检索结果-内蒙古大学图书馆

5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Mallikharjuna Rao, K. Kumar, Ashutosh Agrawal, Deepesh Reyya, Shikhar Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur Naya Raipur India Computer Science and Engineering International Institute of Information Technology Naya Raipur Naya Raipur India

ISBN: (纸本)9789819761050

This article deeply dives into a shift in document security by introducing innovative authentication measures. Conventional digital signature methods are less secure and can easily be forged. Our research explores the integration of multiple signatures authentication and time-constrained signature features, which will boost the security of digital signatures across documents;by introducing a time-constrained signing approach, we are addressing potential issues and establishing robust defense against various threats. Also, multiple files can be uploaded using a single CID in interplanetary file system (IPFS). Besides introducing multiple signers and time-limit features, our research utilizes IPFS for secure and collaborative document management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

Internet of Animal Health Things (IoAHT) Framework for Clinical Mastitis Detection in Dairy Cows 1

Internet of Animal Health Things (IoAHT) Framework for Clini...

引用

1st International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2023

作者： Ankitha, K. Venugopala, P.S. Kunder, Harish Shetty, Ankitha NMAMIT Nitte Dept. of Artificial Intelligence and Data Science Karkala India Alvas Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Moodbidri India

ISBN: (纸本)9798350306637

The Earth serves as a shared habitat not just for humans but also for an array of animal species and organisms. Throughout history, animals have played a crucial role in preserving the natural ecosystem balance and meeting diverse human needs. They contribute significantly to various sectors, such as Agriculture, Dairy, Poultry, and animal husbandry, thus bolstering a country's economy. Human responsibility lies in ensuring the well-being of these animals, which is overseen by Veterinary science. In India, a major milk and milk product producer, the dairy industry actively contributes to the nation's economy. Among the primary milk sources-cows, buffaloes, goats, and sheep-cows hold a revered place in different faiths. Particularly in the dairy industry, cows are vital for milk production. However, monitoring cow health poses challenges. Clinical Mastitis, an inflammation affecting cows' mammary glands, hampers milk production. Automatic milking machines are commonly used, but identifying and segregating mastitis-infected cows from healthy ones remains difficult. The contagious nature of the disease increases the risk of widespread infection among cows. To combat this issue, we propose a solution called the Internet of Animal Health Things (IoAHT), based on the Internet of Things (IoT). IoAHT employs IoT technology for animal health, aiming to accurately identify cases of clinical mastitis in cows. The implementation of IoAHT has the potential to revolutionize health monitoring in the dairy industry, safeguarding cow well-being and ensuring sustainable milk production. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Simultaneous Optimization of the Number and Distribution of Friction Dampers for Seismically Excited Buildings: An AI Approach 8

Simultaneous Optimization of the Number and Distribution of ...

引用

8th International artificial intelligence and data Processing Symposium, IDAP 2024

作者： Kurucu, Mert Can Atam, Ercan Guzelkaya, Mujde Eksin, Ibrahim Istanbul Technical University Faculty of Electrical and Electronics Engineering Istanbul Turkey Boǧaziçi University Institute for Data Science & Artificial Intelligence Istanbul Turkey

ISBN: (纸本)9798331531492

Friction dampers are an economical and effective passive control solution for enhancing the seismic performance of multi-story buildings. Their performance strongly depends on their configuration (distribution or placement) in the building, and from a cost-effectiveness standpoint, the minimum feasible number of dampers should be installed. Therefore, addressing both of these aspects simultaneously is essential. However, the associated optimization problem is a large-scale, non-convex, combinatorial optimization problem. In this paper, we present a deep reinforcement learning-based solution for simultaneous optimization of the number and distribution of friction dampers in multi-story buildings for seismic protection. The presented solution is computationally efficient and is tested on a case study to demonstrate its performance. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

OFDM with Generalized Cascade Index Modulation for Secure Communications

引用

China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. Computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

来源：评论

学校读者我要写书评

暂无评论

Investigating Vulnerabilities of Information Solicitation Process in RPL-Based IoT Networks 11th

Investigating Vulnerabilities of Information Solicitation P...

引用

11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Sahay, Rashmi Sushant, Cherukuri Gaurav Department of Computer Science and Engineering Faculty of Science and Technology IcfaiTech The ICFAI Foundation For Higher Education Hyderabad India Department of Data Science and Artificial Intelligence Faculty of Science and Technology IcfaiTech The ICFAI Foundation For Higher Education Hyderabad India

ISBN: (纸本)9789819967018

The Routing Over Low power and Lossy network (ROLL) working group of IETF proposed the IPv6 Routing Protocol over Low Power and Lossy network (RPL) to fulfill multiple traffic requirements of the Internet of Things (IoT) environments. RPL structures ubiquitous IoT networks in the form of Directed Acyclic Graphs (DAGs) that comprises basic sensor nodes (motes), motes with routing capability, and one or more root (sink) nodes. RPL ensures optimal topological organization of IoT networks by its rank and version number properties. However, vulnerabilities in the RPL routing process pose threats to several protocol violations in the IoT environment. In RPL, information solicitation is the process by which sensor nodes seek network configuration information required to join an IoT network. The paper investigates the vulnerabilities associated with the information solicitation process in RPL-based IoT networks that leads to flooding of control messages. Consequently, there is energy and queue buffer depletion of sensor nodes in IoT networks. On the one hand, queue buffer depletion may lead to the dropping of packets by sensor nodes. On the other hand, energy depletion of sensor nodes may altogether make them non-functioning. Therefore, IoT network administrators must pay attention to the device’s power consumption. Recent research focuses on energy depletion due flooding of control packets. Nevertheless, flooding also affects other significant parameters like, the network convergence time, beacon interval and packet arrival rate. In this work, we perform an in-depth evaluation of the performance of RPL-based IoT networks under the threat of vulnerabilities associated with information solicitation process. The results of our analysis will assist in devising mitigation mechanisms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Classification of Credit Card Delinquent Customers Using Random Forest Algorithm 1

引用

5th International Conference on Computer and Communication Technologies, IC3T 2023

作者： Praveen Kumar, Vadapally Nimmala, Satyanarayana Naresh, Burri Ravikumar, Ch CVR College of Engineering Hyderabad India CVR College of Engineering Hyderabad India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

Customers receive a little plastic payment card called a credit card. It allows the holder access to the market to purchase goods and services mostly on the promise of payment from the holder. The card issuer sets up a revolving account and offers the cardholder a credit line from which they can borrow money for either personal use or to pay for goods or services. To control risks and keep a strong credit portfolio, financial institutions must foresee credit card defaults. In this study, we investigate how to categorize clients who have not paid their credit card balances using the random forest algorithm. We examine the effectiveness of random forest in precisely detecting probable defaulters using a dataset comprising credit card payment history. The study highlights the benefits and drawbacks of the random forest technique through data preprocessing, model application, and performance evaluation. For credit card issuers to make wise judgments and reduce potential financial losses, the findings offer insightful information. The experiment in this study uses real-time data collection obtained from a third-party agency and a random forest technique with tenfold cross-validation. The findings demonstrated that random forest is the best method for categorizing credit card delinquent clients. The classifier outperforms itself in predicting the most likely defaulters with an accuracy of 97.73%, while the classifier’s overall accuracy is 84.703%, according to the experimental data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Risk management

来源：评论

学校读者我要写书评

暂无评论

Adaptive sail fish optimizer based optimized region selection for robust video steganography

引用

International Journal of Information technology (Singapore) 2025年第4期17卷 2417-2427页

作者： Babu, J. Suresh Niranjana, G. Ramana, Kadiyala Department of Computing Science & Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology (Autonomous) Hyderabad India

Securing data transmission in a digital era is a difficult one due to the broad application of the Internet, personal computers, and mobile phones for communication. Traditional video steganography techniques sometimes fail to identify the best pixels for encoding hidden data, resulting in lowerquality and robustness. To solve this issue, this article proposes an innovative steganography technique that improves the security and quality of data hiding. The proposed method takes cover video and secret images as input. The Structural Similarity Index Measurement (SSIM) first collects key frames from the input video. The adaptive sailfish optimizer (ASFO) is then used to find the best regions within these keyframes to insert the secret data in. To improve security, the discrete wavelet transform (DWT) is applied to the specified region, and the LL band is used for the hiding process. The secret image is encrypted by Adaptive elliptical curve cryptography (AECC) and transformed to binary bits, which then get embedded in the chosen band. This method leads to the construction of a stego-video. The extraction procedure is repeated using the same approach, which ensures secure and robust data transmission. Performance metrics, including PSNR, BER, NAE, TAF, and NC are analyzed to compare each method's efficacy. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Adaptive elliptical curve cryptography And stego-video Discrete wavelet transform Structural similarity index measurement Video steganography

来源：评论

学校读者我要写书评

暂无评论

Blockchain-enhanced artificial intelligence for advanced collision avoidance in the Internet of Vehicles (IoV)

引用

Neural Computing and Applications 2025年第6期37卷 4915-4936页

作者： Talaat, Fatma M. Hamza, Alyaa A. Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Engineering Technology Elsewedy University of Technology–Polytechnic of Egypt 10th of Ramadan City44629 Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Bahrain Polytechnic Isa Town Bahrain Badr City11829 Egypt

The convergence of blockchain technology and artificial intelligence (AI) presents a promising solution for enhancing safety within the Internet of Vehicles (IoV) ecosystem. This paper introduces the "Blockchain-Based Collision Avoidance with AI for Vehicles" (BCA-CAR) algorithm, which aims to provide advanced and intelligent collision avoidance capabilities in IoV. BCA-CAR combines the security and data integrity features of blockchain with the real-time decision-making capabilities of AI to prevent collisions and improve road safety. The algorithm consists of five key phases: data Collection and Processing, AI Collision Risk Assessment, Decision and Smart Contract Execution, data Validation and Trust (Blockchain Integration), and Learning and Improvement. In the data Collection and Processing phase, data from vehicle sensors, cameras, V2V and V2I communication, and external infrastructure is collected and preprocessed. The AI Collision Risk Assessment phase utilizes machine learning models to analyze real-time data and predict collision risks. In the Decision and Smart Contract Execution phase, smart contracts on the blockchain automate collision avoidance actions. The data Validation and Trust phase ensures the authenticity and integrity of data through blockchain technology. Finally, the Learning and Improvement phase leverages historical collision data to enhance predictive models and overall system performance. BCA-CAR's primary objective is to enhance safety by preventing collisions, ensuring data trustworthiness, and providing intelligent collision avoidance capabilities. This innovative algorithm has the potential to revolutionize road safety in the era of IoV by reducing accidents, improving traffic management, and enhancing the security and privacy of vehicular communication. The findings highlight that Support Vector Regression (SVR) demonstrates strong predictive accuracy and adaptability within the Internet of Vehicles (IoV), offering a reliable modeli

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Smart Grid Management with AI Driven Analytics in Solar Energy 2

Smart Grid Management with AI Driven Analytics in Solar Ener...

引用

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

作者： Timande, Devanshi Agade, Nishant Gudadhe, Amit Faculty of Engineering & Technology Department of Artificial Intelligence and Data Science Wardha India Faculty of Engineering & Technology Wardha India

ISBN: (纸本)9798331505745

This paper presents an AI-driven framework for real-time smart grid optimization tailored specifically for solar energy integration. The proposed system features a multi-layered architecture that integrates data acquisition from solar irradiance sensors, smart meters, and meteorological sources with advanced pre-processing techniques to ensure data quality and fusion. Using advanced deep learning architectures, such as Long Short-Term Memory networks for forecasting solar irradiance and regression methodologies for predicting load, our proposed framework dynamically adjusts energy dispatch and demand response measures to address the inherent variability associated with solar power. Additionally, a combined strategy that incorporates reinforcement learning is implemented to improve real-time grid stability and operational efficiency, and explainable artificial intelligence methods are used to provide clarity in decision-making processes. Simulation studies conducted on an enhanced IEEE 24-bus test system have shown to reduce forecast error by up to 30% and prevent the occurrence of grid instability events by about 40% in contrast with traditional methods. These results are promising for AI-based analytics since it promises to enhance smart grid functionality and enable more sustainable use of renewable resources. Further research will develop this model with additional renewable energy sources and increase cybersecurity. © 2025 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

database Security: Current Challenges and Effective Protection Strategies 6

Database Security: Current Challenges and Effective Protecti...

引用

6th International Conference on Computing and Informatics, ICCI 2024

作者： Naguib, Ahmed Fouad, Khaled M. Faculty of Information Technology and Computer Science Nile University Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer Science and Engineering New Mansoura University Egypt

ISBN: (纸本)9798350373875

database security has grown to a necessary level of importance today, characterized by the escalating demand for data storage. The surge in data storage requirements, propelled by technological advancements, has led to increased attacks targeting database security vulnerabilities. While multiple storage and protection methods have emerged in tandem with evolving technologies, the discovery of security vulnerabilities has led to new attack vectors. This study aims to repair this gap by securing and fortifying the security of stored data within databases. Fundamental to information security, the study highlights integrity, confidentiality, and accessibility in addressing database security concerns. The study aims to spotlight these issues and increase awareness about security problems and potential solutions. An analysis of the most common security problems in databases forms a central part of the study, accompanied by a discussion of viable solutions for these challenges. Moreover, the study delves into international security standards and secure models to counteract cyber-attacks targeting databases. © 2024 IEEE.

关键词： Cryptography

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：