检索结果-内蒙古大学图书馆

Arabic Fake News Detection Using Deep Learning

computers, Materials & Continua 2022年第5期71卷 3647-3665页

作者： Khaled M.Fouad Sahar F.Sabbeh Walaa Medhat Faculty of Computers&Artificial Intelligence Benha UniversityEgypt University of Jeddah College of Computer Science and EngineeringJeddah21493Saudi Arabia Information Technology and Computer Science Nile UniversityEgypt

Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online ***,because user-generated content is unregulated,it may contain offensive content such as fake news,insults,and harassment *** identification of fake news and rumors and their dissemination on social media has become a critical *** have adverse effects on users,businesses,enterprises,and even political regimes and *** of the art has tackled the English language for news and used feature-based *** paper proposes a model architecture to detect fake news in the Arabic language by using only textual *** learning and deep learning algorithms were *** deep learning models are used depending on conventional neural nets(CNN),long short-term memory(LSTM),bidirectional LSTM(BiLSTM),CNN+LSTM,and CNN+*** datasets were used in the experiments,each containing the textual content of Arabic news articles;one of them is reallife *** results indicate that the BiLSTM model outperforms the other models regarding accuracy rate when both simple data split and recursive training modes are used in the training process.

关键词： Fake news detection deep learning machine learning natural language processing

来源：评论

学校读者我要写书评

暂无评论

The Risks & Realities of Picking an IoT OS

The Risks & Realities of Picking an IoT OS

引用

computer Applications (ICCA), IEEE Conference on

作者： Pyae Sone Hein Zar Zar Linn Master of Engineering in Computer Science Myanmar Institute of Information Technology Faculty of Computer Science Myanmar Institute of Information Technology

ISBN: (数字)9798331534592

ISBN: (纸本)9798331534608

Internet of Things is no longer a new topic. Long ago, the network of things was a rare thing which could be seen only in offices, industries, and labs. Nowadays it is approaching becoming a ubiquitous appliance as a commonplace in everyday environments. Homes and public places such as traffic and city parks have changed to adapt with things within the ability to connect to a network. As a result, even commoners are becoming familiar with IoT. Researchers lay the new technologies into IoT networks. Developers drive them into industry and practitioners carry the aesthetic essence by carrying on the practice among people. In doing so, choosing the correct operating system for the correct device is a step not to be skipped as the learning curve grows up to advanced. This study aims to aid the practitioners by discussing the risks and realities concerned with the role of OS in IoT devices or systems.

关键词： Industries Job shop scheduling Systematics Operating systems Urban areas Switches Systems engineering and theory Real-time systems Internet of Things Stress

来源：评论

学校读者我要写书评

暂无评论

Effect of Using Continuous Integration (CI) and Continuous Delivery (CD) Deployment in DevOps to reduce the Gap between Developer and Operation 23

Effect of Using Continuous Integration (CI) and Continuous D...

引用

23rd International Arab Conference on information technology, ACIT 2022

作者： Mowad, Abrar Mohammad Fawareh, Hamed Hassan, Mohammad A. Zarqa University Faculty of Information Technology Software Engineering Dept Jordan Zarqa University Faculty of Information Technology Computer Science Dept Jordan

ISBN: (纸本)9798350320244

This paper focuses on how to use continuous integration (CI) and continuous Delivery (CD) methodology in DevOps to reduce the developer-operator gap. It also, shows how CI can be a CD bridge. The paper review DevOps and analyze strategies, methodologies, issues, and processes identified for the adoption and implementation of continuing practices. The result of our case studies shows the benefits, and advantages of using CI/CD in software development. Furthermore, this paper presents DevOps as a new model for reducing the gaps between development (Dev) and operations (ops). The Azure tool is used as DevOps CI/CD for the empowerment of continuous delivery of software to enable rapid and frequent releases, this enables rapid responses to changing customer requirements and thus it may be a decisive competitive advantage. This paper also measures the effectiveness of using CI/CD for reducing the time and effort in software development. We also, focus on the DevOps initiative to benefit of CI/CD and to effect of enhances flexibility in delivering the program with the expected quality on time to determine the areas that bridge the gap between Continuous Integration for Continuous Delivery. The methodology used in this paper by exploring and tracking a project developed by the company using the Azure software development tool. The experiment includes a project performance and evaluation. © 2022 IEEE.

关键词： Integration

来源：评论

学校读者我要写书评

暂无评论

Investigating the Determinants of Compliance Intention in Behavior Change Support Systems 29

Investigating the Determinants of Compliance Intention in Be...

引用

29th Annual Americas Conference on information Systems: Diving into Uncharted Waters, AMCIS 2023

作者： Ekpezu, Akon Obu Oinas-Kukkonen, Harri Wiafe, Isaac Faculty of Information Technology and Electrical Engineering Oulu Advanced Research on Service and Information Systems University of Oulu Finland Department of Computer Science University of Ghana Ghana

ISBN: (纸本)9781713893592

The effectiveness of behavior change support systems (BCSS) in promoting health and well-being is unflinching. However, its long-term effectiveness is hindered by non-compliance. Research in BCSS that focuses on compliance is scarce, particularly the determinants of compliance intention are not well established. To address this gap, this study investigated the determinants of compliance intention in BCSS by extending the persuasive system design model to include competence, effectiveness, enjoyment, and persuasiveness. By surveying a sample of prospective BCSS users (N=234), partial least squares structural equation modeling was used to evaluate the proposed model. The findings suggest that the prospective users’ perceptions of primary task support and dialogue support, as well as social support and credibility support significantly influenced their perceptions of self-competence and system effectiveness respectively. These perceptions influenced enjoyment, then persuasiveness, and finally compliance intention. Future studies will validate these findings on the compliance behavior of BCSS. © 2023 29th Annual Americas Conference on information Systems, AMCIS 2023. All rights reserved.

关键词： Behavior Change Support Systems Compliance Intention Persuasive Systems Design Persuasive technology Physical Activity

来源：评论

学校读者我要写书评

暂无评论

Optimization Accuracy of CNN Model by Utilizing CLAHE Parameters in Image Classification Problems

Optimization Accuracy of CNN Model by Utilizing CLAHE Parame...

引用

2023 International Conference on Networking, Electrical engineering, computer Science, and technology, IConNECT 2023

作者： Wanto, Anjar Yuhandri, Yuhandri Okfalisa, Okfalisa Universitas Putra Indonesia Yptk Padang Information Technology Faculty of Computer Science Padang Indonesia Universitas Putra Indonesia Yptk Padang Faculty of Computer Science Department of Information Technology Padang Indonesia Universitas Islam Negeri Sultan Syarif Faculty of Science and Technology Department Informatics Engineering Kasim Riau Indonesia

ISBN: (纸本)9798350331172

This study aims to optimize popular Convolutional Neural Network (CNN) models, such as ResNet50, InceptionV4, VGG19, MobileNetV1, MobileNetV2, MobileNetV3 Small, and MobileNetV3 Large, by utilizing the CLAHE (Contrast Limited Adaptive Histogram Equalization) parameter. This research focuses on the problem of classifying retinal fundus images, which have challenges in analyzing the structure and features of the picture. This study compared the performance of CNN models, which were optimized using the CLAHE method as image pre-processing. The fundus retinal image data used is a publicly available dataset. The optimization process is done by changing the parameters in the CNN model and applying the CLAHE technique to improve image quality before the classification process. The results showed that using the CLAHE parameter significantly contributed to improving the performance of the CNN model in classifying retinal fundus images. Some CNN models optimized with CLAHE produce better accuracy than the unoptimized models. There are also variations in performance between the different models, with some models providing better results in classifying retinal fundus images. This study provides new insights regarding using CLAHE parameters in optimizing the CNN model for retinal fundus image classification problems. The results of this research can be the basis for further development in medical image processing and pattern recognition for more accurate and practical diagnostic applications. © 2023 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Enhancing Convergence Efficiency in Federated Learning Models 25

Enhancing Convergence Efficiency in Federated Learning Model...

引用

25th International Arab Conference on information technology, ACIT 2024

作者： Samara, Ghassan Elhilo, Ala'Mazen Aljaidi, Mohammad Alamleh, Amneh Al-Mousa, Mohammad Rasmi Batyha, Radwan Alazaidah, Raed Bani-Bakr, Alaa Elrashidi, Ali Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Department of Cybersecurity Zarqa Jordan Applied Science Private University Faculty of Information Technology Amman Jordan Zarqa University Faculty of It Department of Cyber Security Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

This study evaluates federated learning models' performance compared to centralized models using the MNIST and CIFAR-10 datasets. Four models were tested: two Federated Learning models and two centralized models. The evaluation focused on accuracy, training rounds, and CO2 emissions, to optimize convergence and reducing energy consumption while maintaining strong model performance. The results showed that Federated learning models outperformed centralized models, with the Federated Learning MNIST model achieving 98.79% accuracy compared to 87.93% for the centralized model. Also, the accuracy of the Federated Learning CIFAR-10 model achieved 91.20% accuracy, exceeding the centralized model's 90.14%. The most accuracy improvements happened in the early training rounds. An adjusted hill-climbing technique was used to the centralized models, decreasing the number of iterations and CO2 emissions without significantly reducing accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation

引用

Energy and AI 2024年第4期18卷 258-280页

作者： Junhao Song Yingfang Yuan Kaiwen Chang Bing Xu Jin Xuan Wei Pang School of Mathematical and Computer Sciences Heriot-Watt UniversityEdinburghUnited Kingdom Faculty of Engineering Imperial College LondonLondonUnited Kingdom Edinburgh Business School Heriot-Watt UniversityEdinburghUnited Kingdom Faculty of Engineering and Physical Sciences University of SurreySurreyUnited Kingdom School of Management Science and Engineering Nanjing University of Information Science and TechnologyNanjingChina

To advance the circular economy(CE),it is crucial to gain insights into the evolution of public attention,cognitive pathways related to circular products,and key public *** achieve these objectives,we collected data from diverse platforms,including Twitter,Reddit,and The Guardian,and utilised three topic models to analyse the *** the performance of topic modelling may vary depending on hyperparameter settings,we proposed a novel framework that integrates twin(single-and multi-objective)hyperparameter timisation op-for CE *** experiments were conducted to determine appropriate hyperparameters under different constraints,providing valuable insights into the correlations between CE and public *** findings reveal that economic implications of sustainability and circular practices,particularly around recyclable materials and environmentally sustainable technologies,remain a significant public *** related to sustainable development and environmental protection technologies are particularly prominent on The Guardian,while Twitter discussions are comparatively *** insights highlight the importance of targeted education programmes,business incentives adopt CE practices,and stringent waste management policies alongside improved recycling processes.

关键词： Circular economy Pulic attention Topic modelling Machine learning Hyperparameter optimisation

来源：评论

学校读者我要写书评

暂无评论

Theoretical Evaluation of Ensemble Machine Learning Techniques 5

Theoretical Evaluation of Ensemble Machine Learning Techniqu...

引用

5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Shah, Milind Kantawala, Harsh Gandhi, Kinjal Patel, Rohini Patel, Kinjal A. Kothari, Ankita Department of Computer Science and Engineering Gujarat Vadodara India Department of Computer Engineering G H Patel College of Engineering and Technology Gujarat Vallabh-Vidhyanagar India Gujarat Law Society University Faculty of Computer Applications and Information Technology Gujarat Ahmedabad India

ISBN: (纸本)9781665474672

The use of ensemble techniques is widely recognized as the most advanced approach to solving a variety of problems in machine learning. These strategies train many models and combine the results from all of those models in order to enhance the predictive performance of a single model. During the period of the last several years, the disciplines of artificial intelligence, pattern recognition, machine learning, neural networks, and data mining have all given a considerable consideration to the concept of ensemble learning. Ensemble Learning has shown both effectiveness and usefulness across a broad range of problem domains and in significant real-world applications. Ensemble learning is a technique thatinvolves the construction of many classifiers or a group of base learneis and the merging of their respective outputs in order to decrease the total variance. When compared to using only one classifier or one base learner at a time, the accuracy of the results achieved by combining numerous dassifiers or the set of base learners is greatly improved. It has been shown that the use of ensemble methods may increase the predicted accuracy of machine learning models for a range of tasks, including classification, regression, and the identification of outliers. This study will discuss about ensemble machine learning techniques and its various methods such as bagging, boosting, and stacking. finally, all the factors involved in bagging, boosting, and stacking are compared. © 2023 IEEE.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

Evaluating collaborative rationality-based decisions: A literature review

Evaluating collaborative rationality-based decisions: A lite...

引用

2022 International Conference on ENTERprise information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care information Systems and Technologies, HCist 2022

作者： Elgendy, Nada Elragal, Ahmed Päivärinta, Tero M3S Faculty of Information Technology and Electrical Engineering University of Oulu Finland Department of Computer Science Electrical and Space Engineering Lulea University of Technology Sweden

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-Ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions. © 2023 Elsevier B.V.. All rights reserved.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

引用

computers, Materials & Continua 2022年第8期72卷 3985-3997页

作者： Rabab Hamed M.Aly Aziza I.Hussein Kamel H.Rahouma The Higher Institute for Management and Information Technology Minya61768Egypt Department of Electrical and Computer Engineering Effat UniversityJeddahKSA Electrical Engineering Department Faculty of EngineeringMinia UniversityMinia6111Egypt

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’*** algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best *** work of this paper consists of two *** first part is based on collecting data of employees to calculate and illustrate the performance of each *** second part is based on the classification and prediction techniques of the employee *** model is designed to help companies in their decisions about the employees’*** classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the *** of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming *** also show that the Grasshopper Optimization,followed by“KF”with the Gradient Boosting Tree as classifier and predictor,is characterized by a high *** proposed algorithm is compared with other known techniques where our results are fund to be superior.

关键词： Metaheuristic algorithm KUWAHARA filter Grasshopper optimization algorithm and Gradient boosting tree

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：