检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Electrical and Computer Engineering Department Western University LondonON Canada Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students’ progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students’ performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

AKQ: A Hybrid Quantum-Classical Image Encryption System

AKQ: A Hybrid Quantum-Classical Image Encryption System

引用

IEEE International Conference on Research, Innovation and Vision for the Future

作者： Tran Khanh Nguyen Nguyen Thi Thanh Truc Vu Tuan Hai Faculty of Computer Science University of Information Technology VNU-HCM Ho Chi Minh city Vietnam Faculty of Software Engineering University of Information Technology VNU-HCM Ho Chi Minh city Vietnam

Image encryption is an essential field between computer vision and security which aims to protect the image before future attacks. Nowadays, it is threatened by quantum computers, which can break some classical cryptography. The encryption system has been upgraded from quantum key exchange protocol to post-quantum key-establishment mechanisms, but they are not explicitly designed for the image encryption task. In this paper, we propose a hybrid quantum-classical theoretical framework and its instance AKQ system which is more secure and suitable in the post-quantum era.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Life on land: An accurate centralized system for ensuring the safety and wellbeing of domestic and wild animals

TechRxiv

引用

TechRxiv 2023年

作者： Heshan, Adithya B.M. Wickramathilake, Shamilka K.W.D. Gunathilake, Dulmi W.M.P. Herath, Hiruni H.M.S. Samarasekara, Piyumika de Silva, Dilshan Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

The web application described above has the potential to make a significant contribution to the conservation of endangered wildlife and the responsible management of domestic animals. The application addresses a critical issue that has far-reaching implications for the health of ecosystems and the well-being of animals. By providing users with tools to track and manage animal populations, monitor their health and behavior, and implement conservation efforts, the application empowers individuals and organizations to take action and make a positive impact on the environment. In addition to its practical benefits, the web application also includes educational resources and supports community engagement to raise awareness about the importance of preserving biodiversity and protecting animal welfare. This holistic approach recognizes the complex and interconnected nature of conservation and management efforts, and highlights the need for collaboration and cooperation among stakeholders. The user-friendly interface of the application ensures that it is accessible to a wide range of users, including individuals with limited technical expertise. The ability to collaborate and share data also promotes transparency and accountability, which are essential for effective conservation and management efforts. Overall, this web application has the potential to be a valuable tool for those involved in animal conservation and management, and it offers hope for the protection of endangered wildlife and the responsible management of domestic animals. © 2023, CC BY.

关键词： Animals

来源：评论

学校读者我要写书评

暂无评论

Validating IP Reputation in Cloud Firewall Systems Using Machine Learning Driven Signature Generation and Detection Techniques

Validating IP Reputation in Cloud Firewall Systems Using Mac...

引用

IEEE Industrial Electronics and Applications Conference (IEACon)

作者： NW Chanka Lasantha Valmik Tilwari MWP Maduranga Ruvan Abeysekara Nilotpal Chakraborty Debashree Sharma Faculty of Graduate Studies IIC University of Technology Phnom Penh Cambodia Department of Electronics & Communication Engineering Indian Institute of Information Technology Guwahati India Department of Software Engineering Sri Lanka Technology Campus Padukka Sri Lanka Department of Computer Science Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798350382976

ISBN: (纸本)9798350382983

This paper offers a new approach to validation for IP address reputation (IPR) using dynamic signatures achieved through the application of machine learning (ML) to conduct an accurate detection of malicious IPs. The proposed solution connects to the Amazon Web Services (AWS) Web Application Firewall (WAF) to improve security by deploying artificial intelligence (AI) models and natural language (NL) processing for automation of information extraction from IP-related information saved in the AWS object storages. All these lead to minimization of the number of false positives, real-time feedback, and optimization of IP reputation checking. The experimental outcome proves the capability of the proposed system to mitigate the IP address of the attacker and improve the protection of web applications against cybercrimes. This new approach is much advanced and a major leap ahead of other conventional IP reputation techniques, which offer a good protection shield against spam, DDoS, and other such evils.

关键词： Radio frequency Accuracy Firewalls (computing) Web services Natural languages Real-time systems IP networks Protection computer crime Optimization

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Deep Learning based Brain Tumor Classification for MR Images using ResNet50

Deep Learning based Brain Tumor Classification for MR Images...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Ömer Miraç Kökçam Aytuğ Boyaci Muhammed Emre Çolak Department of Software Engineering Faculty of Technology Fırat University Elazığ Türkiye Department of Computer Engineering Air Force Academy National Defence University İstanbul Türkiye Department of Artificial Intelligence and Data Science Engineering Faculty of Engineering Fırat University Elazığ Türkiye

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Brain tumors are abnormal cell growths that occur in various parts of the brain, and the accurate classification of these tumors plays a critical role in determining treatment methods. Classification and diagnosis of brain tumors based on artificial intelligence and deep learning models have been made possible due to advances in medical image processing technologies. For instance, such technologies facilitate quick detection of tumors by health experts thereby improving early diagnosis rate and hence enhancing treatment outcomes. Importantly, however, one considers improvement or breakthroughs in artificial intelligence-based classification systems as a crucial step forward towards brain tumor diagnosis as well as its treatment strategy. In this study we use Google Research's ImageNet-21k pre-trained ResNet50 model for classifying brain tumor cases. This model is a deep learning-based image classification tool and is capable of learning from large data sets. The model is specifically trained for high-resolution image recognition and is designed to achieve high accuracy rates. The results showed a classification accuracy of 99.9 percent, which is an exceptional accuracy rate for this model. This accuracy rate indicates that the model is highly effective in detecting and classifying brain tumors. High accuracy rates allow for early diagnosis of patients' diseases and, as it is often said in medicine, “Early Diagnosis Saves Lives”. This result is one of the many proofs of how effective artificial intelligence and deep learning are in medical image analysis.

关键词： Deep learning Image recognition Brain modeling Internet Security Artificial intelligence Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Comparison of IDS in IoT, Mobile and Cloud based Systems

Comparison of IDS in IoT, Mobile and Cloud based Systems

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Görkem Gök Aytuğ Boyacı Mustafa Ulaş Department of Software Engineering Faculty of Technology Fırat University Elazığ Türkiye Department of Computer Engineering Air Force Academy National Defence University İstanbul Türkiye Department of Artificial Intelligence and Data Science Engineering Faculty Of Engineering Fırat University Elazığ Türkiye

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

This study is meant to research the evolution of intrusion detection and network monitoring within computer, cloud-based systems, IIoT, and mobile environments. The source has outlined the novel technologies in IDS, from “Fast Learning Network” to hybrid classifiers aiming at protecting cloud computing. The findings highlight innovation IDS technologies that provide flexible and peculiar solutions to ward off potential cyber threats. In addition, it presented with the use of Nagios, NConf, the state of the art in network traffic monitoring, and deep learning as tools for advanced management and network protection. Besides, the research also reviewed concepts of Big Data Analytics and Federated Learning in bettering direct integration and anomaly detection in IIoT. A good comparison of the two would show that recent technological advancements are much needed for network efficiency and its cybersecurity. Based on the findings, the scope of networked system security develops dynamically amid current challenges.

关键词： Deep learning Performance evaluation Cloud computing Technological innovation Federated learning computer hacking Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

The STIRData Approach to Interoperability of European Company High-Value Datasets

引用

SN computer Science 2024年第4期5卷 1-20页

作者： Klímek, Jakub Chortaras, Alexandros Míšek, Jakub Yang, Jim J. Skagemo, Steinar Tzouvaras, Vassilis Department of Software Engineering Faculty of Mathematics and Physics Charles University Malostranské nám. 25 Prague 118 00 Czech Republic School of Electrical and Computer Engineering National Technical University of Athens 9 Iroon Polytechniou St Athens 157 80 Greece Institute of Law and Technology Masaryk University Faculty of Law Veveří 70 Brno 60200 Czech Republic Norwegian Digitalisation Agency Postboks 1382 Vika Oslo 0114 Norway Brønnøysund Register Centre Postboks 900 Brønnøysund 8910 Norway

The European Commission has published a list of high-value datasets (HVDs) that public sector bodies must make available as open data as part of the Open Data Directive. One of the HVD topics is company data. Although the HVD description contains items that must be included in these datasets, it does not prescribe any technical means of how the data should be published. This is a major obstacle to the interoperability of the datasets once they are published. In this extended paper, we elaborate on the results of STIRData, a project co-financed by the Connecting Europe Facility Programme of the European Union, focusing on various aspects of data interoperability of open data from business registries, covering the company data HVDs topic. These aspects include the semantic, technical, and legal interoperability of this data. The results include a data architecture and a data specification to make the published data technically and semantically interoperable. In addition, we present basic legal interoperability guidelines to ensure legal interoperability of the published data, which is a topic often neglected by technically focused data experts. The project results include proof-of-concept transformations of data from selected European business registries using open source tools and in accordance with the data specification. Moreover, a user-orientated platform for browsing and analysing the data is presented as an example of the possibilities of using the data published in an interoperable way. Finally, we present an example of how compliant data can be processed by data experts for further analysis. © The Author(s) 2024.

关键词： Company data Data spaces High-value datasets (HVDs) Interoperability Linked data Open data

来源：评论

学校读者我要写书评

暂无评论

Critical research trends of mobile technology-supported English language learning: A review of the top 100 highly cited articles

引用

Education and information technologies 2023年第5期28卷 4849-4874页

作者： Monther M Elaish Mahmood H Hussein Gwo-Jen Hwang Benghazi Libya Department of Computer Science Faculty of Information Technology University of Benghazi. Kuala Lumpur Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya. Taipei Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology. Taoyuan Taiwan Yuan Ze University.

Around the world, the number of English speakers and the significance of the English language are constantly increasing. Among various technology-supported instructional styles, Mobile Learning (M-Learning) has been recognized as a promising approach to enhance students' competencies and skills in the English language. By examining previous literature, a number of reviews have been performed to investigate the role of M-learning in the English language. However, none of these studies has highlighted the trends, opportunities, and challenges identified in the most cited articles that focused solely on the English language. Therefore, to address these limitations, this study performed a review of the top 100 most cited articles, published between 2007 and 2020, indexed by the Web of Science, and addressing the English language only. The results revealed that most research in Mobile English Language Learning (M-ELL) followed an experimental design and employed a single mobile learning implementation. Additionally, the current study identified a number of research areas that require additional research attention. For example, further research is needed among students learning from home, more qualitative research is needed, and additional research is required to improve students' higher-order thinking skills. The outcomes of this study provide a reference to researchers and educators who intend to use mobile technologies in the area of language education, especially in the context of the English language.

关键词： Applications in subject areas M-ELL Mobile learning Pedagogical issues Teaching/learning strategies

来源：评论

学校读者我要写书评

暂无评论

An integrated fuzzy adjusted cosine similarity and TOPSIS based recommendation system for information system requirements selection

引用

Decision Analytics Journal 2024年 11卷

作者： Akram, Faiz Ahmad, Tanvir Sadiq, Mohd. Department of Computer Engineering Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi 110025 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi 110025 India

Selecting the requirements for an information system based on their ranking values is one of the critical steps in software development. Various methods for computing the ranking order of the requirements have been developed using multi-criteria decision-making methods. This study shows selecting the requirements for different software releases using a recommendation system is an unresearched problem. We present a recommendation system (PRecSys) for computing the ranking order of the requirements in an information system using fuzzy-based adjusted cosine similarity measure and collaborative filtering to recommend the requirements to stakeholders with similar interests. The ranking order of the information systems requirements in PRecSys is computed using the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The applicability of PRecSys is discussed by considering the requirements for a library information system. The PRecSys is compared with PHandler, an expert system for choosing the requirements, based on the following criteria: agreement measure, the number of judgments by decision-makers, and time complexity. © 2024 The Authors

关键词： Collaborative filtering Fuzzy TOPSIS Library information system Multi-criteria decision making Recommendation system Requirements elicitation

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：