检索结果-内蒙古大学图书馆

5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Mahajan, Deepika Sharma, Sandeep Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

ISBN: (纸本)9789819725496

A disaster is an emergency that leads to extensive damage which is not easy to overcome. Humans are incapable of avoiding disasters, specifically natural disasters. There are more than hundreds of people who lost their lives due to these disasters. Some disasters are natural and some are unnatural. This paper emphasizes the different categories of disasters that happened in the last ten years. It has been observed that of the 4134 disasters that occurred during this period, the majority were Hydrological Disasters. The total number of people affected due to these disasters has been analyzed which includes homeless, injured, and deaths. Disaster management is important to handle the consequences of catastrophes and help the affected people using existing resources. The numerous technologies that have been used in the last few years to prevent, monitor, and help in rescue from the aftermath of disasters are also highlighted and analyzed in this paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Disasters

来源：评论

学校读者我要写书评

暂无评论

Exploring Sign Language Recognition using Convolutional Neural Network 7

Exploring Sign Language Recognition using Convolutional Neur...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Edwin Samuel, M. Samuel Christopher, S. Prem Jacob, T. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

The recognition of sign language holds significant utility in facilitating communication for the hearing-impaired, aiding in robotic interactions, and connecting with non-verbal communities. This field has evolved from early electric signal-based methods to contemporary approaches utilizing machine and machine learning techniques. In this project, the primary objective is to implement sign language recognition (SLR) based on key point detection. The model is trained using a variety of machine learning algorithms, such as random forest, support vector machine, and k-nearest neighbor. The optimal model is determined through testing and evaluation metrics such as f1 score, precision, and recall. Leveraging Media Pipe for hand tracking and key point detection enhances the project's capabilities. The video is captured through a webcam using Open CV, and the model predicts gestures, allowing the robot to respond accordingly based on the recognized sign language gestures. © 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

A New Multi-objective Optimization Algorithm Based on Decomposition with Dynamic Resource Allocation 8

A New Multi-objective Optimization Algorithm Based on Decomp...

引用

8th International Conference on Electrical, Mechanical and computer engineering, ICEMCE 2024

作者： Zhu, Huazheng Zhang, Jun Jiang, Aiping Liu, Yaping Nie, Zicheng Chongqing University of Science & Technology School of Computer Science and Engineering Chongqing China

ISBN: (纸本)9798331506230

The Dynamic Resource Allocation Multi-Objective Optimization Algorithm (MOEA/D-DRA) is a method for solving multi-objective optimization problems (MOPs). This algorithm enhances the performance of the original MOEA/D algorithm through a strategy of dynamically allocating computational resources. However, for some challenging problems, the utility function of MOEA/D-DRA may not effectively allocate resources. This paper proposes an improved MOEA/D-DRA algorithm, introducing a new utility function to more accurately reflect the difficulty of subproblems in the current population, named MOEA/D-DRA-NEF. Experimental comparisons with ten other state-of-the-art MOEA algorithms demonstrate that MOEA/D-DRA-NEF outperforms the other algorithms in terms of convergence and diversity. © 2024 IEEE.

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

computer Vision Techniques for Object Detection

Computer Vision Techniques for Object Detection

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Hemalatha, Raavi Amulya, Godi Lalitha, Ch.S.N. Sai Vishnu Institute of Technology Department of Computer Science & Engineering Bhimavaram534202 India

ISBN: (纸本)9798331543617

The objective of this work is to investigate the applications of computer vision techniques in the field of object identification, as well as the developments that have occurred in these techniques. The primary objective of this research is to investigate the most recent methods that have been developed to support the ability of machines to immediately detect and classify objects that are contained within images and video aqueducts. The utilization of deep literacy models and convolutional neural networks (CNNs) is utilizedto investigate these techniques. The inquiry focuses on infrastructures and algorithms, such as YOLO (which was originally known as You Only Look), SSD (which stands for Single Shot MultiBox Sensor), and Faster R- CNN, with an emphasis on their performance, delicacy, and computational efficiency. YOLO was once known as You Only Look. In addition to this, the research investigates the challenges that are experienced during the process of object detection. One of these challenges is dealing with occlusions, another is recognizing small objects, and yet another is coping with changing lighting conditions. To provide insight into the implicit advancements and new directions related to the enhancement of item-locating skills, the objective of this research is to provide information. Exhaustive testing and analysis are how this is accomplished. The findings of the investigation indicate that the contribution. © 2024 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

A Predictive Model for Marine Debris Prediction (A Comparative Case Study) 2

A Predictive Model for Marine Debris Prediction (A Comparati...

引用

2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Venkatesh, T. Jenila Livingston, L.M. Rajkumar, S. School of Computer Science and Engineering Vellore Institute of Technology-chennai Chennai India

ISBN: (纸本)9798350328202

Efficient removal of marine debris is imperative for safeguarding public health and the environment, particularly marine ecosystems. This research addresses the limitations of conventional methods, such as image detection and satellite data, which often exhibit an accuracy below 85% in estimating waste levels in specific geographic areas. Our primary objective is to enhance predictive accuracy by leveraging machine learning and deep learning algorithms that capitalize on historical waste generation patterns. Through an exhaustive exploration of various algorithms documented in research papers, we aim to identify the most suitable model, whether multimodal or hybrid, to improve accuracy. Performance assessment will be conducted using key metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This comprehensive approach holds the potential for significant advancements in marine debris management and environmental protection. © 2024 IEEE.

关键词： Environmental protection

来源：评论

学校读者我要写书评

暂无评论

Binary Classification of News Articles using Deep Learning

Binary Classification of News Articles using Deep Learning

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Sukhramani, Kritika Kumre, Harshika Rasool, Akhtar Jadav, Abhishek Computer Science and Engineering Maulana Azad National Institue of Technology Bhopal India

ISBN: (纸本)9798350348460

The proliferation of fake news has emerged as a substantial concern in the contemporary digital era. The advent of social media and online news platforms has significantly facilitated the dissemination of misinformation and propaganda. Consequently, there is a pervasive scepticism surrounding conventional news outlets, influencing political outcomes. Thus, it becomes imperative to devise robust strategies for the detection of fake news. In recent times, deep learning has proven to be a potent tool, particularly in the domain of natural language *** scholarly paper introduces an inventive deep learning-based methodology for identifying fake news. The proposed model integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (LSTM) networks, and the transformer-based model BERT to adeptly extract features from textual content within news articles. Through training on a substantial dataset of categorized news articles and evaluation using various metrics, the experimental outcomes compellingly affirm the effectiveness of the proposed approach in accurately discerning fake news, achieving high precision and recall rates. This methodology holds considerable promise in counteracting the proliferation of misinformation on social media and online news platforms © 2024 IEEE.

关键词： convolutional neural networks deep learning fake news transformers

来源：评论

学校读者我要写书评

暂无评论

Efficient Priority Assignment Scheme Based on Heuristic Methodology in Low-Rate DoS Affected IoT Network 4

Efficient Priority Assignment Scheme Based on Heuristic Meth...

引用

4th International Conference on computer, Communication, Control and Information technology, C3IT 2024

作者： Das, Supantha Maity, Dilip Kumar Roy, Surajit Kumar Giri, Chandan Academy of Technology Department of Computer Science and Engineering West Bengal Hooghly India

ISBN: (纸本)9798350389128

Internet of Things (IoT) in modern civilization has created a new insight for uniting and controlling various devices over internet. Detection of Low Rate Denial of Service (LDoS) attack inside an IoT network has emerged a key challenging task in this techno equipped society. LDoS is concealed with other benign network traffic and responsible for transferring periodically small flow of request packets to coordinator node or centralized controller inside an IoT environment. LDoS leads to gradual degradation of network performance and its detection is an arduous process. Priority assignment among multiple IoT nodes keeping the occurrence of low rate DoS attack into mind is also a key aspect for IoT level 5 and IoT level 6. This paper represents priority assessment scheme taking the existence of LDoS attack in IoT environment into account. Simulation results reveal the superior performance of proposed scheme over the state-of-art. © 2024 IEEE.

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

BanFish: A Dataset for Classifying Common Bangladeshi Fish Species 6

BanFish: A Dataset for Classifying Common Bangladeshi Fish S...

引用

6th International Conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Ataullha, Md Jisun, Soumik Paul Jahan, Ishrat Rahman, Mushfiqur Shahjalal University of Science and Technology Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350385779

As a riverine country like Bangladesh, where fish play an indispensable role in both cultural practices and dietary habits, they serve as a primary source of protein in rural households. However, the disheartening reality of the gradual disappearance of numerous traditional indigenous fish species poses an imminent threat to their survival. Compounded by the younger generation's waning familiarity with these culturally significant fish, urgent measures are imperative. Addressing this pressing concern, we propose the implementation of an automatic fish classification system, not just as a timely necessity but as a proactive initiative. Introducing BanFish, the largest fish classification dataset designed for commonly available Bangladeshi fish. Comprising 2,506 images spanning 18 distinct fish species, it aims to fill a critical void in preserving the diversity of indigenous fish. To assess the dataset's quality, we employ a Vision Transformer with a transfer learning model, achieving an impressive overall accuracy of 83%. Beyond contributing a valuable resource for fish classification, our work marks a significant step toward preserving the rich tapestry of traditional fish species in Bangladesh, envisioning a mobile application to complement our system and bridge generational knowledge gaps. © 2024 IEEE.

关键词： Fish

来源：评论

学校读者我要写书评

暂无评论

Radar style transfer for metric robot localisation on lidar maps

引用

CAAI Transactions on Intelligence technology 2023年第1期8卷 139-148页

作者： Huan Yin Yue Wang Jun Wu Rong Xiong College of Control Science and Engineering Zhejiang UniversityHangzhouChina Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologyKowloonHong KongChina

Lidar and visual data are affected heavily in adverse weather conditions due to sensing mechanisms,which bring potential safety hazards for vehicle *** sensing is desirable to build a more robust navigation *** this paper,a cross-modality radar localisation on prior lidar maps is ***,the proposed workflow consists of two parts:first,bird's-eye-view radar images are transferred to fake lidar images by training a generative adversarial network *** with online radar scans,a Monte Carlo localisation framework is built to track the robot pose on lidar *** whole online localisation system only needs a rotating radar sensor and a pre-built global lidar *** the experimental section,the authors conduct an ablation study on image settings and test the proposed system on Oxford Radar Robot Car *** promising results show that the proposed localisation system could track the robot pose successfully,thus demonstrating the feasibility of radar style transfer for metric robot localisation on lidar maps.

关键词： lidar local maps

来源：评论

学校读者我要写书评

暂无评论

Early Detection of Tea Leaf Pathogen using Transfer Learning Techniques 1

Early Detection of Tea Leaf Pathogen using Transfer Learning...

引用

1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Prabha, P Anantha Shaheen Nafiha, S. Thevadharsiny, N. Swastica, K.G. Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350367720

Crop diseases pose a substantial economic threat to societies, impacting plant functionality, growth, and overall agricultural activities. The widespread consumption of tea and its status as a perennial crop face numerous illnesses that threaten its yield. Early detection is crucial for effective disease management and optimal tea production. For maximum tea production and efficient disease treatment, early detection is essential. Visual inspection is a common component of traditional procedures for detecting tea sickness, although it can be laborious and prone to human mistakes. Since many of the prior systems were not very accurate, deep learning models are employed to create the best model possible for the classification of diseases of tea leaves. The suggested method for the categorization of tea leaf disease consists of three hybrid models: VGG16 with Long Short-Term Memory (LSTM), Inceptionv3 with LSTM, and NASNetMobile with LSTM. After training on an extensive dataset comprising healthy and diseased tea leaf images, the effectiveness of three hybrid models is rigorously evaluated using various performance metrics. Subsequently, the best-performing model is identified for the classification of tea leaf diseases. This paper contributes to the advancement of intelligent tea agricultural methods by showcasing the potential of Deep Learning for early disease detection, paving the way for improved disease management and enhanced tea production. © 2024 IEEE.

关键词： Plant diseases

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：