检索结果-内蒙古大学图书馆

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Lendalay, Ashwin Raj Pabba, Rupesh Katta, Sudheer Reddy Ahmed, Mohd Afnan Karthik, R.U. Prasad, Kowdodi Siva Hyderabad Institute of Technology and Management Computer Science with Ai & Ml Hyderabad India Hyderabad Institute of Technology and Management Mechanical Engineering Hyderabad India

ISBN: (纸本)9798350364569

Virtual Reality (VR) simulation has emerged as a transformative technology with applications spanning various fields such as education, healthcare, training, and entertainment. This research paper explores the advancements, methodologies, and impacts of VR simulation, providing a comprehensive review of current technologies and their *** paper details the development and implementation of a Virtual Reality (VR) walkthrough simulation of a college environment using the Unity game engine. The project aims to offer users an immersive and interactive experience that closely mirrors the sensation of navigating through a physical establishment. The simulation integrates lifelike visuals, intuitive navigation, and interactive elements to create a compelling virtual environment. Through this innovative approach, users can experience a realistic and engaging representation of the college, enhancing their understanding and exploration of the space. Traditional architectural models, typically rendered with pen-and-paper blueprints or clay models, often lack detailed visualization and feasibility for quick modifications. This research explores the advantages of utilizing Virtual Reality (VR) for visualizing architectural models of college environments with significantly enhanced detail, both exterior and interior. VR technology allows for the immediate identification and correction of errors, facilitating rapid and efficient updates. Unlike traditional methods, which require redrawing entire blueprints for minor changes, VR's layered structure permits individual modifications without impacting the whole model. Leveraging the Unity game engine, multiple architects can collaborate seamlessly, working together with clients to develop a satisfactory and detailed VR model of the building. This approach not only improves design accuracy but also enhances collaborative efforts, demonstrating the substantial benefits of VR in modern architectural practices. © 2024 IEEE

关键词： Virtual environments

来源：评论

学校读者我要写书评

暂无评论

Analysis on Detecting Cardiac Arrhythmia Using Advanced Technologies 8th

Analysis on Detecting Cardiac Arrhythmia Using Advanced Tech...

引用

8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Radhika, N. Sujatha, D. School of Computer Science & Engineering MRUH Telangana Hyderabad India CSE-AI&ML Department MRCET Telangana Hyderabad India Department of Computational Intelligence MRCET Telangana Hyderabad India

ISBN: (纸本)9789819786657

Essential features of the various cardiac diseases that affect the human heart are provided by the electrocardiogram (ECG). A significant portion of the diagnosis of heart illness comes from the categorization of arrhythmias. An arrhythmia is any alteration to the regular electrical impulse sequence. Conventional techniques for analyzing digital signals, machine learning, and the offshoots, including deep learning approaches are widely used for analyzing and classification of ECG signals and more importantly, for the timely diagnosis of cardiac disorders and arrhythmias. An extensive review of the literature regarding ECG analysis is presented in this document. Also address the computational barriers of machine learning techniques to wearable devices. The study started with a search for pertinent papers and then the gathered data was examined. Examining the assessed ECG features and the machine learning technique for identifying arrhythmias was the second step. The analysis shows that even though the MIT-BIH database requires a large amount of preprocessing work, it chose a sizable proportion of studies. We examine the substantial corpus of literature on pre-recorded ECG data, signal collection, ECG signal processing, and noise reduction, identifying ECG spectrographic states through function technology, classification of ECG signals, and relative perspectives among the examined modules. We reviewed the ultra-modern machine learning strategies for automated arrhythmia detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Optimized Convolutional Neural Network Using Hierarchical Particle Swarm Optimization for Sensor Based Human Activity Recognition

引用

SN computer science 2024年第5期5卷 447页

作者： Ankalaki, Shilpa Thippeswamy, M.N. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka 576104 India Department of Computer Science and Engineering (AI & amp

Hyperparameter optimization poses a significant challenge when developing deep neural networks. Building a convolutional neural network (CNN) for implementation can be an arduous and time-intensive task. This work proposed an approach to optimize the hyperparameters of one dimensional (1D-CNN) to improve the accuracy of human activity recognition (HAR). The framework includes a parametric depiction of 1D-CNNs along with an optimization process for hyperparameters aimed at maximizing the model's performance. This work designed the method called OPTConvNet for hyperparameter optimization of 1D-CNN using Hierarchical Particle Swarm Optimization (H-PSO). The H-PSO algorithm is designed to optimize the architectural, layer and training parameters of 1D-CNN. The H-PSO optimizes the architecture of the 1D-CNN at initial level. Layer and training hyperparameters will be optimized at the next level. The proposed approach employs an exponential-like inertia weight to fine-tune the balance between exploration and exploitation of particles to prevent premature convergence to a local optimum solution in the PSO algorithm. The H-PSO- CNN is evaluated on publicly available sensor- human activity recognition (S-HAR) datasets namely, UCI-HAR, Daphnet Gait, Opportunity and PAMPA2 datasets. © The Author(s) 2024.

关键词： 1D-convolutional neural network Hierarchical particle swarm optimization Human activity recognition Hyperparameters OPTConvNet Optimization

来源：评论

学校读者我要写书评

暂无评论

Drone-Based Intelligent System for Social Distancing Compliance Using YOLOv5 and YOLOv6 with Euclidean Distance Metric

引用

SN computer science 2024年第8期5卷 972页

作者： Parkavi, A. Alex, Sini Anna Pushpalatha, M.N. Shukla, Prashant Kumar Pandey, Ankur Sharma, Sachin Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering (AI and ML) M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Manav Rachna International Institute of Research and Studies Faridabad India

The COVID-19 pandemic forced rigorous social distancing measures to be put in place to halt the virus’s spread;compliance was extremely difficult. In response, we have designed a highly advanced intelligent drone system that maintains and controls distance measures following social distance principles through advanced machine learning skills. This paper describes an intelligent monitoring and compliance system in public places using drone technology with a focus on social distance requirements. Boosting the efficiency of human detection in real-time video streams acquired by drones, our solution employs the You Only Look Once version 5 (YOLOv5) and YOLO version 6 (YOLOv6) object detection algorithms which are the most potent computer vision tools today. The distances between the recognized individuals are computed based on the Euclidean distance measurement to distinguish cases of nonadherence to social distance measures. Our technique integrates the recently developed deep learning models with aerial imagery and monitoring to make it easily scalable for population health surveillance. Thus, we prove impressively that the underlying inventive method works well in different environmental conditions and can potentially contribute to people’s safety in the event of a pandemic. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Drones Euclidean distance metric Social distancing YOLOv6

来源：评论

学校读者我要写书评

暂无评论

ml based Text Summarization for Sentiment Analysis in Information Retrieval using Feature engineering 4

ML based Text Summarization for Sentiment Analysis in Inform...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Shirley, C.P. Berin Jeba Jingle, I. Venkatesan, R. Tamilarasan, R. Yashvanth Ram, R.V. Joyce Beryl Princess, P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Dept. of Ai & Ml Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Sentiment analysis plays a crucial role in understanding user preferences and opinions in the field of information retrieval. This research focuses on developing a system that combines text summarization and machine learning techniques to improve sentiment analysis accuracy. The proposed approach employs extractive and abstractive summarization techniques to reduce lengthy texts into concise summaries while preserving key information. Feature engineering is then applied to extract sentiment-related elements from the summary text, providing valuable insights for the SVM model. The SVM model, enhanced with feature engineering, is expected to effectively summarize text and accurately detect sentiments expressed in documents. This research contributes to the advancement of sentiment analysis techniques and provides valuable insights for decision-making and content development. © 2024 IEEE.

关键词： Support vector machines Measurement Sentiment analysis Accuracy Text summarization Machine learning Information retrieval Feature extraction Vectors Resilience

来源：评论

学校读者我要写书评

暂无评论

Where am I? Large Language Models Wandering between Semantics and Structures in Long Contexts

Where am I? Large Language Models Wandering between Semantic...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Koo, Seonmin Kim, Jinsung Jang, YoungJoon Park, Chanjun Lim, Heuiseok Department of Computer science and Engineering Korea University Korea Republic of Upstage AI

ISBN: (纸本)9798891761643

As the utilization of Large Language Models (LLMs) becomes more widespread, there is a growing demand for their ability to handle more complex and longer external knowledge across various use cases. Most existing evaluations of the open-ended question answering (ODQA) task, which necessitates the use of external knowledge, focus solely on whether the model provides the correct answer. However, even when LLMs answer correctly, they often fail to provide an obvious source for their responses. Therefore, it is necessary to jointly evaluate and verify the correctness of the answers and the appropriateness of grounded evidence in complex external contexts. To address this issue, we examine the phenomenon of discrepancies in abilities across two distinct tasks-QA and evidence selection-when performed simultaneously, from the perspective of task alignment. To verify LLMs' task alignment, we introduce a verification framework and resources considering both semantic relevancy and structural diversity of the given long context knowledge. Through extensive experiments and detailed analysis, we provide insights into the task misalignment between QA and evidence selection. Our code and resources can be found at https://***/seonminkoo/Wai. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

引用

computers, Materials & Continua 2023年第5期75卷 2921-2938页

作者： Muhammad Asfand-e-Yar Qadeer Hashir Syed Hassan Tanvir Wajeeha Khalil Department of Computer Science Center of Excellence in Artificial Intelligence(CoE-AI)Bahria UniversityIslamabad44000Pakistan Department of Computer Science and Information Technology University of Engineering and TechnologyPeshawar25000Pakistan

The growth of the internet and technology has had a significant effect on social *** information has become an important research topic due to the massive amount of misinformed content on social *** is very easy for any user to spread misinformation through the ***,misinformation is a problem for professionals,organizers,and ***,it is essential to observe the credibility and validity of the News articles being shared on social *** core challenge is to distinguish the difference between accurate and false *** studies focus on News article content,such as News titles and descriptions,which has limited their ***,there are two ordinarily agreed-upon features of misinformation:first,the title and text of an article,and second,the user *** the case of the News context,we extracted different user engagements with articles,for example,tweets,i.e.,read-only,user retweets,likes,and *** calculate user credibility and combine it with article content with the user’s *** combining both features,we used three Natural language processing(NLP)feature extraction techniques,i.e.,Term Frequency-Inverse Document Frequency(TF-IDF),Count-Vectorizer(CV),and Hashing-Vectorizer(HV).Then,we applied different machine learning classifiers to classify misinformation as real or ***,we used a Support Vector Machine(SVM),Naive Byes(NB),Random Forest(RF),Decision Tree(DT),Gradient Boosting(GB),and K-Nearest Neighbors(KNN).The proposed method has been tested on a real-world dataset,i.e.,“fakenewsnet”.We refine the fakenewsnet dataset repository according to our required *** dataset contains 23000+articles with millions of user *** highest accuracy score is 93.4%.The proposed model achieves its highest accuracy using count vector features and a random forest *** discoveries confirmed that the proposed classifier would effectively classify misinformat

关键词： Misinformation user credibility fake news machine learning

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient and Congestion Control Technique for IoT Enabled WSN for Video Routing Surveillance: A Survey 15

Energy Efficient and Congestion Control Technique for IoT En...

引用

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

作者： Prathibha, S.B. Supriya, M.C. Dept of computer science and engineering SSIT Sri Siddhartha Academy of Higher Education India Dept of DS and AI&ML SSIT Tumkur India

ISBN: (纸本)9798331300579

Cities all around the world have recently begun to build contemporary smart city infrastructure, which is typically only possible with the usage of cutting-edge *** smart city environments are made possible by intelligent video *** various angle cameras serve as smart sensors that gather visual information from the environment of smart cities and send it for additional visual *** are numerous ways to avoid network communication bottlenecks and enhance the routing *** the majority of the time, unknown attributes caused the network to become unstable and lower the Quality of *** using the right training model to anticipate the congestion properties, this can be *** research offered a unique prediction method by utilizing the matching identification of feature sets to address this *** Probabilistic Rapid March Searching (PRMS) algorithm can be used to do this. © Grenze Scientific Society, 2024.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

A Traffic-Aware and Cluster-Based Energy Efficient Routing Protocol for IoT-Assisted WSNs

引用

computers, Materials & Continua 2024年第8期80卷 1831-1850页

作者： Hina Gul Sana Ullah Ki-Il Kim Farman Ali Department of Computer and Software Technology University of SwatMingora19130Pakistan Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Republic of Korea Department of Applied AI Sungkyunkwan UniversitySeoul03063Republic of Korea

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart *** and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of *** paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such *** proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual *** protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base *** performance of the proposed protocol is evaluated through the network simulator *** different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network *** 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.

关键词： Internet of Things wireless sensor networks traffic load clustering routing energy efficiency

来源：评论

学校读者我要写书评

暂无评论

A Deep Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing 2

A Deep Reinforcement Learning-Based Task Offloading Framewor...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kalpana, Ponugoti Almusawi, Muntather Chanti, Yerrolla Sunil Kumar, V. Varaprasad Rao, M. Avn Institute of Engineering & Technology Department of Computer Science and Engineering Telangana India The Islamic University Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India Cvr College of Engineering Department of Data Science Hyderabad India

ISBN: (纸本)9798350317558

By introducing the processing and resources storage to the network edge, edge computing is a new, promising computing paradigm that drastically decreases network traffic and service latency. Many edge computing applications consist of interdependent tasks, wherein the results of one task are the inputs of another. The important and difficult problem of where to position each running task to optimise Quality-of-Service (QoS) is how to offload these tasks to edge of network. In this work, implemented a novel Deep Reinforcement Learning based Task Offloading (DRLTO) method utilized as the intelligent task offloading that uses a Directed Acyclic Graph (DAG) to represent the dependent tasks and off-policy reinforcement learning powered by a Sequence-to-Sequence (S2S) neural network. This research outcomes show that the DRLTO achieved less cloud processing time, and number of single terminal tasks, and percentage of failed task when compared to Deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) and DeepEdge. © 2024 IEEE.

关键词： and task offloading Deep reinforcement learning Multi-access edge computing sequence to sequence neural networks

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：