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2024 International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2024

作者： Huang, Ruichong Li, Liang Chongqing University of Technology College of Computer Science and Engineering Chongqing Banan China

ISBN: (纸本)9781510680012

In response to the issues of limited annotated data and insufficient semantic information extraction in medical named entity recognition for Chinese electronic medical records, a novel approach based on data augmentation and structural feature fusion is proposed. Firstly, addressing the potential noise introduced by unsupervised data augmentation methods like synonym replacement and random insertion, a rule-based data augmentation technique is introduced. This method effectively diversifies the annotated Chinese electronic medical record data by making use of external resources. Considering that Chinese characters are ideograms and characters with similar radical structures often convey similar semantic information, a semantic information enhancer is proposed. This enhancer utilizes a convolutional neural network to obtain vector representations of Chinese character features. Based on cosine similarity, it extracts several characters with the highest radical structure similarity, and incorporates additional semantic information through an attention-weighted fusion mechanism. This enables the model to extract semantic information more effectively. Lastly, a semantic fusion module is constructed to integrate the semantic information enhancer into the primary named entity recognition model, employing a Conditional Random Field (CRF) to predict the final results. Experimental results on the CCKS 2019 Chinese electronic medical record dataset demonstrate that the model achieves an impressive F1 score of 83.71%, validating the effectiveness of this approach. © 2024 SPIE.

关键词： Semantics

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A Novel Framework Combining VSL and Vehicle Platooning for Freeway Bottleneck

A Novel Framework Combining VSL and Vehicle Platooning for F...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Tong Qi, Liang Luan, Wenjing Liu, Kun Guo, Xiwang Ann Talukder, Qurra Tul College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China

ISBN: (纸本)9781665410205

Freeway bottlenecks caused by traffic incidents contribute significantly to large-scale traffic congestion. Traditional strategies, including variable speed limit (VSL) and ramp metering, are commonly used for freeway traffic congestion management. Recently, vehicle platooning has become a promising way to alleviate traffic bottlenecks. This work proposes a novel framework that combines VSL and vehicle platooning for freeway bottleneck, referred to as VSL-VP, in mixed traffic of connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). First, the upstream road of a bottleneck is divided into two segments, called the former and the latter. VSL limits vehicle speed at the former segment, thereby reducing inflow traffic to the latter one. Then, deep reinforcement learning is employed for CAV platooning at the latter segment, where low traffic flow density and large car-following distance create conditions for smooth lane change and platoon formulation of CAVs. Simulation results demonstrate that VSL-VP significantly enhances the bottleneck throughput and reduces traffic congestion at elevated levels of CAV penetration rates. © 2024 IEEE.

关键词： Traffic congestion

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Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

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CAAI Transactions on Intelligence technology 2023年第1期8卷 95-117页

作者： Pradeep Kumar Roy Sunil Saumya Jyoti Prakash Singh Snehasish Banerjee Adnan Gutub Department of Computer Science and Engineering Indian Institute of Information TechnologySuratIndia Indian Institute of Information Technology DharwadIndia Department of Computer Science and Engineering National Institute of TechnologyPatnaIndia The York Management School University of YorkYorkUK Computer Engineering Department College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic *** have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs *** clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature *** goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)*** final review included 133 *** research themes include question quality,answer quality,and expert *** terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack *** scope of most articles was confined to just one platform with few cross-platform *** with ML outnumber those with ***,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

关键词： answer quality community question answering deep learning expert user machine learning question quality

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Autogenie: Revolutionizing Vehicle Recommendations with Hybrid ML-Based System 5

Autogenie: Revolutionizing Vehicle Recommendations with Hybr...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sharma, Varun Singh, Sidharth Garg, Yash Galgotias College of Engineering and Technology Department of Computer Science and Engineering Gautam Budh Nagar India

ISBN: (纸本)9798350383522

In an ever-changing car market, it is still difficult to choose the best car from a wide range of options. 'AUTOGENIE' offers an innovative solution to this problem combining matrix factorization, collaborative filtering, and content filtering in a hybrid machine learning system In this varied environment, traditional recommendation methods have problems in making recommendations true and best to give in. When a particular algorithm is used, the recommendations it makes fail in some way-whether accuracy, precision, or personal preference-that upsets the user's AUTOGENIE filling this void provides through complementary methods, and improves the accuracy of recommendations by analyzing large data sets and identifying hidden variables Furthermore, this hybrid machine learning algorithm is also able to adapt to simple data and changes the user Preferences Using advances in deep metric multiplication for cross-domain recommendation, AUTOGENIE works exceptionally well for customizing recommendations despite changing user preferences and limited data It uses the information provided are personalized recommendations, so they reflect recommendations that are highly relevant to them. This paper describes the methodology of AUTOGENIE, including its new methodology for studying hybrid devices, experimental results and implications. By combining collaborative filtering, matrix factorization, and content filtering, AUTOGENIE provides a convenient way forward for highly customized vehicle recommendations in the dynamic automotive industry in. © 2024 IEEE.

关键词： Collaborative Filtering Content Filtering Hybrid ML Approach Matrix Factorization Precision Personalization Vehicle Recommendations

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Electronic Medical Record Sharing Using Blockchain in India 10

Electronic Medical Record Sharing Using Blockchain in India

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Smriti, Johanna Vijayalakshmi, S. Priyadarshini, Anita Keerthna, M. Computer Science Engineering Psg College of Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

In the healthcare industry, sharing confidential information is indespensible. This is typically done using Electronic Medical Records(EMR). When used along with Blockchain technology, with its capacity to establish a shared and permanent transaction history, provides a foundation for developing applications that emphasize accountability and transparency in the interactions among the various participants in the healthcare system.. This is an opportunity to create a safe and reliable block chain-based EMR data management system. We discuss our perspectives on how blockchain technology can be used in healthcare data management for research projects, clinical trials, and provider-to-provider EMR data exchange in this paper. The project focuses on EMR data management. The work mainly focuses on giving relevant patient's information to the correct clinical trials. Secondly, to implement the system nationwide which overcomes many medical issues and improves the efficiency of any treatment by providing a patient's complete medical history and improves the quality of clinical trials and research which in turn increases the overall quality of medical care by inventing new cures for many diseases. This project focuses on increasing the scalability by implementing it nationwide, making it as seamless as possible compared to the real-life scenario by including participants like insurers, who can look into the medical activities of the patient and avoid health insurance fraud. Other participants would be hospitals, doctors, patients, insurance companies and the whole network will be overlooked by the Indian government health sector authorities. © 2024 IEEE.

关键词： Electronic health record

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Multi-Dimensional Business Data Fusion Modeling Based on Dynamic Bayesian Network 29th

Multi-Dimensional Business Data Fusion Modeling Based on Dyn...

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29th International Conference on Computational and Experimental engineering and sciences, ICCES 2023

作者： Luo, Tianshu Lu, Dan Han, Qilong Sui, Shanshan College of Computer Science and Technology Harbin Engineering University Harbin150009 China

ISBN: (纸本)9783031425141

Business data fusion from various data sources in enterprises has been one of the hot issues in current research. While previous work has made great efforts to achieve data fusion, existing methods still suffer from three key limitations: (i) Most methods do not guarantee accurate connections to the data sources;(ii) Most approaches disregard the time-varying of business data;(iii) These approaches ignore the fact that the dimensionality of business data is high, resulting in query instances failing. To solve the above problems, we propose a novel multi-dimensional business data fusion method based on Dynamic Bayesian Networks, namely DBN. Specifically, we design a DBN for all the attributes in the data and generate a random variable for each attribute, which can be learned based on domain dependence and correlation or traditional structure. Then, we compute the query using the underlying joint distribution of the Bayesian Network. Moreover, we use Structured Query Language (SQL) engine in an Electric Motor Temperature multi-sensor business data source for query and analysis. The experimental results show that our method can achieve state-of-the-art performance and exhibit competitive accuracy of data source connections. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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An Energy Efficient IoT Based Smart Street Lighting Using Low Cost SOC 1

An Energy Efficient IoT Based Smart Street Lighting Using Lo...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Velu, Anitha Ramamoorthy, Raghu Manasa, S.M. Navulkumar, Devakirubai Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Jayam College of Engineering and Technology Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350371802

Internet of Things (IoT) has radically improved and modernized in every aspect of human existence. The term IoT is a recent trend that states the development a self-configurable network by connecting a variety of hardware objects, to internet without requiring human input to improve service delivery and automate processes by means of data transmission to the cloud. Streetlights are an essential component of any city, although they provide safer roadways, improved night vision, and increased visibility of public spaces it uses a significant amount of electricity and operates at maximum intensity from dusk till dawn. The wastage of electricity can be secured by having lights turn OFF on their own if it is not in use. This work presents an energy efficient IoT-based smart street lighting by using low-cost System On Chip (SOC) circuit. This work deploys an LDR sensor which acts as a decision device, to choose whether to switch ON or OFF the street light based on the level of ambient light. This simple switch has a relay at its output and is activated by either light or dark. This switching may be done using a cost-effective Wi-Fi module namely, ESP8266 for which the LDR sensed value is given as input. Any user can check the status of a street light whether it is ON/OFF from anywhere using internet in real time. It promises great dependability and remarkable stability for long-term. The proposed work is implemented using a NodeMCU board that is designed to supply the required light intensity at different times. © 2024 IEEE.

关键词： Internet of things

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Elevating IoT healthcare security using ProSRN and ICOM methodologies for effective threat management

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International Journal of Information technology (Singapore) 2025年 1-19页

作者： Sowjanya, Yemineni Gopalakrishnan, S. Kumar, R. Dinesh Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Information Technology Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Telangana Hyderabad 500059 India

IoT healthcare security is increasingly important, as the interconnectedness of medical devices in itself introduces a major vulnerability, given the impact on patient safety and data integrity. Past works in this domain have been afflicted by persistent issues of high false-positive rates, resource utilization inefficiency, and failure to adapt to rapidly changing threat spaces. The paper presents a novel framework that incorporates ProSRN, which provides the robustness in threat detection, and ICOM for dynamic weight estimation. ProSRN adopts a residual-connected stacking approach to reach high accuracy of detection while reducing the complexity in training and enhancing responsiveness in real time. It is further complemented by ICOM, which will then dynamically optimize network parameters to yield an adaptive IoT health security model with the capability of addressing evolving threats. Together, ProSRN and ICOM advance both detection and optimization significantly, enhancing the adaptiveness, efficiency, and security in IoT healthcare systems. This creates a concrete basis for developing resilient frameworks that can protect sensitive healthcare data against emerging threats. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： And deep learning Attack detection Classification Healthcare systems Internet of things (IoT) Optimization Security

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Propagation of computer Worms—A Study 6th

Propagation of Computer Worms—A Study

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Rao, Mundlamuri Venkata Midhunchakkaravarthy, Divya Dandu, Sujatha Department of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia Department of Computer Science and Engineering Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819984503

Worms are commonly disseminated through two methods: scanning vulnerable machines in a network as well as spreading through topological neighbors. Modeling worm propagation can assist us in understanding how worms propagate and develop efficient defense tactics. However, most past studies either focused on their intended task or explored detection systems as well as defense systems. Few provide very detailed study in worm propagation modeling, that is useful in building defense mechanisms to deal with worm spread. This work includes a survey as well as comparison of worm propagation models based on two independent worm-spreading strategies. We first define worm features based on their spreading behavior and then classify the numerous target discovery approaches they deploy. In addition, we examine several topologies for modeling worm spreading, analyze numerous models of worm propagation and their effectiveness. On the basis of the analysis of worm spread and current studies, future directions for modeling worm propagation models are offered. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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Designing an AI driven intelligent Tutorial System 5

Designing an AI driven intelligent Tutorial System

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5th IEEE International Conference on Recent Trends in computer science and technology, ICRTCST 2024

作者： Alam, Shamsher Kumar, Surjeet Khursheed, Zeeshan Mahato, Hemant Kumar Bashar, Shadan Suman, Anjali R.V.S College of Engineering & Technology Computer Science and Engineering Jamshedpur India Government Engineering College Sheikhpura Computer Science and Engineering Bihar India Jio Platform Limited DGM Mumbai India NSHM Master of Computer Application Durgapur India R.V.S College of Engineering & Technology Master of Computer Application Jamshedpur India R.V.S College of Engineering & Technology Computer Science & Engineering Student Jamshedpur India

ISBN: (纸本)9798350351378

With the fast-evolving field of education, there is an increase in the demand for intelligent tutorial systems that can adapt to the diverse learning needs of individual students. This research paper explores the design and implementation of an AI-driven Intelligent Tutorial System (ITS) aimed at enhancing the personalized learning experience. The system leverages advanced machine learning algorithms to dynamically adjust instructional content, pace, and assessments based on real-time analysis of individual student performance and preferences. The paper delves into the key components of the proposed ITS, including adaptive learning models, natural language processing capabilities for interactive communication, and data-driven decision-making mechanisms. Emphasis is placed on the seamless integration of AI technologies to create an intuitive and responsive educational *** considerations in the development and deployment of the intelligent tutorial system are discussed, addressing issues related to data privacy, algorithmic bias, and the role of human oversight. The paper also highlights the collaborative potential between AI and human educators, emphasizing the system's capacity to support rather than replace teaching professionals. Through a combination of theoretical frameworks and practical examples, the research examines the potential impact of the AI-driven ITS on student engagement, learning outcomes, and the overall educational experience. Case studies and pilot implementations are presented to illustrate the system's efficacy in diverse educational *** research contributes to the ongoing discourse on AI in education by providing insights into the design principles, implementation challenges, and ethical considerations associated with developing an intelligent tutorial system. The findings aim to inform educators, policymakers, and technologists about the potential of AI to revolutionize personalized learning and contribute to the ong

关键词： Students

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