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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Prashant Khobragade Praveen Kumar Dhankar Manish Motghare Ashish Golghate Nitin Rakesh Hemantkumar Turkar Computer Science and Engineering G H Raisoni College of Engineering Nagpur India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept of Artificial Intelligence G H Raisoni College of Engineering and Management Nagpur India Dept of Computer Science and Engineering (Data Science) S. B. Jain Institute of Technology Management & Research Nagpur India Department of Data Science S.B.Jain Institute of Technology Management and Research Nagpur India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

The field of oncology is changing quickly as more advanced machine learning (ML) models are added to help find cancer earlier and improve treatment results. This paper looks at how machine learning could change oncology by focusing on how it can be used to find cancer early, predict how it will progress, and create personalized treatment plans. Early identification is very important for increasing the number of people who survive, but standard testing methods aren't always accurate or quick enough. Machine learning calculations can discover patterns and exceptions that conventional investigations might miss by utilizing enormous datasets from an assortment of sources, such as electronic wellbeing records, hereditary information, and imaging thinks about. We see at cutting-edge machine learning models, like deep learning neural systems, support vector machines, and gathering strategies, to see how well they work at finding diverse sorts of cancer prior. The paper discuss about some important examples of how these models have done much better than standard diagnostic tools, allowing for earlier treatments and better patient results. The study emphasize about how natural language processing (NLP) can be used to get useful information from unorganized clinical notes. This can help with diagnosis and give us a fuller picture of the patient's background and risk factors. The study also talks about how predictive models can be used to make treatment plans better. Machine learning models can help doctors adjust treatments to each patient's unique profile by looking at patient data and how they respond to treatment. This makes treatments more effective while reducing side effects.

关键词： Accuracy Machine learning Medical services Predictive models Oncology data models Natural language processing Vectors Standards Cancer

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TWO-STAGE PREDICT+OPTIMIZE FOR MIXED INTEGER LINEAR PROGRAMS WITH UNKNOWN PARAMETERS IN CONSTRAINTS

arXiv

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arXiv 2023年

作者： Hu, Xinyi Lee, Jasper C.H. Lee, Jimmy H.M. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Sciences Institute for Foundations of Data Science University of Wisconsin–Madison WI United States

Consider the setting of constrained optimization, with some parameters unknown at solving time and requiring prediction from relevant features. Predict+Optimize is a recent framework for end-to-end training supervised learning models for such predictions, incorporating information about the optimization problem in the training process in order to yield better predictions in terms of the quality of the predicted solution under the true parameters. Almost all prior works have focused on the special case where the unknowns appear only in the optimization objective and not the constraints. Hu et al. proposed the first adaptation of Predict+Optimize to handle unknowns appearing in constraints, but the framework has somewhat ad-hoc elements, and they provided a training algorithm only for covering and packing linear programs. In this work, we give a new simpler and more powerful framework called Two-Stage Predict+Optimize, which we believe should be the canonical framework for the Predict+Optimize setting. We also give a training algorithm usable for all mixed integer linear programs, vastly generalizing the applicability of the framework. Experimental results demonstrate the superior prediction performance of our training framework over all classical and state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

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Blockchain-Based Identity Management: Enhancing Privacy and Security in Digital Identity System

Blockchain-Based Identity Management: Enhancing Privacy and ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： S Saranya Monika K Manikandan Jangam Nagaraju S. Nagendiran B. T. Geetha Computer Science and Engineering Dr. N.G.P. Institute of Technology Coimbatore Mangalmay Institute of Management and Technology Greater Noida India School of Computer Science & Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore Tamilnadu India Department of Data Science MLR Institute of Technology Dundigal Hyderabad Department of Artificial Intelligence and Data Science R. M. K. Engineering College Department of ECE Saveetha School of Engineering (SIMATS) Saveetha University Tamil Nadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Privacy and security are major concerns when it comes to digital identities, particularly in centralized systems where data can be easily hacked or where people engage in identity fraud. These issues can be addressed using blockchain technology since it allows for the creation of a decentralized digital identity that is both secure and tamper-proof. This study employs Kaggle dataset to document the transactions and the devices involved to make the distinctions on how blockchain can boost privacy and security. Predominantly, various machine learning algorithms are used to analyze blockchain applications, and the results emphasize the fact blockchain systems greatly enhance transaction viability and diminish fraudulent actions. Therefore, the study argues that blockchain-based identity management systems offset the shortcomings of the centralized model by enhancing data ownership and combating fraud. Subsequent analysis expanded on the discussion of elaborating the scalability, interoperability, and application of algorithms to blockchains as well as how it can fundamentally change the way that certain industries, particularly identity management, would work with consumers at the center of reliable and secure blockchain solutions.

关键词： Machine learning algorithms Protocols Scalability Instruments Machine learning Blockchains Fraud Security Reliability Interoperability

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FullTransNet: Full Transformer with Local-Global Attention for Video Summarization

arXiv

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arXiv 2025年

作者： Lan, Libin Jiang, Lu Yu, Tianshu Liu, Xiaojuan He, Zhongshi The College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China The School of Data Science Chinese University of Hong Kong Shenzhen518172 China The College of Artificial Intelligence Chongqing University of Technology Chongqing401135 China The College of Computer Science Chongqing University Chongqing400044 China

Video summarization mainly aims to produce a compact, short, informative, and representative synopsis of raw videos, which is of great importance for browsing, analyzing, and understanding video content. Dominant video summarization approaches are generally based on recurrent or convolutional neural networks, even recent encoder-only transformers. We propose using full transformer as an alternative architecture to perform video summarization. The full transformer with an encoder-decoder structure, specifically designed for handling sequence transduction problems, is naturally suitable for video summarization tasks. This work considers supervised video summarization and casts it as a sequence-to-sequence learning problem. Our key idea is to directly apply the full transformer to the video summarization task, which is intuitively sound and effective. Also, considering the efficiency problem, we replace full attention with the combination of local and global sparse attention, which enables modeling long-range dependencies while reducing computational costs. Based on this, we propose a transformer-like architecture, named FullTransNet, which has a full encoder-decoder structure with local-global sparse attention for video summarization. Specifically, both the encoder and decoder in FullTransNet are stacked the same way as ones in the vanilla transformer, and the local-global sparse attention is used only at the encoder side. Extensive experiments on two public multimedia benchmark datasets SumMe and TVSum demonstrate that our proposed model can outperform other video summarization approaches, achieving F-Measures of 54.4% on SumMe and 63.9% on TVSum with relatively lower compute and memory requirements, verifying its effectiveness and efficiency. The code and models are publicly available on GitHub. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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RAUIE: A Relation-Augmented Document-Level Event Extraction Model Based on UIE

RAUIE: A Relation-Augmented Document-Level Event Extraction ...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yiyuan Liang Hong Jia Licai Wang Yangchen Huang Zhe Yang Big data research and development center North China Institute of Computing Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In document-level event extraction tasks, multiple event types often coexist within a document (the multi-event problem), and arguments may play different roles across different events (the argument intertwining problem). Existing methods often suffer from significantly reduced event extraction accuracy, mainly when focusing on scenarios with scarce annotated data and imbalanced event-type distributions. This paper emphasizes enhancing the correlation between event triggers and arguments to address these challenges. Therefore, we propose the Relation-Augmented UIE Model (RADIE), which introduces entity relationships into prompts using prompt-based learning and designs a relation-augmentation algorithm to improve the accuracy and completeness of event extraction. Experimental results demonstrate that this approach significantly outperforms baseline methods, mainly showcasing excellent performance on datasets with long-tail distributions.

关键词： Seminars Accuracy Correlation Focusing Feature extraction data mining Task analysis

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Designing Intelligent Intrusion Detection System Using Deep Learning

Designing Intelligent Intrusion Detection System Using Deep ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Priyanka Wasnik Nekita Chavhan Department of Computer Science and Engineering G H Raisoni College of Engineering Nagpur India Department of Data Science IoT and Cyber Security G H Raisoni College of Engineering Nagpur India

In order to increase the effectiveness and precision of cyber threat detection in computer networks, deep learning techniques are being applied in the construction of intelligent intrusion detection systems (IDS). The suggested IDS can automatically learn and recognize complicated patterns and characteristics from network traffic data by utilizing the capabilities of deep neural networks. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), or deep belief networks (DBNs) are some examples of deep learning architectures that can be built as part of the system design. These models can then be trained on labelled data to distinguish between legitimate and nefarious network behavior. In order to detect both well-known and new threats, the IDS concentrates on capturing both known attack signatures and unusual behaviors. Deep learning models are well suited for real-time intrusion detection in dynamic network environments because they can generalize and adapt to changing threats. By using methods like transfer learning, dimensionality reduction, and visualization, the system also handles issues like feature extraction, scalability, and interpretability. The suggested IDS seeks to achieve higher detection rates, lower false positive rates, and improved resilience against evasion strategies through thorough experimentation and review. The ultimate objective is to create an intelligent IDS that accurately detects and mitigates hostile activity to successfully protect computer networks, improving the overall security posture of the network infrastructure.

关键词：

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An efficient multi-attribute combinatorial auction for 4PL transportation service procurement

An efficient multi-attribute combinatorial auction for 4PL t...

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第35届中国控制与决策会议

作者： Na Yuan Haiming Liang Min Huang Yanfeng Zhang College of Information Science and Engineering Northeastern University Center for Network Big Data and Decision-Making Business School Sichuan University College of Computer Science and Engineering Northeastern University

In this paper,we study the mechanism design problem of combinatorial reverse auction for 4 PL transportation service procurement(TSP) considering price and multiple nonprice *** order to achieve efficient winner determination and transportation request allocation,we develop a 0-1 programing model to minimize social cost,where social cost includes real transportation cost of 3 PLs and the costs incurred by non-price attributes.A multi-attribute VCG(MA-VCG) reverse auction mechanism is proposed,which satisfies efficiency,incentive compatibility,individual rationality,and budget ***,a numerical example is designed to demonstrate the effectiveness and applicability of the proposed ***,sensitivity analysis is implemented to illustrate the influences of attribute preference,cost coefficient and other parameters on auction results.

关键词： Transportation service procurement Mechanism design Combinatorial reverse auction Multi-attribute auction

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Graph-based deep learning models in the prediction of early-stage Alzheimers

Graph-based deep learning models in the prediction of early-...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Bishal Thapaliya Zundong Wu Ram Sapkota Bhaskar Ray Pranav Suresh Santosh Ghimire Vince Calhoun Jingyu Liu Department of Computer Science Georgia State University Atlanta USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Applied Sciences and Chemical Engineering Tribhuvan University Nepal

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications.

关键词： Deep learning Neuroimaging Analytical models Biological system modeling Predictive models Functional magnetic resonance imaging Transformers Brain modeling Alzheimer's disease engineering in medicine and biology

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COMET: NFT Price Prediction with Wallet Profiling

arXiv

引用

arXiv 2024年

作者： Wang, Tianfu Deng, Liwei Wang, Chao Lian, Jianxun Yan, Yue Yuan, Nicholas Jing Zhang, Qi Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Hefei China Microsoft Inc Suzhou China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China Microsoft Inc Beijing China Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evaluations, limiting their practical application. Moreover, the influence of users' multi-behaviour transactions that are publicly accessible on NFT price is still not explored and exhibits challenges. In this paper, we address these gaps by presenting a practical and hierarchical problem definition. This approach unifies both collection-level and token-level task and evaluation methods, which cater to varied practical requirements of investors. To further understand the impact of user behaviours on the variation of NFT price, we propose a general wallet profiling framework and develop a COmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMET profiles wallets with a comprehensive view and considers the impact of diverse relations and interactions within the NFT ecosystem on NFT price variations, thereby improving prediction performance. Extensive experiments conducted in our deployed system demonstrate the superiority of COMET, underscoring its potential in the insight toolkit for NFT investors. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph theory

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Provably Secure Authenticated Key-Management Mechanism for e-Healthcare Environment

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IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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