检索结果-内蒙古大学图书馆

2023 IEEE International Conference on Electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Yadav, Abhay Raj Shekhar, Sushant Vidyarthi, Anurag Prakash, Rishi Gowri, R. Propagation Research Lab Department of Computer Science Engineering Dehradun India Propagation Research Lab Department of Electronics & Communication Engineering Dehradun India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350305111

The tourism and hospitality industries rely heavily on the hotel industry, making it crucial for hotels to optimize their business strategies. Predicting hotel bookings is one strategy that can enable hotel management to make informed decisions about pricing, marketing, and resource allocation. In this paper, we explore the use of machine learning algorithms to predict hotel bookings, focusing on two types of hotels, leisure and business hotels. We use a dataset from Kaggle containing information on hotel bookings, including 32 features such as hotel type, arrival date, departure date, and booking date. We pre-process the data by converting categorical variables into numerical variables, imputing missing values, and performing feature scaling. We evaluate various machine learning algorithms on the dataset, including Gradient Boosting Classifier, Random Forest Classifier, and Logistic Regression Classifier, using accuracy as the evaluation metric. Our results show that these three algorithms perform well on the given dataset, with the Gradient Boosting Classifier achieving the highest accuracy of 86.5%, followed by Random Forest Classifier with an accuracy of 85.2%, and Logistic Regression Classifier with an accuracy of 80.7%. Our findings demonstrate the potential of machine learning techniques in predicting hotel bookings and can be useful to hotel management in optimizing their business strategies. © 2023 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

A Location Validation Technique to Mitigate GPS Spoofing Attacks in IEEE 802.11p Based Fleet Operator's Network of Electric Vehicles 27

A Location Validation Technique to Mitigate GPS Spoofing Att...

引用

27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Samaddar, Ankita Easwaran, Arvind Vanderbilt University Computer Science Department United States Continental-NTU Corporate Lab Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798331505929

Most vehicular applications in electric vehicles use IEEE 802.11p protocol for vehicular communications. Vehicle rebalancing application is one such application that has been used by many car rental service providers to overcome the disparity between vehicle demand and vehicle supply at different charging stations. Vehicle rebalancing application uses the GPS location data of the vehicles periodically to determine the vehicle(s) to be moved to a different charging station for rebalancing. However, a malicious attacker residing in the network can spoof the GPS location data packets of the target vehicle(s) resulting in misinterpretation of the location of the vehicle(s). This can result in wrong rebalancing decision leading to unmet demands of the customers and under utilization of the system. To detect and prevent this attack, we propose a location tracking technique that can validate the current location of a vehicle based on its previous location and roadmaps. We used OpenStreetMap and SUMO simulator to generate the roadmap data from the roadmaps of Singapore. Extensive experiments on the generated datasets show the efficacy of our proposed technique. © 2024 IEEE.

关键词： Charging stations

来源：评论

学校读者我要写书评

暂无评论

Taskgraph Framework: A Competitive Alternative to the OpenMP Thread Model

Taskgraph Framework: A Competitive Alternative to the OpenMP...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Snehal Chavan Prathamesh Nile Sunil Kumar Biswajit Bhowmik Dept. of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Maharshi Patanjali CPS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

OpenMP is the predominant standard for shared memory systems in high-performance computing (HPC), offering a tasking paradigm for parallelism. However, existing OpenMP implementations, like GCC and LLVM, face computational limitations that hinder performance, especially for large-scale tasks. This paper presents the Taskgraph framework, a novel solution that overcomes the limitations of traditional task dependency graphs (TDGs). Unlike conventional TDGs, which require costly reconstruction for dynamic program structures, the Taskgraph framework uses a taskgraph clause with a list of variables, enabling real-time adaptation without complete reconstruction. This approach significantly reduces overhead, making the Task-graph model highly efficient for tasks with minimal dependencies, offering a competitive alternative to the OpenMP thread model, and enhancing efficiency and adaptability in dynamic HPC environments.

关键词： Adaptation models Runtime Parallel programming High performance computing Memory management Parallel processing Real-time systems Intelligent systems Standards Faces

来源：评论

学校读者我要写书评

暂无评论

Detecting COVID-19 Infection Using Customized Convolutional Neural Network

Detecting COVID-19 Infection Using Customized Convolutional ...

引用

Control Instrumentation System Conference (CISCON)

作者： Sahil Kumar Bablu Kisku Sai Harsha Vardhan K Sunil Kumar Biswajit Bhowmik BRICS Laboratory Department of Computer Science and Engineering Maharshi Patanjali CPS Lab National Institute of Technology Karnataka Mangalore India

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

The COVID-19 pandemic has affected 775 million people globally, with an estimated death toll of 7 million. Detection methods like reverse transcription polymer chain reaction (RT-PCR) face multiple challenges, including false positive cases, time-consuming, and high cost. A rapid, precise, affordable screening alternative is essential to expedite COVID-19 detection. Various efforts have focused on expediting COVID-19 detection due to the high costs and logistical challenges associated with traditional methods. This paper proposes a customized deep-learning framework architecture for automatically identifying COVID-19 infection in chest X-ray (CXR) images. Multiple neural networks extract deep features from the CXR images, including popular models such as VGG19, DenseNet201, EfficientNet, MobileNetV2, and InceptionV3. The proposed model undergoes training and testing using the QaTa-COVID-19 dataset. The proposed model achieves classification accuracy of 97.06%, with precision, recall, and F1 score rates for COVID-19 cases recorded at 97.34%, 96.36%, and 97.01%, respectively, for the 4-class cases (COVID vs. Normal vs. Pediatric Bacterial Pneumonia vs. Pediatric Viral Pneumonia).

关键词： COVID-19 Training Analytical models Pneumonia Microorganisms Accuracy Costs Feature extraction Convolutional neural networks X-ray imaging

来源：评论

学校读者我要写书评

暂无评论

Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

引用

Frontiers of computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

来源：评论

学校读者我要写书评

暂无评论

Player Identification for Collectible Card Games with Dynamic Game States 36th

Player Identification for Collectible Card Games with Dynami...

引用

36th International Florida Artificial Intelligence Research Society Conference, FLAIRS-36 2023

作者： Fields, Logan Licato, John Lab Department of Computer Science and Engineering University of South Florida United States

Collectible card games are a fruitful test space for studying resource allocation and battle strategy, given that their structures promote reactionary combat styles and allow players to obtain variable amounts of combat power by expending fixed resources. However, their large action spaces also allow for flexibility in play styles, thus facilitating behavioral analysis at the individual level rather than the aggregate level. When presented with the same options and the same amount of resources, a player's selection of cards and their choice of moves gives insight into their unique play style and decision-making tendencies. As such, we use the virtual collectible card game Legends of Code and Magic to determine whether we can identify a player from their actions and, conversely, predict the future actions of a known player. Our main contributions to this task are the creation of a comprehensive dataset of Legends of Code and Magic game states and actions, as well as the first use of large transformer-based language models to address this problem. © 2023 by the authors. All rights reserved.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Player Identification and Next-Move Prediction for Collectible Card Games with Imperfect Information 19

Player Identification and Next-Move Prediction for Collectib...

引用

19th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2023

作者： Fields, Logan Licato, John Lab Department of Computer Science and Engineering University of South Florida United States

ISBN: (纸本)157735883X

Effectively identifying an individual and predicting their future actions is a material aspect of player analytics, with applications for player engagement and game security. Collectible card games are a fruitful test space for studying player identification, given that their large action spaces allow for flexibility in play styles, thereby facilitating behavioral analysis at the individual, rather than the aggregate, level. Further, once players are identified, modeling the differences between individuals may allow us to preemptively detect patterns that foretell future actions. As such, we use the virtual collectible card game "Legends of Code and Magic" to research both of these topics. Our main contributions to the task are the creation of a comprehensive dataset of Legends of Code and Magic game states and actions, extensive testing of the minimum information and computational methods necessary to identify an individual from their actions, and examination of the transferability of knowledge collected from a group to unknown individuals. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Statistical tests

来源：评论

学校读者我要写书评

暂无评论

SOCluster - Towards Answering Unanswered Questions on Stack Overflow via Answered Questions 23

SOCluster - Towards Answering Unanswered Questions on Stack ...

引用

16th Innovations in Software engineering Conference, ISEC 2023

作者： Kumar, Abhishek Ghadiyali, Deep Chimalakonda, Sridhar Venigalla, Akhila Sri Manasa Lab Department of Computer Science & Engineering Indian Institute of Technology Tirupati India

ISBN: (纸本)9798400700644

Stack Overflow (SO) platform has a huge dataset of questions and answers driven by interactions between users. But the count of unanswered questions is continuously rising, which is observed in various similar community Question & Answering platforms (Q&A) such as Yahoo, Quora and so on. To address this issue, these communities have explored clustering mechanisms to answer unanswered questions using other answered questions in the same cluster, which could also improve the response time for new questions. It is here, we propose SOCluster, an approach and a tool to cluster SO questions using a graph-based clustering approach. We selected four datasets of 10k, 20k, 30k & 40k SO questions without code-snippets or images involved, and performed clustering on them. We have done a preliminary evaluation of our tool by analyzing the resultant clusters using the commonly used metrics of Silhouette coefficient, Calinkski-Harabasz Index, & Davies-Bouldin Index. We performed clustering for 8 different threshold similarity values and analyzed the intriguing trends reflected by the output clusters through the three evaluation metrics. At 90% threshold similarity, it shows the best improvement for the three evaluation metrics on all four datasets. We further manually assessed the content in the clusters to confirm the similarity of elements in clusters. This revealed clusters to correspond to topics such as mouse over effect, speed optimisation, how to perform ĝsome' action in JavaScript, and so on. The source code and tool are available for download on Github at: https://***/rishalab/SOCluster, and the demo can be found here: https://***/Ewm-M_rg_x8. © 2023 ACM.

关键词： HTTP

来源：评论

学校读者我要写书评

暂无评论

Optimizing Feature Selection in Big Data: A Hybrid Spark and Fuzzy Approach

Optimizing Feature Selection in Big Data: A Hybrid Spark and...

引用

Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), International Conference on

作者： Aman Singh Hada Gyanaballav Samir Sahoo Chinnapareddy Krishna Vamsi Anusha Hegde Biswajit Bhowmik Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory National Institute of Technology Mangalore Karnataka Bharat

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

The exponential growth of big data presents both immense opportunities and significant challenges. While vast datasets hold the key to unlocking groundbreaking insights, efficiently extracting value requires sophisticated feature selection techniques. Traditional methods often struggle with the sheer volume and complexity of big data. This paper addresses this challenge by proposing a novel hybrid feature selection algorithm by leveraging Apache PySpark's distributed computing power. Combining a robust feature selection technique with a novel weighting scheme, our method outperforms existing hypercuboid and fuzzy Rough Set methods. The hybrid approach achieves superior accuracy of 72.1% with a reduced feature set, demonstrating its effectiveness in identifying salient features for big data analysis.

关键词： Accuracy Uncertainty Mechatronics Rough sets Big Data Feature extraction Hybrid power systems Complexity theory Sparks Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

An Efficient Deep Learning Approach to Detect Neurodegenerative Diseases Using Retinal Images

An Efficient Deep Learning Approach to Detect Neurodegenerat...

引用

2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Irfanuddin, Chowdhury Mohammad Shafin, Wasique Islam Ahmed, Koushik Khan, Md. Hasib Ashraful Alam, Md. Rahman, Rafeed Dipto, Shakib Mahmud Brac University Department of Computer Science and Engineering Dhaka Bangladesh Research Lab Dept. of Cse Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

Neurodegenerative disorders like Multiple Sclerosis, Parkinson's disease, and Alzheimer's disease typically get diagnosed through brain MRI, CT scans, genetic testing, and various laboratory screening tests that are often tedious, time-consuming, and beyond the reach of the financial capability of many people especially, those living in the developing world. Moreover, they often pose potential hazards for patients with certain pre-existing conditions. To remedy this, we propose an efficient deep-learning approach that enables the detection of such neurodegenerative diseases through retinal images rather than brain images. Our proposed system can proactively detect such disorders simply through retinal scans, which are fast and cost-effective compared to traditional diagnostic approaches, which require expensive and sophisticated equipment. We used a dataset containing retinal cross-sectional images of 21 Multiple Sclerosis patients and 14 healthy individuals for our research, and our model achieved 98.85% accuracy while classifying healthy and diseased individuals from retinal scans. © 2023 IEEE.

关键词： Neurodegenerative 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：