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Fusion of learning automata theory and granular inference systems: ANLAGIS. Applications to pattern recognition and machine learning

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NEUROCOMPUTING 2011年第8期74卷 1237-1242页

作者： Maravall, Dario de Lope, Javier Univ Politecn Madrid Ctr Automat & Robot CSIC Dept Appl Intelligent Syst Madrid Spain Univ Politecn Madrid Dept Artificial Intelligence Fac Comp Sci Sch Telecommun Engn Madrid Spain

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 presynaptic and post-synaptic learning in artificial neural networks. Tc illustrate the capabilities of ANLAGIS some experiments on knowledge discovery in data mining and machine learning are presented. The main, novel contribution of ANLAGIS is the incorporation of learning Automata Theory within its structure;the paper includes also a novel learning scheme for stochastic learning automata. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks Granular computing learning automata theory Pattern recognition machine learning

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Specular reflection Surface Defects Detection by using Deep learning 2019

Specular reflection Surface Defects Detection by using Deep ...

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3rd international conference on Information System and data mining (ICISDM)

作者： Zhang, Zhong Zhang, Borui Akiduki, Takuma Toyohashi Univ Technol Dept Mech Engn 1-1 Hibarigaoka Tenpaku Cho Toyohashi Aichi 4418580 Japan Northeastern Univ Dept Mech Engn 3-11 Wenhua Rd Heping District Shenya Peoples R China

ISBN: (纸本)9781450366359

As you know that defects inspection of specular surface is very difficult because its specular reflection is very strong and defects' reflection is weaker. And the existing computer vision-based industrial parts surface defect detection methods are limited by environmental factors, and the image preprocessing process is complex. On the other hand, with the rapid development of Convolutional Neural Networks (CNN) that is one type of deep learning and has excellent performance for image processing, has led to the rapid development of computer vision research based on deep learning. In this paper, we proposed an ensemble CNN in which integrated two convolutional neural network models for surface defect detection, and obtained better results.

关键词： Specular surface Defect detection Convolutional neural network machine vision Networks

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A Hybrid Approach to Detect Injection Attacks on Server-side Applications using data mining Techniques 3

A Hybrid Approach to Detect Injection Attacks on Server-side...

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3rd international conference on Sustainable Technologies for Industry 4.0 (STI)

作者： Ahmed, Abu Syeed Sajid Shachi, Mehjabeen Brishty, Afsana Afrin Siddiqui, Nurnaby Sakib, Nazmus Ahsanullah Univ Sci & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665400091

Cyber attacks are one of the most serious concerns facing individuals at all levels, particularly in enterprises, as they can maliciously destroy systems and steal data. Cyberattacks are normally carried out by a hacker group to attack a single computer or networks. An attacker launches a serverside attack directly at a listening service. Server-side attacks aim to compromise and infringe on a server's data and applications. Attackers are mostly interested in email services, media players, web browsers, office suites, and other similar apps. Attackers can more easily target server-side applications due to malicious requests. In our work, a hybrid approach is implemented inside our proposed two-layer security firewall that includes both machine learning and non-machine learning approaches to detect malicious codes. In the machine learning-based approach, Adaboost and Random Forest are evaluated as the best classifiers with the accuracy 97.9% for detecting SQL injection attacks. On the other hand, SVM performed better than other classifiers with an accuracy of 91.5% for detecting NoSQL injection attacks.

关键词： Cyber Attacks SQL Injection NoSQL Injection Software Security machine learning data mining.

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Improved collaborative filtering recommendations using quantitative implication rules mining in implication field 2019

Improved collaborative filtering recommendations using quant...

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3rd international conference on machine learning and Soft Computing (ICMLSC)

作者： Hoang Tan Nguyen Hung Huu Huynh Lan Phuong Phan Hiep Xuan Huynh Dept Informat & Commun Dong Thap 12 Tran Phu StWard 1 Cao Lanh City Dong Thap Vietnam Da Nang Univ Univ Sci & Technol 54 Nguyen Luong Bang St Danang City Vietnam Can Tho Univ 3-2 St Can Tho City Vietnam

ISBN: (纸本)9781450366120

Collaborative filtering recommendation based on association rule mining has become a research trend in the field of recommender systems. However, most research results only focus on binary data, whereas in practice sets of transactions are usually quantitative data. Moreover, association rule mining algorithms are designed to focus on optimizing for basket analysis, so that in order to better serve for recommendation, they need to be adjusted. Therefore, a solution for recommender systems to deal with association rules on both binary and quantitative data as well as improve the quality of recommendation based on the rule set is a challenge today. This paper proposes a new approach to improve the accuracy, the performance and the time of recommendation by the model based on quantitative implication rules mining in the implication field.

关键词： Implication field quantitative implication rules implication index implication intensity

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Using VR technology to support the formation of cooperative learning groups

Using VR technology to support the formation of cooperative ...

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3rd IEEE international conference on Advanced learning Technologies

作者： Jong, B Lin, TW Chan, TY Wu, YL Department of Information and Computer Engineering Chung Yuan Christian University Taiwan Department of Computer and Information Science Soochow University Taiwan Department of Electronic Engineering Chung Yuan Christian University Taiwan

ISBN: (纸本)0769519679

In this paper, we used VRML to construct an online Virtual Wildlife Park (VWP). With Virtual Reality's immersive ability, students may interact with others just as in the real world. Students can also interact with virtual animals to help them achieve a greater understanding of those animals. The Virtual Wildlife Park also provides elementary communication facilities promoting cooperation among the students. Furthermore, we use data mining to examine the students' portfolios resulting from their visit to the Virtual Wildlife Park. We then calculate the interrelationship factor of each student, which is useful for drawing up a grouping strategy;this seems to be a help to student learning.

关键词： virtual reality online learning grouping

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Prediction of Coronary Heart Disease using machine learning: An Experimental Analysis 19

Prediction of Coronary Heart Disease using Machine Learning:...

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3rd international conference on Deep learning Technologies (ICDLT)

作者： Gonsalves, Amanda H. Thabtah, Fadi Mohammad, Rami Mustafa A. Singh, Gurpreet Manukau Inst Technol Digital Technol Manukau Stn Rd Auckland New Zealand Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Informat Syst POB 1982 Dammam Saudi Arabia

ISBN: (纸本)9781450371605

The field of medical analysis is often referred to be a valuable source of rich information. Coronary Heart Disease (CHD) is one of the major causes of death all around the world therefore early detection of CHD can help reduce these rates. The challenge lies in the complexity of the data and correlations when it comes to prediction using conventional techniques. The aim of this research is to use the historical medical data to predict CHD using machine learning (ML) technology. The scope of this research is limited to using three supervised learning techniques namely Naive Bayes (NB), Support Vector machine (SVM) and Decision Tree (DT), to discover correlations in CHD data that might help improving the prediction rate. Using the South African Heart Disease dataset of 462 instances, intelligent models are derived by the considered ML techniques using 10-fold cross validation. Empirical results using different performance evaluation measures report that probabilistic models derived by NB are promising in detecting CHD.

关键词： Coronary Heart Disease data mining machine learning Medical Informatics Supervised learning

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Skin Disease Prediction Using machine learning Techniques 3

Skin Disease Prediction Using Machine Learning Techniques

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Bharathi, P.T. Prashanth, G.K. Mounika, S. Siddaganga Institute of Technology Deptment of CSE Tumkur India Siddaganga Institute of Technology Deptment of MCA Tumkur India

ISBN: (纸本)9798350317022

An innovative machine learning-based decision support system for the accurate classification of various skin conditions like eczema, psoriasis, melanoma, and dermatitis. It involves preprocessing images, feature extraction, model training, and rigorous evaluation using metrics like accuracy and precision. A user-friendly interface facilitates seamless integration into clinical practice, with plans to expand the dataset, enhance interpretability, and incorporate real-time data for improved skin disease predictions, benefiting both dermatologists and patients. © 2023 IEEE.

关键词： data mining Decision Tree machine learning Random Forest Skin Disease

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System detection's Hawkeye platform

System detection's Hawkeye platform

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3rd DARPA Information Survivability conference and Exposition

作者： Miller, M System Detection Inc. New York USA

ISBN: (纸本)0769518974

System Detection uses sophisticated data mining, machine learning, and behavioral-based statistical analysis and detection techniques to significantly improve the ability of security managers to detect and protect against threats to the integrity of networks, systems, and applications. System Detection customers benefit from the improved effectiveness and reliability of security initiatives and the boost in productivity its Hawkeye products deliver. And, because System Detection's Hawkeye works with the security products organizations already own, their current investments are protected.

关键词： Information analysis Telecommunication traffic Statistical analysis data security Reconnaissance Aggregates Costs Information security data mining machine learning

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Application of Big data in the Research of Intelligent Information Management System 3

Application of Big Data in the Research of Intelligent Infor...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Sun, Qianqian Xue, Kunlun Huangdao District People's Hospital Shandong Qingdao266499 China Huangdao Branch Network Police Brigade Qingdao Municipal Public Security Bureau Shandong 266499 China

ISBN: (纸本)9798350354171

data analysis and mining play an important role in the research of intelligent information management system, but there is a problem of inaccurate information management. Traditional machine learning cannot solve the information management problem in the research of intelligent information management system, and the effect is not satisfactory. Therefore, this paper proposes the application of intelligent information management system based on big data, and the application of intelligent information management system research an analysis was carried out. Firstly, The key is your guiding theory, es, to reduce the interference factors in data analysis and mining. Then, the dataset theory is used to form a big data analysis and mining scheme, and the data analysis and mining results are comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, big data is better than traditional machine learning in terms of data analysis and mining accuracy, data analysis and mining influencing factor time. © 2024 IEEE.

关键词： data accuracy

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A Comparative Study of machine learning Approaches on learning Management System data 3

A Comparative Study of Machine Learning Approaches on Learni...

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3rd international conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2019

作者： Oreski, Dijana Hajdin, Goran Faculty of Organization and Informatics University of Zagreb Pavlinska 2 Varaždin42000 Croatia

ISBN: (纸本)9781728135724

This paper addresses the analysis of machine learning (ML) effectiveness in learning analytics context. Four different machine learning approaches are evaluated. The results offer information about the usefulness of these approaches and help to decide which of the approaches is the most promising one in learning analytics application. Results substantiate that the neural networks ML model trained on our learning management system (LMS) data exhibits the best performance for predicting the students' academic performance. In our future research, predictive model results will be explained within a pedagogical context in order to be used as part of student support mechanism. © 2019 IEEE.

关键词： data mining

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