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Analysing the Sentiments Of Marathi-English Code-Mixed Social Media data Using machine learning Techniques 5

Analysing The Sentiments Of Marathi-English Code-Mixed Socia...

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5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Patwardhan, Varad Takawane, Gauri Kelkar, Nirmayi Gaikwad, Omkar Saraf, Rutwik Sonawane, Sheetal Pune Institute of Computer Technology Computational Linguistics Research Lab Pune India

ISBN: (纸本)9781665475242

A vast amount of data is generated every day through social media platforms. Various techniques and methodologies are used to bring different forms of data to use. One such form of data is textual data generated from social media platforms in the form of chats, comments, and tweets. the term 'code-mixed data' describes data that combines components of different languages or linguistic subgroups such as text written in several different languages or speech that shifts between languages. Due to increased social media use and worldwide communication, many individuals are using multiple languages in their daily communication, making this type of data even more crucial. machine translation, speech recognition, and text categorization are just a few examples of natural language processing activities that can be performed on code-mixed data. Research on code-mixed data can also aid in the understanding of multilingual communication. In this paper, we present an empirical study on the problem of word-level language identification and text normalisation for Marathi-English code-mixed text. We have created a new dataset of 1009 sentences that exhibit code-mixing of Marathi (Romanised) and English textual data. this data was collected from Whatsapp chats and Youtube comments. © 2023 IEEE.

关键词： Sentiment analysis

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Multivariate and Univariate Anomaly Detection in machine learning: A Bibliometric Analysis 5th

Multivariate and Univariate Anomaly Detection in Machine Lea...

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5th international conference on Information Systems and Management Science, ISMS 2022

作者： Guembe, Blessing Azeta, Ambrose Misra, Sanjay Garg, Lalit Department of Computer and Information Sciences Covenant University Ogun Ota Nigeria Department of Computer Science Namibia University of Science and Technology Windhoek Namibia Department of Computer Science and Communication Ostfold University College Halden Norway Faculty of Information and Communication Technology University of Malta Msida Malta

ISBN: (纸本)9783031311529

Anomaly detection enables the identification of the nature of the outliers to determine if they are errors or new trends that need to be understood and learnt by the model for improved generalisation capability. Multivariate and univariate anomaly detection is used to find an unusual point or pattern in a given set. A substantial number of articles have focused on this research domain;however, they primarily reflect on the impacts and trends of multivariate and univariate time-series data research. However, there is still a lack of bibliometric reports exhibiting the exploration of an in-depth research pattern in multivariate and univariate anomaly detection approaches. this study addresses that gap by analysing the widespread multivariate and univariate anomaly detection activities conducted thus far. this study analysed the Scopus database by using bibliometric analysis in a pool of more than 1385 articles that were published between 2010 and 2022, of which 679 are journal articles, 609 are conference papers, 72 are conference reviews, 13 are book chapters, 8 are reviews, 2 are erratum, and 1 is a book and a short survey. the multivariate and univariate anomaly detection bibliometric analysis was developed using R, an open-source statistical tool, and bibliophily was used to analyse the results. the findings reveal the following: (1) multivariate and univariate anomaly detection in collaboration with machine learning can enhance intrusion detection systems;(2) Researchers are interested in combining multivariate and univariate techniques with machine learning and deep learning classification problems to distinguish normality from abnormality;(3) the most active country in this research domain are the United States, China, France, India, Italy, and Germany;(4) Norway, Sweden, and Taiwan published few articles, however, receive many citations;(5) the United States, China, France, and Italy are the countries that collaborate the most in publishing articles on multiva

关键词： Anomaly detection

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A Knowledge Graph Completion Model Combining Graph Convolutional Networks with Self-Attention Mechanisms

A Knowledge Graph Completion Model Combining Graph Convoluti...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yao Chen Shaorong Li Hao Fang Yaokun Li Jianhong Hu Yunnan Transportation Science Research Institute Co. Ltd. Kunming China School of Software Yunnan University Kunming China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Knowledge graph completion aims to predict missing entities or relationships in a knowledge graph, addressing the issue of data sparsity. In recent years, deep learning-based models, especially Graph Convolutional Network (GCN) and Self-Attention mechanisms, have gained attention for their advantages in handling graph-structured data. We propose a knowledge graph completion model, CSANN, which integrates GCN and self-attention mechanisms. First, GCN module is used to learn the initial representations of entities and relationships, considering the neighboring information of entities. Next, the initial representations of entities and relationships are concatenated to form a sequence, which is further refined by introducing the self-attention mechanism. By combining the local graph structure awareness of GCN and the sequential dependency capturing ability of self-attention mechanisms, CSANN improves the accuracy of knowledge graph completion. Experiments conducted on two public datasets, FB15K-237 and WN18RR, show that the proposed model outperforms many classical models, such as TransE, DistMult, ConvE, and RotatE, in terms of Mean Reciprocal Rank (MRR) and Hit@K.

关键词： Measurement Knowledge engineering Adaptation models Graph convolutional networks Computational modeling Scalability Knowledge graphs machine learning Magnetic heads pattern recognition

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Comparative Analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer Models in Predicting Stock Prices 5

Comparative Analysis of Long Short-Term Memory (LSTM), Gated...

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5th IEEE international conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Umezuruike, Chinecherem Olaniyan, Deborah Olaniyan, Julius Adeniyi, Abidemi Emmanuel Oyebade, Adedoyin Abaneme, David Bowen University Department of Software Engineering Iwo Nigeria Bowen University Department of Computer Science Iwo Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria

ISBN: (纸本)9798331542559

this research explores the application of LSTM, GRU and Transformer models for predicting stock prices, aiming to enhance accuracy in financial forecasting. Stock price prediction is crucial for investment decision-making, yet challenging due to market volatility and complex patterns. the objectives are to evaluate the performance of LSTM, GRU and Transformer models using key metrics such as test loss, MAE, and MSE, and to compare their predictive capabilities. the LSTM model demonstrates robust performance with low test loss and MAE, indicating precise predictions and effective pattern recognition in financial data. In contrast, the Transformer model also shows promising results with relatively low test loss and MAE, albeit with larger errors in MSE and MAE metrics. Both models highlight the potential for accurate stock price prediction, suggesting avenues for future research to optimize model performance and reliability in financial forecasting applications. the experimental results show that GRU Outperformed LSTM and Transformer with an MSE of 0.0008, MAE of 0.0023, and high test accuracy of 0.9833. © 2024 IEEE.

关键词： Analysis Deep learning Financial forecasting machine learning Stock prices

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EVB- dataset Engineering Vehicle Behavior dataset

EVB- Dataset Engineering Vehicle Behavior dataset

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yansong Shi Yining Guo Bowen Wang Gang Shi College of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Using deep learning methods to detect the construction behaviour of construction vehicles from construction site videos is of great significance and research value for ensuring project quality and improving construction site supervision efficiency. the lack of publicly available engineering vehicle datasets limits the in-depth research. this study proposes an improved spatiotemporal action multi-object video dataset annotation method and an innovative dataset conversion strategy, effectively expanding the behavioural dataset into a object detection dataset. A picture-based engineering vehicle object detection dataset and a spatiotemporal analysis engineering vehicle behaviour dataset were constructed. the image-based engineering vehicle object detection dataset collected a total of 6827 pictures, with an annotation volume reaching 22619; the spatiotemporal engineering vehicle behaviour dataset contained 180 videos, annotated video frames totalled 4860 frames, and the annotation volume reached 16638. By employing the SlowFast algorithm to evaluate the dataset, this study achieved a mean average precision (mAP) of 82.41%. We believe that the developed dataset will provide a solid foundation for future research in the field of construction engineering and help drive the continued development of the field.

关键词： machine learning algorithms Annotations Object detection Solids data models Spatiotemporal phenomena pattern recognition Monitoring Videos Meteorology

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Using EEG Nonlinear Dynamic Features and machine learning to Identify Different Organizational Commitment 5

Using EEG Nonlinear Dynamic Features and Machine Learning to...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Zhang, Rui Wang, Zi-Yang Liu, Yu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

As machine learning has greatly improved the depth and width of EEG analysis and psychological research, it has become possible to analyze the relatively stable personalities of individuals based on EEG. In this paper, we recorded the restingstate EEG of subjects, labeled them using their score of organizational commitment, and achieved automatic recognition. In order to complete such a new challenging classification task, we extracted a variety of different nonlinear dynamic features from resting-state EEG, and then used different machine learning models (SVM, GBDT, KNN, LR, Gaussian NB) to classify these features, and next evaluated experimental results based on crossvalidation. the results show that Permutation Entropy and Approximate Entropy achieved best accuracy, which both obtained an overall accuracy of more than 70% based on machine learning. Furthermore, we adopted a stacking strategy and constructed a fusion model to improve the performance. the experimental results show that using the Stacking model to classify the Permutation Entropy of EEG can achieve an overall accuracy of 82.6% with 83.3% recall and 0.827 F1-score. In addition, we also conducted a comparative analysis of EEG signals with different lengths of sample and compare eyes-open and eyes-closed EEG. © 2022 IEEE.

关键词： Entropy

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Hierarchical Classification of Frontotemporal Dementia Subtypes Utilizing Tabular-to-Image data Conversion with Deep learning Methods 27th

Hierarchical Classification of Frontotemporal Dementia Subty...

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27th international conference on pattern recognition, ICPR 2024

作者： Poonam, Km Kotra, Venkata Sathwik Guha, Rajlakshmi Chakrabarti, Partha P. Indian Institute of Technology Kharagpur West Bengal Kharagpur721302 India

ISBN: (纸本)9783031781940

Frontotemporal dementia (FTD) is a group of neurodegenerative disorders characterized by progressive damage to the frontal and temporal lobes of the brain. FTD includes notable changes in social behavior, personality, and language function difficulties. the primary FTD spectrum disorders include behavioral variant FTD, non-fluent variant primary progressive aphasia, and semantic variant. With an aging population, FTD prevalence increases, emphasizing the critical need for research, care, and awareness. this study introduces novel hierarchical classification methods that enhance model prediction capabilities. the dataset used in this work consists of neuropsychiatric tabular data from the Frontotemporal Lobar Degeneration Neuroimaging Initiative database. Innovative methods are needed to effectively leverage deep learning for FTD classification, especially when limited to tabular data. To address this challenge, we proposed an innovative technique, the Tensorised Image Generator (TIG), which transforms tabular data into grid-based image representations or tensors. the TIG algorithm optimizes tabular data visualization by ranking pairwise feature distances and then creating a distance matrix and strategically positioning features in the image to preserve spatial correlation among features. the algorithm further enhances the grid structure with line drawing and intensity adjustments. the results on these images demonstrate high accuracy in detecting FTD subtypes using a Convolutional Neural Networks ensemble with multi-layer perceptron, achieving a test accuracy of 88.89%, which is a 2% increment of hierarchical approach over flat machine learning methods and around 5% increment for combined hierarchical classification with image-based technique compared to best flat method across most metrics (accuracy, precision, recall, and F1-score). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Hand-written Digit recognition using Convolutional Neural Network in Python with Tensorflow

Hand-written Digit Recognition using Convolutional Neural Ne...

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Recent Trends in Computer Science and Technology (ICRTCST), international conference on

作者： Prakash Anand Piyush Ranjan Priyanka Srivastava Department of Computer Science Jharkhand Rai University Ranchi India Department of Computer Science Sarala Birla University Ranchi India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

Recently, deep learning has transformed machine learning by significantly enhancing its artificial intelligence as Artificial Neural Networks (ANN) have become increasingly prevalent. Due of its extensive range of applications in fields such as intelligence, healthcare, medical, athletics, robots, etc., machine learning algorithms is remarkably used in a wide range of industries. At the core of incredible advancements in deep learning are convolutional neural networks (CNN), which integrate artificial neural networks (ANN) and contemporary deep learning algorithms. Across a variety of applications, including pattern classification, phrase classification, voice recognition, image identification, text summarization, documentary analysis, scene recognition, and handwritten digit recognition, it has been used. Our research's objective is to develop a model that can accurately compare image comparisons and identify handwritten numbers. We used the Modified National Institute of Standards and Technology (MNIST) dataset to conduct our experiment CNN.

关键词： Deep learning Handwriting recognition machine learning algorithms Image recognition Text recognition Service robots pattern classification

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Applications and Prospects of Neural Networks in Fault Detection: A Visualization Analysis Based on CiteSpace

Applications and Prospects of Neural Networks in Fault Detec...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Kechen Wu Xingcheng Lu Yixuan Chen Nanjing University of Finance and Economics Nanjing China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

With the rapid development of industrial automation and intelligence, fault detection technology has become key to ensuring system stability and safety. Neural networks, as outstanding data processing and pattern recognition tools, have demonstrated extensive application potential in the field of fault detection. this study utilizes CiteSpace visualization software to conduct a thorough analysis of 2,211 papers on neural networks and fault detection in the Web of Science database from 2020 to 2024, aiming to reveal the research dynamics and development trends in this field. the analysis covered indicators such as authors, research institutions and research direction identifying major research hotspots including process monitoring, rolling element bearing monitoring, structural health monitoring, gas turbine performance assessment, and power system stability. the results indicate that advanced deep learning techniques, the integrated application of digital twins, and the development of the industrial Internet of things are driving fault detection towards greater intelligence. Future research is expected to focus more on algorithm optimization, model interpretability, and the enhancement of real-time monitoring technologies, while also emphasizing the importance of security and privacy protection.

关键词： Fault detection Neural networks Rolling bearings Power system stability Stability analysis Software pattern recognition Visual databases Security Turbines

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Capture and recognition of Bead Weaving Activities using Hand Skeletal data and an LSTM Deep Neural Network. 5

Capture and Recognition of Bead Weaving Activities using Han...

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5th IEEE international conference on Artificial Intelligence and Virtual Reality (IEEE AIVR)

作者： Goddy-Worlu, Rowland Ferreira, Martha Dais Peachey, Matthew Forren, James Nicholas, Claire Reilly, Derek Dalhousie Univ Fac Comp Sci Halifax NS Canada Dalhousie Univ Fac Architecture & Planning Halifax NS Canada Univ Oklahoma Dept Anthropol Norman OK 73019 USA

ISBN: (纸本)9781665457255

Several factors lead to the extinction of tangible cultural heritage, such as globalization, urbanization, intention and accidental neglect, modernization, mechanization, limited usage, migration, and the minimization of skilled practitioners/craft educators and owners. We, the Gesture and Form group, aim to preserve the bead weaving craft, which plays an essential role in culture. We aim to use Augmented reality (AR) and machine learning (ML) to preserve this craft. Preservation in this context means passing the technique from one generation to the other, even without a skilled craftsperson. To achieve this, we aim to use an LSTM deep neural network to classify micro activities in the Peyote stitch with the hand skeletal data from the Microsoft Hololens 2. In this paper, we present our preliminary work and a workflow of the ML process involved with a mini project we called "thumbs-up gesture classification." Using a single subject participant for the gesture classification, we achieve 100% accuracy on the training and test set, and new data from the device. Finally, we present a future direction for our research.

关键词： Augmented Reality Computer-Dependent learning Cultural Heritage Preservation Deep learning LSTM Bead Weaving Gesture recognition Handcraft

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