检索结果-内蒙古大学图书馆

14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Amoruso, Edward L. Zou, Cliff C. Leinecker, Richard University of Central Florida Department of Electrical and Computer Engineering OrlandoFL32816 United States University of Central Florida Department of Computer Science OrlandoFL32816 United States

ISBN: (纸本)9798350304138

The Windows registry stores a glut of information containing settings and data utilized by the Microsoft operating system (OS) and other applications. For example, information such as user credentials, installed programs, recently used applications and documents, accessed resources such as local, remote, and removable devices can all be found in this database. More revealingly, the registry also has time and date stamps that can help build a timeline of user activities. The Windows registry can be easily queried by either malicious or benign applications. This is possible through the Windows Application Program Interface (API) and other OS built-in utilities. In this paper, we develop and demonstrate a program able to collect and infer a user's rich activities by accessing the Windows registry alone. This information, also referred to as the user's digital footprint, can be used to devise an exploit or create a privacy threat. Our custom developed application will demonstrate how a user's digital footprint can be acquired by a malicious application from a Windows registry, without alerting security software. In addition, this information can be exported to a set of comma delimited files, making it easy to import them into other analysis applications. © 2023 IEEE.

关键词： Application programs

来源：评论

学校读者我要写书评

暂无评论

Prediction of Covid-19 Based on Chest X-Ray Images Using Deep Learning with CNN

引用

computer Systems science & engineering 2022年第6期41卷 1223-1240页

作者： Anika Tahsin Meem Mohammad Monirujjaman Khan Mehedi Masud Sultan Aljahdali Department of Electrical and Computer Engineering North South UniversityBashundharaDhaka1229Bangladesh Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

The COVID-19 pandemic has caused trouble in people’s daily lives andruined several economies around the world, killing millions of people thus far. Itis essential to screen the affected patients in a timely and cost-effective manner inorder to fight this disease. This paper presents the prediction of COVID-19 withChest X-Ray images, and the implementation of an image processing systemoperated using deep learning and neural networks. In this paper, a Deep Learning,Machine Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using Chest X-Ray pictures is proposed. In this study, machine learning tools such as TensorFlow were used forbuilding and training neural nets. Scikit-learn was used for machine learning fromend to end. Various deep learning features are used, such as Conv2D, Dense Net,Dropout, Maxpooling2D for creating the model. The proposed approach had aclassification accuracy of 96.43 percent and a validation accuracy of 98.33 percentafter training and testing the X-Ray pictures. Finally, a web application has beendeveloped for general users, which will detect chest x-ray images either as covidor normal. A GUI application for the Covid prediction framework was run. Achest X-ray image can be browsed and fed into the program by medical personnelor the general public.

关键词： Covid-19 prediction covid-19 coronavirus normal deep learning convolutional neural network image processing chest x-ray

来源：评论

学校读者我要写书评

暂无评论

Hybrid Dynamic Optimization for Multilevel Security System in Disseminating Confidential Information

引用

computer Systems science & engineering 2023年第12期47卷 3145-3163页

作者： Shahina Anwarul Sunil Kumar Ashok Bhansali Hammam Alshazly Hany S.Hussein School of Computer Science University of Petroleum and Energy StudiesDehradun248007India Department of Computer Science Graphic Era Hill UniversityDehradunUttarakhand248001India Department of Computer Engineering and Applications GLA UniversityMathuraIndia Faculty of Computers and Information South Valley UniversityQena83523Egypt Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha62529Saudi Arabia Electrical Engineering Department Faculty of EngineeringAswan UniversityAswan81528Egypt

Security systems are the need of the hour to protect data from unauthorized *** dissemination of confidential information over the public network requires a high level of *** security approach such as steganography ensures confidentiality,authentication,integrity,and *** helps in hiding the secret data inside the cover media so that the attacker can be confused during the transmission process of secret data between sender and ***,we present an efficient hybrid security model that provides multifold security *** this end,a rectified Advanced Encryption Standard(AES)algorithm is proposed to overcome the problems existing in AES such as pattern appearance and high *** modified AES is used for the encryption of the stego image that contains the digitally signed encrypted secret *** enciphering and deciphering of the secret data are done using the Rivest–Shamir–Adleman(RSA)*** experiments are conducted on the images of the USC-SIPI standard image *** experimental results prove that the proposed hybrid system outperforms other SOTA(state-of-the-art)approaches.

关键词： Cryptography steganography digital signature rectified AES encryption

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning Enhanced Deep Reinforcement Learning for Volt-Var Control in Smart Grids

Transfer Learning Enhanced Deep Reinforcement Learning for V...

引用

Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES

作者： Kundan Kumar Gelli Ravikumar Department of Computer Science and Electrical and Computer Engineering Iowa State University IA United States

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

The integration of renewable energy resources has made power system management increasingly complex. DRL is a potential solution to optimize power system operations, but it requires significant time and resources during training. The control policies developed using DRL are specific to a single grid and require retraining from scratch for other grids. Training the DRL model from scratch is computationally expensive. This paper proposes a novel TL with a DRL framework to optimize VV C across different grids. This framework significantly reduces training time and improves VVC control performance by fine-tuning pre-trained DRL models for various grids. We developed a policy reuse classifier that transfers the knowledge from the IEEE-123 Bus system to the IEEE-13 Bus system. We performed an impact analysis to determine the effectiveness of TL. Our results show that TL improves the VVC control policy by 69.51 %, achieves faster convergence, and reduces the training time by 98.14%.

关键词： Training Renewable energy sources Energy resources Computational modeling Power system management Transfer learning Robustness Topology Smart grids Optimization

来源：评论

学校读者我要写书评

暂无评论

Network Traffic Prediction with Decomposition and Multi-Scale Autocorrelation in Large-Scale Cloud Data Centers 21

Network Traffic Prediction with Decomposition and Multi-Scal...

引用

21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Meijia Yuan, Haitao Kuang, Zhenwei Ma, Hanbo Bi, Jing Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9798350365221

As the Internet and big data technologies advance, a tremendous amount of data is generated daily. Efficient network operations are essential for handling such data. Accurately predicting future network traffic in real-time enables prompt response from cloud infrastructure and efficient traffic scheduling and allocation, ultimately reducing costs, preventing economic losses, and optimizing the performance of downstream facilities. However, predicting network traffic in large-scale data centers is challenging due to the multidimensional, nonlinear, and high-volatility nature of the time series. Traditional prediction methods, e.g., regression algorithms, struggle to capture non-linear features effectively. Many deep learning models face issues such as gradient explosion or vanishing during their training. Current commonly used prediction methods fail to fully uncover vital information about the frequency domain features in the time series. To do so, this work proposes a novel network traffic prediction model that combines a Savitzky Golay filter, sequence decomposition, multi-scale attention, a temporal convolutional network, an autocorrelation mechanism, and bidirectional long short-term memory. It can accurately predict future network traffic by adaptively extracting important features without requiring excessive feature engineering of the original data. It performs end-to-end sequence prediction, feature selection, and automatic learning of data features and temporal dependencies to achieve accurate time series prediction and avoid redundant data processing. Experiments involving ablation studies and comparisons with advanced prediction models are performed with the Google cluster trace. Experimental results show that the proposed model improves the prediction accuracy by at least 38.52% over the state-of-the-art models. © 2024 IEEE.

关键词： Autocorrelation

来源：评论

学校读者我要写书评

暂无评论

Multi-Context Recommendation Systems (CARS) in Autonomous Driving and Other Applications

Multi-Context Recommendation Systems (CARS) in Autonomous Dr...

引用

2022 International Telecommunications Conference, ITC-Egypt 2022

作者： Ali, Abdullahi Kashef, Rasha Ibrahim, Ahmed Fakhry Toronto Metropolitan University Electrical Computer and Biomedical Engineering Faculty of Engineering and Architectural Science TorontoON Canada Boston University Computer Science Department United States

ISBN: (数字)9781665488082

ISBN: (纸本)9781665488082

Recommendation systems (RS) are important for their instantaneous ability to suggest to users their desired items and for ensuring a smooth and worthwhile user experience. To optimize the user's experience with the RS, it can be programmed to be aware of the various contexts the user has while interacting with the system. Context-aware recommendation systems (CARS) aim to add more nuance to the process of predicting items by considering different factors such as location and time. This paper presents the foundations of recommendation systems, such as categories, evaluation metrics, datasets, and challenges. In this paper, we show the effectiveness of multi-contexts in autonomous driving. Three CARS models are developed and tested. Experimental results show that adding multi-contexts provides more personalized options for drivers, allowing intelligent decisions. © 2022 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

The Role of Artificial Intelligence (AI) in the Accounting and Auditing Professions 10

The Role of Artificial Intelligence (AI) in the Accounting a...

引用

10th IEEE Uttar Pradesh Section International Conference on electrical, Electronics and computer engineering, UPCON 2023

作者： Kaur, Harshdeep Arora, Geeta Salaria, Archana Singh, Arun Rakhra, Manik Dhiman, Gaurav Lovely Professional University Department of Mathematics Phagwara India University Institute of Engineering and Technology Department of Computer Science and Engineering Kathua India School of Computer Science and Engineering Lovely Professional University Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lebanese American University Department of Electrical and Computer Engineering Lebanon School of Sciences and Emerging Technologies Jagat Guru Nanak Dev Punjab State Open University Patiala India

ISBN: (纸本)9798350382471

The fast growth of business, science, and technology has ushered in the age of artificial intelligence, which has had far-reaching effects in every sphere of human existence. This article explains how AI may be used to detect and prevent accounting fraud and enhance the reliability of financial statements. Accounting and auditing firms may benefit or suffer from the advancement and use of artificial intelligence (AI). This article explains how AI can be used to reduce accounting fraud and boost the credibility of financial statements. The use of AI in these fields is not without its merits and drawbacks. Change is inevitable, and the accounting and auditing industry will undergo significant change in the near future. The report concludes by stressing the importance, in the broader context of AI, of accounting staff members developing their own expertise and proficiency. This research highlights the possible impacts of artificial intelligence (AI) on the accounting industry and the steps that need to be taken to prepare for a workforce in the future where AI plays a larger role. © 2023 IEEE.

关键词： component formatting insert style styling

来源：评论

学校读者我要写书评

暂无评论

Sub-terahertz transmissive reconfigurable intelligent surface for integrated beam steering and self-OOK-modulation

引用

Light(science & Applications) 2025年第1期14卷 131-141页

作者： Dongfang Shen Feng Lan Luyang Wang Tianyang Song Munan Yang Tianyu Hu Yueting Li Xiaolei Nie Jiayao Yang Shixiong Liang Hongxin Zeng Hui-Fang Zhang Pinaki Mazumder Ziqiang Yang Yaxin Zhang Tie Jun Cui Sichuan THz Communication Technology Engineering Research Center School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313000China Zhangjiang Laboratory Shanghai201204China School of Microelectronics Tianjin UniversityTianjin300072China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen518100China Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI48109USA Southeast University Nanjing210096China

Boasting superior flexibility in beam manipulation and a simpler framework than traditional phased arrays,terahertz metasurface-based phased arrays show great promise for 5G-A/6G communication *** with the reflective reconfigurable intelligent surface(reflective RIS),the transmissive RIS(TRIS)offers more feasibility for transceiver multiplexing systems to meet the growing demand for high-performance beam tracking in terahertz communication and radar ***,the terahertz TRIS encounters greater challenges in phase shift,beam efficiency,and complex ***,we propose a sub-terahertz TRIS based on the phase shift via Pancharatnam-Berry(PB)metasurface and self-on-off keying(OOK)modulation via Schottky *** electrically reconfigurable unit cell consists of a column-wise phase resonator and a rectangular *** experimental retrieved equivalent lumped-element circuit model is implemented in joint field-circuit simulations and is validated by experiments.A fabricated prototype demonstrates excellent performance of TRIS with the minimum insertion loss of 2.8 dB for operational states,large bandwidth nearly covering the entire W-band for 1-bit phase shift,deep OOK amplitude modulation of 12 dB,and wide scanning range of±60°with low specular *** further implement an integrated platform combining high-speed beam steering and spatial-light modulation,verifying the point-to-point signal transmissions in different directions using the TRIS *** proposed TRIS with high-performance and cost-effective fabrication makes it a promising solution to terahertz minimalist communication systems,radar,and satellite communication systems.

关键词： reconfigurable reflective resonator

来源：评论

学校读者我要写书评

暂无评论

SyREC: A Symbolic-Regression-Based Ensemble Combiner 36

SyREC: A Symbolic-Regression-Based Ensemble Combiner

引用

36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Fong, Kei Sen Motani, Mehul Department of Electrical and Computer Engineering Singapore College of Design and Engineering Singapore Institute of Data Science Singapore N.1 Institute for Health Singapore National University of Singapore Singapore

ISBN: (纸本)9798331527235

Symbolic Regression (SR) is the task of finding a concise white-box mathematical expression that fulfills a given machine learning (ML) objective. In this work, we introduce the first-of-its-kind SR-based ensemble combiner (SyREC) for ML which utilizes the quasi-arithmetic mean (parameterized by a function f) as an ensemble combiner and discovers f via SR. Our Sy REC demonstrates advantages over existing ensemble combiners, i.e., averaging methods and meta-learners. Compared to averaging methods, Sy REC allows for increased combiner complexity via exploring a large function class and provides a learning process to tune the ensemble combiner to the specific dataset. Compared to meta-learners, SyREC preserves important ensemble properties, like idempotency, monotonicity and boundedness, which produce predictions more consistent with the base models. We note SyREC produces a white-box ensemble combiner that lies in the intersection of both categories of existing combiners, addressing the weaknesses while capturing the strengths of each category. To evaluate Sy REC, we present experiments on 16 open-source PMLB datasets and commonly used base models, including a large variety of experiments and analyses across different settings: (i) regression and classification tasks, (ii) equally weighted combiners and custom-weighted combiners, (iii) homogeneous and heterogeneous ensembles, (iv) sequential and parallel ensembles. We find that in all these settings, SyREC shows consistent improvement over existing ensembling approaches. This indicates that SyREC is a robust ensemble combiner, which produces explainable and consistent ensemble predictions. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

SENSITIVE AND SELECTIVE PREGNENOLONE DETECTION WITH NARROW EMISSION GREEN CARBON DOTS 26

SENSITIVE AND SELECTIVE PREGNENOLONE DETECTION WITH NARROW E...

引用

26th International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2022

作者： Zhang, Xueqiao Yeasmin, Sanjida Ullah, Ahasan Cheng, Li-Jing School of Electrical Engineering and Computer Science Oregon State University United States

ISBN: (纸本)9781733419048

We report green-emitting carbon dots (G-CDs) for sensitive and selective detection of pregnenolone (P5) hormone. The G-CDs prepared by a one-step solvothermal synthesis method showed a dual emission peak at 480 and 520 nm under 440 nm excitation. The G-CDs showed superior fluorescence intensity and chemical stability. Their interaction with P5 caused enhanced G-CDs fluorescence, which could result from the formation of ground-state complex and aggregation induced emission enhancement. The fluorescence intensity of G-CDs increased linearly with P5 concentration between 0.01 and 100 μM. The sensor also exhibited selectivity against other analytes. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life sciences. All rights reserved.

关键词： Fluorescence

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：