检索结果-内蒙古大学图书馆

AR-GeoShapes: Development and Evaluation of a Marker Less Augmented Reality Based Geometrical Shapes Learning Framework on Memory Retention and Learning Achievement 18

AR-GeoShapes: Development and Evaluation of a Marker Less Au...

引用

18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Azam, Ayesha Naz, Zubaira Khan, Muhammad Usman Ghani Din, Irfanud Al-Khwarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Information Technology University Lahore Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan New Uzbekistan University Department of Computer Science School of Computing Tashkent Uzbekistan

ISBN: (纸本)9798331508692

A large number of students struggle to grasp geometric principles, especially in three-dimensional (3D) space. In turn, this is why digital educational tools have been integrated more and more into the learning environment: because they entertain students. One of these technologies is Augmented Reality (AR) which has become a widely researched and effective tool for education. Where learning is situated with content embedded within the physical environment (support), AR better enables offerings to be adapted. This paper presents AR-GeoShapes, an augmented reality-based educational tool developed to help students learn solid geometry concepts. Built with the Vuforia marker less SDK in Unity, AR-GeoShapes is an exercise that runs on mobile devices for users to learn shapes interactively. Further to evaluate whether using AR-GeoShapes helps increase memory retention and improve learning achievement, a comparative study involving 58 students was conducted. The results demonstrate that the experimental group, utilizing AR-GeoShapes, exhibited significantly higher memory retention and learning outcomes compared to the control group. These findings suggest that AR-GeoShapes are a valuable tool for enhancing students' comprehension of geometric concepts. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest

引用

Journal of computer science & Technology 2022年第6期37卷 1444-1463页

作者： Ming-Min Shao Wen-Jun Jiang Jie Wu Yu-Qing Shi Tak Shing Yum Ji Zhang College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Department of Computer and Information Sciences Temple UniversityPhiladelphia 19122U.S.A. School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Department of Information Engineering Chinese University of Hong KongHong Kong 999077China Department of Mathematics and Computing University of Southern QueenslandBrisbaneQLD 4350Australia

Friend recommendation plays a key role in promoting user experience in online social networks(OSNs).However,existing studies usually neglect users’fine-grained interest as well as the evolving feature of interest,which may cause unsuitable *** particular,some OSNs,such as the online learning community,even have little work on friend *** this end,we strive to improve friend recommendation with fine-grained evolving interest in this *** take the online learning community as an application scenario,which is a special type of OSNs for people to learn courses *** partners can help improve learners’learning effect and improve the attractiveness of *** propose a learning partner recommendation framework based on the evolution of fine-grained learning interest(LPRF-E for short).We extract a sequence of learning interest tags that changes over ***,we explore the time feature to predict evolving learning ***,we recommend learning partners by fine-grained interest *** also refine the learning partner recommendation framework with users’social influence(denoted as LPRF-F for differentiation).Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy(i.e.,approximate 50%improvements on the precision and the recall)and can recommend learning partners with high quality(e.g.,more experienced and helpful).

关键词： online social network friend recommendation fine-grained interest evolving feature tag online learning community

来源：评论

学校读者我要写书评

暂无评论

Electric Vehicle Range Prediction using Random Forest Regression 8

Electric Vehicle Range Prediction using Random Forest Regres...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Sagar, B.S. Rajasekaran, Mothiram Sakthisaravanan, B. Jyothi, R. Leela Lalitha, K. Murugan, S. Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Pine Candle Way Saint AugustineFL United States Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

This research introduces a new method for predicting electric vehicles (EVs) range that combines cloud computing with random forest regression (RFR) approaches. Predicting the range properly is now critical for user comfort and efficient route planning due to the rising popularity of EVs. Factors like changing driving conditions and battery depletion might make traditional range calculation techniques inaccurate. It uses the processing power of the cloud to collect and analyze massive volumes of real-time data on variables like traffic, weather, and topography that have an impact on EVs range. Next, it uses a powerful machine learning (ML) algorithm for RFR to accurately forecast EVs range by modeling the intricate interactions between these factors. Results from experiments this technique works, with far better range estimate advanced approaches. Cloud computing scalability and flexibility also make it easy to predict models into preexisting EVs navigation systems, so drivers may get accurate range estimations in real time based on their own circumstances. It will improve EVs' usability and dependability, which will lead to more people buying and using these eco-friendly vehicles. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Hybrid Dynamic Optimization for Multilevel Security System in Disseminating Confidential information

引用

computer Systems science & engineering 2023年第12期47卷 3145-3163页

作者： Shahina Anwarul Sunil Kumar Ashok Bhansali Hammam Alshazly Hany S.Hussein School of Computer Science University of Petroleum and Energy StudiesDehradun248007India Department of Computer Science Graphic Era Hill UniversityDehradunUttarakhand248001India Department of Computer Engineering and Applications GLA UniversityMathuraIndia Faculty of Computers and Information South Valley UniversityQena83523Egypt Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha62529Saudi Arabia Electrical Engineering Department Faculty of EngineeringAswan UniversityAswan81528Egypt

Security systems are the need of the hour to protect data from unauthorized *** dissemination of confidential information over the public network requires a high level of *** security approach such as steganography ensures confidentiality,authentication,integrity,and *** helps in hiding the secret data inside the cover media so that the attacker can be confused during the transmission process of secret data between sender and ***,we present an efficient hybrid security model that provides multifold security *** this end,a rectified Advanced Encryption Standard(AES)algorithm is proposed to overcome the problems existing in AES such as pattern appearance and high *** modified AES is used for the encryption of the stego image that contains the digitally signed encrypted secret *** enciphering and deciphering of the secret data are done using the Rivest–Shamir–Adleman(RSA)*** experiments are conducted on the images of the USC-SIPI standard image *** experimental results prove that the proposed hybrid system outperforms other SOTA(state-of-the-art)approaches.

关键词： Cryptography steganography digital signature rectified AES encryption

来源：评论

学校读者我要写书评

暂无评论

Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets

引用

Neural Computing and Applications 2025年第8期37卷 6217-6232页

作者： Nematzadeh, Hossein Mani, Joseph Nematzadeh, Zahra Akbari, Ebrahim Mohamad, Radziah Modern College of Business and Science Muscat Oman Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States Otolaryngology Department School of Medicine Emory University AtlantaGA30322 United States Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Faculty of Computing Universiti Teknologi Malaysia Skudai81310 Malaysia

Abstract: Feature selection poses a challenge in high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There is not a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response,this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

WiFi Indoor Positioning System Based on OpenWRT 20

WiFi Indoor Positioning System Based on OpenWRT

引用

20th International Conference on Smart Technologies, EUROCON 2023

作者： Estrada, Rebeca Valeriano, Irving Aizaga, Xavier Vargas, Lourdes Vera, Nelson Zambrano, Diego Escuela Superior Politécnica Del Litoral Espol Information Technologies Center Electrical Engineering and Computer Science Department Guayaquil Ecuador

ISBN: (纸本)9781665463973

The development of indoor positioning systems is possible thanks to the availability of wireless network infrastructure. Thus, a wireless provider can determine an accurate indoor position taking into account the existing Wifi infrastructure in buildings, malls, or offices. In this work, we develop a system for indoor positioning with the use of several libraries of OpenWRT running on three access points in an office of the CTI building. We develop a server application to retrieve the performance metrics of the WiFi signal from three wireless routers of the study area and load this information to a database. Thus, this application uses the signal strength measured and stored in the database to determine the distance between the mobile devices and each access point. Then, the position of the mobile devices is estimated by averaging the estimation of three Min-Max algorithms and updated in the database. Finally, we also implemented a mobile application that shows on a map the mobile devices position in real time and the position of mobile devices in their proximity. The results show that the mobile devices locations present an average margin error of position equal to 2.43 meters enabling the development of indoor positioning applications using the existing infrastructure of the wireless networks. © 2023 IEEE.

关键词： Indoor positioning systems

来源：评论

学校读者我要写书评

暂无评论

Forecasting Mental Stress Using Machine Learning Algorithms

引用

computers, Materials & Continua 2022年第9期72卷 4945-4966页

作者： Elias Hossain Abdulwahab Alazeb Naif Almudawi Sultan Almakdi Mohammed Alshehri M.Gazi Golam Faruque Wahidur Rahman Department of Software Engineering Daffodil UniversityDhakaBangladesh Collage of Computer Science and Information System University of NajranNajranSaudi Arabia Department of Computer Science and Engineering Khwaja Yunus Ali UniversitySirajganj 6751Bangladesh

Depression is a crippling affliction and affects millions of individuals around the *** general,the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts,which results in lower costs and improved patient ***,this strategy can necessitate a lot of buy-in from a large number of people,as well as additional training and logistical ***,utilizing the machine learning algorithms,patients with depression based on information generally present in a medical file were analyzed and *** methodology of this proposed study is divided into six parts:Proposed Research Architecture(PRA),Data Pre-processing Approach(DPA),Research Hypothesis Testing(RHT),Concentrated Algorithm Pipeline(CAP),Loss Optimization Stratagem(LOS),and Model Deployment Architecture(MDA).The Null Hypothesis and Alternative Hypothesis are applied to test the *** addition,Ensemble Learning Approach(ELA)and Frequent Model Retraining(FMR)have been utilized for optimizing the loss ***,the Features Importance Interpretation is also delineated in this *** forecasts could help individuals connect with expert mental health specialists more quickly and *** to the findings,71%of people with depression and 80%of those who do not have depression can be appropriately *** study obtained 91%and 92%accuracy through the Random Forest(RF)and Extra Tree *** after applying the Receiver operating characteristic(ROC)curve,79%accuracy was found on top of RF,81%found on Extra Tree,and 82%recorded for the eXtreme Gradient Boosting(XGBoost)***,several factors are identified in terms of predicting depression through statistical data *** the additional effort is needed to develop a more accurate model,this model can be adjustable in the healthcare sector for diagnosing depression.

关键词： Depression analysis mental health machine learning model deployment XGBoost ROC-AUC

来源：评论

学校读者我要写书评

暂无评论

Applying t-Distributed Stochastic Neighbor Embedding for Improving Fingerprinting-Based Localization System

IEEE Sensors Letters

引用

IEEE Sensors Letters 2023年第9期7卷 1页

作者： Tarekegn, Getaneh Berie Tai, Li-Chia Lin, Hsin-Piao Tesfaw, Belayneh Abebe Juang, Rong-Terng Hsu, Huan-Chia Huang, Kai-Lun Singh, Kanishk National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu30010 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan Feng Chia University Department of Electronic Engineering Taichung40724 Taiwan Industrial Technology Research Institute Information and Communications Research Laboratories Hsinchu310401 Taiwan

Large-scale location estimation is crucial for many artificial intelligence Internet of Things (IoT) applications in the era of smart cities. This letter proposes a deep learning-based outdoor positioning scheme for large-scale wireless settings using fingerprinting techniques. We first developed a feature extraction technique using t-distributed stochastic neighbor embedding (t-SNE) to extract dominant and distinguishable features while eliminating noises from the radio fingerprints. Afterward, we developed a deep learning-based coarse-fine localization framework to improve positioning performance significantly. Based on our numerical analysis, the proposed scheme reduces computation time by 64.41%, and the average positioning error is 38 cm. Therefore, the proposed approach significantly improved positioning accuracy and reduced computation time. © 2017 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Massive Fabrication of Carbon Nanotube Transistors by Surface Tension-Driven Inkjet-Printing Method 5

Massive Fabrication of Carbon Nanotube Transistors by Surfac...

引用

5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023

作者： Park, Soohyun Shin, Minhye Kang, Hongki Lee, Yoonhee Division of Electronics and Information System Daegu42988 Korea Republic of Department of Electrical Engineering and Computer Science Daegu42988 Korea Republic of

ISBN: (纸本)9781665457330

Carbon nanotube field-effect transistors (CNTFETs) have been ideal nanoelectronics in semiconductor technologies with exceptional electrical properties. Scaling the CNT -FET fabrication enables the expansion of the application fields. However, placing nanotubes with uniform alignment and controlled density to the electrode contacts has been the maj or challenge in massive manufacturing. Here, we introduce the in-place inkjet-printing method for fabricating CNTFETs with controlled numbers of connected CNTs at a single CNT level. A picoliter (pL) drop of CNT ink is printed to the pre-patterned electrode arrays in parallel over a four-inch silicon wafer, allowing adaptive manufacturing without additional lithographic processes. Drops form thin films on the metal electrode patterns driven by the surface tension flow, diluting the surface density of CNT on the one-micrometer channel gap and avoiding bundled coffee ring edges. Finally, single CNTs are correctly bridged to the electrodes with high statistical yield. This process enables reproducible and high-throughput single CNT deposition, integrating into the FET manufacturing process, and can be used for versatile nanoelectronics applications, such as biosensors. © 2023 IEEE.

关键词： Carbon nanotubes

来源：评论

学校读者我要写书评

暂无评论

Overlay CR-NOMA Assisted Intelligent Transportation System Networks with Imperfect SIC and CEEs

引用

Chinese Journal of Electronics 2023年第6期32卷 1258-1270页

作者： LI Xingwang GAO Xuesong LIU Yingting HUANG Gaojian ZENG Ming QIAO Dawei Physics and Electronic Information Engineering Henan Polytechnic University School of Electronic and Information Engineering Lanzhou Jiaotong University State Grid Gansu Electric Power Research Institute School of Electronic Engineering Beijing University of Posts and Telecommunications Department of Electrical and Computer Engineering Laval University School of Emergency Management Henan Polytechnic University

With the development of the mobile communication and intelligent information technologies,the intelligent transportation systems driven by the sixth generation(6G) has many opportunities to achieve ultralow latency and higher data transmission rate. Nonetheless, it also faces the great challenges of spectral resource shortage and large-scale connection. To solve the above problems, non-orthogonal multiple access(NOMA) and cognitive radio(CR) technologies have been proposed. In this regard, we study the reliable and ergodic performance of CR-NOMA assisted intelligent transportation system networks in the presence of imperfect successive interference cancellation(SIC) and non-ideal channel state information. Specifically, the analytical expressions of the outage probability(OP) and ergodic sum rate(ESR) are derived through a string of calculations. In order to gain more insights, the asymptotic expressions for OP and ESR at high signal-to-noise ratio(SNR) regimes are discussed. We verify the accuracy of the analysis by Monte Carlo simulations, and the results show: i) Imperfect SIC and channel estimation errors(CEEs) have negative impacts on the OP and ESR; ii) The OP decreases with the SNR increasing until convergence to a fixed constant at high SNR regions; iii) The ESR increases with increasing SNR and there exists a ceiling in the high SNR region.

关键词： NOMA Analytical models Interference cancellation Spectral efficiency System performance Channel estimation Reliability

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：