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Predicting Student Academic Performance using Support Vector machine and Random Forest 2020

Predicting Student Academic Performance using Support Vector...

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2020 3rd international conference on Education Technology Management

作者： Leena H. Alamri Ranim S. Almuslim Mona S. Alotibi Dana K. Alkadi Irfan Ullah Khan Nida Aslam Imam Abdulrahman Bin Faisal University Kingdom of Saudi Arabia

ISBN: (纸本)9781450388757

The use of machine learning and data mining in the educational field to predict student performance, known as educational data mining, which has always been an important study area. An early prediction of student performance may help the responsible entities to provide solutions to the students with low performance. Student performance in the final exam could be affected by many factors (e.g., previous assignment grades, social life, parents’ job, and absence frequency). This paper aims to predict student academics performance to enhance the performance of educational organizations to get better academic results of their students. In this paper, classification algorithms and techniques applied were Support Vector machines (SVM) and Random Forest (RF). Binary classification and regression techniques have been applied with both SVM and RF. In this work, we predict the final grade of mathematics course and Portuguese language course, the dataset consists of 369 and 649 records, respectively. The experimental results for both SVM and RF algorithms applied to both datasets showed that the accuracy in the case of binary classification achieves a superior accurate prediction reach to 93%, while in regression, the lowest RMSE is 1.13 in case of RF.

关键词： random forest machine learning support vector machine supervised learning student performance

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Methodology-Driven Characteristics of Scientific Research Collaboration Networks in the Field of Economic Management: mining and Analysis Based on Big data 3rd

Methodology-Driven Characteristics of Scientific Research Co...

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3rd international conference on Big data and Security, ICBDS 2021

作者： Xu, Hao Chen, Shihui Yi, Mengxue Feng, Ke Ding, Xi Liu, Yilin Chen, Yiyang Nanjing Institute of Technology Nanjing211167 China Nanjing University Nanjing210023 China University of St. Andrews St. AndrewsKY16 9AJ United Kingdom

ISBN: (纸本)9789811908514

Objective: Research takes the methodological knowledge in the full text of academic literature as a knowledge production element, and divides it into four categories: Theory &Method, data, Model, and Tool & Software. Identify the complex network characteristics of scientific research collaboration in economic management field driven by it, in order to achieve efficient promotion of knowledge production. Methods: Develop a crawler by python, and uses CNKI as data source to obtain the full text data of 5,564 papers (about 50,076,000 characters) from 2000 to 2019 in "Management World", then combines the TF-IDF, fusion rules and manual annotation to extract 6946 records of methodological knowledge data and its subsidiary information. The relationship were visualized by Gephi, and characteristics are analyzed. Results/Conclusions: The overall collaboration network and scientific research institutions driven by Theory &Method are the most complexity and multi-mode (complete and continuous development model are included), while it has a cohesive collaborative subnet. Methodological knowledge-driven scientific research collaboration networks have different characteristics: Theory & Method driven has the highest complexity, a long duration, and much more mature in economics and management field. The network formed by institutions has a low density, means a cross-institutional, large-scale collaboration model has not yet been formed, which may be restricted by geographical factors and research topics. Among the four types of methodological knowledge, the Theory &Method and data type drive the research collaboration of institutions more obviously. Limitations: The types and numbers of data sources in this study need to be expanded, and the extraction of specific methodological knowledge for the full text of academic literature needs to be further expanded by relying on machine learning and other methods. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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Toward a Deep Smart Waste Management System based on pattern recognition and Transfer learning

Toward a Deep Smart Waste Management System based on Pattern...

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international conference on Advanced Communication Technologies and Networking (CommNet)

作者： Aissam Jadli Mustapha Hain SISM Team I2SI2E Laboratory ENSAM Casablanca University Hassan II Casablanca Morocco

ISBN: (数字)9781728187044

ISBN: (纸本)9781728187051

Smart cities essentially combine the use of Information and communications technology (ICT) to provide services for better living conditions inside. The recent developments in the Internet of Things (IoT) opened vast opportunities for researchers and developers to implement various systems and applications in the field of Smart Cities and Intelligent Transportation Systems (ITS). It is a diverse topic of discussion with several application areas such as smart traffic management, smart street lights or gas and water leak detection. Among these applications, Efficient Waste Collection (WC) is considered a fundamental service for helping maintain a healthier environment for the citizens while reducing operating costs. This paper proposes a new architecture for a smart wastes management system based on artificial intelligence techniques and focuses on the combined use of the Internet of Things (IoT) and surveillance systems as an assistive technology for high Quality of Service (QoS) in waste collection. By integrating deep learning techniques in this sector, important cost reductions can be made while maintaining optimal performance.

关键词： machine learning Training Feature extraction Waste management Support vector machines Urban areas Task analysis

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Survey of Heart Disease Prediction and Identification using machine learning Approaches

Survey of Heart Disease Prediction and Identification using ...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： Ramya G. Franklin B. Muthukumar School of Computing Sathyabama Institute of Science and Technology Chennai India Mohammad Sathak College Chennai India

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Heart disease is highlighted as the major one among the various death factors. Detecting heart disease tends to be a bit complex due to insufficient knowledge and experience of the medical practitioners concerning warning signs of heart failure. There exist innumerable data volumes in the healthcare sector. By adopting the best appropriate data mining techniques, early detection of heart-related diseases can be achieved and also preventing it from occurring. Both the machine learning (ML) and data mining (DM) techniques prove to be effective and significant in the domain of the medical industry. The objective of the current research work is to examine various risk parameters highlighted in the investigation of Heart disease and also it targets to discover multiple techniques for the identification and prediction of heart disease along with evaluating the drawbacks of the existing work. The article utilizes the DM techniques to summarize existing researches concerning heart disease prediction, examining a combination of DM techniques to reveal the best suitable and effective technique. The CNN, LSTM is being proposed for heart disease identification and prediction which yields in improvised output than the rest of the prevailing techniques. Various levels involved in the proposed approach are a collection of dataset, training, and testing, collection of user symptoms, securely forwarding the data by utilizing AES, and eventually, result is being generated in PDF format. Comparative performance in datasets concerning medicine is adopted in predicting heart disease techniques in comparison to the rest of other ML approaches.

关键词： Heart Diseases Prediction algorithms Classification algorithms Support vector machines Mathematical model Clustering algorithms

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international conference on data Science and Business Analytics (ICDSBA)

作者： Tieqiang Wang Peng Lu Xin Cao Xiaodong Yang Wei Wang Hao Lv Chunxian Feng Chao Tian Pushi Wang State Grid Hebi Electric Power Company Shijiazhuang Hebei Province China North China Electric Power University Baoding Hebei Province China

ISBN: (数字)9781728146447

ISBN: (纸本)9781728146454

In order to utilize the large amount of historical data stored in the power system efficiently, and to provide the data support for the static security analysis, feature pattern extraction and situational awareness of the power grid, the idea of similarity matching of power grid running sections is proposed in this paper. The deep learning idea and model are introduced, and a similarity matching method for grid running section based on stacked autoencoder (SAE) is proposed. The algorithm process is divided into two stages which are layer-by-layer unsupervised pre-training and supervised fine-tuning. The effectiveness of the proposed method is validated on the IEEE 10-unit 39-bus system. The results show that the proposed method has high matching accuracy. In addition, the method greatly shorten the simulation time of training samples, with better performance and potential application value.

关键词： Feature extraction Training Power grids Deep learning data mining Power system stability Stacking

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Cyber Security Fraud Detection Using machine learning Approach

Cyber Security Fraud Detection Using Machine Learning Approa...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Ramakrishnan Raman Mohit Tiwari Dharam Buddhi Snehal Trivedi Shivaji Bothe R. Ponnusamy Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Paschim Vihar Delhi Uttaranchal Institute of Technology Uttaranchal University Department of Mech Parul University Department of IT & Business Analytics Sri Balaji University Pune (SBUP) Department of Computer Science & Engineering Chennai Institute of Technology

It is notoriously harder to identify fraudsters for its fluid nature in terms of recognizable trends. Latest technological breakthroughs were used by scammers. Individuals overcome the protection, incurring millions of dollars of lost revenue. data mining approaches could be employed to crunch numbers and discover out-of-the-ordinary behaviors, allowing it to be pursued to its cause, in this case a fraudulent payment, activities. In this study, we really would like to evaluate and contrast various popular ml algorithms, namely k-nearest peer (KNN), randomized forest (RF), and support vector (SVM), along with popular deep neural networks, including auto - encoder, Classifiers, Multiple solutions, and multi - layer perceptron (DBN). The European Union (EU), Canada, and Netherlands information will be used. The measures utilized for evaluations are the Region That under Fitted Model (AUC), the Matthew R Squared (MCC), and the cost of failing.

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Improving Design Quality of Software Using machine learning Techniques

Improving Design Quality of Software Using Machine Learning ...

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international conference on Advanced Computing and Communication Systems (ICACCS)

作者： C. Lakshmi Prabha N. Shivakumar Computer Science and Engineering Thiagarajar College of Engineering Madurai India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151984

Design patterns commonly pre-owned in Object-Software oriented Development. It provides Information helping to overcome recurrent design challenges (problems). recognition of patterns can be described as identification and representation of information based on knowledge obtained from statistical data derived from patterns. Detection of design patterns provides the advantages of an extensive investigation of object-oriented software applications. When using various pattern detection techniques depending on static research issues to identify patterns it detects similar pattern structures. For this, we seek to identify patterns in design of Software by using classification-based methods and information metrics. The JHotDraw, QuickUML, and Junit of object-oriented software are used to detect software problems in the patterns of designing a Software. The datasets are manual creations which are designed utilizing software criteria for classifying learners. It is carried out in two steps, namely the development of a statistics-oriented dataset and the identification of trends in software trends in software design. Tests are carried out by Usage of 3 open source computer programs to test the process, then the tests are examined.

关键词： Object oriented modeling Unified modeling language machine learning Measurement data mining Software design

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Study on Text mining Algorithm for Ultrasound Examination of Chronic Liver Diseases Based on Spectral Clustering

Study on Text Mining Algorithm for Ultrasound Examination of...

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2018 3rd international conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2018)

作者： Bingguo Chang Xiaofei Chen School of Information Science and Engineering Hunan University

Ultrasonography is an important examination for the diagnosis of chronic liver disease. The doctor gives the liver indicators and suggests the patient's condition according to the description of ultrasound report. With the rapid increase in the amount of data of ultrasound report, the workload of professional physician to manually distinguish ultrasound results significantly increases. In this paper, we use the spectral clustering method to cluster analysis of the description of the ultrasound report, and automatically generate the ultrasonic diagnostic diagnosis by machine learning. 110 groups ultrasound examination report of chronic liver disease were selected as test samples in this experiment, and the results were validated by spectral clustering and compared with k-means clustering algorithm. The results show that the accuracy of spectral clustering is 92.73%, which is higher than that of k-means clustering algorithm, which provides a powerful ultrasound-assisted diagnosis for patients with chronic liver disease.

关键词： component Text mining Ultrasonic examination report Spectral clustering learning algorithm Chronic liver disease

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Pre Cardiac Failure Detection using different supervised machine learning Methods with CNN

Pre Cardiac Failure Detection using different supervised Mac...

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international Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Amit Gupta Meeravali Shaikh G. Jithender Reddy Arun Singh Chouhan Department of Cyber Security School of Engineering Malla Reddy University Telangana Department of CSE School of Engineering Malla Reddy University Telangana GITAM University Visakhapatnam

For aspect, a convolution Neural Network (CNN) for cardiac arrest would help establish a better output. Whilst a cardiac arrest occurs, the limited interventions available to shop-affected person’s lives are related to badly affected outcomes. Therefore, the best way to enhance affected person results and lower related healthcare prices might be to prevent cardiac arrest. This remark highlights the significance of prediction models that continuously perceive excessive-risk individuals and assist healthcare providers in providing centered care to the appropriate patient at the appropriate time for the early identification of cardiac arrest patients who may need immediate medical attention. Moreover, the significant aspect lies in the proper symmetry formation or can, say, the better scenario to compute or make a clear recognition of the image having a clear resolution of the image. Starting with a small model to determine things as simple as CNN (Convolution Neural Network). In this proposed paper, we are used in seven different supervised machine learning method for simulation of ECG data set results in different time interval with different duration and amplitude. In the CNN with a partially connected convolution layer, the image has a single neuron connected to a small layer region. The CNN (convolution Neural Network) image classification takes the input, processes it and classification is done on the dimensions organized in three dimensions width, height, and depth.

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Research on Algorithm of Online Merchant Loan P2P Platform Based on data mining 23

Research on Algorithm of Online Merchant Loan P2P Platform B...

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Proceedings of the 3rd international conference on Electronic Information Technology and Smart Agriculture

作者： Zihao Zhu Department of International Politics University of International Relations China

ISBN: (纸本)9798400716775

With the rapid development of Internet finance, P2P (Peer-to-Peer) lending platforms have become a part of the financial market that cannot be ignored. These platforms provide an innovative way to lend money by connecting borrowers and investors. In order to improve the service quality and operational efficiency of the platform, it has become particularly important to use advanced data mining technology to analyze loan applications. This study aims to explore how to use data mining algorithms to improve the credit assessment and risk management of online merchant loan P2P platforms. We conduct experiments using a real data set containing 100,000 loan application records, which is provided by a well-known P2P lending platform and has been made public on the Kaggle platform. Through detailed preprocessing and feature engineering of the dataset, we selected a variety of data mining models for training and evaluation. Experimental results show that some models can accurately predict the repayment status of loans, providing valuable risk assessment information for P2P platforms. This study not only provides scientific methodological support for the risk management of P2P lending platforms, but also lays a solid foundation for future research in this field.

关键词： P2P lending credit assessment data mining feature engineering machine learning model evaluation risk management

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