检索结果-内蒙古大学图书馆

30th ICE IEEE/ITMC Conference on engineering, Technology, and Innovation, ICE/ITMC 2024

作者： Manta, Ourania Vasileiou, Nikolaos Giannakopoulou, Olympia Bromis, Konstantinos Georgas, Konstantinos Vagenas, Theodoros P. Kouris, Ioannis Haritou, Maria Matsopoulos, George Koutsouris, Dimitris Institute of Communication and Computer Systems Biomedical Engineering Laboratory Athens Greece

ISBN: (纸本)9798350362435

Balance disorders pose a significant health concern, especially among the elderly, often leading to falls and a reduced quality of life. The TeleRehabilitation of Balance clinical and economic Decision Support System (TeleRehaB DSS) project aims to tackle these challenges by integrating personalised interventions with advanced digital health technologies. This multidisciplinary effort, funded under the Horizon 2020 framework, leverages cutting-edge technologies such as artificial intelligence and augmented reality to deliver tailored rehabilitation programs. TeleRehaB DSS aims to optimise treatment efficacy, improve patient engagement, and enhance the overall quality of life for individuals with balance disorders. Additionally, the project emphasises personalisation based on multiple criteria, including socio-economic factors and digital skills, to provide the most effective intervention for each participant. The overall concept will be assessed during a clinical validation phase to be conducted in 5 countries and hospitals. © 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

A deep decentralized privacy-preservation framework for online social networks

引用

Blockchain(Research and Applications) 2024年第4期5卷 163-174页

作者： Samuel Akwasi Frimpong Mu Han Emmanuel Kwame Effah Joseph Kwame Adjei Isaac Hanson Percy Brown School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212013China Department of Computer Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Electrical&Electronic Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Computer Science and Information Systems Ashesi UniversityAccra3042Ghana Department of Telecommunication Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana

This paper addresses the critical challenge of privacy in Online Social Networks(OSNs),where centralized designs compromise user *** propose a novel privacy-preservation framework that integrates blockchain technology with deep learning to overcome these *** methodology employs a two-tier architecture:the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm(ePSOGSA)for optimizing feature selection,while the second tier employs an enhanced Non-symmetric Deep Autoencoder(e-NDAE)for anomaly ***,a blockchain network secures users’data via smart contracts,ensuring robust data *** tested on the NSL-KDD dataset,our framework achieves 98.79%accuracy,a 10%false alarm rate,and a 98.99%detection rate,surpassing existing *** integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.

关键词： Preprocessing Privacy-preservation Blockchain Deep learning Online social network

来源：评论

学校读者我要写书评

暂无评论

On the Passive Fingerprinting Attack for IoT Device Classification 7

On the Passive Fingerprinting Attack for IoT Device Classifi...

引用

7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Kamal, Tabina Hashim, Fazirulhisyam Rasid, Mohd. Fadlee A. Ahmad, Faisul Arif Department of Computer and Communication Systems Engineering Selangor Serdang43400 UPM Malaysia

ISBN: (纸本)9798350392074

The rapid growth of the Internet of Things (IoT), expected to exceed 29 billion devices by 2027, presents a significant security challenge. Device fingerprinting, which identifies devices through unique network traffic patterns, is a valuable security tool but can be exploited by attackers for undetectable reconnaissance. This paper explores the feasibility of passive traffic fingerprinting attacks from an attacker's perspective, focusing on a small-scale testbed with four devices from an undisclosed single vendor. Using minimal resources and open-source tools, traffic patterns were analyzed, confirming unique device behaviors. A Random Forest (RF) classifier was developed, demonstrating high precision with instances where 100% classification accuracy can be achieved in a specific experimental scenario using just two features: Source Port and Destination Port. Key aspects of the model included feature selection, hyperparameter tuning, and a tree depth optimization of 20. Notably, forward feature selection proved more effective than Principal Component Analysis (PCA). These preliminary results underscore the vulnerabilities of single-vendor IoT ecosystems and highlight the simplicity and replicability of this low-cost attack methodology. The ease with which attackers can implement this approach underscores the urgent need for robust defenses as IoT devices proliferate across various sectors. © 2024 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Jamming Attack Mitigation in Wireless Federated Learning Networks Using Bayesian Games

IEEE Networking Letters

引用

IEEE Networking Letters 2024年第4期6卷 247-251页

作者： Barkatsa, Sofia Diamanti, Maria Charatsaris, Panagiotis Tsiropoulou, Eirini Eleni Papavassiliou, Symeon Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Zografou15780 Greece University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM87131 United States

Federated Learning (FL), an emerging distributed Artificial Intelligence (AI) technique, is susceptible to jamming attacks during the wireless transmission of trained models. In this letter, we introduce a jamming attack mitigation mechanism for the uplink of wireless FL networks using the power-domain Non-Orthogonal Multiple Access (NOMA) technique. The problem of transmission power allocation for all clients (legitimate and malicious) is formulated and solved distributively as a Bayesian game with incomplete information. The clients aim to successfully transmit their model parameters, minimizing transmission time and consumed power, while having probabilistic knowledge about the malicious behavior of the other clients in the game. © 2019 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Optical Hyperuricemia Sensor with Uricase Biofunctionalized Stacked Nanocomposite 10

Optical Hyperuricemia Sensor with Uricase Biofunctionalized ...

引用

10th IEEE International Conference on Photonics, ICP 2024

作者： Onifade, O.A. Bakar, M.H.B.A. Mahdi, M.A. Noor, A.S.B.M. Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Serdang Malaysia

ISBN: (纸本)9798350383164

—This paper presents an advanced optical sensor based on surface plasmon resonance (SPR) designed to detect hyperuricemia, characterized by elevated uric acid (UA) levels. The sensor features a stacked nanocomposite comprising gold-graphene quantum dots (GQD) biofunctionalized with uricase enzyme, ensuring high sensitivity and selectivity for UA detection over other potentially competing interferents like ascorbic acid, glucose, D-cystine, urea, and creatinine commonly found in biological samples. The developed sensor achieves an impressive sensitivity of 0.093◦/(mg/dL) for UA at 12 mg/dL, significantly higher than the recorded sensitivity of 0.015◦/(mg/dL) for all other potential interferents at the same concentration. This highlights the sensor’s exceptional precision in discerning UA. Moreover, the sensor demonstrates notable selectivity for UA even at low concentrations, registering a resonance angle shift of 0.371◦ at 0.5 mg/dL, compared to a lower angle shift of 0.185◦ for other interferents at higher concentrations of 12 mg/dL. These findings emphasize the sensor’s accuracy and rapid response in distinguishing UA, positioning it as a promising tool for early diagnosis and management of hyperuricemia in clinical settings, addressing a critical need for reliable and efficient UA detection. ©2024 IEEE.

关键词： Graphene quantum dots

来源：评论

学校读者我要写书评

暂无评论

ABES: attention bi-directional ensemble SVM for early detection of brain tumors

引用

Neural Computing and Applications 2024年第26期36卷 16179-16193页

作者： Subramaniam, Erana Veerappa Dinesh Krishnasamy, Valarmathi Department of Computer Science and Business Systems Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

引用

Digital communications and Networks 2024年第5期10卷 1245-1257页

作者： Abeer A.Z.Ibrahim Fazirulhisyam Hashim Aduwati Sali Nor K.Noordin Keivan Navaie Saber M.E.Fadul Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Wireless and Photonics Networks Research Centre(WiPNET) Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Department of Communication and Computer Engineering College of Engineering and Medical SciencesKhartoum11111Sudan School of Computing and Communications Lancaster UniversityLancasterLA14YWUnited Kingdom Department of Electrical and Electronics Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia

The deployment of distributed multi-controllers for Software-Defined Networking(SDN)architecture is an emerging solution to improve network scalability and ***,the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low ***,we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane *** fault-tolerance strategy has been applied to distributed SDN control architecture,which allows each switch to migrate to next controller to enhance network *** this paper,the Reliable and Dynamic Mapping-based Controller Placement(RDMCP)problem in distributed architecture is framed as an optimization problem to improve the system reliability,quality,and *** considering the bound constraints,a heuristic state-of-the-art Controller Placement Problem(CPP)algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular *** algorithm identifies the optimal controller location,minimum number of controllers,and the expected assignment costs after failure at the lowest effective cost.A metaheuristic Particle Swarm Optimization(PSO)algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and *** effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline *** findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

关键词： Software defined networking Dynamic mapping Particle swarm optimization Reliability Multi-objective optimization Evolutionary

来源：评论

学校读者我要写书评

暂无评论

Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

引用

computers, Materials & Continua 2024年第4期79卷 117-142页

作者： Ankita Gangwar Vijaypal Singh Dhaka Geeta Rani Shrey Khandelwal Ester Zumpano Eugenio Vocaturo Department of Computer and Communication Engineering Manipal University JaipurJaipurIndia Department of IoT and Intelligent Systems Manipal University JaipurJaipurIndia Department of Computer Engineering ModellingElectronics and Systems(DIMES)University of CalabriaRende(Cosenza)Italy National Research Council Institute of Nanotechnology(NANOTEC)Rende(Cosenza)Italy

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20%of the total *** increase of 3.3%in consumption is predicted from 2024 to *** are also rich in iron,potassium,antioxidant lycopene,vitamins A,C and K which are important for preventing cancer,and maintaining blood pressure and glucose ***,tomatoes are globally important due to their widespread usage and nutritional *** face the high demand for tomatoes,it is mandatory to investigate the causes of crop loss and minimize *** are one of the major causes that adversely affect crop yield and degrade the quality of the tomato *** leads to financial losses and affects the livelihood of ***,automatic disease detection at any stage of the tomato plant is a critical *** learning models introduced in the literature show promising results,but the models are difficult to implement on handheld devices such as mobile phones due to high computational costs and a large number of ***,most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf ***,the existing techniques lack in recognizing multiple diseases on the same *** address these concerns,we introduce a customized deep learning-based convolution vision transformer *** achieves an accuracy of 93.51%for classifying tomato leaf images with plain as well as complex backgrounds into 13 *** requires a space storage of merely 5.8 MB which is 98.93%,98.33%,and 92.64%less than stateof-the-art visual geometry group,vision transformers,and convolution vision transformermodels,*** training time of 44 min is 51.12%,74.12%,and 57.7%lower than the above-mentioned ***,it can be deployed on(Internet of Things)IoT-enabled devices,drones,or mobile devices to assist farmers in the real-time monitoring of tomato *** p

关键词： Tomato disease transformer deep learning mobile devices

来源：评论

学校读者我要写书评

暂无评论

Comparison of Segmentation Algorithms for Extraction of Stone from Kidney X-ray Image 4th

Comparison of Segmentation Algorithms for Extraction of Ston...

引用

4th International Conference on Smart IoT systems: Innovations and Computing, SSIC 2023

作者： Nisha, N. Dhaka, Vijaypal Singh Sinwar, Deepak Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India Director of School of Computing and Intelligent Systems Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819751990

Healthcare systems are gaining popularity because of the advancement of new technology aiding healthcare professional for diagnosis of disease from medical modalities. The detection and classification of disease using AI and its technologies are active research areas in the last decade. Radiologist usually detects stone from the kidney using different imaging like CT scan, MRI scan, and X-ray. For the detection of stone in kidney X-ray images, various segmentation and classification methods are used. These different classification and segmentation methods are used to reduce errors and time of healthcare professional in diagnosis of stone in X-ray image. In this paper, comparison of different algorithms like thresholding algorithm, contour-based algorithm, deep learning algorithms like U-Net, U-Net++, V-Net, V-Net++, and Transformer Net are used for segmentation of stone from kidney X-ray images. The thorough comparison of different deep learning architectures in diagnosing stone from kidney X-ray images is also made in this paper. Most of the papers work on classification of stone from different medical modalities before segmentation which cause data leakage problem. In this research paper, comparisons of different segmentation algorithms help in achieving higher accuracy and reduce the data leakage problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Operational Current Amplifier-Based Quadrature Oscillators Family

引用

IEEE Access 2025年 13卷 87281-87291页

作者： Alzaher, Hussain A. Alkhalifa, Ibrahim M. Department of Electrical Engineering Dhahran31261 Saudi Arabia Irc for Communication Systems and Sensing Dhahran31261 Saudi Arabia Department of Electrical and Computer Engineering Thuwal23955 Saudi Arabia

This paper investigates the application of the operational current amplifier (OCA) in the design of quadrature sinusoidal oscillators. There are numerous quadrature sinusoidal oscillators based on the three fundamental amplifiers namely the operational amplifier (OpAmp), the operational transconductance amplifier (OTA), and the operational transresistance amplifier (OTRA);however, their counterparts based on the OCA have not been reported. In this paper, several possible quadrature oscillator structures based on the OCA are systematically developed from their voltage mode active-RC counterparts using adjoint network theorem. Consequently, it is shown that the resulted oscillators do not necessarily preserve the attractive features of the voltage mode quadrature oscillators;namely providing explicit quadrature current output signals with equal amplitudes. When attempting to retain these features, it is found that the minimum OCAs needed is three. Analyzing this issue has paved the way for developing a novel OCA based integrator that promotes the invention of a new quadrature oscillator using only two OCAs. Simulation results obtained from a standard 150 nm CMOS process are provided. It is demonstrated that the proposed oscillator consumes approximately 350μ W while working in the 3 MHz range. Measurement results obtained from a prototype implemented using CMOS OCA ICs also confirms the proposed theory. © 2025 IEEE.

关键词： Circuit oscillations

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：