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A part-of-speech-based exploratory text mining of students’ looking-back evaluation 11th

A part-of-speech-based exploratory text mining of students’...

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11th international Symposium on Natural Language Processing, SNLP-2016 and 1st workshop in Intelligent Informatics and Smart Technology, 2016

作者： Minami, Toshiro Hirokawa, Sachio Ohura, Yoko Hashimoto, Kiyota Kyushu Institute of Information Sciences 6-3-1 Saifu DazaifuFukuoka818-0117 Japan Kyushu University Fukuoka Japan Prince of Songkla University Phuket Thailand

ISBN: (纸本)9783319700151

In our lectures at universities, we observe that the students’ attitudes affects a lot to their achievements. In order to prove this observation based on data, we have been investigating to find effective methods that extract students’ attitudes from lecture data;such as examination score as an index to student’s achievement, attendance and homework data for his/her effort, and answer texts of the term-end questionnaire as information source of attitude. In this chapter, we take another approach to investigate the influences of words used in the answer texts of students on their achievements. We use a machine learning method called Support Vector machine (SVM), which is a tool to create a model for classifying the given data into two groups by positive and negative training sample data. We apply SVM to the answer texts for analyzing the influences of parts of speech of words to the student’s achievement. Even though adjectives and adverbs are the same in the sense that they modify nouns and verbs, we found that adverbs affects much more than adjectives, as a result. From our experiences so far, we believe that analysis of answers to the evaluations of students toward themselves and lectures are very useful source of finding the students’ attitudes to learning. © Springer international Publishing AG 2018.

关键词： students

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mining stock market tendency using GA-based support vector machines

Mining stock market tendency using GA-based support vector m...

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1st international workshop on Internet and Network Economics, WINE 2005

作者： Yu, Lean Wang, Shouyang Lai, Kin Keung Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100080 China School of Management Graduate University Chinese Academy of Sciences Beijing 100039 China College of Business Administration Hunan University Changsha 410082 China Department of Management Sciences City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

ISBN: (纸本)3540309004

In this study, a hybrid intelligent data mining methodology, genetic algorithm based support vector machine (GASVM) model, is proposed to explore stock market tendency. In this hybrid data mining approach, GA is used for variable selection in order to reduce the model complexity of SVM and improve the speed of SVM, and then the SVM is used to identify stock market movement direction based on the historical data. To evaluate the forecasting ability of GASVM, we compare its performance with that of conventional methods (e.g., statistical models and time series models) and neural network models. The empirical results reveal that GASVM outperforms other forecasting models, implying that the proposed approach is a promising alternative to stock market tendency exploration. © Springer-Verlag Berlin Heidelberg 2005.

关键词： learning systems

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1D CNN Based Human Respiration pattern recognition using Ultra Wideband Radar 1

1D CNN Based Human Respiration Pattern Recognition using Ult...

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1st international Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Kim, Seong-Hoon Han, Gi-Tae Gachon Univ Dept IT Convergence Engn Seongnam Si South Korea Gachon Univ Dept Comp Engn Seongnam Si South Korea

ISBN: (纸本)9781538678220

The respiration status of a person is one of the vital signs that can be used to check the health condition of the person. The respiration status has been measured in various ways in the medical and healthcare sectors. Contact type sensors were conventionally used to measure respiration. The contact type sensors have been used primarily in the medical sector, because they can be only used in a limited environment. Recent studies have evaluated the ways of detecting human respiration patterns using Ultra-Wideband (UWB) Radar, which relies on noncontact type sensors. Previous studies evaluated the apnea pattern during sleep by analyzing the respiration signals acquired by UWB Radar using a principal component analysis (PCA). However, it is necessary to measure various respiration patterns in addition to apnea in order to accurately analyze the health condition of an individual in the healthcare sector. Therefore, this study proposed a method to recognize four respiration patterns based on the 1D convolutional neural network from the respiration signals acquired from UWB Radar. The proposed method extracts the eupnea, bradypnea, tachypnea, and apnea respiration patterns from UWB Radar and composes a learning dataset. The proposed method learned data through 1D CNN and the recognition accuracy was measured. The results of this study revealed that the accuracy of the proposed method was up to 15% higher than that of the conventional classification algorithms (i.e., PCA and Support Vector machine (SVM)).

关键词： UWB Radar Vital Signs Respiration pattern Healthcare 1D Convolutional Neural Network

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CALDS: Context-aware learning from data streams

CALDS: Context-aware learning from data streams

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1st international workshop on Novel data stream pattern mining Techniques, streamKDD'10, Held in Conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery and data mining

作者： Gomes, João Bártolo Menasalvas, Ernestina Sousa, Pedro A.C. Universidad Politécnica de Madrid Facultad de Informática Madrid Spain Universidade Nova de Lisboa Faculdade de Ciências e Tecnologia Lisboa Portugal

ISBN: (纸本)9781450302265

Drift detection methods in data streams can detect changes in incoming data so that learned models can be used to represent the underlying population. In many real-world scenarios context information is available and could be exploited to improve existing approaches, by detecting or even anticipating to recurring concepts in the underlying population. Several applications, among them health-care or recommender systems, lend themselves to use such information as data from sensors is available but is not being used. Nevertheless, new challenges arise when integrating context with drift detection methods. Modeling and comparing context information, representing the context-concepts history and storing previously learned concepts for reuse are some of the critical problems. In this work, we propose the Context-aware learning from data streams (CALDS) system to improve existing drift detection methods by exploiting available context information. Our enhancement is seamless: we use the association between context information and learned concepts to improve detection and adaptation to drift when concepts reappear. We present and discuss our preliminary experimental results with synthetic and real datasets. © 2010 ACM.

关键词： Population statistics

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Monitoring Cyber Peer-Led Team learning: A Multimodal Human-in-the-loop Approach 1

Monitoring Cyber Peer-Led Team Learning: A Multimodal Human-...

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1st international workshop on Human-in-the-Loop Applied machine learning, HITLAML 2023

作者： DSouza, Karen Varma-Nelson, Pratibha Fang, Shiaofen Mukhopadhyay, Snehasis Department of Computer Science Indiana University Purdue University IndianapolisIN United States Department of Chemistry Indiana University Purdue University IndianapolisIN United States

The recent adoption of generative artificial intelligence (AI) tools in education has transformed education and AI-assisted learning. However, researchers embracing applied machine learning (ML) in pedagogical settings continue to face challenges. Lack of publicly available multimodal datasets involving gesture and emotion recognition specifically in education is a bottleneck for building AI-enabled e-learning platforms. In the paper, we take a constructive stance at monitoring group behavior in cyber peer-led team learning (cPLTL) classes in Organic Chemistry using ML. Although past studies have attempted to quantify student engagement in e-learning, their use in the cPLTL use case for AI modeling has not yet been established. The hypothesis underlying our proposed framework is that online peer group behavior can be characterized by a human-in-the-loop model that relies on multiple input modalities. Thus, the aim is to identify behavioral patterns in head and facial movements that are augmented by lexical based sentiment and audio feature extraction. To combat the small data challenge, we propose a framework for the human-in-the-loop (HITL) system that actively learns the past group modalities. HITL strategies enable the algorithm to learn more efficiently from less data iteratively. The model will be implemented using active learning, measures of uncertainty, random sampling and entropy which are key in the design of the study. A qualitative comparison of sentiment modality with ChatGPT's participant performance evaluation has been discussed. The study will increase the use of AI in tools that support educators in universities using pedagogies of active engagement in science, technology, engineering, and math. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Artificial intelligence

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Condense Mixed Convexity and optimization with an application in data service optimization 1st

Condense Mixed Convexity and optimization with an applicatio...

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1st international workshop on machine learning, Optimization, and Big data, MOD 2015

作者： Tokgöz, Emre Kumin, Hillel School of Engineering Quinnipiac University HamdenCT06518 United States School of Industrial and Systems Engineering University of Oklahoma NormanOK73071 United States

ISBN: (纸本)9783319279251

Elements of matrix theory are useful in exploring solutions for optimization, data mining, and big data problems. In particular, mixed integer programming is widely used in data based optimization research that uses matrix theory (see for example [13]). Important ele- ments of matrix theory, such as Hessian matrices, are well studied for con- tinuous (see for example [11]) and discrete [9] functions, however matrix theory for functions with mixed (i.e. continuous and discrete) variables has not been extensively developed from a theoretical perspective. There are many mixed variable functions to be optimized that can make use of a Hessian matrix in various fields of research such as queueing the- ory, inventory systems, and telecommunication systems. In this work we introduce a mixed Hessian matrix, named condense mixed Hessian matrix, for mixed variable closed form functions g: Zn × Rm → R, and the use of this matrix for determining convexity and optimization results for mixed variable functions. These tasks are accomplished by building on the definition of a multivariable condense discrete convex function and the corresponding Hessian matrix that are introduced in [14]. In addition, theoretical condense mixed convexity and optimization results are obtained. The theoretical results are implemented on an M/M/s queue- ing function that is widely used in optimization, data mining, and big data problems. © Springer international Publishing Switzerland 2015.

关键词： data mining

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students' Performance Prediction Model Using Meta-classifier Approach 20th

Students' Performance Prediction Model Using Meta-classifier...

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20th international Conference on Engineering Applications of Neural Networks (EANN) /1st international workshop on Pervasive Intelligence (PEINT)

作者： Hassan, Hasniza Anuar, Syahid Ahmad, Nor Bahiah Univ Teknol Malaysia Johor Baharu 81310 Johor Malaysia Univ Teknol Malaysia Kuala Lumpur 54100 Malaysia

ISBN: (纸本)9783030202576;9783030202569

students' performance is vitally important at all stages of education, particularly for Higher Education Institutions. One of the most important issues is to improve the performance and quality of students enrolled. The initial symptom of at-risks' students need to be observed and earlier preventive measures are required to be carried out so as to determine the cause of students' dropout rate. Hence, the purpose of this research is to identify factors influencing students' performance using educational data mining techniques. In order to achieve this, data from different sources is employed into a single platform for pre-processing and modelling. The design of the study is divided into 6 different phases (data collection, data integration, data pre-processing such as cleaning, normalization, and transformation, feature selection, patterns extraction and model optimization as well as evaluation. The datasets were collected from a students' information system and e-learning system from a public university in Malaysia, while sample data from the Faculty of Engineering were used accordingly. This study also employed the use of academic, demographical, economical and behaviour e-learning features, in which 8 different group models were developed using 3 base-classifiers;Decision Tree, Artificial Neural Network and Support Vector machine, and 5 multi-classifiers;Random Forest, Bagging, AdaBoost, stacking and Majority Vote classifier. Finally, the highest accuracy of the classifier model was optimized. At the end, new students' Performance Prediction Model was developed. The result proves that combination demographics with behaviour using a meta-classifier model with optimized hyper parameter produced better accuracy to predict students' performance.

关键词： student performance prediction Educational data mining E-learning Higher education Classification Ensemble model

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learning decision trees and tree automata for a syntactic pattern recognition task

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1st Iberian Conference on pattern recognition and Image Analysis, IbPRIA 2003

作者： Sempere, José M. López, Damián Departamento de Sistemas Informáticos y Computación Universidad Politécnica de Valencia Valencia Spain

ISBN: (纸本)3540402179

Decision trees have been widely used for different tasks in artificial intelligence and data mining. Tree automata have been used in pattern recognition tasks to represent some features of objects to be classified. Here we propose a method that combines both approaches to solve a classical problem in pattern recognition such as Optical Character recognition. We propose a method which is organized in two stages: (1) we use a grammatical inference technique to represent some structural features of the characters and, (2) we obtain edit distances between characters in order to design a decision tree. The combination of both methods benefits from their individual characteristics and is formulated as a coherent unifying strategy. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Decision trees

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Fast pattern matching approach for intrusion detection systems

Fast pattern matching approach for intrusion detection syste...

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1st international Conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2012

作者： Manjunath, M. Srinivasa, K.G. Muppalla, Anil Kumar Machine Learning and Data Mining Laboratory Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9788132211563

Intrusion detection system (IDS) consists of set of techniques and methods for collection of packets from host system or network and analyzes those packets for anomalous content. IDSs mainly fall into two categories: signature-based IDSs and anomaly detection systems. A rule-based IDS compares the incoming packets against rule set in order to detect intrusion. A common approach followed is to build rule trees or finite automata with rule set and traverse it using a packet as input string. 30-60 % of total signature-based IDS processing time is spent on pattern matching [1]. The existing signature-based IDS cannot meet the speed demands imposed by both high network speeds and increasing number of signatures, and more CPU time is spent on searching for rules that match each packet. In this paper, we are going to present an analysis on IDS that is combined with other methods and techniques to produce greater results and hence contribute to the improvement of IDS. © 2014 Springer India.

关键词： pattern matching

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Support vector learning for gender classification using audio and visual cues

Support vector learning for gender classification using audi...

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1st international workshop on pattern recognition with Support Vector machines

作者： Walavalkar, L Yeasin, M Narasimhamurthy, A Sharma, R Penn State Univ Dept Comp Sci Pond Lab 220 University Pk PA 16802 USA

ISBN: (纸本)354044016X

Computer vision systems for monitoring people and collecting valuable demographic information in a social environment is an important research problem. It is expected that such a system will play an increasingly important role in enhancing user's experience and can significantly improve the intelligibility of a human computer interaction (HCI) system. For example, a robust gender classification system can provide a basis for passive surveillance and access to a smart building using demographic information or can provide valuable consumer statistics in a public place. The option of an audio cue in addition to the visual cue promises a robust solution with high accuracy and ease-of-use in human computer interaction systems. This paper investigates gender classification using Support Vector machines (SVMs). The visual (thumbnail frontal face) and the audio (features from speech data) cues were considered for designing the classifier. Three different representations of the data, namely, raw data, principle component. analysis (PCA) and non-negative matrix factorization (NMF) were used for the experimentation with visual signal. For speech, mel-cepstral coefficient and pitch were used for the experimentation. It was found that the best overall classification rates obtained using the SVM for the visual and speech data were 95.31% and 100%, respectively, on data set collected in laboratory environment. The performance of the SVM was compared with two simple classifiers namely, the nearest prototype neighbor and the k-nearest neighbor on all feature sets. It was found that the SVM outperformed the other two classifiers on all datasets. To further understand the robustness issues, the proposed approach has been applied on a large balanced (roughly equal distribution of gender, ethnicity and age group) data-base consisting of 8000 faces collected in real world environment. While, the results are very promising it indicates more to be done to make a statistically meaningful co

关键词： gender classification support vector machines (SVMs) PCA NMF mel-cepstrum linear and nonlinear classifies

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