检索结果-内蒙古大学图书馆

15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Rahmani, Zeinab Pinto, Armando N. Barbosa, Luis S. Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal Instituto de Telecomunicações Aveiro Portugal International Iberian Nanotechnology Laboratory Braga Portugal Department of Computer Science University of Minho Braga Portugal Institute of Systems and Computer Engineering Technology and Science Braga Portugal

ISBN: (纸本)9783031856990

Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. This paper introduces a two-party quantum SMC protocol designed for evaluating binary Boolean functions using single qubits. Complexity analyses demonstrate a reduction of 66.7% in required quantum resources, achieved by utilizing single qubits instead of multi-particle entangled states. However, the quantum communication cost has increased by 40% due to the amplified exchange of qubits among participants. Furthermore, we bolster security by performing additional quantum operations along the y-axis of the Bloch sphere, effectively hiding the output from potential adversaries. We design the corresponding quantum circuit and implement the proposed protocol on the IBM Qiskit platform, yielding reliable outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

Adapting to Evasive Tactics through Resilient Adversarial Machine Learning for Malware Detection 18

Adapting to Evasive Tactics through Resilient Adversarial Ma...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Krishna, G. Bala Sravan Kumar, G. Ramachandra, Mummadi Sampurnima Pattem, K. Rani, D. Sandhya Kakarla, Geeta Cvr College of Engineering Department of Computer Science Engg. Rangareddy Ibrahimpatnam India Cvr College of Engineering Department of Computer Science Engg.-Cyber Security Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Hyderabad India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

This paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial Learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet's superiority over traditional models like the Support Vector Machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM's 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM's 80.3%). Notably, ARCNet's detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. These results confirm ARCNet's efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. The study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Block chain Blockchains Challenges of Block chain Consensus algorithm Data privacy Digital signatures Distributed ledger Faces Organizations Smart contracts

来源：评论

学校读者我要写书评

暂无评论

Security Preserving Distributive n-Party Computation 38

Security Preserving Distributive n-Party Computation

引用

38th IEEE Region 10 Conference, TENCON 2023

作者： Dutta, Mou Dhal, Subhasish Saxena, Ashi Sarkar, Mahasweta Indian Institute of Information Technology Department of Computer Science & Engineering Assam Guwahati India Dr. Akhilesh das Gupta Institute of Technology and Management Department of Computer Science & Engineering India San Diego State University Department of Electrical & Computer Engineering United States

ISBN: (纸本)9798350302196

In n-party Computation, data from different sources are integrated to achieve a common goal, which can be beneficial for different applications. However, this goal may be at risk due to the fact that this corresponding data can be the target of attacks from outside attackers as well as inside ones. On the other hand, in Secure n-party Computation, the data can be processed by preserving security and privacy in such a way that no party can know the data of the others. Several schemes have been proposed to address the issues regarding n-party computation. However, some of them are not efficient in terms of computation and communication, or some of them have used a Trusted Initializer (TI), a third party, which is considered as trusted, for communicating between different parties. Therefore, to address these issues, we propose an n-party secure computation protocol without using any Trusted Initializer (TI). We formally simulate our protocol using Automated Verification of Internet Security Protocols and Applications (AVISPA) as well as we elaborate on the formal and informal security analysis of our proposed protocol. Performance analysis of our proposed protocol is also carried out and it is observed our proposed protocol is secure and protects the privacy of the system. © 2023 IEEE.

关键词： Privacy-preserving techniques

来源：评论

学校读者我要写书评

暂无评论

FabricSense: A Tactile-Based Fabric Structure Detection Methodology for Automatic Temperature Adjustment

FabricSense: A Tactile-Based Fabric Structure Detection Meth...

引用

2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Dayananda, Chaithanya Hemashree, P. Harshini, M. Sai Guruprasad, N. Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India Annamcharya Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350302820

Based on tactile information and content-based image retrieval (CBIR), this methodology proposes a novel way for fabric structure detection and automatic temperature control. The technology accurately identifies fabric structures and establishes the ideal ironing temperature by combining tactile qualities, visual elements retrieved from fabric photographs, and programmed thermostat sensors. Unlike standard visual-based methods, this approach analyses colour, texture, and shape properties of fabric samples using tactile information and CBIR algorithms, enabling reliable fabric structure detection. Through testing and improvement, the system's performance is confirmed, providing exact temperature adjustment and improving the ironing experience. © 2023 IEEE.

关键词： Iron

来源：评论

学校读者我要写书评

暂无评论

Automated Crack Detection in Pipelines Using Advanced Image Processing 2

Automated Crack Detection in Pipelines Using Advanced Image ...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Subramani, Nandhagopal Jithesh, M.D. Harish, R. Rithish Hari, G. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Mechanical Engineering Coimbatore India

ISBN: (纸本)9798331505745

The automated detection of pipeline cracks is vital for maintaining the safety and operational lifespan of this crucial infrastructure. Early crack detection prevents costly repairs and minimizes the risk of catastrophic failures and environmental damage. Existing methods primarily rely on manual inspections and traditional image processing techniques, which often lead to inaccuracies and inefficiencies. These methods suffer from high false-negative rates and poor adaptability to different environmental conditions. This study proposes a deep learning framework utilizing YOLOv10 to automate the detection of pipeline cracks with enhanced accuracy. Studies using deep learning report detection accuracies between 80% and 92%, although performance varies across different studies. The range reflects the influence of factors such as dataset and model specifics. © 2025 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Parkinson’s Disease Prediction Using Machine Learning and Nature-Inspired Optimization Technique 5th

Parkinson’s Disease Prediction Using Machine Learning and N...

引用

5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Mittal, Ayushi Tayal, Devendra Kumar Gupta, Charu Srivastava, Neha Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women Kashmere Gate New Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology Delhi India

ISBN: (纸本)9789819760350

Parkinson’s disease (PD) is a nervous system condition that gradually becomes worse with the passing of time and is caused by injury to brain cells responsible for producing dopamine. When between 60 and 80% of dopamine-producing are lost, the production of dopamine diminishes, leading to the manifestation of motor symptoms associated with PD. Artificial intelligence (AI), particularly machine learning (ML), has become increasingly pivotal in medicine. Optimization techniques play a vital role in enhancing the accuracy and efficiency of disease prediction models. In the proposed work, we have implemented a nature-inspired firefly optimization technique to see its results for the correct disease classification. It is implemented using KNN, SVM, decision tree, and random forest combined with a firefly algorithm for the classification while minimizing the features selected. The highest results achieved are using random forest as a classifier, with a maximum accuracy of 87.22%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

computer Vision for Agricultural Automation - Algorithmic Solutions for Crop Yield Predictions

Computer Vision for Agricultural Automation - Algorithmic So...

引用

2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Fernandez, F. Mary Harin Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats Department of Computers Science and Engineering Chennai India Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Computer Engineering Department Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

computer vision is defined in this study and one of the applications demonstrated is crop yield prediction in agricultural automation. Here we have emphasized on its use for computer vision for the agricultural domain. Using multispectral images that can be obtained from drones satellites and pairs with ground truths of average crop yield, weather conditions and parts of the soil treated with practices performed by an agronomist the proffered methodology would take advantage of the uniqueness of the concept to predict crop production. Further, elaborate machine learning methods such as line regression, decision trees, Random Forest, Support Vector Machine and CNN models are applied in order that the ultimate selection of the model would be contingent on the accuracy in relation to a crop yield. The DL based approaches as seen majority of this work and particularly the CNN exhibited improved results over regression models since there are more of classification than regression;In addition DL models are more appropriate for spatial relations and variations characteristic of crop data. Besides that, data fusion methods which map the accuracy of the images, weather and fields data develops a more accurate forecast as proven by the above crop modeling that involves multidimensional data. While transferring on fresh test data, it also demonstrates that the trained models have the capability to use the collected data in order to not only provide intelligent advices to the farmers on resource goodwill and crop productivity that will pave way for better results. Most importantly therefore for the hear of it the research show that with computer visions and machine learning the agricultural process can be reinvented this is how food security can be improved as the environment evolves. © 2024 IEEE.

关键词： Agricultural automation computer vision Crop yield prediction Deep learning Machine learning Multispectral imagery

来源：评论

学校读者我要写书评

暂无评论

Data-driven strategies for digital native market segmentation using clustering

引用

International Journal of Cognitive Computing in engineering 2024年第1期5卷 178-191页

作者： Uddin, Md Ashraf Talukder, Md. Alamin Ahmed, Md. Redwan Khraisat, Ansam Alazab, Ammar Islam, Md. Manowarul Aryal, Sunil Jibon, Ferdaus Anam School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Information Technology Crown Institute of Higher Education 116 Pacific Highway North SydneyNSW2060 Australia Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Centre for Artificial Intelligence and Optimization Torrens University Australia

The rapid growth of internet users and social networking sites presents significant challenges for entrepreneurs and marketers. Understanding the evolving behavioral and psychological patterns across consumer demographics is crucial for adapting business models effectively. Particularly, the emergence of new firms targeting adolescents and future generations underscores the importance of comprehending online consumer behavior and communication dynamics. To tackle these challenges, we introduce a Machine Learning-based Digital Native Market Segmentation designed to cater specifically to the interests of digital natives. Leveraging an open-access prototype dataset from social networking sites (SNS), our study employs a variety of clustering techniques, including Kmeans, MiniBatch Kmeans, AGNES, and Fuzzy C-means, to uncover hidden interests of teenage consumers from SNS data. Through rigorous evaluation of these clustering approaches by default parameters, we identify the optimal number of clusters and group consumers with similar tastes effectively. Our findings provide actionable insights into business impact and critical patterns driving future marketing growth. In our experiment, we systematically evaluate various clustering techniques, and notably, the Kmeans cluster outperforms others, demonstrating strong segmentation ability in the digital market. Specifically, it achieves silhouette scores of 63.90% and 58.06% for 2 and 3 clusters, respectively, highlighting its effectiveness in segmenting the digital market. © 2024 The Authors

关键词： Cluster analysis

来源：评论

学校读者我要写书评

暂无评论

The Development of Inference in Healthcare System using FEFL System 4

The Development of Inference in Healthcare System using FEFL...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Kumar, Vikram Saini, Dimple Rastogi, Shailesh Raman, Ramakrishnan Verma, Apurv Meenakshi, R. Md Srv Media Pvt. Ltd India India Symbiosis Institute of Business Management Nagpur Department of Cse Pune India Department of Cse India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

In the modern world, access to health care remains an important issue, especially in impoverished regions where farmers lack access to the highest quality medical centers and expert practitioners. computer technology has proven groundbreaking in the healthcare sector, corresponding its good effect in different industry sectors. Wearable technologies are important in sophisticated medical applications, leveraging the tracking and analysis of bodily data from healthcare device sensors. The inclusion of Fog computing covers delays that come with cloud computing, critical for enabling quick analysis of bodily data. However, the fog setting presents difficulties such as delay in emergency health state and system overloading. Addressing these problems, this study suggests a novel Fog-enabled Flexible Clinical Decision-making System (FICDSS) health design for improved treatment and diagnostic efficiency. The system features four layers: a sensor layer, edge layers fog layer, while cloud layer. Patient data from worn or non-wearable sensors is transferred to the peripheral layer, the network layer responsible for gathering, processing, and sending information into the fog layer below. A Fuzzy Law Inference The system (FLIS) beginning at the boundary layer uses timing modifications to data to predict a user's immediate form health state. The FLIS system uses fuzzy language factors, produced through fuzzification, to express the user's health state on the patient's nurse or doctor. The fog layer is utilizing fitness parameter traits, determines a person's health declare, with both the dense fog and cloud elements reacting quickly based on that user's medical history. A thorough test in MATLAB confirms the suggested fuzzy logic prediction system's superiority in terms of delay, processing time, as well as detection accuracy. © 2024 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Wireless Sensor Networks: Revolutionizing Explosive Detection 5

Wireless Sensor Networks: Revolutionizing Explosive Detectio...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sujith Kumar, P.S. Anand, C. Vasuki, N. Ramesh, V. Santhoshkumar, S.P. Sree Buddha College of Engineering Department of Computer Science and Engineering Kerala Alappuzha India Nandha College of Technology Department of Information Technology Erode India Government College of Engineering Department of Computer Science and Engineering Erode India School of Computing Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India

ISBN: (纸本)9798331518097

One of the important technologies for protecting civilian life from terrorist attacks is explosive detection techniques. The global incidence of terrorist activities has been considerably reduced by the development of explosive detection techniques, which has been made possible by ongoing technology improvements. Wireless sensor networks (WSNs) are one of these developments that has become an innovative tool for explosives detection. This work explores recent empirical research on the application of WSNs to explosives detection and includes statistical analysis of the investigations. Wireless sensor network with compatible specialized sensors is required for effective explosive detection. Three main wireless sensor system aspects that are relevant to explosives detection are covered in this research. It first looks at how scalable wireless sensors are in terms of explosives detection technologies. In the second section, it looks at the mobility and communication capabilities of WSNs and sensors in explosives detecting systems. Finally, it highlights potential hazards and weaknesses and addresses the crucial topic of data security inside wireless sensor networks used for explosive detection. The study also suggests incorporating drone assisted detection as a novel way to improve the coverage and effectiveness of explosives detection systems, adding to the continuous advancement of security technologies meant to shield civilian populations from terrorist threats. © 2024 IEEE.

关键词： Terrorism

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：