of Cloud computing facilities.

关键词： economic framework Artificial intelligence Machine Learning Knowledge generation Cybersecurity sustainable development innovation

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data Analysis of Portfolio Optimization using Artificial Neural Network in China's Stock Market* 5

Data Analysis of Portfolio Optimization using Artificial Neu...

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Wang, Chia-Hung Lee, Chia-Jung Fujian University of Technology College of Information Science and Engineering Fuzhou City350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications No. 33 South University Road University Town Fujian Province Fuzhou City350118 China Soochow University School of Big Data Management No. 70 Linhsi Road Shihlin District Taipei City11102 Taiwan

ISBN: (纸本)9781728141176

This paper presents a neural network model for studying the portfolio optimization in China's stock market. An investment scheme is proposed by using an artificial intelligence algorithm based on the trained neural network. In the empirical study, six key indicators of historical transaction data for stocks are used as the input characteristics of our neural network model, which are the opening price, the closing price, the trading volume, the highest and lowest price on the day, and Week time scale. Besides, we use Python technology to process the collected stock data and develop the learning process of the studied neural network for predicting the closing price for the next period. Based on the presented data analysis in the paper, the investors of the stock market in China could make a rational decision-making on the portfolio optimization. © 2019 IEEE.

关键词： Neural network models

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Automating Literature Reviews: Searching for Behavioral big data in Operations Management 5

Automating Literature Reviews: Searching for Behavioral Big ...

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Lee, Hsiao-Hui Mach, Patrizia Shmueli, Galit Yahav, Inbal National Chengchi University Taipei Taiwan National Tsing Hua University Hsinchu Taiwan Tel Aviv University Tel Aviv Israel

ISBN: (纸本)9781728141176

From patient waiting time to consumer shopping, firms collect more and more human behavior data to assist their decision making process. This trend in business also affects academic research, especially in operations management (OM), a research area that often relies on mathematical modeling to guide business decisions. However, it is both time and labor intensive to identify applications and opportunities that use behavioral big data (BBD) in the large and growing published literature. In this paper, we introduce a procedure that applies various data mining approaches to survey a vast number of research articles across three different OM journals, and identify articles that use BBD. The goal is to reduce the number of articles that must be read manually and yet reduce the false negatives (missed BBD papers);in other words, in this classification task we emphasize the importance of sensitivity over specificity with respect to detecting BBD papers. Testing different feature engineering and classification approaches, we find that the highest sensitivity and specificity are provided by a Random Forest classifier, applied to a bag-of-words set of features. © 2019 IEEE.

关键词： Information management

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A Realtime Crypto computing and Block-Dependability 5

A Realtime Crypto Computing and Block-Dependability

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Abhilash, Kancharla Park, Nohpill Oklahoma State University Computer Science StillwaterOK United States

ISBN: (纸本)9781728141176

This paper proposes an analytical approach how to design and realize a crypto computing (Ethereum blockchain-based) under stringent real-time requirement. In order to evaluate the efficacy of the approach, a new analytical metric is defined and developed to estimate the dependability, referred to as the block-dependability. The proposed block-dependability precisely models the probability for the pending transactions to be posted within the current, in other words, within the target block delay, namely, within the deadline required if their expected execution times are within the temporal range of the deadline. Various methods how to prioritize and select transactions in the pending transaction pool in order to facilitate those transactions to be executed within their deadline requirements, such as the normal, random, sorted, and stratified, are proposed and simulated. A set of performance variables, or parameters, such as the number of pending transactions in the pool, the average speed of the transactions, gas fees, deadlines as well as the number of miners, are identified and taken into the block-dependability in order to reveal the influence of each variable on the block-dependability, versus each of those proposed prioritization and selection methods. Extensive parametric simulations are conducted and results are observed and discussed in the cases of the random transaction selection method and the sorted. © 2019 IEEE.

关键词： Ethereum

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A data-Driven Knowledge-Enriched Framework for Dialogue Modeling 5

A Data-Driven Knowledge-Enriched Framework for Dialogue Mode...

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Huang, Jhih-Yuan Lee, Wei-Po Tsai, Hsin-Wu Lee, King-Teh National Sun Yat-sen University Department of Information Management Kaohsiung Taiwan Kaohsiung Medical University Department of Healthcare Administration and Medical Informatics Kaohsiung Taiwan

ISBN: (纸本)9781728141176

In recent years, it has been advocated to build social dialogue systems to achieve human-machine interaction. In this study, we adopt deep learning models to develop a dialogue system. In contrast to other studies that mainly concentrate on the model structure and the learning method, our work emphasizes the importance of knowledge and explores the possibility of exploiting knowledge instead. Our system includes a procedure of training a deep learning model to generate answers in response to the users' questions, and most importantly, a set of strategies to exploit various knowledge resources from different perspectives. We have conducted a series of experiments to evaluate the presented approach, and the results confirm its usefulness and effectiveness. © 2019 IEEE.

关键词： Contrastive Learning

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Leveraging Reinforcement Learning to Allocate Bandwidth in the Agent-based Resource Management System 5

Leveraging Reinforcement Learning to Allocate Bandwidth in t...

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Wang, Mu-Cheng Hershey, Paul C. Ioakimidis, Theodore E Raytheon Space and Airborne Systems MarlboroughMA United States Raytheon Intelligence Information and Services DullesVA United States

ISBN: (纸本)9781728141176

In this research, we utilize an agent-based resource management technique, namely the real-time Network Communication Resource Manager (NCRM) to manage battlefield network resources. The NCRM addresses the allocation problem by providing a process, an architecture, and an algorithm to allocate network resources to a mission based on its requirements and currently available resources. This approach enables resilient centralized command (i.e., mission analyses and resource allocation), distributed control, and decentralized execution (i.e., resource reservation and reallocation). Our solution focuses on maximizing the effectiveness of available (remaining) network assets to maintain integrity and efficiency of mission critical assets. NCRM can: (A) through intelligent path selection, deliver mission traffic over the optimal performing paths based on its characteristics or requirements and real-time path status;and (B) effectively converge to a new optimum that dynamically adapts itself to the latest network condition (e.g., newly introduced or lost assets/links). © 2019 IEEE.

关键词： Reinforcement learning

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SoLar: sinkhorn label refinery for imbalanced partial-label learning 22

SoLar: sinkhorn label refinery for imbalanced partial-label ...

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Proceedings of the 36th international conference on Neural Information Processing Systems

作者： Haobo Wang Mingxuan Xia Yixuan Li Yuren Mao Lei Feng Gang Chen Junbo Zhao Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University Department of Computer Sciences University of Wisconsin-Madison College of Computer Science Chongqing University and Center for Advanced Intelligence Project RIKEN

ISBN: (纸本)9781713871088

Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario that may not hold in many real-world applications. Empirically, we observe degenerated performance of the prior methods when facing the combinatorial challenge from the long-tailed distribution and partial-labeling. In this work, we first identify the major reasons that the prior work failed. We subsequently propose SoLar, a novel Optimal Transport-based framework that allows to refine the disambiguated labels towards matching the marginal class prior distribution. SoLar additionally incorporates a new and systematic mechanism for estimating the long-tailed class prior distribution under the PLL setup. Through extensive experiments, SoLar exhibits substantially superior results on standardized benchmarks compared to the previous state-of-the-art PLL methods.

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Architecture to Speed up Redundant data Processing in a MapReduce Cloud 5

Architecture to Speed up Redundant Data Processing in a MapR...

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Huang, Tzu-Chi Chu, Kuo-Chih Zeng, Xue-Yan Jou, Fu-Lin Tsai, Ming-Fong Shieh, Ce-Kuen Lunghwa University of Science and Technology Department of Electronic Engineering Taoyuan Taiwan National United University Department of Electronic Engineering Miaoli Taiwan National Cheng Kung University Department of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9781728141176

A cloud owns many resources usable to applications, but economically utilizing or saving the resources always is an important issue on cloud computing nowadays. A cloud probably wastes resources to do extra unnecessary computations due to processing redundant data such as MapReduce applications calculating the page ranking of hot news or rendering map zooms of a geographic location. Now, a cloud can utilize the Architecture to Speed up Redundant data Processing (ASRDP) proposed in this paper in order to speed up the processing of redundant data in MapReduce. With the help of ASRDP, A cloud can greatly save the execution time of an application that processes redundant input data, especially when the application has high complexity in its Map function. According to the prototype implementation and related experiments with several popular applications in this paper, a cloud utilizes ASRDP to not only greatly improve performance when processing redundant input data but also intelligently avoid overheads of ASRDP when non-redundant input data. © 2019 IEEE.

关键词： MapReduce

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