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Text Classification on Imbalanced data using Graph Neural Networks and Adversarial Weight Balancer

Text Classification on Imbalanced Data using Graph Neural Ne...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Badiei, Fatemeh Kananian, Makan Ghahramani, S. AmirAli Gh. Sharif University of Technology Department of Computer Engineering International Campus Kish Island Iran

ISBN: (纸本)9798350341072

Text classification in imbalanced datasets poses a significant challenge in natural language processing (NLP) applications. In this paper, we address this issue by treating text data as graphs, with words as nodes, and employ two strategies for establishing connections between words within these graphs. The first strategy employs a fixed-size context window to connect words that lie within the same window, while the second strategy leverages word embedding vectors and a k-nearest neighbors (KNN) approach to connect each word to its k most similar neighbors. To further enhance the classification performance, we combine Graph Convolutional Neural Networks (GCNs) and Long Short-Term Memory (LSTM) networks to process the textual information. Importantly, we address the challenge of class imbalance using an adversarial loss framework. We introduce separate weight generator networks for each class within the dataset, responsible for dynamically assigning weights to individual samples during training. While the classifier aims to minimize its weighted cross-entropy loss, the weight-generating networks strategically assign higher weights to misclassified samples and reduce the weights assigned to correctly classified ones, thus increasing the classifier's loss in subsequent epochs. This adversarial loss, in conjunction with the LSTM-GCN architecture, results in finely tuned sample weights, leading to significantly improved accuracy and F1-Score compared to conventional methods for handling imbalanced text classification problems. Our experimental results demonstrate the effectiveness of our approach in addressing the challenges of imbalanced text classification, offering a solution for a wide range of NLP applications where imbalanced datasets are prevalent. © 2023 ieee.

关键词： Long short-term memory

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Exploring Deep Time-Series Imaging for Anomaly Detection of Building Energy Consumption

Exploring Deep Time-Series Imaging for Anomaly Detection of ...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Copiaco, Abigail Himeur, Yassine Amira, Abbes Mansoor, Wathiq Fadli, Fodil Atalla, Shadi College of Engineering and It University of Dubai Dubai United Arab Emirates University of Sharjah Department of Computer Science United Arab Emirates Qatar University Department of Architecture & Urban Planning Doha2713 Qatar

ISBN: (纸本)9781665453059

Although deep anomaly detection (DAD) is crucial to optimize energy management in smart buildings, there is a lack of efficient research investigating DAD of energy consumption time-series. Besides, analyzing one-dimensional (1D) energy time-series does not provide many options to detect abnormal energy con-sumption. By contrast, converting 1D signals to two-dimensional (2D) representations open the doors to benefit from the success of time-series imaging in related research fields. In this paper, we explore transfer learning in anomaly detection of energy consumption. The presented method converts time-series inputs to images, which in turn serve as inputs for pre-trained CNN models. Specifically, this helps benefit from the inherent spatial invariance since the best characteristics are efficiently provided for convolutional layers. The experimental evaluation on real and simulated datasets demonstrates the promising performance of the proposed method, primarily when the support vector machine (SVM) is used to classify the features extracted using AlexNet. Accordingly, 92.24% and 99.19% weighted F1-score rates have been reached under both scenarios, respectively. © 2022 ieee.

关键词： Time series

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Interpretable Movie Review Analysis Using Machine Learning and Transformer Models Leveraging XAI

Interpretable Movie Review Analysis Using Machine Learning a...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Ahmed, Farzad Sultana, Samiha Reza, Md Tanzim Joy, Sajib Kumar Saha Alam, Md. Golam Rabiul Ahsanullah University of Science and Technology Department of Cse Dhaka Bangladesh Brac University Department of Cse Dhaka Bangladesh

ISBN: (纸本)9781665453059

Text classification has been a common topic of interest for many years. A lot of advanced models has been developed so far in this area. But it is very difficult to understand how the models behave while predicting the class of the text. In our work, we utilized some models to classify the sentiment of movie reviews from text data and observed how the models behaved using Explainable Artificial Intelligence (XAI). At first dataset was collected and pre-processed. Then the processed dataset was separated into different train and test sets. The train set was used to classify using different different machine learning and neural network based models. The test set was used after training to evaluate the trained classifiers. Finally, the performance of the classifiers were compared and evaluated. After different variations of pre-processing and training steps, the best accuracy score of 91% was obtained using Roberta LSTM model. In the trained models, we sent texts that are correctly classified by RoBERTa models but misclassified by other models. Finally we figured out the reasons of misclassification with the help of LIME Algorithm. © 2022 ieee.

关键词： Statistical tests

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Tools for Animation and Shape Manipulation: A Scoping Review

Tools for Animation and Shape Manipulation: A Scoping Review

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Farzinnejad, Forouzan Habibi, Jafar Khezrian, Navid Afshin Hemmatyar, Ali Mohammad Baghaei, Nilufar Coburg University of Applied Sciences Electrical Engineering and Computer Science Coburg Germany Sharif University of Technology Computer Engineering Tehran Iran University of Queensland Information Technology and Electrical Engineering Brisbane Australia

ISBN: (纸本)9781665453059

Objectives: This paper presents a review of the latest, state-of-the-art research on animation tools, shape de-formation and manipulation published over the last 20 years (from 2001 to 2021). By presenting a systematic classification of available tools and expressing the methods and limitations of each of these tools, our goal was to answer some of the questions faced by researchers who are seeking to work in this field. We posed three questions and answered them by reviewing the literature. Materials and Methods: Four databases (ieee Xplore, ACM Digital Library, Springer, and Google Scholar) were searched for studies that introduce animation, deformation, or manipulation tools. The initial search yielded 296 articles. After evaluating them against the input criteria, 32 studies remain. Results: Out of the 32 studies we reviewed, four of these studies were related to shape manipulation and deformation, and the rest were related to animation tools. The results show that 24 of the tools developed are suitable for 2D manipulation or animation. Also, 28 cases were suitable for novice users. Conclusion: We presented a review of animation, deformation or manipulation tools, including methods and limitations. We also By referring to this work, the new researchers can identify their target community and determine what features previous papers have focused on and what limitations they have, and finally what choices they should be made to get started. © 2022 ieee.

关键词： Animation

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Using Novel Hybrid Convolutional Neural Network for Dysarthria Diagnosis

Using Novel Hybrid Convolutional Neural Network for Dysarthr...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Thilakarathne, Navod Neranjan Galajit, Kasorn Karnjana, Jessada Pa, Win Pa Yassin, Hayati Universiti Brunei Darussalam Faculty of Integrated Technologies Brunei Nectec National Science and Technology Development Agency Pathum Thani Thailand University of Computer Studies Yangon Myanmar

ISBN: (纸本)9798350341072

Dysarthria is a motor speech disorder characterized by articulation and phonation difficulties resulting from speech muscle weakness, paralysis, or incoordination. A precise and timely diagnosis of dysarthria is essential for effective treatment and management of the condition, as it may deteriorate over time or be a precursor to a much more serious disease. On the other hand, this is becoming a severe problem in recent times owing to the rising ageing population and the prevalence of neurological disorders among such people. Hence early detection of dysarthria is deemed essential for the timely management of the disease. In recent years, Artificial Intelligence (AI) applications have shown promising results in various audio processing tasks and incorporated into pathological voice analysis for disease diagnosis. The majority of previous studies on dysarthria detection employed Machine Learning (ML) and Deep Learning (DL) models as the disease classification models. In light of this, this study presents a novel hybrid approach for classifying dysarthria based on audio data using Convolutional Neural Network (CNN) and Support Vector Machine (SVM). According to the experimental results, the proposed classification schema achieves an accuracy of 98.25 % compared to previous research work. Overall, our proposed method aims to automate the classification process, enabling faster and more reliable diagnoses. © 2023 ieee.

关键词： Support vector machines

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Performance and Evaluation of Classification Algorithms Using Machine Learning with Comparative Study for Heart Disease Prediction

Performance and Evaluation of Classification Algorithms Usin...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Hossain, Md. Manik Ashraful Alam, Md. Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

The leading cause of unseasonable death worldwide is heart complaints. Day by day the causes of heart disease are increasing at a rapid-fire rate and it's veritably important and concerning to prognosticate any such disease beforehand. Predicting how illness will affect a person is a delicate challenge. Machine learning is being applied in different fields around the world. In the healthcare sector, there is no exception. data classification models and machine learning algorithms stoutly introduce diagnostic guidelines and enable experts to increase the effectiveness of the diagnostic process. The body's remaining organs are given advanced precedence over the nucleus. It provides the body with oxygen. The distribution of heart illnesses among medical practitioners can be estimated via data exploration. Medical facilities can examine various disorders and evaluate emerging diseases thanks to data collecting. Predicting the condition based on recent medical research has the biggest impact. To learn statistics more effectively, a variety of methods are being investigated in the scientific community. The renovation has had a big impact on the metropolitan community's way of life in addition to improving it. In this situation, it's crucial to offer a comprehensive tool that will enable medical professionals to foresee the sickness. Different machine learning applications suggest varying prediction precision. It should be analyzed with Logistic Regression, KNN, Decision tree, Random Forest, SVM, Gaussian NB, Ada Boost Classifier Gradient Boosting Classifier, Quadratic Discriminant Analysis, and MLP Classifier with comparative mean of three data sets. It will be better to investigate the accuracy of prediction from the most concerning algorithms of Machine learning with recall and f-score of the heart data and present it in the table with visual representation. This underpinning research has shown that multilayer perceptron with cross-validation has surpassed all other alg

关键词： Heart

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The Practicality of Machine Learning for Airline Forward Sales Forecast

The Practicality of Machine Learning for Airline Forward Sal...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Gounder, Muni Prashneel Kumar, Ravnesh Kumar, Kunal University of Fiji School of Science and Technology Department of Computer Science and Mathematics Lautoka Fiji

ISBN: (纸本)9781665453059

Forecasting is used in the aviation industry, among other things, to estimate the number of passengers for each trip. This is advantageous for the practice of revenue management because the forecast serves as the foundation for determining the cost of each flight. In order to maximize profit, it is therefore important to know whether these forecasts are accurate. Today, the price of an airline ticket can vary significantly and quickly during the same journey, even for neighboring seats in the same cabin. Customers search for the greatest offer while airlines seek to maintain a high overall revenue and maximize their profit. Airlines use a range of computational techniques, including as pricing discrimination and demand forecasting, to increase their revenue. Demand forecasts have a crucial role in how well airline pricing and revenue management systems perform. The demand for different fare classes is assumed independent by traditional airline forecasting models, which ignore the demand's sensitivity to the airline's inventory control procedures, price variations, and the range of available alternatives. This study looks at several forecasting algorithm implementations in the context of sales booking prediction for the aviation sector, along with its benefits and drawbacks. The research is accompanied with use cases in which Time Series forecasting algorithms ARIMA and Random Forest are tested against a passenger data for American Airlines against performance indicators: confidence interval, usability, and performance. Academics, business intelligence and revenue analysts, airline operators, the revenue management team, and functional silos across the organization are the main beneficiaries of this article as sales forecast will be utilized to plan and budget flights (capacity) and to make strategic decision. © 2022 ieee.

关键词： Sensitivity analysis

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Detecting Privacy Policies Violations Using Natural Language Inference (NLI)

Detecting Privacy Policies Violations Using Natural Language...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Alshamsan, Abdullah R Chaudhry, Shafique A Clarkson University Department of Computer Science Potsdam United States David D. Reh School of Business Clarkson University Potsdam United States

ISBN: (纸本)9781665453059

data privacy laws like the General data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) provide guidelines for collecting personal information from individuals and processing it. These frameworks also require service providers to inform their customers on how clients' data is gathered, used, protected, and shared with other parties. A privacy policy is a legal document used by service providers to inform users about how their personal information is collected, stored, and shared with other parties. It is expected that the privacy policies adhere to the data privacy regulations. However, it has been observed that some policies may deviate from the practices recommended by data protection regulations. Detecting instances where a policy may violate a certain regulation is quite challenging because the privacy policy text is long and complex, and there are numerous regulations. To address this problem, we have designed an approach to automatically detect whether a policy violates the articles of GDPR. This paper demonstrates how we have used Natural Language Inference (NLI) tasks to compare privacy content against the GDPR to detect privacy policies' text in violation of GDPR. We provide two designs using the Stanford Natural Language Inference (SNLI) and the Multi-Genre Natural Language Inference (MultiNLI) datasets. The results from both designs are promising as our approach detected the deviations with 76% accuracy © 2022 ieee.

关键词： data privacy

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Myers-Briggs Type Indicator and the Big Five Model-How Our Personality Affects Language Use

Myers-Briggs Type Indicator and the Big Five Model-How Our P...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Radisavljevic, Dusan Batalo, Bojan Rzepka, Rafal Araki, Kenji Graduate School of Information Science and Technology Hokkaido University Sapporo Japan Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan Hokkaido University Faculty of Information Science and Technology Sapporo Japan

ISBN: (纸本)9781665453059

Recently there has been an increasing interest in personality computing, a research field that mainly concerns itself with problems of personality perception, synthesis and recognition. In order to provide personality measure for these tasks, two models have been most commonly applied: the Myers-Briggs Type Indicator (MBTI) and the Big Five model (BF). Since some early scientific works have been critical of MBTI, the Big Five model has historically been more favoured by the scientific community. However, in recent years there has been an increasing number of researchers examining the possible application of MBTI in personality computing. Although the correlation between the set of measures offered by these two models has been proven to exist, works that directly compare the two, especially from the aspect of language usage, have been scarce. In this paper we examine the linguistic similarities between individuals labelled with MBTI types and Big Five dimensions by conducting an analysis using Linguistic Inquiry and Word Count (LIWC). Our study has further substantiated previous linguistic qualities of the Big Five model, while providing a novel insight into how MBTI types influence language use. Additionally, the results of our research have indicated existence of language use patterns shared by these two models. To the best of our knowledge, this is the first time a comparison of effects the two personality models have on language usage has been made. © 2022 ieee.

关键词： Linguistics

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Two-Stage Fine-Tuning For Low-resource Englishbased Creole with Pre-Trained LLMs

Two-Stage Fine-Tuning For Low-resource Englishbased Creole w...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Amanda Tan, Shu Hui Aung, Eint Sandi Yamana, Hayato Department of Computer Science and Communications Engineering Waseda University Tokyo Japan Waseda University Faculty of Science and Engineering Tokyo Japan

ISBN: (纸本)9798350341072

Sentiment analysis adopts natural language processing (NLP) techniques to determine the emotional tone of the text. Sentiment analysis research has predominantly been conducted on commonly spoken languages, such as English and Mandarin Chinese. However, less widely spoken languages, such as Englishbased Creoles, have received limited attention due to the need for large-scale labeled datasets. English-based Creoles are derived from English, resulting in many similarities with English. However, English-based Creoles differ from English in some aspects, making the Creole unintelligible to other English speakers. In sentiment analysis research conducted on Singapore English-based Creole (Singlish), most existing research does not leverage the capabilities of large-language models, such as pretrained BERT. To our knowledge, only Gotera et al.'s work relates to a large-language model;however, their contribution is creating a new pre-trained model for Singlish. However, creating a new pre-trained model is computationally expensive, and pre-training a less widely spoken language will result in ineffective because of the small dataset. Hence, we propose a new two-stage fine-tuning framework for pre-trained models, targeting a low-resource English-based Creole, Singlish. Our proposed framework initially clusters the dataset into two based on each data point's percentage of English words. Then, two-stage fine-tuning is performed by transferring the pre-trained BERT model onto the clustered dataset with the higher English percentage in the first stage. The model is further transferred onto the clustered dataset with the lower English percentage in the second stage. Our proposed framework outperforms the traditional fine-tuning framework, achieving a weighted F1 score of 0.8344 for sentiment analysis. © 2023 ieee.

关键词： Sentiment analysis

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