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BERT-BiLSTM Architecture to Modelling Depression Recognition for Indonesian Text from English Social Media 4

BERT-BiLSTM Architecture to Modelling Depression Recognition...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Chowanda, Andry Andangsari, Esther Widhi Yesmaya, Violitta Chen, Tin-Kai Fang, Hsiao-Lin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities Psychology Department Jakarta11480 Indonesia Tainan University of Technology Creative Computing Centre Department of Comic Art Taiwan Taiwan Tainan University of Technology Living Service Industry Department Taiwan Taiwan

ISBN: (纸本)9798350318432

Depression is a common mental health disorder. It can greatly affect our daily lives. Depressed people are generally prone to negative emotions. Hence, recognising a sign of depression is an important task. Several techniques are proposed to model automatic depression recognition from several modalities. However, there is a limited number of datasets and research done in a local language (i.e. Indonesian). Expressing thoughts and feelings are unique based on their backgrounds (e.g. race, religion and culture). Hence, fine-tuning the model to a local language or culture is also important. this research aims to build a model using deep learning to recognise depression signs from the text in the local language (i.e. Indonesian). Seven models are proposed in this research to model depression recognition from social media. the result illustrates that combining Bidirectional Long short-term memory with Bidirectional Encoder Representations from Transformers architecture can improve the performance of the model. © 2023 IEEE.

关键词： Deep learning

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4th international conference on Computing, Communications, and Cyber-Security, IC4S 2022

4th International Conference on Computing, Communications, a...

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4th international conference on Computing, Communications, and Cyber-Security, IC4S 2022

ISBN: (纸本)9789819914784

the proceedings contain 67 papers. the special focus in this conference is on Computing, Communications, and Cyber-Security. the topics include: Resource Sharing in Back Haul Satellite-Based NOMA Network;a Review on Various Deepfakes’ Detection Methods;Proposed Framework for Implementation of Biometrics in Banking KYC;paddy Pro: A MobileNetV3-Based App to Identify Paddy Leaf Diseases;Cryptanalysis of RSEAP2 Authentication Protocol Based on RFID for Vehicular Cloud Computing;a Survey on Code-Mixed Sentiment Analysis Based on Hinglish Dataset;An Alternative to PHP for the Development of Web applications: Java Server Pages Engine;Drone Ecosystem: Architecture for Configuring and Securing UAVs;novel Load Balancing Technique for Microservice-Based Fog Healthcare Environment;Self-improved COOT Algorithm for Resource Allocation in Cloud Data Centers;XGBoost-Based Prediction and Evaluation Model for Enchanting Subscribers in Industrial Sector;cleanO-Renewable Energy-Based Robotic Floor Cleaner;Impact of "COVID-19 Pandemic" on Children Online Education: A Review and Bibliometric Analysis;digital-Based learning in Indian Government’s Higher Education: Initiatives and Insights;Autonomous Vehicles Adoption Classification for Future Mobility in UAE Using machine learning;an Augmented Reality Framework as a Solution to Enhance the Experience of Visiting a Museum;Hybrid Real-Time Implicit Feedback SOM-Based Movie Recommendation Systems;Automatic SMS Spam Filtering for Disaster Response Using Classification Algorithms;an Improved Neural Network-Based Routing Algorithm for Mobile Ad Hoc Networks;Lung Cancer Diagnosis Using X-Ray and CT Scan Images Based on machine learning Approaches;hybrid machine learning Algorithm for Prediction of Malaria;yoga Pose Estimation Using machine learning;COVID Detection from Chest X-Ray Images Using Deep learning Model;preface.

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Design and Fabrication of Adhesive application System for Magnets in Motors 4

Design and Fabrication of Adhesive Application System for Ma...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sanjeev Kumar, S. Shree Arryaman, A.S. Suganthprabhu, R. Pritima, D. Arun Kumar, P. Sri Krishna College of Engineering and Technology Department of Mechatronics Engineering Coimbatore India Mahle Electric Drives India Pvt Ltd Coimbatore India

ISBN: (纸本)9798350300093

Electric Motors requires Permanent Magnets in them to function. the following paper explains the design, construction and critical analysis of an Adhesive applicant machine for magnets in rotor casings [1] [2]. through the integration of both human and mechanical systems, this innovation addresses the problem of work efficacy in an industry. We used a scientific research model to analyze and answer the question. the industry we chose for our model is on a small scale and is very simple to work with and analyze for its full potential. Our main objective is to construct a cartridge-based method to load the workpiece into the machinery and to create the ideal end-effector system for Glue delivery on the specified path [3]. Compared to electromagnets, permanent magnets on a rotor can both simplify the design and improve the efficiency of electric machines. the absence of automated assembly techniques for large machinery is a limitation, too. the strategy for resolving this issue is building a cohesion environment where people and machines work as a unified unit capable of producing more products. © 2023 IEEE.

关键词： Permanent magnets

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A Time Series Clustering Method for Network Big Data 29

A Time Series Clustering Method for Network Big Data

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29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhu, Yujia Min, Geyong Wu, Yulei Wang, Haozhe Science and Economy University of Exeter Falcuty of Environment Department of Computer Science ExeterEX4 4QF United Kingdom

ISBN: (纸本)9798350330717

In the era of big data, network data increase rapidly in a distributed manner, giving birth to the network big data. Network big data with the extra features such as distributed and decentralized data collection and storage, distributed and parallel data processing, more complex and evolving relationships among data, and heterogeneous data representation, pose opportunities together with challenges to the traditional network analysis algorithms. A hopeful solution is combining machine learning techniques with network big data analysis. In this paper, we proposed a novel time series clustering method which effectively combines machine learning techniques with network big data analysis for fault diagnosis task based on network logs. Verification experimental results on classic HDFS dataset demonstrate the outstanding performance of the proposed method. © 2023 IEEE.

关键词： deep clustering hidden representations network big data Time series clustering

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application of Vision-Series Transformer in screening for coronary heart diseases using coronary CT angiography 23

Application of Vision-Series Transformer in screening for co...

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4th international conference on Computing, Networks and Internet of things, CNIOT 2023

作者： Wang, Kunlun Meng, Hanyang Wang, Xingjun Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798400700705

With the development of computer vision, more and more researches support the utilization of machine learning (ML) in the diagnosis of coronary heart disease (CHD) using CT angiography. Artificial intelligence shows strength in assisting researchers and clinicians in CT scan information reading. CTA data provides a series of scans of images from patients and thus could be reconstructed as three dimensions (3-D) images, also known as volume rendering images. We use the projection of those 3-D images as a dataset and developed a deep neural network with Transformer to diagnose coronary heart diseases automatically. Compared with several traditional and classic deep learning methods, i.e. simple CNN, VGGNet, and ResNet which show accuracy of 52.5%, 57.82%, and 76.3%, respectively, vision transformer(ViT) improves it to 81.5%. Besides, we take great advantage of the vision transformer and series of CTA. Based on that, we developed a new deep learning model Vision-Series Transformer(ViST). It has better performance in accuracy(83.78%). 5-fold cross-validation was used to determine whether it performs better in other statistical indicators. It shows the same high sensitivity and specificity as ViT, better than that of other models. the results show that both ViT and ViST could be used in screening for CHD, while ViST has better statistical indicators. © 2023 Owner/Author.

关键词： Diseases

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Base Station Traffic Prediction based on Feature Selection and Stacking Ensemble learning 23

Base Station Traffic Prediction based on Feature Selection a...

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4th international conference on Computing, Networks and Internet of things, CNIOT 2023

作者： Zhao, Long Huang, Youzhi Wang, Yanyan Xu, Yin Feng, Qiangzhong Chen, Enhong School of Computer Science and Technology University of Science and Technology of China Innovation + Research Institute GuoChuang Cloud Technology LTd Hefei China Innovation + Research Institute GuoChuang Cloud Technology LTd. Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9798400700705

Accurately predicting base station network traffic is of great significance to improve network service quality and reduce base station operating costs. Aiming at the problem of low prediction accuracy of single model in the existing base station traffic prediction methods, a multi-model fusion prediction method based on feature selection and stacking ensemble learning is proposed. Firstly, a large number of features are constructed on the historical data, and then feature selection and correlation verification are carried out based on the tree model, and the features with high correlation are retained as the input of the predictive model to improve the performance and interpretability of the model. On this basis, a stacking ensemble learning prediction model with GDBT, XGBoost, LightGBM as the base learner and MLP as the meta-learner is established, and finally experimental verification is carried out on the real 1731 base stations. the results show that the mean squared error (MSE) and mean absolute error (MAE) of this method are reduced by 9.8% and 4.3%, respectively, compared with the single machine learning prediction model, and have better prediction accuracy and generalization ability. © 2023 ACM.

关键词： Base stations

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4th international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3D IT-MSP&DL 2022

4th International Conference on 3D Imaging Technologies- Mul...

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4th international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3D IT-MSP and DL 2022

ISBN: (纸本)9789819912292

the proceedings contain 51 papers. the special focus in this conference is on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning. the topics include: Research on the application of 3D Modeling Technology in Mechanical Structure Teaching;2D Numerical Model Used to Investigate the Influence of Vegetation on Geomorphological Evolution of Mudflats;design and Experimental Verification of High Functional Density Cubesat System;space Design of Exhibition Hall Based on Virtual Reality;Identification of Expressway Traffic States Based on the Enhanced FCM Algorithm;algorithms Applied in Soft Tissue Deformation Simulation: A Survey;soft Tissue Cutting Based on Position Dynamics;research on application Technology of Multi-source Fusion of 3D Digital Resources in Power Grid;rapid Identification of Herbaceous Biomass Based on Raman Spectrum Analysis;a Knowledge Representation Method for Hierarchical Diagnosis Decision-Making Under Uncertain Conditions;research on Spam Detection with a Hybrid machine learning Model;research on Weld Defect Object Detection Based on Multi-channel Fusion Convolutional Neural Network;Design and Research of Highway Tunnel Construction Positioning System Based on UWB;research on Cross Domain Data Analysis and Data Mining Technology of Power Grid Digital Benefits;graph Convolutional Neural Networks for Drug Target Affinity Prediction in U-Shaped and Skip-Connection Architectures;SOC Estimation of Lithium Titanate Battery Based on Variable Temperature Equivalent Model;research and Implementation of Hybrid Storage Method for Multi-source Heterogeneous Data of Electric Distribution Network;Research on Measuring Method of Ball Indentation Based on MATLAB Image Edge Detection;multi-feature Extraction of Mineral Zone of Tabling through Deep Semantic Segmentation.

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machine learning based Novel Framework for Fake News Detection and Prevention using Blockchain 10

Machine Learning based Novel Framework for Fake News Detecti...

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Faridi, Arman Rasool Singh, Rahul Masood, Faraz Salmony, Monir Yahya Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9789380544472

there has been a dramatic increase in the transmission of false information on social media over time. Concerns have been expressed about the effects of false information on the industry, not only by ordinary people but also by marketers. False information is often produced for commercial purposes to draw viewers and gather money through advertising. the whole world is affected by fake news. False and appealing news articles have been published by some unreliable sources, and this news cannot be confirmed because no regulatory system exists. As a result, these unreliable sources are free to post whatever they want, which in some circumstances, even causes social chaos. Due to the internet's ease, unsuitable news can spread faster than ever. However, it has been shown that influential media organizations with nefarious intentions have created false news to impact global events and policies. A conceptual framework for fake news detection using machine learning and blockchain is proposed in this paper. the idea is to give ratings to the content by experts and ordinary people and check for the authenticity of the content. Ethereum blockchain is used to make the system decentralized, and the InterPlanetary File System is used as off- chain storage for blockchain. A Decentralized-application will also be created in Ethereum, where users can read the news. InterPlanetary File System will be used to store the data, and the hash of the metadata will be stored in the blockchain. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Smart contract

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Real Estate Forecasting System Using machine learning Algorithms 6

Real Estate Forecasting System Using Machine Learning Algori...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kaur, Harshpreet Kushwanth, A. Sai Praveen, M. Akshay, Y. Kiran, Y. Surya Kartheek, B. Ganesh Kamal Reddy, G. Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350304480

the idea of this paper is to predict the value of houses and properties of the market value. the aim of the presented paper is to estimate the market value of the asset being sold. this application assists in determining a location's beginning pricing based on location-specific factors. the below mentioned idea to find the price of a location based on location variables. the below discussed models helps to find the market value of any property in India through different databases depending upon the locations. the presented paper suggests the buyer in determining the prices of different houses with the similarity of characteristics and properties. According to the survey made the prices of properties always rise every year, so a buyer needs a real estate forecasting system. this research paper can help estimate the price of a house which helps both seller and buyer for a reasonable price to buy a property. the result showed that machine learning algorithms in linear regressions model can accurately predict area price with high accuracy rates. However, some challenges such as property value based on area, characteristics, locality, facilities provided. It highlights the importance of machine learning algorithms for price predictions to research and address the challenges. © 2023 IEEE.

关键词： Data Science Google collab House price prediction Linear regression model machine learning Python

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Comparative Analysis of Web APIS: RESTful and GraphQL 4th

Comparative Analysis of Web APIS: RESTful and GraphQL

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4th international conference on machine learning, Advances in Computing, Renewable Energy and Communication, MARC 2023

作者： Seth, Aparna Kumar, Vinay Kulshrestha, Vaibhav Vij, Aanchal School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819752300

the two well-liked methods for creating web services, REST and GraphQL APIs, are thoroughly compared in this study paper. To give a thorough assessment of both systems, the study examines a number of factors, including performance, flexibility, scalability, and developer experience. this research aims to identify the strengths and weaknesses of each approach through extensive testing, benchmarking, and real-world use cases, providing insights to assist developers, architects, and organizations in selecting the best API technology for their particular needs. the results highlight subtle trade-offs between REST and GraphQL, illuminating their best applications and informing further API design decisions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： application programs

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