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2020 7th international conference on soft computing and machine Intelligence, ISCMI 2020

2020 7th International Conference on Soft Computing and Mach...

引用

7th international conference on soft computing and machine Intelligence, ISCMI 2020

ISBN: (纸本)9781728175591

the proceedings contain 52 papers. the topics discussed include: evaluation of deep learning based human expression recognition on noisy images;NichePSO and the merging subswarm problem;melt-pool defects classification for additive manufactured components in aerospace use-case;a multiscale approach to detect the semantics of locomotion without positioning information;a comprehensive diagnostic tool for skin cancer using a multifaceted computer vision approach;comparison of swarm and graph algorithms for solving travelling salesman problems;and GPQ: greedy partial quantization of convolutional neural networks inspired by submodular optimization.

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Genetic Lateral Tuning of Membership Functions as Post-processing for Hybrid Fuzzy Genetics-based machine learning 7

Genetic Lateral Tuning of Membership Functions as Post-proce...

引用

Joint 7th international conference on soft computing and Intelligent Systems (SCIS) and 15th international Symposium on Advanced Intelligent Systems (ISIS)

作者： Nojima, Yusuke Takahashi, Yuji Ishibuchi, Hisao Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Sakai Osaka Japan

ISBN: (纸本)9781479959556

Genetic fuzzy systems (GFS) have been actively studied in the field of fuzzy classifier design. GFS can generate simple and accurate classifiers with a number of fuzzy if-then rules by evolutionary computation (EC). One general concern is how to discretize numerical attributes into fuzzy partitions. Most GFS use homogeneous fuzzy partitions without considering the class distribution of each attribute. this is the simplest idea, but more accurate classifiers could be obtained by optimizing fuzzy partitions. there are three approaches. One is pre-processing where inhomogeneous fuzzy partitions are specified according to the class distribution before applying EC. Another approach is to simultaneously optimize both a set of fuzzy if-then rules and fuzzy partitions by EC. the other is post-processing where fuzzy partitions used in the obtained classifier are optimized afterward. In this paper, we examine the effect of post-processing where fuzzy partition optimization is applied to the obtained classifier by GFS. In computational experiments, we first use our hybrid fuzzy genetics-based machine learning with homogeneous fuzzy partitions for standard data sets and its parallel distributed implementation for large data sets. then we apply genetic lateral tuning as post-processing to shift the positions of membership functions according to the pattern distribution.

关键词： genetics-based machine learnig fuzzy classifier design genetic lateral tuning pattern classification

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Competitive Self-Training Technique for Sentiment Analysis in Mass Social Media 7

Competitive Self-Training Technique for Sentiment Analysis i...

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Joint 7th international conference on soft computing and Intelligent Systems (SCIS) and 15th international Symposium on Advanced Intelligent Systems (ISIS)

作者： Hong, Sola Lee, Jaedong Lee, Jee-Hyong Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea

ISBN: (纸本)9781479959556

this paper aims to analyze user's emotion automatically by analyzing Twitter using "data without sentiment labels", not only "data with sentiment labels", to increase accuracy of sentiment analysis through an improved Self-Training, one of Semi-supervised learning techniques. Self-Training has a weak point that a classification mistake can reinforce itself. Self-Training iteratively modifies the model based on the output of the model. thus, if the model generates wrong output, the model can be wrongly modified. For alleviate this weak point, we propose a competitive Self-Training technique. We create three models based on the output of the model and choose the best. three models are created by binary mixture perspectives: the threshold, the same number, and the maximum number for updates. We repeat step that creating model and choosing a best model highest to get F-measure. Finally, we can improve the performance of sentiment analysis model.

关键词： Twitter Sentiment Analysis Semi-supervised learning Self-Training technique Support Vector machine

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machine learning Approach in Melanoma Cancer Stage Detection 7

Machine Learning Approach in Melanoma Cancer Stage Detection

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Chitte, Aniruddha Pawar, Aniket Nale, Mukund JSPM's Rscoe Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9798350304268

Finding the stage of melanoma cancer is important for cancer research. Because of the lack of awareness of the signs, the number of mortalities has dramatically increased, making early detection of skin cancer essential. Due to its capacity to learn from data and create predictions based on that learning, the machine learning techniques that use deep learning algorithms such as Convolutional Neural Network (CNN) have been widely used in this sector. © 2023 IEEE.

关键词： CNN Melanoma Stage 1 Stage 2

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Vertical Collaborative Clustering using Generative Topographic Maps 7

Vertical Collaborative Clustering using Generative Topograph...

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7th international conference of soft computing and Pattern Recognition (SoCPaR)

作者： Sublime, Jeremie Grozavu, Nistor Bennani, Younes Cornuejols, Antoine AgroParisTech INRA UMR MIA Paris 518 16 Rue Claude Bernard F-75231 Paris France Univ Paris 13 Sorbonne Paris Cite LIPN UMR CNRS 7030 F-93430 Villetaneuse France

ISBN: (纸本)9781467393607

Collaborative clustering is a recent field of machine learning that shows similarities with both transfer learning and ensemble learning. It uses two-step approaches where different clustering algorithms first process data individually and then exchange their information and results with a goal of mutual improvement. In this article, we introduce a new collaborative learning approach based on collaborative clustering principles and applied to the Generative Topographic Mapping (GTM) algorithm. Our method consists in applying the GTM algorithm on different data sets where similar clusters can be found (same feature spaces and similar data distributions), and then to use a collaborative framework on the generated maps with the goal of transferring knowledge between them. the proposed approach has been validated on several data sets, and the experimental results have shown very promising performances.

关键词： learning systems

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Taxonomy of machine learning Techniques in Test Case Generation 7

Taxonomy of Machine Learning Techniques in Test Case Generat...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Singh, Ajmer DCRUST Murthal CSE Department Sonipat India

ISBN: (纸本)9798350397253

machine learning empowers computer systems to automatically learn and improve their performance without being explicitly programmed for each specific task. It has revolutionized many industries and continues to drive advancements in artificial intelligence, enabling computers to perform complex tasks with greater efficiency and accuracy machine learning algorithms use training data to learn patterns and make predictions or decisions about new data. It involves a range of techniques such as supervised learning, unsupervised learning, reinforcement learning, deep learning, and others. machine learning (ML) can be applied to test case generation in several ways: Like Prioritization, Test case reduction, Test case minimization etc. machine learning techniques can be beneficial in test case generation in several ways. this article reviews the research work on application of machine learning algorithms in test case generation. © 2023 IEEE.

关键词： software testing

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Harmful Comments Extraction from a Bulletin Board System using Word Meaning and Impression on thread Context 7

Harmful Comments Extraction from a Bulletin Board System usi...

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Joint 7th international conference on soft computing and Intelligent Systems (SCIS) and 15th international Symposium on Advanced Intelligent Systems (ISIS)

作者： Nishihara, Yoko Iwasa, Kazuki Fukumoto, Junichi Yamanishi, Ryosuke Ritsumeikan Univ Coll Informat Sci & Engn 1-1-1 Noji Higashi Kusatsu Shiga 5258577 Japan

ISBN: (纸本)9781479959556

Harmful documents make readers unpleasant on the Web. In order to hide the harmful documents from the public, machine learning methods have been proposed, which learn words used in harmful documents and hide them automatically. the learned words often have bad meanings. though word meanings are not changed, word impression may be changed on context. Even if a word with bad impression is contained in a document, the previous learning methods can not learn the word, and fail to hide documents. We select the following approach: word impression may be changed on context. If a word has been used with other words of good meaning, it is considered that impression of the word is also good. In contrast, if a word has been used with others of bad meaning, impression of the word may be bad. this paper proposes a new extraction method of harmful comments in a thread of a Bulletin Board System. the proposed method extracts comments using word meanings and word impression on thread context. We evaluated the proposed method using comments collected from four threads in Japanese BBS "2-channel." the averaged precision of extraction was 0.47, and the averaged recall was 0.68. We verified that the proposed method was suitable for extraction of harmful comments from a thread of a BBS.

关键词： Internet document handling feature extraction learning (artificial intelligence) Japanese BBS bad meaning bulletin board system harmful comments extraction method harmful documents harmful documents hiding machine learning methods thread context word impression word meaning word meanings Context Data mining Educational institutions Postal services TV Web pages

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learning Analytics in Blended Mode During Covid-19 Pandemic 7

Learning Analytics in Blended Mode During Covid-19 Pandemic

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Tejal, R.K. Kanchana, V. Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India

ISBN: (纸本)9798350304268

Personality and behavior are great factors when considering students' learning strategies. Students tend to utilize their best capacity when the environment around them is comforting and the situation go hand in hand with their personality. learning analytics is a study based on theories and until recently, was not a topic any researcher would show interest in. During 2019- 2020, a severe change was caused not only to students but the entire education system when the Covid-19 pandemic was caused. Exploring the factors which affected students' performance during and after the pandemic is a topic yet to be researched. this entire study is based on students' feedback on their performance during and after the pandemic has nearly ended © 2023 IEEE.

关键词： Anova blended learning data mining learning analytics SVM-Support vector machine

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Model-Driven Quantum Federated learning (QFL) 23

Model-Driven Quantum Federated Learning (QFL)

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7th international conference on the Art, Science, and Engineering of Programming (Programming)

作者： Moin, Armin Badii, Atta Challenger, Moharram Tech Univ Munich Munich Germany Univ Antwerp Antwerp Belgium Univ Reading Reading Berks England Flanders Make Lommel Belgium

ISBN: (纸本)9798400707551

Recently, several studies have proposed frameworks for Quantum Federated learning (QFL). For instance, the Google TensorFlow Quantum (TFQ) and TensorFlow Federated (TFF) libraries have been deployed for realizing QFL. However, developers, in the main, are not as yet familiar with Quantum computing (QC) libraries and frameworks. A Domain-Specific Modeling Language (DSML) that provides an abstraction layer over the underlying QC and Federated learning (FL) libraries would be beneficial. this could enable practitioners to carry out software development and data science tasks efficiently while deploying the state of the art in Quantum machine learning (QML). In this position paper, we propose extending existing domain-specific Model-Driven Engineering (MDE) tools for machine learning (ML) enabled systems, such as MontiAnna, ML-Quadrat, and GreyCat, to support QFL.

关键词： model-driven engineering quantum computing federated machine learning

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An Overview of Various Approaches for Automated Skin Diseases Prognosis 7

An Overview of Various Approaches for Automated Skin Disease...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Supe, Meghana Bendre, Varsha PCCOE Dept. Electronics & Telecommunication Pune India

ISBN: (纸本)9798350304268

the majority of diseases in the world are skin-related. the complexity of the skin's structure makes treating skin problems a difficult task for the global health community. Because of this, early detection of this disease is crucial. Using diverse techniques, a lot of work has been done in the past, to diagnose the diseases. Early skin disease identification is aided by the development of a system that uses image processing, deep learning, and machine learning. the survey offers a thorough analysis of various approaches for skin disease predictions. © 2023 IEEE.

关键词： classifiers deep learning image processing machine learning neural network skin lesion

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