检索结果-内蒙古大学图书馆

2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Doshi, Nisarg Bhavsar, Sagar Rajeswari, D. Srinivasan, R. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350315905

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear *** first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values. © 2023 IEEE.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

Smart Home Based on IoT by Using Machine Learning 1

引用

6th International Conference on Microelectronics and Telecommunication Engineering, ICMETE 2022

作者： Lavanya, K. Archana, K.S. CSE Vels Institute of Science Technology & Advanced Studies Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9789811995125

ISBN: (纸本)9789811995118

Smart homes have surfaced as the coming wave of technological advancement. It is a collection of smaller systems connected to home network and comprising a variety of communication technologies which can assist inhabitants’ convenience, security, safety, and comfort. This paper examines several IoT based smart house approaches, which were divided as 2 categories depending on application: smart home security system and smart home automation. We explored several sensors, doors, and communication protocols utilized in IoT-based smart homes in this paper. We also discussed about research problems and also open research objectives. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

引用

China Communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

来源：评论

学校读者我要写书评

暂无评论

Enhancing lung cancer detection through integrated deep learning and transformer models

引用

Scientific Reports 2025年第1期15卷 1-28页

作者： Durgam, Revathi Panduri, Bharathi Balaji, V. Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Department of Data Science AVN Institute of Engineering and Technology Hyderabad India Department of Information Technology Gokaraju Rangaraju Institute of Engineering & Technology Hyderabad India Department of CSE-AIML Vardhaman College of Engineering Hyderabad India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science and Engineering Kebri Dehar University Kebri Dehar Ethiopia Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India

Lung cancer has been stated as one of the prevalent killers of cancer up to this present time and this clearly underlines the rationale for early diagnosis to enhance life expectancy of patients afflicted with the condition. The reasons behind the usage of the transformer and deep learning classifiers for the detection of lung cancer include accuracy, robustness along with the capability to handle and evaluate large data sets and much more. Such models can be more complex and can help to utilize multiple modalities of data to give extensive information that will be critical in ascertaining the right diagnosis at the right time. However, the existing works encounter several limitations including reliance on large annotated data, overfitting, high computation complexity, and interpretability. Third, the issue of the stability of these models’ performance when applied to actual clinical datasets is still an open question;this is an even bigger issue that will greatly reduce the actual utilization of these models in clinical practice. To tackle these, we develop a novel Cancer Nexus Synergy (CanNS), which applies of A. Swin-Transformer UNet (SwiNet) Model for segmentation, Xception-LSTM GAN (XLG) CancerNet for classification, and Devilish Levy Optimization (DevLO) for fine-tuning parameters. This paper breaks new ground in that the presented elements are incorporated in a manner that co-operatively elevates the diagnostic capabilities while at the same time being computationally light and resilient. These are SwiNet for segmented analysis, XLG CancerNet for precise classification of the cases, and DevLO that optimizes the parameters of the lung cancer detection system, making the system more sensible and efficient. The performance outcomes indicate that the CanNS framework enhances the detection’s accuracy, sensitivity, and specificity compared to the previous approaches. © The Author(s) 2025.

关键词： And classification Deep learning Disease detection Lung Cancer Optimization Segmentation Transformer models

来源：评论

学校读者我要写书评

暂无评论

Wireless Acoustic Sensor Networks and Edge computing for Rapid Acoustic Monitoring

引用

IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 64-74页

作者： Zhengguo Sheng Saskia Pfersich Alice Eldridge Jianshan Zhou Daxin Tian Victor C.M.Leung the Department of Engineering and Design University of Sussex Infineon Technologies AG the Department of Music University of Sussex Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety ControlSchool of Transportation Science and EngineeringBeihang University the Department of Electrical and Computer Engineering The University of British Columbia

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity *** scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ *** and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination *** tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.

关键词： Acoustic sensor networks edge computing energy efficiency

来源：评论

学校读者我要写书评

暂无评论

A Novel Pipeline for Compressing Image Buffers in Remote Education Video Conferencing using Harris Corner Detection and Pixel Map Array 5

A Novel Pipeline for Compressing Image Buffers in Remote Edu...

引用

5th IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Paul, Gita Alekhya Sharma, Ansh Jagnani, Yashvardhan Saxena, Abhishek Supraja, P. SRM Institute of Science and Technology Networking and Communications Department Chennai India SRM Institute of Science and Technology Computing Technologies Department Chennai India SRM Institute of Science and Technology Data Science and Business Systems Department Chennai India

ISBN: (纸本)9781665493604

The COVID-19 pandemic has compelled educational institutions worldwide to shift to remote online education. Addressing the growing trend, an Oxford University Press report titled 'Addressing the Deepening Digital Divide' states that poor digital access is the most significant barrier to digital learning according to 68 percent of academicians. Students in many remote parts of India frequently have access to limited bandwidth internet, which is insufficient for the modern standards of network-hogging online video conference software solutions. This paper provides an algorithmic compression of image buffers to aid low-cost remote online video education. This compression can be done by translating the teacher's blackboard images to pixel arrays projected on a canvas on the student's dashboard while the instructor constantly communicates via real-time voice. The image is first converted to grayscale and dilated with a square kernel. Using Harris Corner Detector, probable board corners are identified and compared to a geometrical center of the points and the corners recovered by cornerSubPix. An adaptive threshold is employed, distinguishing the board's contents from the backdrop on the cropped picture based on the recovered points. The pixel-mapped array is then transmitted to the students through the webRTC real-time protocol, which includes support for two-way audio, allowing the teacher to deliver lectures. Using Canvas API on the application front-end, the array is projected onto the student's device as a dot matrix display. This paper has achieved an effective rate in the video transmission format, aiding online remote education on low-bandwidth network devices. © 2023 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

An Enhanced CNN Model for Motor Imagery-Based Brain Computer Interface 3

An Enhanced CNN Model for Motor Imagery-Based Brain Computer...

引用

3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

作者： Kavin, Balasubramanian Prabhu Selvaraj, Jeeva Sarveshwaran, Velliangiri Prakash, P. SRM Institute of Science and Technology Department of Data Science and Business Systems Tamilnadu India SRM Institute of Science and Technology Department of Computational Intelligence Tamilnadu India SRM Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350338263

An essential Brain-Computer Interface (BCI) paradigm, Motor Imagery (MI) based BCI calls for robust classifiers. In recent years, deep learning technology has been widely adopted, and numerous attempts have been made to apply it to the field of BCI. As much progress has been made in improving the accuracy of deep learning-based MI-BCI systems in comparison to more conventional methods, there is still a significant challenge in accurately interpreting the deep learning models. This study offers an Optimized Complex Structure based 1-D Convolutional Neural Network (OCS-1D-CNN) model for automatic detection using MI image classification to overcome these concerns. Enhancing its properties for distinguishing normal conditions and other levels, 2D maps can be turned into feature signals using two-round 1D convolutional procedures in the layer. Gray Relational Analysis (GRA) investigation classifiers are used to screen the different layers in the classification layer due to their simple mathematical operation. The Artificial Jellyfish Search Algorithm (AJSO) finds the best combination of summation layer weight and GRA weight. Using two big, publicly available databases containing data on 42 and 62 people, the suggested model is put through its paces. Based on the findings, the models outperformed conventional methods. © 2023 IEEE.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

IoT Smart City Air Quality Monitoring System Using Arduino

IoT Smart City Air Quality Monitoring System Using Arduino

引用

2023 Intelligent computing and Control for Engineering and Business systems, ICCEBS 2023

作者： Gopinath, N. Chinnasamy, A. Sachin, K. Sakthiraj, S. Ravindharan, S. Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srmist Kattankulathur Campus Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350394580

Many factors, including population growth, increased vehicle use, industrialization, and urbanisation, have contributed to an increase in pollution levels throughout time, which has a negative impact on human wellbeing by adversely affecting the health of those exposed to it. To keep an eye on In this project, we'll build an IOT-based air pollution monitoring system that will track the air quality via an internet-connected web server and sound an alarm whenit drops below a certain threshold, which occurs when harmful gases like CO2, smoke, alcohol, benzene, and NH3 are present in sufficient quantities. On the LCD and on the website, the air quality will be displayed in PPM for easy monitoring. You can use your mobiledevice to check the pollution level as part of this IOT project from anywhere. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Estimation of Neurological Diseases and Age Using Brain MRI 4th

Estimation of Neurological Diseases and Age Using Brain MRI

引用

4th International Conference on Smart systems: Innovations in computing, SSIC 2023

作者： Ashokkumar, K. Dharshan, K. Ragupathi, T. Pritam, Anem Sagar, Amujuru Gnana Department of Computer Science Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Data Science and Business systems School of Computing SRM Institute of Science and Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819736898

Leveraging D-NN trained on neuroimaging data, we can effectively estimate the chronological ages of normal persons;this projected brain age has potential as a biomarker for identifying age-related disorders. The suggested technique employs a CNN cascade network from DL and the Support Vector Machine (SVM) algorithm from ML (SVM). These techniques have been employed to train MRI scans of the brain, classifying them into regular (unaffected by illness), Alzheimer’s disorder (AD), and neurocognitive disability. That we also have the ability to determine ages thanks to the age classifications on the photos. The brain MRI collection is trained using CNN and SVM, on which the classifications and age estimate will be carried out. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving data Publishing Models, Challenges, Applications, and Issues 1st

Privacy-Preserving Data Publishing Models, Challenges, Appli...

引用

1st International Conference on Expert Clouds and Applications, ICOECA 2023

作者： Jayapradha, J. Prakash, M. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India

ISBN: (纸本)9789819917440

The distribution of Electronic Health Records is highly needed for various analysis purposes and medical studies. However, the data should be disclosed to the data recipient in such a way that the privacy of the individual is ensured. Privacy-preserving data publishing is a challenging task because the distributed data should be protected against multiple privacy threats. Several privacy models, methods, techniques have been proposed and studied in earlier works, however, the study concerning privacy models considering adversary background knowledge is very limited. In this study, various privacy models and their attack models have been analyzed and depicted, the main focus of the study is the adversary’s background knowledge privacy-preserving data publishing model. Various research challenges in security and privacy have been identified and summarized. Furthermore, the various applications of privacy-preserving data publishing such as Cloud, E-health, Social Network, Agriculture, and Smart City have been studied and the need for privacy is briefed. Finally, the paper highlighted the study's intuitions regarding present unresolved challenges as well as probable future approaches in privacy-preserving data publishing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Records management

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：