检索结果-内蒙古大学图书馆

A hybrid blockchain and AI-based approach for attack protection to secure internet of medical things networks

Alexandria Engineering Journal 2025年 128卷 223-230页

作者： Hadeel Alsolai Fahd N. Al-Wesabi Ali M. Al-Sharafi Asma Alshuhail Abdulbasit A. Darem Bandar M. Alghamdi Fouad Shoie Alallah Abdulrahman Alzahrani Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha 67714 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia

The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling real-time monitoring, remote diagnosis, and seamless data exchange among medical devices and providers. However, the interconnected and distributed nature of IoMT systems exposes them to significant cybersecurity threats, including data breaches and unauthorized access. This study aims to develop a secure and scalable framework to safeguard IoMT environments using blockchain technology, federated learning, and dynamic consensus algorithms. The proposed approach integrates artificial intelligence to detect threats and employs federated learning to maintain patient privacy while training models collaboratively. Experimental evaluation of the framework shows improved data security, reduced response times, and enhanced system trustworthiness. The results suggest that the integration of blockchain and AI-driven mechanisms offers a robust solution to IoMT cybersecurity challenges, paving the way for more reliable and efficient healthcare systems.

关键词： Internet of medical things (IoMT) Blockchain security Federated learning AI-driven anomaly detection Cyber threat mitigation

来源：评论

学校读者我要写书评

暂无评论

AI-Enable Heart Sound Analysis: PASCAL Approach for Precision-Driven Cardiopulmonary Assessment 8th

AI-Enable Heart Sound Analysis: PASCAL Approach for Precisio...

引用

8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Kumar, Ankit Singh, Kamred Udham Kumar, Gaurav Choudhury, Tanupriya Singh, Teekam Kotecha, Ketan Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Pune411045 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819994885

A large number of medical professionals depended on manual methods to assess the features of heart sounds. Because of the hands-on nature of this method, it was necessary for them to generate waveforms representing heart sounds and then carefully analyses the various components of these sounds. In contrast, the findings of our most recent research provide an improved methodology that automates the process of separating heart sound data and extracting the relevant factors from those signals. This update is very helpful for applications of machine learning in thoracentesis, and it aligns well with the developments of the most recent clinical imaging technologies. A novel "accordion" approach is presented in our methodology, which we have dubbed PASCAL. This method not only makes use of reference data that is determined by sudden shifts, but it also incorporates an improved classification model that has been customised for the highest possible level of precision. This is particularly clear when looking at the Dataset that We Have for Characterising Heart Rate. It is of the utmost importance to emphasise the fact that the performance as well as the false positive (Fp) rates of our classification phase were thoroughly evaluated. While this was going on, we looked at how the features of the ECG signal changed over time. The findings of our investigation are really positive. A fantastic F1-score of 94.38% and a respectable accuracy rate of 93.52% were reached by our suggested technique for identifying and classifying the capabilities of a healthy cardiopulmonary system. The primary sound (S1 duration), the subsequent sound (S2 duration), the overall cardiac cycle, the duration of ventricular systole, the time taken for ventricular contraction, and the ratio between the systolic and diastolic phases were some of the aspects of heart sounds that we examined in great detail. This ground-breaking method makes it possible to conduct an accurate study of the two most important p

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis in Social Media Marketing: Leveraging Natural Language Processing for Customer Insights 8th

Sentiment Analysis in Social Media Marketing: Leveraging Nat...

引用

8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Choudhury, Tanupriya Singh, Teekam Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Pune411045 India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819994885

In this day and age of online marketing, social media platforms have evolved into essential tools for companies to use in order to communicate with their consumers, as well as to advertise the goods and services they provide. Understanding client emotion, tastes, and views may be significantly aided by perusing the vast amounts of user-generated information that can be found on these many platforms. This study investigates the use of sentiment analysis, a branch of natural language processing (NLP), as a potent instrument for gleaning useful consumer insights from data collected from social media platforms like as Facebook and Twitter. The application of computer methods in the process of automatically determining and categorising the sentiment that is represented in textual data, such as tweets, Facebook posts, and online reviews, is what is known as sentiment analysis. In this article, the primary methodology and techniques used in sentiment analysis, such as lexicon-based, machine learning, and deep learning approaches, are dissected and discussed. In addition to this, it sheds light on the difficulties and factors to take into account that are unique to sentiment analysis when applied to the context of data from social media, such as the management of noisy and unstructured language, the management of sarcasm, and the management of context-dependent sentiment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Explainable-AI for DoS Attacks Detection in 5G Network Using Deep Learning Models

Explainable-AI for DoS Attacks Detection in 5G Network Using...

引用

Intelligent computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Amjad Albashayreh Yahya Tashtoush Abdallah Aldosary Omar Darwish Firas Albalas Department of Computer Science The University of Jordan Amman Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan The Department of Computer Engineering Prince Sattam bin Abdulaziz University Ar Riyadh Saudi Arabia Information Security and Applied Computing Department Eastern Michigan University Ypsilanti MI USA

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

With the emergence of the fifth-generation (5G) net-work, numerous revolutionary applications are enabled, including low-latency and machine-type communications. This great in-crease creates a broader security threat concerns, such as denial-of-service (DoS) attacks, which can disrupt network functionality. The complexity and decentralization of 5G networks create new vulnerabilities for adversaries, necessitating comprehensive security procedures to identify, mitigate, and prevent DoS attacks in 5G networks. This paper introduces a novel approach for DoS detection in 5G networks, utilizing deep learning and machine learning models, along with Local Interpretable Model-Agnostic Explanations (LIME), to interpret model predictions and identify the significant role of data features in detecting DoS attacks. The results revealed that the random forest model demonstrated superior recall of 99.98, while BiLSTM demonstrated exceptional performance with a recall of 98.02.

关键词： Deep learning Radio frequency 5G mobile communication Predictive models Denial-of-service attack Feature extraction Data models Security Low latency communication Random forests

来源：评论

学校读者我要写书评

暂无评论

An EfficientNet to Classify Monkeypox-Comparable Skin Lesions Using Transfer Learning

An EfficientNet to Classify Monkeypox-Comparable Skin Lesion...

引用

Omni-layer Intelligent Systems (COINS), International Conference on

作者： Md. Rifat Hossen Nazia Alfaz Adnan Sami Sharia Arfin Tanim Talha Bin Sarwar Md. Kamrul Islam Department of Computer Science and Engineering American International University-Bangladesh (AIUB) Dhaka Bangladesh Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Faculty of Computing College of Computing and Applied Science Universiti Malaysia Pahang Pekan Malaysia

Monkeypox is an infectious illness caused by the DNA-based monkeypox virus, which has raised public health concerns due to its rapid transmission to over 50 countries. Direct physical interaction with infected humans or infected animals is the main reason behind the spread of this virus. The appearance of skin problems such as smallpox and rashes are the most frequently reported symptoms of this virus. Since cases of monkeypox are increasing rapidly around the world, stopping the spread of this zoonosis by providing early diagnosis and treatment is crucial before the emergence of a pandemic similar to COVID-19. In this study, we aim to propose a transfer learning-based approach using the EfficientNet-B0 architecture to identify monkeypox subjects by using skin lesion image samples. However, distinguishing monkeypox from other comparable infectious skin illnesses like chickenpox and measles is challenging. Therefore, additionally, this study identifies other diseases that also cause blisters and rashes on the skin, like chickenpox, and measles. During the data distribution phase, 5-fold cross-validation is used to validate the work's reliability by assuring that every sample is utilized for training and validation. For the evaluation of the model's classification performance, accuracy and loss are recorded for each training epoch. Moreover, precision, recall, F1-score, and confusion matrix are generated upon completion of the model training. This proposed approach is experimented on a public dataset and has shown remarkable performance by providing an overall 96.53% classification accuracy, 96.57% precision, 96.53% recall, and 96.52% F1-score.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms

Improving Existing VANETs by Incorporating UAVS Using Ant Co...

引用

Data Engineering and Communication Systems (ICDECS), International Conference on

作者： S Thangam Aniket Dixit Alok Kumar Karn Arnav Thakur Cg Arunbalaji J Jesy Janet Kumari A. Saleem Raja Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Sultanate of Oman

Vehicular Ad Hoc Networks (VANETs) are a type of wireless communication network that enable vehicles to communicate with each other and with roadside infrastructure in a peer-to-peer fashion; despite their vast applications, they come with several shortcomings. The key challenges in their real-world implementation include scalability, connectivity, and coverage issues. Unmanned Aerial Vehicles (UAVs), also known as drones, can complement existing VANETs in several ways to enhance their functionality and address some of their limitations. This paper studies the incorporation of UAVs in VANETs to overcome the challenges faced by the present networks. The approach is based on the dynamic deployment of UAVs in the most optimal positions, found by utilizing Particle Swarm Optimization (PSO) and Ant Colony algorithms which analyze the vehicle density, and previous coverage information in the network. The deployment of UAVs is intended to provide a seamless network coverage for ground vehicles. The impact of dynamic UAV mobility in communicating VANETs is comparatively studied. The simulation is done using Network Simulator-3 (NS3) simulator to evaluate the performance of 4 VANET protocols, AODV, DSR, OLSR, and DSDV, in terms of packet delivery ratio (PDR), Average End-to-End Delay, Throughput, Average Throughput per Packet, Packet Drop Rate, and Normalized Routing Load after incorporating the proposed modification designs. The paper concludes that the incorporation of Ant Colony is better suited to enhance VANETs than PSO.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Pruned Multi-Scale Skip Connected and Asymmetric-Convolution-Based U-Net Model for Land Cover Classification

A Pruned Multi-Scale Skip Connected and Asymmetric-Convoluti...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Almusawi, Muntather Parameshachari, B.D. Sunil, G. Reddy Muvva, Vijaya Bhaskar Siri, Dharmapuri The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Sr University Department of Computer Science & Artificial Intelligence Warangal India University of Technology and Applied Sciences -Shinas College of Computing and Information Sciences Department of Information Technology Shinas Oman Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

Recently, land cover classification became an evolutionary study in Remote Sensing (RS) satellite interpretations. While extracting features from satellite images, spatial information causes the overfitting issue. In addition, computational complexity is occurring in land cover segmentation due to the usage of complex architectures. Thus, in this research, Multi-scale Skip connected and Asymmetric-Convolution-based U-Net (MACUNet) with pruning method is introduced to reduce the computational complexity. To reduce the overfitting, Batch Normalization (BN) layer is employed, simultaneously 3D Convolutional Neural Network (3D CNN) for feature extraction. Initially, the input is taken as satellite images from the GID dataset, where spatial and temporal features are extracted using 3D CNN model with BN layer. After that, these features are given as input to the MACUNet method to improve segmentation performance. Then, classification is done with class_num value utilization based on identified pixel region maps and classified into six classes such as build up, forest, water, farmland, meadows and others. From the results, the proposed pruned-MACUNet model produces better results than existing GeohashNet in terms of Mean Intersection over Union (MIoU) as 80.45% and f1-score as 90.23% respectively. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Transmission Line Fault Detection and Classification Using Feature Extraction Based Self-Attention Convolutional Neural Network with Time Series Image 3

Transmission Line Fault Detection and Classification Using F...

引用

3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Almusawi, Muntather Babu, D. Raja Mazumder, Debarshi Bala Dhandayuthapani, V. Saranya, N. Naga College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India College Of Computing And Information Sciences University Of Technology And Applied Sciences Department Of It Shinas campus Oman Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

The transmission lines continuously experience the number of shunt faults and its effective in practical system increases the instability, load damages and line restoration cost. This work implements an advanced self-attention convolutional neural network (SAT-CNN) method for fault detection and classification (FDC) of high voltage transmission lines experiencing a constant number of shunt faults. Faults cause load damage in real-time applications, increase instability, and increase the cost of line restoration. Therefore, a precise model is needs to identify and categorize the flaws to quickly restore he problematic phases. In this research, implemented SAT-CNN feature extraction model with imaging-based on time series that can accurately detect and classify faults. By employing a number of input signals, including, current, voltage, and combined current and voltage signals, at different sampling frequencies, the efficacy of implemented SAT-CNN model is evaluated. Implemented SAT-CNN method obtains high performance when compared to existing methods including weight-sharing network (WSCN), Truncated singular value decomposition and Human urbanization algorithm based Recurrent Perceptron Neural Network (TSVD-HUARPNN), and Graph convolutional neural network (GCN), and result achieved 99.90% accuracy value. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Adaptive Strategies for Scalable and Heterogeneous VANET Network

Adaptive Strategies for Scalable and Heterogeneous VANET Net...

引用

Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： S Thangam Priyanshu Jha Jawed Hawari Nabin Kumar Sah Nikunj Jain J Jesy Janet Kumari A.Saleem Raja Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Sultanate of Oman

Despite technological advancements, Vehicular Ad-hoc Networks (VANETs) continue to face challenges related to reliability and high-speed mobility. This study focuses on enhancing scalability, improving mobility, and addressing network heterogeneity in the context of data dissemination. Data dissemination involves the distribution or transmission of statistical data to users. Tools like NS3, SUMO utilizing OSM for realistic traffic simulations, MATLAB for Data Analysis and evaluation of various protocols like ADHOC, Dynamic Source Routing (DSR), Optimized Link State Routing Protocol (OLSR), Destination Sequenced Distance Vector Routing (DSDV). The research aimed to refine VANET simulations for effective decision making, as its primary objective data dissemination i.e. distribution or transmission of statistical data to users, forming the central focus of this investigation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Smart Traffic Management in Green VANETs: Ant-Inspired Routing Strategies with Local Search Optimization

Smart Traffic Management in Green VANETs: Ant-Inspired Routi...

引用

Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： Thangam S Jahnavi M Chandana K Dheeraj K J Jesy Janet Kumari A Saleem Raja Dept. Of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Sultanate of Oman

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

Green Vehicular Ad-hoc Networks (VANETs) are gaining significance in smart mobility because of growing concerns about the environment. The research introduces a novel method for managing traffic in such networks by combining ant-inspired routing methods with local search optimization approaches. The solution, which draws inspiration from the efficient path-finding abilities of ants, facilitates decentralized communication and sharing of traffic data among cars. It allows rapid adaptation to changing traffic circumstances and environmental elements. Local search optimization enhances routing decisions by considering the near vicinity of each vehicle. Extensive simulations demonstrate notable enhancements in traffic efficiency, decreased congestion, and improved energy conservation as compared to conventional routing methods. The suggested solutions also enhance the development of environmentally friendly and enduring transportation networks.

关键词： Technological innovation Costs Green products Energy conservation Vehicular ad hoc networks Transportation Routing Sustainable development Optimization Pattern matching

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：