检索结果-内蒙古大学图书馆

3rd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2023

作者： Vekariya, Daxa Rastogi, Ajay Priyadarshini, R. Patil, Manoj Kumar, M. Sunil Bhasker Pant Parul University Department of Cse Parul Institute of Engineering and Technology Gujarat Vadodara India Teerthanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Datta Meghe Institute of Medical Sciences Research and Development Jawaharlal Nehru Medical College Wardha India Department of Cse School of Computing AP Tirupathi India Cse Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The Internet of Things (IoT), which allows computers from different settings to connect and cooperate on a same activity while ensuring better levels of privacy and safety for some of the connected equipment, must manage the whole process as manufacturing procedures grow considerably more complex. Some well-liked things that solve important issues and provide third-party security. Since the problem is still open, privacy and security must come first. According to this research, Mengers identification, a unique and potent security technique made possible by blockchain, would foster confidence in a variety of industries. In this instance, access control carries out security. To allow authentication across linked devices while protecting device privacy, the Mengers authenticating mechanism is defined. Before delivering keys to linked parties, an appropriate path must first be established. The findings show improved performance during the verification process when the simulation is run in the simulation environment. © 2023 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Cloud computing Security through Deep Learning and Attention Mechanism Intrusion Detection Systems

Enhancing Cloud Computing Security through Deep Learning and...

引用

Intelligent Technologies (CONIT), International Conference on

作者： Sunil Shukla Jagendra Singh T. Ramya Satyakam Rahul Arnab Kumar Mallick Pawan Pandey School of Computing Graphic Era Hill University Dehradun India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CSE-DS CVR College of Engineering Hyderabad India School of Computer Science Engineering MIT ADT University Pune India Department of Computer Science Engineering Indian Institute of Technology Madras Chennai India Department of Computer Science Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

This research investigates the enhancement of cloud computing security through adaptive neural network intrusion detection systems, employing Deep Belief Networks (DBN) and Attention Mechanism. The study aims to address the growing concerns surrounding security threats in cloud environments by leveraging advanced machine learning techniques. Experimental evaluations were conducted to assess the performance of the proposed approach, with specific emphasis on detection accuracy, false positive rate, and computational efficiency. Results indicate that the DBN-based intrusion detection system achieved an average detection accuracy of $94.4 \%$, with a false positive rate of $2.2 \%$ and a computational efficiency of $\mathbf{1 5 8. 4}$ milliseconds. In comparison, the IDS utilizing Attention Mechanism demonstrated superior performance, with an average detection accuracy of $96.2 \%$, a false positive rate of $1.1 \%$, and a computational efficiency of 153.2 milliseconds. These findings underscore the effectiveness of DBN and Attention Mechanism in bolstering the security posture of cloud computing environments. By integrating adaptive neural network-based intrusion detection systems, organizations can enhance threat detection capabilities, mitigate risks, and safeguard sensitive data and critical assets in the cloud.

关键词： Attention mechanisms Accuracy Adaptive systems Cloud computing security Intrusion detection Clustering algorithms Threat assessment

来源：评论

学校读者我要写书评

暂无评论

Mobile edge fog, Blockchain Networking and computing-A survey 1

Mobile edge fog, Blockchain Networking and Computing-A surve...

引用

1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bhatla, Shruti Mishra, Divya Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Abes Engineering College Ghaziabad India

ISBN: (纸本)9798350338027

Mobile edge computing (MEC) and blockchain technology are two rapidly evolving areas in computer science that have the potential to significantly impact the design and operation of distributed systems. MEC involves the deployment of computing resources and services at the edge of the network, closer to the end users, enabling lowlatency, high-bandwidth applications such as augmented reality and machine learning. Blockchain technology, on the other hand, is a distributed database that allows for secure, transparent, and immutable record-keeping, with potential applications in a wide range of industries. In this project, we explore the integration of MEC and blockchain technology for the management of distributed computing resources at the edge of the network. We review the state of the art in these two areas, including existing projects and prototypes, and describe our materials and method for evaluating their potential for integration. Our results show that the combination of MEC and blockchain has the potential to enable new applications and services that require both low latency and secure, decentralized data management. We conclude by summarizing the main contributions of our work and discussing future directions for research in this area. Our findings have implications for the design and operation of distributed systems, and have the potential to enable new applications and services in a wide range of industries. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Smart Irrigation: Revolutionizing Water Management in Agriculture for Sustainable Practices and Improved Crop Yield 6

Smart Irrigation: Revolutionizing Water Management in Agricu...

引用

6th International Conference on Recent Trends in Advance computing, ICRTAC 2023

作者： Devendiran, Ramkumar Turukmane, Anil V. Sathiyaraj, A. Srinivasa Rao, P. Prasad, B.B.K. Pulipati, Sreedhar VIT-AP Univeristy School of Computer Science and Engineering Andra Pradesh Amaravati522 237 India Sathyabama Institute of Science and Technology School of Computing Tamilnadu Chennai60 0119 India Vasireddy Venkatadri Institute of Technology Department of Cse Nambur522508 India Dept of It Bapatla India

ISBN: (纸本)9798350394702

An inventive method of managing water in agriculture, smart irrigation makes use of automation, sensors, and technology to maximize water utilization and minimize waste. As water scarcity becomes a greater worry and agricultural practices need to be sustainable, smart irrigation is becoming a vital tool for farmers all over the world. Farmers would be able to better manage water resources with the help of the proposed smart irrigation systems, which would provide them real-time data on soil moisture, weather, and crop water needs. By using that data, farmers may optimize water usage and minimize water loss by making well-informed decisions regarding watering schedules and amounts. Increased crop yield, reduced water consumption, and cost savings are just a few of the many benefits that farmers may reap from smart irrigation systems. Smart irrigation systems are able to automatically modify watering schedules and amounts to suit the unique requirements of individual crops by employing sensors that track the weather and the amount of moisture in the soil. This leads to less energy and fertilizer use and increased crop quality and yield. Smart irrigation systems can lower labor costs and boost farm profitability in addition to increasing crop yield and using less water. Farmers may save time and energy by automating their irrigation systems, which allows them to irrigate crops more precisely and at the proper times. This may result in lower expenses, higher output, and better environmental effects. Another sustainable technique that encourages water resource saving is smart irrigation. It is transforming the way that water is managed in agriculture. Smart irrigation systems will continue to advance through further research and development, becoming more widely available, affordable, and conducive to sustainable farming practices that will increase crop output. © 2023 IEEE.

关键词： Water management

来源：评论

学校读者我要写书评

暂无评论

Quantitative and Qualitative Analysis of PCC-based Change detection methods over Agricultural land using Sentinel-2 Dataset 3

Quantitative and Qualitative Analysis of PCC-based Change de...

引用

3rd International Conference on computing, Analytics and Networks, ICAN 2022

作者： Singh, Gurwinder Sethi, Ganesh Kumar Singh, Sartajvir Chandigarh University University Institute of Computing Mohali India Punjabi University Department of Computer Science Patiala India Multani Mal Modi College Department of Computer Science Patiala India Chitkara University Chitkara University School of Engineering and Technology Himachal Pradesh India

ISBN: (纸本)9781665499446

To plan production, the sowing, and harvesting of a particular crop, and the performance of marketing activities information about yields is important for both the traders and producers. In this study, various efforts have been made to extract critical information for agriculture land use classification areas using Sentinel-2 datasets, which was not possible with the help of multi-spectral datasets. As part of the current work, the artificial neural networks (ANN) classifier is combined with the post-classification comparison (PCC), thereby predicting seasonal variability from satellite imagery. The ANN classifier is incorporated into the post-classification comparison procedure, called ANN-based change detection. As part of the demonstration, the datasets were acquired using Sentinel-2 datasets during the period 2017 - 2018 over the agricultural land in Block Khamanon, District Fatehgarh Sahib, Punjab State, India. This process cross-validated the performance of ANN with a conventional maximum likelihood classifier (MLC) for confirmation. In comparison with the conventional PCC-MLC model (classified maps have an average of 86 - 88.8%, and change maps have an average of 83.6 - 84.2%), the PCC-ANN model achieved accuracy (classified maps have an average of 90.4 - 93.4%, and change maps have an average of 87.4 - 90%). In addition to identifying water surfaces, crop types, and man-made features, this study can also help in performing a wide range of land-use patterns. © 2022 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Joint Optimization of Model Splitting and Device Task Assignment for Private Multi-hop Split Learning

Joint Optimization of Model Splitting and Device Task Assign...

引用

Annual Conference on Information sciences and Systems (CISS)

作者： Dongyu Wei Qianqian Zhang Yuchen Liu Mingzhe Chen Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Department of Electrical and Computer Engineering Rowan University Glassboro NJ USA Department of Computer Science NC State University Raleigh NC USA Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

In this paper, a deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdropper from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the eavesdropped information while meeting the model training energy consumption and delay constraints. To solve this problem, we propose an actor-critic deep reinforcement learning (AC-DRL) framework that enables a centralized agent to dynamically determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device while considering network topology and device properties (e.g., energy and privacy constraints). The proposed AC-DRL framework integrates an actor network to generate optimal actions and a critic network to stabilize the learning process by evaluating the cumulative rewards. Simulation results demonstrate that the proposed method improves the convergence rate by 3× and the accumulated reward by up to 40% compared with the proximal policy optimization (PPO) algorithm.

关键词： Training Performance evaluation Privacy Network topology Simulation Heuristic algorithms Data models Delays Optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

Pipette: Efficient Fine-Grained Reads for SSDs

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第12期42卷 4721-4734页

作者： Bai, Shuhan Wan, Hu Huang, Yun Sun, Xuan Wu, Fei Xie, Changsheng Hsieh, Hung-Chih Kuo, Tei-Wei Xue, Chun Jason Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China City University of Hong Kong Department of Computer Science Pokfulam Hong Kong Imperial College London Department of Computing LondonSW7 2BX United Kingdom Yeestor Microelectronics Company Ltd. Department of Research and Development Shenzhen518057 China National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Big data applications, such as recommendation system and social network, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices upon the traditional storage system rely on the paging mechanism to migrate pages to the host DRAM, tending to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, an efficient fine-grained read framework, Pipette, is proposed in this article as an extension to the traditional I/O framework. With adaptive design for caching, merging, and scheduling, Pipette explores locality and acceleration for fine-grained read requests to establish an efficient byte-granular read path upon the dedicated byte-addressable interface. When the Pipette prototype on an SSD runs popular workloads, we measured throughput gains by up to 50% and 54% with traffic reduction in the range of 41.3× and 56.5× . © 1982-2012 IEEE.

关键词： Merging

来源：评论

学校读者我要写书评

暂无评论

Multi-Task Decision-Making for Multi-User 360° Video Processing over Wireless Networks

arXiv

引用

arXiv 2024年

作者： Badnava, Babak Chakareski, Jacob Hashemi, Morteza Department of Electrical Engineering and Computer Science University of Kansas United States College of Computing New Jersey Institute of Technology United States

We study a multi-task decision-making problem for 360° video processing in a wireless multi-user virtual reality (VR) system that includes an edge computing unit (ECU) to deliver 360° videos to VR users and offer computing assistance for decoding/ rendering of video frames. However, this comes at the expense of increased data volume and required bandwidth. To balance this trade-off, we formulate a constrained quality of experience (QoE) maximization problem in which the rebuffering time and quality variation between video frames are bounded by user and video requirements. To solve the formulated multi-user QoE maximization, we leverage deep reinforcement learning (DRL) for multi-task rate adaptation and computation distribution (MTRC). The proposed MTRC approach does not rely on any predefined assumption about the environment and relies on video playback statistics (i.e., past throughput, decoding time, transmission time, etc.), video information, and the resulting performance to adjust the video bitrate and computation distribution. We train MTRC with real-world wireless network traces and 360° video datasets to obtain evaluation results in terms of the average QoE, peak signalto- noise ratio (PSNR), rebuffering time, and quality variation. Our results indicate that the MTRC improves the users' QoE compared to state-of-the-art rate adaptation algorithm. Specifically, we show a 5.97 dB to 6.44 dB improvement in PSNR, a 1.66X to 4.23X improvement in rebuffering time, and a 4.21 dB to 4.35 dB improvement in quality variation. Copyright © 2024, The Authors. All rights reserved.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Identification of Fish and Shrimp Species using computer Vision echniques and Convolutional Neural Networks

Identification of Fish and Shrimp Species using Computer Vis...

引用

Mobile computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Levina Tukaram Swati Shalet Benvin Swapnil Parikh Harshal Patil Ramya Maranan Department of Computer Science & Engineering K N S Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Waghodia Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

The objective of this work is to identify fish and shrimp species using Artificial Intelligence (AI) and computer vision techniques. Artificial Intelligence (AI) techniques include Artificial Neural Networks (ANN). Therefore, neural networks can be used as a way to improve the industry, aiming to reduce costs and improve processes. This work proposes a transform network model for fish identification. Eight species of fish and one species of shrimp, were used. The database has already been consolidated and made available by the company, with a substantial amount of data, which contributed to the selection. The results regarding the number of hits were in the order of 99%, which demonstrates promising results that will contribute to the reliability of seafood species identification. The 99% accuracy of the proposed model indicates good performance for identifying fish species. Of all the classes used, only five were not fully utilized, including the black sea sprat (I), Gilt Head Bream (II), Hourse Makerel (III), red mullet (IV), red sea bream (V), sea bass (VI), shrimp (VII), striped red mullet (VIII), and trout (IX). However, the identification errors did not exceed 2%.

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：