检索结果-内蒙古大学图书馆

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Supreeth, A. Jeeva Ganesh, A. Subash, N. Santhosh, R. Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509828

Accurate and efficient land-use classification is important for the formulation of plans by urban planners, environmental monitoring, and even sustainable development. The paper explores the use of transfer learning methods that utilize pre-trained models to improve LSTM and DNNs' performance when applied on metropolitan settings for LAC. This can be done using transfer learning to learn from large datasets and adapt to the complexities of urban land-use patterns even with limited labeled data. This study uses the UC-Merced-LandUse dataset and heatmap analysis to visualize the models' classification decisions in demonstrating the effectiveness of this approach. This research contributes to the advancement of land-use classification techniques, which enable more accurate and informed urban planning and environmental management. © 2025 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

DeepVNE: Deep Reinforcement and Graph Convolution Fusion for Virtual Network Embedding 16

DeepVNE: Deep Reinforcement and Graph Convolution Fusion for...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Keerthan Kumar, T.G. Kb, Aneesh Siddheshwar, Anagha Marali, Amulya Kamath, Anupama Koolagudi, S.G. Addya, Sourav Kanti National Institute of Technology Department of Computer Science and Engineering Karnataka India Siddaganga Institute of Technology Department of Computer Science and Engineering India Pes University Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Network virtualization (NV) plays a crucial role in modern network management. One of the fundamental challenges in NV is allocating physical network (PN) resources to the demands of the virtual network requests (VNRs). This process is known as a virtual network embedding (VNE) and is NP-hard. Most of the existing approaches for VNE are based on heuristic, meta-heuristic, and exact strategies with limited flexibility and the risk of being stuck in local optimal solutions. In this concern, we provide a deep reinforcement learning (DRL) and graph convolution network (GCN) fusion for VNE (DeepVNE) for maximizing the revenue-to-cost ratio. The DeepVNE takes advantage of the power of actor-critic models within the DRL framework to detect network states and provide optimal solutions matched to current conditions. DeepVNE effectively captures the structural dependencies of both VNRs and PN resources by GCNs, allowing better decision-making during the embedding. Considering several features in the agents throughout the training phase improves resource utilization. The experiments show that DeepVNE outperforms the baselines by gaining a 51% acceptance ratio and 28% revenue-to-cost ratio. © 2024 IEEE.

关键词： Graph Convolutional Network Network Virtualization Reinforcement Learning Virtual Network Embedding

来源：评论

学校读者我要写书评

暂无评论

Hyper-personalized employment in urban hubs: multimodal fusion architectures for personality-based job matching

引用

Neural Computing and Applications 2024年 1-16页

作者： Jain, Dipika Sangwan, Saurabh Raj Kumar, Akshi Department of Computer Science and Engineering Delhi Technological University New Delhi India School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computing Goldsmiths University of London London United Kingdom

In the evolving landscape of smart cities, employment strategies have been steering towards a more personalized approach, aiming to enhance job satisfaction and boost economic efficiency. This paper explores an advanced solution by integrating multimodal deep learning to create a hyper-personalized job matching system based on individual personality traits. We employed the First Impressions V2 dataset, a comprehensive collection encompassing various data modalities suitable for extracting personality insights. Among various architectures tested, the fusion of XceptionResNet with BERT emerged as the most promising, delivering unparalleled results. The combined model achieved an accuracy of 92.12%, an R2 score of 54.49%, a mean squared error of 0.0098, and a root mean squared error of 0.0992. These empirical findings demonstrate the effectiveness of the XceptionResNet + BERT in mapping personality traits, paving the way for an innovative, and efficient approach to job matching in urban environments. This work has the potential to revolutionize recruitment strategies in smart cities, ensuring placements that are not only skill-aligned but also personality-congruent, optimizing both individual satisfaction and organizational productivity. A set of theoretical case studies in technology, banking, healthcare, and retail sectors within smart cities illustrate how the model could optimize both individual satisfaction and organizational productivity. © The Author(s) 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

引用

computer Systems science & engineering 2023年第2期44卷 1451-1466页

作者： A.Jameer Basha S.Aswini S.Aarthini Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of ComputationalMathematics Science and Engineering(CMSE) Michigan State University East LansingMI48824USA.

Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart *** power is one of the most signiﬁcant resources in *** enhancing a power factor,the clustering techniques are *** the forward of data in WSN,more power is *** the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and *** the existing system,it does not deal with end-to-end delay and deliv-ery of *** overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is *** Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness *** Swarm Optimization(CSO)helps to solve the complex opti-mization ***,it consists of chickens,hens,and *** divides the chicken into *** Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the *** proposed GA-CSO with LBCM improves the life-span of the ***,it minimizes the energy consumption and also bal-ances the load over the *** proposed method outperforms by using the following metrics such as energy efﬁciency,ratio of packet delivery,throughput of the network,lifetime of the sensor ***,the evaluation result shows the energy efﬁciency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

关键词： Energy efﬁciency sensor nodes chicken swarm optimization load balanced clustering method wireless sensor network cluster heads load-balancing ﬁtness function

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

引用

computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

来源：评论

学校读者我要写书评

暂无评论

Dual Attention and Question Categorization-Based Visual Question Answering

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2023年第1期4卷 81-91页

作者： Mishra, Aakansha Anand, Ashish Guha, Prithwijit Department of Computer Science & Engineering Guwahati781039 India Department of Electronics and Electrical Engineering Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati Guwahati781039 India

Visual question answering (VQA) aims at predicting an answer to a natural language question associated with an image. This work focuses on two important issues pertaining to VQA, which is a complex multimodal AI task: First, the task of answer prediction in a large output answer space, and second, to obtain enriched representation through cross-modality interactions. This work aims to address these two issues by proposing a dual attention (DA) and question categorization (QC)-based visual question answering model (DAQC-VQA). DAQC-VQA has three main network modules: First, a novel dual attention mechanism that helps toward the objective of obtaining an enriched cross-domain representation of the two modalities;second, a question classifier subsystem for identifying input (natural language) question category. The second module of question categorizer helps in reducing the answer search space;and third, a subsystem for predicting answer depending on the question category. All component networks of DAQC-VQA are trained in an end-to-end manner with a joint loss function. The performance of DAQC-VQA is evaluated on two widely used VQA datasets, viz., TDIUC and VQA2.0. Experimental results demonstrate competitive performance of DAQC-VQA against the recent state-of-the-art VQA models. An ablation analysis indicates that the enriched representation obtained using the proposed dual-attention mechanism helps improve performance. © 2022 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Human Gait Recognition Based on Sequential Deep Learning and Best Features Selection

引用

computers, Materials & Continua 2023年第6期75卷 5123-5140页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Usman Tariq Ye Jin Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxilaPakistan Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Gait recognition is an active research area that uses a walking theme to identify the subject *** Gait Recognition(HGR)is performed without any cooperation from the ***,in practice,it remains a challenging task under diverse walking sequences due to the covariant factors such as normal walking and walking with wearing a ***,over the years,have worked on successfully identifying subjects using different techniques,but there is still room for improvement in accuracy due to these covariant *** paper proposes an automated model-free framework for human gait recognition in this *** are a few critical steps in the proposed ***,optical flow-based motion region esti-mation and dynamic coordinates-based cropping are *** second step involves training a fine-tuned pre-trained MobileNetV2 model on both original and optical flow cropped frames;the training has been conducted using static *** third step proposed a fusion technique known as normal distribution serially *** the fourth step,a better optimization algorithm is applied to select the best features,which are then classified using a Bi-Layered neural *** publicly available datasets,CASIA A,CASIA B,and CASIA C,were used in the experimental process and obtained average accuracies of 99.6%,91.6%,and 95.02%,*** proposed framework has achieved improved accuracy compared to the other methods.

关键词： Human gait recognition optical flow deep learning features fusion feature selection

来源：评论

学校读者我要写书评

暂无评论

An effective compressed image authentication scheme based on N-variant AMBTC

引用

Multimedia Tools and Applications 2024年第2期83卷 3801-3829页

作者： Su, Guo-Dong Chang, Chin-Chen Lin, Chia-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Road Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

Image authentication is one technique that provides integrity protection for digital images, making them sensitive to any slight modification. Recently, the detectability of absolute moment block truncation coding (AMBTC) compressed images has been attracting more attention due to its integrity authentication concerns. Thus, this paper proposes an effective N-variant AMBTC based image authentication scheme for AMBTC compressed images. First, a novel AMBTC compression code is derived using a variant AMBTC technique that can provide a better quality of compressed images. Then, each AMBTC compression code is further eliminated into a version requiring fewer bits using the proposed N-variant AMBTC technique, and those vacated N-bits are used to conceal authentication codes, which is generated by hashing the AMBTC compression code and location information. Experimental results confirm that the proposed scheme has superior stability and accuracy in tampering detection while providing better visual quality of the watermarked image. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

The Impact of 5G Technology on the Functionality and Performance of Internet of Things (IoT) Devices 8

The Impact of 5G Technology on the Functionality and Perform...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Yesankar, Prajyot Gourshettiwar, Palash Gote, Pradnyawant Jiet, Moses Makuei Gadkari, Ayush Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798350376425

The design of wireless mobile devices of the next generation 5G promises to address the demands of complex IOT designs in terms of connectivity technologies. This The study illuminates the architecture, benefits, and needs that5G can satisfy. drawbacks of the 5G network. Numerous sites throughout the world have conducted extensive analyses of IOT devices with 5G capabilities and research on this kind of network. A new era of communication has begun with the introduction of 5G technology, which promises unheard of velocity, low latency, and enormous capacity. The significant effects of 5G on the operation and efficiency of connected Internet of Things (IoT) gadgets are examined in this research. IoT devices stand to gain from improved communication capabilities as 5G networks proliferate, opening the door for creative applications in a variety of industries. The first section of the article gives a summary of the main characteristics of 5G technology, highlighting how it has the potential to completely change how Internet of Things devices interact and communicate. Real-time data transfer is made possible by 5G networks far lower latency and higher capacity, which open up possibilities for faster and smooth interactions between IoT *** report also explores how 5G may affect how much electricity Internet of Things devices use. 5G has the ability to solve a long-standing issue in the IoT ecosystem by extending the life of the batteries of IoT devices with the adoption of cost-effective communication protocols and increased network efficiency. A solution that can efficiently handle enormous volumes of information being transferred at very high capacity is required as the Internet of Things gains popularity One of the main demands or goals that need to be reached in the near future, or the next generation of IOT devices, is more capacity, a faster data transfer speed and less delay. © 2024 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Probe Attack Detection Using an Improved Intrusion Detection System

引用

computers, Materials & Continua 2023年第3期74卷 4769-4784页

作者： Abdulaziz Almazyad Laila Halman Alaa Alsaeed Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11421Saudi Arabia

The novel SoftwareDefined Networking(SDN)architecture potentially resolves specific challenges arising from rapid internet growth of and the static nature of conventional networks to manage organizational business requirements with distinctive ***,such benefits lead to a more adverse environment entailing network breakdown,systems paralysis,and online banking fraudulence and *** one of the most common and dangerous threats in SDN,probe attack occurs when the attacker scans SDN devices to collect the necessary knowledge on system susceptibilities,which is thenmanipulated to undermine the entire ***,high performance,and real-time systems prove pivotal in successful goal attainment through feature selection to minimize computation time,optimize prediction performance,and provide a holistic understanding of machine learning *** the extension of astute machine learning algorithms into an Intrusion Detection System(IDS)through SDN has garnered much scholarly attention within the past decade,this study recommended an effective IDS under the Grey-wolf optimizer(GWO)and Light Gradient Boosting Machine(Light-GBM)classifier for probe attack *** InSDN dataset was employed to train and test the proposed IDS,which is deemed to be a novel benchmarking dataset in *** proposed IDS assessment demonstrated an optimized performance against that of peer IDSs in probe attack detection within *** results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 99.8%accuracy,99.7%recall,99.99%precision,and 99.8%F-measure.

关键词： GWO IDS InSDN LightGBM probe attack SDN

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：