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Towards an optimal personalization strategy in MOOCs

SMART LEARNING ENVIRONMENTS 2020年第1期7卷 1-18页

作者： Qaffas, Alaa A. Kaabi, Kaouther Shadiev, Rustam Essalmi, Fathi Univ Jeddah Coll Business Management Informat Syst Dept Jeddah Saudi Arabia Univ Kairouan Higher Inst Comp Sci & Management Kairouan ISIGK Kairouan Tunisia Nanjing Normal Univ Sch Educ Sci Nanjing Peoples R China Univ Tunis Higher Sch Sci & Technol Tunis ESSTT Res Lab Technol Informat & Commun & Elect Engn La Tunis Tunisia

Several successful initiatives have resulted from the OER movement. One of them is Massive Open Online Courses (MOOCs) which is a popular learning mode as it offers an affordable and flexible way to learn. However, the evolution of the MOOCs has some challenges. One of the major problems of MOOCs is the diversity of learners and the need to personalize the content as well as the way of delivering it. The origin of this problem is the one size does not fit all. In fact, learners have different characteristics such as their learning styles, levels of knowledge, and so on. The selection of the most suitable parameters (set of complementary learners' characteristics) to be considered in learner's profile is not easy in the presence of a considerable number of learners in MOOCs. One reason is because a course can be attended by many learners with varied profiles from different regions of the world. This plurality of learner profiles makes it important to develop content that can meet the needs and objectives of each learner in MOOCS. Our solution to solve this problem consists of personalizing the content of MOOC for each learner. We propose a new approach which allows to optimize the selection of the personalization parameters and to apply the appropriate personalization strategy, based on a classification algorithm. The proposed approach aims to improve the retention rate and the quality of learning in MOOCs. This approach is validated by experiments which test its success when applied to many combinations of strategies and learner profiles.

关键词： Online learning OER MOOCs Personalization strategies Retention classification algorithm Learner characteristics Educational games

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Feature Analysis and Optimization of Underwater Target Radiated Noise Based on t-SNE 10

Feature Analysis and Optimization of Underwater Target Radia...

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10th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Chen, Yuechao Du, Shuanping Quan, Hengheng Hangzhou Appl Acoust Res Inst Sci & Technol Sonar Lab Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538661192

The analysis and optimization for underwater target radiated noise recognition are addressed. The t-SNE (t-distributed stochastic neighbor embedding) algorithm is taken for dimension reduction of the underwater target radiated noise spectrum bands segmented by frequency and the visual results can be obtained. The average distance between classes and coincidence rate are constructed as indicators of the visualization results. By analyzing the separability of each frequency band, the optimal features can be obtained. Then the optimal features are recognized by three classification algorithms which are Random Forest, SVM (Support Vector Machine) and AdaBoost. The processing results of experimental signal spectrum with two types of target are as follows. The separability of the target signal spectrum decreases with the increase of frequency. The spectrum band of 10-150Hz has the best separability. SVM is sensitive to the increase of the data dimension with non optimal separability. Random Forests and AdaBoost can apply separability components in wider frequency bands and have greater tolerance. The overall recognition accuracy of AdaBoost is the highest, but the computation efficiency is the lowest. These analysis results shows that the t-SNE algorithm can be used to optimize the underwater target radiated noise spectrum features for the purpose of improving the accuracy and efficiency of the classification algorithm.

关键词： underwater target radiated noise t-distributed stochastic neighbor embedding classification algorithm feature optimization spectrum

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Dynamic Partial Reconfiguration implementation of the SVM/KNN multi-classifier on FPGA for Bioinformatics application 37

Dynamic Partial Reconfiguration implementation of the SVM/KN...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Hussain, Hanaa M. Benkrid, Khaled Seker, Huseyin PAAET Coll Technol Studies Elect Engn Technol Dept Shuwaikh 70654 Kuwait Univ Edinburgh Sch Engn & Elect Kings BldgMayfield Rd Edinburgh EH9 3JL Midlothian Scotland Northumbria Univ Dept Comp Sci & Digital Technol Fac Engn & Environm Newcastle Upon Tyne NE1 8ST Tyne & Wear England

ISBN: (纸本)9781424492701

Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scientists such as Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The latter have shown to be efficient and high in performance. In recent years, FPGAs have been benefiting from dynamic partial reconfiguration (DPR) feature for adding flexibility to alter specific regions within the chip. This work proposes combing the use of FPGAs and DPR to build a dynamic multi-classifier architecture that can be used in processing bioinformatics data. In bioinformatics, applying different classification algorithms to the same dataset is desirable in order to obtain comparable, more reliable and consensus decision, but it can consume long time when performed on conventional PC. The DPR implementation of two common classifiers, namely support vector machines (SVMs) and K-nearest neighbor (KNN) are combined together to form a multi-classifier FPGA architecture which can utilize specific region of the FPGA to work as either SVM or KNN classifier. This multi-classifier DPR implementation achieved at least similar to 8x reduction in reconfiguration time over the single non-DPR classifier implementation, and occupied less space and hardware resources than having both classifiers. The proposed architecture can be extended to work as an ensemble classifier.

关键词： bioinformatics field programmable gate arrays support vector machines DPR feature FPGA K-nearest neighbor KNN classifier SVM-KNN multiclassifier bioinformatics application bioinformatics data classification algorithm dynamic partial reconfiguration implementation graphical processing units support vector machine Bioinformatics classification algorithms Computer architecture Field programmable gate arrays Hardware Support vector machines Training

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Two-layered ensemble Kohonen nets for imbalanced streaming data

Two-layered ensemble Kohonen nets for imbalanced streaming d...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Roy, Asim Arizona State Univ Dept Informat Syst Tempe AZ 85287 USA

ISBN: (纸本)9781509006229

One of the dominant application areas of machine learning will be the industrial internet (Internet of Things), although most of the data will actually be confined to smaller internal networks and will not use the Internet. In this world of the industrial internet, streaming sensor data with rare events, such as machine or process failures and disruptions, will be the dominant form of data and it poses a particular problem for machine learning because its methods are generally not designed to handle imbalanced data. In this paper, I present a new classification algorithm for streaming imbalanced data that uses Kohonen nets at its core. To handle high-velocity streaming data, the proposed algorithm can be implemented on hardware to take advantage of massive parallelism. I provide an outline of the algorithm and some preliminary computational results.

关键词： Kohonen nets ensemble learning imbalanced data streaming data industrial internet classification algorithm

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Thyroid Disease Prediction Using XGBoost algorithms

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Journal of Mobile Multimedia 2022年第3期18卷 917-934页

作者： Sankar, S. Potti, Anupama Naga Chandrika, G. Ramasubbareddy, Somula Department of CSE Sona College of Technology Salem India Department of CSE Sree Dattha Institute of Engineering and Science Hyderabad India Department of IT VNRVJIET Hyderabad India

Nowadays, thyroid disease is increasing rapidly all over the world. Significantly, one out of ten people is affected by the thyroid in India. In recent years, many researchers have done various research works on thyroid disease detection. Therefore, the early stage of thyroid disease prediction is difficult to protect and avoid the worst health condition. In this regard, the machine learning plays a crucial role to detect the disease accurately. We consider the UC Irvin knowledge discovery dataset. So, this paper proposes the XGBoost algorithm to predict thyroid disease accurately. The best features are selected using XGBoost function. The proposed XGBoost algorithm’s efficacy is compared to decision tree, logistic regression, k-Nearest Neighbor (kNN) methods. The performance of all four algorithms is compared and analyzed. It is observed that the accuracy of the XGBoost algorithm increases by 2% than the KNN algorithm. © 2022 River Publishers.

关键词： classification algorithm Disease prediction Hormone K-nearest neighbor Machine leaning Thyroid disease XGBoost

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Outcome Prediction of DOTA2 Based on Naive Bayes Classifier 16

Outcome Prediction of DOTA2 Based on Naive Bayes Classifier

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16th IEEE/ACIS International Conference on Computer and Information Science (ICIS)

作者： Wang, Kaixiang Shang, Wenqian Commun Univ China Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781509055074

Although DOTA2 is a popular game around the world, no clear algorithm or software are designed to forecast the winning probability by analyzing the lineups. However, the author finds that Naive Bayes classifier, one of the most common classification algorithm, can analyze the lineups and predict the outcome according to the lineups and gives an improved Naive Bayes classifier. Using the DOTA2 data set published in the UCI Machine Learning Repository, we test Naive Bayes classifier's prediction of respective winning probability of both sides in the game. The results show that Naive Bayes classifier is a practical tool to analyze the lineups and predict the outcome based on players' choices.

关键词： Naive Bayes classification algorithm DOTA2

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A Review Paper on Machine Learning Techniques and Its Applications in Health Care Sector 1

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2nd International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

作者： Gautam, Priya Dehraj, Pooja Noida Inst Engn & Technol Greater Noida India

ISBN: (数字)9789811925412

ISBN: (纸本)9789811925412;9789811925405

Healthcare data analysis has seen a significant rise in recent years and thus becoming a prominent research field. Healthcare data types include Sensor data, Clinical data, and Omics data. Clinical data are generally collected whereas a patient is being treated whereas multiple wearable and wireless sensor devices are used for collecting the Sensor data. Omics data includes transcriptome, genome, and proteome data types, all of which are complicated and high-dimensional. Since the data type varies and manually dealing with the raw data becomes a challenging task. This is where Machine Learning emerges as a significant tool for analyzing the data. To precisely forecast the healthcare data results, Machine Learning utilizes varied statistical techniques and advanced algorithms. For analysis ML utilizes different algorithms such as Reinforcement Learning, Supervised Learning, and Unsupervised Learning. This paper discusses the Machine learning algorithms of many forms that can be used to analyse and survey enormous volumes of healthcare data.

关键词： classification algorithm Disease diagnostics Healthcare data Machine learning

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An Evolutionary Data-Conscious Artificial Immune Recognition System 13

An Evolutionary Data-Conscious Artificial Immune Recognition...

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15th Genetic and Evolutionary Computation Conference (GECCO)

作者： Tay, Darwin Poh, Chueh Loo Kitney, Richard I. Nanyang Technol Univ Div Bioengn Singapore 639798 Singapore Imperial Coll London Dept Bioengn London SW7 2AZ England

ISBN: (纸本)9781450319638

Artificial Immune Recognition System (AIRS) algorithm offers a promising methodology for data classification. It is an immune-inspired supervised learning algorithm that works efficiently and has shown comparable performance with respect to other classifier algorithms. For this reason, it has received escalating interests in recent years. However, the full potential of the algorithm was yet unleashed. We proposed a novel algorithm called the evolutionary data-conscious AIRS (EDC-AIRS) algorithm that accentuates and capitalizes on 3 additional immune mechanisms observed from the natural immune system. These mechanisms are associated to the phenomena exhibited by the antibodies in response to the concentration, location and type of foreign antigens. Bio-mimicking these observations empower EDC-AIRS algorithm with the ability to robustly adapt to the different density, distribution and characteristics exhibited by each data class. This provides competitive advantages for the algorithm to better characterize and learn the underlying pattern of the data. Experiments on four widely used benchmarking datasets demonstrated promising results - outperforming several state-of-the-art classification algorithms evaluated. This signifies the importance of integrating these immune mechanisms as part of the learning process.

关键词： Artificial immune recognition system classification algorithm Evolutionary computation

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Affective-feature-based Sentiment Analysis using SVM Classifier 20

Affective-feature-based Sentiment Analysis using SVM Classif...

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20th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Luo, Fang Li, Cheng Cao, Zehui Tsinghua Univ Sch Aerosp Beijing Peoples R China Wuhan Univ Technol Dept Comp Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781509019151

Based on the methods of the traditional topic-based text classification, machine learning method was performed to the coarse-grained sentiment classification of reviews. Sentiment classification involved a lot of problems. In this paper, the sentiment Vector Space Model (s-VSM) was used for text representation to solve data sparseness. In addition, the critical issues of the sentiment classification, i.e. the selection of classification algorithms, the determination of feature selection method and the selection of feature dimension, are verified by experiments. Furthermore, in order to consider the entire corpus contribution of features and each category contribution of features, the feature selection method of Chi-square Difference between the Positive and Negative Categories (CDPNC) was proposed. It combined DF with CHI and had the better performance. Experiments showed that the Macro-F and Micro-F achieved 90.18% and 90.08% respectively.

关键词： Reviews Sentiment classification Machine learning classification algorithm Feature selection

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Text categorization method based on Extension Theory

Text categorization method based on Extension Theory

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International Conference on Natural Language Processing and Knowledge Engineering

作者： Yi, Y Zheng, Y He, ZS Wu, ZF Chongqing Univ Inst Comp Sci Chongqing 400044 Peoples R China

ISBN: (纸本)0780379020

This paper introduces a new text categorization method utilizing Machine Learning based on Extension Theory. This dependent degree based on the Extension Theory represents the extent to which the element belongs to the predefined categories. The "closeness degree" between the input document vector and standard range of each predefined category can be calculated. The new method is conceptually simple;it can be used with relatively low complexity, and high flexibility: The algorithm is highly scalable. It can be effectively applied to text categorization, of which various features are consecutive values. Furthermore, this algorithm can be widely applied to computational linguistics.

关键词： text categorization extension theory classification algorithm machine learning

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