检索结果-内蒙古大学图书馆

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kulkarni, Abhishek V. Ravindra, Addanki Ranga Sangeethi, Maheswarla Sai Sreevidya, B. Rajesh, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Online food ordering management system with recommendation is a full-fledged software model based on the Database Management, SQL and *** to minimize the manual work for the workers of the hotel. This helps to even customers to order their food easily in this evolving software world. This model has many features like Ordering food, Menu and Category updating by the Admin, Customers order history, Total payment done in a day, selling report of the hotel from a date to date, Login and mainly the recommendation for the customer. The software and hardware requirements for this software model are easily available and easy to work with. This helps us to keep track of all the data for long years. As the KNN classifier recommends the user. This software is completely based on the administration level as the DB admin can manage. The total workflow of the project and the work of DB admin explained in the methodology section. © 2024 IEEE.

关键词： DBMS Python Recommendationsystem SQL

来源：评论

学校读者我要写书评

暂无评论

Rumor Source Detection in Subgraphs: An ML Approach 5

Rumor Source Detection in Subgraphs: An ML Approach

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Deepthi, L.R. Nair, Lekshmi S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Social networking sites offer a vast platform for sharing various types of content, such as information, photos, videos, and audio clips. However, the credibility of the information shared on these sites is a significant concern since there is no prior verification process as the information spreads across the networks. This increases the chances of disseminating false or misleading information, often called rumors. The spread of rumors in today's information environment creates severe problems for the credibility of information and public confidence. The quick spread of rumors, frequently conveyed via social media and other online platforms, can affect public opinion and potentially have real-world repercussions. Maintaining the integrity of information ecosystems and averting the potentially damaging impacts of disinformation require detecting and mitigating rumors. In today's social media and online communication age, it is crucial to identify rumors to prevent their spread and mitigate their potential negative impact. In this study, our primary goal is to discover and identify individual sources of rumors. This work addresses two aspects of rumor management: firstly, identifying the difference between rumors and facts, and secondly, tracking the source of the rumor. In conjunction with the attributes, state-of-the-art methods such as SVM, AdaBoost, Random Forest, Logistic Regression, and Graph Convolutional Networks (GCN) are used to accomplish these goals. This approach contributes to the broader goal of combating disinformation and preserving the Accuracy of information. © 2024 The Authors. Published by Elsevier B.V.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

Ransome Ware Detection Using Machine Learning and Deep Learning Models 1

引用

12th International Conference on Recent Trends in computing, ICRTC 2024

作者： Vivek, Vinnakota Sai Lokesh, Vadlamudi Kaushik, Tata Kumaran, U. Umah, B. Devarajan, Gopal Ganesh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Uttar Pradesh Ghaziabad India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

This research addresses the critical challenge of ransomware detection through the use of deep learning and machine learning methods. Because ransomware is a serious threat to cybersecurity, it is imperative that advanced techniques be used for prompt detection and mitigation. Using a large dataset, the study concentrates on pertinent features necessary for efficient machine learning model training. The suggested model shows promising results in identifying and averting ransomware attacks by utilizing cutting-edge algorithms. The model’s effectiveness is confirmed by the evaluation metrics (F1 score, recall, accuracy, and precision). By providing a proactive defense against emerging ransomware threats, the findings enhance cybersecurity measures. The study not only provides a strong methodology for ransomware detection but also encourages more research in the area with the goal of continuously bolstering cyber defenses against evolving cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Learning in sinusoidal spaces with physics-informed neural networks

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第3期5卷 985-1000页

作者： Wong, Jian Cheng Ooi, Chin Chun Gupta, Abhishek Ong, Yew Soon Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research Singapore138632 Singapore Agency for Science Technology and Research Singapore138632 Singapore Data Science and Artificial Intelligence Research Centre School of Computer Science and Engineering Nanyang Technological University Singapore138632 Singapore

A physics-informed neural network (PINN) uses physics-Augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensure its output is consistent with fundamental physics laws. However, it turns out to be difficult to train an accurate PINN model for many problems in practice. In this article, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs. By analyzing behavior at model initialization, we first show that a PINN of increasing expressiveness induces an initial bias around flat output functions. Notably, this initial solution can be very close to satisfying many physics PDEs, i.e., falling into a localminimum of the PINN loss that onlyminimizes PDE residuals, while still being far from the true solution that jointly minimizes PDE residuals and the initial and/or boundary conditions. It is difficult for gradient descent optimization to escape from such a local minimum trap, often causing the training to stall. We then prove that the sinusoidalmapping of inputs-in an architecture we label as sf-PINN-is effective to increase input gradient variability, thus avoiding being trapped in such deceptive local minimum. The level of variability can be effectively modulated to match high-frequency patterns in the problem at hand. A key facet of this article is the comprehensive empirical study that demonstrates the efficacy of learning in sinusoidal spaces with PINNs for a wide range of forward and inversemodeling problems spanning multiple physics domains. Impact Statement-Falling under the emerging field of physicsinformed machine learning, PINN models have tremendous potential as a unifying AI framework for assimilating physics theory and measurement data. However, they remain infeasible for broad science and engineering applications due to computational cost and training challenges, especially for more complex problems. Instead of focusing on empirical demonstration of appli

关键词： Gradient methods

来源：评论

学校读者我要写书评

暂无评论

A Semi-supervised Protein Complex Identification Algorithm Based on Sparseness Constraint

A Semi-supervised Protein Complex Identification Algorithm B...

引用

2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

YOLOv9 in Action: A Futuristic Approach to Real-Time License Plate Recognition and Analysis

YOLOv9 in Action: A Futuristic Approach to Real-Time License...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Kumar, M Ranjith Sendur, G Jeevan Kumar, S Praveen Annamalai, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379525

The automated recognition and identification of license plates is an essential element of intelligent transportation systems that enable effective traffic management, security measures, and the development of efficient e-payment algorithms in toll booths and parking facilities. Even though license plate recognition (LPR) technology is fairly advanced, significant efforts are being made to enhance its accuracy and resilience The growing adoption of deep learning technologies across multiple industry domains, particularly computer vision, presents a novel idea implemented in this paper. To develop an approach to improve detection accuracy using YOLOv9 and transfer learning. Through combining the power of YOLOv9 with transfer learning, it is feasible to tackle the traditional LPR system's inherent flaws. Character recognition, picture pre-processing, license plate extraction, and character segmentation are the four main parts of the proposed system. Each step involves unique methodologies, primarily focusing on character recognition methodologies using YOLOv9 which has not been adequately tackled in existing research. By thorough testing and analysis, this study seeks to further the current developments in LPR systems, ensuring discernibly better-established intelligent transportation solutions. We have achieved a mAP50 Score of 99.5% and a mAP50-95 score of 71 % © 2024 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

On - Device Translation of Dynamically Typed and Interpreted Languages 15

On - Device Translation of Dynamically Typed and Interpreted...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Pranav, H. Sriharshini, Muppavarapu Tarun, Rachuri Belwal, Meena Amrita Viswa Vidyapeetham Amrita school of computing Dept of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350370249

On Device - translation of expression form languages with dynamic nature and using interpreted languages like MATLAB and Python requires a precise and automated form of processing the error-free conversion. With the help of the automation, the code in MATLAB is well-defined to Python so that translation is precise and the originality as well as functionality is ensured. This paper describes a robust and redundant methodology for converting maintaining the functionality and originality of the MATLAB code helping in smooth transition of code from MATLAB to python. The procedure being entirely local also ensures to protect privacy of the code. © 2024 IEEE.

关键词： Code Generator Dynamical Type Interpreting language MATLAB On - device Code Translation Parser Python Token Tokenizer

来源：评论

学校读者我要写书评

暂无评论

Federated Multi-Agent Reinforcement Learning for Resource Allocation in NR-V2X Mode 2

引用

IEEE Internet of Things Journal 2025年第13期12卷 23402-23417页

作者： Saad, Malik Muhammad Tariq, Muhammad Ashar Ajmal, Mahnoor Kim, Dongkyun Srivastava, Gautam Kyungpook National University School of Computer Science and Engineering Dongseo University Republic of Korea and the School of Computer Science Busan Daegu Korea Republic of Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India

The Third Generation Partnership Project (3GPP) introduced Cellular Vehicle-to-Everything (C-V2X) for vehicular communications. In the standard, C-V2X Mode 4 is defined for the distributed resource selection. Subsequently, in 3GPP Release 16, NR-V2X is introduced with Mode 1 and Mode 2 for vehicular communications. Likewise C-V2X Mode 4, NR-V2X Mode 2 is used for decentralized resource scheduling. The vehicles select the resources based on their local observations by utilizing the Semi-persistent Scheduling (SPS). Since, the vehicles select the resources based on the local observation, sensing nature of SPS is challenged by the hidden node problem that lead to resource conflict. To resolve the contention, 3GPP also introduced the Physical Sidelink Feedback Channel (PSFCH) to assist the distributive resource scheduling based on the receiver feedback. However, this incurred a signaling overhead. In this work, federated learning is exploited for distributive training via offline method and distributive multi-agent-based resource scheduling is performed following the principles of NR-V2X Mode 2. Distributed training favors the model accuracy by accommodating the varying affect of the environment due to the high mobile dynamics. Simulation is conducted by integrating SUMO in conjunction with 3GPP NR-V2X standard. Performance results demonstrate a substantial improvement compared to other deep learning methods, where centralized training and random resource selection procedures are employed. This research marks a significant stride towards efficient and conflict-resilient resource allocation in vehicular communications. © 2014 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Algorithmic Approaches, Practical Implementations and Future Research Directions in Machine Learning 1

Algorithmic Approaches, Practical Implementations and Future...

引用

1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Vaishali Sharma, Deepank Kumar Punia, Sanjeev Galgotias University School of Computing Science and Engineering Greater Noida India G L Bajaj Computer Science and Engineering Department Greater Noida India

ISBN: (纸本)9798350356816

Machine learning has become a disruptive force that is advancing technology and changing industries. With an emphasis on algorithmic techniques, real-world applications, and important future research avenues, this study examines the rapidly changing field of machine learning. It explores the fundamentals of supervised, unsupervised, and semi-supervised learning algorithms and highlights how they are used in a variety of fields, including autonomous systems, healthcare, and finance. The report highlights the potential of several important future research directions, such as explainable AI, robust and privacy-preserving learning, quantum machine learning, and multimodal AI, to overcome present constraints and open up hitherto unheard-of possibilities. These multidisciplinary research avenues emphasize the value of interdisciplinary cooperation in addressing difficult problems and guaranteeing the creation of morally sound and significant AI systems. In order to help academics and practitioners who want to progress the subject, this review attempts to give a thorough grasp of the present situation and potential future direction of machine learning. © 2024 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Accent-Enhanced Automatic Speech Recognition for Multilingual Classrooms 15

Accent-Enhanced Automatic Speech Recognition for Multilingua...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Jagadeeshvaran, M. Sarah Michelle, H. Shruti Bala, S. Srinivasa Vinayak, M. Deepika, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

In an increasingly interconnected world, universities face the challenge of hosting diverse students from different backgrounds. The biggest obstacle to effective communication and learning in multicultural classrooms is teacher and student diversity. To overcome this challenge, we propose the development of noise-enhanced automatic speech recognition (ASR), specifically for multilingual classrooms. The system is designed to provide instant feedback on lessons while preserving the subtleties of speech, thus improving understanding and creating a learning environment. Using the advanced capabilities of the Transformer model together with the convolutional neural network (CNN) highlight classifier, the proposed system provides powerful and accurate transcription capabilities. This article provides an overview of the design, implementation, and evaluation of voice-enhanced ASR, demonstrating its potential to transform communication and learning in a variety of educational settings. © 2024 IEEE.

关键词： Accent Enhancement Accent Nullification Automatic Speech Recognition Inclusive Learning Multilingual Classrooms Speech Transcription

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：