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共 1150 条记录，以下是181-190 订阅

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Diabetes Prediction based on Supervised and Unsupervised learning Techniques - A Review 3

Diabetes Prediction based on Supervised and Unsupervised Lea...

引用

3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Sivaraman, M. Sumitha, J. Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Diabetes is a disorder that develops in the human body when blood glucose or sugar levels are extremely high. machine learning (ML) is subfield of Artificial Intelligence (AI) that is built on the idea that systems and machines will evaluate and interpret data, learn from it, and make a decision with no human participation. Medical fields that operate with massive amounts of data have recognized by the of machine learning technology. Health care systems can work more effectively and gain a competitive advantage by extracting insights from this data. The general goal of this study is to discover and observe the working and performance of various machine-learning algorithms. This paper work discusses the possibilities of employing machine-learning technology in healthcare. © 2022 IEEE.

关键词： data mining

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Human Action Recognition using 4W1H and Particle Swarm Optimization Clustering

Human Action Recognition using 4W1H and Particle Swarm Optim...

引用

3rd Annual international conference on Human System Interaction (HSI)

作者： Palafox, Leon Hashimoto, Hideki Univ Tokyo Inst Ind Sci Tokyo Japan

ISBN: (纸本)9781424475629

Tracking and recording human activities have been a major interest in the iSpace, for this purpose different recognition and clustering techniques have been used, like using a learning Classifier System and data mining Techniques. These techniques share the common factor of database dependence and there was actually little effort into making the system to understand the way human were behaving in a given time in the space. Using Artificial Intelligence techniques, we present a work that reads and classifies user object activity

关键词： 4W1H Clustering machine learning PSO

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Using Domain Top-page Similarity Feature in machine learning-based Web Phishing Detection

Using Domain Top-page Similarity Feature in Machine Learning...

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3rd international conference on Knowledge Discovery and data mining

作者： Sanglerdsinlapachai, Nuttapong Rungsawang, Arnon Natl Elect & Comp Technol Ctr Comp Emergency Response Team Pathum Thani Thailand Kasetsart Univ Mass Informat & Knowledge Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9780769539232

This paper presents a study on using a concept feature to detect web phishing problem. Following the features introduced in Carnegie Mellon Anti-phishing and Network Analysis Tool (CANTINA), we applied additional domain top-page similarity feature to a machine learning based phishing detection system. We preliminarily experimented with a small set of 200 web data, consisting of 100 phishing webs and another 100 non-phishing webs. The evaluation result in terms of f-measure was up to 0.9250, with 7.50% of error rate.

关键词： phishing anti-phishing machine learning semantic similarity domain top-page

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Prediction of Liver Diseases Based on machine learning Technique for Big data 3rd

Prediction of Liver Diseases Based on Machine Learning Techn...

引用

3rd international conference on Advanced machine learning Technologies and Applications (AMLTA)

作者： El-Shafeiy, Engy A. El-Desouky, Ali I. Elghamrawy, Sally M. Mansoura Univ Comp & Syst Dept Fac Engn Mansoura Egypt MISR Higher Inst Engn & Technol Comp Engn Dept Al Mansora Egypt

ISBN: (纸本)9783319746906;9783319746890

Liver diseases have produced a big data such as metabolomics analyses, electronic health records, and report including patient medical information, and disorders. However, these data must be analyzed and integrated if they are to produce models about physiological mechanisms of pathogenesis. We use machine learning based on classifier for big datasets in the fields of liver to Predict and therapeutic discovery. A dataset was developed with twenty three attributes that include the records of 7000 patients in which 5295 patients were male and rests were female. Support Vector machine (SVM), Boosted C5.0, and Naive Bayes (NB), data mining techniques are used with the proposed model for the prediction of liver diseases. The performance of these classifier techniques are evaluated with accuracy, sensitivity, specificity.

关键词： Classifier techniques machine learning Liver diseases data mining Big data

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mining the Big data: The Critical Feature Dimension Problem 3

Mining the Big Data: The Critical Feature Dimension Problem

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3rd IIAI international conference on Advanced Applied Informatics (IIAI-AAI)

作者： Liu, Qingzhong Sung, Andrew H. Ribeiro, Bernardete Suryakumar, Divya Sam Houston State Univ Dept Comp Sci Huntsville TX 77341 USA Univ So Mississippi Sch Comp Hattiesburg MS 39406 USA Univ Coimbra Dept Informat Engn P-3030290 Coimbra Portugal Wipro Technol Ltd Appl Machine Learning Mountain View CA 94043 USA

ISBN: (纸本)9781479941735

In mining massive datasets, often two of the most important and immediate problems are sampling and feature selection. Proper sampling and feature selection contributes to reducing the size of the dataset while obtaining satisfactory results in model building. Theoretically, therefore, it is interesting to investigate whether a given dataset possesses a critical feature dimension, or the minimum number of features that is required for a given learning machine to achieve "satisfactory" performance. (Likewise, the critical sampling size problem concerns whether, for a given dataset, there is a minimum number of data points that must be included in any sample for a learning machine to achieve satisfactory performance.) Here the specific meaning of "satisfactory" performance is to be defined by the user. This paper addresses the complexity of both problems in one general theoretical setting and shows that they have the same complexity and are highly intractable. Next, an empirical method is applied in an attempt to find the approximate critical feature dimension of datasets. It is demonstrated that, under generally reasonable assumptions pertaining to feature ranking algorithms, the critical feature dimension are successfully discovered by the empirical method for a number of datasets of various sizes. The results are encouraging in achieving significant feature size reduction and point to a promising way in dealing with big data. The significance of the existence of crucial dimension in datasets is also explained.

关键词： data mining machine learning feature ranking dimension reduction critical dimension

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data mining in Healthcare Informatics:Techniques and Applications 3

Data Mining in Healthcare Informatics:Techniques and Applica...

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3rd international conference on Computing for Sustainable Global Development (INDIACom)

作者： Anand, Tanvi Pal, Rekha Dubey, Sanjay Kumar Amity Univ Uttar Pradesh Dept Comp Sci & Engn Sec 125Expressway Noida UP India

ISBN: (纸本)9789380544199

The evolution of modern approach in knowledge systems, decision support systems and clinical constraints estimation algorithms that formulate machine learning, soft computing and data mining in presenting a new outlook for health informatics domain. Health is then clearly understood as the essential part while describing a person's sense of well-being. The delivery of health care services therefore considers as higher proportion, and played an efficient role in information and communication technologies for its effective distribution mechanism. data mining in health informatics are developing into optimistic area for producing vision from diverse data set. data mining techniques are proved to he as a valuable resource for health care informatics. The main scope of writing this paper is to analyse the effectiveness of data mining techniques in health informatics and compare various techniques, approaches or methods and different tools used and its effect on the healthcare industry. The main motive of using data mining application in healthcare systems is to exploit a machine driven tool for identifying and circulating useful and relevant healthcare information.

关键词： data mining Healthcare Informatics Classification Association Clustering

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Risk Management in customs using Deep Neural Network 3

Risk Management in customs using Deep Neural Network

引用

3rd IEEE international conference on Computing, Communication and Security (ICCCS)

作者： Regmi, Ram Hari Timalsina, Arun K. Minist Finance Dept Customs Kathmandu Nepal Tribhuwan Univ Inst Engn Cent Campus Lalitpur Nepal

ISBN: (纸本)9781538662274

Increasing trade volume adds up various challenges and risks for customs to maintain balance between trade facilitation and strong border control. With limited resources and manpower, it's quite difficult to have exhaustive physical examination of all import and export consignments. To balance control and facilitation Revised Kyoto Convention (RKC) and World Trade Organization (WTO) Trade Facilitation Agreement (TFA) have clearly stated about implementation of effective risk management system. In this paper, deep learning model was trained and tested to segregate high risk and low risk consignment on randomly selected 200,000 data from Nepal Customs of the year 2017. Model was tested using supervised learning utilizing inspection result provided by Nepal Customs. Deep learning has improved accuracy and seizure rate than that of decision Tree (DT) and Support Vector machine (SVM). All three methods have achieved a better result than current rule based risk management system. ANN had achieved better result than DT and SVM, by achieving 81% of seizure rate under 9% inspection.

关键词： Risk Management Customs risk artificial neural network risk based on compliance fraud detection data mining machine learning

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Application of text mining technology in power data prediction 3

Application of text mining technology in power data predicti...

引用

2022 3rd international conference on Computer Information and Big data Applications, CIBDA 2022

作者： Guo, Jian Guo, Wu College of Architecture and Civil Engineering Qiqihar University Qiqihar China College of Communication and Electronic Engineering Qiqihar University Qiqihar China

ISBN: (纸本)9783800758760

This paper analyses the main research methods of power text mining technology in detail, and discusses the research hotspots of power text named entity recognition and named entity relationship extraction based on machine learning and deep learning. On this basis, the limited Boltzmann machine is further used to predict the power data. The test results show that the analysis of power text features in this paper is accurate, can effectively analyse the key text information, and the prediction results meet the requirements. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Character recognition

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Educational data mining: Using knowledge tracing as a tool for student success 3

Educational data mining: Using knowledge tracing as a tool f...

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3rd international Multidisciplinary Information Technology and Engineering conference, IMITEC 2021

作者： Mandlazi, Joel Jadhav, Ashwini Ajoodha, Ritesh University of the Witwatersrand School of Computer Science and Applied Mathematics Johannesburg South Africa University of the Witwatersrand Science Teaching and Learning Unit Faculty of Science Johannesburg South Africa

ISBN: (纸本)9781665417495

Student success is important to institutions of learning. learning institutions offer academic support to their students to ensure success. Knowledge Tracing (KT), the task of modelling student knowledge based on their learning history is a significant topic in the field of Educational data mining and has numerous applications in the development of interactive and adaptive learning technology. KT can be used to understand each student's unique learning behaviour, individual needs, and skill-levels. machine learning classification models were implemented to trace the knowledge state of students. The Support Vector machine outperformed all other models in this article, with Random Forest performing the worst, with accuracy of 76.8% and 70.76%, respectively. One important goal of KT is to personalise study materials in order to assist student's understand learning concepts more efficiently. © 2021 IEEE.

关键词： data mining

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BLS: A learning Search Algorithm with Bayesian learning 3

BLS: A Learning Search Algorithm with Bayesian Learning

引用

3rd IEEE international conference on Cloud Computing and Big data Analysis (ICCCBDA)

作者： Zhang, Wei Northeastern Univ Coll Comp Sci & Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538643013

In learning search, it is assumed that a search algorithm has adequate opportunities to traverse the problem state space, and to sense and learn the topological (or probability) knowledge about the state space. In this paper, a new search algorithm based on Bayesian learning (algorithm BLS) is proposed, which finds the solution path for any given state pair (s, t) in the state space efficiently. The algorithm depicts the search as a process of pattern recognition for the current path. During its depth-first search, the BLS classifies the current path, depending on the probability distribution functions acquired in previous Bayesian learning, and prunes it if necessary. It is proved that the mean complexity of BLS grows linearly with the length of the solution path after sufficient training.

关键词： machine learning learning search big graph search and mining heuristic search Bayesian learning pattern recognition

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