检索结果-内蒙古大学图书馆

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Gupta, Swati Kumar, Himanshu Vijarania, Meenu Gautam, Anjali Negi, Neerja Dadhich, Priyanka Kr Mangalam University Department of Computer Science Centre of Excellence Haryana Gurugram India Kr Mangalam University Department of Cse Haryana Gurugram India School of Computer Applications Manav Rachna International Institute of Research & Studies India

ISBN: (纸本)9798350387490

India's poor air quality is responsible for many health concerns, with chronic exposure to fine particulate matter (PM) leading to various health issues. Recently, the pollution levels in India have undergone various changes in the status of pollutants present in the air. Our paper aims to provide a comprehensive analysis of these issues highlighting the severe implications of poor air quality on public health in India and try to predict future health concerns due to poor air quality. In our work we have taken three machine learning modelslinear regression, decision tree regression, and K-Nearest Neighbour into the consideration to train our model. We have evaluated the models by using 'R-squared score' and 'mean square error', to determine which was the best suitable model in predicting the Air quality with better accuracy. Our work contribute to ongoing efforts to improve the air quality forecasts model (AQI) and create effective solutions for a healthier India. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Aircraft Detection in Satellite Images 1

Aircraft Detection in Satellite Images

引用

1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Singh, Abhimanyu Nene, Manisha J. Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665450461

Object detection is one of the key areas for all the researchers in the field of computer science. The research is to find the types of objects in the image and provide their temporal and spatial characteristics. In the recent times there have been a lot of improvements in the field of satellite image detection with varying data sets being available and has left high impact on the performance of such analysis. A number of algorithms have evolved over a period of time in object detection and analysis namely different versions of YOLO, CNN, DETR etc. There is a need to deploy a study which enables the performance comparison of different versions of these algorithms on specific data set to understand their efficacy. The study in the paper contributes to understanding, evaluating and analyzing the performance characteristics of YOLO v7 algorithm with varying parameters. © 2022 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Optimization Techniques for Machine Learning Models to Improve the Efficiency of Classification

Optimization Techniques for Machine Learning Models to Impro...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： J. Anita Smiles M. Sakthivanitha A. Bharathi D. Narayani. S. Sudha M. Mohamed Sirajudeen Department of Computer Applications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Computer Applications Vels institute of Science Technology and Advanced Studies (VISTAS) Tamil Nadu India Department of Computer Applications Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Computer Science PG and Research Nilgiri College of Arts and Science (Autonomous) Thaloor The Nilgiris Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The objective of the study is to identify the most optimal set of hyperparameters for a machine learning (ML) or deep learning(DL) algorithms that improves its performance on a certain task.. This study uses five machine learning methods like Decision Tree(DT), Random Forest(RF), Gradient Boost Models(XGBoost), Support Vector Machine(SVMs) and K-Nearest Neighbhor(KNN). The model specific parameters were applied to all these ML methods to improve the accuracy of the models. The ML models performance with its hyperparameter tuning are evaluated for performance using the performance metrics like accuracy, precision, Recall and F-score. These findings indicate that XGBoost models performed significantly in terms of accuracy, precision, recall, and F1-score. Gradient boosting models are extremely adaptable, but they are also sensitive to hyperparameters such as the learning rate, number of estimators, and tree depth. Tuning these parameters can dramatically improve performance. The optimal model and tuning method are determined by the dataset, task specifications, and computing power.. The contribution of the study to suggests a suitable with right hyperparameter settings to develop a highly flexible model that can adapt to a variety of datasets. The study and application of model-specific hyperparameters in ML continues to evolve, resulting to advances that improve productivity, durability, and generalization.

关键词： Support vector machines Adaptation models Accuracy Machine learning algorithms Computational modeling Forestry Vectors Decision trees Tuning Random forests

来源：评论

学校读者我要写书评

暂无评论

FMCW Monopulse Radar with an Orthogonally Polarized Dual-Beam Dielectric Resonator Antenna

FMCW Monopulse Radar with an Orthogonally Polarized Dual-Bea...

引用

2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Lee, Chanhee Shin, Kyeong-Han Lee, Ji-Hoon Lee, Hyo-Won Kim, Jeong-Wook Yu, Jong-Won School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Electrical and Computer Engineering Ajou University Suwon Korea Republic of

ISBN: (纸本)9798350364774

In this paper, frequency-modulated continuous-wave (FMCW) radar system using dielectric resonator antenna (DRA), which has two beams with orthogonal linear polarization to each other, is introduced to function as a monopulse radar. The DRA operates at 24.3 GHz with a 16.8% bandwidth and provides over 40 dB isolation between V-pol and H-pol ports. Simulations demonstrate effective target detection and advanced directional information extraction, similar to monopulse radar, using a signal processing technique to address overlapping beam regions. © 2024 IEEE.

关键词： Monopulse radar

来源：评论

学校读者我要写书评

暂无评论

An Agent-Based Basic Educational Model for the Children with ASD Using Persuasive Technology

An Agent-Based Basic Educational Model for the Children with...

引用

2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Hasan, Nazmul Nene, Manisha J. Defence Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665425773

Children with Autism Spectrum Disorder (ASD) suffer most in communication and behavioral aspect which hampers their regular basic education and cognitive development. Technological development enhanced a lot in every sector where educational sector is no exception of that. The cognitive development of the children with ASD can be taken care of effectively by using of advanced information technology. This study has focused on the prospect of designing the digital learning platform for the children with ASD to fulfill the existing research gap for developing persuasive learning mechanism for the children with ASD. This study has proposed an agent-based model for developing a persuasive learning tool where Sanket is an agent of a touch screen based application which pursues the children with ASD regarding the learning of basic education (numbers, alphabets), which forms a basis to enable Artificial Intelligence for automation of the learning process and help the children with ASD. © 2022 IEEE.

关键词： Touch screens

来源：评论

学校读者我要写书评

暂无评论

nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering 7

nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering

引用

7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Zheng, Yan Wu, Song Ren, Yazhou Pu, Xiaorong Xu, Zenglin He, Lifang Yi, Zhang University of Electronic Science And Technology of China School of Computer Science And Engineering Chengdu China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Harbin Institute of Technology Shenzhen School of Computer Science And Technology Shenzhen China Lehigh University Department of Computer Science And Engineering Bethlehem United States Sichuan University Intelligent Interdisciplinary Research Center College of Computer Science Chengdu China

ISBN: (纸本)9798350359145

Deep multi-view clustering (MVC) has recently gained significant interest for its capability to harness complementary information across multiple views through deep neural networks, enhancing clustering performance. However, existing deep MVC methods still have two issues: (1) They separate the feature learning objective and the clustering objective, potentially leading to suboptimal embedding features due to the neglect of the global clustering structure. (2) Most works utilize the target distribution obtained by the clustering objective to supervise feature learning. However, they simply sharpen the soft clustering distribution to obtain the target distribution, lacking the dynamics of the clustering process. To address these issues, in this paper we propose a novel end-to-end self-supervised multi-view clustering approach based on NeuralODEs, namely nmODE-MVC. Our approach operates in two stages: In the first stage, all view-embedded features are extracted, and then unified them into a global feature, facilitating global clustering assignments by exploiting multi-view data's complementarity. The second stage leverages the dynamic attributes of NeuralODEs to dynamically refine the data features and generate a target distribution that actively drives the clustering decisions. We formulate these learning processes as a unified optimization problem, facilitating iterative training and refinement. The effectiveness of nmODE-MVC is validated through extensive experiments on multiple real-world multi-view datasets. © 2023 IEEE.

关键词： Ordinary differential equations

来源：评论

学校读者我要写书评

暂无评论

DropAttack: A Random Dropped Weight Attack Adversarial Training for Natural Language Understanding

引用

IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 364-373页

作者： Ni, Shiwen Li, Jiawen Yang, Min Kao, Hung-Yu Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518000 China Guangdong Ocean University Maritime College Zhanjiang524000 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan

Adversarial training has been proven to be a powerful regularization technique to improve language models. In this work, we propose a novel random dropped weight attack adversarial training method (DropAttack) for natural language understanding. Our DropAttack improves the generalization of models by minimizing the internal adversarial risk caused by a multitude of attack combinations. Specifically, DropAttack enhances the adversarial attack space by intentionally adding worst-case adversarial perturbations to the weight parameters and randomly dropping the specific proportion of attack perturbations. To extensively validate the effectiveness of DropAttack, 12 public English natural language understanding datasets were used. Experiments on the GLUE benchmark show that when DropAttack is applied only to the finetuning stage, it is able to improve the overall test scores of the BERT-base pre-trained model from 78.3 to 79.7 and RoBERTa-large pre-trained model from 88.1 to 88.8. Further, DropAttack also significantly improves models trained from scratch. Theoretical analysis reveals that DropAttack performs potential gradient regularization on the input and weight parameters of the model. Moreover, visualization experiments show that DropAttack can push the minimum risk of the neural network to a lower and flatter loss landscape. © 2014 IEEE.

关键词： Speech processing

来源：评论

学校读者我要写书评

暂无评论

Forensic Analysis of Windows 11 Prefetch Artifact 4

Forensic Analysis of Windows 11 Prefetch Artifact

引用

4th IEEE Bombay Section Signature Conference, IBSSC 2022

作者： Budhrani, Akash Singh, Upasna Singh, Bhupendra Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665492911

The Operating System creates numerous objects to improve its efficiency and user experience and such objects are called artifacts. These artifacts record crucial data about the user activity. Such artifacts are the start point of any investigation as they can be an additional lead to a forensic triage. Prefetch file is one among various objects, presence of which confirms the execution of a particular application. Prefetch gives additional inside for the purpose of investigation. Thus, this paper brings out the forensic value of it, the tools required to decode the information it contains and also look in various caveats in interpreting this artifact to learn its strength and weaknesses to properly incorporate in support of opinion derived by the analyst. In this work, Prefetch is forensically examined to bring out its forensic value, knowledge it contains and all of that in whole or in parts can be used to help advance in investigation. Paper also brings out the difference in format of this artifact among various version of Windows OS. © 2022 IEEE.

关键词： Windows operating system

来源：评论

学校读者我要写书评

暂无评论

Enhancing Lung Cancer Detection Accuracy through the Integration of Ant Colony Optimization and Neural Network

Enhancing Lung Cancer Detection Accuracy through the Integra...

引用

2024 International Conference on Emerging Innovations and advanced Computing, INNOCOMP 2024

作者： Bhati, Sunita Singh, Jagendra Shelke, Nitin Arvind Nagda, Vinita Sahu, Hemant Bhatt, Monica JECRC University Jaipur India School of Computer Science Engineering & Technology Bennett University Greater Noida India Mohan Lal Sukhadia University Department of Computer Science Udaipur India Department of CS & IT Udaipur India Geetanjali Institute of Technical Studies Udaipur India

ISBN: (纸本)9798350376470

The combination of machine learning algorithms and optimization techniques shows great potential in lung cancer detection. The current study explores the use of Convolutional Neural Networks (CNN) and VGG16 models with Ant Colony Optimization (ACO) for better performance of predictive modeling in lung cancer detection. In the study, a dataset of 4,236 images from Computed Tomography scans was used. The dataset comprised both CT scans of cancerous lungs at different stages and healthy, non-cancerous lung images. First, ACO was used to select important features, then, the selected features were used to train the two models. As a result, high accuracy of the models was observed, with CNN having 95.46% accuracy and VGG16 - 97.87%. Comprehensive evaluation scores, including precision, recall, and F1-score, also showed the models' high-performance level in differential analysis of disease and healthy lung tissue. The results further emphasize that the use of advanced machine learning algorithms with optimization techniques has the potential to revolutionize lung cancer detection, providing the medical community with powerful tools both for early detection and personalized treatment. However, continued efforts in research and development are needed to refine the performance of predictive models and ultimately better fight lung cancer and improve prognosis for patients. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Real-Time Eyes Monitoring System Using OpenCV Autonomous filtering using RNN 3

Real-Time Eyes Monitoring System Using OpenCV Autonomous fil...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Prajapati, Dhruvansh Joshi, Jaynil Das, Nibedita Satapathy, Santosh Pandit Deendayal Energy University Information & Communication Technology Gujarat Gandhinagar India Institute of Advanced Research Department of Computer Science and Engineering Gujarat Gandhinagar India

ISBN: (纸本)9798350354171

This paper introduces a novel Real-Time Eye Gazing and Monitoring System aimed at objectively and continuously assessing individuals across various environmental conditions. Addressing the challenge of accurate performance in diverse lighting conditions, the system employs Recurrent Neural Network (RNN) trained filters to enhance eye detection accuracy. Traditional methods often falter in non-ideal lighting, prompting the need for adaptive solutions. Through extensive experimentation, optimal filter combinations were identified, ensuring robustness across a spectrum of scenarios. Implementation via an OpenCV facilitates efficient processing without compromising detection accuracy. This paper achieved the list of main filters and their combinations which can be helpful to detect eyes in different environmental conditions. © 2024 IEEE.

关键词： Recurrent 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：