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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Kumar, Vinayshekhar Bannihatti Kumar, Sujay S. Saboo, Varun PES Inst Technol Dept Comp Sci Bengaluru Karnataka India

ISBN: (纸本)9781467391870

Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. In this research paper, we provide an approach to detect various kinds of these diseases. We use a dual stage approach which effectively combines Computer Vision and machine learning on clinically evaluated histopathological attributes to accurately identify the disease. In the first stage, the image of the skin disease is subject to various kinds of pre-processing techniques followed by feature extraction. The second stage involves the use of machine learning algorithms to identify diseases based on the histopathological attributes observed on analysing of the skin. Upon training and testing for the six diseases, the system produced an accuracy of up to 95 percent.

关键词： Dermatology Image Processing Computer Vision machine learning data mining Computational Intelligence Automated Disease Diagnosis

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Analyses of the Technological Accumulation over the 2nd and the 3rd AI Boom and the Issues Related to AI Adoption by Firms

Analyses of the Technological Accumulation over the 2nd and ...

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Portland international conference on Management of Engineering and Technology (PICMET) - Managing Technological Entrepreneurship - The Engine for Economic Growth

作者： Miyazaki, Kumiko Sato, Ryusuke Tokyo Inst Technol Dept Technol & Innovat Management Tokyo Japan IBM Corp Global Business Serv Watson Delivery 2 Tokyo Japan

ISBN: (纸本)9781890843373

Although AI was proposed by *** back in 1956, 60 years later, we are beginning to witness a surge of interest in the practical applications of AI in many sectors. However, the actual adoption rate of AI in businesses has been quite low. The current 3rd AI boom follows the 2nd AI boom in the 1980s which focused mainly on expert systems. In this paper, an attempt is made to analyze the technological accumulation process from the 2nd to the 3rd AI boom and to identify forthcoming key application areas. The methodology is based on a 3 step approach. In the first step, a bibliometric analyses of Artificial Intelligence was carried out to analyze the technological accumulation during the 2nd and the 3rd AI boom. Having done the desk work and interviewed several experts in AI, we decided that 2013 was the year which marked the boundary between the two AI booms. A bibliometric analyses based on countries and institutions over the 2 periods was followed by co-occurrence analyses of the author keywords in the 2 periods, before and after 2013. In the third stage, interviews were carried out with some corporate members to do a qualitative analysis on the possible application areas of AI and issues to be solved for adopting AI were identified. The results showed that in the 3rd AI boom, machine learning, deep learning, genetic algorithm have been identified as the key technologies. Furthermore, prediction, forecasting, data mining, fault diagnosis, pattern recognition were identified as important areas in the 3rd AI boom. It was also revealed that firms' R&D has been changing to focus more on AI applications.

关键词： machine learning Bibliometrics Companies Neural networks Market research Expert systems

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Construction Model Using machine learning Techniques for the Prediction of Rice Produce for Farmers 3

Construction Model Using Machine Learning Techniques for the...

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3rd IEEE international conference on Image, Vision and Computing (ICIVC)

作者： Inyaem, Uraiwan Rajamangala Univ Technol Thanyaburi Fac Sci & Technol Dept Math & Comp Sci Pathum Thani Thailand

ISBN: (纸本)9781538649916

Nowadays, the researches in data mining area have been continuous increasing. Appling data mining to agriculture;for example, the prediction of rice produce for farmers is still challenging. The objective of the research is to propose a model using machine learning Techniques comparing between Decision Tree Technique and Neural Network Technique (ANN) for the prediction of rice produce for farmers. Farmers can predict volume of rice produce and selling price. It is helpful for farmers to increase their income. The process of the research follows Cross-industry standard process for data mining (CRISP-DM) process. The model pattern is classified by machine learning techniques experiment with a dataset of farmer records. Performance measure of model pattern uses four options such as Test Options, Cross-Validation Folds 10, Split 80-20, and Use Training Set. After that, four options will be averaged for accuracy. The experimental result shows that the best technique which has highest accuracy can be helpful for farmers in real world.

关键词： data classification machine learning decision tree technique rice product prediction

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Comparisons of machine learning Algorithms for Driving Behavior recognition Using In-Vehicle CAN Bus data 8

Comparisons of Machine Learning Algorithms for Driving Behav...

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Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and pattern recognition (icIVPR) / international conference on Activity and Behavior Computing (ABC)

作者： Chen, Wen-Hui Lin, Yu-Chen Chen, Wei-Hao Natl Taipei Univ Technol Grad Inst Automat Technol Taipei Taiwan Feng Chia Univ Dept Automat Control Engn Taichung Taiwan

ISBN: (纸本)9781728107882

Driving behavior recognition is an active research topic as it has many potential applications, such as fleet management, vehicle anti-theft, and planning of car insurance policies. Nowadays, the most successful approaches to driving behavior recognition are based on machine learning algorithms. Each machine learning algorithm has its pros and cons, and no single algorithm fits all problems. Therefore, how to determine an appropriate algorithm suitable for discovering driving patterns is a critical step in driving behavior recognition. This paper aims to conduct an empirical study for driving behavior recognition and evaluate the recognition performance of popular machine-learning algorithms. The experimental results showed that many sensor values gathered from the CAN bus are either highly correlated with one another or less important attributed to driving behavior identification. Among traditional machine learning approaches, ensemble tree-based algorithms, such as Random Forests and Decision Trees have better performance when compared with other approaches.

关键词： driving behavior recognition machine learning the controller area network (CAN) bus

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mining useful Macro-actions in Planning 3

Mining useful Macro-actions in Planning

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Castellanos-Paez, Sandra Pellier, Damien Fiorino, Humbert Pesty, Sylvie Univ Grenoble Alpes LIG F-38000 Grenoble France

ISBN: (纸本)9781467391870

Planning has achieved significant progress in recent years. Among the various approaches to scale up plan synthesis, the use of macro-actions has been widely explored. As a first stage towards the development of a solution to learn on-line macro-actions, we propose an algorithm to identify useful macroactions based on data mining techniques. The integration in the planning search of these learned macro-actions shows significant improvements over six classical planning benchmarks.

关键词： automated planning macro actions pattern mining data mining learning

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The fast image recognition system based on neuro-fuzzy units and its online learning for data stream mining tasks 3

The fast image recognition system based on neuro-fuzzy units...

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3rd IEEE international conference on data Stream mining and Processing, DSMP 2020

作者： Kulishova, Nonna Bodyanskiy, Yevgeniy Timofeyev, Volodymyr Kharkiv National University of Radioelectronics Media Systems and Technologies Department Kharkiv Ukraine Kharkiv National University of Radioelectronics Control Systems Research Laboratory Kharkiv Ukraine Kharkiv National University of Urban Economy Department of Information Technologies Kharkiv Ukraine

ISBN: (纸本)9781728132143

In recent years, the need for real-time pattern recognition applications has sharply increased. Along with deep and probabilistic neural networks, hybrid architectures such as neo-fuzzy networks and networks based on neo-fuzzy units turned out to be quite effective for solving this problem. In this paper, we consider the task of developing an architecture and a learning algorithm for a real-time pattern recognition system built on neo-fuzzy units. The objective function based on cross-entropy allows formulating a learning criterion that provides a high learning rate. © 2020 IEEE.

关键词： Image recognition

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A Predictive Analytics System for Forecasting Student Academic Performance: Insights from a Pilot Project at Eastern Washington University 8

A Predictive Analytics System for Forecasting Student Academ...

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作者： Das, Arindam K. Rodriguez-Marek, Esteban Eastern Washington Univ Dept Engn & Design Cheney WA 99004 USA

ISBN: (纸本)9781728107882

Our work is focused on determining whether any correlation exists between preparatory classes taken by Electrical Engineering (EE) students at Eastern Washington University (EWU) early in their academic careers (e.g. Calculus and Physics sequences) and their departmental GPAs upon graduation. Using academic data from prior EE graduates, a machine learning algorithm was trained to predict with 85% certainty whether a student's GPA will fall above/below one standard deviation from the mean. This prediction can be used to channel university resources to support those students who need it the most. However, there is a significant prediction overlap for average students, i.e. those who fall within approximately one standard deviation around the mean. It is our conjecture that incorporating more major-specific data (e.g. grades in a set of core introductory level departmental courses) or a customized general aptitude test administered at the end of the sophomore year could improve the prediction accuracy for the average group.

关键词： Educational data mining predictive analytics classification machine learning

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ANLAGIS: Adaptive Neuron-Like Network Based on learning Automata Theory and Granular Inference Systems with Applications to pattern recognition and machine learning

ANLAGIS: Adaptive Neuron-Like Network Based on Learning Auto...

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3rd international Work-conference on the Interplay Between Natural and Artificial Computation

作者： Maravall, Dario de Lope, Javier Univ Politecn Madrid E-28040 Madrid Spain

ISBN: (纸本)9783642022630

In this paper the fusion of artificial neural networks, granular computing and learning automata theory is proposed and we present as a final result ANLAGIS, an adaptive neuron-like network based on learning automata and granular inference systems. ANLAGIS can be applied to both pattern recognition and learning control problems. Another interesting contribution of this paper is the distinction between pre-synaptic and post-synaptic learning in artificial neural networks. To illustrate the capabilities of ANLAGIS some experiments on knowledge discovery in data mining and machine learning are presented. Previous work of Jang et al. [1] on adaptive network-based fuzzy inference systems, or simply ANFIS, can be considered a precursor of ANLAGIS. The main, novel contribution of ANLAGIS is the incorporation of learning Automata Theory within its structure.

关键词： Artificial Neural Networks Granular Computing learning Automata Theory pattern recognition machine learning

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An Algorithm Research of Supervised LLE Based On Mahalanobis Distance and Extreme learning machine

An Algorithm Research of Supervised LLE Based On Mahalanobis...

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3rd international conference on Consumer Electronics, Communications and Networks (CECNet)

作者： He, Ling-Min Jin, Wei Yang, Xiao-Bin Wang, Kang-Jian China Jiliang Univ Coll Informat Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781479928606

The Locally Linear Embedding (LLE) is one of the efficient nonlinear dimensionality reduction techniques. But for some high dimensional data, it is not taking the class information of the data into account and Euclidean distance can not accurately reflect the similarity among samples. The paper proposes an improved Supervised LLE which combines class labeled data and Mahalanobis Distance (MSP-LLE). First, the approach learns a Mahalanobis Distance from the existing data. Then the Mahalanobis Distance and label information are combined to choose neighborhoods. Finally, ELM is using to map the unlabeled data to the feature space, which easily implement fault pattern recognition. The experiment result shows its good performance on reduction and recognition for high-dimensional and similar data.

关键词： locally linear embedding extreme learning machine Mahalanobis distance supervised reduction recognition

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data mining -: New challenges for statisticians

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SOCIAL SCIENCE COMPUTER REVIEW 2000年第4期18卷 442-449页

作者： Hand, DJ Univ London Imperial Coll Sci Technol & Med Dept Math London SW7 2BZ England

data mining is the science of finding unexpected, valuable, or interesting structures in large data sets. It is an interdisciplinary activity, taking ideas and methods from statistics, machine learning, database technology, and other areas. It poses novel challenges, in part arising from the sheer size of modem data sets. Although there is no doubt that it addresses important questions, there are deep issues to be resolved relating to data quality and the nature of inference. Statisticians have an important role to play in resolving these issues.

关键词： data mining large data sets machine learning patterns models data quality

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