检索结果-内蒙古大学图书馆

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

引用

computer Modeling in engineering & Sciences 2024年第2期138卷 1951-1979页

作者： Salma Khan Muhammad Gulistan NasreenKausar Seifedine Kadry Jungeun Kim Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityP.O.Box 13-5053ByblosLebanon Department of Software and CMPSI Konju National UniversityCheonan31080Korea

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying *** approach can capture the imprecision and ambiguity often present in human *** the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making *** implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

关键词： q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator

来源：评论

学校读者我要写书评

暂无评论

Robust In-Vehicle Diagnostic-Based Intrusion Detection System

Robust In-Vehicle Diagnostic-Based Intrusion Detection Syste...

引用

International Symposium on Networks, computers and Communications (ISNCC)

作者： Ahmed Shafee Tasneem A. Awaad Ahmed Moro Department of Engineering and Computer Science Adams State University Alamosa CO USA Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt Siemens EDA Cairo Egypt Integrated Electrical Systems Segment Siemens Digital Industries Software Cairo Egypt

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic Control Units (ECUs) have evolved to become the central control units for critical car functions such as engines and brakes, experiencing rapid technological advancements. However, this swift progression in ECU technology has also made them prime targets for cyber attacks. This vulnerability has spurred researchers to focus on securing ECUs. Numerous studies have proposed intrusion detection systems (IDS) to protect against attacks on ECUs in vehicles. Yet, these IDSs are not impenetrable; attackers can exploit them by launching evasion attacks, which can trigger numerous false positive alarms. Such false alarms can be disruptive and potentially hazardous for drivers. Additionally, attackers can evade IDSs from detecting malicious data that can cause harm to the vehicle. Accordingly, in this paper, we propose a novel training framework to train a robust in-vehicle IDS that can encounter evasion attacks. Our methodology is based on implementing two rounds of mimic learning technique for training Random Forest (RF) based IDS. RF has been chosen to incorporate the randomness of the RF architecture to enhance the robustness of the model. Additionally, in each round of the two rounds of the mimic learning technique, an RF model with a different architecture is chosen to improve the resilience of the model against evasion attacks without affecting its accuracy. Our Experimental results have shown the effectiveness of our framework against evasion attacks.

关键词： Radio frequency Training Computational modeling Intrusion detection computer architecture Robustness Automobiles Random forests Vehicles Resilience

来源：评论

学校读者我要写书评

暂无评论

Dependency Update Strategies and Package Characteristics

arXiv

引用

arXiv 2023年

作者： Jafari, Abbas Javan Costa, Diego Elias Shihab, Emad Abdalkareem, Rabe Lab The Department of Computer Science and Software Engineering Concordia University Montreal Canada Montreal Canada Department of Computer Science The Faculty of Science Omar Al-Mukhtar University Libya

Managing project dependencies is a key maintenance issue in software development. Developers need to choose an update strategy that allows them to receive important updates and fixes while protecting them from breaking changes. Semantic Versioning was proposed to address this dilemma but many have opted for more restrictive or permissive alternatives. This empirical study explores the association between package characteristics and the dependency update strategy selected by its dependents to understand how developers select and change their update strategies. We study over 112,000 npm packages and use 19 characteristics to build a prediction model that identifies the common dependency update strategy for each package. Our model achieves a minimum improvement of 72% over the baselines and is much better aligned with community decisions than the npm default strategy. We investigate how different package characteristics can influence the predicted update strategy and find that dependent count, age and release status to be the highest influencing features. We complement the work with qualitative analyses of 160 packages to investigate the evolution of update strategies. While the common update strategy remains consistent for many packages, certain events such as the release of the 1.0.0 version or breaking changes influence the selected update strategy over time. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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

Learning Shape-Preserving Autoencoder for the Reconstruction of Functional Data from Noisy Observations 23rd

Learning Shape-Preserving Autoencoder for the Reconstructi...

引用

23rd International Conference on Computational Science, ICCS 2023

作者： Krzyżak, Adam Rafajlowicz, Wojciech Rafajlowicz, Ewaryst Department of Computer Science and Software Engineering Concordia University 1455 De Maisonneuve Blvd. West MontrealQCH3G 1M8 Canada Szczecin Poland Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031360206

We propose a new autoencoder preserving input functions’ general shape (monotonicity, convexity) after their reconstruction without imposing a priori constraints. These properties are inherent to the coefficients of the Bernstein-Durrmeyer polynomials that serve here as theoretical descriptors. Their estimates, computed from noisy observations by the coder, play the role of latent variables. The approach is purely nonparametric, i.e., no prior finite-dimensional model is assumed. The answer to the question of how many latent variables should be used for an acceptable reconstruction accuracy of a family of functional data is inferred from learning based on the proposed approximation of the Akaike Information Criterion. A distinguishing feature of this autoencoder is that the coder and encoder are designed as precomputed and stored matrices with the Bernstein polynomial entries. Thus, after selecting the number of latent variables, the autoencoder usage has a low computational complexity since it is linear with respect to observations. The proposed computational algorithms are tested on real data arising in mechanical engineering when control of damping vibrations is necessary. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Polynomials

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

Medical Expert System to Diagnose the Most Common Psychiatric Diseases 21

Medical Expert System to Diagnose the Most Common Psychiatri...

引用

7th International Conference on engineering and MIS, ICEMIS 2021

作者： Alsagheer, Eman A. Rajab, Hamzah A. Elnajar, Khadiga M.Y. Department of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Department of Software Engineering Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (纸本)9781450390446

Expert Systems are programs that employ artificial intelligence and mimic the performance of human experts in a certain field by gathering and capturing expert information. In this paper, we present a rule-based expert system to provide the diagnosis for the most common psychiatric diseases to facilitate the diagnosis process. This system diagnoses four types of psychiatric diseases Anxiety Disorder (generalized anxiety, panic disorder, OCD, and phobic disorder), Stress-related disorder, Depression, and Schizophrenia using the International Classification of Diseases 10th Revision. The knowledge used in this system is acquired from domain experts, and relevant medical books and documents to capture knowledge that is valid as much as possible. Then, the acquired knowledge is demonstrated using a diagnostic chart, and decision table that represents the signs and symptoms involved in the diagnosis. A rule-based expert system and production rules that represent the domain knowledge are built using SWI-PROLOG Editor. The system is based on backward chaining which starts with possible goals and attempts to collect information that confirms it. The testing result shows the average accuracy of the system register 80% in diagnosing patients with psychiatric diseases. © 2021 ACM.

关键词： Expert systems

来源：评论

学校读者我要写书评

暂无评论

Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects

引用

SN computer Science 2021年第3期2卷 219页

作者： Feroze, Aqeel Daud, Ali Amjad, Tehmina Hayat, Malik Khizar Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering Jeddah University Jeddah Saudi Arabia Department of Information Technology Faculty of Information Technology and Engineering The University of Haripur Haripur Pakistan

Anomaly detection has evolved as a successful research subject in the areas such as bibliometrics, informatics and computer networks including security-based and social networks. Almost all existing anomaly detection techniques have some limitations and do not focus specifically on detecting anomalous groups. Anomaly detection is also a crucial problem in processing large-scale datasets when our goal is to find abnormal values or unusual events. The authors decided to survey existing group anomaly detection techniques because there is a need to consider group anomalies for mitigation of risks, prevention of malicious collaborative activities, and other interesting explanatory insights by identifying groups that are not consistent with regular group patterns. In this research, we bifurcated group anomaly detection techniques into activity-based and graph-based methods. The graphical methodologies are then further classified under static versus dynamic and attributed versus plain graph methods. We have also listed the datasets used in various studies to detect group anomalies along with detected anomalies and the various performance measures used to validate the results. Towards the end, we have provided various applications of group anomaly detection and the research challenges that group anomaly detection presents to the scientific community and enlisted some of the future trends for this particular research area. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Academic social network Dynamic Graph-centric features Group anomaly detection Performance metrics Static

来源：评论

学校读者我要写书评

暂无评论

Studies on the Quality Assessment of PRNG for Q-Trits Quantum Cryptography Protocols 8

Studies on the Quality Assessment of PRNG for Q-Trits Quantu...

引用

8th IEEE International Conference on Problems of Infocommunications, Science and Technology, PIC S and T 2021

作者： Gnatyuk, Sergiy Okhrimenko, Tetiana Fesenko, Andrii Dorozhynskyi, Sergiy Computer And Software Engineering National Aviation University Faculty of Cybersecurity Kyiv Ukraine Taras Shevchenko National University of Kyiv Faculty of Information Technology Kyiv Ukraine

ISBN: (纸本)9781665406826

Cybersecurity contains three basic features such as confidentiality, integrity and availability. Data confidentiality ensuring by encryption has faced quantum computing threat, but this problem can be solved by using post-quantum cryptography and/or quantum cryptography (quantum key distribution). For cryptographic applications statistical properties of the generated pseudorandom sequences should not be differ from random sequences. To determine such properties a number of statistical testing methods are used, but they all are focused on the evaluation of binary sequences. In quantum cryptography there are some protocols with Q-trits (qutrits), but these sequences cannot be assessed effectively by binary statistic methods. From this position in the paper quality assessment approach for Q-trits pseudo random number generators and formed sequences was proposed. Also the experimental study of proposed approach (in comparison with NIST STS) was carried out. Given results show that NIST STS technique cannot be used to evaluate the quality of the Q-trit sequences and generators, but developed method is suitable to use in practice for evaluating Q-trit sequences (generators) quality. © 2021 IEEE.

关键词： Random number generation

来源：评论

学校读者我要写书评

暂无评论

QoE-oriented Soft Caching with Content Recommendation for Edge Computing Networks

QoE-oriented Soft Caching with Content Recommendation for Ed...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Chenyang Chen, Yan Sun, Chuan Jia, Bosen Wang, Xiaofei Taleb, Tarik Leung, Victor C. M. Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen518132 China Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum Germany Nanyang Technological University College of Computing and Data Science Singapore China Unicom Digital Technology Co. Ltd Beijing China Tianjin University College of Intelligence and Computing Tianjin China University of British Columbia Vancouver Canada

ISBN: (纸本)9798350351255

Mobile Edge Caching (MEC) can potentially alleviate Internet transmission congestion by delivering content at the network edge. However, current MEC solutions suffer from low resource utilization efficiency and often fail to meet user Quality of Experience (QoE), primarily due to dynamic user requests and obsessive pursuit of direct caching hits. Given the prevalence of recommendation systems, users often lack precise requests when using recommendation-based applications like TikTok and Taobao, insted passively enjoying recommended content. In this paper, we introduce a recommendation-enabled MEC architecture to enhance resource utilization and QoE. We develop a recommendation-enabled soft caching model and formulate the optimization problem as maximizing joint system revenue. To address this, we propose an attention-assisted federated learning deep Q-network algorithm. We conduct the simulations by using the real-world MIND dataset. The results demonstrate that our proposed algorithm outperforms existing baselines, demonstrating its effectiveness in improving resource utilization and QoE. © 2024 IEEE.

关键词： Mobile edge computing

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：