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A data mining approach using machine learning algorithms for early detection of low-performing students

INTERNATIONAL JOURNAL OF INFORMATION AND learning TECHNOLOGY 2022年第2期39卷 122-132页

作者： Khor, Ean Teng Nanyang Technol Univ Natl Inst Educ Singapore Singapore

Purpose The purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance. Design/methodology/approach For the first step, the author performed exploratory data analysis to analyze the dataset. The process was then followed by data pre-processing and feature engineering (Step 2). Next, the author conducted data modelling and prediction (Step 3). Finally, the performance of the developed models was evaluated (Step 4). Findings The paper found that the decision trees algorithm outperformed other machine earning algorithms. The study also confirms the significant effect of the academic background and virtual learning environment (VLE) interactions feature categories to academic performance. The accuracy enhancement is 17.66% for decision trees classifier, 3.49% for logistic regression classifier and 4.89% for neural networks classifier. Based on the results of CorrelationAttributeEval technique with the use of a ranker search method, the author found that the assessment_score and sum_click features are more important among academic background and VLE interactions feature categories for the classification analysis in predicting students' academic performance. Originality/value The work meets the originality requirement.

关键词： learning analytics Classification data mining machine learning algorithms learning process Academic performance MOOCs

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Performance Analysis of machine learning algorithms on Diabetes Dataset using Big Data Analytics

Performance Analysis of Machine Learning Algorithms on Diabe...

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International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS)

作者： Kumar, P. Suresh Pranavi, S. Kakatiya Inst Technol & Sci Warangal Andhra Pradesh India Kakatiya Univ Warangal Andhra Pradesh India

ISBN: (纸本)9781538605141

New Technologies such as Big Data and Cloud is playing a vital role in providing solutions to Healthcare problems. Now-a-days healthcare data is growing very drastically day-by-day and it requires an efficient, effective and timely solution to reduce the mortality rate. One of the most critical chronic healthcare problems is diabetes. In Long run, this problem may leads to damage eyes, heart, kidneys and nerves of diabetes patient if improper medication is done which also leads to death. The aim of this paper is to analyze and compare different machine learning algorithms to identify a best predicting algorithm based on various metrics such as accuracy, kappa, precision, recall, sensitivity and specificity. A comprehensive study is done on diabetes dataset with Random Forest (RF), SVM, k-NN, CART and LDA algorithms. The achieved results shows that RF is giving more accurate predictions with compared to other algorithms.

关键词： Big data predictive analytics Hive and R Diabetes machine learning algorithms

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Predictive Modeling for ***. Nursing Placement Using machine learning algorithms 3

Predictive Modeling for ***. Nursing Placement Using Machine...

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International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Chavan, Ranjana Dumbre, Dipali Devi, Seeta Symbiosis Int Deemed Univ SIU Pune Symbiosis Coll Nursing SCON Pune Maharashtra India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The demand for well-trained nursing professionals is ever-increasing, necessitating efficient placement processes post-graduation. This study explores the application of machine learning (ML) algorithms to predict ***. Nursing placement outcomes, aiming to optimize workforce management and graduate placement efficiency. Traditional placement methods often suffer from subjectivity and inefficiency. Leveraging historical placement data and relevant features, ML algorithms discern patterns to forecast individual placement likelihood accurately. Various ML techniques including logistic regression, decision trees, support vector machines, and ensemble methods are employed and evaluated for their predictive efficacy. The CN2 rule inducer model emerges as the top-performing model, demonstrating superior discriminatory ability and accuracy. These findings offer significant implications for nursing education institutions and healthcare organizations, facilitating better alignment of education with workforce demands. However, the study also emphasizes the importance of balancing model performance with interpretability. Further research and implementation efforts are warranted to refine predictive modeling approaches and seamlessly integrate them into nursing education and workforce management practices, ultimately contributing to the advancement of nursing practice and patient care delivery.

关键词： Placement Nursing students Prediction Artificial Intelligence machine learning algorithms

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Assessment of machine learning algorithms for Near-Sensor Computing under Radiation Soft Errors 27

Assessment of Machine Learning Algorithms for Near-Sensor Co...

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27th IEEE International Conference on Electronics, Circuits and Systems (IEEE ICECS)

作者： Trindade, M. Garay Garibotti, R. Ost, L. Letiche, M. Beaucour, J. Bastos, R. Possamai Univ Grenoble Alpes CNRS Grenoble INP TIMAInst Engn F-38000 Grenoble France Pontificia Univ Catolica Rio Grande do Sul Sch Technol BR-90619900 Porto Alegre RS Brazil Loughborough Univ Loughborough LE11 3TU Leics England Inst Laue Langevin F-38042 Grenoble France

ISBN: (纸本)9781728160443

machine learning (ML) algorithms have been regaining momentum thanks to their ability to analyze substantial and complex data, supporting artificial intelligence decisions in cloud computing but also in near-sensor computing in end-point devices. Both cloud and near-sensor computing are liable to radiation-induced soft errors, especially in automotive and aerospace safety-critical applications. In this regard, this paper contributes by comparing the accuracy of two prominent machine learning algorithms running on a low-power processor upset by radiation-induced soft errors. Both ML algorithms have been assessed with the help of a fault injection-based method able to natively emulate soft errors directly in a development board. In addition, neutron radiation test results suggest the most critical situations in which mitigation solutions should address.

关键词： Radiation-induced soft errors machine learning algorithms low-power processors

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Classification of Terahertz Reflection Spectra using machine learning algorithms 47

Classification of Terahertz Reflection Spectra using Machine...

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47th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)

作者： Kristensen, Mathias Hedegaard Cielecki, Pawel Piotr Skovsen, Esben Aalborg Univ Dept Mat & Prod Sect Phys & Mech Skjernvej 4A DK-9220 Aalborg Denmark

ISBN: (纸本)9781728194271

A successful implementation of terahertz screening systems requires a development of reliable and efficient identification algorithms. Dimensionality reduction methods are applied to lower the dimensionality of multivariate data while retaining most of the information. Here, we focus on Principal component analysis (PCA) and linear discriminant analysis (LDA) for analysis and classification of terahertz reflection spectra. The complete data set consists of more than 5000 reflection spectra of six active materials. We found that LDA is better for grouping the spectra resulting in highly accurate classification of terahertz spectra. Furthermore, we compare the classification of referenced and non-referenced reflection spectra eligible for real-world applications of terahertz spectroscopy.

关键词： Dimensionality reduction Spectroscopy machine learning algorithms Reflection Linear discriminant analysis Classification algorithms Reliability

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Identifying Students At-Risk with an Ensemble of machine learning algorithms 3

Identifying Students At-Risk with an Ensemble of Machine Lea...

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3rd Annual Conference on Information-Communications-Technology-and-Society (ICTAS)

作者： Soobramoney, Ranjin Singh, Alveen Durban Univ Technol Dept Informat Technol Durban South Africa Durban Univ Technol Dept Informat Technol ICT & Soc Res Grp Durban South Africa

ISBN: (纸本)9781538673652

Accurately predicting the academic performance of students is an important process for the long term sustainability of higher education institutions (HEIs). A reliable and timely identification of students at-risk will enable HEIs to take proactive measures to assist these students. Owing to large student numbers with varying backgrounds, identifying students at-risk is often primarily a manual process involving an analysis of students' prior academic results and little else. While prior academic results do play an important role in indentifying students at-risk, there could be several other factors that may be overlooked. The demographic and financial circumstances of the student for instance could play a key role in more accurately identifying students at-risk. It may be impractical to consider all of these factors when manually trying to identify students at-risk. We propose that classification models using an ensemble of machine learning algorithms (MLAs) have promising prospects to more effectively predict students at-risk by including several factors that may often not even be practical with manual methods.

关键词： Classification Prediction machine learning algorithms At-risk Feature Selection Ensemble methods

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Comparison of Statistical Signal Processing and machine learning algorithms for Spectrum Sensing

Comparison of Statistical Signal Processing and Machine Lear...

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IEEE Global Conference on Communications (GLOBECOM) / Workshop on Wireless Networking and Control for UAV

作者： Tiwari, Ayush Chenji, Harsha Devabhaktuni, Vijay Univ Toledo Dept Elect Engn & Comp Sci 2801 W Bancroft St Toledo OH 43606 USA Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA

ISBN: (纸本)9781538647271

In a cognitive radio network, what is the trade off between statistical signal processing and machine learning algorithms that perform the same task? When should a system use the former and when should it use the latter? In this paper we present an empirical comparison study of different techniques for two tasks: detecting multiple transmitters in the same time-frequency domain, and automatic modulation classification. We develop and improve upon an unsupervised learning technique for the former, based on the log-Rayleigh distribution. Results are based on data generated from GNU Radio Companion, and implementations of these algorithms in software. They show that there is a tradeoff between accuracy and computation/implementation complexity signal processing has a several orders of magnitude advantage over machine learning, but slightly lower accuracy. Thus, there is a need for an overarching framework that can meld machine learning and statistical signal processing.

关键词： Signal processing algorithms machine learning algorithms Sensors Mixture models Transmitters Clustering algorithms Signal processing

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Predictive Analytics for Inventory Management in E-commerce Using machine learning algorithms 3

Predictive Analytics for Inventory Management in E-commerce ...

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International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Manoharan, Geetha Sharma, Anupama Vani, V. Divya Raj, Vijilius Helena Jain, Rishabh Nijhawan, Ginni SR Univ Sch Business Warangal Telangana India BK BirlaInst Engn & Technol Dept Math Pilani 333031 Rajasthan India Inst Aeronaut Engn Hyderabad India NewHorizon Coll Engn Dept Appl Sci Bangalore Karnataka India Constituentof Symbiosis Int Univ Symbiosis Ctr Management Studies Plot 47 &amp 48Block AInd AreaSect 62 Noida Uttar Pradesh 20130 India Lovely Profess Univ Phagwara India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

A description of the abstract for the research paper titled "Predictive Analytics for Inventory Management in E-commerce Using machine learning algorithms" is as follows: In this research, a novel strategy for implementing force operations in e-commerce is presented. This strategy involves the utilization of predictive analytics and machine literacy algorithms. When it comes to the ever-changing landscape of online retail, efficient force operation is necessary to satisfy customer demand while simultaneously minimizing the costs that are connected with overstocking and stockouts. To predict future demand for specific products, prophetic analytics methods are utilized, which involve the utilization of literal deal data in addition to other relevant elements. To create accurate prophetic models that are capable of locating complicated patterns and trends in the data, machine literacy methods such as arbitrary timbers, support vector machines, and neural networks are utilized. Through the incorporation of these prophetic models into the process of force operation, e-commerce businesses canoptimize force circumstances, expedite operations, and improve customer happiness. In addition to making a contribution to the expanding body of literature on data-driven approaches to force operation, this investigation demonstrates the potential for predictive analytics and machine literacy to be utilized in the process of tackling the specific issues that are associated with e-commerce.

关键词： Predictive Analytics Inventory Management E-commerce machine learning algorithms Inventory Forecasting Data-driven Decision Making Supply Chain Optimization

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A Comprehensive Review on Email Spam Classification using machine learning algorithms 35

A Comprehensive Review on Email Spam Classification using Ma...

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35th International Conference on Information Networking (ICOIN)

作者： Raza, Mansoor Jayasinghe, Nathali Dilshani Muslam, Muhana Magboul Ali Charles Sturt Univ Sch Comp & Math Study Ctr Melbourne Vic 3000 Australia Al Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Dept Informat Technol Riyadh 11432 Saudi Arabia

ISBN: (纸本)9781728191010

Email is the most used source of official communication method for business purposes. The usage of the email continuously increases despite of other methods of communications. Automated management of emails is important in the today's context as the volume of emails grows day by day. Out of the total emails, more than 55 percent is identified as spam. This shows that these spams consume email user time and resources generating no useful output. The spammers use developed and creative methods in order to fulfil their criminal activities using spam emails, Therefore, it is vital to understand different spam email classification techniques and their mechanism. This paper mainly focuses on the spam classification approached using machine learning algorithms. Furthermore, this study provides a comprehensive analysis and review of research done on different machine learning techniques and email features used in different machine learning approaches. Also provides future research directions and the challenges in the spam classification field that can be useful for future researchers.

关键词： Spam Detection Spam Classification Spam Filter E-mail Supervised learning machine learning algorithms Email Classification Spam Email Detection Email Categorization Email Feature Set Analysis Spam Detection Using machine learning algorithms

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Methodologies and Application of machine learning algorithms to Classify the Performance of High Performance Cluster Components 16

Methodologies and Application of Machine Learning Algorithms...

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16th IEEE International Conference on Cluster Computing (CLUSTER)

作者： Romero, Phil Idler, Craig Los Alamos Natl Lab High Performance Comp 1 Los Alamos NM 87545 USA Los Alamos Natl Lab High Performance Comp 5 Los Alamos NM USA

ISBN: (纸本)9781479955480

High Performance Computing Clusters are designed to host highly parallelized applications, often in excess of thousands of nodes allocated to a job. These jobs, especially those that require a high level of synchronous communication, can be greatly affected by a single poor, or even sub-standard performing component. These components, often referred to as a node, are typically comprised of CPUs, accelerator processors, memory, a communication bus, and so on. Consequently it is important to identify and eliminate these sub-standard performing nodes before a job is scheduled onto them. In this paper we will describe the process used to measure and the methodology used to quantify poor performing nodes or classify suspect performing nodes into groups, or clusters, that can be later used to identify future performance issues. This process is more involved than simply running a scientific calculation across all the nodes, finding one that was "slow", and labeling it as a bad node. At Los Alamos, this methodology has been used successfully to find problem nodes and has helped characterize the components of other clusters to aid in the proactive elimination of potential problems.

关键词： learning (artificial intelligence) parallel processing performance evaluation CPU accelerator processors high performance computing clusters parallelized applications poor performing nodes scientific calculation suspect performing nodes synchronous communication Bandwidth Clustering algorithms Graphics processing units Principal component analysis Standards Testing synchronous communication Clustering algorithms central processing units PARALLEL PROCESSING (COMPUTERS) Ganglia Bandwidth Performance Evaluation machine learning algorithms principal components analysis Graphics Processing Unit jobs machine APPLICATIONS

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