检索结果-内蒙古大学图书馆

Predictive Data Analysis to Support Decision-Making Based on Long-Term Impacts of Disasters 1

5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Mishra, Shailendra Kumar Rahamatkar, Surendra Amity School of Engineering and Technology Department of Computer Science and Engineering Amity University Chhattisgarh Raipur India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Research into disaster relief is a field with a lot of potential, and predictions made about its future are based on historical data. It is a difficult task to analyze the long-term consequences of a disaster. Governments, NGOs, and other organizations struggle to make sense of disaster data. It is hard to assess the impact of different factors, such as education, employment, agriculture, and the social sector, on people living in affected areas. This research paper represents a framework to evaluate the long-term effects of disasters, by means of machine learning algorithms. The algorithms mentioned in this paper are Random Forest, Decision Tree, Gradient-boosted Decision Tree (GBDT), and XGBoost. It seeks to analyze historical disaster data, identify impacts, and predict the effects of similar disasters in the future. The framework also suggests preemptive measures and the impact on those involved. Finally, the proposed framework will be able to make long-term analyses and recommendations, using data from past events. © 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

Cloud-Based Object Detection Model Using Amazon Rekognition 1

引用

International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Singh, Darshita Arora, Deepak Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India

ISBN: (数字)9789819998111

ISBN: (纸本)9789819998104

Object identification is a well-known research subject in the field of computer vision, with various applications like surveillance, autonomous driving, and robotics. The integration of machine learning with cloud computing has enabled organizations to automate many procedures and tasks, cut costs, and boost efficiency. With the help of a wide range of machine learning (ML) services offered by cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), organizations may take advantage of ML’s potential without the need for specialized equipment or costly staff. A cloud-based ML service called Amazon Rekognition offered by Amazon Web Services is a powerful tool for object identification. Through this paper, the authors offer a study on the application of Amazon Rekognition for object detection and recognition. The idea is to detect objects in the provided images using machine learning and deep learning algorithms provided by Amazon Rekognition. The effectiveness of Amazon Rekognition in recognizing objects in images is precisely examined by the authors, who compare the discovered objects with state-of-the-art object detection algorithms and then provide the result with a corresponding confidence percentage. Experimental results show that Amazon Rekognition handles object detection tasks well, achieving a good balance between accuracy and speed. It is an effective tool for object detection with high average precision and recall values for many object categories. However, accuracy may vary depending on the complexity of the objects in the image, the lighting conditions, and other factors. Amazon Rekognition is a managed service that makes use of encryption, access control, compliance, monitoring, and logging. While the infrastructure and security are handled by AWS, it’s crucial to incorporate security best practices within the application for maximum security. It is important for developers to carefully evaluate the perf

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Crafting a Machine Learning Model for Discerning Synthetic and Authentic Images 2

Crafting a Machine Learning Model for Discerning Synthetic a...

引用

2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Shah, Misha Jaiswal, Gautam Sharma, Dolly Amity School of Engineering and Technology Amity University Noida India Department of Computer Science and Engineering Amity University Noida India

ISBN: (纸本)9798331541217

This paper shows the development of a Convolutional Neural Network (CNN) model with an attention mechanism and data augmentation for discerning synthetic and authentic images. Given the increase in AI-generated content there is a need for robust methods to identify an image's authenticity. The model employs ensemble learning by using ResNet50 and VGG16 architectures which are combined with a custom CNN. The project has detailed explanations of CNNs, data augmentation, TensorFlow, CUDA, ensemble models and the mathematical concepts underlying these techniques. With a dataset of 2.7 million images, the model aims for high accuracy in classification tasks. © 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

Research on Enhanced Multi-head Self-Attention Social Recommendation Algorithm Based on Graph Neural Network

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2024年第7期51卷 754-764页

作者： Teng, Yue Yang, Kai Yue Teng is a postgraduate student at the School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In today’s society, communication among people has become more frequent and extensive due to the rapid development of science, technology, and the Internet. This vast communication occurs in real life and virtual online worlds. However, with the overwhelming amount of information available, people often struggle with finding and accessing the information they need, leading to frustration. To address this challenge, we conducted a study to extract valuable information and insights from users’ social relationships. While existing models can help us understand and analyze users’ social relationships to some extent, there are still significant challenges in extracting valuable insights. Therefore, we conducted in-depth research to improve the GraphRec model and created a new social recommendation model called the CWYGNN model. The CWYGNN model is unique because it can identify deeper connections in users’ social interactions through in-depth learning of the social sequences between users. This is important for understanding users’ behavioral patterns, predicting their needs, and providing personalized services. To validate the effectiveness of the CWYGNN model, we conducted experiments and comparisons on several public datasets. The experimental results show that the CWYGNN model outperforms similar approaches in processing user social relationships and extracting valuable insights. This result provides a new way of thinking and methodology to help us better understand and serve users and ultimately enhance their overall experience. © (2024) International Association of Engineers. All rights reserved.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Recognition of Valid QR Codes with Machine Learning 13

Recognition of Valid QR Codes with Machine Learning

引用

13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Minocha, Arshia Goyal, Aman Gandhi, Rashmi Amity School of Engineering and Technology Amity University Auup Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350305463

The widespread use of Quick Response (QR) codes has made QR codes an attractive target for cyberattacks, posing a security and privacy concern. Quick Response (QR) codes have revolutionized marketing strategies by providing a fast and efficient way to connect offline and online platforms. However, the security issues arise as the usage of QR codes. This paper introduces an automatic detector that identifies QR code images and lets the user know whether it is malicious or benign. To achieve this, a dataset of 10,000 original and malicious QR code images (both) is taken. Convolutional Neural Network (CNN) is employed to classify original and malicious codes. Additionally, histogram density analysis is incorporated to enhance feature identification, leading to improved classification accuracy. This paper implemented ResNet50, MobilenetV3 and InceptionV3 CNN models for evaluating the abilities to predict the malicious codes. The InceptionV3 model emerges as the most promising, attaining an accuracy of 98.13%, offering an efficient and accurate solution for document forensics and counterfeit detection. © 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

Predictive Analytics in Sports: Using Machine Learning to Forecast Outcomes and Medal Tally Trends at the 2024 Summer Olympics 4

Predictive Analytics in Sports: Using Machine Learning to Fo...

引用

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

作者： Moolchandani, Jhankar Chole, Vikrant Sahu, Santosh Kumar, Rahul Shukla, Anshita Kumar, Ashok Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

This paper focuses on using machine learning approaches in predicting the medal projections and analyzing the medal distribution pattern in the 2024 Summer Olympics. Due to the availability of a large number of variables both at the individual athlete level and the country level as well as event specific measures, various statistical models are trained to predict the number of medals and unearthing traits that are associated with success in Olympics. The evaluation of the predictions of the models is done using linear regression and random forest along with support vector classifier, as well as artificial neural network classifiers. Using the research findings, it becomes evident that predictive analytics has the capacity of offering a better understanding of the performance of sport teams and formulation of strategies for future competition events. As is observed from the above findings the random forest model sampled the highest accuracy of 99. 034 percent in the general set out of the four models;thus, it is recommended ideal for complicated set data investigations Based on the results of the experiments, it is clearly observed that the random forest has the highest accuracy of 99. 034. © 2024 IEEE.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Interpretation of Sign Language Using Machine Learning 2

Interpretation of Sign Language Using Machine Learning

引用

2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Srivastava, Palak Hazela, Bramah Singh, Shikha Asthana, Pallavi Pandey, Deependra Singh, Kamlesh Kumar Singh, Vineet Amity University Amity School of Engineering & Technology Department of Computer Science Engineering Uttar Pradesh India Amity University Amity School of Engineering & Technology Department of Electronics & Communication Engineering Uttar Pradesh India

ISBN: (纸本)9798350364699

Sign language detection using machine learning has emerged as a crucial area of research aimed at bridging communication barriers between individuals with hearing impairments and the broader community. This paper explores the application of machine learning techniques for the automatic recognition and interpretation of sign language gestures. A systematic review of existing methodologies, including traditional machine learning approaches and recent advancements in deep learning architectures, reveals key challenges such as variability in signing styles, regional dialects, and real-time processing requirements. To address these challenges, we propose a novel approach that leverages convolutional neural networks (CNNs) and long short-term memory (LSTM) models, which have demonstrated significant improvements in accuracy and generalization across diverse datasets. Our experimental results show the efficacy of these models on benchmark sign language datasets, achieving high levels of accuracy and robustness in real-world conditions. Furthermore, this study discusses the potential societal impacts of sign language detection systems in education, healthcare, and everyday communication, emphasizing the role of technological innovation in fostering inclusivity. By advancing machine learning methodologies, we contribute to the development of accessible communication solutions for individuals with hearing impairments. © 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

Document retrieval using clustering-based Aquila hash-Q optimization with query expansion based on pseudo relevance feedback

引用

International Journal of computers and Applications 2024年第7期46卷 496-507页

作者： Inje, Bhushan Nagwanshi, Kapil Kumar Rambola, Radhakrishna Amity School of Engineering and Technology Amity University Jaipur India NMIMS University MPSTME Shirpur Shirpur Dist Dhule India Department of Computer Science and Engineering SoS Engineering and Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495009 India

A document retrieval system helps users to retrieve the relevant documents corresponding to their query quickly and easily. In the real world, document retrieval is a difficult task due to high volumes of data, unstructured data, and different formats of data. Even though many research techniques are introduced, major problems like vocabulary mismatch and non-linear matching still need to be solved. In this work, the Aquila hash-q optimizer is the proposed matching technique with the clustering technique to retrieve the document in a time-efficient manner for the user query without collision. First, preprocessing is done by eliminating the stop words from the document, stemming, and grouping documents in a cluster into a single document using Hierarchical Density-based Sampling Spatial Cluster of Applications with Noise (HDBSSCAN) clustering. This clustering algorithm is powerful, robust to noise, and scalable and identifies clusters of documents that are related to each other. Additionally, the sampling technique used in this clustering algorithm increases the clustering speed by reducing the size of the document which improves the performance of document retrieval systems. Secondly, the queries are searched using the Aquila hash-q optimizer matching technique by which the relevant documents are retrieved. The Aquila hash-q optimization works by pre-computing a hash table of the terms in a document collection and then using this hash table to quickly identify the relevant documents from the given query. This can significantly improve the speed of document retrieval, especially for large document collections. Aquila hash-q optimization can improve the accuracy, efficiency, and scalability of document retrieval systems. The effectiveness of the Hierarchical Density-Based Clustering Aquila Optimization approach is determined by various analyses through NPL, LISA, and CACM data in terms of precision @ 5 (0.497), precision @ 10 (0.425), Mean Average Precision (MAP) (0.4

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Analytical Approach Towards Cybersecurity Through AI-Enabled Threat Intelligence 11

Analytical Approach Towards Cybersecurity Through AI-Enabled...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Singh, Anurag Kanishka Dubey, Sanjay Kumar Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

A growing variety of cyber hazards are appearing as the world becomes more and more digital. The sophistication and rate of innovation of emerging threats puts organizations of all sizes at danger, and traditional cybersecurity solutions frequently fall short of keeping up. Threat intelligence powered by artificial intelligence, or AI-TI, offers a fresh and cutting-edge method for tackling cybersecurity issues. To find patterns and anomalies that could point to a cyber-attack, AI-TI solutions can examine massive amounts of data from a variety of sources, including as internal networks, security logs, and open threat intelligence feeds. Then, with the use of this information, security teams can be made aware of prospective dangers and assist in the creation of defense measures. Compared to conventional cybersecurity solutions, AI-TI systems have a number of benefits: Automation: AI-TI systems have the potential to automate a number of threat detection and response-related duties, freeing up security staff to concentrate on more strategic projects. Continuous improvement: AI-TI systems have the ability to continuously learn and get better with time, which enables them to stay on top of the most recent threats. Advanced threat detection: AI-TI systems can be used to identify and react to threats that are too sophisticated or subtle for traditional approaches to identify. © 2024 IEEE.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Advanced Predictive Modelling for Bitcoin and Ethereum Price Dynamics 7

Advanced Predictive Modelling for Bitcoin and Ethereum Price...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Waseem, Mohd Singh, Shailendra Narayan Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350350067

Over the last decade, Bitcoin and Ethereum have become cryptocurrencies that have attracted the attention of the financial world with their potential for business transactions and the use of new blockchain technology. This study is dedicated to predicting Bitcoin and Ethereum price trends from 2014 to 2024, covering the main period of growth and change in the cryptocurrency market. This research aims to develop a good forecasting model through a comprehensive analysis of historical price data, market trends, economic progress and macroeconomy. It uses techniques such as machine learning, time series analysis, and sentiment analysis to try to predict future price movements of Bitcoin more accurately and Ethereum. By revealing the fundamental principles influencing cryptocurrency prices, this research leads to a deeper understanding of the dynamics driving digital assets and their impact on the financial sector. © 2024 IEEE.

关键词： Ethereum

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：