检索结果-内蒙古大学图书馆

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bhavitha, Medikonda Rakshitha, K. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the current digital era, it is crucial to preserve sensitive data, particularly when it comes to images. Encrypting images is essential for ensuring the confidentiality and integrity of image data both in transit and storage. To address this need, this exploration design delves into the operation of four important encryption ways Advanced Encryption Standard (AES), Data Encryption Standard (DES), Rivest-Shamir-Adleman (RSA), and the Paillier homomorphic algorithm. Regarding the security of image data, each of these approaches has a distinct set of benefits and features. This study aims to explore their use in image encryption and decryption and assess their performance, security, and practicality in safeguarding images from unauthorized access and malicious threats. © 2024 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Geopolitical Risk Analysis and its Impact on Market Volatility 9

Geopolitical Risk Analysis and its Impact on Market Volatili...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Rathinavel, Keshav Indira Kumar, A.K. Sahoo, Anshuman Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India Universiti Tunku Abdul Rahman Dept. of Internet Engineering & Computer Science Malaysia

ISBN: (纸本)9798350394474

This study addresses the influence of geopolitical risks on financial markets, as noticed in recent events like the COVID-19 pandemic and the Russia-Ukraine war. This study showcases a model combining financial indices with the Geopolitical Risk Index (GPRI), employing Bi-LSTM networks with attention mechanisms to analyze the data. This approach captures the nuanced effects of geopolitical events on market volatility. Focused on the pandemic period, our model offers insights into market dynamics during times of geopolitical unrest and serves as a predictive tool for market behavior in response to global events. © 2024 IEEE.

关键词： Risk analysis

来源：评论

学校读者我要写书评

暂无评论

Shellcode Generation: A Resource Efficient Approach using Fine-tuned LLMs 15

Shellcode Generation: A Resource Efficient Approach using Fi...

引用

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

作者： Ram, Madhav Nair, Priyanka C. Krishna, Mohith V. Gupta, Deepa Pranavkrishnan, M. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350370249

In order to create better shellcode for offensive cybersecurity, this study investigates the use of large language models (LLMs) such as Mistral and Llama. It focuses on LLM optimizations to improve shellcode accuracy and efficiency with the goal of quickly locating and taking advantage of software vulnerabilities. To optimize shellcode generation, several model parameters are tuned through controlled experiments, with the BLEU score serving as the primary criterion for impartial evaluation. This study achieved a BLEU-1 score of 0.8506, highlighting the effectiveness of the optimization and finetuning strategies. The study explores the subtle aspects of LLMs that influence the creation of shellcode, such as their capacity for learning, their ability to adjust to the peculiarities of cybersecurity, and their ability to replicate complex patterns into effective shellcode. This project intends to expand knowledge of using LLMs for shellcode development by submitting significant experimentation and research, providing insights into more efficient vulnerability exploitation strategies in cybersecurity. ©2024 IEEE.

关键词： cybersecurity large language models Llama LLM finetuning LoRA Mistral Shellcode generation

来源：评论

学校读者我要写书评

暂无评论

Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

引用

computers, Materials & Continua 2023年第5期75卷 4293-4315页

作者： Muhammad Hammad Waseem Malik Sajjad Ahmed Nadeem Ishtiaq Rasool Khan Seong-O-Shim Wajid Aziz Usman Habib Department of Computer Sciences and Information Technology University of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Computer Science and AI College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of AI&DS School of ComputingNational University of Computer and Emerging SciencesIslamabad44000Pakistan

Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target ***,improving predictive accuracy is a crucial step for informed *** the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or *** ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification *** network weights and the activation functions are the two crucial elements in the learning process of an *** weights affect the prediction ability and the convergence efficiency of the *** traditional settings,ANNs assign random weights to the *** research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random *** proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer *** system computes the confusion matrix-based metrics for traditional and proposed *** proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other *** results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research ***,the proposed framework is of use to predict and classify cancer patients ***,this will facilitate the effective management of cancer patients.

关键词： ANN decision support systems gene-expression data hybrid classification machine learning predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Improvising energy efficiency of heterogeneous servers using AWS services and Machine Learning approaches 15

Improvising energy efficiency of heterogeneous servers using...

引用

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

作者： Reddy, P. Praneeth Shruthi, P. Sai Tharneesh, P. Beena, B.M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The rapid expansion of data centers has led to a substantial increase in energy consumption, prompting a need for more energy-efficient solutions. This paper presents a comprehensive approach to enhancing the energy efficiency of heterogeneous servers by leveraging Amazon Web Services (AWS) and Machine Learning (ML) techniques. Our proposed solution involves developing an ML model that uses a dataset of VM cloud which consists of various parameters like the cloud ID, timestamp, CPU performance, memory usage, network traffic, energy efficiency and so on. The model uses regression algorithms like Linear Regression, Decision Tree Regressor, K-Neighbor Regressor, Polynomial Regression, and AdaBoost Regressor to predict the energy-efficiency of a system based on the relevant features. The model will be evaluated on the basis of Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, R2 Score, and Mean Absolute Percentage Error and Linear Regression model is noted to provide the least error scores. This model helps in reducing server workloads and dynamically manages resource allocation to minimize energy usage while maintaining optimal performance. The developed ML model is then deployed on the AWS EC2 free tier instance by encapsulating the ML models within a Docker container. This study provides a scalable and economical approach to enhance the energy efficiency in the data centers, with a potential impact on the wide range of cloud -based infrastructures. © 2024 IEEE.

关键词： AWS EC2 Container Docker Energy efficiency Green computing Regression

来源：评论

学校读者我要写书评

暂无评论

Comparative Study of Cryptographic Algorithms in Post Quantum computing Landscape 5

Comparative Study of Cryptographic Algorithms in Post Quantu...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Sahoo, Anshuman Indira Kumar, A.K. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

As the advent of quantum computing looms, the cryptographic landscape faces unprecedented challenges that could render traditional algorithms like AES, RSA, and ECC vulnerable. This paper delves into a comparative study of these classical cryptographic algorithms, examining their foundational principles and evaluating their resilience against quantum computing threats, particularly Shor's and Grover's algorithms. Through a combination of theoretical analysis and practical simulations, we assess the potential weaknesses and strengths of AES, RSA, and ECC in the post-quantum era. The study highlights the need for quantum-resistant cryptographic methods and explores how current algorithms can be adapted or replaced to withstand the sophisticated computational capabilities of quantum computers. By bridging the gap between classical and quantum cryptography, this research offers crucial insights into the future of secure digital communication, underscoring the urgency for innovation in cryptographic practices to safeguard data in an increasingly quantum-dominated world. © 2024 IEEE.

关键词： Encryption algorithms

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

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

Secure Student Record Management using Visual Cryptography in Educational Systems 9

Secure Student Record Management using Visual Cryptography i...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Kiranchandu, E. Gireesh, C. Kiran, G. Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

Cloud computing is vital for data transmission and storage in the digital era, highlighting the urgent requirement for strong data security measures. Conventional encryption techniques such as RSA, while dependable, have considerable computational burden and energy requirements owing to their extensive key sizes. This study examines the efficacy of Elliptic Curve Cryptography (ECC) as a superior solution for data security in cloud environments. The suggested method utilizes ECC's reduced key sizes to attain improved computational efficiency and energy saving, all while upholding rigorous encryption standards. A comparison research indicates that ECC surpasses RSA in performance and resource efficiency, rendering it especially appropriate for extensive cloud systems handling significant amounts of sensitive information. This research examines the implementation of the Advanced Encryption Standard (AES) algorithm in Cipher Block Chaining (CBC) mode for image encryption. The AES-CBC technique efficiently safeguards images by transforming them into encrypted formats, so obstructing illegal access. Comparative assessments using alternative encryption methods highlight AES's enhanced security and computational efficacy. Collectively, these findings underscore the capability of ECC and AES to overcome the shortcomings of conventional encryption, providing secure and resource-efficient solutions for contemporary cloud computing issues. © 2024 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

Vision Enhancer: A Voice-Driven System For Obstacle Detection

Vision Enhancer: A Voice-Driven System For Obstacle Detectio...

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Supraja, P. Ganapathi, Ananya Ram, K. Sushmitha Vaishnavi, V. Jyotsna, C. Dept. of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331505134

The eyes serve as the primary sensory organ for individuals, providing vital visual information that aids in perception, recognition, and understanding of the surrounding environment. However, for those with visual impairments, accessing visual information poses significant challenges, limiting their freedom, particularly in navigating through obstacles. The paper attempts to address the obstacles faced by visually impaired individuals by leveraging object detection techniques such as TensorFlow to identify obstacles in their surroundings. By incorporating depth estimation, the system aims to determine the distance to these obstacles, providing crucial spatial awareness. The system generates warnings through voice feedback, aiding users in making informed decisions to navigate safely. The implementation of this detection system is active on handheld devices, predominantly utilizing webcams or mobile devices. Through this approach, the system achieves a commendable accuracy rate of 98%, thereby enhancing the autonomy and safety of individuals with visual impairments in their daily lives. © 2024 IEEE.

关键词： Feature extraction

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：