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A Semi-supervised Approach of Cluster-Based Topic Modeling for Effective Tweet Hashtag Recommendation

SN computer science 2024年第7期5卷 951页

作者： Pattanayak, Pradipta Kumar Tripathy, Rudra M. Padhy, Sudarsan Research Scholar BPUT(Rourkela) Department of Computer Science and Engineering Silicon University Patia Odisha Bhubaneswar 751024 India School of Computer Science and Engineering XIM University Harirajpur Odisha Bhubaneswar 752050 India Institute of Mathematics and Applications Andharua Odisha Bhubaneswar 751029 India

Twitter has emerged as a significant source of data to be used for text summarization, Topic Modeling, document clustering, information retrieval, sentiment analysis, etc. Using hashtags, Twitter users may categorize their tweets as hashtags provide the essential meta-information in connecting tweets to the underlying themes. However, the majority of tweets do not have hashtags, which makes it challenging to search for a particular theme. The proposed model is designed to recommend appropriate hashtags for tweets by considering their categorization into sports, politics, health, or technology. In this paper, we proposed a novel heuristic for recommending hashtags of tweets. Taking 20,000 tweets, which includes 5000 tweets from each of the specified four topics along with their respective hashtags. These hashtags were manually assigned by a group of experts, which were subsequently excluded during the topic modeling process. The basic data-cleaning technique is applied to clean and tokenize the tweets. Then Word2Vec technique is used to vectorize the tokens which captured the semantic meaning of the words in the tweets and overcomes the data sparsity issues. The dimension of the data is reduced using Singular Value Decomposition (SVD) followed by t-SNE (t-distributed Stochastic Neighbor Embedding). The reduced data is divided into four clusters and a semi-supervised method is introduced to link these clusters to the aforementioned topics, which eventually helped to produce the hashtag for a list of tweets. On comparison of our results with existing techniques, it is observed that our model performance is better with respect to the metrics: precision, recall, and F1-score. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gibbs sampling Dirichlet mixture model Linear Dirchlet allocation Singular value decomposition Topic modeling Word2Vec

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CSE-NER: A Category Semantic Enhanced Multi-Task Learning Framework for Named Entity Recognition 5

CSE-NER: A Category Semantic Enhanced Multi-Task Learning Fr...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Dai, Dongbo Zhang, Guangjie Li, Shengzhou Song, Na Xu, Yubao Tang, Zheng Zhang, Huiran School of Computer Engineering and Science Shanghai University Shanghai China University of Tsukuba Department of Computer Science Tsukuba Japan Shanghai University Research Center of Nanoscience and Nanotechnology Shanghai China University of Toyama Faculty of Engineering Toyama Japan

ISBN: (纸本)9798350385557

Named Entity Recognition (NER) is a fundamental and crucial task in natural language processing. Currently, the use of large pre-trained models has become the mainstream for NER. However, when there is a distributional difference between pre-training data and downstream task data, such extensive prior training may lead to issues like 'knowledge hallucination' or 'domain shift' in the downstream task. To address this, we propose a Category Semantic Enhanced framework for Named Entity Recognition (CSE-NER) in this paper. By introducing more downstream task information during the fine-tuning phase, we enhance the model's adaptability. Specifically, we incorporate a Category Semantic Embedding layer, providing category semantic information distinct from context embeddings. Additionally, we introduce an auxiliary task, Entity Type Classification, to help the model effectively utilize the added category semantic information. Experiments on five datasets demonstrate the effectiveness of our proposed framework, outperforming various state-of-the-art methods. © 2024 IEEE.

关键词： Semantics

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Using Knowledge Graph Embeddings in Embedding Based Recommender Systems 20

Using Knowledge Graph Embeddings in Embedding Based Recommen...

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20th International Conference on Language Engineering, ESOLEC 2022

作者： Ragab, Ahmed Hussein El-Kafrawy, Passant School of Information Technology and Computer Science Computer Science Department Nile University Cairo Egypt School of Information Technology and Computer Science CIS Research Center Nile University Computer Science Department Egypt Effat Collage of Engineering Jedah Saudi Arabia

ISBN: (纸本)9781665453226

This paper proposes using entity2rec [1] which utilizes knowledge graph-based embeddings (node2vec) instead of traditional embedding layers in embedding based recommender systems. This opens the door to increasing the accuracy of some of the most implemented recommender systems running in production in many companies by just replacing the traditional embedding layer with node2vec graph embedding without the risk of completely migrating to newer SOTA systems and risking unexpected performance issues. Also, Graph embeddings will be able to incorporate user and item features which can help in solving the well-known Cold start problem in recommender systems. Both embedding methods are compared on the movie-Lens 100-K dataset in an item-item collaborative filtering recommender and we show that the suggested replacement improves the representation learning of the embedding layer by adding a semantic layer that can increase the overall performance of the normal embedding based recommenders. First, normal Recommender systems are introduced, and a brief explanation of both traditional and graph-based embeddings is presented. Then, the proposed approach is presented along with related work. Finally, results are presented along with future work. © 2022 IEEE.

关键词： Recommender systems

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Developing an Adaptive Security Framework for Real-Time Threat Detection and Response in Cloud-Network Systems

Developing an Adaptive Security Framework for Real-Time Thre...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Paramesh, J. Sriram, K.P. Anbalagan, E. Sasikumar, S. Vimal Kumar, M. Guru Saveetha Engineering College Department of Computer Science and Engineering Chennai India Information Technology St.Joseph's Institute of Technology OMR Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha University Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

The study is centred on examining an intelligent security system for real-time threat identification and mitigation in cloud-network systems. The more cloud computing environments are being constructed;conventional models of security do not offer optimal solutions to counteract changing cyber threats. This framework incorporates the use of sophisticated algorithms in machine learning and real-time data analysis to achieve a more proactive form of defence measures. It incorporates anomaly detection, classification algorithms and predictive analysis in the improvement of threat detection and response time. The framework also has active response mechanisms for timely deliberation on anything that may be seen to have a detrimental effect on the cloud. Analyzes reveal high levels of detection, timely responses and scalability proving the suitability of the framework to handle new threats and/or blend with existing cloud components. The findings of this study can be considered as valuable contribution to cloud security since the approach proposed in this paper can be effectively employed to enhance protection against new types of threats in specific organizational contexts. © 2024 IEEE.

关键词： Cloud security

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An Ensemble based Recession Prediction 5

An Ensemble based Recession Prediction

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Kumar, Mukesh Rapate, Gauri Sameer Kumari, Tanaya Kumar, Dharmendra Chatterjee, Pinaki Sankar Sahoo, Biswajit Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India Pes University Department of Computer Science and Engineering Karnataka Bangalore India Kiit University School of Computer Engg. Bhubaneswar India Tiet Department of Eied Punjab 147004 India

ISBN: (纸本)9798331522100

Predicting the economic crises of the upcoming years is vital, but difficult, given this superior situation. The research paper presents an ensemble-based approach for predicting economic recessions. The research emphasizes the importance of leveraging the strengths of multiple base classifiers to develop more accurate and reliable early warning systems for economic downturns. In this work we have analyze the recession prediction with different ensemble classifier with various machine learning algorithms. As ensemble machine learning algorithms, we have analyzed data using voting, stacking, and the Xgboost classifier in addition to Support Vector Machine (SVM), Logistic Regression, and Decision Tree. We have used four performance measures to evaluate the performance on two datasets. The results can inform the development of such systems to better anticipate and prepare for potential recessions. © 2025 IEEE.

关键词： Logistic regression

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Bibliometric Analysis of Smart Technology for Virtual Museum Research 9

Bibliometric Analysis of Smart Technology for Virtual Museum...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Carollina, Donna Purbasari, Mita Sidharta Bina Nusantara University School of Design Visual Communication Design Department Jakarta Indonesia Bina Nusantara University Graphic Design & New Media Program Visual Communication Design Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350370126

In recent years, the development of intelligent technology has had a significant impact on the development of virtual museums. Therefore, knowledge mapping is needed to describe and analyze trends, implementation, and benefits of using smart technology in virtual museums. This study used bibliometric methods. The results is smart technology for virtual museum research tends to a downward trend in research productivity. The productive year for this study was 2019. While the type of documents related to this research on Scopus indexation is dominated by conference papers with 51 documents or 63.7%. The dominant publication source for this research is Lecture Notes in computer science including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics with 11 documents. The affiliation with the most documents is MARA University of Technology with 2 documents, and the most productive country in this study is China with 15 documents. From the results of research mapping, it was found that research in the field of smart technology for virtual museums still has great opportunities to be applied to applicable themes such as the application of local wisdom content that can enrich research topics. © 2023 IEEE.

关键词： Mapping

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A Novel Approach of Machine Learning Application in Astrophysics: Morphological Feature Wrapping Based Ensemble Method for Galaxy Shape Classification Using GAMA dataset 1

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International Conference on Advances in Communication Technology and computer Engineering, ICACTCE 2023

作者： Sinha, Anurag Shahid, Mohammad Nandan, Amritesh Iwendi, Celestine Giri, Abhijeet Kumar Anand, Saprit Department of Computer Science and Information Technology IGNOU New Delhi India Department of Computer Science and Information Technology Amity University Jharkhand Ranchi India Department of Computer Science and Information Technology Graphic Era Deemed to be University Dehradun India School of Creative Technologies University of Bolton BoltonBL3 5AB United Kingdom Department of Computer Science and Engineering KIIT Bhubaneswar India

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

The numerous strategies for the automated morphological categorization of galaxies, which uses a variety of supervised machine learning techniques, have not been well examined or compared. As the majority of star galaxy classifiers in use today use condensed summary data from catalogues, rigorous feature extraction and selection are required. With the aid of Deep Convolutional Neural Networks (CNN), a development in machine learning, it may automate the process of feature detection from data by a computer, therefore lowering the demand for qualified human input. Low-level artificial classification has made great progress. While this is the case, Deep Learning consistently outperforms traditional computers. analyzing large datasets while learning. We examine three machine learning techniques for categorizing morphological galaxies: Support Vector Machines (SVM), Random Forests (RF), and Naive Bayes (NB). We examine the efficacy of several machine learning algorithms on each feature representation of a galaxy using a collection of morphological features produced by image analysis as well as the raw image pixel data compressed using PCA (Principal Component Analysis) into PCA features. According to our experiments, RF outperformed SVM and NB. The remainder of the time, morphological features outperformed our PCA features in performance. Thus, the current mechanism is not extremely scalable. A probabilistic classifier that can scale, is based on source data, and requires the least amount of human interaction is essential to resolving these problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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A Brief Comparative Study of Metaheuristic Approaches for Hyperparameter Optimization of Machine Learning Model 1

A Brief Comparative Study of Metaheuristic Approaches for Hy...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Dilip, Kumar Ghantasala, G. S. Pradeep Rathee, Manisha Kallam, Suresh Bathla, Priyanka Alliance University Department of Computer Science and Engineering Bengaluru India School of Computer and System Sciences Jawaharlal Nehru University India School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350341737

Machine learning models have been successfully applied in numerous fields. Training a model is the most important aspect of machine learning for its successful application for the problem. To improve the training of a machine learning model thereby improve the performance, the selection of features and setting optimal parameters is crucial. Mainly two kinds of parameters are required to deal with, namely internal and external parameters. Internal parameters are model parameters and configurable such as weights of neural networks and their estimation can be done using data set. The hyperparameters such as learning rate, size of layers, number of layers, loss function etc, are external parameters and its values cannot be determined using the data set and it is not the part of the model. Its estimation can be done by the domain expert or using some trial-and-error techniques until it achieves some acceptable values. However, these techniques are highly time-consuming and cannot ensure the optimal values for these hyperparameters. In recent years different metaheuristic techniques have been applied to determine the optimal values of hyper parameters for machine learning models. In this paper we have conducted a brief comparative study of a few popular metaheuristic approaches applied for the hyperparameter optimization for various machine learning models. In this paper various evaluation measures have been considered for comparative analysis of metaheuristic approaches for hyperparameter optimization for deep learning model. © 2023 IEEE.

关键词： Ant colony optimization

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Magnetic Resonance Image Analysis: A Healthcare System 4

Magnetic Resonance Image Analysis: A Healthcare System

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dubey, Shivendra Singh, Shweta Dubey, Sakshi Nair, Rajit Sharma, Rahul Manipal University School of Engineering Department of Computer Science and Engineering Department of Aiml Rajasthan Jaipur India Jagran Lakecity University School of Engineering and Technology Madhya Pradesh Bhopal India Rkdf University Department of Electronics and Communication Madhya Pradesh Bhopal India Vit Bhopal University Department of Computer Science and Engineering Madhya Pradesh Bhopal India Parul Institute of Technology Parul University Department of Computer Science and Engineering Gujarat Vadodara India

ISBN: (纸本)9798331518981

In today's medical image, analyses are captured very rapidly due to early detection of the brain tumour being very important. The tumour could be plainly visible in the neurological magnetic resonance imaging study, but the doctor must also quantify the tumour area at a certain level in order to conduct additional research. Machine learning has witnessed remarkable consideration over the past few years. From the analysis of medical images to natural language processing, several machine learning models spanning many fields appear to be able to detect tumours and are widely used in applications in academia, business, and medical science. These advancements have a significant impact on healthcare in a broad range of areas, including healthcare, medical imaging technologies, medical diagnostics, and medical data analysis. We give a broad summary of current developments in machine learning used for medical processing and evaluation of images as well as related difficulties. Magnetic Resonance Image (MRI) analysis using IoT involves collecting data from connected medical devices and sensors to create MRI images. Machine learning algorithms can then be used to analyze this data in order to find trends and forecast the health of patients. It can assist medical professionals in making better decisions that will enhance patient care and outcomes. © 2024 IEEE.

关键词： Support vector machines

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Stacking multiple optimal transport policies to map functional connectomes 57

Stacking multiple optimal transport policies to map function...

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57th Annual Conference on Information sciences and Systems, CISS 2023

作者： Dadashkarimi, Javid Rosenblatt, Matthew Karbasi, Amin Scheinost, Dustin Yale University Department of Computer Science New Haven United States Yale University Department of Biomedical Engineering New Haven United States Yale University Department of Electrical Engineering Department of Computer Science Department of Statistics and Data Science New Haven United States Yale School of Medicine Department of Radiology and Biomedical Imaging New Haven United States

ISBN: (纸本)9781665451819

Connectomics is a popular approach for under-standing the brain with neuroimaging data. However, a con-nectome generated from one atlas is different in size, topology, and scale compared to a connectome generated from another. Consequently, connectomes generated from different atlases can-not be used in the same analysis. This limitation hinders efforts toward increasing sample size and demonstrating generalizability across datasets. Recently, we proposed Cross Atlas Remapping via Optimal Transport (CAROT) to find a spatial mapping between a pair of atlases based on a set of training data. The mapping transforms timeseries fMRI data parcellated with an atlas to form a connectome based on a different one. Crucially, CAROT does not need raw fMRI data and thus does not require re-processing, which can otherwise be time-consuming and expensive. The current CAROT implementation leverages information from several source atlases to create robust mappings for a target atlas. In this work, we extend CAROT to combine existing mappings between a source and target atlas for an arbitrary number of mappings. This extension (labeled Stacking CAROT) allows mappings between a pair of atlases to be created once and re-used with other pre-trained mappings to create new mappings as needed. Reconstructed connectomes from Stacking CAROT perform as well as those from CAROT in downstream analyses. Importantly, Stacking CAROT significantly reduces training time and storage requirements compared to CAROT. Overall, Stacking CAROT improves previous versions of CAROT. © 2023 IEEE.

关键词： Mapping

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