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检索条件"丛书名=Advances in Intelligent Systems and Computing"

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Performance Analysis of Different Models for Twitter Sentiment 1

引用

2nd International Conference on Machine Learning and Information Processing, ICMLIP 2020

作者： Joshi, Deepali J. Kankurti, Tasmiya Padalkar, Akshada Deshmukh, Rutvik Kadam, Shailesh Vartak, Tanay Vishwakarma Institute of Technology Upper Indira Nagar Bibwewadi Pune Maharashtra India

ISBN: (数字)9789813348592

ISBN: (纸本)9789813348585

Sentiment analysis is the classification of emotions (such as positive negative and neutral) in the textual data. It helps companies to know the sentiments of customers regarding their products and services that they provide. Today many customers express their reviews on particular product on social media sites. If we analyze those reviews using sentiment analysis, we could know whether customers are happy or not with certain products. Twitter represents the largest and most dynamic datasets for data mining and sentiment analysis. Therefore, Twitter Sentiment Analysis plays an important role in the research area with significant applications in industry and academics. The purpose of this paper is to provide an optimal algorithm for Twitter sentiment analysis by comparing the accuracy of various machine learning models. In this context, nine well-known learning-based classifiers have been evaluated based on confusion matrices. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Coarse-Grained Genetic Algorithm for Flexible Job Scheduling Problem 1

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12th International Conference on Soft computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020

作者： Bytyçi, Eliot Berisha, Artan Geci, Fis University of Prishtina Prishtina Serbia

ISBN: (数字)9783030736897

ISBN: (纸本)9783030736880

Flexible Job Scheduling Problem presents a relaxation of the Job Scheduling Problem. It falls into the category of NP-hard problems. Our approach is to tackle the problem through usage of a coarse-grained genetic algorithm. Coarse-grained extends the genetic algorithm and thus increasing chances of more fitness landscape to be examined. Solutions are benchmarked with other approaches, through two of the most used datasets. Even though the results are less favourable than other approaches, in few cases our approach reaches the upper bound of the benchmarked solutions, thus impelling us to further investigate on the problem solution. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Towards Agent Design for Forming a Consensus Remotely Through an Analysis of Declaration of Intent in Barnga Game

Towards Agent Design for Forming a Consensus Remotely Throug...

引用

4th International Conference on intelligent Human systems Integration (IHSI) - Integrating People and intelligent systems

作者： Maekawa, Yoshimiki Yamaguchi, Tomohiro Takadama, Keiki Univ Electrocommun Chofugaoka 1-5-1 Chofu Tokyo Japan Nara Coll Natl Inst Technor 22 Yata Cho Yamato Koriyama Nara Japan

ISBN: (纸本)9783030680176;9783030680169

This paper explores the key-factors that can promote people to form a consensus remotely in such an Internet environment, and designs the agent according to the found key-factor for a better online communication. To address this issue, this paper focuses on "declaration of intent" and regards that people form a consensus when sharing one thought with declaring their intents. To investigate an influence of declaration of intent in a group, this paper employs cross-cultural game, Barnga, in which the players are forced to form a consensus for progressing the game. Through the human subject experiments of Barnga, the following insights are revealed: (1) the players are promoted to form a consensus when their rate of declaration of intent is high in the early stage of the game;and (2) the relationship between the rate of declaration of intent and the winning rate of the players shows the weak positive correlation.

关键词： Human-Agent interaction Barnga Remote communication Forming a consensus Declaration of intent

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Using Fuzzy Probabilistic Implication in Z-set Based Inference 1

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11th World Conference on intelligent systems for Industrial Automation, WCIS 2020

作者： Gardashova, Latafat A. Azerbaijan State Oil and Industry University Azadlig 35 Nasimi Baku Azerbaijan

ISBN: (数字)9783030680046

ISBN: (纸本)9783030680039

In our daily life the reasoning is characterized by imperfect information. This information is expressing by using fuzzy and probabilistic information. The main advantage of fuzzy logic is possibility of application in solving complex, nonlinear problems which described by words based on human perception. According to these in scientific literature there are fuzzy reasoning approach and probabilistic approximate reasoning approach, which can not operate imperfect information. This paper deals Z-set based inference using probabilistic implication. Numerical example with Z-set-based inference for approximate reasoning is considered. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Approximation theory

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Maximizing an Organization’s Operating Enactment in the Areas of Planning and Management 1

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20th International Conference on Hybrid intelligent systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Mohamed, Sajid Ahmed Devaraj, Kinslin Noorul Islam University Kanyakumari DistrictTamil Nadu629180 India MENA College of Management Dubai430 470 United Arab Emirates

ISBN: (数字)9783030730505

ISBN: (纸本)9783030730499

The study examines the organization’s operational effectiveness with respect to the business process domains that focuses on the organizational results. The researcher seeks to determine the causes of the organization’s business process inadequacy. The researcher identified critical business success factors that influence organization’s development. Among them, the management process’s effectiveness is one of the critical reasons for the organization’s growth. The effectiveness of the management process comes under the reengineering, review, optimization and implementation culture. Also, the organizations did not intervene with the management process’s effectiveness experienced unprecedented process inadequacy and performance fluctuations. The researcher also understood that the organization’s development lies chiefly in implementing the top to the bottom management process. The research focuses on the management process’s efficacy and the challenges of implementing the top to the bottom level’s management process. The secondary emphasis was also on the difficulties of verifying the implementation of the management process. The researcher had a notion that Good organizations have an effective method of management and verify execution at all levels. The data were gathered through the standard questionnaire. And it is checked by the recent Data Analysis Software version of SPSS. Automated Standards Decision-Making standard input/output processes, Automatic Alarming of unauthorized decisions with reference numbers, Pre-Audit before Decision Making and Post-Automated Decision Making are vibrant to Organizational Development. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Accounting Fraud Detection Using K-Means Clustering Technique 1

引用

2nd International Conference on Machine Learning and Information Processing, ICMLIP 2020

作者： Sahoo, Giridhari Sahoo, Sony Snigdha School of Commerce and Economics KIIT Deemed To Be University Bhubaneswar India Department of Computer Science and Applications DDCE Utkal University Bhubaneswar India

ISBN: (数字)9789813348592

ISBN: (纸本)9789813348585

Background: Accounting fraud has become quite prevalent in current times. This kind of frauds or scandals comprise any act of manipulation of financial statements as a means for hiding financial misdeeds. Such kind of fraudulent practices can be brought to the fore with data mining techniques. One can recognize patterns for fraud in an accounting data set with data mining. Methodology: One such data mining technique is K-means clustering. This divides the data set into k distinct non-overlapping partitioned clusters. Though a number of statistical models have been used at length for revealing patterns in such scams, they have been found to be complicated and highly time-consuming. In such scenario, K-means may form the simplest preliminary basis of fraud detection by segregating the data set into fraudulent and non-fraudulent sets which may be further followed by more complex practices for establishing the same. This work has been carried out on an accounting data set, and the factors considered are bank statements (credit and debit in the cashbook), asset value and net profit, on which K-means clustering has been simulated. Conclusion: This work is an effort toward showing a simple application of K-means. The experimental results indicate high accuracy and significant relation between misrepresented values of these factors and fraud. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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ALCESTE X IRAMUTEQ: Comparative Analysis of the Use of CAQDAS in Qualitative Research 1

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5th World Conference on Qualitative Research, WCQR 2021

作者： Monteiro, Lucas de Melo, Rodrigo Braga, Beatriz de Sá, Júlia Monteiro, Lorenna Cunha, Maria Gêda, Thais Canuto, Angela Federal University of Alagoas Maceió AL57072970 Brazil Centro Universitário Tiradentes Maceió AL57038000 Brazil

ISBN: (数字)9783030701871

ISBN: (纸本)9783030701864

Introduction: Nowadays the use of Computer-Assisted Qualitative Data Analysis Software (CAQDAS) is essential in qualitative research methodology. However, programs of this line such as ALCESTE and IRAMUTEQ have as many positive as negative points. Knowing this, it is important to analyze the usage of the aforementioned Software packages in qualitative research. Objectives: To analyze the functionalities, accessibility, and limitations of CAQDAS as main methodological processing in different qualitative surveys. Methods: Critical Comparison between two quanti-qualitative surveys previously performed by the authors of this scientific analysis. Results: The use of each software was efficient in the management of the data of non-categorical research. However, IRAMUTEQ proved to be more advantageous than ALCESTE. Discussion: The use of a CAQDAS is essential in researches with an extensive sample space of qualitative data meaning, in the current context, a great advance of this research segment, since it puts it in a level of greater assertiveness. However, the selection of which program to use varies with the affinity of the researcher in relation to them and which resources they wish to be used to generate the results, being IRAMUTEQ the software with more options for that. Conclusion: Therefore, the management of CAQDAS is one of the most promising methodological paths for qualitative research. Its benefits overcome the limitations that its algorithms have and more research on it is necessary for the execution of others to be improved. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Surveys

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Improving Classification Models Using the Frequency of Terms and a Percentage Relation Approach Between Classes for Emergency Calls 1

引用

Future of Information and Communication Conference, FICC 2021

作者： Orellana, Marcos Trujillo, Andrea Acosta, María Inés Universidad del Azuay Av. 24 de Mayo 7-77 Cuenca Ecuador

ISBN: (数字)9783030731038

ISBN: (纸本)9783030731021

The analysis of informal texts is essential in text mining applications, such as the classification ones. The division of classes into informal and non-structured text is necessary because it is possible to extract knowledge for each category. The problem emerges in languages because those have high dimensionality. Therefore, the complexity should be reduced by applying techniques that include dimensionality decrease. Based on these arguments, a technique that is pruning the words in medium and long texts was applied through its frequency in all documents, and frequency in each class. This study aims to determine irrelevant and relevant terms that proper fit for each category and to improve the classification performance. This study, including a four-step methodology: i) data preprocessing, ii) analyzing term frequency, iii) reduction language dimensionality, and iv) classification model structuring. For this, a series of experiments were proposed to determine the word frequency threshold, with a decisive index that establishes the words to be eliminated or conserved. The experimentation showed that the application of the word pruning approach significantly improves the performance of the binary classification according to its evaluation measures. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Text processing

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Solving Dynamic Bag-of-Tasks Scheduling Problem in Heterogeneous Multi-cloud Environment Using Hybrid Bi-Level Optimization Model 1

引用

20th International Conference on Hybrid intelligent systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Karaja, Mouna Ennigrou, Meriem Said, Lamjed Ben SMART Laboratory Institut Supérieur de Gestion Université de Tunis Tunis Tunisia

ISBN: (数字)9783030730505

ISBN: (纸本)9783030730499

Task scheduling problem has attracted a lot of attention since it plays a key role to improve the performance of any distributed system. This is again more challenging, especially for multi-cloud computing environment, mainly based on the nature of the multi-cloud to scale dynamically and due to heterogeneity of resources which add more complexity to the scheduling problem. In this paper, we propose, for the first time, a new Hybrid Bi-level optimization model named HB-DBoTSP to solve the Dynamic Bag-of-Tasks Scheduling Problem (DBoTSP) in heterogeneous multi-cloud environment. The proposed model aims to minimize the makespan and the execution cost while taking into consideration budget constraints and guaranteeing load balancing between Cloud’s Virtual Machines. By performing experiments on synthetic data sets that we propose, we demonstrate the effectiveness of the algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Budget control

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A Novel Clustering Based Undersampling Algorithm for Imbalanced Data Sets Using Artificial Bee Colony Algorithm 1

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11th International Conference on Innovations in Bio-Inspired computing and Applications, IBICA 2020 and 10th World Congress on Information and Communication Technologies, WICT 2020

作者： Ajilisa, O.A. Jagathyraj, V.P. Sabu, M.K. Department of computer Applications Cochin University of Science and Technology Kerala India School of Management Studies Cochin University of Science and Technology Kerala India

ISBN: (数字)9783030736033

ISBN: (纸本)9783030736026

Datasets that follow imbalanced class distribution impose a critical challenge to the machine learning research community. Standard machine learning approaches assume that datasets have a balanced class distribution and can’t handle imbalanced datasets. There are several other methods to deal with imbalanced datasets. This paper proposed an Artificial Bee Colony (ABC) clustering-based undersampling approach for imbalanced datasets. The proposed approach has been applied on several benchmark datasets from the KEEL dataset repository, and the obtained AUC value is compared and analyzed. We strongly recommend the Artificial Bee Colony-based undersampling method for dealing with imbalanced classification problems based on obtained results. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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