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Incorporating Attribute Information into Joint Random Walk for Interpretable Local Community Detection 10

Incorporating Attribute Information into Joint Random Walk f...

引用

10th International conference on Systems and Informatics, ICSAI 2024

作者： Chen, Tianchao Ma, Huifang Li, Ju Lanzhou Vocational Technical College Department of Electronic and Information Engineering Lanzhou China College of Computer Science and Engineering Northwest Normal University Lanzhou China

ISBN: (纸本)9798331510138

Local community detection on the attribute graph aims to locate the community that contains query nodes and satisfies structure cohesion and attribute homogeneity. Most of the traditional local community detection methods focus on topology information and fail to capture attribute information, making it difficult to capture dense subgraphs with high similarity of attributes. Aiming at the above issue, an interpretable Local Community Detection method based on incorporating Attribute information into joint Random walk (ARLCD) is proposed. Specifically, a heterogeneous graph is constructed to capture multiple of structural information. Then we leverage the spectral properties of nearly uncoupled Markov Chain to prolong the influence of user's past preferences on the successive steps of the walk, allowing the walker to explore the underlying features more comprehensively. In addition, to eliminate the seed dependence of random walk, we design a joint random walk with reinforcement mechanism. The experimental results on several datasets show our proposed method can find the community with high structure cohesiveness and attribute similarity. © 2024 IEEE.

关键词： Markov chains

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Penetration of Deep Learning in Human Health Care and Pharmaceutical Industries;the Opportunities and Challenges 3

Penetration of Deep Learning in Human Health Care and Pharma...

引用

3rd International conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Raman, Ramakrishnan Radha, H.R. Inbamalar, T.M. Subhahan, D. Abdus Kumar, Ashok Bathrinath, S. Sarkar, Swagata Symbiosis Institute of Business Management Pune & Symbiosis International Maharashtra Pune India K. S. School of Engineering and Management Department of Chemistry Karnataka Bangalore India R.M.K. College of Engineering Department of Electronics and Communication Tamil Nadu Thiruvallur India B v Raju Institute of Technology Department of Computer Science Telangana Narsapur India Banasthali Vidyapith Department of Computer Science Rajasthan India Kalasalingam Academy of Research Department of Mechanical Engineering Tamil Nadu Krishnankoil India Sri Sai Ram Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350336238

Computational medicine has emerged as a result of the advancement of medical technology, which has led to the emergence of the big data era in the biomedical area, which is supported by artificial intelligence technology. To advance the development of precision medicine, people must be able to extract the valuable information from this vast biomedical data. In the past, professionals in the field of feature engineering and domain knowledge were typically utilised to extract the features from the biological data using machine learning techniques, which took a lot of time and resources. Modern machine learning techniques like deep learning (DL) have an advantage over them in that they can automatically find strong, complex features from fresh data without the necessity for succeeding engineering. The study of DL's applications in the fields of genomics, drug development, electronic health records, and medical imaging suggests that deep learning has clear advantages in maximising the use of biomedical data. Deep learning is becoming increasingly important in the field of medicine and health due to its large range of potential applications. The lack of data, interpretability, data privacy, and heterogeneity are some of the limitations of deep learning in computational medical health. A resource for improving the use of deep learning in medical health is provided by the analysis and discussion of these difficulties. © 2023 IEEE.

关键词： Health care

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Develop a Smart Android Application to Provide Laundry Service 15

Develop a Smart Android Application to Provide Laundry Servi...

引用

15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jeyapradesh, Samuel Jebaseeli, T. Jemima Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798331300579

In the era of the Internet and smart applications, companies and institutions providing various services face significant challenges. The digital landscape, characterized by limitless connectivity and accessibility, poses a new paradigm where services transcend traditional boundaries of time and space. To remain at the forefront in their respective domains, businesses must embrace the electronic realm. Notably, web programming languages like PHP and ASP, alongside mobile application development languages such as Android have emerged as pivotal tools in this technological evolution. This design focuses on the development of an Android application integrated with a web control panel tailored for the service sector, with a specific emphasis on public laundry facilities. The study encompasses the system's architecture technological frameworks employed, and subsequently delves into the target audience, market requirements, and additional services to be offered. Throughout this exploration, the design aims to harness the potential inherent in these programming languages, drawing parallels to human language for enhanced user engagement and accessibility. © Grenze Scientific Society, 2024.

关键词： Laundries

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Smart Health Monitoring Wearable with Accessibility using IoT

Smart Health Monitoring Wearable with Accessibility using Io...

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2024 International conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Thangam, S. Gurupriya, M. Aaron Xavier, S. Reddy, Akhil Rajmohan, Navneeth Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350369175

In our day-to-day lives, monitoring health regularly is essential to ensure the body functions well under various conditions. This paper outlines the development of a smart health monitoring wearable device utilizing an Arduino Uno as the core processing unit. The device is designed to monitor health parameters continuously, transmitting data wirelessly for real-time analysis and feedback. Modern connectivity options enable data sharing with a centralized platform, allowing efficient health management and user interaction through mobile or web applications. This wearable aims to enhance healthcare by providing immediate health insights and alerting providers to potential health issues, embodying a proactive approach to health monitoring in everyday life. © 2024 IEEE.

关键词： electronic health record

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An Improved Ant Colony Clustering Algorithm and Application in Classification of Tree Species 7

An Improved Ant Colony Clustering Algorithm and Application ...

引用

7th International conference on computer Network, electronic and Automation, ICCNEA 2024

作者： Zhang, Jiashan Chongqing Vocational Institute of Engineering Chongqing China

Clustering is one of the most challenging research topics and has attracted the attention of many researchers. Clustering aims to divide a given set of objects into several categories according to certain rules. It is widely applied to practical problems, such as knowledge discovery, artificial intelligence, scientific data analysis, business, biology, medical diagnosis, text mining, and web data mining and so on. Ant colony clustering algorithm (ACCA), which integrates ACA into clustering, is an efficient swarm intelligence algorithm. There are some weakness in the basic ant colony clustering algorithm, such as low solving efficiency, poor calculation accuracy and iteration stagnation. In this article, an improved Ant Colony Clustering Algorithm (IACCA), in which novel picking-up and dropping strategy is introduced, is proposed and applied to classification of tree species. The experiments indicate efficiency of IACCA for classification of tree species. © 2024 Institute of Physics Publishing. All rights reserved.

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Many-to-Many Transfer Learning on Motor Imagery BCI

Many-to-Many Transfer Learning on Motor Imagery BCI

引用

2024 International conference on Decision Aid sciences and Applications, DASA 2024

作者： Altindiş, Fatih Yilmaz, Bulent Congedo, Marco Abdullah Gül University Electrical and Electronic Engineering Kayseri Turkey Gulf University for Science and Technology Electrical Engineering Hawally Kuwait GIPSA-lab University of Grenoble Alpes Grenoble-Image-sPeach-Signal-Automatics Lab CNRS Grenoble France

ISBN: (纸本)9798350369106

This paper presents many-to-many domain adaptation strategy, named group learning, for motor imagery brain-computer interfaces (BCIs). Group learning, grounded in Riemannian geometry, simultaneously aligns multiple domains in a unified model, whereas fast alignment approach integrates new, unseen domains without re-estimating alignment matrices for all domains. Group learning creates a single machine learning model using data from previous subjects and/or sessions. Fast alignment utilizes the already trained model for an unseen domain without requiring any additional classifier training. The tests on five publicly available motor imagery databases demonstrate the robustness of group learning against negative learning. The classification accuracy scores of binary and multiclass databases show comparable, if not superior, performance to conventional subject-wise learning method. © 2024 IEEE.

关键词： Federated learning

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EMOWELL: Emotional Well-Being Web Application for Better Understanding Using NLP Techniques 5

EMOWELL: Emotional Well-Being Web Application for Better Und...

引用

5th International conference on electronics and Sustainable Communication Systems, ICESC 2024

作者： Nagaraj, P. Gurusigaaamani, A.M. Khan, Arsalan Ahmad Sathish, S. Ritika, M. Mohiuddin, Ahmad Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar Tamilnadu Krishnankoil India

ISBN: (纸本)9798350379945

In the digital age, the need for comprehensive emotional well-being platforms has become increasingly evident. EMOWELL, utilizing the MERN technological stack and the advanced BERT model, addresses this need by offering a holistic approach to emotional self-reflection. Designed with modern users in mind, EMOWELL integrates advanced features such as mood tracking, meditation exercises, and journaling, all enhanced with sentiment analysis, to provide robust support for emotional health. The implementation of EMOWELL is supported by robust security measures and a commitment to continual improvement, ensuring the platform's effectiveness in promoting emotional resilience and sustained user engagement. Empirical findings highlight the EMOWELL's potential as an efficient tool in digital health solutions, marking a significant stride towards wellness and emotional intelligence in the technology driven society. © 2024 IEEE.

关键词： electronic health record

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Deep Graph Learning on Bipartite Observed Networks 2

Deep Graph Learning on Bipartite Observed Networks

引用

2nd Asia-Pacific conference on Communications Technology and computer science, ACCTCS 2022

作者： Wei, Qiang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781665400343

In real-world settings, network structure observations are often incomplete due to the difficulties of data collection. However, most existing network analysis methods assume that the whole underlying structure is observed. In this paper, we focus on a common but overlooked network observation, named bipartite observation. We systematically study the influence of bipartite observations, and give an analytical representation using the stochastic block model. Moreover, we propose a new framework LP-GNN, which combines link prediction, sparsification, and graph neural networks for node classification tasks in bipartite observations. The effectiveness of LP-GNN is illustrated by experimental results on real datasets. © 2022 IEEE.

关键词： Graph neural networks

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A Multi-View Graph-Based Scheme for Drug-Drug Interactions Categorization 16

A Multi-View Graph-Based Scheme for Drug-Drug Interactions C...

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16th International conference on Advanced computer Theory and engineering, ICACTE 2023

作者： Lin, Canxin Liang, Zexiao Xie, Hongmei Tan, Guoliang Li, Jiangzhong Li, Qian School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Integrated Circuits Guangdong University of Technology Guangzhou China School of Automation Guangdong University of Technology Guangzhou China Guangdong Prov. Key Laboratory of Electronic Information Products Reliability Technology Guangzhou China

ISBN: (纸本)9798350317534

With the increasing significance of poly-drug therapy, the research on drug-drug interaction (DDI) has garnered more attention. Predicting potential DDI is important for ensuring patient safety and optimizing drug combinations. Recently, intelligent algorithms, such as machine learning and deep learning techniques, have emerged as effective tools for predicting potential DDI. However, most existing techniques only utilize single features for learning and describe the extraction and process of DDI data roughly. In this paper, a Multi-View Graph-Based Scheme for Drug-Drug is proposed, named MVGDDI, which aims to comprehensive DDI prediction. This scheme consists of two major parts including data acquisition and graph-based analysis. Specifically, data acquisition extracts relevant drug information from multiple drug databases using untangle. Then, the data is assembled in two different ways to meet specific requirements. At last, the multiple features are organized into a multi-view dataset for comprehensive learning. Subsequently, a graph-based model is adopted to learn the relationships among DDI data and categorize DDIs. The experiments with the actual anticancer drug demonstrate the effectiveness and superiority of MVGDDI compared to the conventional machine learning methods. © 2023 IEEE.

关键词： Data acquisition

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Blockchain-based trusted dispute resolution service architecture

Blockchain-based trusted dispute resolution service architec...

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International conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sharma, Ravi Mohan Krishna, V. Rama Saikia, Tirtha Suri, Ashish Rivera, Richard Mavaluru, Dinesh Department of Computer Science and Applications Makhanalal Chaturvedi National University of Journalism and Communication Madhya Pradesh Bhopal India CVR College of Engineering Hyderabad India Economics Mittal School of Business Lovely Professional University India School of Electronics and Communication Engineering Shri Mata Vaishno Devi University India Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9781032669663

In the traditional service architecture (Service-Oriented Architecture, SOA), the Web Service provider registers its service description in the registry for service consumers to discover and call services. Although this architecture can provide open service calls, it lacks a dispute resolution mechanism. The trusted invocation of services between service consumers and providers who do not trust each other cannot be guaranteed. Blockchain technology has significant advantages in decentralization and anti-tampering and can be reasonably applied in traditional SOA to solve the problem of service credibility. Trusted SOA architecture based on blockchain is proposed to realize credible service invocation by combining traditional SOA architecture and blockchain technology. During a credible service invocation, or service consumption, The provider encrypts the parameters and sends them to the target service provider;the service provider receives the encrypted parameters and completes the decryption;the service provider completes the service execution and completes the encryption of the output result;finally, the service provider sends the encrypted result to the service provider Consumers complete the construction and chaining of trusted credentials at the same time. Based on the above, when a service dispute occurs, the ruling smart contract will be triggered to handle the service dispute correctly. The experimental results show that compared with the traditional invocation, the proposed method can correctly handle service disputes between service providers and requesters under the premise of ensuring that the growth rate of most Service trusted call times is not more excellent than 30%. by combining traditional SOA architecture and blockchain technology by combining traditional SOA architecture and blockchain technology. © 2024 The Author(s).

关键词： Blockchain

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