检索结果-内蒙古大学图书馆

8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Annie Silviya, S.H. Seetha, R. Raja, S. Sriman, B. Rupesh, S. Gopala Krishnan, J. Rajalakshmi Institute of Technology Computer Science Engineering Chennai India Vellore Institute of Technology Computer Science Engineering and Information Systems Vellore India Panimalar Engineering College Computer Science and Engineering Chennai India Institute of Science and Technology Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&d Chennai India Rajalakshmi Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798350367904

The mental health and well-being of children are critical components of their overall development and future success. In India, only 1 in 6 8 children are diagnosed with autism, since monitoring and addressing the mental health issues among children is a challenge because of various factors. Studies have proven that autism detection can be made possible with the assessment of specific facial features that are used as primary markers for the proposed model. The proposed solution aims to leverage technological advancements, such as deep learning, for detecting autism in children using the aforementioned markers. The model gathers data on the children's facial features and predicts any underlying mental disorder. Since the application collects and analyzes sensitive information from the users, an additional security feature is implemented in the proposed solution using blockchain. Key features of the solution include standardized assessment tools, real-time tracking capabilities, personalized interventions, and seamless communication channels between parents, caregivers, and mental health professionals. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Zero-DCE++Inspired Object Detection in Less Illuminated Environment Using Improved YOLOv5

引用

computers, Materials & Continua 2023年第12期77卷 2751-2769页

作者： Ananthakrishnan Balasundaram Anshuman Mohanty Ayesha Shaik Krishnadoss Pradeep Kedalu Poornachary Vijayakumar Muthu Subash Kavitha Centre for Cyber Physical Systems Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Information and Data Sciences Nagasaki UniversityNagasaki8528521Japan

Automated object detection has received the most attention over the *** cases ranging from autonomous driving applications to military surveillance systems,require robust detection of objects in different illumination ***-of-the-art object detectors tend to fare well in object detection during daytime ***,their performance is severely hampered in night light conditions due to poor *** address this challenge,the manuscript proposes an improved YOLOv5-based object detection framework for effective detection in unevenly illuminated nighttime ***,the preprocessing strategies involve using the Zero-DCE++approach to enhance lowlight *** is followed by optimizing the existing YOLOv5 architecture by integrating the Convolutional Block Attention Module(CBAM)in the backbone network to boost model learning capability and Depthwise Convolutional module(DWConv)in the neck network for efficient compression of network *** Night Object Detection(NOD)and Exclusively Dark(ExDARK)dataset has been used for this *** proposed framework detects classes like humans,bicycles,and *** demonstrate that the proposed architecture achieved a higher Mean Average Precision(mAP)along with a reduction in model size and total parameters,*** proposed model is lighter by 11.24%in terms of model size and 12.38%in terms of parameters when compared to baseline YOLOv5.

关键词： Object detection deep learning nighttime road scenes YOLOv5 DWConv Zero-DCE++ CBAM

来源：评论

学校读者我要写书评

暂无评论

MalFSM: Feature Subset Selection Method for Malware Family Classification

引用

Chinese Journal of Electronics 2023年第1期32卷 26-38页

作者： KONG Zixiao XUE Jingfeng WANG Yong ZHANG Qian HAN Weijie ZHU Yufen School of Computer Science and Technology Beijing Institute of Technology School of Space Information Space Engineering University

Malware detection has been a hot spot in cyberspace security and academic research. We investigate the correlation between the opcode features of malicious samples and perform feature extraction, selection and fusion by filtering redundant features, thus alleviating the dimensional disaster problem and achieving efficient identification of malware families for proper classification. Malware authors use obfuscation technology to generate a large number of malware variants, which imposes a heavy analysis burden on security researchers and consumes a lot of resources in both time and space. To this end, we propose the MalFSM framework. Through the feature selection method, we reduce the 735 opcode features contained in the Kaggle dataset to 16, and then fuse on metadata features(count of file lines and file size)for a total of 18 features, and find that the machine learning classification is efficient and high accuracy. We analyzed the correlation between the opcode features of malicious samples and interpreted the selected features. Our comprehensive experiments show that the highest classification accuracy of MalFSM can reach up to 98.6% and the classification time is only 7.76 s on the Kaggle malware dataset of Microsoft.

关键词： Correlation Fuses Filtering Cyberspace Machine learning Metadata Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Review on Smart Healthcare employing Quantum Internet of Things

引用

IEEE engineering Management Review 2024年 1-9页

作者： Sutradhar, Kartick Venkatesh, Ranjitha Venkatesh, Priyanka Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India Department of Computer Science and Engineering GITAM School of Technology Bengaluru India Department of Computer Science and Engineering Presidency University Bengaluru India

The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Advancing Virtual Reality Interaction through Real-Time Hand Gesture Recognition for Immersive Experiences 3

Advancing Virtual Reality Interaction through Real-Time Hand...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Singh, Vinayak Kumar Kalinathan, Lekshmi Marimuthu, Marimuthu Vadivel, Sengazhani Murugesan Palani, Saravanan Vellore Institute of Technology School of Computer Science and Engineering Chennai India VIT Business School Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350372120

Virtual Reality technology offers immersive experiences, but reliance on handheld controllers can limit immersion. This study explores real-time hand gesture recognition to enhance natural interaction in VR environments. Motivated by VR's diverse applications in gaming, education, and training, it seeks to improve engagement and realism. The study provides an overview of current hand gesture recognition techniques, including computer vision and deep learning methods. Understanding the state of real-time gesture recognition informs future research and development in VR interaction. It enables researchers, developers, and practitioners to make informed decisions about integrating and enhancing gesture recognition technologies in various VR applications © 2024 IEEE.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Performance Improvement of Model View Controller based Applications through Linda's-Key

Performance Improvement of Model View Controller based Appli...

引用

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Suhas Reddy, P. Anala, Jayanth Krishnamurthy, Vallidevi Balasubramanian, Surendiran Sujithra Kanmani, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India National Institute of Technology Puducherry Department of Computer Science and Engineering India

ISBN: (纸本)9798350312126

The Model-View-Controller (MVC) design pattern is widely used in software engineering for developing user interfaces. While MVC offers many benefits, handling data in a way that is efficient and effective can be a challenge. One approach to optimising the performance of MVC applications is converting lists to dictionaries. This paper discusses the benefits and drawbacks of this approach and presents the findings of recent research on this topic. The main advantage of converting lists to dictionaries is that it can improve the performance of MVC applications by offering faster access times and making code easier to read and maintain. However, there are drawbacks to this approach, such as increased memory usage and slower performance for certain operations. Several studies have been conducted on the performance of MVC applications when using lists versus dictionaries, with varying results. This paper overviews this research and highlights the implications for MVC development. © 2023 IEEE.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

引用

science China(information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

来源：评论

学校读者我要写书评

暂无评论

Exploring News-Feed Credibility using Emerging Machine Learning and Deep Learning Models

引用

Journal of engineering science and technology Review 2022年第3期15卷 31-37页

作者： Aju, D. Kumar, Kakelli Anil Lal, Anisha M School of Computer Science and Engineering Vellore Institute of Technology TN Vellore India

In the recent past, the phenomenal growth, availability, and access of information on social media have made it perplexing to discern between real and fake information. The faster and easier dissemination of information through various means has accelerated the explosive growth of its falsehood. At the same time, the newsfeeds and their credibility in the social networks are in danger since the fake news is alarmingly disseminating very fast. Henceforth, the credibility of the newsfeeds or any information has become a real research challenge to cross-check the respective news or information. The cross verification can be performed concerning its source, the exact content, and the respective publisher to catalog it into fact or fake. Despite a few constraints, machine learning plays a crucial and significant role in classifying the respective news feeds. Various machine learning methodologies such as BLR (Bilinear logistic regression), NB (Naive Bayes), SVM (Support Vector Machine), and RF (Random Forest) have been reviewed and experimented with for detecting the fact and fake news feeds. After the experimentation, the limitations of the respective machine learning methods were explored and noted. Henceforth, a deep learning method called BERT is implemented and it is observed that BERT provides better efficiency than the machine learning algorithms. Furthermore, it is ascertained that the deep learning method provided the best accuracy of 99.79 % with the available dataset. © 2022. school of science, IHU. All rights reserved.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A Study Of Cyclone Tracking Mechanism Using Deep Learning Techniques

A Study Of Cyclone Tracking Mechanism Using Deep Learning Te...

引用

2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Paul, Tushar Dhanalakshmi, R. Masters of Computer Application School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350315127

With over 90 storms emerging worldwide each year, tropical cyclones (TCs) are the most damaging weather systems to emerge over the tropical oceans. For the purpose of providing advanced warning to the impacted areas, fast TC detection and tracking is essential. Remote sensing is essential for the detection of these storms since they originate over open oceans that are far from the continents. Here, we describe an innovative deep learning approach-based automated technique for TC detection from satellite photos. Our research presents a three-phase deep learning architecture for TC detection that consists of three components: 1) a classifier - convolutional neural network (CNN);2) a wind speed filter;and 3) a detector - Mask convolutional neural network (CNN). Using Bayesian optimization, the hyperparameters of the pipeline as a whole are tuned to display optimal performance. Results show that the suggested method produces test images with specificity (97.59%), high precision (97.10%) and accuracy (86.55%). © 2023 IEEE.

关键词： Tropical cyclone

来源：评论

学校读者我要写书评

暂无评论

Hand-Gesture Based Emoji Identification and Chat Integration 5

Hand-Gesture Based Emoji Identification and Chat Integration

引用

5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Tare, Sanskar S Patil, Madhav J Alam, Adnan Swarnalatha, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India

ISBN: (纸本)9798350321425

Every aspect of human life is slowly getting computerized and gradually turning electronic. This has made a rangeof tasks a lot easier and accessible for people from all walks of life. Unfortunately, this rapid computerization and the ever changing landscape of electronics has given birth to a new classof computer - illiterate individuals. Such individuals exist due to the lack of accessibility to latest electronic gadgets, or humansocial problems like old age. There is a need to make technology fun and easier to use for individuals belonging to such classes. The very primary use of internet-based networking is text-based communications namely: e-mails, texts chats, blogs, etc. The text based interaction is mainly facilitated using a keyboard which makes it the most focal human-computer interaction involved. The modern usage of this text-based communication involves extensive usage of emoticons. While most handheld devices feature a customised emoji-keyboard, the emoji keyboard is difficult to access in personal computers. This can adversely affect the interactions the aforementioned computer-illiterates partake in. This project aims to deal with this problem by creating a hand-detection based emoji detection algorithm in python through OpenCV and Mediapipe for detection of hand landmarks and implementing it in one such communication channel. The algorithm consists of two modules: a Hand-Tracking Moduleand an emoji detector capable of matching the tracked hand to existing emoji database. The resulting application is consistent, allow frequent users to use shortcuts and make the user feel more in control. The work also strives to integrate error prevention and simple error handling in existing emoji detection technologies along with managing short term memory load. © 2023 IEEE.

关键词： Palmprint recognition

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：