检索结果-内蒙古大学图书馆

5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Vada, Yaswanth Reddy Oshiojum, Blessing Emmanuella Krishna, Ardhala Bala Kamboj, Vikram Kumar Salesforce Administrator The Los Angeles Film School Los Angeles United States Salesforce Administrator Numed Inc TX United States Department of Computer and Information Science Concordia University St PaulMN United States Department of Electrical and Electronics Engineering Kkr and Ksr Institute of Technology and Sciences Andhra Pradesh India Schulich School of Engineering University of Calgary Alberta Canada

ISBN: (纸本)9781665474672

Recent augmented swarm intelligence Sparrow Search optimizer (SSO) was prompted by anti-predation, gathering, and group premised activities of sparrow birds. Its effectiveness also tested and proved on several benchmarks. And the outcomes of speed reducer design challenge are not analyzed with other optimizers. Due to regular changes in Artificial Intelligence era, it still needs to test for sophisticating uses at global market. In this proposed research paper, simulation experiments are conducted on four optimal engineering structural designs to prove complex challenges solving nature with efficacy of novel SSO algorithm. Optimal engineering structural design of I beam optimal design mean fitness value is 0.006626, gear train optimal design fitness value is 1.0939E-2l, compression spring optimal fitness value is0.012674, and multi plate clutch optimal structure fitness value is 0.389650 respectively. And outcomes are validated with AOA and RIFO optimizers, analysis reveals that SSO algorithm outperforms other in terms mean fitness value in precision, consistency, and resilience, according to simulation findings. © 2023 IEEE.

关键词： Structural design

来源：评论

学校读者我要写书评

暂无评论

Implementing Multi-factor Authentication in Cloud Services for Enhanced Security

Implementing Multi-factor Authentication in Cloud Services f...

引用

Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： K. Sashi Rekha V.M. Sivagami R. Usharani T Amutha S. Pushparani Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Chennai Tamil Nadu India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The cloud services which are now the most common data transmission and endanger organizations’ confidential information, it’s more and more visible that security of any data should be a main priority for companies. MFA, which consists of adopting multi factor approach to authentication and thereby provides multiple types of verification thus ensures effective security, is leveraged to ensure resource access. This paper proposes a full-fledged way of implementing MFA for cloud services in order to strengthen the security given and manage the constatoing risks. The proposed methodology consists of main stages like requirements determining, technology reviewing, risks assessment, technology design, integration with cloud services, users training, pilot deployment and testing, implementation and configuration, monitoring and maintenance as well as constant improvement. Through a systemic approach, the organizations can be equipped with the capability to personalize the MFA solutions according to their security goals, to bring the cloud environments on board, as well as to ensure that the organizations comply with the security standards as set by various governing bodies. This research adds a grain of salt to the development of cloud security good practices through offering a disciplined approach to MFA implementation, which allows partners to harden their cloud assets and protect against growing cyber threats.

关键词： Training Cloud computing Multi-factor authentication Standards organizations Companies Regulation Maintenance Security Protection Monitoring

来源：评论

学校读者我要写书评

暂无评论

Classification of Automatic Detection of Plant Disease in Leaves and Fruits using Image Processing with Convolutional Neural Network 4

Classification of Automatic Detection of Plant Disease in Le...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Muthuselvan, S. Rajasekhar, Veeramreddy Shankar, Bhukya Mohana, P. Doddabasappa, N. Suresh, Badugu Kcg College of Technology Department of Information Technology Tamil Nadu Chennai India Malla Reddy Engineering College Department of Computer Science Engineering Kompally Rangareddy Dt Secunderabad Hyderabad India Cvr College of Engineering Department of Electronics and Communications Engineering Telangana Ibrahimpatnam India Scientist - C Centre for Remote Sensing and Geoinformatics Sathyabama Institute of Science and Technology Tamilnadu Chennai119 India School of Electrical and Electronic Engineering Reva University Karnataka Bengaluru India Koneru Lakshmaiah Educational Foundation Department of Electronics and Communications Engineering Andhra Pradesh Guntur Vaddeswaram India

ISBN: (纸本)9798350300888

Detection and identification of disease in plants is the fundamental step to avoid various constraints and to enhance productivity. In agricultural farm, the crops are prone to various bacterial and fungal disease. It is initiated due to the varied ecological conditions and soil categories. The earlier prediction of diseases helps to prevent the complete degradation of crops. The disease is detected through small patches on the leaves of the plant. The evolution of automation and innovations helps in the earlier detection of disease in the leaves and fruits of the crops. It is achieved by artificial intelligence. This is done through the image processing techniques. It uses the comparison of healthy plant leaves to that of the infected leaves and fruits. The classification of disease in the fruits and leaves are done accurately through the convolution neural network. This helps to detect the type and cause of the bacterial and fungal infections. This includes image pre-processing, data acquisition and image classification with feature extraction. Thus the CNN model helps to detect and classify the disease in plant and fruits in agricultural farm. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

False Data Injection Attack Detection Based on Wavelet Packet Decomposition and Random Forest in Smart Grid 23

False Data Injection Attack Detection Based on Wavelet Packe...

引用

23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Zhenyu Yuan, Shuai Wu, Longfei Guan, Zhitao Du, Xiaojiang School of Control and Computer Engineering North China Electric Power University China Brock University Department of Finance Operations and Information Systems ON Canada Fayetteville State University Department of Mathematics and Computer Science Fayetteville United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9781665494571

As one of the critical infrastructures, the safety and reliability of the smart grid are directly associated with the development and stability of society. However, studies have shown that the power grid is at risk when the parameters are manipulated and cyber-attacks are generated against the state estimation, i.e., under false data injection attack (FDIA). Currently, a rich body of literature has studied on the FDIA defense methods, but most of them focus on the direct current (DC) scenario. This paper proposes a novel detection model that combines the wavelet packet decomposition (WPD) technique with the random forest (RF) algorithm. The WPD is able to capture the deviation of parameters from the normal conditions, whereas the RF is developed to classify these features and effectively identify the malicious data. The proposed model is also evaluated using real-world data on IEEE 118-bus power system. The results demonstrate excellent performance on precision rate and recall rate under varying scenarios. © 2021 IEEE.

关键词： Radio frequency Training data Interference Power system stability Feature extraction Data models Wavelet packets

来源：评论

学校读者我要写书评

暂无评论

Investigating Innovative User Interfaces for Data Visualization and Analysis

Investigating Innovative User Interfaces for Data Visualizat...

引用

International Conference on Futuristic Technologies (INCOFT)

作者： V. Mathan Kumar Balakumar P Shilpa M. Katikar Satendra Singh Rakesh Kumar Yadav R Kamalraj Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Department of Mechanical Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India

This paper will explore current studies and developments in consumer interfaces for records visualization and analysis. Mainly that specialize in how innovative consumer interfaces can make choices better than present-day ones. The studies will look at various consumer interfaces, discover and explore their strengths and weaknesses, and discuss opportunities for further studies and innovation in this location. Examples of revolutionary consumer interfaces discussed within the paper might be evaluated for their capability to grow a person's information and engagement with records, lessen the cognitive load, improve enjoyment, and better combine documents with meaningful visualizations. The consequences of this research could be priceless in terms of assisting in designing effective and person-pleasant document visualization consumer interfaces on multiple systems, including desktop, mobile devices, and virtual pills.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IoT Based Alcohol Detection and Vehicle Control System

IoT Based Alcohol Detection and Vehicle Control System

引用

Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： I. Sapthami V V Narasimha Raju V. Vaithianathan P. Chinnasamy G Kumaran Ramesh Kumar Ayyasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad Telangana India Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

It is of the utmost importance to be aware of safety measures when operating a motor vehicle in the contemporary world, characterized by a fast-paced and hazardous environment. It has become necessary to develop innovative technology in order to improve road safety as a result of the alarmingly high number of incidents that can be linked to drivers who are under the influence of alcohol. As a solution to this significant problem, this study uses the capabilities offered by the Internet of Things (IoT) to develop an integrated system for detecting and controlling alcohol in commercial vehicles. This study aims to reduce the dangers connected with driving under the influence of alcohol by allowing for the seamless integration of cutting-edge alcohol detection technologies with real-time vehicle control mechanisms. The system makes use of specific sensors for the detection of alcohol. It interacts with the control mechanisms of the vehicle in order to restrict its functioning when alcohol is present. To make this possible, the hardware component of the system mounted in the vehicle uses the MQ3 sensor, also known as the alcohol sensor. This sensor is responsible for monitoring the alcohol molecules in the air around the driver to determine whether or not the driver is intoxicated. A buzzer, a DC motor, an LCD, a GPS, and a GSM are all components utilized by the system for data transfer. Additionally, the gear is powered by a 12-volt battery. As soon as the system determines that there is alcohol present in the vehicle, it immediately locks the accelerator. Using the GPS, the driver can obtain their location coordinates.

关键词： Refining Control systems Sensor systems Road safety Sensors Safety Internet of Things Reliability Vehicles Global Positioning System

来源：评论

学校读者我要写书评

暂无评论

Multi-View Incongruity Learning for Multimodal Sarcasm Detection

arXiv

引用

arXiv 2024年

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model’s generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL’s advancement in mitigating the effect of spurious correlation. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Unsupervised learning of Data-driven Facial Expression Coding System (DFECS) using keypoint tracking

arXiv

引用

arXiv 2024年

作者： Tripathi, Shivansh Chandra Garg, Rahul Department of Computer Science and Engineering Indian Institute of Technology Delhi Delhi110016 India Amar Nath and Shashi Khosla School of Information Technology Indian Institute of Technology Delhi Delhi110016 India National Resource Centre for Value Education in Engineering Indian Institute of Technology Delhi Delhi110016 India

The development of existing facial coding systems, such as the Facial Action Coding System (FACS), relied on manual examination of facial expression videos for defining Action Units (AUs). To overcome the labor-intensive nature of this process, we propose the unsupervised learning of an automated facial coding system by leveraging computer-vision-based facial keypoint tracking. In this novel facial coding system called the Data-driven Facial Expression Coding System (DFECS), the AUs are estimated by applying dimensionality reduction to facial keypoint movements from a neutral frame through a proposed Full Face Model (FFM). FFM employs a two-level decomposition using advanced dimensionality reduction techniques such as dictionary learning (DL) and non-negative matrix factorization (NMF). These techniques enhance the interpretability of AUs by introducing constraints such as sparsity and positivity to the encoding matrix. Results show that DFECS AUs estimated from the DISFA dataset can account for an average variance of up to 91.29 percent in test datasets (CK+ and BP4DSpontaneous) and also surpass the variance explained by keypoint-based equivalents of FACS AUs in these datasets. Additionally, 87.5 percent of DFECS AUs are interpretable, i.e., align with the direction of facial muscle movements. In summary, advancements in automated facial coding systems can accelerate facial expression analysis across diverse fields such as security, healthcare, and entertainment. These advancements offer numerous benefits, including enhanced detection of abnormal behavior, improved pain analysis in healthcare settings, and enriched emotion-driven interactions. To facilitate further research, the code repository of DFECS has been made publicly accessible. Copyright © 2024, The Authors. All rights reserved.

关键词： Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Fake Review Classification Using Supervised Machine Learning 25th

Fake Review Classification Using Supervised Machine Learning

引用

25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Khan, Hanif Asghar, Muhammad Usama Asghar, Muhammad Zubair Srivastava, Gautam Maddikunta, Praveen Kumar Reddy Gadekallu, Thippa Reddy Pakistan Department of Math and Computer Science Brandon University BrandonMB Canada School of Information Technology and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9783030687984

The revolution of social media has propelled the online community to take advantage of online reviews for not only posting feedback about the products, services, and other issues but also assists individuals to analyze user’s feedback for making purchase decisions, and companies for improving the quality of manufactured goods. However, the propagation of fake reviews has become an alarming issue, as it deceives online users while purchasing and promotes or demotes the reputation of competing brands. In this work, we propose a supervised learning-based technique for the detection of fake reviews from the online textual content. The study employs machine learning classifiers for bifurcating fake and genuine reviews. Experimental results are evaluated against different evaluation measures and the performance of the proposed system is compared with baseline works. © 2021, Springer Nature Switzerland AG.

关键词： Fake detection

来源：评论

学校读者我要写书评

暂无评论

Wital: A COTS WiFi Devices Based Vital Signs Monitoring System Using NLOS Sensing Model

arXiv

引用

arXiv 2023年

作者： Zhang, Xiang Gu, Yu Yan, Huan Wang, Yantong Dong, Mianxiong Ota, Kaoru Ren, Fuji Ji, Yusheng The School of Computer and Information Hefei University of Technology China I+ Lab School of Computer Science and Engineering University of Electronic Science and Technology of China China The Dept. of Sciences and Informatics Muroran Institute of Technology Japan The National Institute of Informatics Tokyo101-8430 Japan

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras;however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs. © 2023, CC BY.

关键词： Wireless local area networks (WLAN)

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：