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Language Prediction of Twitch Streamers using Graph Convolutional Network

Language Prediction of Twitch Streamers using Graph Convolut...

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international Image Processing, Applications and systems conference (IPAS)

作者： Md. Nahid Hasan Nakshi Saha Md. Anisur Rahman Dept. of CSE Dhaka International University Dhaka Bangladesh CSE Discipline Khulna University Khulna Bangladesh

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Social Network is a common example of the graph structure. there are many algorithms available for predicting different elements of social networks. Language is one of the elements. Prediction problems generally stay within 2D information. Graphs provide more structural information like edge relationship edge feature etc. While predicting social network elements, information about graph structure can be very useful. Graph Convolutional Network gives the opportunity to use graph structure as a piece of new information. Twitch is a popular social media platform where people stream gaming-related content. In our work, we have proposed a Graph Convolutional Network model for predicting the language of Twitch gamers. We also showed that it outperforms other machine learning and deep learning algorithms like Decision trees, Random Forest, Support Vector Machine, Neural Network and others. While other models could only reach 45.8% accuracy, GCN was able to achieve 73.43% by utilizing the graph structure.

关键词： Deep learning Support vector machines Machine learning algorithms Social networking (online) Graph convolutional networks Neural networks Streaming media Predictive models Prediction algorithms Random forests

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Analysis of the Multispectral and SAR Image 6

Analysis of the Multispectral and SAR Image

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6th IEEE international conference on Computer and Communication systems, ICCCS 2021

作者： Zhang, Shuchun Zhang, Yun Chou, Yonabin Wang, Ziheng Shi, Yifu Sun, Zhenyu Simulation Technology and Service CO. Ltd Avic International Shanghai China Institute of Electronic Engineering Technology Harbin Institute of Technology Haerbin China Northeastern University Department of Electrical and Computer Engineering Boston United States

ISBN: (纸本)9780738126043

the SAR image and the multispectral image are both used for dynamic monitoring, mineral resources investigation, urban and rural monitoring and evaluation, traffic network exploration, forest resources investigation, desertification monitoring, and so on. the multi-spectral and SAR image fusion to improve the classify quality is discussed in this paper, compared the common fusion algorithms of the SAR image and multi spectral images, that is standard color transform (Brovey) method, phase recovery (Gram-Schmidt) method and color space transform (HSV) method, principal component transformation super resolution (PCA) method and Bias method (Pansharp), by which the fused image is more relative with the multi-spectral and SAR. © 2021 IEEE.

关键词： Synthetic aperture radar

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Resource-Constrained Image Processing for Scientific CubeSats 6

Resource-Constrained Image Processing for Scientific CubeSat...

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6th international conference on algorithms, computing and systems, ICACS 2022

作者： Kozaris, Ioannis Gioulekas, Fotios Katsaros, Panagiotis School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Directorate of Informatics 5th Regional Health Authority Larissa Greece

ISBN: (纸本)9781450397407

CubeSat systems are preferred for short-term space missions, like those with a scientific objective, since they make it possible to complete them in an affordable cost. On the other hand, the resource-constrained nature of such systems poses significant challenges in software design, for the system payload. We focus on image compression and processing for scientific space missions, a vital functionality, due to the limited onboard storage capacity and the infrequent time periods, in which a satellite in orbit can send images to the ground, through a limited bandwidth connection. For CubeSat systems, the performance of image processing depends - even more than other space systems - on trade-offs, which are influenced by size, power and complexity constraints. In this context, we present the design challenges and the image compression/processing solution developed, for a university CubeSat built to carry on a biological experiment. All changes of the performance capability in the different parts of the payload image data chain (image resolution, onboard storage capacity, communication channel bandwidth and error characteristics) are taken into account. Moreover, any decision that increases the risk of incorrect reception of images from the experiment is weighted against the benefits of improved performance. We provide experimental results that show how our image processing solution resolves the associated trade-offs, while fulfilling the mission requirements. © 2022 ACM.

关键词： Image compression

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Optimisation of a Convolutional Neural Network Based Model for thermal Image Recognition of Faces

Optimisation of a Convolutional Neural Network Based Model f...

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IEEE international conference on Power, Intelligent computing and systems (ICPICS)

作者： Yang Lu Jiali Su Yisong Shi Maynooth International Engineering College Fuzhou University Fuzhou Fujian China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In this paper, we propose an optimization method for face infrared image recognition model based on convolutional neural network for the challenge of limited computational resources of edge computing devices. the method reduces the computational resource requirements by structurally pruning the VGG19 convolutional neural network architecture, which significantly compresses the number of parameters of the model while ensuring high recognition accuracy. the Terravic face infrared dataset is used to test the performance of the optimized model. After the experiments, we obtain that the optimized model has a high accuracy of 99.78 % in the face infrared image classification task, which is only 0.19% lower than the accuracy before optimization. the number of parameters contained in the model is reduced by 98.5%, and the compression rate is as high as 66.69. the optimization method proposed in this paper provides a new approach for deploying convolutional neural networks on edge computing devices with limited computational resources, showing significant practical application value.

关键词： Deep learning Performance evaluation Accuracy Image recognition Image coding Computational modeling Face recognition Optimization methods Convolutional neural networks Edge computing

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4th international conference on Intelligent, Interactive systems and Applications, IISA2019

4th International Conference on Intelligent, Interactive Sys...

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4th international conference on Intelligent, Interactive systems and Applications, IISA2019

ISBN: (纸本)9783030343866

the proceedings contain 102 papers. the special focus in this conference is on Intelligent, Interactive systems and Applications. the topics include: Research and Application of Process Object Modeling Method Based on Deep Learning;Security Issues for Smart Grid EC System;a Novel Smart Grid Model for Efficient Resources;the Idea of Deep Neural Network algorithms Application in Sharing Economic Dynamic Value CO-creation Mechanism;performance Assessment of Smart Electricity Meters Based on Max Margin Bayesian Classifiers;operating Performance Assessment of Smart Meters Based on Bayesian Networks and Convex Evidence theory;research on Object-Oriented Design Defect Detection Method Based on Machine Learning;design of Electromagnetic Energy-Saving Intelligent Vehicle;new Energy Vehicle Reliability Research by Large Data;One-Step Prediction for GM(1,1) with a Sliding Data Window;evaluation on Well-Canal Combined Irrigation Modes for Cotton Fields in the South of Xinjiang, China Based on an Algorithm of Fuzzy Neutral Network;grape Downy Mildew On-line Detection Based on Accurate Image Processing Method Preformed on Embedded Ambient Intelligence System;an Improved Bisecting K-Means Text Clustering Method;research on Intelligent Patent Classification Scheme Based on Title Analysis;PLC-Based Control System Design of Intelligent and Efficient Multiple Parking Device;sparse Acceleration Algorithm Based on Likelihood Upper Bound;optimization Design of Quad-Rotor Flight Controller Based on Improved Particle Swarm Optimization Algorithm;diversified Development Path of Cultural Exhibition Industry: Perspective of Service Value Chain;Research on Text Classification Method Based on Word2vec and Improved TF-IDF;Research on Automatic Text Summarization Method Based on TF-IDF;message Transmission-Driven Infectious Disease Propagation Model.

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Chest X-ray Image Classification Using Convolutional Neural Network to Identify Tuberculosis 6th

Chest X-ray Image Classification Using Convolutional Neural ...

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6th international conference on Intelligent computing and Optimization, ICO 2023

作者： Promy, Fahmida Nusrat Chowdhury, Tasnia Afrin Imam, Omar Tawhid Alam, Farhana Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka1212 Bangladesh Department of Electrical and Electronics Engineering Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1341 Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh

ISBN: (纸本)9783031362453

Tuberculosis (TB) is a severe bacterial infection that can be spread by inhaling small droplets from an infected person’s cough and sneeze. TB claimed the lives of 1.5 million individuals in 2020, including 214,000 HIV-positive people. TB is the world’s second most widespread infectious lethal disease and the 13th leading cause of mortality. As a result, predicting whether someone has tuberculosis or not is critical. We experimented with chest X-ray images of healthy and tuberculosis patients. For our studies, we applied the CNN models VGG16, VGG19, Xception, ResNet50, InceptionResNetV2, DenseNet201, InceptionV3, and MobileNetV2. We also developed two models utilizing convolutional layers, max-pooling, and other techniques. In our study, VGG-16, Xception, and DenseNet201 provide any model’s highest training and validation accuracy. Densenet201 has the highest accuracy, with 99.7% in validation and 99.7% in training. One model we have developed has good training and validation accuracy, with 90.7% in training and 90% in validation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Convolution

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Crime Prediction based on Classification Approaches 6

Crime Prediction based on Classification Approaches

引用

6th international conference on Futuristic Trends in Networks and computing Technologies, FTNCT 2024

作者： Abubakera, Howida Muchtar, Farkhana Degan, Kiran Sanjay Mohd Azmi, Kaiyisah Hanis Abd Hamid, Fatimah Khairiah Binti Along, Nurul Zainab Bin Khairuddin, Alif Ridzuan Faculty of Computing Universiti Teknologi Malaysia Johor Johor Bahru81310 Malaysia Department of Electronic System Engineering MJIIT Universiti Teknologi Malaysia 54100 Malaysia College of Engineering Univertsiti Teknologi MARA Johor Branch Pasir Gudang Campus Johor Masai81750 Malaysia Symbiosis International University India

this study assesses and contrasts the effectiveness of four supervised learning algorithms which are Decision Tree, Extra Tree, Random Forest, and XGBoost in predicting crime types by utilizing the San Francisco crime dataset. the main goal is to identify the most efficient model for forecasting crime incidents based on past data. the results demonstrate that the Random Forest algorithm surpasses the other algorithms with an accuracy rate of 34%. this research highlights the importance of machine learning classifiers in helping security agencies optimize resources and improve public safety by accurately predicting crimes. the findings suggest that machine learning methods can greatly enhance crime prediction abilities and offer valuable insights for proactive policing strategies. © 2025 the Author(s). Published by Elsevier B.V.

关键词： Random forests

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Generic Recommendation System for Business Process Modeling 6th

Generic Recommendation System for Business Process Modeling

引用

6th international conference on Innovative computing and Communication, ICICC 2023

作者： Shreya, J.L. Saini, Anu Kumari, Sunita Jain, Astha Department of Computer Science and Engineering Dr. SPM International Institute of Information Technology Naya Raipur India Department of Computer Science and Engineering G. B. Pant DSEU Okhla-1 Campus DSEU New Delhi India Tata Consultancy Services Limited Uttar Pradesh Noida India

ISBN: (纸本)9789819930098

Process modeling helps in the comprehension and reorganization of corporate activity. Manual process modeling takes longer time and is liable for errors. We propose to build and provide a supporting system for people who manage the creation and application of business process models for their organizations. Any person intending to build and get recommendations for business process models can utilize the proposed recommender system, depending upon the kind of organization the person is working for. the core functions of the recommender system are to recommend fragments of a business process model as well as in-built process models with the benefit of creating, storing, and utilizing them for multiple purposes. the recommendations are based on a number of factors such as the number of people who prefer a particular model along with the models’ ratings. Here, we use the basic principle of collaborative filtering. this recommendation system provides a user-friendly interface, with a lucid structure of query interface to help users surf easily. the recommendation system also avoids the risk of misusing users’ personal details since it does not use cookies. Users can freely utilize this recommendation system even if they are novice. this extended work takes into account all of the specifications of a business process model for a range of industries, such as education, software, food, medical, automotive, banking, and so on, thus developing a generic system. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Power Curve Estimation of a Wind Turbine Considering Different Weather Conditions using Machine Learning algorithms 23

Power Curve Estimation of a Wind Turbine Considering Differe...

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23rd international Middle East Power systems conference, MEPCON 2022

作者： Al-Gabalawy, Mostafa Ramadan, Haitham S. Mostafa, Mohammed A. Hussien, Shimaa A. El-Safawa High Institute of Engineering Electronics Communications Computers Engineering Department Cairo Egypt Zagazig University Faculty of Engineering Electrical Power and Machines Dept Zagazig Egypt Must University Faculty of Engineering Electrical Power and Machines Dept 6th October Cairo Egypt Pyramids Higher Institute for Engineering and Technology Electrical Power and Control Department 6th October Cairo Egypt

ISBN: (纸本)9781665463638

theoretically, the output of the wind turbine might be estimated based the most known power equation that depends mainly in the wind speed. there are many issues appeared in the phase of the estimating and control while applying this equation due to ignoring many weather conditions. this paper introduces a multivariate estimation for the power curve of the wind turbine considering the weather conditions such as wind speed, air density, wind turbulence, and wind share. there variables are termed features, and a lot of measurement has been occurred to collect all possible data for these features, where measurements (system data) exceed 47,000 points. this data is proceeded mainly by three steps of the data sciences;exploratory data analysis (EDA), data processing, and building the model, using Python programming language, where it gives more flexibility more than the other languages. the power curve estimation is executed using different machine learning tools such as linear regression, polynomial regression, random forest regression, gradient boost (G Boost), and extreme gradient regression (XGBoost). A comparative study is introduced considering the R-square and the root mean square error. From the results, XGBoost learning tool provides the best performance in terms of root mean square error (RMSE). the RMSE value decreases to 6.404 while using the proposed algorithm compared to (6.631, 6.6721, and 9.072) attained through the alternative G Boost, forest random, and 4th-degree polynomial respectively. © 2022 IEEE.

关键词： Wind turbines

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13th international conference on Complex, Intelligent, and Software Intensive systems, CISIS 2019

13th International Conference on Complex, Intelligent, and S...

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13th international conference on Complex, Intelligent, and Software Intensive systems, CISIS 2019

ISBN: (纸本)9783030223533

the proceedings contain 94 papers. the special focus in this conference is on Complex, Intelligent, and Software Intensive systems. the topics include: From BPMN Models to SoaML Models;message Dissemination Using Nomadic Lévy Walk on Unit Disk Graphs;modelling a Smart Motorway;personalized Protocols for Data Division and Knowledge Management;the Spatiotemporal Prediction Model of Opioids Spread Trend Based on Grey Correlation;transferring Informal Text in Arabic as Low Resource Languages: State-of-the-Art and Future Research Directions;consumers’ Attitude Toward Cloud Services: Sentiment Mining of Online Consumer Reviews;HPC, Cloud and Big-Data Convergent Architectures: the LEXIS Approach;Performance Analysis of WMNs by WMN-PSOHC-DGA Simulation System Considering Random Inertia Weight and Linearly Decreasing Vmax Router Replacement Methods;knowledge Sharing System Database Architecture for Global Knowledge Sharing;an Intelligent Predictive Analytics System for Transportation Analytics on Open Data Towards the Development of a Smart City;multi-criteria Group Decision Making and Group Agreement Quotient Analysis Based on the Delphi Method;Graph-Based Semantic Query Optimization for Intensional XML Data;Design and Implementation of Security System for IPv6 Sensor Networks;A UML Profile for the Service Discovery in the Enterprise Cloud Bus (ECB) Framework;HPC-as-a-Service via HEAppE Platform;a Distributed Environment for Traffic Navigation systems;smart Scheduling Strategy for Lightweight Virtualized Resources Towards Green computing;ioT Cloud Platform Based on Asynchronous Processing for Reliable Multi-user Health Monitoring;ioT Node Selection and Placement: A New Approach Based on Fuzzy Logic and Genetic Algorithm;towards Cloud-Based Personalised Student-Centric Context-Aware e-Learning Pedagogic systems;intelligent Fall Detection with Wearable IoT.

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