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Developing active learning experiences for adaptive personalised elearning

3rd International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH 2004

作者： Dagger, Declan Wade, Vincent P. Conlan, Owen Knowledge and Data Engineering Group Department of Computer Science Trinity College Dublin Ireland

ISBN: (纸本)3540228950

Developing adaptive, rich-media, eLearning courses tends to be a complex, highly-expensive and time-consuming task. A typical adaptive eLearning course will involve a multi-skilled development team of technologists, instructional developers, subject matter experts and integrators. Even where the adaptive course attempts to reuse existing digital resources, considerable effort is still required in the integration of the adaptive techniques and curriculum. This paper tackles the fundamental challenges of extending adaptivity across not only content (based on prior knowledge, goals, learning styles, connectivity etc.) but also across adaptive pedagogic approaches, communication tools and a range of e-activity types which are required for effective, deeper learning. This paper identifies key activities and requirements for adaptive course construction and presents the design of a tool to allow the rapid construction of such courses. The paper outlines the usage of this tool in the form of a case study and presents its research findings. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Teaching

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Evaluation of Modified VGG16 Learning Model for Classifying Skin Cancer Lesions

Evaluation of Modified VGG16 Learning Model for Classifying ...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Naser Alaubaidy, Mateen Ismail Abdullah, Abdulqadir Mousa Alwan, Fatimah College of Engineering and Computer Science Lebanese French University Department of Information Technology Kurdistan Region Erbil Iraq College of Engineering Knowledge University Department of Computer Engineering Kurdistan Region Erbil Iraq College of Engineering and Computer Science Bayan University Department of Computer Science Kurdistan Region Erbil Iraq

ISBN: (纸本)9798350300826

One of the most serious types of cancer that is prevalent around the world is skin cancer. It is widely known that skin cancer characterized by uncontrolled cell and tissue growth, presenting a challenge in detecting or diagnosis skin cancer in early stage. Accurate segmentation of skin lesions in dermatoscopic images holds great importance in diagnosis and assessment of skin cancer. Thus, Fast, precise, and real-time algorithms are essential in effectively detecting skin cancer to aid physicians in their treatment decision-making process. In this paper, we evaluated the performance of the modified VGG16 with using three common convolutional neural networks(CNN) in detecting the skin cancer with dermoscopic skin lesions images. In this study, the Kaggle dataset is used to classify skin cancer images. To classify skin lesions, the model modified VGG16 is compared to the most common models such as VGG16, mobileNetV2 and Inception ResNet v2. All models are trained on the same dataset and their performance is evaluated using metrics such as: accuracy, precision, F1-score, and recall. Finally, The comparative analysis revealed a promising performance of the modified VGG16 model in detecting multiclass skin cancer, which highlights its promising potential in classifying. © 2023 IEEE.

关键词： Diagnosis

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Supply Chain Strategies Supported by Blockchain Technology: Open Problems and Challenges

Supply Chain Strategies Supported by Blockchain Technology: ...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Rana, Sumit Kumar Rana, Arun Kumar Naz, Sardar M N Islam Singh, Ashutosh Kumar Bhushan, Bharat Panipat Institute of Engineering and Technology Department of Computer Science and Engineering Haryana Panipat India Galgotia College of Engineering Knowledge Park I Department of Computer Science Engineering Uttar Pradesh Greater Noida201310 India ISILC Victoria University Melbourne Australia Kurukshetra University NIT Department of Computer Applications Kurukshetra India Sharda Universi Knowledge Park III Department of Computer Science Engineering Greater Noida201310 India

ISBN: (纸本)9798350396485

The most prevalent issue in the supply chain ecosystem is introducing transparency to the system. Another issue is fostering trust among various stakeholders to support successful process execution. Depending on the domain and the stakeholders, the supply chain process may change. Thus, solving the issue is challenging. Blockchain technology, sometimes referred to as distributed ledger technology, is beneficial in many ways in a variety of industries, including finance and healthcare. Blockchain based model can resolve the issues of traditional supply chain ecosystem. It creates a decentralized and transparent system where every decision is taken with the help of consensus of the associated stakeholders. This supports an immutable ledger so unauthorized modification is not supported. In this article, we address the issues that the supply chain ecosystem faces as well as the benefits of blockchain technology in resolving these issues. As well as some of the remaining challenges and opportunities, other aspects of blockchain technology are explored that will enable it flourish in future supply chains. © 2023 IEEE.

关键词： Supply chains

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A data science approach to flagging non-retention in engineering enrollment data

A data science approach to flagging non-retention in enginee...

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2020 ASEE Virtual Annual Conference, ASEE 2020

作者： Boujelbene, Mariem Damak, Khalil Sener, Asuman Cagla Acun Hieb, Jeffrey Lloyd Bego, Campbell R. Ralston, Patricia A. Nasraoui, Olfa Knowledge Discovery and Web Mining Lab Department of Computer Science and Computer Engineering University of Louisville United States Department of Engineering Fundamentals University of Louisville United States

This research paper discusses a new, data-driven metric for measuring retention. First and second year retention and retention rates are now well established as metrics in the engineering education research landscape, with many research studies exploring the impact of individual performance, noncognitive, and preparation characteristics on retention in engineering. Researchers at the University of Louisville, a large Research Institution in the Midwest, have compiled survey results and enrollment data for students in the engineering college since 2010, with the intention of conducting retrospective studies of engineering retention using this data. Using degree earned in six years or less to label students as retained makes over half the dataset unusable. First and second year retention are options, but these can have both false positives and false negatives. Using a data science pipeline, we analyzed the number of consecutive non-enrolled terms, referred to as enrollment gaps, and found that the best short-term criteria is three consecutive semesters not enrolled in engineering. With this criterion, we can reliably label a given student as not-retained. The proposed retention threshold approach has the following advantages: It does not rely on the requirement of earning a degree in engineering and could be applied across a variety of fields of study, it is not based on enrollment at a fixed point in time, and it can be used as the data set continues to grow. Most importantly, while other common heuristics use grades, success in certain consecutive courses, or even demographics;our method only uses enrollment (and hence enrollment gap) data. This is a significant advantage given that the enrollment data is always available;whereas other commonly used feature heuristics for retention determination are not always available or may only apply to subsets of students. © American Society for engineering Education 2020.

关键词： Students

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Clustering moving objects in spatial networks

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12th International Conference on Database Systems for Advanced Applications

作者： Chen, Jidong Lai, Caifeng Meng, Xiaofeng Xu, Jianliang Hu, Haibo School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE Department of Computer Science Hong Kong Baptist University

ISBN: (纸本)9783540717027

Advances in wireless networks and positioning technologies (e.g., CPS) have enabled new data management applications that monitor moving objects. In such new applications, realtime data analysis such as clustering analysis is becoming one of the most important requirements. In this paper, we present the problem of clustering moving objects in spatial networks and propose a unified framework to address this problem. Due to the innate feature of continuously changing positions of moving objects, the clustering results dynamically change. By exploiting the unique features of road networks, our framework first introduces a notion of cluster block (CB) as the underlying clustering unit. We then divide the clustering process into the continuous maintenance of CBs and periodical construction of clusters with different criteria based on CBs. The algorithms for efficiently maintaining and organizing the CBs to construct clusters are proposed. Extensive experimental results show that our clustering framework achieves high efficiency for clustering moving objects in real road networks.

关键词： spatial-temporal databases moving objects clustering spatial networks

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Distance-based feature selection from probabilistic data

Distance-based feature selection from probabilistic data

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14th International Conference on Web-Age Information Management, WAIM 2013

作者： Zhao, Tingting Pei, Bin Zhao, Suyun Chen, Hong Li, Cuiping Key Lab of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China China

ISBN: (纸本)9783642385612

Feature selection is a powerful tool of dimension reduction from datasets. In the last decade, more and more researchers have paid attentions on feature selection. Further, some researchers begin to focus on feature selection from probabilistic datasets. However, in the existing method of feature selection from probabilistic data, the distance hidden in probabilistic data is neglected. In this paper, we design a new distance measure to select informative feature from probabilistic databases, in which both the distance and randomness in the data are considered. And then, we propose a feature selection algorithm based on the new distance and develop two accelerative algorithms to boost the computation. Furthermore, we introduce a parameter into the distance to reduce the sensitivity to noise. Finally, the experimental results verify the effectiveness of our algorithms. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Random processes

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Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

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Journal of computer science & Technology 2018年第1期33卷 58-78页

作者： Jian Liu Yun-Peng Chai Xiao Qin Yao-Hong Liu Division of Computer Science and Engineering Louisiana State University Baton Rouge LA 70803 U.S.A. Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Shelby Center for Engineering Technology Department of Computer Science and Software Engineering Samuel Ginn College of Engineering Auburn University Auburn AL 368~9-53~7 U.S.A.

Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based （i.e., SSD-based） re^d cache cm, be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes （e.g., LRU and LFU） significantly shorten SSDs＇ lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution -- LOP-Cache to greatly improve tile write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular （LOP） data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its pertbrmance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs＇ lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.

关键词： data deduplication solid state drive （SSD） flash cache endurance

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Few-Shot Partial Multi-View Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 11824-11841页

作者： Zhou, Yuan Guo, Yanrong Hao, Shijie Hong, Richang Luo, Jiebo Ministry of Education Hefei University of Technology The Key Laboratory of Knowledge Engineering with Big Data Hefei230009 China Hefei University of Technology The School of Computer Science and Information Engineering Hefei230009 China University of Rochester The Department of Computer Science RochesterNY14627 United States

It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views;(ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness. The codes will be released at https://***/zhouyuan888888/UGDA. © 1979-2012 IEEE.

关键词： Neural networks

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Impacts of oxytactic microorganisms and viscous dissipation in Carreau nanoliquid featuring ferromagnetic nanoparticles

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Partial Differential Equations in Applied Mathematics 2025年 13卷

作者： Tabrez, Muhammad Hussain, I. Khan, W.A. Khan, S.U. Waqas, M. Rashid, Rzgar Farooq Anwar, M.I. Ammar, Mohamed Ben Zamri, Nurnadiah Abiyev, Ruzimurod Garalleh, Hakim AL Department of Mathematics Mohi-ud-Din Islamic University Nerian Sharif Azad Jammu and Kashmir 12010 Pakistan Department of Mathematics Namal University Mianwali 42250 Pakistan NUTECH School of Applied Sciences and Humanities National University of Technology Islamabad 44000 Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut 22200 Malaysia Department of Medical Laboratory Science College of Science Knowledge University Erbil 44001 Iraq Department of Medical Laboratory Technique College of Medical Technology AL-Kitab University Kirkuk Iraq Department of Mathematics School of Sciences and Humanities Nazarbayev University Astana Kazakhstan Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Mathematics and its Teaching Methods in Primary Education Tashkent State Pedagogical University Tashkent Uzbekistan Department of Mathematical Science College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia

Owing to rapid progress in field of nanotechnology, various mathematical model has been proposed regarding the flow of nanofluids. Based on boosted thermal properties, multidisciplinary applications of such materials has been claimed in various engineering and industrial processes. In current work, a theoretical analysis is performed for bioconvective flow of Carreau fluid with applications of magnetic dipole. The significance of magnetic dipole are presented with interaction of ferrofluid. The applications of viscous dissipation have also been entertained. The flow is subject to applications of stretched surface. Similarity variables are used for making transformation to obtain set of nonlinear ODEs from set of PDEs. The numerical computation is also made by using of well-known numerical method termed as bvp4c. Influences of various non dimensional fluid parameters like Brownian motion, viscous dissipation, Curie temperature, Weissenberg number, motile difference parameter and bioconvection Paclet number are examined and expressed graphically. It is perceived that ferrofluid's concentration declines when Brownian diffusive variable is augmented while opposite characteristics are reported for Schmidt number. Current results conveying applications in the biofuels, better heat transport medium, fertilizers, catalysts, electronics, paints, semiconductors, speakers, drug delivery, fertilizers etc. © 2024 The Author(s)

关键词： Bioconvection Carreau fluid Ferromagnetic flow Magnetic dipole Viscous dissipation

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A Novel system for Tracking and Screening COVID Patients

A Novel system for Tracking and Screening COVID Patients

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2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Sasikala Devi, S. Arulkumar, V. Kumar, Saroj Priyanka, D. PPG College of Arts and Science College Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu India Chandigarh University Computer Science and Engineering Gharuan India Institute of IT Saveetha School of Engineering Department of Knowledge Engineering Chennai India

ISBN: (纸本)9781665495158

The wandering of older persons with Covid is one of the several behavioral difficulties that they experience, and it is the source of the most anxiety for their caregivers. Using a novel mobile phone-based safety assistance system, we have been able to relay information about a wandering individual's whereabouts to others who are near that person. The wearable sensor is made up of a GSM module, an Arduino UNO, and a GPS module. It is possible to determine the position of the wandering individual after they have beyond a particular meter. When the old person leaves the area, the GSM transmits the position of the wandering elderly person, which is determined with the aid of GPS. The server computer sends an SMS to the caregiver to notify him or her of the situation. The caregiver may keep track of the wandering person's whereabouts on a map by connecting to the Internet. © 2022 IEEE.

关键词： Global system for mobile communications

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