检索结果-内蒙古大学图书馆

Robust Human Interaction Recognition Using Extended Kalman Filter

computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

来源：评论

学校读者我要写书评

暂无评论

Acute Lymphoblastic Leukemia Detection Using DenseNet Model from Microscopic Blood Smear Images

Acute Lymphoblastic Leukemia Detection Using DenseNet Model ...

引用

International Conference on Data science and Network engineering, ICDSNE 2023

作者： Prasath, Navamani Jasthi, Monika Saikia, Rabul Loganathan, Muralidaran Devi, Salam Shuleenda Department of Electronics and Communication Engineering NIT Meghalaya Meghalaya Shillong793003 India Department of Computer Science and Information System Engineering St. Joseph University Dae Es Salaam Tanzania United Republic of

ISBN: (纸本)9789819967544

Acute lymphoblastic leukemia (ALL) is a cancerous condition that affects the bone marrow and blood. It is a fast developing illness that, if not identified and treated as soon as possible, could be fatal. ALL is often identified by looking at blood and bone marrow smears under a microscope. Leukemia can be detected and classified using detailed cytochemical tests. However, these procedures are expensive, time-consuming, and dependent on the knowledge and skills of the specialists involved. Using image processing techniques that examine microscopic blood smear images to search for the leukemic cells, leukemia can be detected. These methods are simple, quick, cost-effective, and unaffected by the judgments of experts. The suggested study describes a computer-aided diagnosis method that uses deep convolutional neural networks (CNNs) that have already been trained to compare leukemia images to normal images. The public dataset ALL-IDB 2 was used for the proposed research. The study uses the pre-trained model DenseNet-201 for performing the classification. With the DenseNet201 pre-trained networks employed in the study for the ALL_IDB2 dataset, a classification accuracy of 94.6% is achieved. In all of the classifications carried out, optimization strategies such as cross-validation, fine-tuning, and real-time augmentation are also compared. Also use Pre-trained series models like ResNet-50, VGG-19, Inceptionv3, MobileNet-v2, Xceptionv3, and VGG-16 for performing the comparison. The experimental result gives an improvement in accuracy (17.76, 10.6, and 13.2%) in comparison to the other approaches namely, residual neural network, customized combined CNN, and conVNet neural network, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

引用

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A novel ontological-based trust aware hybrid key management scheme (OTAHKMS) to enhance network lifetime and energy usage in wireless sensor networks (WSNs)

引用

International Journal of Information Technology (Singapore) 2024年第3期16卷 1429-1435页

作者： Barve, Amit Pallavi, R. Deepak, Shashikant Murugan, R. Yadav, Dhananjay Singh, Ashutosh Kr. Sharma, Meenakshi Shalini, S. Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Computer Science Engineering Presidency University Karnataka Bangalore India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Noida India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology UP Noida India Department of Education Sanskriti University UP Mathura India Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India

Security, heterogeneity, and interoperability are the prominent concerns in wireless sensor networks (WSNs) with restricted amounts of resources such as energy, transmission range, and processing of packets. Moreover, secured and trusted communication among WSNs is required to enhance network lifetime and security, thereby reducing energy consumption in the network. Thus, the given paper presents an ontological-based trust aware hybrid key management scheme (OTAHKMS) for WSNs. The proposed approach is a two-fold approach, where the first phase involves the encryption process for maintaining data confidentiality and integrity, while the second phase involves the use of ontology for capturing trust relationships and representing contextual information among several nodes in the network. The performance of the proposed trusted approach is validated and compared with existing cited works based on evaluation metrics such as communication trust value (CTV), network throughput (NT), packet delivery ratio (PDR), and energy consumption (EC). The results show that the proposed ontological-based trusted approach efficiently outperforms existing protocols, thereby enhancing network lifetime and energy usage in WSNs. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Encryption Packets transmission Security Trust validation Wireless sensor networks (WSNs)

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of RouteSegmentation Algorithm in Identifying Perimeter Area Around Travel Route 16

Performance Analysis of RouteSegmentation Algorithm in Ident...

引用

16th International Conference on Information Technology and Electrical engineering, ICITEE 2024

作者： Bachmid, Muhammad Avied Anshari Haq, Muhammad Yasir Mumtaz, Muhammad Rafly Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Informatics Engineering Malang Indonesia Universitas Brawijaya Faculty of Computer Science Malang Indonesia Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Information System Malang Indonesia

ISBN: (纸本)9798350375817

Prior study has developed the RouteSegmentation algorithm to identify the perimeter area surrounding a route. In this study, a comparative experiment was carried out to investigate the performance of the RouteSegmentation algorithm implementation in contrast to the RouteBoxer algorithm. Both algorithms play a crucial role in geographical information systems (GIS), particularly in enhancing navigational tools by identifying points of interest along specified routes. Through systematic experimentation using various perimeter distance values and types of route, the trade-offs between processing efficiency and the granularity of the area identified by these algorithms were evaluated. The results demonstrate that RouteSegmentation maintained relatively consistent performance across different perimeter distance values and statistically significantly faster processing time than RouteBoxer. Furthermore, while the RouteBoxer algorithm benefits from larger perimeter distance values by reducing processing time, it compromises the coverage results of the algorithm and potentially undermines its usefulness in practical scenarios. This study not only provides insights into the optimal use of perimeter distance values for each algorithm but also guides users in selecting the appropriate algorithm based on their specific application needs, balancing detailed geographical analysis and runtime efficiency. © 2024 IEEE.

关键词： Transportation routes

来源：评论

学校读者我要写书评

暂无评论

An Efficient Attack Detection Framework in Software-Defined Networking using Intelligent Techniques 3

An Efficient Attack Detection Framework in Software-Defined ...

引用

3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Hassan, Heba A. El-Din Hemdan, Ezz Shokair, Mona El-Samie, Fathi E. Abd El-Shafai, Walid Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Menouf32952 Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Communication Engineering Menouf32952 Egypt Prince Sultan University Security Engineering Laboratory Department of Computer Science Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350323511

Recently, Software-Defined Networking (SDN) architecture has offered great benefits due to the separation between the control and network elements such as routers and switches. Unfortunately, the enormous growth of attacks hinders the wide adoption of SDN. Intrusion Detection systems (IDSs) are used as significant tools to detect and mitigate network attacks of anomalous nature. Several Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs), have been utilized for building IDSs in cyber security in recent years, because they achieve the desired detection results. Nonetheless, little work has been introduced on IDSs in SDN systems. Hence, this paper presents an efficient framework based on intelligent techniques for predicting attacks in SDN systems. This work focuses on the early detection of abnormal attacks existing in an SDN environment. When malicious traffic is identified in an SDN topology, the Artificial Intelligence (AI) module employs Machine Learning (ML) or DL models to identify and stop the attack source. The architecture presented in this research allows for comparing several ML and DL classification techniques that can be used to identify different sorts of network attacks. The proposed framework is tested on the InSDN dataset using several learning models like Random Forest (RF), AdaBoost (AB), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT), and Logistic Regression (LR) classifiers. In addition, DL models such as Deep CNN and Long Short Term Memory (LSTM) are considered. The results demonstrate that the proposed Deep CNN model for multi-class attack data classification achieves the highest accuracy of 99.85% compared to RF, AB, KNN, NB, DT, LR, and LSTM classifiers with accuracy levels of 95%, 88 %, 97%, 93 %, 90 %, 98%, and 88.31 %, respectively. © 2023 IEEE.

关键词： Software defined networking

来源：评论

学校读者我要写书评

暂无评论

Text to Image Generation Using Attentional Generative Adversarial Network 1

引用

1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Rao, Supriya B. Shetty, Shailesh S. Singh, Chandra M, Srinivas P. Bekal, Anush Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Karnataka Mangaluru India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Department of Electronics & Communication Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India Department of Computer Science Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

AttnGan – Attentional Generative Adversarial Network (GAN) is a type of GANs that is used for text to image generation. Unlike other art models, it focuses on fine-grained word level information and once the model is trained by mapping the images and the corresponding text in the dataset, the model will generate image part-by-part using the image description given by the user as an input to the model. Current paper, focuses on the conversion of text into images based on the description provided by the user. This enables them to translate their ideas in the form of text into images of their required choices. The model is initially trained by the description of the text and images and when the user provides the text as an input to the system, it is used as a testing data. By analyzing the text word-by-word, the respective images pertaining to the text is generated part-by-part by considering the text description into account. This can heavily help the users in a applications such as in editing aids, documental archives, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Robust Spectral Efficiency Maximization in 5G Networks Using Non-Linear Stochastic Precoding 2

Robust Spectral Efficiency Maximization in 5G Networks Using...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Ponnaian, Geetha Jayanthi, N. Sarasu, R. Sajitha, L.P. Ramalakshmi, R. Manimegalai, L. Department of Computer Science and Engineering Thandalam Chennai India Pallavaram Tamil Nadu Chennai600117 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences India R. M. K Engineering College Department of Computer Science and Business System Tamilnadu Kavaraipettai India Ramco Institute of Technology Department of Electronics and Communication Engineering Rajapalayam India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350371055

This research focuses on enhancing the spectral efficiency of 5G networks through the utilization of nonlinear stochastic precoding techniques. The proposed approach aims to mitigate the detrimental effects of channel impairments and noise by employing advanced precoding schemes that exploit the statistical properties of the wireless channel. By incorporating nonlinear precoding algorithms and stochastic optimization methods, the system can dynamically adapt to varying channel conditions, resulting in improved data transmission rates and overall spectral efficiency. Through extensive simulations and analysis, the effectiveness of the proposed method is demonstrated, showcasing its potential to significantly enhance the performance of 5G networks in real-world scenarios. © 2024 IEEE.

关键词： 5G networks channel impairments nonlinear stochastic precoding spectral efficiency stochastic optimization

来源：评论

学校读者我要写书评

暂无评论

Secure Key Management in 5G Networks Using Elliptic Curve Integrated Encryption Scheme (ECIES) for Low-Power IoT Devices 4

Secure Key Management in 5G Networks Using Elliptic Curve In...

引用

4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Chitra Kiran, N. Maindola, Meenakshi Kumara, Bharath Kaliappan, S. Bhatt, Pooja Maranan, Ramya Alliance University Department of Electronics and Communication Engineering Bengaluru India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Ramaiah University of Applied Science Department of Electronics and Communication Engineering Bengaluru India Lovely Professional University Division of Research and Development Phagwara India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Post Limda India Saveetha School of Engineering Simats Department of Research and Innovation Chennai India

ISBN: (纸本)9798350352931

The rapid proliferation of the Internet of Things (IoT) has led to a surge in cyber threats, demanding the development of robust security frameworks. This paper introduces an advanced threat detection model, leveraging Artificial Intelligence (AI) and Cyber Twin Technologies for enhanced IoT security. The proposed framework integrates a Cyber Twin-a digital replica of physical IoT devices-with real-time data analytics to detect, predict, and respond to sophisticated cyber-attacks. The Cyber Twin continuously monitors IoT networks, identifying abnormal behaviors and enabling the implementation of dynamic security measures through AI-driven intrusion detection systems (IDS). A hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) is utilized to enhance anomaly detection and threat classification accuracy. The system's ability to simulate various attack scenarios within a digital environment facilitates the development of effective countermeasures while minimizing the impact on actual IoT operations. Experimental results demonstrate significant improvements in detection accuracy, reduced false positives, and enhanced system resilience. The proposed AI and Cyber Twin-based threat detection model sets a new benchmark in safeguarding IoT infrastructures against emerging cyber threats. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Maximizing Energy Efficiency in IRS-Assisted Phase Cooperative PS-SWIPT-Based Self-Sustainable IoT Network

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the communications Society 2025年 6卷 4311-4327页

作者： Sadia, Haleema Kamal Hassan, Ahmad Haq Abbas, Ziaul Abbas, Ghulam Baker, Thar Saeed, Nasir United Arab Emirates University College of Engineering Department of Electrical and Communication Engineering Al Ain United Arab Emirates Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Electrical Engineering Topi23460 Pakistan Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Computer Science and Engineering Topi23460 Pakistan University of Brighton School of Architecture Technology and Engineering BrightonBN2 4GJ United Kingdom

Power splitting based simultaneous wireless information and power transfer (PS-SWIPT) appears to be a promising solution to support future self-sustainable Internet of Things (SS-IoT) networks. However, the performance of these networks is constrained by radio frequency signal strength and channel impairments. To overcome this challenge, intelligent reflecting surfaces (IRSs) have recently been introduced in IoT networks to improve network efficiency by controlling signal reflections. In this article, we propose a novel IRS-enabled phase cooperative framework that aims to maximize the energy efficiency (EE) of the PS-SWIPT based SS-IoT network. Our framework leverages the idea of phase cooperation between two distinct networks without requiring additional hardware resources. We employed transmit beamforming (BF) at access points (APs) and phase shift optimization at the IRS with effective phase cooperation between APs. The EE maximization problem turns out to be NP-hard, so first, an alternating optimization (AO) is solved for the user’s network using heuristic BF approaches and phase optimization via semidefinite relaxation (SDR) approaches. To combat the computational complexity of AO, we also propose a low-complexity alternative solution by exploiting heuristic BF schemes and an iterative algorithm, i.e., the element-wise block-coordinate descent method for phase shifts optimization. The proposed solutions’ analysis shows low computational complexity and fast convergence, achieving near-optimal EE performance for different network settings. We conducted extensive simulations to evaluate our proposed framework’s performance and unveiled significant EE performance achieved by the IoT network consistent with the numerical findings. This work demonstrates the potential of IRS phase cooperation for enhancing the EE of different networks without constraining hardware resources. © 2020 IEEE.

关键词： Energy transfer

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：