检索结果-内蒙古大学图书馆

Science, Engineering Management and Information technology First International Conference, SEMIT 2022

作者： Odeh, Ammar Keshta, Ismail Al-Fayoumi, Mustafa Department of Computer Science King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Amman Jordan Department of Computer Science College of Applied Sciences AlMaarefa University Riyadh Riyadh Saudi Arabia

ISBN: (纸本)9783031403972

Visual object tracking has become a very active research area in recent years. Each year, a growing number of tracking algorithms are proposed. Object detection and tracking is a critical and challenging task in many critical computer vision applications, including automated video surveillance, traffic monitoring, autonomous robot navigation, and intelligent environments. Object tracking is segmenting an object of interest and tracking its velocity, orientation, and occlusion in a video scene to extract useful information. Over the last two decades, several object tracking approaches have been developed to design a robust object tracker that covers all practical obstacles in real-world operations. This paper reviews recent trends and advances in tracking and assesses the reliability of various trackers based on feature extraction techniques. In video processing, visual tracking has a wide range of applications. When a target is identified in one video frame, it is frequently advantageous to track that object in subsequent frames. Every successful frame in which the target is tracked yields more information about the target’s identity and activity. Because tracking is more straightforward than detection, tracking algorithms can require fewer computational resources than object detectors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Exploring Cultural Learning with Vertical Chatbot: Korda 8th

Exploring Cultural Learning with Vertical Chatbot: Korda

引用

8th International Conference on the Applications of Science and Mathematics, SCIEMATHIC 2022

作者： Kasinathan, Vinothini Mustapha, Aida Sewnundun, Neekita Bing, Diong Jien Fang, Lee Khoon Yi, Cindy Koh Xin School of Computing and Technology Asia Pacific University Taman Teknologi Malaysia Jalan Teknologi 5 Kuala Lumpur57000 Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Malaysia KM1 Jalan Pagoh Pagoh Johor 84600 Malaysia

ISBN: (纸本)9789819928491

Cultural heritage is the tangible and tacit legacy of attributes of society inherited from past generations. Its preservation relies on the memory of communities and individuals, hence limiting it from widespread access. To address this issue, this paper proposes the use of a vertical chatbot that serves as the conversational interface between the general public and the expert knowledge. The chatbot is called Korda that focuses on the lost heritage of a traditional dance in Malaysia;the Kuda Kepang. The prototype of Korda is developed on the Chatfuel framework and published as a Messager bot. User acceptance testing of Korda indicated positive feedback due to lack of prior knowledge in the cultural dance. Korda is hoped to promote access to and enjoyment of cultural diversity in Johor, Malaysia, in particular the Kuda Kepang. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Acceptance tests

来源：评论

学校读者我要写书评

暂无评论

Asthma Disease Risk Prediction Using Machine Learning Techniques

Asthma Disease Risk Prediction Using Machine Learning Techni...

引用

2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Selvaraj, P.A. Kumar, Kamdev Aadarsh, V. Mariya Arockiya Akash, A. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences IT Department Oman

ISBN: (纸本)9798350348217

The signs of asthma, a long-term inflammatory condition of the airways, include wheezing, throat tightness, coughing, and breathing difficulties. The attack of an asthma, which can be fatal, is the fast worsening of these symptoms. Another severe, irreversible airflow restriction in the lungs is caused by respiratory COPD, which encompasses emphysema and chronic bronchitis. In this project, a machine learning-based algorithm is for predicting asthma risk is presented (ML). PEFR, which are widely used external tools like peak flow meters and recognized asthma risk predictors, are frequently monitored. This study shows a relationship between the ambient particle matter(PM) and the weather outdoors. According to the best peak flow value each person was able to acquire, the results are divided into two groups: Safe and Risk circumstances. The link between indoor PM and weather data is mapped to the found values using a convolutional neural network (CNN) architecture. The suggested method's root mean square and mean absolute error accuracy metrics are contrasted with those of current deep neural network (DNN)-based methods. Additionally, the accuracy of the classification methods KNN and SVM are carried out. The new data set's asthma category may be predicted more accurately thanks to the application of SVM, KNN, and CNN classification. Python 3.7 is the coding language employed. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A Multi-Module Spatial Semantic Network with Gradient Boosting for Efficient Crop Classification

A Multi-Module Spatial Semantic Network with Gradient Boosti...

引用

2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Husain, Saif O. Meghana, A. Soujanya, Rajireddy Sunil, G. Reddy Muvva, Vijaya Bhaskar The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India University of Technology and Applied Sciences -Shinas College of Computing and Information Sciences Department of Information Technology Shinas Oman

ISBN: (纸本)9798350375442

For the past few years, accurate crop classification is critical for efficient agricultural management and monitoring. While traditional classification techniques have leveraged spectral indices, they often struggle to fully capture the complex and diverse multispectral features present in agricultural landscapes. This challenge is particularly evident when differentiating crops with similar spectral signatures, leading to misclassification and reduced accuracy in traditional models. To address this problem, a hybrid approach that combines a deep learning model namely Multi-module Spatial Semantic Network (MSSNet) with the Gradient Boosting Classifier (GBC) is proposed. The dataset utilized in this research is sentinel-2, where the median filter is used to remove noise at the preprocessing stage. Then, MSSNet architecture is applied in feature extraction where pre-trained residual network (ResNet50), embedded receptive field module (RFB) and skip connection are incorporated to extract multi-spectral and spatial features. Finally, the extracted features are fed into a GBC, which is suitable for the robust performance in pixel mapping. This MSSNet+GBC combination leverages the strengths and offers improved accuracy in distinguishing between crop types and conditions. The experimental results demonstrate that MSSNet+GBC outperforms conventional methods such as 3D-Convolutional Neural Network (CNN) with higher accuracy of 98.67% using a sentinel-2 dataset for precise crop classification. © 2024 IEEE.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

An Optimal Cluster Head and Secure Route Path Selection Using Multi Objective—GWO Approach in Wireless Sensor Networks

An Optimal Cluster Head and Secure Route Path Selection Usin...

引用

International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Muvva, Vijaya Bhaskar Reddy Almoussawi, Zainab abed Naik, Narayan Madhavan, Sowmya Praveena, Hirald Dwaraka Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences -Shinas Shinas Oman College of Islamic Science Ahl Al Bayt University Karbala Iraq Department of IS&E Canara Engineering College Visvesvaraya Technological University Karnataka BantwalBelagavi India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Andhra Pradesh Tirupati India

ISBN: (纸本)9789819778751

Wireless Sensor Networks (WSNs) play a crucial role ranging from environmental control to queue tracking. These networks are established by deploying numerous tiny sensor nodes that communicate with each other wirelessly. However, the routing paths in WSNs often suffer from delays in timely information delivery. The proposed Multi Objective Grey Wolf Optimization (MOGWO) aims to address these challenges where information transmission may converge slowly and routing paths may struggle in large-dimensional spaces. The objective of MOGWO is to efficiently send information by selecting the shortest path based on the regions. The MOGWO focuses on achieving high efficiency by choosing the shortest path to explore information. In the context of network routing MOGWO optimizes the path to the destination considering the energy levels of the sensor nodes. The proposed MOGWO method demonstrates superior performance compared to existing methods such as PSO, Black Widow Optimization (BWO) and Ant Colony Optimization (ACO). Specifically, the MOGWO method yields better results, achieving an energy consumption of 43 J, Packet Delivery Ratio (PDR) of 90%, and End-To-End Delay (ETED) of 33 s. This outperforms existing methods, highlighting the effectiveness of the MOGWO approach in enhancing network routing paths in WSN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

来源：评论

学校读者我要写书评

暂无评论

Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams

Towards Differentiating Between Failures and Domain Shifts i...

引用

2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024

作者： Wojak-Strzelecka, Natalia Bobek, Szymon Nalepa, Grzegorz J. Stefanowski, Jerzy Jagiellonian Human-Centered AI Lab Mark Kac Center for Complex Systems Research Institute of Applied Computer Science Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. prof. Stanislawa Lojasiewicza 11 Krakow30-348 Poland Institute of Computing Science Poznań University of Technology Poznań60-965 Poland

Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures, while sudden, isolated changes in the data indicate anomalies. However, in many practical applications, changes in the data do not always represent abnormal system states. Such changes may be recognized incorrectly as failures, while being a normal evolution of the system, e.g. referring to characteristics of starting the processing of a new product, i.e. realizing a domain shift. Therefore, distinguishing between failures and such"healthy" changes in data distribution is critical to ensure the practical robustness of the system. In this paper, we propose a method that not only detects changes in the data distribution and anomalies but also allows us to distinguish between failures and normal domain shifts inherent to a given process. The proposed method consists of a modified Page-Hinkley changepoint detector for identification of the domain shift and possible failures and supervised domain-adaptation-based algorithms for fast, online anomaly detection. These two are coupled with an explainable artificial intelligence (XAI) component that aims at helping the human operator to finally differentiate between domain shifts and failures. The method is illustrated by an experiment on a data stream from the steel factory. © 2024 Copyright for this paper by its authors.

关键词： Data streams

来源：评论

学校读者我要写书评

暂无评论

Empowering Aging Populations: Implementing Robotics Solutions for Elderly

Empowering Aging Populations: Implementing Robotics Solution...

引用

IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Tewfiq El Maliki Gabriel Strano Information Technology Department-Hepia University of Applied Sciences Western Switzerland Geneva Switzerland Computing and communication systems University of applied sciences-HES-SO Geneva Switzerland

ISBN: (数字)9798350396133

ISBN: (纸本)9798350396140

The anticipated doubling of the global elderly population by 2050 brings the increased risk and severe consequences of falls to the forefront, especially for those living on their own. This demographic trend underscores the critical need for forward-thinking solutions that enable seniors to live independently while ensuring their health and safety are not compromised. Addressing this challenge, we developed an allencompassing service for fall detection, which integrates meticulous monitoring and tracking capabilities. Utilizing the latest technology, including cameras and smartphones, an effective system is implemented for detecting falls. Moreover, this system can be enhanced by integrating a smart robot to provide customized assistance from alerting emergency services to contacting family members, depending on the individual's preferences and situation. Innovative technology is integrated with considerate and personalized care to empower seniors to maintain their independence and enjoy a high quality of life, ensuring their safety and well-being are prioritized.

关键词： Technological innovation Robot vision systems Cameras Libraries Complexity theory Health and safety Older adults Fall detection Usability Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Parameter identification in PDEs by the solution of monotone inclusion problems

arXiv

引用

arXiv 2024年

作者： Gautam, Pankaj Grasmair, Markus Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee India Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway

In this paper we consider the solution of monotone inverse problems using the particular example of a parameter identification problem for a semilinear parabolic PDE. For the regularized solution of this problem, we introduce a total variation based regularization method requiring the solution of a monotone inclusion problem. We show well-posedness in the sense of inverse problems of the resulting regularization scheme. In addition, we introduce and analyze a numerical algorithm for the solution of this inclusion problem using a nested inertial primal dual method. We demonstrate by means of numerical examples the convergence of both the numerical algorithm and the regularization *** Codes 65J20, 47H05, 47J06, 49J20 © 2024, CC BY.

关键词： Parameter estimation

来源：评论

学校读者我要写书评

暂无评论

An Overview of Tools and Techniques in Network Forensics 25

An Overview of Tools and Techniques in Network Forensics

引用

25th International Arab Conference on Information technology, ACIT 2024

作者： Asassfeh, Mahmoud Al-Mousa, Mohammad Rasmi Al-Dweikat, Hasan Al-Mashagbeh, Malak Hamad Afaneh, Suha Samara, Ghassan Zaid, Ali Abu Khouj, Mohammed Al Daoud, Essam Zarqa University Department of Cybersecurity Zarqa Jordan Applied Science Private University Cybersecurity and Cloud Computing Department Amman Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Zarqa Jordan

ISBN: (纸本)9798331540012

Network forensics pertains to a subfield of digital forensics that deals with network security. It is utilized in conjunction with computer network traffic monitoring and analysis, which acts as an intrusion detection system and firewall and facilitates the collection of data and digital proof for network security. This platform is designed to analyze and monitor data in a real-time network environment. It is a crucial instrument for identifying and resolving instances of real-time network misuse. This research shows the differences between network forensics and digital forensics as well as the tools and techniques used in network forensics. It also discusses building forensics platforms, developing network analysis methodologies, and talking about the future of network forensics. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

A Pruned Multi-Scale Skip Connected and Asymmetric-Convolution-Based U-Net Model for Land Cover Classification

A Pruned Multi-Scale Skip Connected and Asymmetric-Convoluti...

引用

2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Almusawi, Muntather Parameshachari, B.D. Sunil, G. Reddy Muvva, Vijaya Bhaskar Siri, Dharmapuri The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Sr University Department of Computer Science & Artificial Intelligence Warangal India University of Technology and Applied Sciences -Shinas College of Computing and Information Sciences Department of Information Technology Shinas Oman Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

Recently, land cover classification became an evolutionary study in Remote Sensing (RS) satellite interpretations. While extracting features from satellite images, spatial information causes the overfitting issue. In addition, computational complexity is occurring in land cover segmentation due to the usage of complex architectures. Thus, in this research, Multi-scale Skip connected and Asymmetric-Convolution-based U-Net (MACUNet) with pruning method is introduced to reduce the computational complexity. To reduce the overfitting, Batch Normalization (BN) layer is employed, simultaneously 3D Convolutional Neural Network (3D CNN) for feature extraction. Initially, the input is taken as satellite images from the GID dataset, where spatial and temporal features are extracted using 3D CNN model with BN layer. After that, these features are given as input to the MACUNet method to improve segmentation performance. Then, classification is done with class_num value utilization based on identified pixel region maps and classified into six classes such as build up, forest, water, farmland, meadows and others. From the results, the proposed pruned-MACUNet model produces better results than existing GeohashNet in terms of Mean Intersection over Union (MIoU) as 80.45% and f1-score as 90.23% respectively. © 2024 IEEE.

关键词： Convolutional 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：