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Contrastive Consensus Graph Learning for Multi-View Clustering

IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2027-2030页

作者： Shiping Wang Xincan Lin Zihan Fang Shide Du Guobao Xiao the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China the College of Computer and Control Engineering Minjiang UniversityFuzhou 350108China

Dear Editor,This letter proposes a contrastive consensus graph learning model for multi-view *** are usually built to outline the correlation between multi-model objects in clustering task,and multiview graph clusteri... 详细信息

关键词： letter integrate property

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Research on Service Composition Based on DQN in Multiple Clouds 24

Research on Service Composition Based on DQN in Multiple Clo...

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5th International Conference on computer science and Management Technology, ICCSMT 2024

作者： Yang, Yanhua Zhan, Xinrong College of Computer Engineering Jimei University Fujian Xiamen China School of Data and Computer Science Xiamen Institute of Technology Fujian Xiamen China

ISBN: (纸本)9798400709999

Service organized all kinds of services to one for the *** the cloud environment, the services and related QoSs (Quality of Services) in every cloud may be *** this paper, how to compose those services together in the multiple clouds environment is the *** use a SCDQN (Service Composition based a Deep-Q-Network) method, and consider the execution time, privacy level, cost and availability of the service and the service request in the multiple *** show that SCDQN has a lower value in the execution time and cost while satisfying privacy level and availability requirement. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： DQN

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Traffic flow prediction based on graph convolutional networks with a parallel attention network and stacked gate recurrent units

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Multimedia Tools and Applications 2025年第15期84卷 14329-14358页

作者： Xia, Dawen Ao, Yuce Wei, Xiaoduo Li, Yunsong Chen, Yan Hu, Yang Li, Yantao Li, Huaqing College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Department of Automotive Engineering Guizhou Traffic Technician and Transportation College Guiyang550008 China College of Computer Science Chongqing University Chongqing400044 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

Accurate traffic flow prediction is essential to address traffic issues and assist traffic managers make informed decisions in intelligent transportation systems. Extracting potential features from traffic data is challenging due to the complex topology of urban road networks and the time-varying traffic flow. To capture the global spatiotemporal characteristics of traffic flow, we propose a novel model based on graph convolutional networks with a parallel attention network and stacked gated recurrent units (PAGCN-SGRU). First, the parallel attention (PA) network enhances the feature representation of global traffic road nodes and road segments. Then, the graph convolutional networks (GCN) are designed to extract spatial characteristics. Next, the stacked gate recurrent units (SGRU) are employed to capture temporal features. Finally, PAGCN-SGRU discovers global spatiotemporal features for traffic flow prediction. The experimental results demonstrate that the accuracy of PAGCN-SGRU under the SZ-dataset is improved by 9.76%, 72.54%, 5.76%, 16.07%, 2.07%, 1.82%, 3.35%, and 6.59%, respectively, compared to that of HA, ARIMA, SVR, GCN, T-GCN, A3T-GCN, ST-GCN, and DCRNN. In the Los-dataset, the accuracy values increase by 7.11%, 8.94%, 6.66%, 7.77%, 3.43%, 2.45%, 2.28%, and 4.09%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Design and Evaluation of a Medical Chatbot Built on BERT Language Model for Remote Health Assistance 3

Design and Evaluation of a Medical Chatbot Built on BERT Lan...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Rao, D. Rajeswara Thottempudi, Kokila Surla, Bharath Kumar Satapathy, Ashutosh Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350381931

Telemedicine refers to delivering healthcare services, including diagnosis, treatment, and consultations, remotely through digital communication tools such as video calls, phone calls, and online messaging. Access to quality healthcare remains a significant challenge in underserved rural areas worldwide. Due to transportation issues and financial support in remote areas, people can only visit hospitals close to their location. Current telemedicine applications provide video conversations with doctors but need more seamless services due to the huge population increase in cities and the unavailability of internet services for people in rural areas. This article presents a medical chatbot application trained on a customized health assistant corpus to generate fast responses in text format to queries related to common health issues. The solution is built on the BERT model to train the chatbot, which includes MLM and NSP, which is one of the best ways to obtain accurate results. The BERT model trained on customized health assessment corpus consists of multiple health assessment corpora and health assistant queries/responses collected from different sources and achieved 96.5% test accuracy. Since the model is pre-trained, it will generate the response as fast as possible. In addition to that, the application provides 24/7 availability, user-friendly interfaces, remote monitoring, and robust data security. © 2024 IEEE.

关键词： Rural areas

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Enhancing Malicious Domain Detection Using Advanced Machine Learning Techniques 6

Enhancing Malicious Domain Detection Using Advanced Machine ...

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6th IEEE Pune Section International Conference, PuneCon 2023

作者： Nashikkar, Rishi Niraj Padhye, Yash Nimish Ingle, Rajesh Vpkbiet Artificial Intelligence and Data Science Baramati India Iiit Nr Computer Science and Engineering Raipur India

ISBN: (纸本)9798350324204

The necessity of comprehensive cybersecurity measures cannot be emphasized in today's quickly expanding digital world. Due to the rising complexity and frequency of cyber attacks, detecting malicious domains has become an urgent problem. Conventional methods often struggle to accurately identify sophisticated attacks, highlighting the need for innovative solutions. This research aims to address the urgent issue of detecting malicious domains by introducing a novel approach that harnesses the strengths of both the HistGradientBoosting-Classifier and Artificial Neural Networks. Through the synergistic combination of diverse algorithms, we propose a solution that has the potential to redefine cybersecurity, enhancing both accuracy and effectiveness. It is crucial to emphasize the significance of this problem within the current cybersecurity landscape, where threat actors continuously adapt their tactics to exploit vulnerabilities. Our study involves the meticulous curation of a dataset, enriched with essential features, showcasing substantial progress in enhancing detection accuracy. Through rigorous comparative analysis, we establish that the integration of various machine learning algorithms and domain name system features has the potential to revolutionize malicious domain detection. The implications of these findings extend well beyond the realm of academic research and hold a profound impact on practical cybersecurity. The incorporation of the Histogram Gradient Boosting Classifier and Artificial Neural Network represents a forward-thinking approach that can proactively address emerging challenges. By integrating these machine learning-driven insights into cyber-security strategies, organizations can significantly bolster their capabilities in safezuardina diaital infrastructures. © 2023 IEEE.

关键词： Decision trees

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Heart disease prediction using machine learning, deep Learning and optimization techniques-A semantic review

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Multimedia Tools and Applications 2024年第39期83卷 86895-86922页

作者： Bhavekar, Girish Shrikrushnarao Das Goswami, Agam Vasantrao, Chafle Pratiksha Gaikwad, Amit K. Zade, Amol V. Vyawahare, Harsha Department of Artificial Intelligence & Data Science CSMSS Chh. Shahu College of Engineering Aurangabad431136 India Department of SENSE VIT-AP University Andhra Pradesh 522237 India Department of AI & DS CSMSS Chh. Shahu College of Engineering Maharashtra Aurangabad431136 India Department of Computer Science & Engineering GH Raisoni University Maharashtra Amravati444701 India Department of Computer Science & Engineering Sipna College of Engineering & Technology Maharashtra Amravati444701 India

Cardiovascular disease holds the position of being the foremost cause of death worldwide. Heart Disease Prediction (HDP) is a difficult task as it needs advanced knowledge with better experience. Moreover, it encounters numerous significant challenges in clinical data analysis. While many researchers have focused on predicting heart disease, the performance metric, namely prediction accuracy, remains suboptimal. The accurate HDP can help the person to prevent himself from life threats and at the same time, inaccurate prediction can prove to be fatal. To solve these issues, in this review work several Deep Learning (DL), Machine Learning (ML) and optimization based HDP techniques are discussed. In recent times, many researchers have been utilizing different DL and ML algorithms to help the professionals and health care industry for the prediction of heart disease. Further, it discussed about various optimization-based algorithms and its performance analysis. Therefore, this review paper suggests that the optimization-based HDP algorithm could assist doctors in predicting the occurrence of heart disease in advance and offering suitable treatment. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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From Pixels to Payouts -A Satellite Based Solution

From Pixels to Payouts -A Satellite Based Solution

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Deepthi, R.S.S.V. Priya, A Leela Revanth, D. Ajay, K. Madhavi, M. Velagapudi Ramakrishna Siddhartha Engineering College Depratment of Artificial Intelligence and Data Science Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Depratment of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350359688

The Pradhan Mantri Fasal Bima Yojana (PMFBY) stands as a crucial initiative for farmers' protection, yet grapples with challenges like delayed claims, inaccurate loss assessments, and uneven aid distribution [1]. This project proposes an innovative solution that addresses these issues by introducing a precision agriculture and risk management framework. The current crop insurance policies rely on an area-based approach and limited weather indices, resulting in an inadequate assessment of individual crop health and losses [2]. This shortfall leads to unjust aid distribution, leaving deserving farmers underserved [3]. The proposed solution advocates for a paradigm shift, incorporating a personalized and technology-driven approach. By integrating remote sensing technology, machine learning algorithms, and comprehensive data analysis, the system optimizes insurance resource allocation, assessing crop and regional risk profiles with precision. Furthermore, the project implements advanced image processing, segmentation, and machine learning models, such as SVM and CNN, for crop detection and yield estimation. The Google Maps API enhances decision support through geo-mapping, providing a transparent and data-driven basis for aid distribution. This innovative approach fosters equity, transparency, and active farmer participation, overcoming the limitations of existing methodologies. With a focus on precision, transparency, and equity, the proposed solution aims to revolutionize the effectiveness of PMFBY, ensuring accurate assessment and targeted aid distribution for farmers facing crop stress and losses. © 2024 IEEE.

关键词： Risk management

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Smart AI-Based Delivery Robot

Smart AI-Based Delivery Robot

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Vemulapalli, Lavanya Charitha, Kolanti Harshitha, Kuralla Durga Lakshmi Sri, Vallepu Udaya Velagapudi Ramakrishna Siddhartha Engineering College Dept.of Computer Science and Engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Dept.of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331512965

Humans are responsible for the majority of delivering the packets over the area of a network. When compared to classic delivery system the Autonomous Delivery Robot(ADR's) is meant to deliver the goods instead of a human involvement. The delivery robots are a contact-less and alternative to human interaction. At their core, we design a budget-friendly prototype for an Autonomous Delivery Robot (ADR) that can deliver packages to their destinations safely. This robot uses a GPS (like a car's navigation system) to find its way around. We use a special kind of computer brain called Artificial Intelligence to understand the best way to go. Once delivery robot know the right path, it follows until it reaches to the final stop. The robot keeps packets secure by putting them in a special container that needs a secret code to open. The secret code to open the container can be sent to the customer mobile when they order something. Once the robot delivers your package, it can find its own way back to where it started. © 2025 IEEE.

关键词： Budget control

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Emergency Vehicle Detection Using Deep ConvNet2D and computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

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2023 IEEE Conference on engineering Informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 IEEE.

关键词： data visualization

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