检索结果-内蒙古大学图书馆

5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Rawat, Abhishek Suryavanshi, Raghuraj Singh Nagar, Vishal Yadav, Divakar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Institute of Engineering and Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819771776

Blockchains improve safety and faster teamwork for emergency medical transport in critical care. Emergency medical transport is not the only healthcare sector that has become better and different because of current blockchains. This report also illustrates how blockchains can alter the safety, integrity, and effectiveness of emergency medical transport operations, including a wide-ranging examination of these technologies’ consequences on the healthcare sector. Traditional Emergency Medical Transportation Systems face different challenges, and this paper provides a framework for a complete understanding of the possible benefits and creative solutions that Blockchain offers to the healthcare industry. This paper has given a compelling business example for applying Blockchain to enhance Emergency Medical Transportation. It gives valuable perspectives on smart steps that can be used to improve care and well-being and streamline pool offerings and a feasibility rate that could help to create an effective outcome for Emergency Medical Transport Services with the usage of Blockchain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Healthcare industry Medical Data History Transportation

来源：评论

学校读者我要写书评

暂无评论

Comparative Evaluation of Mango Fruit Diseases Classification Using Machine Learning

Comparative Evaluation of Mango Fruit Diseases Classificatio...

引用

2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Kumar, Sandeep Gupta, Bhupesh Chhabra, Mohit Garg, Lucy Department of Computer Science & Engineering Mullana India Panipat Institute of Engineering Technology Department of Computer Science & Engineering Panipat India

ISBN: (纸本)9798350376470

A fruit valued for its great flavor, scent, and nutritional content;the mango (Mangifera indica L.) is one of the most significant tropical fruits in the world economically. However, several illnesses that compromise fruit quality and output significantly hinder the commercial cultivation and trading of mangoes. This widespread illness has significant financial consequences and represents a danger to mango farming worldwide. A key component of effectively managing the illness and protecting crops is the timely and accurate assessment of the disease's severity. The message highlights how critical it is to detect illnesses as soon as possible and with precision to enable effective crop management. The main illnesses that afflict mango fruits are covered in detail in this document, along with information on their genesis, symptoms, epidemiology, and management techniques. The primary disorders covered are anthracnose, mango malformation, powdery mildew, mango bacterial black spot, and mango abrupt decline (mango decline-associated virus). Every disease is thoroughly investigated, covering its causes, methods of spread, symptoms, and effects on fruit output and quality. Machine learning (ML) approaches have become more effective tools for agricultural system disease detection and categorization in recent years. To shed light on the efficacy and relevance of machine learning (ML) algorithms for categorizing mango fruit illnesses, this research study compares and contrasts them. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Securing Autonomous UAV Cluster With Blockchain-Based Threshold Key Management System Utilizing Crypto-Asset and Multisignature

引用

IEEE Transactions on Mobile Computing 2025年第7期24卷 5765-5778页

作者： Kharjana, Mebanjop Sahana, Subhas Chandra Saha, Goutam North-Eastern Hill University Department of Information Technology Shillong793022 India National Institute of Technology Department of Computer Science and Engineering Durgapur713209 India

Unmanned aerial vehicles deployed in remote locations rely on self-governed key management for their protection. However, conventional key management depends on a centralized ground-based station or single vehicle. Such a system is vulnerable to compromised certificate authority problems and single-points-of-failure. This paper proposed to resolve these vulnerabilities using a blockchain-based threshold key management system. The proposed system utilized blockchain’s concepts of crypto-asset and multisignature. Keys are defined as crypto-assets to improve their management in the blockchain network. Multisignature facilitates collaboration during key management based on a threshold value. The threshold value is also configurable to meet systems’ security and performance requirements. The proposed system secured the process of re-enforcement, sub-clustering, re-merging, and inter-cluster migration. Security analysis revealed that the proposed system complied with most key management security guidelines. The custom signature module used to authenticate intra-cluster communication was also verified as safe. Threats to the cluster were identified, assessed for risk, and mitigated accordingly. Performance analysis found that both AODV and DSDV routing protocols offer consistent performance but DSDV prevailed during the worst-case network scenario. The paper finally identified research gaps, including the requirement for an optimized mechanism for collecting consent signatures. © 2002-2012 IEEE.

关键词： Blockchains Autonomous Aerial Vehicles Security Authentication Drones Public Key Mobile Computing Smart Contracts Collaboration Global Positioning System Autonomous UAV Cluster Threshold Key Management System Blockchain Crypto Asset Multisignature Unmanned Aerial Vehicles Autonomous Unmanned Aerial Vehicles Key Management System Unmanned Aerial Vehicles Cluster Worst Case Scenario Performance Requirements Security Analysis Security Requirements Certificate Authority Energy Consumption Application Programming Interface Secret Key Directed Acyclic Graph Public Key Denial Of Service Elliptic Curve Best Case Scenario Secret Sharing Smart Contracts Public Key Infrastructure Key Operations Unmanned Aerial Vehicles Networks Distributed Ledger technology Signature Scheme Digital Signature Blockchain Platform Random Oracle Model Bitcoin

来源：评论

学校读者我要写书评

暂无评论

Optimized graph transformer with molecule attention network based multi class attack detection framework for enhancing privacy and security in WSN

引用

Multimedia Tools and Applications 2025年第15期84卷 14273-14304页

作者： Anoop, M. Mary, L. William Wilson, Allan J. Kiran, W.S. Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram campus Chennai600089 India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India

In today's rapidly evolving network landscape, cybersecurity has become increasingly crucial. However, wireless sensor networks face unique challenges due to their limited resources and diverse composition, high computational demands, diminished accuracy rates, making them susceptible to a wide array of cyber threats. To tackle these challenges, this research introduced a novel method known as Optimized Graph Transformer with Molecule Attention Network (OGTMAN) on the fusion of Secure Multi-party Computation (SMC) and differential privacy aimed at enhancing privacy and security. This approach starts with data preprocessing, employing min–max normalization to address dataset imbalances. N-Tuple Contrastive Learning with Generative transformer(N-TCLG)feature extractor distinguish between different classes of data instances and effectively learns representations that capture relevant information about network traffic patterns Further, feature selection by enhanced chi-square statistics with kepler algorithm, is utilized to assess the significance of each feature in characteristic between normal network behavior and malicious activities further refines the feature selection method by optimizing the selection criteria based on the statistical properties of the dataset. Finally, the proposed OGTMAN classifies various attacks based on their behavioural patterns with the help of enhanced ant aphid optimization with adaptive step factor. Ultimately, this fine-tuning yield superior model training outcomes, resulting more accurate attack classification outcomes. To evaluate the effectiveness of this approach, datasets encompassing with KDD Cup 99 datasets are employed. The efficiency of the introduced method is demonstrated when compared with prevailing attack detection methods in terms of accuracy, recall, precision and F-measure of 99.98%, 99.5%.,99.9% and 99.5% respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer N

关键词： Molecules

来源：评论

学校读者我要写书评

暂无评论

mLBOA-DML: modified butterfly optimized deep metric learning for enhancing accuracy in intrusion detection system

引用

Journal of Reliable Intelligent Environments 2023年第3期9卷 333-347页

作者： Prabhakaran, Varun Kulandasamy, Ashokkumar Department of Computer Science and Engineering St.Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Intrusion detection is a prominent factor in the cybersecurity domain that prevents the network from malicious attacks. Cloud security is not satisfactory for securing the user’s information because it is based on standard protocols. Hence, cloud users cannot fully trust the security offered by cloud service providers. The state-of-the-art techniques create clusters for classes and manually label the unknown classes to detect novel attacks. This notion binds the network traffic associated with each attack together and drifts the similarity between the same attacks. These techniques are often prone to errors and degrade performance. To overcome this drawback, various researchers have developed different intrusion detection system which relies on specific attack patterns to distinguish between normal and abnormal behavior. This paper presents a modified Lagrange interpolated Butterfly optimization algorithm-based deep metric learning (mLBOA-DML) architecture for intrusion detection to detect both host-based and network attacks. DML architecture parameters are optimized utilizing mLBOA algorithm via its global optimization capability for increasing the accuracy of attack prediction. DML algorithm does both feature extraction and classification processes. When evaluated using the UNSW-NB15 and NSL-KDD datasets, the proposed model offers improved accuracy near 99% for both datasets. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

A comparative study of recommender systems in different domains 4th

A comparative study of recommender systems in different doma...

引用

4th International Conference on Computational Methods in Science and technology, ICCMST 2024

作者： Rohit Sharma, Aditi Kumar, Aditya Singh, Prabal Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India

ISBN: (纸本)9781032911571

Recommender systems are techniques designed to enhance user experience in various domains. They suggest relevant items to users based on their behavior and preferences (Linyuan et al. Feb 2012). These systems are being extensively utilized in domains such as e-commerce, social media, and video streaming platforms. A recommender system aims to provide personalized recommendations to users based on their past behavior, preferences, and other similar data. The recommender system receives input data from users. Several recommender systems are available nowadays, making it challenging to select one. It would be beneficial to conduct a comparative analysis of various recommendation systems. The research paper attempts to conclude the performance of various recommender systems by using a variety of evaluation metrics. Evaluation metrics provide insights into the effectiveness of the recommendation algorithms. This guides the selection of the most appropriate model for a given application (Oona et al. 2024). The comparative study aims to examine and analyze different recommender systems across various domains. We evaluate the performance of these systems in improving user experiences and increasing user interaction. By studying the performance of recommender systems in different domains, we gain insights into the strengths and limitations of each approach. © 2025 the Author(s).

关键词： collaborative content-based evaluation metric hybrid recommendation recommender

来源：评论

学校读者我要写书评

暂无评论

Efficient straggler task management in cloud environment using stochastic gradient descent with momentum learning-driven neural networks

引用

Cluster Computing 2024年第4期27卷 4673-4685页

作者： Swain, Smruti Rekha Parashar, Anshu Singh, Ashutosh Kumar Lee, Chung Nan Department of Computer Applications National Institute of Technology Haryana Kurukshetra136119 India Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Kaohsiung804201 Taiwan

In the modern era, large-scale computing systems distribute tasks into smaller units, allowing them to be executed simultaneously, accelerating job completion, and reducing energy usage. However, cloud computing systems face a significant challenge: the Long Tail problem. This problem arises when a small subset of slow-performing tasks impedes the overall progress of parallel job execution, resulting in longer service response times and decreased system efficiency. To reduce task execution time and energy consumption, we propose an efficient straggler task management framework for cloud data centers in this paper. A neural network-based resource predictor is initially developed and tuned with the Stochastic Gradient Descent with Momentum mechanism to analyze and classify heterogeneous tasks into stragglers and non-stragglers. Then, after identifying the straggler tasks, they are further classified into two categories: Resource Hunters and Long-Tail stragglers, based on their specific resource requirements. A task management policy is implemented to achieve parallelism and enhance sustainability in the cloud infrastructure. Considering the task category, this policy effectively schedules and allocates resources among user job requests. To evaluate the effectiveness of the proposed work, extensive simulations are performed using the Google Cluster Dataset (GCD). The results obtained from these simulations are subsequently compared to state-of-the-art techniques for a comprehensive analysis. The experimental results reveal substantial improvements in various metrics, including power consumption and active servers showing reductions of up to 55.16% and 35%, respectively. Furthermore, there has been a reduction in execution time of up to 67.74%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

To Enhance the Security System of Mobile Phones by Using GSM technology

To Enhance the Security System of Mobile Phones by Using GSM...

引用

2024 International Conference on Advances in Computing Research on Science engineering and technology, ACROSET 2024

作者： Mangal, Mayank Tiwari, Gagan Malviya, Kanchan Sahu, Vivek Pathare, Kalpesh Vaidya, Kaustubh Computer Engineering Alamuri Ratnamala Institute of Engineering & Technology Mumbai India Alamuri Ratnamala Institute of Engineering & Technology Department of Computer Science & Engineering Mumbai India

ISBN: (纸本)9798350388800

Nowadays to interconnect with the world, smart phones become an important part of our life, smart phones help us in various ways like keeping lots of important application and data. Using this we can store contacts messages photos videos. But what if you forget your phone at home when you go to work? You might need to make an urgent call, but you don't have the number memorized. So, this model is an Offline mobile device security method based on SMS. Which help to overcome this problem. This paper offered a comprehensive suite of features accessible via text message commands. It empowered users to remotely control their devices, facilitating tasks such as retrieving contact information, locating the device, changing the sound profile, and even ring the phone, and all without the need for internet access. This proposed model aimed to bridge the gap in mobile security by providing a robust offline solution that surpassed traditional online methods. Through these innovations, the model aimed to safeguard users' mobile devices and mitigate the risks associated with loss or theft. © 2024 IEEE.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Multilevel Augmentation for Identifying Thin Vessels in Diabetic Retinopathy Using UNET Model

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 2273-2288页

作者： A.Deepak Kumar T.Sasipraba Faculty of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India

Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different methods and *** Learning(ML)techniques are used for analyz-ing the images andﬁnding out the location of the *** restriction of the ML is a dataset size,which is used for model *** problem has been overcome by using an augmentation method by generating larger datasets with multidimensional *** models are using only one augmentation tech-nique,which produces limited features of dataset and also lacks in the association of those data during DR detection,so multilevel augmentation is proposed for *** proposed method performs in two phases namely integrated aug-mentation model and dataset correlation(***).It eliminates overﬁt-ting problem by considering relevant *** method is used for solving the Diabetic Retinopathy problem with a thin vessel identiﬁcation using the UNET *** based image segmentation achieves 98.3%accuracy when com-pared to RV-GAN and different UNET models with high detection rate.

关键词： Image segmentation diabetic retinopathy image augmentation semantic segmentation CNN

来源：评论

学校读者我要写书评

暂无评论

Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach 38

Protected Test-Time Adaptation via Online Entropy Matching: ...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bar, Yarin Shaer, Shalev Romano, Yaniv Department of Computer Science Technion-Israel Institute of Technology Israel Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Israel

We present a novel approach for test-time adaptation via online self-training, consisting of two components. First, we introduce a statistical framework that detects distribution shifts in the classifier's entropy values obtained on a stream of unlabeled samples. Second, we devise an online adaptation mechanism that utilizes the evidence of distribution shifts captured by the detection tool to dynamically update the classifier's parameters. The resulting adaptation process drives the distribution of test entropy values obtained from the self-trained classifier to match those of the source domain, building invariance to distribution shifts. This approach departs from the conventional self-training method, which focuses on minimizing the classifier's entropy. Our approach combines concepts in betting martingales and online learning to form a detection tool capable of quickly reacting to distribution shifts. We then reveal a tight relation between our adaptation scheme and optimal transport, which forms the basis of our novel self-supervised loss. Experimental results demonstrate that our approach improves test-time accuracy under distribution shifts while maintaining accuracy and calibration in their absence, outperforming leading entropy minimization methods across various scenarios. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

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：