检索结果-内蒙古大学图书馆

Cybernation and Computation (CYBERCOM), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Abhay Upadhyay Mridul Gupta Shiv Ashish Dhondiyal Deepak Suresh Asudani Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

In this paper, we study the multi-layered security approaches for cooperative relay networks against passive eavesdropping adversaries. Specifically, we investigated physical layer approaches (i.e., beamformed and artificial noise injections) to improve the signal defence ability at a much lower eavesdropping capacity. Trade-offs in these cryptographic methods were studied in terms of encryption strength vs. decryption time under different loads on the network. The security of the network layer was assessed by measuring packet delivery ratios and data integrity violations under malicious nodes. Results show that multi-layer security is performed at the cost of performance, but much better network defence mechanisms are proposed overall. While this end-to-end approach strikes a reasonable balance between security and real-world performance considerations, it presents itself as a sensible solution for securing network communications against eavesdropping threats.

关键词： Simulation Noise Data protection Relay networks Network security Physical layer Encryption Security Protection Eavesdropping

来源：评论

学校读者我要写书评

暂无评论

Metamaterial Assisted Multi-Wide Band Electrically Small Antennas

Metamaterial Assisted Multi-Wide Band Electrically Small Ant...

引用

International Conference on computer and Information technology (ICCIT)

作者： Sakib Sadman Abdul Khaleque A B M Arafat Hossain Kumary Sumi Rani Shaha Mst. Sumaya Akter Dept. of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Electrical & Computer Engineering University of Houston Houston Texas USA Dept. of Electrical Electronic and Communication Engineering Pabna University of Science and Technology Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Metamaterials have gained significant interest in antenna technology nowadays due to their exceptional features. This article presents metamaterial (MTM) based two new antennas with a vision to precisely enhance the number of bands and maintain a suitable bandwidth level. A sequential rise in band numbers from 2 to 4 and then to 5 is noticed by the applications of split-ring resonator (SRR) and complementary structure of dual negative metamaterial (dual NG-MTM) in a standard patch antenna. Initially, a dual NG-MTM was attached at the back of the patch antenna and it presented four separate bands at 2 GHz, 6.8 GHz, 9.1 GHz, and 9.6 GHz, respectively with comparatively larger bandwidth. At last, inclusion of an SRR takes the band-width to another level in addition to providing five bands and its electrical size is then compact as 0.22λ 0 × 0.19λ 0 × 0.0006λ 0 , with a ka (antenna size indicator) value of less than 1. These extraordinary features of the above-mentioned MTM antennas lead to promising applications including television broadcasting and 5G system.

关键词： Wireless LAN TV 5G mobile communication Patch antennas Bandwidth Broadcasting Metamaterials Resonance Information technology Standards

来源：评论

学校读者我要写书评

暂无评论

Clear Text Visibility: A CNN-LSTM Approach for Frame of Interest Identification in Book Flipping Videos

Clear Text Visibility: A CNN-LSTM Approach for Frame of Inte...

引用

Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Gaurav Buddhawar Dharin Dave Krupa Jariwala Chiranjoy Chattopadhyay Dept. of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Surat India Dept. of Computer Science and Engineering (AI & ML) Pimpri Chinchwad College of Engineering Pune India School of Computing and Data Sciences FLAME University Pune India

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Book flipping videos present a distinctive challenge for information extraction, requiring the identification of frames with clear text visibility during dynamic page turns. This paper introduces a novel approach to frame classification in book flipping videos, leveraging the combined effect of Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs). In particular, a CNN-LSTM model is employed, where a pre-trained CNN captures spatial features, and LSTMs extend the model’s capability to distinguish temporal dependencies critical for page turn detection. Unlike conventional methods, this approach processes entire video sequences, enabling the model to learn intricate Frame of Interest (FoI) detection features indicative of page turns and text visibility. The proposed model offers improved interpretability, effectively classifying frames without compromising transparency. The research opens avenues for automated book digitization, digital library creation, and educational technology applications, enhancing information extraction and reliability from book flipping videos.

关键词： Digital transformation Video sequences Educational technology Information retrieval Feature extraction Libraries Convolutional neural networks Reliability Long short term memory Videos

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-driven Antenna Selection Strategies for Fading MIMO Systems

Machine Learning-driven Antenna Selection Strategies for Fad...

引用

Advancements and Key Challenges in Green Energy and Computing (AKGEC), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

In this study, we investigate better performing antenna selection strategies in wireless communication systems based on machine learning algorithms. Reinforcement learning as well as neural networks among others are analysed for different metrics such a bit error rate (BER), signal-to-noise ratio (SNR) and data throughput. These results show that these methods are working well against the dynamic changing of channel conditions to provide reliable and efficient. The computational complexity analysis suggests affordable overhead, allowing the feasibility of real-time implementation. Although this method can simulate the scenario in a real environment, it is not scalable due to simulation fidelity and antenna array. On the one hand, future research will refine and expand upon these ML models with real-time adaptation capabilities - On the other hand significant field trials are needed to put this work into practice for specific applications making sure that we develop robust adaptive wireless communication systems in practice.

关键词： Measurement Hands Adaptation models Machine learning algorithms Computational modeling Neural networks Reinforcement learning Throughput Real-time systems Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

JARVIS - Smart AI Support System

JARVIS - Smart AI Support System

引用

Electrical Power Engineering, Communication and Computing technology(ODICON), Odisha International Conference on

作者： V. Ramya Manaswi Meduri Saketh Mandumula Sai Ravi Tejha Atluri Sai Karthikeya Dharani Chandra Prakash Ahmad Alkhayyat Dept. of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Dept. of computer science engineering with specialisation of cybersecurity GITAM university Hyderabad India Dept. of Electronics & Communication Engineering K.G Reddy College of Engineering and Technology Hyderabad Telangana India Dept. of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating “Air Canvas” facilitated by OpenCV. This AI-driven virtual assistant offers users a natural and intuitive experience, allowing them to effortlessly perform tasks such as browsing the web, interacting with a chatbot, and executing dynamic voice- controlled actions. Moreover, the system showcases advanced capabilities including motion detection and facial recognition with an accuracy of 95% in multiple runs. Leveraging the power of computer vision, the Air Canvas feature empowers users to express creativity through fluid hand gestures, while voice commands effortlessly manage diverse tasks. This innovative project presents an approachable way to interact with technology in the world of AI.

关键词： Emotion recognition Video on demand Speech recognition Chatbots Motion detection Internet of Things Web sites Security Artificial intelligence Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

Dual trustworthy mechanism for illness classification with multi-modality data

Dual trustworthy mechanism for illness classification with m...

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Qing Wang Jia Zhu Changfan Pan Jianyang Shi Chaojun Meng Hanghui Guo Dept. Computer Science and Technology Zhejiang Normal University Jinhua China Dept. Education Zhejiang Normal University Jinhua China

Multi-modality classification has flourished in recent years. Traditional methods mainly focus on advancing deep neural networks (DNN) to meet high performance. However, the interpretability of these methods remains blind due to the complexity and ambiguity of DNN, which also causes distrust. This problem is enlarged in sensitive areas, such as biomedical computing. Hence, we propose a novel dual trustworthy mechanism for multi-modality classification (DTMC), which can make the process and results of DNN more credible and interpretable while increasing performance. Specifically, a confidence attention mechanism is performed from local and global views to improve the process’ confidence by evaluating the attention scores and distinguishing the abnormal information. A confidence probability mechanism from local and global perspectives is conducted in the prediction stage to enhance the results’ confidence. Extensive experiments on multi-modality medical classification datasets show superior performance with the interpretability of the proposed method compared to the state-of-the-art (SOTA) methods. Our resources are open at https://***/ghh1125/data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

YOLOv8-Based Pothole Detection for Autonomous Driving Safety

YOLOv8-Based Pothole Detection for Autonomous Driving Safety

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Poongothai P Sabareswari T C R Ramesh Veeramalai Sankaradass R. Ponnusamy Dept of Mathematics Chennai Institute of Technology Tamil Nadu India Dept of Electronics and Communication Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Dept of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Artificial Intelligence and Data Science Madha Engineering College Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Undeniably, autonomous vehicles are the most dramatic evolution of transportation yet. They offer new safety, efficiency, and convenience levels that have never been seen before. Still, some natural and unexpected conditions continually strike a blow against the safety of self-driving systems, such as potholes on roads. This paper introduces a new, cloud-based safety solution for autonomous vehicles through artificial intelligence and implementation with the YOLOv8 object detection model, coupled with the deep learning platform Weights & Biases, or wandb. As the real-time pothole-detecting system is developed, issues related to navigation appear while increasing the safety level. This work adopts the YOLOv8 object detection model in the use case due to its higher accuracy and much faster object detection rate. This can classify and predict the object boundaries of a single network, thus being a good fit for quick and accurate detection. Upgrades will be made in sync with new conditions in roads and types of potholes as the right solution gains ready to change the future of transportation about autonomous transportation, paving the path for safer and more reliable self-driving vehicles. It directly addresses the acute challenge of pothole-related hazards. It builds the way to an even safer and more efficient future for autonomous mobility, thus showing tremendous potential for transforming advanced AI technologies.

关键词： Accuracy Navigation Roads Transportation Object detection Real-time systems Safety Reliability Artificial intelligence Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Integer Wavelet Transform based Secured Image Steganography using LSB and Coefficient Value Differencing 2

Integer Wavelet Transform based Secured Image Steganography ...

引用

2nd International Conference on Secure Cyber Computing and Communications, ICSCCC 2021

作者： Mandal, Pratap Chandra Mukherjee, Imon Indian Institute of Information Technology Kalyani B.P. Poddar Institute of Management and Technology Dept. of Computer Science and Engineering Kolkata India Indian Institute of Information Technology Kalyani Dept. of Computer Science and Engineering India

ISBN: (纸本)9781665444156

A steganography technique based on the Integer Wavelet Transform (IWT) is proposed. High-frequency coefficients are exploited more to embed the confidential message since they are less sensitive to the human eye. Based on this notion, the 2-LSB method is employed in the wavelet coefficients of the approximation subband which contains most of the signal energy and the 4-LSB method is applied in the wavelet coefficients of the diagonal subband. The Coefficient Value Differencing (CVD) method has been introduced in the horizontal and vertical subbands. To obtain the highest embedding capacity with an acceptable distortion, a threshold value has been applied in the CVD method. The proposed technique achieves an average embedding capacity of 2.28 bpp for all the stego images while maintaining a lower distortion. It shows strength against the standard tool, StirMark Benchmark 4.0, and withstands the steganalysis attacks. © 2021 IEEE.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Real-Time Sign Language Recognition using Machine Learning and CNN-based EfficientNetB3 for Accessible Communication

Real-Time Sign Language Recognition using Machine Learning a...

引用

Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Zabir Ibna Mizan Farzam Aidelkhani Khandaker Tasfia Reza Tanbir Hasan Bhuiyan Munzirul Alom Saiful Islam Tuhin Dept. of Electrical and Computer Engineering (ECE) North South University (NSU) Dhaka Bangladesh Dept. of Electrical and Electronic Engineering (EEE) Independent University Bangladesh (IUB) Dhaka Bangladesh Dept. of Computer Science Engineering Engineering (CSE) Bangladesh University of Business and Technology (BUBT) Dhaka Bangladesh Dept. of Electrical and Electronic Engineering (EEE) Bangladesh University of Business and Technology (BUBT) Dhaka Bangladesh

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research analyzes the challenges faced by Sign Language Recognition (SLR) systems by evaluating the performance of EfficientNetB3, Inception-v3, and GoogLeNet models. Using a dataset of 27,000 American Sign Language (ASL) images across 27 classes, EfficientNetB3 emerged as the top performer. After fine-tuning the model with the full dataset, it achieved a test accuracy of 99.53%. We then integrated this optimized model into a real-time, user-friendly Android app for sign language recognition, offering a valuable tool for enhancing communication accessibility, particularly for the deaf and hard-of-hearing. This research work contributes to advancing SLR and promoting inclusive communication.

关键词： Training Sign language Renewable energy sources Accuracy Computational modeling Face recognition Machine learning Programming Real-time systems Robustness

来源：评论

学校读者我要写书评

暂无评论

LLM4CVE: Enabling Iterative Automated Vulnerability Repair with Large Language Models

arXiv

引用

arXiv 2025年

作者： Fakih, Mohamad Dharmaji, Rahul Bouzidi, Halima Araya, Gustavo Quiros Ogundare, Oluwatosin Al Faruque, Mohammad Abdullah Dept. of Electrical Engineering and Computer Science University of California IrvineCA United States Siemens Technology PrincetonNJ United States

Software vulnerabilities continue to be ubiquitous, even in the era of AI-powered code assistants, advanced static analysis tools, and the adoption of extensive testing frameworks. It has become apparent that we must not simply prevent these bugs, but also eliminate them in a quick, efficient manner. Yet, human code intervention is slow, costly, and can often lead to further security vulnerabilities, especially in legacy codebases. The advent of highly advanced Large Language Models (LLM) has opened up the possibility for many software defects to be patched automatically. We propose LLM4CVE - an LLM-based iterative pipeline that robustly fixes vulnerable functions in real-world code with high accuracy. We examine our pipeline with State-of-the-Art LLMs, such as GPT-3.5, GPT-4o, Llama 3 8B, and Llama 3 70B. We achieve a human-verified quality score of 8.51/10 and an increase in ground-truth code similarity of 20% with Llama 3 70B. To promote further research in the area of LLM-based vulnerability repair, we publish our testing apparatus, fine-tuned weights, and experimental data on our website. © 2025, CC BY-NC-SA.

关键词： Software testing

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：