检索结果-内蒙古大学图书馆

An Effective Online Collaborative Training in Developing Listening Comprehension Skills

computer systems science & engineering 2021年第8期38卷 131-140页

作者： Shakeel Ahmed Munazza Ambreen Muneer Ahmad Abdulellah A.Alaboudi Roobaea Alroobaea NZ Jhanjhi Department of Secondary Instructors Education Faculty of EducationAllama Iqbal Open UniversityIslamabadPakistan Department of Information Systems Faculty of Computer Science&ITUniversiti Malaya50603 Kuala LumpurMalaysia College of Computer Science Shaqra UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500Malaysia

The COVID-19 outbreak severely affected formal face-to-face classroom teaching and ***-based online education and training can be a useful measure during the *** the Pakistani educational context,the use of ICT-based online training is generally sporadic and often unavailable,especially for developing English-language instructors’listening comprehension *** major factors affecting availability include insufficient IT resources and infrastructure,a lack of proper online training for speech and listening,instructors with inadequate academic backgrounds,and an unfavorable environment for ICT-based training for listening *** study evaluated the effectiveness of ICT-based training for developing secondary-level English-language instructors’listening comprehension *** this end,collaborative online training was undertaken using random ***,60 private-school instructors in Chakwal District,Pakistan,were randomly selected to receive online-listening training sessions using English *** experimental group achieved significant scores in the posttest ***,there were substantial improvements in the participants’listening skills via online *** the unavailability of face-to-face learning during COVID-19,this study recommends using ICT-based online training to enhance listening comprehension *** policymakers should revise curricula based on online teaching methods and modules.

关键词： COVID-19 online training remote teaching computers in education listening comprehension English language

来源：评论

学校读者我要写书评

暂无评论

Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

引用

2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ibrahim, Abdelhameed Gatzoulis, Christos Eid, Marwa M. Rizk, Faris H. Abualigah, Laith El-Kenawy, El-Sayed M. Isa Town Bahrain Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Applied Science Private University Applied Science Research Center Amman11931 Jordan Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9798350351408

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform;the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Watermarking of OFDM for Pseudonymetry: Analysis and Experimental Results

Watermarking of OFDM for Pseudonymetry: Analysis and Experim...

引用

IEEE International Conference on Communications Workshops, ICC

作者： Meles G. Weldegebriel Jie Wang Greg Hellbourg Ning Zhang Neal Patwari Electrical & Systems Engineering Washington University St. Louis Computer Science & Engineering Washington University St. Louis Cahill Center for Astronomy and Astrophysics California Institute of Technology

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

This paper presents new results for pseudonymetry, a closed-loop feedback mechanism in which active transmissions are watermarked with a pseudonym that, if it interferes with a protected passive radio receiver, can be demodulated and used to force the transmitter off of the band. This paper addresses amplitude-based watermarking of orthogonal frequency division multiplexing (OFDM) packets. We quantify the ability of a passive receiver to decode the watermark at very low signal-to-noise ratio (SNR) and the impact on the intended communication link. We demonstrate, using a testbed of software-defined radios, that the experimental implementation of pseudonymetry matches the theoretical analysis very closely. Our results quantify a fundamental trade-off in the design of pseudonymetry for OFDM and provide a practical pseudonym receiver design.

关键词： Wireless communication Protocols Databases OFDM Conferences Radio transmitters Watermarking

来源：评论

学校读者我要写书评

暂无评论

Optimization and Optimizers for Adversarial Robustness

arXiv

引用

arXiv 2023年

作者： Liang, Hengyue Liang, Buyun Peng, Le Cui, Ying Mitchell, Tim Sun, Ju Department of Electrical & Computer Engineering University of Minnesota United States Department of Computer Science & Engineering University of Minnesota United States Department of Industrial & Systems Engineering University of Minnesota United States Department of Computer Science Queens College City University of New York United States

Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations entails solving nontrivial constrained optimization problems. Existing numerical algorithms that are commonly used to solve them in practice predominantly rely on projected gradient, and mostly handle perturbations modeled by the l1, l2 and l∞ distances. In this paper, we introduce a novel algorithmic framework that blends a general-purpose constrained-optimization solver PyGRANSO with Constraint-Folding (PWCF), which can add more reliability and generality to the state-of-the-art RE packages, e.g., AutoAttack. Regarding reliability, PWCF provides solutions with stationarity measures and feasibility tests to assess the solution quality. For generality, PWCF can handle perturbation models that are typically inaccessible to the existing projected gradient methods;the main requirement is the distance metric to be almost everywhere differentiable. Taking advantage of PWCF and other existing numerical algorithms, we further explore the distinct patterns in the solutions found for solving these optimization problems using various combinations of losses, perturbation models, and optimization algorithms. We then discuss the implications of these patterns on the current robustness evaluation and adversarial training. Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Prediction for Start-Up Success through Deep Learning based Stacked DAE Model

Probabilistic Prediction for Start-Up Success through Deep L...

引用

Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： Shakir Syed Lakshminarayana Reddy Kothapalli Sondinti Puneet Sapra Amit Joshi Shobana M Deepan Chakravarthi A V Corporate Partners India US Bank India Department of Computer Science Engineering Rayat Bahara University Mohali India Department of Information Technology ISMA University RĪga Latvia Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Karur India Department of Civil Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Researchers and financial entities have long been fascinated by the ability to accurately predict the success of start-ups. Modern start-ups, which are creative, tech-focused, and quickly growing companies, face this challenge more than most. This study introduces a deep learning Stacked Denoising Autoencoder (SDAE) model that uses important technology domains to forecast the likelihood of a startup's success. The three primary steps of the proposed approach are as follows: preprocessing, feature extraction, and training the model. To ensure statistical independence of signal components in multivariate datasets, preprocessing steps include min-max normalisation for data standardisation and Independent Component Analysis (ICA) for feature extraction. The predictive power of the SDAE model is then enhanced by training. The proposed model outperforms traditional methods like Decision Trees (DT) and Denoising Autoencoders (DAE) with an average accuracy of 94.28%, according to the experimental data. This makes it a strong contender for predicting startup success in changing technical contexts.

关键词： Training Deep learning Accuracy Computational modeling Noise reduction Independent component analysis Companies Predictive models Feature extraction Integrated circuit modeling

来源：评论

学校读者我要写书评

暂无评论

Streamlining Inspection Processes for Error-Free Vehicles in the Automobile and Aerospace Industries: Integration of computer Vision, Machine Learning, Control systems

SSRN

引用

SSRN 2023年

作者： Ramesh, Kaki Deshmukh, Sandip S. Ray, Tathagata Parimi, Chandu Department of Mechanical Engineering Department of Computer Science and Information Systems Department of Civil Engineering Birla Institute of Science & Technology Pilani Hyderabad Campus Telangana Hyderabad500078 India

Automobile and Aerospace industries of vehicle assembly are facing significant challenges in ensuring the production of high-quality, faultless vehicles within a limited time frame. Conventional inspection methods relying on human inspection are prone to errors and cannot guarantee the precise placement of assembled components in the vehicle. These issues can lead to vehicle failures and user hazards, resulting in significant financial and reputational losses for the industry. This research presents a novel solution to these challenges by developing an automatic inspection system that combines computer vision, machine learning, image processing, and control systems. The proposed system is controlled by an Arduino microcontroller and stepper motors for the movement of the highly calibrated camera, allowing for precise and efficient inspection of the vehicle. It utilizes the well-known YOLOv5 model for object detection, specifically hole marks and holes on the vehicle before assembly. The deep learning model (YOLOv5) achieved an accuracy of 0.95 mAP. The output images of the model are then processed with image processing techniques for the precise position of the object. Through this, we have achieved acceptable results with an error rate of 0.09%. The results of the study demonstrate that the proposed automatic inspection system can significantly reduce the processing time of inspection, improve the accuracy of identifying faults, and enhance the overall quality of the final product. The combination of computer vision, machine learning, image processing, and control systems presents a novel and effective solution to the challenges faced by the industries. © 2023, The Authors. All rights reserved.

关键词： Quality assurance

来源：评论

学校读者我要写书评

暂无评论

Society 5.0: a simulation optimisation study of dynamic scheduling for a grocery store

引用

International Journal of Simulation and Process Modelling 2021年第2-3期17卷 178-186页

作者： Mykoniatis, Konstantinos Angelopoulou, Anastasia Nichols, Samira Shirzaei Department of Industrial and Systems Engineering Auburn University AuburnAL36849 United States TSYS School of Computer Science Columbus State University ColumbusGA31907 United States Computer Information Systems and Analytics College of Business University of Central Arkansas AR72035 United States

Society 5.0 refers to a technology-based human-centred society that integrates cyber-physical systems and uses advanced technology to improve everyday life. Queuing systems, such as grocery stores, will transition to Society 5.0. Waiting time in the queue of such systems makes it difficult to determine the necessary number of resources to operate cost-effectively and keep customers satisfied. This work studies a grocery store with two types of resources: cashier-staffed billing counters and self-checkout machines. The objective of the study is to dynamically schedule the required number of resources such that the arriving customers experience reduced waiting times in the queue, while the store maintains low operational costs. The work uses the Simio simulation software and the OptQuest simulation optimisation engine to determine the staffing levels and achieve the aforementioned objectives. The optimisation experiment results showed that using dynamic resource scheduling can assist in maintaining satisfactory customer waiting times and operational costs. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Discrete event simulation

来源：评论

学校读者我要写书评

暂无评论

Advanced Interoperable Framework for Real-Time Predictive Analysis Leveraging Machine Learning and IoT in Smart Health Monitoring systems

Advanced Interoperable Framework for Real-Time Predictive An...

引用

2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Pradeesh, D. Antony Syamala, Y. Priya, P. Sripathi, Madhavi Kaza, Srilakshmi Rohith Bala Jaswanth, B. Kg College of Arts and Science Department of Electronics and Communication Systems Coimbatore India Seshadri Rao Gudlavalleru Engineering College Department of IoT Andhra Pradesh Vijayawada India The Quaide Milleth College for Men Department of Computer Science Tamilnadu Chennai India Rao Gudlavalleru Engineering College Department of Business and Management Studies Seshadri Andhra Pradesh Vijayawada India Seshadri Rao Gudlavalleru Engineering College Department of Electronics and Communication Engineering Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350327816

As health care evolves rapidly, devices enabled by the Internet of Things, and algorithms driven by Machine Learning have surfaced as transformative elements. However, these technologies often fall short due to a lack of interoperability between existing systems, real-time predictive capabilities, and strong security measures. This paper presents a novel framework designed to overcome these challenges and optimize Internet of Things applications in health monitoring systems. Our framework has been specifically created to work seamlessly across the Internet of Things devices while monitoring a wide array of health parameters. It integrates advanced machine learning models, including Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting, Random Forest and Neural Network to improve prediction accuracy. The framework also allows real-time analysis and prediction services with real-time updates available through an Internet portal. Furthermore, we employ robust security and privacy measures to safeguard patient data. Experimental results illustrate our framework's superior performance over existing systems, demonstrating its promise for creating efficient, secure smart health monitoring systems. © 2023 IEEE.

关键词： internet of things machine learning predictive analysis smart health care

来源：评论

学校读者我要写书评

暂无评论

Enhancing Road Safety: Automated Traffic Violation Detection and Counting System Using YOLO Algorithm

Enhancing Road Safety: Automated Traffic Violation Detection...

引用

Mediterranean Smart Cities Conference (MSCC)

作者： Ayoub Charef Zahi Jarir Mohamed Quafafou LAMIGEP Research Laboratory EMSI Moroccan School of Engineering Sciences Marrakech Morocco Laboratory of Computer Systems Engineering (LISI) Cadi Ayyad University Marrakech Marrakech Morocco Department of Computer Science University of Aix-Marseille Marseille France

ISBN: (数字)9798350374001

ISBN: (纸本)9798350374018

Ensuring compliance with traffic regulations, such as wearing helmets and obeying traffic signals, is crucial for enhancing road safety, particularly among motorcycle riders. In this study, we propose an automated approach for detecting helmet wearing and traffic light violations using computer vision techniques. Our methodology involves leveraging the YOLO-v8 object detection model pretrained on the COCO dataset to identify motorcycles, persons, traffic lights, and helmets in video footage captured at intersections in Marrakech. We conducted manual counting as a benchmark for evaluating the performance of our automated system. Our results demonstrate a strong alignment between our automated approach and manual counting for both helmet detection and traffic light violations. However, occasional discrepancies were observed, particularly during specific time slots characterized by high motorcycle speeds. Contextual factors such as traffic density and vehicle speed were identified as influencing factors. Despite these challenges, our automated system shows promise as a valuable tool for monitoring and enforcing traffic regulations. Ongoing refinement and optimization are essential to address these challenges and enhance the accuracy and reliability of automated detection systems. Our study highlights the potential of automated technologies in improving road safety measures and underscores the importance of considering contextual factors in interpreting detection results.

关键词： Head Motorcycles Manuals Benchmark testing Regulation Road safety Safety Reliability Monitoring Optimization

来源：评论

学校读者我要写书评

暂无评论

Multi-Graph Spatio-Temporal Convolution for Traffic Flow Prediction Focusing on Edge Derived Imbalanced Data From Highway Electronics

引用

IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Ding, Weilong Zhang, Tianpu Gao, Honghao Yu, Qi Wang, Jianwu Zhao, Zhuofeng School of Information Science and Technology North China University of Technology Beijing China Beijing China School of Computer Engineering and Science Shanghai University Shanghai China Department of Information Systems University of Maryland Baltimore County USA

The proliferation of Internet of Things (IoT) and the adoption of Edge Computing paradigms have led to a substantial increase in data volume. Data imbalance is inevitable in distributed environment composed of edge nodes, due to inherent diversity in processing capabilities, network connectivity, and geographical distribution. In specific highway domain, such edge derived data makes predictive precision of crucial traffic flow hard to guarantee. First, data imbalance would deteriorate the predictive performance at certain locations. Second, various spatial dependencies existed in highway network have not been sufficiently explored. To address these challenges in highway domain, we propose a novel method for daily traffic flow prediction on imbalanced data. On the one hand, a pre-processing strategy is presented to normalize traffic flow data in long-tail distribution. The data is collected from highway electronics through edge nodes. On the other hand, a multi-graph convolution model is designed to capture spatio-temporal features in distinct physical, statistical and latent perspectives. Incorporating external characteristics like meteorological and calendar features, prediction can be achieved precisely. Through extensive experiments and case studies conducted on a Chinese provincial highway, our method demonstrates a significant improvement of predictive accuracy than baselines and outstanding effects in a practical project. IEEE

关键词： Network topology

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：