检索结果-内蒙古大学图书馆

Exploring Students Engagement Towards the Learning Management system (LMS) Using Learning Analytics

computer systems science & Engineering 2021年第4期37卷 73-87页

作者： Shahrul Nizam Ismail Suraya Hamid Muneer Ahmad A.Alaboudi Nz Jhanjhi Department of Information Systems Faculty of Computer Science&Information TechnologyUniversity MalayaKuala Lumpur50603Malaysia Department of Information systems College of Computer and Information SciencesShaqra UniversitySaudi Arabia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500Malaysia

Learning analytics is a rapidly evolving research discipline that uses theinsights generated from data analysis to support learners as well as optimize boththe learning process and environment. This paper studied students’ engagementlevel of the Learning Management system (LMS) via a learning analytics tool,student’s approach in managing their studies and possible learning analytic methods to analyze student data. Moreover, extensive systematic literature review(SLR) was employed for the selection, sorting and exclusion of articles fromdiverse renowned sources. The findings show that most of the engagement inLMS are driven by educators. Additionally, we have discussed the factors inLMS, causes of low engagement and ways of increasing engagement factorsvia the Learning Analytics approach. Nevertheless, apart from recognizing theLearning Analytics approach as being a successful method and technique for analyzing the LMS data, this research further highlighted the possibility of mergingthe learning analytics technique with the LMS engagement in every institution asbeing a direction for future research.

关键词： Learning analytics student engagement learning management system systematic literature review

来源：评论

学校读者我要写书评

暂无评论

A Design and Implementation of Water Heater Model Plant with Temperature Estimation Model for IOT Studies 7

A Design and Implementation of Water Heater Model Plant with...

引用

7th International Conference on Vocational Education and Electrical Engineering, ICVEE 2024

作者： Pradhana, Anak Agung Surya Funabiki, Nobuo Kotama, I Nyoman Darma Desnanjaya, I Gusti Made Ngurah Radhitya, Made Leo Mahajaya, Nyoman Sarasuartha Okayama University Graduate School of Environmental Life Natural Science and Technology Okayama Japan Okayama University Department of Electrical and Communication Engineering Okayama Japan Institute of Business and Technology Indonesia Department of Computer System Engineering Denpasar Indonesia Institute of Business and Technology Indonesia Department of Informatic Technology Denpasar Indonesia Institut Teknologi Dan Bisnis Stikom Departement of Magister Information Systems Denpasar Indonesia

ISBN: (纸本)9798331505103

This study presents the development of an Internet of Things (IoT) system for a water heater model, focusing on enhancing reliability during sensor malfunctions that could disrupt operations. Using the SEMAR IoT platform, the system efficiently collects and analyzes data, while the integration of the Holt-Winter algorithm ensures accurate temperature estimates, even with unreliable or missing sensor data. The algorithm successfully auto-calibrates sensors, predicts temperature at specific time intervals based on real sensor behavior, and fills in missing data during sensor failures. Tests show strong correlations between estimated and actual temperatures, allowing uninterrupted operation. This solution offers a flexible, cost-effective alternative to traditional redundancy, ensuring system reliability during sensor failures. Future research will explore alternative predictive models and conduct real-world testing to validate the system's resilience under complex conditions. © 2024 IEEE.

关键词： Temperature measurement

来源：评论

学校读者我要写书评

暂无评论

Improving Energy Efficiency in Piezoelectric Effect Based Synchronous Multicast Protocol (PESM) in Wireless Sensor Networks 2nd

Improving Energy Efficiency in Piezoelectric Effect Based Sy...

引用

2nd International Conference on Mechanical and Energy Technologies , ICMET 2021

作者： Geleto, Tadele Degefa Suresh Gnana Dhas, C. Kaliyaperumal, Karthikeyan Noordeen, S. Computer Science Department Ambo University-Hachalu Hundesa Campus Oromia Ethiopia Information Technology Department Ambo University HH Campus Oromia Ethiopia Department of Computer Science SAS-AV Campus VMRF Chennai India

ISBN: (纸本)9789811901072

The main aim of the proposed system is to optimize the consumption of energy during transmission in Wireless Sensor Networks (WSNs). This is achieved using Piezoelectric Effect based Synchronous Multicast approach (PESM), which is used to conserve energy from sensor nodes and it improves the network lifetime. This technique regenerates electrical effect, which is produced from natural resources to increase the network lifetime. The scavenging process is carried out by piezoelectric crystals in sensor notes. The experiments process that proposed method improves well the network lifetime in WSN more than other methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Piezoelectricity

来源：评论

学校读者我要写书评

暂无评论

Real-Time Face Recognition Attendance system Based on Video Processing

Real-Time Face Recognition Attendance System Based on Video ...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar Y S, Rajath Sindhu, D. Shashikala, S.V. Arpitha, K. Chaithra, K.N. Adichunchanagiri University Bgs Institute of Technology Department of Computer Science Bengaluru India Adichunchanagiri University Bgs Institute of Technology Department of Information Science Bengaluru India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The advent of big data and the commercial value of facial recognition technology have created a bright future for the technology, with a large market potential. This article proposes a face recognition participation framework based on continuous video processing. The article explores four aspects of the framework: the accuracy of facial recognition in offline recordings, the accuracy of facial recognition in continuous video processing, the dropout rate of the face recognition participation framework with continuous video processing, and the interface settings of the face recognition participation framework with continuous video processing. By analyzing these aspects, the article proposes a framework for participation based on facial recognition technology and conducts research on the framework with continuous video processing. Experimental results show that the accuracy of the video facial recognition framework can reach 82%. Compared with the traditional registration method, the face recognition participation framework can reduce the dropout rate by more than 60%. The dropout rate of missing classes has also been significantly reduced, which can effectively address the problems of students missing classes and leaving classes early. © 2023 IEEE.

关键词： Attendance system Face recognition attendance Face recognition technology Video processing Video recognition

来源：评论

学校读者我要写书评

暂无评论

An improvised CNN model for fake image detection

引用

International Journal of information technology (Singapore) 2023年第1期15卷 5-15页

作者： Hamid, Yasir Elyassami, Sanaa Gulzar, Yonis Balasaraswathi, Veeran Ranganathan Habuza, Tetiana Wani, Sharyar Information Security and Engineering Technology Abu Dhabi Polytechnic Abu Dhabi United Arab Emirates Department of Management Information System College of Business Administration King Faisal University Riyadh Saudi Arabia Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chengalpattu India Department of Computer Science and SWE College of Info. Tech UAEU Al Ain United Arab Emirates Department of Computer Science International Islamic University Malaysia Kuala Lumpur Malaysia

The last decade has witnessed a multifold growth of image data courtesy of the emergence of social networking services like Facebook, Instagram, LinkedIn etc. The major menace faced by today’s world is the issue of doctored images, where-in the photographs are altered using a rich set of ways like splicing, copy-move, removal to change their meaning and hence demands serious mitigation mechanisms to be thought of. The problem when seen from the prism of Artificial intelligence is a binary classification one, where-in the characterization must be drawn between the original and the manipulated images. This research work proposes a computer vision model based on Convolution Neural Networks for fake image detection. A comparative analysis of 6 popular traditional machine learning models and 6 different CNN architectures to select the best possible model for further experimentation. The proposed model based on ResNet50 employed with powerful preprocessing techniques results in a perfect fake image detector having a total accuracy of 0.99 having an improvement of around 18% performance with other models. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Artificial Intelligence Classification Convolution neural networks Doctored images Splicing

来源：评论

学校读者我要写书评

暂无评论

Deep Federated Feature Recommendation 13

Deep Federated Feature Recommendation

引用

13th International Conference on Electronics, Communications and Networks, CECNet 2023

作者： Xue, Bin Zheng, Qinghua Li, Zhinan Zhao, Weihu Zhang, Weihang Feng, Xue Department of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Information and Communications National University of Defense Technology Wuhan China Department of Automation Tsinghua University Beijing China Laboratory of Flexible Electronics Technology Tsinghua University Beijing China

ISBN: (纸本)9781643684802

Feature recommendation is one of the most critical and challenging problems in modern digital intelligence system. However, it is difficult to ensure privacy protection in many situations. To address this challenge, a deep federated feature recommendation method, called DF_Rec, is designed. Particularly, deep decentralized federated aggregation learning (DFAL) is jointly developed based on the ingenious combination of several deep frameworks and federated aggregation schemes. Extensive experiments are performed on three authoritative datasets, demonstrating that DF_Rec outperforms existing outstanding systems significantly. © 2024 The authors and IOS Press.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Deep Learning based Pulmonary Embolism Detection using Convolutional Feature Maps of CT Pulmonary Angiography Images 5

Deep Learning based Pulmonary Embolism Detection using Convo...

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Sukumar, Sanjana Harish, A. Shahina, A. Sanjana, B. Nayeemulla Khan, A. Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai India School of Computer Science and Engineering Vellore Institute of Technology-Chennai Vandalur-Kelambakkam Road Tamilnadu Chennai600127 India

Pulmonary Embolism (PE) is a blood clot in the pulmonary arteries of the lungs. Currently, computerized Tomography Pulmonary Angiography (CTPA) scans are used to diagnose this condition. However, to manually locate the presence of a PE in the scan is laborious. In this paper, the detection of PE and prediction of its features like location, Right Ventricle to Left Ventricle (RV/LV) ratio, and chronicity are automated. First, for the detection of the embolisms, image features from the RSNA Pulmonary Embolism CT (RSPECT) dataset - containing over 12,000 CTPA studies, are extracted by several pre-trained Convolutional Neural Networks (CNN) - AlexNet, Inception V3, ResNet-18, and ResNet-50. Then, various Machine Learning (ML) classifiers are trained on these features and their performances are compared. Second, for the prediction of labels, multi-label classification is performed by training these networks with Binary Cross Entropy Logits (BCE Logits) loss. The results showed that for the detection task, k-NN classifier trained on ResNet-50 features achieved the highest sensitivity of 0.98 with 1.7 false positives per scan and an AUROC score of 97.4%. This system also improved on the state-of-the-art sensitivity score by 0.0975 and AUROC score by 3.4%. For multi-label classification, ResNet-18 performed comparatively better with the best validation loss of 0.07 and weighted macro-average ROC AUC score of 0.61. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Security Distributed Social Media Service Based on IoTA and IPFS 1

引用

5th International Conference on Innovative Computing, IC 2022

作者： Chu, Ting-Hong Chang, Jia-Wei Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

Social platforms have become an important tool for sharing information and communication in the modern times. The common centralized management system has design vulnerabilities that lead to numerous information security incidents, so we build a system that uses IoTA as cryptocurrency and IPFS as storage system, which is faster than traditional blockchain. Both technologies are based on DAG, so they are very compatible. IPFS ensures that data is not maliciously tampered with by third parties, and it does not have the problem of single node failure in the data center and cannot be read. Because IPFS does not have file encryption, we have added a security authentication mechanism to ensure that user files are not accessed by unauthorized users. We use AES and RSA encryption as the authentication mechanism. Though AES encryption is faster, RSA is still more suitable for key delivery. We have created a secure decentralized social media service with IoTA and IPFS. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning

引用

computers, Materials & Continua 2021年第3期66卷 2397-2407页

作者： Rachid Zagrouba Muhammad Adnan Khan Atta-ur-Rahman Muhammad Aamer Saleem Muhammad Faheem Mushtaq Abdur Rehman Muhammad Farhan Khan Department of Computer Information System College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science Riphah International UniversityLahore CampusLahore54000Pakistan Hamdard Institute of Engineering and Technology Hamdard UniversityIslamabad44000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan School of Computer Science NCBA&ELahore54000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan

Novel Coronavirus-19(COVID-19)is a newer type of coronavirus that has not been formally detected in *** is established that this disease often affects people of different age groups,particularly those with body disorders,blood pressure,diabetes,heart problems,or weakened immune *** epidemic of this infection has recently had a huge impact on people around the globe with rising mortality *** levels of mortality are attributed to their transmitting behavior through physical contact between *** is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be *** latest global coronavirus epidemic(COVID-19)has brought new challenges to the scientific *** Intelligence(AI)-motivated methodologies may be useful in predicting the conditions,consequences,and implications of such an *** forecasts may help to monitor and prevent the spread of these *** article proposes a predictive framework incorporating Support Vector Machines(SVM)in the forecasting of a potential outbreak of *** findings indicate that the suggested system outperforms cutting-edge *** method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in *** findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational *** proposed SVM system model exhibits 98.88%and 96.79%result in terms of accuracy during training and validation respectively.

关键词： Coronavirus outbreak machine learning artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Prediction of Cardiovascular Disease and Respiratory Disease: A Review

引用

SN computer science 2024年第1期5卷 196页

作者： Parashar, Gaurav Chaudhary, Alka Pandey, Dilkeshwar Amity Institute of Information Technology AMITY University Sector-125 Uttar Pradesh Noida India Department of Computer Science and Engineering KIET Group of Institutions Muradnagar Uttar Pradesh Ghaziabad India

Cardiovascular (CVD) and respiratory diseases (RD) have been in the active domain for machine learning (ML) researchers as these diseases significantly contribute to mortality in humans. Some studies suggest that CVD problems such as cerebrovascular problems, dysrhythmia, inflammatory heart disease, ischemic heart disease (IHD), and RD related problems remain high even after COVID-19 infection clears up. To the best of our knowledge, this is the only study that surveyed these two diseases. This paper’s goal is to explore the existing state of the art in the application of ML in the detection, categorization, and prediction of disorders related to CVD and RD. The review highlights ML algorithms used in prediction of CVD and RD related diseases, datasets used by the articles, technique used for feature selection, features selected for the study, dataset used in the article was unimodal or multimodal, and performance of the algorithm. In CVD category, it was observed that about 15 studies had their performance metrics range between 91% and 100%, 7 studies had between 81% and 90% and about 2 studies had their performance between 70% and 80%. CNN is the most used Feature Selection technique. Only three studies were found in our set that worked on the multimodal dataset and others used the unimodal dataset. In case of RDs, it was observed that about 15 studies had their performance metrics range between 91% and 100%, 7 studies had between 81% and 90% and about 2 studies had their performance between 70% and 80%. CNN is the most used feature selection technique. Only three studies were found in our set that worked on the multimodal dataset and others used the unimodal dataset. The intent of this review is to stimulate the interest of scientists in this challenging field and to acquaint them with current advances in the field. To design a system that predict CVD or RD in a patient using uni or multi modal datasets, approaches such as data cleaning, feature selection, region

关键词： Cardiovascular disease Heart disease prediction Lung disease prediction Machine learning Respiratory disease

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：