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共 1096 条记录，以下是1081-1090 订阅

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Using Ensemble Decision Tree Model to Predict Student Dropout in Computing science

Using Ensemble Decision Tree Model to Predict Student Dropou...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Mohammed Naseem Kaylash Chaudhary Bibhya Sharma Aman Goel Lal School of Computing Information and Mathematical Sciences The University of the South Pacific Suva Fiji

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

science, Technology, engineering and Mathematics (STEM) professionals play a key role in the development of an economy. STEM workers are critical thinkers as they contribute immensely by driving innovations. There is a high demand for professionals in the STEM fields but there is also a shortage of human resource in these areas. One way to reduce this problem is by identifying students who are at-risk of dropping out and then intervening with focused strategies that will ensure that these students remain in same the programme till graduation. Therefore, this research aims to use a data mining classification technique to identify students who are at-risk of dropping out from their Computing science (CS) degree programmes. The Random Forest (RF) decision tree algorithm is used to learn patterns from historical data about first-year undergraduate CS students who are enrolled in a tertiary institute in the South pacific. A number of factors are used which comprise of students demographic information, previous education background, financial information as well as data about students' academic interaction. Feature selection is performed to determine which factors have greater influence in students' decision in dropping out. Cross-validation techniques are used to ensure that the models are not over-fitted. Two models were built using a 5fold and 10-fold cross-validation and the results were compared using several measures of model performance. The results show that the factors corresponding to students' academic performance in a first-year programming course had the greatest impact student attrition in CS.

关键词： Predictive models Classification algorithms data mining Decision trees Education Prediction algorithms data models

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Sliding Window-based Regularly Frequent Patterns Mining Over Sensor data Streams

Sliding Window-based Regularly Frequent Patterns Mining Over...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Md Mamunur Rashid Joarder Kamruzzaman Saleh Wasimi School of Engineering and Technolog CQ University Melbourne Australia School of Science Engineering and Technology Federation University Churchill Australia

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

WSNs generate a large amount of data in the form of data stream; and mining these streams with well used support metric-based sensor association rule mechanism can result in extracting interesting knowledge. Support metric-based sensor association use occurrence frequency of pattern as criteria, but the occurrence frequency of a pattern may not be an appropriate criterion for finding significant patterns. However, temporal regularity in occurrence behavior can be considered as another important measure for assessing the importance of patterns in WSNs. A frequent pattern that occurs after regular intervals in WSNs called as regularly frequent sensor patterns. Even though mining regularly frequent sensor patterns from sensor data stream is extremely required in real-time applications, no such algorithm has been proposed yet. Therefore, in this paper we propose a novel tree structure, called regular frequent sensor pattern stream tree (RFSPS-tree) and a new technique, called regularly frequent sensor pattern mining of data stream (RFSPMS), using sliding window-based regularly frequent sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an RFSPS-tree, our RFSPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering regularly frequent sensor patterns over sensor data stream.

关键词： data mining Wireless sensor networks databases Windows Real-time systems Australia Knowledge discovery

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Predicting Media Literacy Level of Secondary School Students in Fiji

Predicting Media Literacy Level of Secondary School Students...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Pritika Reddy Kaylash Chaudhary Bibhya Sharma Department of Computing Science and Information Systems Fiji National University Suva Fiji School of Computing Information and Mathematical Sciences The University of the South Pacific Suva Fiji

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

The digital revolution has set a platform for all the information and means of communication to be digitised, thus creating a digital media society. This explosion of digital media requires individuals to have a set of skills and knowledge to survive in this lifelong digital media society. In such a context, many countries around the world are now leveraging on Media Literacy to enhance the necessary skills of individuals and improve upon responsible media engagement. Therefore, predicting media literacy of students is essential so that suitable interventions can be put in place. This paper presents an analysis of Media Literacy status of Year 12 and Year 13 students at randomly selected secondary schools in Fiji, and it presents a set of predictive models using classification techniques. A quantitative study using a reliable survey was conducted to determine the Media Literacy of students using a Likert scale of 1-5. The analysis for this study was using the R software whereby classification algorithms such as Random Forest Classifiers, Decision Trees and Support Vector Machine Algorithm (SVM) were used to build the predictive models. These models will be used to derive appropriate interventions to improve Media Literacy of students. The baseline data from the study provide information on media literacy of Fijian students. The paper concludes with the important attributes that contribute towards an individual's competency on media literacy.

关键词： Media Education Support vector machines Decision trees Forestry Internet Classification algorithms

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Workload Analysis of Cloud Resources using Time Series and Machine Learning Prediction

Workload Analysis of Cloud Resources using Time Series and M...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Sivasankari Bhagavathiperumal Madhu Goyal Centre for Artifical Intelligence School of Computer Science University of Technology Sydney Australia

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

Most of the businesses now-a-days have started using cloud platforms to host their software applications. A Cloud platform is shared resource that provides various services like software as a service (SAAS), infrastructure as a service (IAAS) or anything as a service (XAAS) that is required to develop and deploy any business application. These cloud services are provided as virtual machines (VM) that can handle the end user's requirements. The cloud providers must ensure efficient resource handling mechanisms for different time intervals to avoid wastage of resources. Auto-scaling mechanisms would take care of using these resources appropriately along with providing an excellent quality of service. Auto-scaling supports the cloud service providers achieve the goal of supplying the required resources automatically. It use methods that will calculate the number of requests and decides the resources to release based on workload. The workload consists of some quantity of application program running on the machine and usually some number of users connected to and communicating with the computer's applications. The researchers have used various approaches to perform autoscaling which is a process to predict the workload that is required to handle the end users request and provide required resources as Virtual Machines (VM) disruptively. Along with providing uninterrupted service, the businesses also only pay for the service they use, thus increasing the popularity of Cloud computing. Based on the workload identified the resources are provisioned. The resource provisioning techniques is a model used for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, applications, and services) required resources are released. In this regard, the aim of this paper is to develop a framework to predict the workload using deep learning which would be able to handle provisioning of cloud resources dynamically. This fr

关键词： Integrated circuits

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A Framework for Sedimentological data Structure Extraction in Gold Mining Facies Delineation

A Framework for Sedimentological Data Structure Extraction i...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Phumlani Nhlanganiso Khoza Scilinx Research School of Computer Science & Applied Mathematics University of the Witwatersrand Johannesburg South Africa

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

Quartz-pebble conglomerate (reef) horizons of the Witwatersrand Basin are the most important sources of gold (Au) in South Africa. Concentration of gold to economic ore-grades in the Witwatersrand-type gold deposits has been linked to sedimentary processes having operated as principal driving forces. Therefore, in Witwatersrand-type gold deposits, sedimentological data are often utilized to make qualitative assessment of in-situ gold-grade. This paper presents a framework that prescribes a normative and principled analysis of sedimentological data in order to quantify in-situ gold-grades. Its significance is the provision of a scalable process for extracting correlational structures, regardless of the attribute value-types. The framework applies network analysis, and a bespoke clustering algorithm to support visual analysis of spatial correlation between sedimentological data samples. A key result is the preservation of the semantic interpretation of the resulting cluster assignments via a topologically invariant transformation. This is achieved by applying non-uniform weighting to the attributes to emphasize the influence of non-numeric attributes in cluster assignment. Additionally, sedimentological attributes that are strongly correlated with gold-grade are identified by adopting total variance reduction from decision tree regression as a guiding principle. Using this approach, and data collected from a mining company located in the Witwatersrand Basin, spatial correlation structures defined over sedimentological data attributes are reported. As a byproduct, observations that lack spatial correlation with the rest of the data are identified. These isolated observations can inform quality assurance and quality control departments about potential irregularities in the reporting of sedimentological data within exploration and mining companies.

关键词： Machine learning data mining Gold Uncertainty Mineralization Estimation

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Capitalizing on Learning Analytics Dashboard for Maximizing Student Outcomes

Capitalizing on Learning Analytics Dashboard for Maximizing ...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Gomathy Suganya Ramaswami Teo Susnjak Anuradha Mathrani School of Natural and Computational Studies Massey University Auckland New Zealand

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

With ongoing advancements in technology enabled learning, an opportunity has risen for educators to enhance student learning with use of Learning Analytics. Educational institutions are using Learning Analytics Dashboards (LAD) to provide students with timely and personalized feedback in visual format. LAD use advanced analytical capabilities that capitalize on online learner activities that are extracted from log files. They provide data-driven insights on current learning contexts and inform management on any learning intervention strategies that may be needed to support students in achieving their learning outcomes. Besides students, the perspective of instructors too is considered. Using easy-to-read graphical reporting formats (e.g., line graphs, scatter plot, bar charts, etc.), the LAD reveals a consolidated view of how online learning is taking place. In this manner, a snapshot depicting details of student learning patterns can enable instructors to monitor their students' learning strategies. At the same time, the LAD assists students too by providing them with a personalized environment to help them engage better with the learning outcomes. Therefore, LAD is increasingly used as a pedagogical approach to motivate students and help build their self-learning capacity. In this study, we propose to develop a real-time dashboard that pulls online student data from various sources including a learning management system (Moodle), University's library and from the student management system (SMS) that is used by the staff.

关键词： Tools data visualization data mining Bars Libraries History Learning systems

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Advanced data Balancing Method with SVM Decision Boundary and Bagging

Advanced Data Balancing Method with SVM Decision Boundary an...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Md. Yasir Arafat Sabera Hoque Shuxiang Xu Dewan Md. Farid United International University United City Dhaka Bangladesh School of Engineering and ICT University of Tasmania Hobart TAS Australia United International University United City Madani Avenue Badda Dhaka Bangladesh

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

In the field of data mining applications, the most challenging research work is to classify imbalanced data in supervised learning. From the beginning of the machine learning era, a huge number of researches has done by inventing a lot of successful data balancing methods, however, these are not yet fully compatible to learn from imbalanced data. The key reason is data is producing in every moment and randomly by various sources, like humans, machines, sensors, robots, and so on. To date, the existing machine learning algorithms mostly biased to the majority class instances and neglecting the minority class instances, therefore they have a crucial effect on the prediction capacity and exactness on the final results. To deal with imbalanced data the most popular classifiers are under-sampling and over-sampling, cost-sensitive learning and ensemble learning. In this paper, we have presented a novel data balancing method by employing clustering technique with SVM especially decision boundary instances for creating balanced data. The proposed approach selects the most informative majority class and minority class support vectors nearest to the decision boundary or hyperplane, and finally, combines them to establishes a novel method. We have experimented with the performance of our proposed technique with some popular single classifiers such as C4.5 Decision Tree (DT) and Naive Bayes (NB) classifier and ensemble classifiers such as Random Forest and AdaBoost on thirteen standard real-life imbalanced datasets. The experimental result shows apparently, that the proposed idea generates improved accuracy when classifying both the minority and majority class instances compared to other existing methods.

关键词： Support vector machines Forestry data mining Boosting Machine learning algorithms Decision trees

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Practices of Software Testing Techniques and Tools in Bangladesh Software Industry

Practices of Software Testing Techniques and Tools in Bangla...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Trina Saha Rajesh Palit Department of Electrical and Computer Engineering North South University Dhaka

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

For high-quality software development, Software Quality Assurance (SQA) is an important factor where testing is at the base of this factor. Software industry used to maintain software quality using manual testing process, which is being replaced by automated testing tools. Using the automation testing tools, testing has become easier and user-friendly to the software testers. Bangladesh software industry started its journey around three decades ago, and over the 2 decades, it has been flourished. Currently, there are approximately 500 software firms enlisted with the Bangladesh Association of Software and Information Services (BASIS). BASIS officially conducted a research on export income that comes from software and service products, and it turned out that Bangladesh exported software worth of $800 million (6,720 corer) last year. With the rapid growth of the economy, there has been an increased internal demand for quality software. Moreover, the government of Bangladesh is patronizing this sector so that Bangladesh can earn foreign exchange from the software export. As a result, growth of the software industry has become essential through utilizing the youth force and applying modern tools and techniques for developing high-quality software. However, in Bangladesh, use of modern and automated testing tools and techniques are far from satisfactory. In this paper, we surveyed 10 leading software firms to assess the current practices of software testing techniques and tools in the industry. We also wanted understand the present barriers and challenges that impeding the industry from producing better products. The responses from each firm have been presented in the beginning, and an overall comparative analysis on the usage of testing tools are given later in this paper. This analysis will assist the professionals in this industry to deeply understand the present situation in Bangladesh.

关键词： Tools Software testing Industries Software quality Software engineering

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Student perceptions of the Safety and Security Risk of On-campus Student Housing Facilities

Student perceptions of the Safety and Security Risk of On-ca...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Solomon Adisa Fredrick Simpeh Department of Construction Management and Quantity Surveying Cape Peninsula University of Technology Cape Town South Africa

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

In the recent times, studies conducted on the provision of student housing facility in universities in South Africa, indicate that security and safety are major issues across the universities. Hence, this paper aims to present the result of an assessment of the risk associated with the absence/lack of security and fire safety measures in the student housing facilities (SHFs) in two universities in South Africa from the students' perspective. This was done with the aim of addressing the security and safety challenges which has become problematic in most of the on-campus university SHFs in South Africa. The study adopted quantitative survey approach with questionnaire used as instrument for data collection from the students who are registered members of the SHFs from both universities. Analysis of the data was done through relevant descriptive and inferential statistics using a four-step analysis approach. The study found that the majority of SHFs at university A lack security and fire safety measures such as: CCTV, access control with functional smart-card, security alarm, smoke detectors, emergency help line, and water sprinkler system. Although the SHFs at university B had a better provision of security measures, it become evident that some important security and fire safety measures such as; access control with functional smart-card, CCTV, water sprinkler system and weapon detectors were found missing in some of the SHFs. The majority of respondent were of the opinion that absence of those security and fire safety measures pose a very high risk in the SHFs. There is no significant difference in the level of risk students attached to the absence/lack of security and fire safety measures in the SHFs across both universities. The recommendations can guide university health and safety officers (SHE unit), hostel and facility managers to make provision for adequate security and fire safety measures in order to mitigate the security and safety challenges in the on-campu

关键词： Fires Access control Instruments data collection Smoke detectors Weapons Detectors

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Impact of climate change on future water savings of rainwater tank in Adelaide, Australia

Impact of climate change on future water savings of rainwate...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Upendra Paudel Monzur A. Imteaz Department of Civil and Construction Engineering Swinburne University of Technology Melbourne Australia

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

Climate change has been identified as one of the significant factors for alteration of weather conditions around the world. The changes in rainfall amount and temperature might have severe impact on reliability of conventional water supply system as it is totally depends on ground and/or surface water resources. To reduce the dependency on traditional water supply system, government authorities are promoting sustainable alternative technologies like rainwater harvesting system in various countries including Australia. So, It is imperative to analyze the future annual water saving efficiency of rainwater tank on the perspective of climate change as it is also depends on the amount of precipitation and its distribution throughout the year. The outcomes of such study will help rainwater tank user and policy makers on selection of appropriate rainwater tank size to fulfill their future water demand in the context of climate change. This paper presents the impact of the climate change on future water savings for Adelaide airport region, South Australia. The study shows that in year 2040, all models projected (except CSIRO RCP 8.5) annual savings for different combination of roof areas, demand and tank sizes are 4%-42% less than the observed annual savings. Similarly for year 2065, only SDSM generated rainfall data shows reduction in the annual savings by 18%-23% than historical annual savings for lower roof areas irrespective of tank size. But for the same period and higher roof areas all the model shows 6%-27% reduction in annual savings. Likewise in the year 2090, all the model generated rainfall data produced 11%-53% less annual savings for lower roof areas but for the higher roof areas, SDSM and CSIRO higher concentration pathways models only have shown 12%-36% reduction in annual savings. Overall, in the context of the climate change, rainwater tank's efficiency for the future representative years was found lower than the historical average annual savings.

关键词： Meteorology Calibration data models Water resources Australia Hidden Markov models Predictive models

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