检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Yilmaz, B. Turkmen, M. Meral, S. Akagündüz, E. Tileylioglu, S. Dept. of Modeling and Simulation Graduate School of Informatics METU Ankara Turkey Turkish Aerospace Industries Inc. Ankara Turkey Dept. of Civil Engineering Kadir Has University Istanbul Turkey

Assessing seismic hazards and thereby designing earthquake-resilient structures or evaluating structural damage that has been incurred after an earthquake are important objectives in earthquake engineering. Both tasks require critical evaluation of strong ground motion records, and the knowledge of site conditions at the earthquake stations plays a major role in achieving the aforementioned objectives. Site conditions are generally represented by the time-averaged shear wave velocity in the upper 30 meters of the geological materials (Vs30). Several strong motion stations lack Vs30 measurements resulting in potentially inaccurate assessment of seismic hazards and evaluation of ground motion records. In this study, we present a deep learning-based approach for predicting Vs30 at strong motion station locations using three-channel earthquake records. For this purpose, Convolutional Neural Networks (CNNs) with dilated and causal convolutional layers are used to extract deep features from accelerometer records collected from over 700 stations located in Turkey. In order to overcome the limited availability of labeled data, we propose a two-phase training approach. In the first phase, a CNN is trained to estimate the epicenters, for which ground truth is available for all records. After the CNN is trained, the pre-trained encoder is fine-tuned based on the Vs30 ground truth. The performance of the proposed method is compared with machine learning models that utilize hand-crafted features. The results demonstrate that the deep convolutional encoder based Vs30 prediction model outperforms the machine learning models that rely on hand-crafted features. This suggests that our computational model can extract meaningful and informative features from the accelerometer records, enabling more accurate Vs30 predictions. The findings of this study highlight the potential of deep learning-based approaches in seismology and earthquake engineering. © 2024, CC BY.

关键词： Shear waves

来源：评论

学校读者我要写书评

暂无评论

Human Activity Classification using G-XGB

Human Activity Classification using G-XGB

引用

2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Kataria, Aman Puri, Vikram Pareek, Piyush Kumar Rani, Sita Amity Institute of Defence Technology Amity University Noida Campus Sector-125 Noida India Duy Tan University Center of Visualization and Simulation Da Nang Viet Nam Cell Nitte Meenakshi Institute of Technology Dept. of Artificial Intelligence and Machine Learning and Ipr Bengaluru India Guru Nanak Dev Engineering College Department of Computer Science and Engineering Ludhiana India

ISBN: (纸本)9798350301595

Wearable body sensors have recently gained popularity because they monitor user behaviors and vital indicators. These sensors are designed to monitor various physiological factors, such as motion, temperature, and heart rate, among others. Making sense of the data gathered by these sensors requires separating the dependent and independent features. The dependent parameters are those that body-worn sensors can read in this scenario. Examples of such elements include blood oxygen levels, heart rate, temperature, mobility, and airflow. These sensors capture data about a person's activity and health. The independent parameters in wearable body sensors are related to the dependent parameters, whereas the dependent parameters are those that the sensors can measure directly. Such characteristics include age, sex, weight, and environmental influences. For example, humidity and airflow might impair a temperature sensor's accuracy. The user's age or level of physical fitness may also influence the accuracy of a heart rate monitor. Activity monitors are used in this study to capture the actions of various people. Static and dynamic properties are derived from the activity monitor recording. The performance evaluations of the eight distinct machine-learning models that were created using the retrieved features are discussed in this paper. The Grid Search method is used to enhance the performance of the XGB model. © 2023 IEEE.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

Method of Coding Video Flow Segments Containing Motion Objects

Method of Coding Video Flow Segments Containing Motion Objec...

引用

International Conference on Modern Problems of Radio engineering, Telecommunications and Computer Science

作者： Roman Onyshchenko Volodymyr Barannik Kyrylo Revva Oleh Kulitsa Pavlo Pertsev Mykhailo Bielous Dept. of Information network engineering Kharkov National University of Radio Electronics Kharkiv Ukraine Dept. of Information network engineering National University of Civil Defence of Ukraine Cherkasy Ukraine Dept. of Scientific research department simulation modeling Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

This article shows that one of the most important directions in the development of information communication systems (ICS) is the construction of new generations of wireless technologies. Including such a direction is important for the creation of information technology support systems and decision-making for the management of critical infrastructure objects. It is shown that increasing the bit rate of the coded video stream is a relevant area of research. Such an advantage is proposed to be formed on the basis of structural and positional coding of segments of dynamic objects. A model for evaluating the informativeness of a transformant is created based on the fact that it is represented in the spectral space according to an uneven diagonal scheme. The construction of the information technology of the code representation of diagonals in the structural-positional space of transformants is considered.

关键词： Wireless communication Image coding Motion segmentation Redundancy Dynamic range Streaming media Market research Encoding Telecommunications Information technology

来源：评论

学校读者我要写书评

暂无评论

AI-based botnet attack classification and detection in IoT devices

AI-based botnet attack classification and detection in IoT d...

引用

Machine Learning and Applied Network Technologies (ICMLANT), IEEE International Conference on

作者： Vikram Puri Aman Kataria Vijender Kumar Solanki Sita Rani Center of Visualization and Simulation Duy Tan University Da Nang Vietnam CSIR-CSIO Chandigarh India Dept. of Computer Science and Engineering CMR Institute of Technology Hyderabad TS India Dept. of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana Punjab India

ISBN: (数字)9781665487016

ISBN: (纸本)9781665487023

End-user Internet of Things (IoT) devices, including security cameras, smart appliances, home monitors, and thermostats, are becoming more prevalent in households. Additionally, the proliferation of devices facilitates the propagation of security concerns like DoS and spoofing. However, it is difficult for conventional rule-based security systems to recognize IoT assaults due to the development of heterogenous devices in the IoT ecosystem. Artificial Intelligence (AI) techniques can be a solution which enables the creation of an effective security model based on actual data from each device. In this work, IoT botnets are detected and classified using machine learning (ML) and deep learning (DL) based algorithms. Six ML models and three DL models are used to assess the system's performance. The best-performing model is also implemented as an API.

关键词： Deep learning Home appliances Machine learning algorithms Botnet System performance Ecosystems Internet of Things

来源：评论

学校读者我要写书评

暂无评论

An Explainable AI-Enabled Framework for the Diabetes Classification

An Explainable AI-Enabled Framework for the Diabetes Classif...

引用

Machine Learning and Applied Network Technologies (ICMLANT), IEEE International Conference on

作者： Cu Kim Long Vikram Puri Vijender Kumar Solanki Gloria Jeanette Rincón Aponte Information Technology Center Ministry of Science and Technology (MOST) Hanoi Vietnam Center of Visualization and Simulation Duy Tan University Da Nang Vietnam Dept. of Computer Science and Engineering CMR Institute of Technology Hyderabad TS India Universidad Cooperativa de Colombia Bogota Colombia

Nowadays, Diabetes has become a prominent health issue that spans several categories determined by characteristics such as clinical presentation, disease progression, and pathophysiology. A real-time monitoring and management system is necessary to mitigate this life-threatening disease and safeguard individuals. The potential of Artificial Intelligence (AI) to change the management of Diabetes and enhance outcomes for persons with the condition is substantial. While AI shows promise in its ability to forecast Diabetes, it also presents a challenge due to its black-box nature. To address this concern, a proposed framework incorporating explainable AI is suggested for classifying Diabetes. This framework serves to elucidate the decisions made by the AI model. Furthermore, it was assessed using three distinct datasets categorized according to Diabetes categorization. Understanding the decision-making process of AI in the medical field fosters trust between healthcare providers and patients.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

引用

IEEE International Conference on Web Services (ICWS)

作者： Renjie Zhou Zhenming Jin Jian Wan Jilin Zhang Xingyu Guo Qiang Liu Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education ITMO Joint Institute Hangzhou Dianzi University Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Dept. of Computer and Information Technology Zhejiang Police College Hangzhou China

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user’s shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Human Activity Classification using G-XGB

Human Activity Classification using G-XGB

引用

Data Science and Network Security (ICDSNS), International Conference on

作者： Aman Kataria Vikram Puri Piyush Kumar Pareek Sita Rani Amity Institute of Defence Technology Amity University Noida India Center of Visualization and Simulation Duy Tan University Da Nang Vietnam Dept. of Artificial Intelligence and Machine Learning and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India

关键词：

来源：评论

学校读者我要写书评

暂无评论

Blockchain-enabled Transparent and Trustworthy Regulation System for Smart Cities

Blockchain-enabled Transparent and Trustworthy Regulation Sy...

引用

Cyber Physical Systems, Power Electronics and Electric Vehicles (ICPEEV), International Conference on

作者： Vikram Puri Vijender Kumar Solanki Aman Kataria Cu Kim Long Center of Visualization and Simulation Duy Tan University Da Nang Vietnam Dept. of Computer Science and Engineering CMR Institute of Technology Hyderabad TS India Amity Institute of Defence Technology Amity University Uttar Pradesh India Information Technology Center Ministry of Science and Technology (MOST) Hanoi Vietnam

The development and growth of the shared economy in the smart cities are significantly influenced by technology. An online community-based ecosystem that enables peer-to-peer trade between buyers and sellers as well as share- and rental-based access to goods and services. Lack of trust and transparency is the main problem in the shared economy system. A blockchain-enabled architecture is created for the shared economy ecosystem to address this problem. In this study, product registration and product services policies are implemented using smart contracts. This technology aids in distinguishing between genuine and counterfeit goods while also giving consumers access to the product’s metadata via QR codes. An experimental testbed that makes use of the polygon network is taken into consideration in order to validate the suggested framework.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DLT Based Smart Medical Ecosystem

DLT Based Smart Medical Ecosystem

引用

Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Vikram Puri Aman Kataria Sita Rani Piyush Kumar Pareek Center of Visualization and Simulation Duy Tan University Da Nang Vietnam Amity Institute of Defence Technology Amity University Noida Uttar Pradesh India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Dept. of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

The healthcare system has become more reliant on the data collection capabilities of fast evolving IoT devices. Patient healthcare records (PHR) now contain additional information as a result. However, securing identities and guaranteeing data security have emerged as critical challenges in the healthcare system. When it comes to regulating PHR, many of the centralized and decentralized methods now in use struggle difficulties such as single point of failure, high transaction fees, slow throughput, and excessive latency. To address these concerns, a distributed ledger technology (DLT)-based framework named IOTA rather than a blockchain-based platform such as Ethereum or Polygon is proposed. The proposed framework considered the example of a hospital with several patients on the same and various floors. Furthermore, the suggested approach employs a real-time experimental setup for calculating the time and power consumption of the devices.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Smart Microgrid Approach for Distributed Network Combined with Power Line Fault Location Detection

A Smart Microgrid Approach for Distributed Network Combined ...

引用

2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021

作者： Khushi, Farha Motakabber, S.M.A. Bhattacharjee, Amit Zahirul Alam, A.H.A. Wong Azman, Amelia Hussain, Fayaz Kulliyyah of Engineering IIUM Dept. of Electronic and Computer Engineering Gombak Malaysia The City University Of New York Department Of Electrical And Computer Engineering New YorkNY United States Ton Duc Thang University Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9781665403436

For every researcher, uncertainties connected to the Smart Grid and Distribution Network are a common element. Thousands of sources are part of the renewable energy system and they are situated at varying distances. In due course, certain sources may be inefficient, or a natural disaster can destroy the source causing power supply failures or require routine maintenance to improve service. Some of these defects grow slowly and some have a strong impact. The systems, however, contain safety components to remove the problem, but impact the generation of electricity and conduct intelligent grid errors. An intelligent microgrid should feature an intelligent fault location detection and fault removal device to avoid unwanted loads and insecurities. This research shaped a smart grid system, together with a defective technique to identify uncertainty efficiently. The originality of this research is to model an SMG based on an isolated small area's SGC mechanism in conjunction with a defective detecting system to manage uncertainties in an efficient way. The FLD technique is focused upon wavelet trigger signal and mathematical morphology method. In this approach, the wavelet activator signal created by the equivalent current or voltage for a short period of time is simulated to go to both terminals to determine that the fault takes place in the short branch using the mathematical morphological technique. The SMG modeled system is separated into a few branches for the FLD system. A smart technique is presented via mixed human machine interface (HMI) for SMG performance and FLD monitoring. The integrated process of the SMG system modeling in the MATLAB performance validation simulation platform. © 2021 IEEE.

关键词： Mathematical morphology

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：