检索结果-内蒙古大学图书馆

3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Bagla, Piyush Mishra, Amit Kumar Pandey, Neeraj Kumar Sharma, Ravi National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India Graphic Era Hill University Department of Computer Scienece and Engineering Dehradun India Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350309409

Nowadays machine learning is increasing its usage in multiple domains such as bio medical, information security, firewall protection, medical science, internet of things and mobile application security. Hence the need of machine learning is increased more and more for several applications and in this current article we try to use machine learning for identifying the web or online attacks on mobile devices. Limiting the personal information leakage and privacy has become a difficult challenge as smartphone usage has increased quickly. Impersonation is a main consequence of this type of leakage. Since current defensive measures (such as passcodes and fingerprinting) are unable to regularly monitor usage and determine whether a user is authorized, it is practically impossible to stop this kind of unlawful usage. In general, once the user got authorized by the device then complete control will be under the user and no protection mechanism can stop the complete access of device data. Therefore, a multi-view bagging that is an approach based on machine learning is used to gather sequential tapping information on the keyboard of smart phone. To continually authenticate the user while they type, we build a sequential tapping biometrics approach. By conducting various experiments on our proposed model by taking sample dataset CLaMP (Classification of Malware with Portable headers) from Kaggle website and tested the ensemble classification model on that dataset. According to empirical and theoretical studies that compare our model to widely used shallow machine techniques we conclude that an 8.42% equal error rate, a 94.41% H-mean, and a 94.24% accuracy can be achieved by our system with the use of accelerometer and only five keyboard taps. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

CBBM-WARM:A Workload-Aware Meta-Heuristic for Resource Management in Cloud Computing

引用

China Communications 2025年第6期22卷 255-275页

作者： K Nivitha P Pabitha R Praveen School of Computer Science and Engineering Vellore Institute of TechnologyVellore 632014India Department of Computer Technology Anna UniversityMIT CampusChennaiTamil Nadu600044India Department of Computer Science and Engineering National Institute of Technology PuducherryKaraikalPuducherry 609609India

The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud *** process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous ***,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic *** this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud *** CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the ***,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud *** further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal *** is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA *** experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.

关键词： autonomic resource management cloud computing coot bird behavior model SLA violation cost workload

来源：评论

学校读者我要写书评

暂无评论

Content-Based CT Image Retrieval for Emphysema Using Texture and Shape Features

引用

SN computer Science 2024年第7期5卷 1-12页

作者： Prakash, Ankur Singh, Vibhav Prakash Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad Prayagraj India

The growing volume of medical imaging data presents significant challenges in terms of storage, management, and utilization. Content-based medical image retrieval (CBMIR) systems serve as a supportive tool for radiologists and doctors, enabling healthcare professionals to efficiently compare current cases with previous ones, thereby facilitating accurate and timely diagnoses and treatments. This paper explores various clinically correlated features using the k-nearest neighbors algorithm to retrieve Computed Tomography images of emphysema and with no emphysema based on query content. We examine and compare several combinations of feature extraction techniques, including local binary patterns (LBP), local ternary patterns (LTP), and Zernike moments (ZMs), to evaluate retrieval performance. Our results demonstrate that the integration of LBP, LTP, and ZMs features performs well. However, the retrieval performance of ZMs alone is also quite encouraging compared to the other feature sets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CBMIR Emphysema Local binary patterns Local ternary patterns Zernike moments

来源：评论

学校读者我要写书评

暂无评论

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

引用

IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 377-390页

作者： Honghao Zhu MengChu Zhou Yu Xie Aiiad Albeshri IEEE School of Computer and Information Engineering Bengbu UniversityBengbu 233030China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Helen and John C.Hartmann the Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewark NJ 07102 USA College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

关键词： Credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator

来源：评论

学校读者我要写书评

暂无评论

Impact of COVID-19 on People 1

引用

4th International Conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Nandal, Neha Tanwar, Rohit Saketh, Meduri Pilania, Urmila Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India School of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science and Technology Manav Rachna University Faridabad India

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

COVID-19 affected each and every sector of human life. The food sector, security, employment, agriculture, etc. converged a lot during the pandemic. The coronavirus (COVID-19) pandemic has had a significant impact on the world. The virus was first identified in Wuhan, China, in December 2019, and has since spread to nearly every country. The World Health Organization declared it a pandemic on March 11, 2020. As of January 2021, the virus has infected over 89 million people and resulted in over 1.9 million deaths globally. The pandemic has had wide-ranging effects on society, including health impacts, economic consequences, and changes to daily life and social interactions. Many countries have implemented measures such as lockdowns, travel restrictions, and mask mandates in an effort to slow the spread of the virus. The impact on the healthcare system was significant due to the overwhelming number of hospitalizations and intensive care needs in some countries. The economy took a hit as well as many businesses closed and job loss became widespread. This review provides insight into the impact of the coronavirus on people and how different measures have been taken up to have a control over this pandemic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

The Evolution and Impact of Price Comparison Websites: A Technological and Economic Analysis 4

The Evolution and Impact of Price Comparison Websites: A Tec...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Patidar, Mukesh Vekariya, Daxa Pradhan, Preeti Gupta, Kartik Patidar, Praveen Kumar Khan, Pirmohammad Parul University Parul Institute of Engineering and Technology Department of Computer Science & Engineering Gujarat Vadodara India Madhya Pradesh Indore India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Madhya Pradesh Indore India Parul University Parul Institute of Technology Department of Computer Science & Engineering Gujarat Vadodara India

ISBN: (纸本)9798331518981

Price comparison websites (PCWs) have become integral to the online shopping experience, enabling consumers to make informed decisions by comparing prices, features, and reviews from multiple retailers. These platforms not only benefit consumers by saving time and money but also influence market competition by increasing pricing transparency and driving businesses to optimize their strategies. This research explores the evolution of PCWs, their technological infrastructure, and their business models. Moreover, it examines their impact on consumer behavior, the challenges they pose for retailers, and the future trends shaping the industry. Through an analysis of current practices, technological innovations, and economic effects, this work highlights the growing importance of PCWs in the global e-commerce ecosystem. © 2024 IEEE.

关键词： Marketplaces

来源：评论

学校读者我要写书评

暂无评论

Adaptive Neural Network-Based Improved Fabric Defect Detection Scheme 12th

Adaptive Neural Network-Based Improved Fabric Defect Detecti...

引用

12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Prabha, P. Anantha Abirami, S. Varshini, N. Amirtha Garishma, K. Priya, M. Deva Karthick, S. Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Efficient detection of defects in fabric plays a dominant role in automating quality control. It significantly enhances efficiency as well as accuracy of quality assurance in textile manufacturing. Skilled humans who physically examine defective designs in numerous sectors use traditional defect detection techniques. The main drawbacks of manual methods include deficiency of concentration, inaccuracy, weariness and increased time consumption. computer Vision (CV) and Digital Image Processing (DIP) techniques may be efficient in overcoming these shortcomings. The proposed approach involves data acquisition to record images which are subsequently pre-processed using histogram equalization to improve the defective area. A distinctive Neural Network (NN) is used for defect detection. This method improves precision and efficiency in identifying defects. The proposed method is built using Adaptive NNs (ANNs) along with Radial Basis Functions (RBFs) and is aimed at revolutionary identification of fabric flaws. The networks are built using attributes derived from images segmented using Expectation Maximization (EM) depending on target’s region attributes. As a result, the type of defects is classified based on the extracted features, thus yielding increased detection accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Radial basis function networks

来源：评论

学校读者我要写书评

暂无评论

Deep Q-learning based Resource Scheduling in IoT Edge Computing 7

Deep Q-learning based Resource Scheduling in IoT Edge Comput...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Vijayasekaran, G. Duraipandian, M. New Horizon College of Engineering Department of Computer Science & Engineering Karnataka Bangalore India Hindusthan Institute of Technology Department of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350359299

Edge computing (EC) has emerged as a viable solution for resource-intensive Internet of Things (IoT) applications seeking low-latency services at the network edge. However, the limited computing power of edge servers poses challenges in scheduling application jobs and allocating resources effectively. This study addresses the task scheduling and resource allocation problem in the EC scenario, aiming to maximize the long-term satisfaction of tasks assigned to virtual machines (VMs) deployed at the edge server. Formulated as a Markov decision process (MDP), the problem involves states, actions, state transitions, and rewards. Subsequently, a Deep Q Neural Network (DQN)-based resource allocation algorithm is developed to devise an optimal offloading decision and resource allocation scheme in an edge computing environment with constrained resources and latency limits. Simulation results demonstrate that the proposed DQN-based methods outperform existing approaches in the literature, achieving superior performance in terms of average waiting time and efficiency. This research contributes to enhancing the effectiveness and efficiency of resource allocation in edge computing environments, thereby facilitating the deployment of latency-sensitive IoT applications. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Recommendation System for Riyadh Restaurants: Integrating Content-Based and Context-Aware Techniques 4

A Novel Hybrid Recommendation System for Riyadh Restaurants:...

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Rohilla, Vinita Kaur, Mandeep Chakraborty, Sudeshna Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Maharaja Surajmal Institute of Technology Department of Computer Science and Engineering New Delhi India Galgotia University Department of Computer Science and Technology Greater Noida India

ISBN: (纸本)9798350364729

Recommendation systems are often used to deliver consumers a customised set of services. Their main purpose is to provide recommendations or concepts (such as restaurants, tourist attractions, medicines, movies, etc.) that effectively address consumer issues and may be used across several sectors. In today's environment, when there are so many eating alternatives, selecting the ideal restaurant to meet our interests may be difficult. Saudi Arabia's tourist industry has lately emerged as an important economic engine. It is becoming more difficult for customers to choose among the vast array of restaurants available in Riyadh due to the industry's fast expansion in recent years. This research presents a comprehensive recommendation system for Riyadh restaurants that integrates content-based filtering and context-aware location analysis to provide personalised suggestions. Utilising a dataset from Kaggle with information on 20,000 restaurants, the system employs a hybrid approach combining TF-IDF and cosine similarity for content-based filtering with geographic distance calculations for location-based recommendations. The hybrid model balances content relevance with geographic proximity, enhancing user satisfaction by delivering accurate and contextually relevant restaurant recommendations. The outcomes demonstrate an effectiveness of a hybrid method, outperforming individual techniques by generating more relevant and practical recommendations. The uniqueness of this study resides in the use of hybrid filtering, which has not before been used in comparable investigations. A more detailed and easily digestible list of suggested models is what this study report is aiming to provide. According to the findings and conclusion, the proposed strategy is effective in recommending restaurants in Riyadh. © 2024 IEEE.

关键词： Wiener filtering

来源：评论

学校读者我要写书评

暂无评论

Implementation of YOLO V8 for Advanced Autonomous Vehicle Detection Techniques 15

Implementation of YOLO V8 for Advanced Autonomous Vehicle De...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Duraisamy, Premkumar Deepika, A. Shivadharshini, G. Sudharshini Swetha, S. Kumar, Naveen S. Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Chemical Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331300579

Addressing the escalating necessity for proficient and dependable autonomous vehicle systems, this research proposes an advanced object detection approach harnessing the YOLOv8 algorithm. Object detection plays a pivotal role in the functionality of autonomous driving systems, enabling vehicles to perceive and react to their surroundings promptly. YOLOv8, an upgraded iteration of the YOLO algorithm, is acclaimed for its rapidity and precision in object detection endeavors. Our proposed model, distinguished by enhancements in network architecture and training methodologies, surpasses existing models in terms of detection precision and computational efficacy. Through an exhaustive examination, we delve into the network architecture, training regimen, and assessment metrics of the YOLOv8-based model. Experimental findings showcase the model's commendable performance in both quantitative and qualitative assessments, highlighting its resilience in identifying pedestrians, vehicles, traffic signs, and other pertinent objects across varied driving scenarios. The model's predicted outputs attest to its adeptness in precisely localizing and categorizing objects of interest, thereby augmenting the safety and efficiency of autonomous driving systems. © Grenze Scientific Society, 2024.

关键词： Pedestrian safety

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：