检索结果-内蒙古大学图书馆

Navigating Anxiety in a Cloud E-learning Virtual Environments: The Moderating Role of Anxiety on Lecturers’ Adoption of Cloud Computing E-learning

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2024年第17期18卷 115-138页

作者： Armah, Elisha D’Archimedes Sathik Ali, I. Department of Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Technology Cape Coast Technical University Cape Coast Ghana Department of Information Technology B.S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India

The rapid expansion of cloud computing e-learning systems has empowered educators to reach learners worldwide. However, a critical study gap exists: the impact of lecturers’ technological anxiety on their adoption intentions remains unexplored in the technical universities in Ghana. This study addresses this gap by investigating anxiety’s influence on lecturers’ cloud computing e-learning adoption intentions within a virtual environment (VE). We examine the mediating role of competitive pressure and the moderating role of anxiety. Using PLSSEM, we analysed data from surveys conducted among 1395 lecturers across all 10 technical universities. With a high response rate of 90.2% (n = 1258), our analysis reveals moderate anxiety among lecturers regarding cloud e-learning adoption. The proposed model explains 21.1% of the variance in adoption intentions. Interestingly, competitive pressure on lecturers mediates the relationship between cloud e-learning usefulness, security, and adoption decisions. Additionally, anxiety moderates the effects of cloud computing complexity, security, and adoption intentions. We have recommended organizing regular training workshops, ensuring user-friendly platforms, implementing robust security measures, and providing psychological support for lecturers using cloud computing technologies. © 2024 by the authors of this article.

关键词： Virtual environments

来源：评论

学校读者我要写书评

暂无评论

Mobile Edge Computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

引用

IEEE Transactions on Cognitive Communications and networking 2024年第3期11卷 1845-1853页

作者： Liu, Weiqi Hossain, Mohammad Arif Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computIng reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computIng resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

IDedupNet: A MobileNetV3-Based Deep Learning Framework for Efficient Image Deduplication in Cloud Computing Environments

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第13期49卷 143-162页

作者： Mohiuddin, Mohd Hasan Tamilselvan, Latha Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science &Technology Vandalur Chennai600048 India Department of Information & Technology B.S. Abdur Rahman Crescent Institute of Science &Technology Vandalur Chennai600048 India

Image deduplication is becoming increasingly important for cloud storage infrastructures to handle the increasing amount of multimedia material. Through increased storage efficiency, effective picture deduplication may optimize resources and save expenses. It also improves performance by facilitating quicker access, utilizing less bandwidth, and enhancing data integrity. Although heuristic-based classical deduplication techniques work well in various storage infrastructures, they cannot keep up with the dynamic nature of cloud storage resources. This study presents IDedupNet, a revolutionary DL-based framework that improves infrastructure performance in cloud computing by efficiently detecting duplicate and near-duplicate photos. Our approach leverages MobileNetV3 for feature extraction and CNN-based encodings for image deduplication, enabling it to manage duplicate photos in highly dynamic contexts efficiently. Additionally, we provide a Learning-Based Image Deduplication (LBID) approach that improves deduplication performance by extending the use of the IDedupNet model. Experimental evaluation demonstrates a high accuracy of 98.68% on benchmark datasets, consistently outperforming existing models. The underlying technique and deep learning framework may be easily integrated into real-time cloud storage systems to increase customer satisfaction and infrastructure efficiency. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Cloud storage

来源：评论

学校读者我要写书评

暂无评论

Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

引用

Neural Computing and Applications 2024年第36期36卷 22867-22887页

作者： Thendral, M. Sathya Abinaya, S. Devi, M. K. Kavitha Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions from agriculture. However, the database utilized for prediction has thousands of missing values and imbalanced data records due to various factors, such as environmental disasters, sensor failure, maintenance issues, and budgetary constraints. Many researchers have either completely discarded records with missing values or have only partially addressed the issue, leading to less precise predictions. This study proposes a machine learning-based ensemble approach that uses multiple imputation by chained equations (MICE), K-nearest neighbors (KNN), and MissForest techniques to impute the missing values by reducing uncertainties and improves robustness in predictions. Furthermore, Synthetic Minority Oversampling Technique (SMOTE R) addresses data imbalance in CO2 emissions prediction by generating synthetic data to balance the target variable's distribution in the regression problem. Thereby the proposed SMOTE R and ensemble imputation approach aims to improve prediction accuracy and reliability by tackling data completeness and distribution issues simultaneously. The experiments are conducted on the GRACEnet database shows that the proposed approach outperforms the existing work in measure of R2, MAE, and RMSE metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Greenhouse gas emissions

来源：评论

学校读者我要写书评

暂无评论

A crop disease severity index derived from transfer learning and feature fusion using enhanced OPTICS algorithm

引用

International Journal of Hybrid Intelligent Systems 2024年第3期20卷 207-221页

作者： Subbiah, Priyanga Tyagi, Amit Kumar Krishnaraj, N. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperfor

关键词： deep learning disease detection enhanced OPTICS algorithm Image processing image segmentation infection severity tomato plant diseases transfer learning

来源：评论

学校读者我要写书评

暂无评论

Patient activity recognition for robust performance under varied illumination and cluttered background in indoor environment

引用

International Journal of computers and Applications 2024年第12期46卷 1219-1245页

作者： Sain, Manoj Kumar Laskar, Rabul Singha, Joyeeta Saini, Sandeep Department of Computer Science Banasthali Vidyapaith Niwai India Department of Electronics and Communication National Institute of Technology Silchar India

Human Activity Recognition (HAR) holds significant importance in health and human-machine interaction. However, recognizing actions from 2D information faces challenges like occlusion, illumination variation, cluttered backgrounds, and view invariance. These hurdles are particularly pronounced in indoor patient monitoring settings due to fluctuating lighting conditions and cluttered backgrounds, which compromise the accuracy of activity recognition systems. A new architecture named illuminationRevive has been proposed to tackle this issue. Which utilizes an encoder-decoder convolutional neural network and image post-processing blocks to enhance the image's visual appearance. A new dataset comprising seven indoor physical activities has been proposed, created with contributions from thirty individuals aged 20 to 45. A hybrid fusion architecture is proposed to classify activities, integrating motion sequence information and body joint features. The proposed classification model incorporates generated Skeleton Motion History Images (SMHIs), collected human joint motion features from video frames, and novel kinematic and geometric features within window frames as inputs. The model can extract spatial and temporal feature vectors by integrating ResNet50-ViT (Residual Network-50 layers, Vision Transformer) and CNN-BiLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) layers. The suggested classification model was evaluated alongside state-of-the-art models using the LNMIIT-SMD (The LNM institute of Information technology-Skelton Motion Dataset) and established NTU-RGBD (Nanyang Technological University's Red Blue Green and Depth information) dataset. The evaluation aimed to assess the effectiveness of the proposed classification model architecture. Results demonstrate the superiority of the proposed model, achieving impressive accuracies of 98.21% on real-time data, 98.45% on the proposed dataset, and 97.12% on the NTU-RGBD dataset. © 2024 Informa UK

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Gesture and Emotion Detection using Quantum Computing for Enhanced Recognition and Analysis 4

Gesture and Emotion Detection using Quantum Computing for En...

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Suraj Jai Krishna, S. Anish, M. Posonia, A. Mary Albert Mayan, J. Asha, P. Sathyabama Institute of Science and Technology Department of Computer Science and Technology Chennai India

ISBN: (纸本)9798350385793

The research on 'Gesture and Emotion Detection using Quantum Computing' is driven by an increasing need to overcome the linguistic and emotional barriers encountered by the Deaf community. With a deep understanding of the complexity of American Sign Language (ASL), this research bridges the communication gap by seamlessly translating ASL into speech, thereby increasing the accessibility for a wider audience. Going beyond mere translation, this research study aims to integrate the converted speech with the emotional expressions inherent in sign language. The proposed research idea stems from the recognition that existing communication technologies often overlook the rich emotional content conveyed through ASL. By integrating facial emotion identification, this research study aims to capture a diverse range of emotions embedded in sign language communication. This dual approach not only increases human understanding but also enhances the inclusivity in linguistic and emotional experiences for both Deaf and hearing communities. Ultimately, the objective of this research study is to develop a comprehensive system capable of accurately recognizing ASL, transforming it into articulate speech, and simultaneously conveying the emotional depth inherent in sign language expression. Through this research study, the emotion-enhanced sign language to speech conversion research seeks to redefine the communication accessibility, enabling a more interconnected and empathetic society. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Single-Valued Neutrosophic Graph with Heptapartitioend Structure

引用

Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

来源：评论

学校读者我要写书评

暂无评论

Automated Brain Tumor detection using multi-label images of MRI scans and CNNs 2

Automated Brain Tumor detection using multi-label images of ...

引用

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and networking Technologies, ViTECoN 2023

作者： Patel, Aman Priya, Nidumoli Gowthami Divya, G. SRM Institute of Science and Technology Department of Networking and Communications Tamilnadu Kattankulathur India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India

ISBN: (纸本)9798350347982

Numerous medical diagnostic applications now heavily rely on automatic defect detection in medical imaging. Automatically detecting tumors by MRI is essential for treatment planning because it offers details on aberrant tissues. Due to the volume of data required, this strategy is impracticable. As a result, to lower the rate of human death, trustworthy and automatic classification techniques are required. Automated tumor detection techniques are therefore being created to free up radiologist time and attain proven accuracy. Brain tumor, which develops as a result of the abnormal development and division of brain cells, eventually turn into brain cancer. The study of human health benefits greatly from the use of computer vision since it is used to eliminate the requirement for precise human judgement. The most reliable diagnostic tools include MRI scans, CT scans, and X-rays. Secure imaging techniques within magnetic-resonance imaging (MRI). In this study, the noises present in an MR image were removed using a morphological opening to the pre-processing. Binary thresholding and Neural Network segmentation methods were then used to accurately detect tumors. Our model will assess if the person has a brain tumor or not. To increase the accuracy of different models and scaling methods such as Efficient B2, B3, and B6, we want to test and experiment with them. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Segmentation of Retinal Pigment Epithelium Layer in Retinal OCT Images 5

Segmentation of Retinal Pigment Epithelium Layer in Retinal ...

引用

5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Sheeba, T.M. Antony Raj, S. Albert Anand, M. SRM Institute of Science and Technology Kattankulathur Faculty of Science and Humanities Department of Computer Applications Chennai India SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350305449

Optical Coherence Tomography (OCT) is a relatively recent procedure for studying eyes that has proven to be quite beneficial. We propose automatic detection of the RPE (Retinal Pigment Epithelium) layer in Retinal OCT images in this work. The bottom layer of the retina in retinal OCT images is known as the RPE layer, which is extracted in this study and may be studied to diagnose retinal disorders. After effectively increasing the quality of the OCT image using the Wiener filter, we extract the Retinal Pigment Epithelium layer using the Threshold approach. The proposed approach is tested using 40 OCT images of the retina. The results demonstrate 95.50 percent accuracy in RPE layer segmentation. © 2023 IEEE.

关键词： Optical tomography

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：