检索结果-内蒙古大学图书馆

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Chennupati, Anjali Prahas, Bhamidipati Ghali, Bharadwaj Aaditya Nidhin Prabhakar, T.V. Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The proposed framework addresses the critical need for secure multimedia data sharing by integrating image-to-audio encryption with advanced AI-based data-hiding techniques. The core concept involves transforming static visual content into dynamic auditory experiences, injecting semantic depth and artistic creativity into the resulting audio output. Unlike conventional methods, this approach goes beyond translating pixels into sound waves. It holds promise in multimedia communication, artistic expression, and data protection. To enhance security, this framework introduces AIenhanced data-hiding techniques, leveraging adversarial training and reinforcement learning. This extra layer ensures resistance to unauthorized access and tampering. In summary, this approach fuses image-to-audio encryption and AI-enhanced data hiding, aiming to revolutionize multimedia, thus offering a robust solution, transcending boundaries by safeguarding visual data through the immersive medium of audio. © 2024 IEEE.

关键词： Data hiding Hide-Net Image to audio encryption and decryption Reveal-Net Steganography

来源：评论

学校读者我要写书评

暂无评论

Adapting Deep Learning Transformer Model for Depression-Specific Named Entity Recognition: A Transfer Learning Approach 3

Adapting Deep Learning Transformer Model for Depression-Spec...

引用

3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Pawar, Dipti Phansalkar, Shraddha Department of Computer Science Engineering MIT School of Computing MIT Art DesignTechnology University Pune India

ISBN: (纸本)9798331532420

Named Entity Recognition (NER) is essential in the biomedical domain, particularly in mental health studies focused on disorders like depression. It helps extract structured information from unstructured text, enabling accurate recognition of key entities such as symptoms, disorder, antidepressants, brands, and therapies. This step is crucial for recommending diagnoses, personalizing treatments, and advancing research. Traditional methods, like rule-based or dictionary-driven approaches, often fall short in flexibility and scalability, as they rely heavily on predefined lexicons and corpora. The proposed work presents an advanced deep learning model aimed at improving NER performance on a customized dataset. Our model focuses on identifying critical entities disorders, brands, antidepressants, and therapies by addressing the limitations of conventional techniques. It uses a hybrid approach that combines a BERT-based model, fine-tuned through transfer learning, with a knowledge-based dictionary (MedDRA). This integration allows for enhanced semantic enrichment, improving the identification and categorization of biomedical entities. By leveraging BERT’s contextual capabilities alongside the structured knowledge from MedDRA, the model delivers better accuracy and classification in mental health data. Preliminary results show high confidence levels, with substantial improvements in key metrics like precision, recall, and F1 score, further advancing NER's role in mental health research. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Integrated Approach to Detect Cyberbullying Text:Mobile Device Forensics Data

引用

computer Systems science & engineering 2022年第3期40卷 963-978页

作者： G.Maria Jones S.Godfrey Winster P.Valarmathie Department of Computer Science and Engineering Saveetha Engineering CollegeChennai602105India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengalpattu603203India Department of Information Technology Saveetha Engineering CollegeChennai602105India

Mobile devices and social networks provide communication opportunities among the young generation,which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among *** paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics *** describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying *** use forensics techniques,Machine Learning(ML),and Deep Learning(DL)algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the *** on a real-time dataset reveal better results for the detection of cyberbullying *** Random Forest in ML approach produces 87%of accuracy without SMOTE technique,whereas the value of F1Score produces a good result with SMOTE *** LSTM has 92%of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.

关键词： Mobile forensics cyberbullying machine learning investigation model suspicious pattern

来源：评论

学校读者我要写书评

暂无评论

Adaptive delay-energy balanced partial offloading strategy in Mobile Edge computing networks

引用

Digital Communications and Networks 2023年第6期9卷 1310-1318页

作者： Shumei Liu Yao Yu Lei Guo Phee Lep Yeoh Branka Vucetic Yonghui Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyang110819China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Electrical and Information Engineering University of SydneySydneyNSW2006Australia

Mobile Edge computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy *** this paper,we propose a flexible MECbased requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy *** address the dimensional differences between time and energy,we introduce two normalized parameters and then derive the computational overhead of processing *** from existing works,this paper considers practical variations in the user request patterns,and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay,task workload and power *** the resulting problem is non-convex,we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading *** experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.

关键词： Mobile edge computing(MEC) Delay Energy consumption Dynamic balance Partial computation offloading

来源：评论

学校读者我要写书评

暂无评论

Optimizing Federated Semantic Learning in Distributed AIGC-Enabled Human Digital Twins: A Multi-Criteria and Multi-Shard User Selection Framework

引用

IEEE Transactions on Mobile computing 2025年第7期24卷 5916-5933页

作者： Okegbile, Samuel D. Gao, Haoran Talabi, Oluwasegun Cai, Jun Niyato, Dusit Shen, Xuemin Concordia University Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada University of the Fraser Valley School of Computing AbbotsfordBCV2S 7M8 Canada Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Artificial intelligence-generated content (AIGC) has been proposed as a solution to meet the requirements of ultra-reliable, secure, and privacy-preserving connectivity in human digital twin (HDT) networks. In such an AIGC-enhanced HDT, contents representing the true statuses of physical twins are generated in the virtual environment for the immediate update and evolution of the corresponding virtual twins (VTs). However, adopting a distributed AIGC in HDT presents several challenges, including the need for personalized VTs, data privacy concerns, and insufficient contextual understanding. This paper introduces a multi-layer federated semantic learning framework to address these challenges, incorporating batch learning to meet the training requirements for semantic-channel encoders and decoders. Furthermore, we introduce a novel user association framework to maximize the overall system performance under shard formation constraints. We then formulate a long-term joint optimization problem for user selection over finite learning periods. A novel Lyapunov-based online optimization strategy was proposed to mitigate the impact of time-varying and unpredictable training conditions. Additionally, we introduce a multi-arm bandit-based method and a context-centric user selection approach to solve the optimization problem. The results demonstrate that the proposed user association framework addresses the limitations of existing approaches, thereby improving the overall performance of the multi-shard AIGC-enhanced HDT. © 2002-2012 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Healthcare: Cloud-Based Health Information Exchange and Disease Prediction

Revolutionizing Healthcare: Cloud-Based Health Information E...

引用

2024 International Symposium on computing and Intelligent Systems, SCI 2024

作者： Nayini, Sai Nithin Gade, Vishwas Gudapareddy, Mani Prakash Reddy Peddineni, Varshith Beena, B.M. Department of Computer Science &Engineering Amrita School of Computing AmritaVishwa Vidyapeetham Karnataka Bangalore560035 India

A cloud-based system called Health Information Exchange (HIE) is. Hosted on Amazon Web Services (AWS). This system provides logins, for doctors, administrators and patients. The primary objective of this work is to revolutionize healthcare data sharing by providing a convenient platform. Patients are empowered to manage their health by selecting their symptoms and receiving disease predictions. AWS is utilized to orchestrate the cloud-based infrastructure, which includes SageMaker for constructing machine learning models RDS for a PostgreSQL database, EC2 for hosting the Django server, Lambda for deploying machine learning model APIs CloudWatch for resource monitoring and IAM, for access control. The core element of this system is the implementation of a classification framework that optimizes the organization and retrieval of data. This innovative approach enhances data accessibility while streamlining healthcare processes to facilitate decision making and patient care. By integrating AWS services, it also creates a scalable foundation that supports the growth of a dynamic retail environment. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Decision Support System for Spontaneous Volunteers During Emergency Response 15

Decision Support System for Spontaneous Volunteers During Em...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Anand, Sruthy Rao, Sethuraman N. Nandan, A.D. Dev Gopal, B.Madan Das, Madhav A. Amritapuri India School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Disasters affect a large number of people yearly and recurrently at many locations. Emergencies compel the local community to participate in disaster response;they are mostly the first responders during any disaster. Hence, it is necessary to devise solutions to support the local community and enhance their resilience. This work proposes a comprehensive platform for the victims, administrators, and volunteers during a catastrophe. It encourages the participation of the local community in managing local resources, requesting help, and getting acknowledged for the service they lend. The platform helps recognize the local community's strengths and provides a method to receive information from disaster managers. The platform will also bring more situational awareness to disaster managers and volunteers. It also helps disaster managers assess the disaster's potential impact and vulnerability levels of the community at an early stage. The platform envisions providing a decision support system for the spontaneous volunteers to co-work with disaster managers in a regulated fashion by eliminating fake volunteers and messages. The research also focuses on the characteristics of technology adoption for the widespread acceptance of the solution. © 2024 IEEE.

关键词： Community Resilience Disaster Management Emergency Response Mobile Application Risk Reduction Spontaneous Volunteers

来源：评论

学校读者我要写书评

暂无评论

Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

引用

computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Control Strategy of Wind Turbines for Their Withdrawl of Frequency Regulation 3

Control Strategy of Wind Turbines for Their Withdrawl of Fre...

引用

3rd International Conference on Energy, Power and electrical Technology, ICEPET 2024

作者： Gao, Haishu Zhang, Feng Wu, Guanghao Cornelusse, Bertrand Shandong University School of Electrical Engineering Jinan China University of Liege Department of Electrical Engineering and Computer Science Liège Belgium

ISBN: (纸本)9798350352658

The simultaneous withdrawal of multiple WTGs after wind farm participation in frequency regulation will cause a large power plunge, which in turn will cause a serious secondary frequency drop in the system. Moreover, due to the large area of large-scale wind farms, the wind conditions in which each WTG is located vary greatly, and the volatility of wind speed is more complicated, so the timing arrangement of WTG withdrawal becomes more difficult. Aiming at this problem, this research takes the power dips and the output frequency regulation energy into account, at the moment of withdraw of WTG, establishes a wind speed differentiated withdraw control mode for WTG frequency regulation, and based on this, a novel control strategy of wind turbines for their withdraw of frequency regulation is proposed, which staggers the time of speed restoration of the WTG groups at the wind farm level to mitigate the power dips caused by the simultaneous withdraw of multiple WTGs. © 2024 IEEE.

关键词： Windmill

来源：评论

学校读者我要写书评

暂无评论

Enhancing Energy Efficiency and Security in Wireless Sensor Networks: A Survey and Proposed Methodology Integrating Prediction Algorithms and Deep Neural Networks 6

Enhancing Energy Efficiency and Security in Wireless Sensor ...

引用

6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Shafiuddin, Shaik Krishna, Konda Hari Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331505790

This research study provides a complete assessment of energy- and trust-aware techniques in IoT-WSNs, emphasizing the significant problems and limits of current methodologies. Traditional methodologies frequently face challenges such as excessive processing overhead, difficulty reaching real-time optimal solutions, and increased delay, especially in large-scale or resource-constrained contexts. To solve these issues, the paper suggests a new approach that combines powerful prediction algorithms, deep neural networks, and strong security frameworks. The process entails initializing exact inputs and simulation settings, modeling advanced prediction algorithms, developing deep neural networks, and constructing resilient sensor nodes, all while implementing extensive security measures. The suggested approach seeks to considerably improve energy efficiency, network lifetime, and security by focusing on mobile sink movement prediction and network operation optimization. This study not only covers existing methodologies, but also shows the promise of the proposed methodology through extensive analysis and preliminary results, demonstrating its ability to overcome the limits of present WSN deployments. © 2024 IEEE.

关键词： Prediction models

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：