检索结果-内蒙古大学图书馆

Intrusion Detection in 5G Cellular Network Using Machine Learning

computer Systems science & engineering 2023年第11期47卷 2439-2453页

作者： Ishtiaque Mahmood Tahir Alyas Sagheer Abbas Tariq Shahzad Qaiser Abbas Khmaies Ouahada Knowledge Unit of Systems and Technology UMT Sialkot CampusSialkot51040Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Faculty of Computer Science National College of Business Administration and EconomicsLahore54660Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia Department of Computer Science&IT University of SargodhaSargodha40100Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O Box 524Auckland ParkJohannesburg2006South Africa

Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing *** devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended *** on Internet of Things interfaces must be closely monitored in real time for effective safety and *** the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular *** to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 *** gives you the most recent *** countries have already established this technology within their *** is the most crucial aspect of using a 5G *** of the absence of study and network deployment,new technology first introduces new gaps for attackers and *** Protocol(IP)attacks and intrusion will become more prevalent in this *** efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this *** research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/*** applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.

关键词： Intrusion detection system machine learning confidentiality integrity availability

来源：评论

学校读者我要写书评

暂无评论

Medical Healthcare System with Hybrid Block based Predictive models for Quality preserving in Medical Images using Machine Learning Techniques

Medical Healthcare System with Hybrid Block based Predictive...

引用

2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Aruna, M. Arulkumar, V. Deepa, M. Latha, G. Charlyn Pushpa SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Chennai603203 India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu India Vellalar College of Engineering and Technology Tamilnadu India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Knowledge Engineering Department of IT Chennai India

ISBN: (纸本)9781665495158

Cloud technology is a business strategy that aims to provide the necessary material to customers depending on their needs. Individuals and cloud businesses alike have embraced the cloud storage service, which has become the most widely used service. The industries outsource their data to cloud storage space to relieve themselves of a load of dealing with redundant data contents. This must be protected to prevent the theft of personal belongings, and privacy must be improved as well. Different research projects have been suggested to ensure the safe management of the information included within the data content. The security of current research projects, on the other hand, still needs improvement. As a result, this method has been suggested to address the security concerns associated with cloud computing. The primary goal of this study effort is to offer a safe environment for cloud users while also increasing the profit of cloud resource providers by managing and securely delivering data contents to the cloud users. The bulk of sectors, including business, finance, the military, and the healthcare industry, do not store data in cloud-based storage systems. This technique is used to attract these kinds of customers. Increasing public acceptance Medical researchers are drawn to cloud computing because it allows them to store their study material in a centralized location and distribute and access it in a more flexible manner. They were collected from numerous individuals who were being evaluated for medical care at the time. Scalable and Enhanced Key Aggregate Cryptosystem is a protected data protection method that provides highly effective security in the health care industry. This approach handles disagreements in the outflow of sensitive information and guarantees the data security deployment of a Cloud-based Intelligent Health Monitoring system for the parties involved in the dispute. Using the suggested method, the encrypted data format of medical and health-care

关键词： Aggregates

来源：评论

学校读者我要写书评

暂无评论

Augmentation of Intelligent Agent for Multiple Access Protocols in Wireless Sensor Networks 2

Augmentation of Intelligent Agent for Multiple Access Protoc...

引用

2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Majidha Fathima, K.M. Santhiyakumari, N. Suganthi, M. Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Knowledge Institute of Technology Department of Electronics Communication Engineering Salem India Mahendra College of Engineering Department of Electronics Communication Engineering Salem India

ISBN: (纸本)9781665400527

In any wired or wireless network, the huge data introduced into it are the data generated by the sensors placed in the environment. Wireless Sensor Networks play an important role in capturing the data (including big data), performing the computation and returning the result back to the user. (viz., a Healthcare Information System). Due to the enormous access of internet by the users distributed geographically, the logical channel must be granted access in an even fashion. Multiple access protocols gain their importance in resolving the access conflict to the shared medium by the sender, receiver and the other intermediate nodes. When two nodes decide to transmit data at the same time, access is granted by dividing the channel to slots based on frequency, time or code. If packets collide in the network, collision avoidance is enhanced by Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. An intelligent agent is incorporated in the multiple access protocols for knowledge based collision avoidance. In particular, the learning agent feeds the system by observing the environment, feeds data into the inference engine, processes data, fetches the output. The learning element is responsible for making improvements like efficient bandwidth allocation and energy conservation. It uses feedback from the critic. The performance element is authoritative for selecting external actions. It takes in percepts and decides on actions. Hence the network support system built on Multi Protocol Label Switching (MPLS) identifies the unique traffic being sent from diverse sources. Network Virtualization ensures the data are aggregated and segregated at the respective ingress and egress routers. © 2022 IEEE.

关键词： multi-protocol label switching

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor Detection using Deep Features

Brain Tumor Detection using Deep Features

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Nijhawan, Rahul Bisht, Rajendra Singh Pandey, Mukesh Kumar, Sanyam Mendirtta, Neha Awasthi, Poojita Sagar Thapar Institute of Engg. & Technology Computer Science Engineering Patiala India Graphic Era Hill University Department of Cse Bhimtal India University of Petroleum & Energy Studies School of Computer Science Dehradun India Medical Knowledge Group New York City United States Chandigarh University Punjab India University of Petroleum & Energy Studies Computer Science Engineering Dehradun India

ISBN: (纸本)9798350313413

An excessive proliferation of aberrant cells in the brain, which may result in a brain tumor. Moreover, it is very difficult for doctors to detect brain tumors at an early stage. However, with the help of MRI images, they can now monitor the condition. Due to the complexity of the disease, it is very difficult for doctors to determine its exact causes. With the help of brain tumor images, we can now monitor the condition and identify the possible cause of the disease. In this paper, we have proposed a deep learning technique for brain tumor detection. We employed a pre-trained VGG-16 architecture for detection of brain tumour. Our proposed approach outperformed the state-of-the-art algorithms with an accuracy of 86.6%. We compared the results by computing different statistical measures such as AUC, precision, recall, and F1 score. © 2023 IEEE.

关键词： deep learning Detection MRI Tumor

来源：评论

学校读者我要写书评

暂无评论

Next-Generation Artificial Intelligence Solutions for Comprehensive Analysis and Improvement of Heat Dissipation in High-Intensity Boiling Environments

Next-Generation Artificial Intelligence Solutions for Compre...

引用

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Gadiyar, H. Manoj T. Kumar, S Vinod Raj, Vijilius Helena Yadav, Dinesh Kumar Hasoni, Mortada Kareem Sabi Lakhanpal, Sorabh Canara Engineering College Department of Computer Science and Business System India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology AlTalb Collage of Nursing Dhi Qar64001 Iraq Lovely Professional University Phagwara India

ISBN: (纸本)9798350373783

This research delves into the realm of AI-driven heat dissipation, offering a suite of algorithms to address the challenges posed by high-intensity boiling environments. The proposed predictive modeling algorithm, rooted in linear regression, provides a systematic and robust approach through 20 meticulously designed steps. This algorithm, along with neural networks, reinforcement learning, generative adversarial networks (GANs), and genetic algorithms, forms a versatile toolbox for thermal management. Linear regression, through its step-by-step predictive modeling process, ensures accurate heat dissipation predictions. Neural networks, employing backpropagation, showcase adaptability and complexity, excelling in real-time adaptation. Reinforcement learning, specifically Q-Learning, introduces dynamic decision-making strategies, vital for navigating complex thermal scenarios. GANs contribute by generating realistic synthetic data, an invaluable asset for simulating high intensity boiling environments. Genetic algorithms, resembling natural selection, prove versatile and scalable in evolving optimal solutions. The research evaluates these algorithms comprehensively, demonstrating the proposed method's superiority in prediction accuracy, energy efficiency, adaptability, and overall performance. The proposed predictive modeling algorithm stands out as a benchmark, showcasing its potential for practical implementation and optimization in diverse scenarios. This comprehensive exploration sets the stage for innovative solutions to the intricate challenges of thermal management, as continuous adaptation and fine-tuning remain pivotal in this dynamic landscape. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Real-time Settings and Their Transformation through Distributed Intelligence Communication Technologies

Real-time Settings and Their Transformation through Distribu...

引用

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Manjunatha Krishna, M. Hari Rai, Vijay Kumar Singh, Navdeep Kumar, Ashwani Habelalmateen, Mohammed I. New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Hyderabad Dundigal India Ies College of Technology Department of Computer Science & Engineering M. P Bhopal India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India The Islamic University Najaf Iraq

ISBN: (纸本)9798350373783

Modern real-time settings and dispersed intelligence communication technologies require new solutions to improve efficiency, flexibility, and scalability. This research introduces dynamic edge-cloud orchestration for real-time intelligence. The approach uses the Dynamic Task Allocation Algorithm (DTAA), the Predictive Resource Scaling Algorithm (PRSA), and the Adaptive Communication Routing Algorithm. DTAA distributes work between edge devices and the cloud based on priority, processing time, and resources. PRSA estimates future resource needs, making edge and cloud scaling easier. ACRA adjusts communication routes based on latency, job priority, and network circumstances. We use numbers to compare the new strategy to six popular ones. These include SDA, TFA, RBI, FBA, and PRT. The proposed option outperforms the others in latency, scalability, security, reliability, freedom, and resource utilization. Due to its comprehensive framework for smart task assignment, resource management, and communication enhancement, DECO-RTI is a major advance in real-time settings. The approach can adapt to changing situations and automatically scale up resources, making it ahead of the curve for distributed intelligent communication systems. © 2024 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Translation of code mixed language to monolingual languages using rule-based approach

引用

International Journal of Cloud Computing 2021年第4期10卷 278-298页

作者： Attri, Shree Harsh Prasad, T.V. RamaKrishna, Gavirneni Department of Computer Science and Engineering JIMS Engineering Management Technical Campus Knowledge Park III 48/4 Greater Noida Knowledge Park III Uttar Pradesh Noida201303 India GIET Institutions NH-16 Chaitanya Knowledge City Rajahmundry Andhra Pradesh 533296 India Department of Computer Science and Engineering KL University Green Fields Andhra Pradesh Vaddeswaram522502 India

The demand of bilingual code switching has significantly increased due to cross-lingual communication and information exchange at a global scale and specifically in India. The core component of MT system is identification and translation of morphological inflections and PoS word ordering with respect to language structure. Indian languages are morphologically richer than English language and have multiple inflections during translation into English language. This paper focuses on the analysis and translation of code mixed language, i.e., Hinglish into pure Hindi and pure English languages. The experiments based on the algorithms in the paper are able to translate code mixed sentences to pure Hindi with a maximum success rate of 91% and to pure English with a maximum success rate of 84%. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Modulation

来源：评论

学校读者我要写书评

暂无评论

Complex Event Processing in Web Streams with Ontology-Based Abstraction Layers for Smart City Frameworks 2

Complex Event Processing in Web Streams with Ontology-Based ...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Krishna, M Hari Manjunatha Kumar, Rakesh Singh, Navdeep Kumar, Ashwani Ftaiet, Adnan Allawi Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

Smart cities embody the integration of information technology, urban infrastructure, and citizen engagement to optimize city functions and drive economic growth. A critical component in achieving this integration is the ability to process complex events in real-time, particularly from diverse web streams. This paper introduces a novel framework for Complex Event Processing (CEP) in web streams by employing ontology-based abstraction layers, aiming to enhance the interpretability and interoperability of heterogeneous data sources within smart city frameworks. The proposed approach uses a layered architecture where the bottom layer deals with raw data streams from various sources, including IoT devices, social media, and sensor networks. The middle layer employs an ontology-based model to abstract and enrich the raw data, providing a unified and semantic representation. This representation facilitates the identification and correlation of complex events, which are pertinent to city management and planning. The top layer involves the application of advanced data mining techniques to predict, detect, and respond to urban events in a timely and efficient manner. Through this methodology, the framework addresses the challenges of scalability, semantic heterogeneity, and real-time processing needs inherent in smart city applications. Empirical evaluations demonstrate the efficacy of the approach in various urban scenarios, showing promising improvements over existing methods. This work lays a foundational architecture for future research and development in smart city technologies, aiming to make urban areas more livable, sustainable, and efficient. © 2024 IEEE.

关键词： Complex Event Processing Data Mining Ontology-Based Abstraction Smart Cities Web Streams

来源：评论

学校读者我要写书评

暂无评论

Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective

arXiv

引用

arXiv 2023年

作者： Abu-Rasheed, Hasan Weber, Christian Fathi, Madjid Department of Electrical Engineering and Computer Science Institute of Knowledge Based Systems and Knowledge Management University of Siegen Siegen Germany

Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner’s current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context-aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. During the last decade, several approaches have emerged in the literature to define the learning context and the factors that may capture it. Those approaches led to different definitions of contextualized learner-profiles. In this paper, we review the state-of-the-art approaches for defining a user’s learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport 25

Enhancing Unsupervised Graph Few-shot Learning via Set Funct...

引用

Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and Data Mining V.1

作者： Yonghao Liu Fausto Giunchiglia Ximing Li Lan Huang Xiaoyue Feng Renchu Guan Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (纸本)9798400712456

Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among researchers. Recent advancements in graph few-shot learning models have exhibited superior performance across diverse applications. Despite their successes, several limitations still exist. First, existing models in the meta-training phase predominantly focus on instance-level features within tasks, neglecting crucial set-level features essential for distinguishing between different categories. Second, these models often utilize query sets directly on classifiers trained with support sets containing only a few labeled examples, overlooking potential distribution shifts between these sets and leading to suboptimal performance. Finally, previous models typically require necessitate abundant labeled data from base classes to extract transferable knowledge, which is typically infeasible in real-world scenarios. To address these issues, we propose a novel model named STAR, which leverages Set funcTions and optimAl tRansport for enhancing unsupervised graph few-shot learning. Specifically, STAR utilizes expressive set functions to obtain set-level features in an unsupervised manner and employs optimal transport principles to align the distributions of support and query sets, thereby mitigating distribution shift effects. Theoretical analysis demonstrates that STAR can capture more task-relevant information and enhance generalization capabilities. Empirically, extensive experiments across multiple datasets validate the effectiveness of STAR. Our code can be found https://***/KEAML-JLU/STAR here.

关键词： few-shot learning

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：